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Optimizing Sensing, Computing, andCommunication for Energy Harvesting IoTs:

A SurveyDong Ma, Student Member, IEEE, Guohao Lan, Member, IEEE, Mahbub Hassan, Senior Member, IEEE,

Wen Hu, Senior Member, IEEE, and Sajal K. Das, Fellow, IEEE

Abstract—With businesses deploying a growing number ofInternet of Things (IoTs), battery maintenance has become alimiting factor for realizing a sustainable IoT infrastructure. Toovercome battery limitations, industry is now considering energyharvesting as a viable alternative to autonomously power IoTdevices as much as possible, which has resulted in a number ofbatteryless energy harvesting IoTs (EH-IoTs) appearing in themarket in recent years. Standards activities are also underway,which involve wireless protocol design suitable for EH-IoTs aswell as testing procedures for various energy harvesting methods.Despite the early commercial and standards activities, IoT sens-ing, computing and communication under unpredictable powersupply still faces significant research challenges and has becomea topic of intense research. This paper surveys recent advancesin EH-IoTs from several perspectives. First, it surveys the recentcommercial developments for EH-IoT in terms of both productsand services, followed by a review of initial standards activitiesin this space. Then it surveys methods that enable use of energyharvesting hardware as a proxy for conventional sensors to detectcontexts more power-efficiently. The advancements in efficientcheckpointing and timekeeping for intermittently powered IoTdevices are reviewed next. We also survey recent research innovel wireless communication techniques for EH-IoTs, includingapplications of reinforcement learning to optimize power alloca-tions on-the-fly under unpredictable energy productions as well aspacket-less IoT communication and backscatter communicationtechniques for energy impoverished environments. We concludethe paper with a discussion of future research directions.

Index Terms—Energy Harvesting, Internet of Things, Sensing,Transient Computing, Energy Harvesting Communication

I. INTRODUCTION

With the advancements in low-power and miniature elec-tronics, recent years have witnessed a dramatic increase ofInternet of Things (IoTs), covering a wide range of areas

Dong Ma is with the School of Computer Science and Engineering,University of New South Wales (UNSW), Sydney, Australia, and also withthe Data61, CSIRO, Eveleigh, Australia (E-mail: dong.ma1@unsw.edu.au).

Guohao Lan is with the Department of Electrical and Computer En-gineering, Duke University, Durham, NC 27708, USA (E-mail: guo-hao.lan@duke.edu). Most of the work was done while the author was withthe University of New South Wales, Sydney, Australia.

Mahbub Hassan is with the School of Computer Science and Engineering,University of New South Wales (UNSW), Sydney, Australia (E-mail: mah-bub.hassan@unsw.edu.au).

Wen Hu is with the School of Computer Science and Engineering,University of New South Wales (UNSW), Sydney, Australia (E-mail:wen.hu@unsw.edu.au).

Sajal K. Das is with the Department of Computer Science, MissouriUniversity of Science and Technology, Rolla, MO 65409 USA (e-mail:sdas@mst.edu).

including civil infrastructure, home automation, consumerelectronics, wearable devices, and industrial/agricultural mon-itoring. This trend is unstoppable as we continue to automateprocesses in every sector of our economy. Forecasts suggestthat by 2025, a massive 25 billions of IoT devices will bedeployed worldwide [1].

As most IoT devices are designed as small portable con-sumer electronics, they are expected to be powered by on-device power supply, which is currently realized by varioustypes and sizes of batteries depending on the applicationrequirements. Unfortunately, for large scale IoT deployments,the battery technology suffers from major limitations. Becausebatteries store a finite amount of energy, they either need tobe recharged or replaced, which can be inconvenient, costly,or even not possible in certain deployments. To prolongbattery life, IoT devices could be configured to be activeless frequently, but at the cost of reduced utility. In somedeployments, batteries may pose safety risks. Finally, dumpingbillions of toxic batteries is not environmental-friendly. Dueto these reasons, powering massive number of sensors isnow recognized as one of the grand challenges of the IoTrevolution [2], [3].

Harvesting energy from the ambient environment to per-petually power electronic sensors is a promising solution toeliminate dependency on batteries and thus accelerate thedeployments of IoTs. Indeed, recent advancements in energyharvesting materials, devices, and processes have already madeit possible to realize certain IoT circuits that can operatewithout batteries. Examples of such energy harvesting IoTs(EH-IoTs) include wireless switches [4], which harvest kineticenergy from each push of the switch button to transmit alow-power wireless message to an actuator to turn on/off aglobe or pull down/up a curtain, and so on. Other exam-ples include smart shoes [5]–[7] harvesting energy from footstrikes, smartwatches powered by kinetic [8], solar [9], orthermal [10] energy harvesting, energy meters [11] poweredby electromagnetic energy harvesting, and so on. To promoteinteroperability between products from different manufactur-ers, the International Organization for Standardization (ISO)and the International Electrotechnical Commission (IEC) haveinitiated standardization of EH-IoTs, which involve wirelessprotocol design suitable for EH-IoTs as well as testing pro-cedures for various types of energy harvesting methods. Suchstandardization activities are expected to further accelerate the

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development and deployment of energy harvesting IoTs in thecoming years.

Despite the early commercial and standards activities, thereremain significant research challenges to realize efficient andreliable EH-IoTs. For small form factor devices, current energyharvesting technology can produce only small amounts ofpower, which is also very dynamic and unpredictable. Contin-uously powering various sensors for 24/7 context monitoringusing tiny amounts of harvested power is fundamentally chal-lenging. Unpredictability of power generation causes furtherchallenges for reliably completing various computing tasksand optimizing power allocation for wireless communication.How to optimize sensing, computing and communication forEH-IoTs, therefore, has become a topic of intense research.Figure 1 shows that the number of publications dealing withchallenges in EH-IoTs have grown exponentially in recentyears confirming the popularity of the topic.

The concept of energy harvesting is not new and thereexists publications surveying the recent advances in energyharvesting materials and techniques [12]. There also existscomprehensive surveys on research challenges and solutionsfor wireless communications powered by energy harvesting[13], [14]. These surveys, however, do not capture many recentadvances in EH-IoT, such as those related to commercializa-tion, standardization, sensing optimizations as well as manynew methods for optimizing computing and communications.This survey fills this gap in the current literature and thetaxonomy is illustrated in Figure 2.

The survey is organized as follows. Following a brief intro-duction to the basic architecture of an EH-IoT device, SectionII surveys the recent commercial and standards developmentsin this area. Section III surveys methods that enable use ofenergy harvesting hardware as a proxy for conventional sen-sors to detect contexts more power-efficiently. The advance-ments on energy harvesting computing, including efficientcheckpointing and timekeeping for intermittently poweredscenarios are reviewed in Section IV. Section V surveys

1https://app.dimensions.ai/discover/publication

EH- IoTs

Commercial and Standards Activities

Sensing Optimizations

Computing Optimizations

Communications Optimizations

Commercial products andservices

Standards for wireless protocoland transducer test method Checkpointing optimization

Timekeeping across powerfailures

Packet-less communication

Reinforcement learning basedcommunication optimization

Backscatter communication

Kinetic EH sensing Thermal EH sensing Solar EH sensing RF EH sensing

Fig. 2: Taxonomy of our survey coving the recent advancesin commercializations, standardizations, and optimizations forsensing, computing and communications for EH-IoT.

recent research in novel wireless communication techniquesfor EH-IoTs, including applications of reinforcement learningto optimize power allocations on-the-fly under unpredictableenergy productions as well as packet-less and backscattercommunication of basic IoT notifications. Future researchdirections are envisaged in Section VI, before we concludethe survey in Section VII.

II. COMMERCIAL AND STANDARDS DEVELOPMENTS INEH-IOTS

Although we are far from pervasive deployment of EH-IoTs,we are beginning to witness some early commercial successesin this area. There are also fully working prototypes of EH-IoTs demonstrated in various research laboratories. Finally,as summarized in Table I, there is an upward trend in thenumber of third party vendors specializing in energy harvest-ing components and solutions suitable for IoTs. Collectively,these developments provide compelling evidence supportingthe feasibility and promise of using renewable energy to self-power the next generation of IoTs. This section reviews theserecent developments in EH-IoTs and summarizes initial effortsfor standardizing wireless communications suitable for energyharvesting IoTs. To gain an insight to the working principle ofthese EH-IoTs, we start this section by examining the systemarchitecture of these devices.

A. System Architecture of Emerging EH-IoTs

It is important to design the architecture of EH-IoT to en-sure existing electronic components such as microprocessors,sensors, and radios can be seamlessly reused in these newbreed of devices. Although the actual details of the devicearchitecture will vary from vendor to vendor, we capture thebasic components and connectivity in Figure 3. As we cansee, the current design of EH-IoT allows us to simply replacethe battery by an energy harvesting module, and the remainingIoT electronic components can be seamlessly integrated andpowered by the module. This design simplicity is achieved bybreaking the energy harvesting component into two subcom-ponents, an energy harvesting transducer followed by a powermanagement module, which includes storage element, DC-DC

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TABLE I: Vendors specializing in energy harvesting components and solutions suitable for IoTs.

Company Source Energy Harvester EH Solution Application Foundation YearPiezo Systems Kinetic X × piezoelectric energy harvesting 1988

MIDE Technology Kinetic X × piezoelectric energy harvesting 1989

EnOcean Kinetic/Solar X X Building 2001

Perpetuum Kinetic × X Transportation 2004

Bionic Power Kinetic X × Wearables /Military 2007

PaveGen Kinetic X X Infrastructure /Entertainment 2009

Eight19 Solar X X Health care/Retail /Infrastructure 2010

SolePower Kinetic × X Wearables 2012

PsiKick Kinetic/Solar/Thermal × X Industry 2012

Greengineering Thermal × X Building/Industry/Transportation 2012

ReVibe Energy Kinetic X X Industry/Transportation 2013

Enerbee Kinetic × X Building/Industry 2014

AMPY Kinetic X × Wearables 2014

8power Kinetic × X Industry 2015

Freevolt RF X × Building/Wearables 2015

Nowi Energy RF X X Industry/Wearables/Transportation 2015

ParkHere Kinetic X X Infrastructure 2015

Aqua Robur Kinetic X X Industry/Irrigation 2015

Kinergizer Kinetic X X Industry/Wearables/Transportation 2016

Trameto Multiple × × Power management integrated circuits 2016

Otego Thermal X X Industry 2016

Zolitron Technology Solar X X Industry/Transportation 2016

Lightricity Solar X × Industry/Wearables/Building 2017

EnergIoT Kinetic X X Industry/Transportation 2017

Energy Transducer

AC Unstable DC

Stable DC

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Kinetic (AC)Photovoltaic (DC)Thermoelectric (DC)RF (DC) Buck Converter

Boost Converter

CapacitorSupercapacitorRechargeable battery

Rectifier

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DC-DCConverter

Storage Element

System Load(MCU, sensor,

radio, etc.)

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Battery

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radio, etc.)

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Fig. 3: Generic system architecture for (a) conventional IoTpowered by battery and (b) emerging EH-IoT, which simplyreplaces the battery with an energy harvesting module.

converter, and rectifier if power source produces alternatingcurrent (AC).

The fundamental purpose of the transducer is to convertambient energy available in the environment in various formsinto usable electricity so it can be used to power IoT elec-tronics. Different transduction techniques and materials can beused to harvest energy from different sources. There are fourmain sources of ambient energy suitable for IoTs. They arekinetic (a.k.a motion, vibration, and mechanical), solar, radiofrequency (RF), and thermal. Different sources produce energyunder different contexts. For example, kinetic energy harvest-ing is only possible if the object moves, vibrates, bends, or

experiences some type of motion. In contrast, a solar cell canproduce electricity even for static objects, but it cannot workin the dark. A thermal source can generate electricity even forstatic objects in the dark as long as temperature differences inspace or time are present. Finally, RF energy harvesting canconvert ambient radio waves into usable electricity and wouldwork for all type of objects as long as it is within the coverageof some type of radio transmitters. More details about themechanisms, characteristics, and applications of these energysources and the corresponding transduction techniques can befound in [15], [16], but we compare and contrast them in TableII from the perspective of their use in EH-IoTs. As we canobserve, given different application scenarios and requirementsof the EH-IoT, we need to select the proper energy harvestingtransduction techniques. For example, there is no point toemploy solar energy harvesting if the object is expected tooperate mostly in dark environments.

The transducer, however, can only generate electricity asenergies become available in the environment, which meansthat the transducer output is highly unstable and cannot beused directly to power conventional electronic componentsthat are designed for stable direct current (DC) supply. Apower management module is therefore required to furtherregulate the electricity generated by the transducer to producestable power supply for the IoT electronics. Such regulationmay involve simple DC-to-DC conversion, or if the transducerproduces alternating current (AC), it may also require AC-to-DC conversion (i.e., rectifier) as part of the energy harvest-ing regulation. Finally, as electricity generation can be very

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TABLE II: Typical energy harvesting sources and techniques used in EH-IoTs.

Source Transduction Signal Principle Requirement Energy Density

Kinetic

Piezoelectric AC Apply compression on crystalline materials

Motions or vibrations

Walking: 49µW/cm2@3km/h,

Electromagnetic AC Change of magnetic field under movement piezoelectric EH, knee bending [17]

Electrostatic AC Change of electrical field under movement Wind: 370µW/cm2@15m/s,

Triboelectric AC Frictional contact of two different materials triboelectric EH [18]

ThermalThermoelectric DC Spatial temperature gradient

Temperature difference14uW/cm2 on human wrist,

Pyroelectric AC Temporal temperature difference thermoelectric EH [19]

Solar Photovoltaic DC Convert light into electricity Bright environmentTransparent:7mW/cm2@128klux [20]Opaque: 26.7mW/cm2@128klux [21]Opaque: 16µW/cm2@400lux [22]

RF RF radiation AC Convert electromagnetic wave into electricity Radio coverageTV tower:60µW@distance 4.1km [23]Wi-Fi: 100µW@distance 2 feet [24]

dynamic and intermittent, it is often necessary to store theharvested electricity for a while before using it. The powermanagement module therefore often includes a capacitor or arechargeable battery for this purpose.

B. Commercial and Academic EH-IoTs

With the efforts of researchers and engineers, there is a widerange of self-powered IoT devices presented in the academiaand market. These devices achieve energy autonomous oper-ation merely relying on the energy harvested from the envi-ronment. The application domain varies from smart building,transportation, to wearable and implantable medical devices. Inthis subsection, we review the existing battery-free prototypesor products in different application domains.

1) Smart building: A smart building deploys many differentIoT devices, such as temperature sensors, smoke detectors, andwireless switches, to enable a smart and convenient control ofthe environment. However, the deployment and maintenanceof large number of IoT devices incur heavy labor and highcost. Meanwhile, indoor environment provides a variety ofenergy sources like light, radio frequency (e.g., Wi-Fi), andkinetic energy from interaction with human beings. Therefore,collecting such energy to power the IoT devices would bepossible to reduce the deployment (by eliminating wires) andmaintenance (by eliminating the replacement of battery) cost.Many prototypes/products in this category have been presentedfrom both academia and market.

In [27], the authors presented a self-powered airflow sensingand control system. By mounting the piezoelectric energyharvester to the outlets of the air conditioning system, it canharvest energy from the airflows and power the sensor nodeswhich report the airflow speed to a server, thereby achiev-ing real-time monitoring and control. In [11], a batterylesspower meter named Monjolo was developed. By attaching anelectromagnetic energy harvester to the power line, based onthe Faraday’s law, electricity will be generated when currentflows through. Once the harvested energy reaches a certainvalue, it was used to transmit a pulse, and eventually, powerload can be estimated based on pulse reception frequency/rate.Using the electromagnetic energy harvester as well, the authors

in [40] designed a batteryless sensor, Tethys, which harvestsenergy from water flows. The harvested energy was leveragedto transmit the time stamp of the start and end of a showerprocess, thereby estimating the amount of water consumed.The authors in [41] proposed an in-home living activityrecognition system which utilizes passive infra-red sensors anddoor sensors to detect different activities, like eating, sleeping,cooking, working and so forth. The sensors are equipped withsolar panels to harvest energy from sunlight or bulb light.

There are also some self-powered IoTs for smart buildingin the market. The Wireless Switch [4] from EnOcean is ableto transmit a radio telegram for remote monitoring of switchstatus when it is pushed every time. It also provides convenientcontrol of lighting, temperature and miscellaneous electricloads. CleanSpace Tag [26] is a batteryless air pollutionmonitor that harvests energy from the ambient RF signalsradiated by Wi-Fi access points or cellular base stations. Itcan track air pollution condition and record users’ exposure toharmful carbon monoxide so that protects them from harmfulair pollutions.

WISP [28] is a radio frequency powered IoT device withcapability of environment sensing, and has been widely used inacademic research. It has two descendant versions, Moo [42]and SocWISP [43], which are upgraded in term of micro-controller, flash memory, and software firmware. EnHANTTags [44] harvest energy from indoor light using organic solarcells and exchange tag IDs with the neighboring Tags throughUltraWideband Impulse Radio (UWB-IR) for object tracking,e.g., locating a misplaced book in a library [45].

2) Wearable devices: Harvesting energy from human activ-ities or body heat are viable solutions to power the wearabledevices. In [32], the authors proposed a shoe based battery-free wearable sensing platform where the power is generatedfrom the two feet when people are walking. The designedpiezoelectric energy harvester achieved a power output of 1-2mW , which enables the operation of sensors, microcontroller(MCU), and radio with reasonable duty-cycling. Similarly,Salar Chamanian et. al [46] presented a batteryless sensorplatform which harvests energy from human motions. Thesensor node was equipped with an electromagnetic energyharvester and was able to adjust its sensing rate and data

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TABLE III: Examples of self-powered IoTs: commercial products and research prototypes.

Domain Product EH A/C Radio Release Energy Function Energy Harvesting CapabilityTechnique Year Storage (Form Factor)

Sensors [25] Solar/Photovoltaic C 863MHz - CapacitorTemperature 5min @400lux to send the first telegrammonitoring from cold start (solar cell: 50×20mm)

WirelessKinetic/Piezoelectric C 868MHz - -

Dimming/shutter Each push and release of button can actuateSwitch [4] control a telegram transmission

Smart CleanSpaceRadio frequency C BLE 2015 -

Air quality-

Tag [26] monitoringBuilding

[27] Kinetic/Piezoelectric A Zigbee 2013 BatteryAir conditioning Generate 667µW at 5.5m/s airflow speedmonitoring (piezoelectric EH: 20cm2)

Monjolo [11] Electromagnetic A Zigbee 2013 Capacitor Energy metering Generated 4mW at 60W loadWISP [28] Radio frequency A 928MHz 2008 Capacitor Context monitor Generate 310µW with 0dBm input powerEnHANT [29] Solar/Photovoltaic A UWB-IR 2013 Battery Object tracking Generated 70µW/cm2 at lab environmentLunar

Solar/Photovoltaic C BLE 2017 BatteryStep counting, Exposure at > 10K lux for 1 hour can

Watch [9] Sleep tracking support 24 hours operationSequent Kinetic/

C BLE 2017 BatteryActivity, heart -

Watch [8] Electromagnetic rate tracking

PowerWatch [10]Thermal/

C BLE 2017 BatteryStep counting, Can operate using the energy converted

Thermoelectric sleep tracking from body heat

Smartboots [5]Kinetic/

CWi-Fi/

2012 -Wearable and Generate 100mW

Electromagnetic Cellular military motoring (form factor: shoe)Wearable INSTEP [6] Kinetic C BLE 2011 Battery Activity tracking, Generate 1W@walking (form factor: shoe)

PowerWalk [7] Kinetic/Piezoelectric C - 2007 -Wearable and Generate 11W from knee bendingmilitary motoring (form factor: knee brace)

AMPY [30]Kinetic/

C BLE 2014 Battery Power chargerGenerate milliwatts from activity

Electromagnetic (form factor: power bank)

Sunglasses [31] Solar/Photovoltaic A - 2017 CapacitorTemperature and Generate 400µW at 500luxlight monitoring (form factor: glass lens, solar cell 31cm2)

[32] Kinetic/Piezoelectric A BLE 2016 Capacitor Activity tracking Generate 1-2mW (piezoelectric EH: 50cm2)

HiPER-D [33]Kinetic/

C - 2016Energy harvesters tha Generate 3mW at vibration of 1g and 20Hz

Electromagnetic support customized (cylinder: height 75mm and diameter 35mm)Industry Model Kinetic/

C - 2013asset condition Generate 150mW at vibration of 1g and 60Hz

A/D/Q [34] Electromagnetic monitoring, tracking, etc. (form factor: power bank, cylinder battery)Fenix Hub [35] Kinetic/turbine C LoRa 2015 Battery Irrigation Generate up to 4W (200×160×65mm)

Z-Node [36] Solar/Photovoltaic CBLE/

2016 BatteryAsset tracking

(form factor: 80×90×12mm)NB-IoT Smart traffic

Pacemaker [37] Kinetic/Piezoelectric A - 2014 - Drive pacemaker Generate 3 µJ/cm3/heartbeat(Cylinder 21mm3)IMD Heart monitor [38] Kinetic/Triboelectric A - 2016 Capacitor Heartbeat monitor Generate 19.5nW at heart beat of 80 bpm

Pacemaker [39] Biofuel/Glucose A - 2013 - Drive pacemakerGenerate 470mV voltage, 5mA current(form factor: 10×4×0.3cm)

*A/C represents Academia prototype or Commercial product.

transmission rate according to jogger activity. The authorsin [47] developed SensorTile, a self-powered wristband whichis integrated with three photovoltaic strips. Combining withthe on-board sensors, the wristband was able to detect humanactivities and communicate with a smartphone using BluetoothLow Energy (BLE). Similarly, a sunglass that is able to mea-sure the sunlight intensity and ambient temperature withoutthe need of battery was developed in [31]. The two lensesof the sunglass were fitted with semi-transparent solar cells,which harvests energy from the sunlight.

Some self-powered wearable products also appear in themarket. A smart shoe from Instep NanoPower [6] enableshuman activity recognition, where the energy is harvested fromdaily walking and running. There are several smartwatcheswhich can operate battery-free by harvesting energy fromhuman body. The Sequent Watch [8] is equipped with anelectromagnetic energy harvester and relies on human wristmotions to power the functions. Matrix PowerWatch [10]harvests energy from body heat through a thermoelectric

energy harvester, while the Lunar Watch [9] equipped withtransparent solar cells harvests energy from the sunlight. Allthese smartwatches support popular wearable applications likestep counting and health monitoring.

3) Industry/transportation: Self-powered IoT devices havealso been deployed in transport and industrial domains. Differ-ent motion energy harvesters are designed to cater for a varietyof applications, such as process control, asset condition mon-itoring, tracking, and railway condition monitoring. Exampleslike HiPER from Kinergizer [33] as well as modelA/modelDfrom ReVibe Energy [34], have been adopted in practicalscenarios. For instance, when HiPER is mounted on thelower flange of the track, it is able to harvest energy frompassing trains and power the integrated sensors for monitoringservices, like condition monitoring of wheels and bearings.Similarly, in [48], the authors presented a batteryless wirelesssensor node equipped with a piezoelectric energy harvesterto collect energy from vibrations caused by passing vehicles.The harvested energy was used to power the radio for data

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transmission so that the remote server was able to analyze thetraffic-flow.

4) Implantable medical devices (IMDs): In the pastdecades, implantable medical devices (IMDs) have been de-signed and implemented to observe human physical actions,enhance the functionality of some damaged or degraded or-gans, and deliver drugs for the therapy of special diseases.Various medical devices, such as cardiac pacemakers, cochlearimplants, tissue stimulator and so forth [49], [50], have beenwidely used to provide physical treatment as well as assisthealthcare tracking services in clinical practices.

Given that reliability is a critical concern, IMDs typicallyrely on batteries to maintain a sustainable operation. However,one major drawback of battery-powered IMDs is its limitedlifespan with only several years [51], which often requirebattery replacement through surgery. Therefore, research ex-ploring energy harvesting IMDs has been conducted in recentyears.

In [52], energy harvested from heart motions of a swinewas collected using an implanted piezoelectric device and theresults showed the feasibility to drive artificial pacemakers.A triboelectric generator with output voltage up to 14V andoutput current up to 5uA was designed and fabricated in [38].Implanting the generator into an adult swine for over 72 hours,the results demonstrated that it can achieve self-powered wire-less transmission for real-time heartbeat monitoring. In [39],the authors used a single biofuel cell to harvest energy fromhuman body and designed a power management module toactivate a pacemaker. The results show that it is able to powerthe pacemaker for at least 5 hours.

To conclude, self-powered IoTs have already started ap-pearing in several application domains and new companiescontinue to innovate, design, and market novel energy har-vesting solutions for IoTs. From Table III, we can observe thatkinetic energy gains most popularity for wearables, industryand IMDs, while smart building has the opportunity to adopta wide range of energy sources.

C. Standardization Activities for EH-IoTs

To promote interoperability, the International Organizationfor Standardization (ISO) and the International Electrotechni-cal Commission (IEC) have taken initial steps to standardizetwo important aspects of EH-IoT, wireless communicationprotocols and transducer testing methods. In the following wedescribe these activities and provide a summary in Table IV.

1) Standardization of wireless protocols: This activity seeksto standardize new wireless protocols that are suitable forenergy harvesting devices, which have access to extremelysmall amount of energy and may not have consistent supplyof energy. ISO and IEC have jointly released a standard forwireless short packet (WSP) protocol optimized for energyharvesting. This protocol is targeted for smart home appli-cations such as lighting, heating, energy management, blindscontrol, different forms of security control and entertainment(audio and video) where various sensors and switches transmitvery short command and control messages. The WSP isdesigned to carry such short messages using minimal number

TABLE IV: Standardization activities for EH-IoTs.

Aspect Type Standard Y DomainAmplitude ISO/IEC

2012Wireless modulation 14543-3-10 [53] Home

Protocol Frequency ISO/IEC2016

electronicmodulation 14543-3-11 [54]

Transducer

Piezoelectric

IEC 62969-3 [55] 2018 Vehicle sensor

IEC 62830-4 [56] 2018

IEC 62830-1 [57] 2017

EHElectromagnetic

IEC 62830-3 [58] 2017Consumer/

IEC 62407-28 [59] 2017Military/

ThermoelectricIEC 62830-2 [60] 2017

IndustryIEC 62830-5 [61] 2018

TriboelectricIEC 62830-6 [62] 2018

IEC 62830-7 [63] 2018*Y represents Release Year.

of bits to improve the chances of successful transmission evenif the amount of harvested energy is extremely small.

OSI layers 1-3 have been specified and the medium accesscontrol (MAC) does not enforce the conventional carrier sens-ing or listen before talk (LBT) prior to transmission. Instead,devices are allowed to transmit on the channel straightawaywhenever they want to, which is referred to as random accesswithin the standard. The random access mechanism servestwo important functions. First, devices that are equipped onlywith transmitters, but have no receivers, can still effectivelyparticipate in the IoT eco-system. Second, even if the devicehas a receiver, it can choose to turn it off when transmittingif the harvested energy cannot power both the transmitter andthe receiver at the same time. To avoid collisions, the standardrecommends such devices to operate at very low duty cyclingor implement retransmissions at higher layers.

Two different modulation schemes, amplitude modulation(AM) and frequency modulation (FM), have been proposedfor WSP to deal with both energy efficiency and mobility ofIoTs. AM is more energy efficient, but is less effective formobile objects because the antenna impedance gets affectedwhen held in hand or placed on metal surfaces. It also affectsthe amplitude linearity but not the frequency. FM is thereforerecommended for mobile devices. While low frequencies, suchas 315 MHz can be used for AM, frequencies above 800MHz is recommended for FM communications to achievea reasonably small size for the antenna which suits mobiledevices.

2) Standardization of test methods for energy harvestingtransducers: This activity seeks to devise standard methodsfor testing and evaluating specific types of transducers, suchas those used for vibration and thermal energy harvesting. IECreleased nine standards so far, which cover testing methods forpiezoelectric, electromagnetic, thermoelectric, and triboelec-tric energy harvesting. The applications of these transducersinvolve consumer, military, and industrial electronics.

These initial standardization activities are expected to accel-

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Energy Transducer

Context DetectionAlgorithm

Energy Storage

Energy Transducer

Context DetectionAlgorithm

Energy Storage

(a) (b)

Inst

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Fig. 4: Illustration of using (a) Instantaneous and (b) Accu-mulated power signal for context sensing.

erate the development and deployment of energy harvestingIoTs in the coming years. Despite such commercial andstandards activities, there remain plenty of opportunities tofurther optimize the sensing, computing, and communicationstasks of an EH-IoT, which has become a topic of intenseresearch in recent years. We survey these research works inthe remaining part of the paper.

III. CONTEXT SENSING FROM EH PATTERNS

Sensing various user and environmental contexts is themain application for many IoTs. For example, wearable healthmonitoring IoTs continuously detect various daily activities ofthe user, while an industrial IoT may be required to monitor theheat or temperature of certain surfaces. In conventional IoTs,such sensing is achieved with the help of specialized sensors.For instance, motion sensor, such as accelerometer, is used tocapture the motion of the users, while the temperature sensoris used to measure the temperature changes in the ambientenvironment. All these specialized sensors however requireexternal power sources to operate, making their extended useproblematic for EH-IoTs, especially during energy-starvingperiods.

Fortunately, EH-IoTs open up a new opportunity for contextsensing by reusing the energy harvesting signals. For example,kinetic-powered wearable IoT is able to detect and count theuser’s step, as the energy harvester generates distinguishablepeaks in energy harvesting signal each time the legs hit theground [64]. Similarly, a thermoelectric energy harvester wasable to detect any changes in surface temperature simply fromthe variations in the generated energy harvesting signal [65],[66]. Those examples encourage the use of energy harvestersas self-powered sensors for EH-IoTs, which yields tangiblepower saving opportunities as well as simpler and morecompact hardware.

In recent years, many researchers have successfully demon-strated the detection of a wide variety of contexts by reusingkinetic, thermoelectric, solar, and RF energy harvesting sig-nals. Irrespective of the type of energy source, there are twomain approaches for sensing from energy harvesting signals asillustrated in Figure 4. The first approach analyzes the patternsof the instantaneous power generated by the energy harvestingtransducer, while the second approach uses the amount of thetotal energy accumulated in the storage over a specific periodof time. The first approach allows the detection of a rich setof contexts at the expense of more frequent sampling of the

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Fig. 5: Illustration of (a) instantaneous AC voltage and (b)accumulated capacitor voltage when a subject is walking andrunning wearing a piezoelectric energy harvester. The patternsof the AC voltage and the slopes of the capacitor voltage aredifferent for walking and running.

fluctuating power values. In contrast, the second approach canoffer more significant power saving by sampling the storedenergy only once in a while at the expense of more coarse-grained context sensing. In this section, we survey existingstudies that apply those two approaches for context sensingand present a summary in Table V.

A. Context Sensing from Kinetic EH

Because the KEH harvests energy from the external kineticcontexts (e.g., human motion, activities, mechanical vibra-tions) that strain or vibrate the transducer, the generatedenergy signal contains patterns and signatures of the externalcontexts. Thus, by using appropriate signal processing andpattern recognition algorithms, the KEH signal can be usedas the proxy to sense and detect the external kinetic context.Following this intuition, researchers have attempted to detecta range of contexts directly from the KEH without usingany conventional motion sensors (e.g., accelerometer). In thefollowing, we overview KEH-based sensing techniques foreight different contexts, discuss their performances as wellas any reported power saving obtained when compared toconventional sensor-based context sensing.

1) Human Activity Recognition: For KEH-powered wear-able IoTs, such as fitness bands or smart shoes, harvestedenergy is significantly influenced by the activity performed bythe user. For two activities, walking and running, Figure 5illustrates both the AC voltage (instantaneous power) fromthe transducer as well as the capacitor voltage (accumulatedenergy) collected from a KEH-powered wearable worn by atest subject in our laboratory. In case of the instantaneouspower signal, the maximum of the AC voltage signal gen-erated by running is higher than that from walking. Similarbehavior can be observed in Fig.5 (b), in which the chargingrate of the capacitor voltage (i.e., the slope of the signal)is distinct between the two activities. It means that humanactivity recognition (HAR) can be realized without engagingany specialized sensors. The question is whether such KEH-based activity detection can scale beyond just walking andrunning.

8

TABLE V: Summary of recent research on context sensing from energy harvesting signals.

Source Application Method/Algorithm Performance

Kinetic

Activity recognition

Waveform analysis [67] 90% accuracy on 6 activities with 3 subjects

Machine learning [68]–[70]80% accuracy on 5 activities with 10 subjects, save 79% power compared toaccelerometer based method

Transport mode detection Machine learning [71] 85% accuracy on 3 motorized modes

Calorie estimation Linear regression [72] Estimation accuracy close to that obtained from an accelerometer

Step counting

Waveform analysis [73] Counting error less than 4%

Packet counting [74] Counting error ranges from 4% to 20% on flat surfaces

Peak detection [64] 96% accuracy with 570 steps under 4 surfaces including stairs

Gait recognition MSSRC [75], [76] 95% accuracy with 20 subjects, save 92% power compared to accelerometer

Hotword detection Machine learning [77] Up to 85% accuracy when spoken from 3cm with 8 subjects

Airflow speed monitoring Peak detection [27] Estimation error of 0.2m/s

Acoustic communication ON-OFF keying [78], [79] 5 bit/s data rate with a bit error rate of 1% at distance of 80cm

Thermal

Water flow detection Binary indication [65] Extend the battery life of water flow sensors to 20 years

Heat appliance monitoring Packet interval [66] Successfully implemented to monitor stoves, radiators, and hot water flow

Chemical reaction detection Pulse amplitude [80], [81] A remote receiver can detect the reaction type from the received pulse energy

Solar

Localization Machine learning [82], [83] Achieve a distance estimation accuracy of 21cm

Positioning Sunlight map [84] Infer the longitude and latitude of a location

Place recognition Machine learning [85] 86.2% accuracy with 9 places using 5 types of solar cell

Gesture recognitionPeak counting [86] Detect 3 different hand gestures

Machine learning [87], [88] 96% accuracy with 6 gestures, save 44% power compared to photodiode

Visible light communication OFDM [89] 11.84 Mbps data with a bit error rate of 1.6× 10−3

RF Environment sensing Impedance changing [90] Convert RFID tags into light sensor and temperature sensor

Using piezoelectric energy harvester inside shoes, Han etal. [67] conducted an experiment with three subjects per-forming six different activities — normal walking, strolling,brisk walking, jogging, ascending stairs, and descending stairs.By simply using the relationships between peak values, timelength, and slopes of transducer AC waveforms, they were ableto classify these six activities with over 90% accuracy. Thisexperiment demonstrates that it is feasible to use KEH as aself-powered sensor for HAR.

Khalifa et al. [68] conducted further experiments to assess(a) how well KEH can detect human activities when wornon other body parts, such as wrist, instead of in the foot2,(b) how accurately KEH can detect activities compared toaccelerometers, and (c) how much power can be saved ifKEH, which does not require a power supply, is used forHAR instead of an accelerometer. They also used piezo-electric KEH and conducted experiments with ten subjectsperforming five different activities, standing, walking, running,ascending stairs, and descending stairs, while holding theKEH device in the hand. To enable comparison between KEHand accelerometer, the handheld device was also instrumentedwith an accelerometer. Using machine learning, they foundthat accelerometer could classify these 5 activities with an

2Note that it is relatively easier to detect human motions when the sensoris worn in the foot, but the performance drops when worn in hands.

accuracy of 95%, while KEH could achieve only 80%. Thisoutcome revealed that although KEH has the potential forHAR, it may be challenging to achieve recognition accuraciescomparable to conventional sensor-based systems, especiallywhen the wearable is worn in the hand. On the positiveside, a detailed power consumption experiment revealed thatKEH-based detection consumed 79% less device power thanthe accelerometer-based system. In fact, power consumptionfor KEH-based HAR can be saved even further when theaccumulated energy in the capacitor is used once in a whileto detect different activities instead of continuously samplingthe transducer AC voltage to detect patterns [91].

2) Transportation Mode Detection: As noted in Table II,KEH can harvest energy from vibrations. Given that a wear-able device is subjected to different vibration patterns when theuser travels via different transportation modes, it is expectedthat KEH can be used to sense the transportation mode ofan individual’s everyday travel. Applying machine learningto KEH AC voltage time series data from a wearable piezo-electric energy harvesting device, Lan et al. [71] was able toachieve 85% accuracy in determining whether the user wastraveling by car, bus, or train. A more detailed study furtherrevealed that KEH data can also identify the specific trainroutes traveled by a user [92].

9

Estimated Calorie

AnthropomorphicFeatures

Piezoelectric Transducer

Regression Model

AC Voltage

Weight, Age,Height

Fig. 6: Signal pipeline for calorie estimation from piezoelectricenergy harvesting voltage.

3) Estimation of Calorie Expenditure: During physical ac-tivities, people produce kinetic energy by expending or burningsome calories. Since a wearable KEH harvests kinetic energyproduced during physical activities, the voltage output of awearable piezoelectric transducer should contain informationthat can be used to estimate the amount of calorie expended bythe wearer. To prove this hypothesis, Lan et al. [72] conductedan experimental study with ten volunteers performing twodifferent physical activities — walking and running. Theexpended calorie is estimated using the following regressionmodel (the signal pipeline is shown in Figure 6):

C = Xβ + ε (1)

where, C indicates the estimated calorie expenditure at the kth

minute. X denotes the vector of input signals, including theanthropometric features of the volunteers, i.e, their weight,height, and age, as well as the output AC voltage signalsfrom the transducer. The β and ε are the vector of coefficientsand residual error, respectively. It was found that, for mostsubjects, the calorie estimations obtained from the outputvoltage of KEH were very close to those obtained from a3-axial accelerometer for both walking and running.

4) Step Counting: Several researchers have investigated thepossibility of counting steps from the kinetic energy harvestedfrom a wearable device. In [73], a ferroelectric energy har-vesting device was designed in the form factor of an insoleto harvest energy from human walking. The capacitor voltagewaveform is leveraged for the purpose of step-counting, as itexhibits a stair-like pattern where each ‘stair’ corresponds to asingle step of walking. Their experimental results showed anestimation error less than 4% in step counting.

In [74], the authors also used KEH inside shoes, but the ca-pacitor voltage was used in a different way to count the steps.Once the capacitor voltage exceeds a pre-defined threshold,the capacitor would be discharged to power a Bluetooth bea-con transmission, which is received by a nearby smartphonerunning the step counting app. When the capacitor voltagedecreases below the voltage threshold, Bluetooth transmissionwould stop until the capacitor voltage reaches the thresholdagain. Thus by counting the number of Bluetooth packetsreceived, the smartphone app can estimate the number of stepstaken. The reported error for step counting ranged from 4%to 20% for walking on a flat surface.

Finally, Khalifa et al. [64] observed that KEH power gener-ation exhibits distinctive peaks for each step, which could beaccurately detected using existing peak detection algorithms.They tested their peak detection based step counting algorithm

with four subjects under different walking scenarios includingstairs covering a total of 570 steps and were able to detect thesteps with 96% accuracy.

5) Gait Recognition: It is well known that human gait hasdistinctive motion patterns for different individuals, which canbe detected accurately using accelerometers for user authen-tication [93], [94]. With 20 volunteers, Xu et al., [75], [95]set out an experiment to determine whether KEH AC voltagesignal can also recognize human gait. In their experiments,they considered two different types of KEH, one based onpiezoelectric energy harvester and the other on electromagneticenergy harvester. They found that both types of transducerswere able to detect gait, but with conventional classifica-tion techniques, which operate over a single step, KEH-Gait achieves approximately 6% lower accuracy compared toaccelerometer-based gait recognition. They proposed a novelclassification method, called Multi-Step Sparse RepresentationClassification (MSSRC), which can match the performanceto that of accelerometer by intelligently fusing informationfrom multiple steps. They also showed that by eliminatingaccelerometer from the processing pipeline, KEH-based gaitrecognition can reduce power consumption by 92%.

The experiments in [75], [95] employed isolated transducerswithout connecting them to a capacitor to store energy. Later,Ma et al., [76] discovered that when a capacitor is used to storeenergy, the capacitor voltage interferes with the AC voltagewaveform of the transducer, which reduces gait recognitionaccuracy. To address this problem, the authors in [76] proposeda filter to minimize the influence of the capacitor voltage onthe instantaneous AC signal, thereby achieving simultaneousenergy harvesting and sensing for KEH.

6) Hotword Detection: Human voice creates vibrations inthe air, which could be potentially picked up by the KEHhardware inside a mobile device. Based on this observation,Khalifa et al. [77] studied KEH’s feasibility and accuracy fordetecting hotwords, such as “OK Google”, which are used byvoice control applications to delineate user commands frombackground conversations. Using eight subjects, they eval-uated two types of hotword detection, speaker-independent,which does not require speaker-specific training, and speaker-dependent, which relies on speaker-specific training. Theyfound that when spoken from 3cm, piezoelectric transduc-ers can detect hotwords with accuracies of 73% and 85%,respectively, for speaker-independent and speaker-dependentdetections. They also reported that orientation of the piezo-electric beam relative to the speaker has a significant impacton hotword detection accuracy.

7) HVAC Airflow Monitoring: IoT can play a significantrole in reducing the energy consumption of heating, ventilationand air conditioning (HVAC) systems by adaptively controllingits output, e.g., the air speed through the vents, to the currentpopulation density of the serving area. To this end, small IoTdevices fitted with sensors have to be installed near the ventsto periodically measure the output air speed, temperature, etc.and send them to a smart home control box. For these IoTsto be self-powered with energy harvesting, sensing powerconsumption has to be minimal. However, typical airflowsensors consume on the order of hundreds of mW, which

10

was a motivation for Xiang et al. [27] to study the feasibilityof HVAC airflow sensing using the voltage of piezoelectricenergy harvesters. Through experiments, they demonstratedthat the peak voltage of piezoelectric harvester is a functionof the airflow speed and the voltage value under a given speedhas a variation only up to 0.06V, which suggests that usingvoltage to infer airflow speed has an error of only 0.2m/s.They were able to ‘catch’ the peak voltage by sampling thetransducer output only once every 100 ms, which resulted inan overall power consumption of only 500 µW when KEHwas used as an airflow sensor.

For on-demand infrastructural sensing (i.e., only sense thecontext when desired events occur), conventional approachesusually adopt duty-cycling or adaptive sampling to save systempower. However, using the energy harvester as a passive eventdetector further pushes the power consumption to zero. In [96],Liu et al. presented ECOVIBE, which exploits a vibrationenergy harvester to detect the arrival of trains and thenactivates the sensors for rail condition monitoring. Meanwhile,the harvested energy was used to power the sensors during themonitoring process.

8) Acoustic Communication: Lan et al. [78] proposed toleverage the piezoelectric KEH as a communications receiverto receive data packets transmitted by a nearby speaker bymodulating sound waves within the resonance frequency of thetransducer using simple ON-OFF keying. Experiments with areal KEH device revealed that, at a distance of 80 cm, a laptopspeaker with the proposed modulation scheme can successfullytransmit information to the KEH device at 5 bits per secondat a target bit error rate of less than 1%. While this schemewould enable a KEH-powered IoT to receive commands fromother speaker-enabled devices, such as a laptop, TV, musicplayer, etc., acoustic data transmissions would be audibleand could be annoying to some users. A more advancedmodulation scheme [79] was later proposed that enables thetransmitter to completely hide the acoustic data transmissionwithin background music so the data transmission is notaudible anymore.

B. Context Sensing from Thermoelectric EH

As we have discussed in Section II, thermoelectric energyharvesting can convert any temperature difference in spaceor time into usable electricity. Therefore, it is possible touse a thermoelectric energy harvester as a sensor to detecttemperature-related contexts. Indeed, researchers have suc-cessfully demonstrated such potentials for detecting waterflow, activities of various heating appliances, as well aschemical reactions taking place in a reactor.

1) Water Flow Detection: Currently, wireless water flowmonitoring in residential water fixtures requires installingsensors with access to electrical wiring or replacing batteriesfrequently. Martin et al. [65] proposed a thermoelectric energyharvesting solution, called DoubleDip, which harvests energyfrom the pipe’s thermal gradient, i.e., the temperature differ-ence between the pipe and the room temperature, when hotwater flows through the pipe. The harvested thermal energy isused to both wake up the sensor from deep sleep mode and

to compensate battery energy expenditure. Because the sensoris automatically waked up only when it is needed, instead ofduty cycling at fixed intervals, it can save energy significantly.The authors claim that DoubleDip can extend the battery lifeof water flow sensors to 20 years or even to perpetuity.

2) Heat Appliance Monitoring: Campbell et al. [66] pro-posed and demonstrated that thermoelectric generators (TEGs)can be effectively used as sensors to remotely monitor anyheat-generating appliances. The key idea is to wake up thesensing device when sufficient energy has been harvested andforce it to transmit small wireless packets as long as thereis energy available to harvest, which is basically the sameprinciple applied by the authors of [74] to sense human stepsusing KEH. Since thermal energy is produced only when theappliance becomes active, it is then possible to monitor theactivity of the appliance by simply monitoring the timing ofthe received packets at a nearby receiver. The authors of [66]have successfully implemented this idea to monitor stoves,toaster ovens, radiators, as well as hot water flow throughshower heads.

3) Chemical Reaction Detection: In the context of Internetof nano things, Zarepour et al. [80], [81] proposed a self-powered sensing architecture, called SEMON, for remotedetection of chemical reactions. It is proposed that pyroelectricnanogenerators, which can harvest energy from temperaturevariation in time domain, are fitted with Graphene-based nano-antennas radiating in the Terahertz band (0.1-10THz) andembedded in the catalyst surface where different types ofchemical reactions take place. Each reaction consumes ordissipates some heat, which causes temperature fluctuationson the catalyst surface. When a particular reaction takes placeon the catalyst surface, a SEMON node harvests the energyconsumed or dissipated by the reaction and turns it into aterahertz radio pulse with an amplitude proportional to the har-vested energy. A pyroelectric nanogenerator harvests electricalenergy from each temperature fluctuation and uses the energyto transmit a THz pulse of proportional amplitude. Becausedifferent types of reactions dissipate different amounts ofenergy, the authors were able to show via simulation that aremote receiver can detect the reaction type from the receivedpulse energy.

C. Context Sensing from Solar EH

Similar to KEH revealing motion patterns or thermoelectricenergy harvester recognizing temperature changes, solar en-ergy harvesting provides information about lighting conditions.A wide range of light-based sensing applications, includinglocalization, gesture recognition, and even data transmission,have been proposed based on the characteristics of solar energyharvesting. We survey these developments in this section.

1) Localization and Positioning: Because different loca-tions experience different lighting conditions under the samelighting infrastructure, it is possible to detect location by ana-lyzing the received lights. In [82], [83], the authors proposedand prototyped a wearable solar cell based indoor positioningsystem called LuxTrace. In their prototype, solar cells areattached to the shoulder of the user, which not only harvest

11

PhotocurrentTransparent Solar Panel

light

Gesture Recognition

Energy Harvesting

Fig. 7: Illustration of a transparent solar powered smartwatchwith solar-based gesture recognition.

energy from the ambient indoor light, but detect the receivedlight strength (RLS). By utilizing the RLS, a trained modelwas exploited to estimate the relative distance between theuser’s location and the light sources. Experimental evaluationindicated that LuxTrace can achieve a distance estimationaccuracy of 21cm (80% quantile).

In [85], different types of solar cells are exploited to identifyplaces in daily living spaces. The underlying principles are: (1)the amount of energy generated by solar cells is almost linearwith the environment illuminance and (2) solar cells manufac-tured with different materials, e.g., silicon and organic, showdistinct response to the wavelength of light. Using machinelearning with the data collected from five different types ofsolar cells in nine different places, such as laboratory, toilet,elevator, outdoor, etc., the authors demonstrated that a placerecognition accuracy of 86.2% can be achieved using two typesof solar cells.

The idea of using light to detect location can be extendedto a global scale. Every location on Earth has a unique solarsignature, like a unique sunrise and sunset time, which can beused to create a sunlight map of the Earth. Chen et. al. [84]used such solar signatures to design a system, called SunSpot,which is able to infer a location’s longitude and latitudeseparately, based on the sunlight map.

2) Gesture Recognition: Many future IoT devices are likelyto use solar panels for energy harvesting. By moving a handclose to the solar panel, it is possible to influence the solarenergy harvesting, which in turn can be exploited to realizegesture recognition capabilities in any solar-powered IoT de-vices. Motivated by this idea, researchers have investigatedthe gesture recognition capability of different types of solarcells in different conditions. For conventional opaque solarcells, Varshney et al. [86] designed a thresholding circuit todetect if the solar energy falls below a given threshold. Thisallows them to detect when a hand is moved very close to thesolar panel, which causes the solar energy to drop below thethreshold. By simply counting the number of times the handis moved near the solar panel, they were able to detect threedifferent hand gestures.

Ma et al. [88] investigated gesture recognition feasibil-ity for transparent solar cells. Transparent solar cell is anemerging solar energy harvesting technology that allows usto see through these cells. This revolutionary discovery is

Solar PanelResistor

10cm

Up

Down

Up

UpDown

Down

DownUp

Fig. 8: Illustration of the potential for solar energy harvestingto detect gestures. The left figure shows the experiment setupand the right shows the distinct solar photocurrent waveformsgenerated under four different hand gestures.

creating unique opportunities to turn any mobile device screen,such as the screen of a smartwatch (see Figure 7), intosolar energy harvester. Transparency, however, means that theabsorption efficiency of the solar cell in the visible light bandis significantly lower compared to opaque cells. The lowerabsorption rate results in weaker responsiveness to the visiblelight, making gesture recognition challenging. To overcomethis challenge, machine learning was considered to analyzethe solar photocurrent waveforms produced during differentgestures. As shown in Figure 8, the waveforms of differentgestures are different, which can be detected using machinelearning. Experimental results from [88] demonstrate that fivehand gestures can be detected by transparent solar cells withan average accuracy of 95% and transparent solar cells canrecognize some of these gestures almost as good as the opaquecells.

A key challenge for solar-based gesture recognition researchis lack of access to many new types of solar cells, such astransparent solar cells, which still remain in research labs. Thismakes solar energy harvesting based sensing research out ofreach for many researchers. To facilitate gesture recognitionresearch with solar cells, Ma et al. [87] developed a simulator,called SolarGest, which can be used to generate photocurrentwaveforms for any arbitrary solar cells and hand gestures(the simulator code is released for public use [97]). Theyvalidated the simulator through various gesture experimentswith both opaque and transparent solar cells. To furtherimprove the robustness of solar-based gesture recognitionunder non-deterministic operating conditions, they combineddynamic time warping with Z-score transformation in a signalprocessing pipeline to pre-process each gesture waveformbefore it was analyzed for classification. Their experimentswith 6,960 gesture samples for six different gestures revealedthat even with transparent cells, SolarGest can detect 96% ofthe gestures while consuming 44% less power compared tolight sensor based systems.

3) Visible Light Communication: Visible light communi-cations (VLC) is seen as a promising new alternative toconventional RF-based data communication. VLC harnessesa portion of the electromagnetic spectrum that is currentlylicense-exempt and offers vast amount of bandwidth for high-speed wireless data communication without any interference to

12

VOFF

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VON

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Instr 1Instr 2 Instr 3Instr 4 Instr 5Instr 6 Instr 7Instr 8 Instr 9Instr 10 

Checkpoint 1

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Power failure 1

Power failure 2, 3, 4,…Sisyph

ean Loop

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oltage

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Checkpoint 1

Checkpoint 2

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(b)

Fig. 9: Illustration of Sisyphean execution. (a) Intermittent power supply and execution. Power fails and program executionstops when capacitor voltage falls below a minimum threshold (VOFF ). Power comes back and execution restarts from thelast checkpoint when capacitor voltage reaches to VON . (b) First power failure occurs after completing the 5th instruction; theexecution restarts from instruction 4 (checkpoint 1), but the energy reserve runs out just before reaching checkpoint 2. Thus,the execution reaches up to instruction 8 only to roll back to instruction 4 repeatedly forming a ‘Sisyphean loop’.

existing radio communication systems. VLC uses light emit-ting diode (LED) as transmitter and photodiode as receiver.However, these photodiodes require a power supply to operate.Wang et al. [89] proposed to use standard solar panels asVLC receivers that can demodulate VLC data signal withoutthe need of an external power supply. Using orthogonalfrequency division multiplexing (OFDM), they were able toachieve a data rate of 11.84 Mbps with a bit error rate(BER) of 1.6×10−3. This outcome suggests that simultaneouscommunication and energy harvesting can be realized withsolar panels.

D. Context Sensing from RF EH

Passive radio frequency identification (RFID) tags arebattery-free devices ubiquitously deployed today to monitormany different types of events. These tags are powered byinductive coupling with RF energy generated by a nearbytransmitter known as RFID reader. An RFID tag has an an-tenna which harvests RF energy from the reader’s transmissionand also reflects the reader’s signal to encode the ID of thetag. With conventional RFID, the reader is only able to readthe ID of the tag. These tags are often embedded in animalsand objects that are being monitored. If a reader can detect thepresence of a given ID at certain location and time, then theknowledge can be used to monitor the activity or trajectory ofthe animal or object.

Wang et al. [90] explored an idea of embedding passivesensors, such as phototransistors whose resistance dependson the amount of ambient light received, into RFID tags toalter the impedance of the antenna and hence the amount ofRF energy radiated from its antenna. This would make thereceived signal strength (RSS) at the reader dependent onthe current lighting condition of the RFID tag. The authorsof [90] have successfully demonstrated this outcome for bothlight sensing (using phototransistor) and temperature sensing(using thermistor) using battery-free RFID tags. The authors

claim that the idea is generic and can be used to sense a rangeof other contexts including detecting color, humidity, pressure,or even hand gestures.

IV. COMPUTING OPTIMIZATIONS FOR BATTERYLESSEH-IOTS

Unlike batteries, power supply from energy harvesting isuncertain and often unpredictable. As a result, it is not unlikelyfor an EH-IoT device to experience frequent power outage,which can be as extreme as several times per second forcertain devices [98]. As all variables and registers stored involatile memory during program execution are completelylost when power supply fails, program execution on EH-IoTsmust rely on checkpointing, a technique to periodically savevolatile states in non-volatile memory, so that when powerreturns, the program execution can restart from a known state.Although checkpointing can be effective to survive powerfailures, it gives rise to the following performance issues foran intermittently powered EH-IoT:

• Energy overhead of checkpointing. During runtime, theprogram has to copy all state variables from volatilememory to non-volatile memory at each checkpoint,which will then have to be reloaded to volatile memorywhen power returns. Thus, the total energy overhead ofcheckpointing can be significant if checkpointing is usedfrequently. In addition, the number of state variables tobe saved vary when checkpointing at different stages ofa program. For example, less volatile variables need tobe copied to non-volatile memory if the checkpointingprocess is executed right after a function call rather thanduring the function call. Therefore, proper selection ofthe stage/location for checkpointing is critical.

• Sisyphean execution. As mentioned earlier, checkpoint-ing allows a device to roll-back on the execution time-line and start from a previous saved known state when

2The key is obtained by p = 3, q = 5⇒ n = 3× 5 = 15, E = 3.

13

Pseudocode Intermittent execution runtime with power failure & checkpointing

// InitializeNon-volatile Glucose = read(Glucose sensor);Non-volatile Encrypted_Glucose = 1;Volatile i = 0;

Checkpoint

// Calculate Encrypted_Glucose Repeat

Encrypted_Glucose = Encrypted_Glucose * Glucose;i = i + 1;

Until i = 3Encrypted_Glucose = Encrypted_Glucose mod 15;

// Transmit Encrypted_Glucose transmit(Encrypted_Glucose);

// InitializeNon-volatile Glucose = 2; // sensor value is 2Non-volatile Encrypted_Glucose = 1;Volatile i = 0;

Checkpointing // save i = 0 to non-volatileEncrypted_Glucose = 1* 2 = 2;i = 0 + 1 = 1;

Power failureReboot // i is set to checkpointed value of 0

Encrypted_Glucose = 2* 2 = 4;i = 0 + 1 = 1;Encrypted_Glucose = 4* 2 = 8;i = 1+ 1 = 2;Encrypted_Glucose = 8* 2 = 16;i = 2 + 1 = 3; // meets conditionEncrypted_Glucose = 16 mod 15 = 1;transmit(1); // Error: ‘1’ is transmitted instead of ‘8’!

Fig. 10: An example of encrypting glucose reading using RSA public key (3,15)3. The original glucose reading, stored invariable Glucose, represents one of the 15 different levels of glucose denoted as 0 to 14. The encrypted value is obtained asGlucose3 mod 15. (a) The source pseudocode showing that all variables except index i are non-volatile. (b) Execution of thecode with a glucose reading of 2, which should be encrypted as 23 mod 15 = 8. With the power failing after completing theloop once, the index i is reset to 0 and the loop is eventually executed 4 times producing the encrypted value of 1, instead of8.

power returns following an earlier failure. Thus, at mini-mum, power failures will cause some roll-back preventingsmooth execution of program on EH-IoT devices. Amore serious problem can occur when the accumulatedenergy since the previous power cut-off is not enoughto reach the next checkpoint. In that case, the programmakes a small progress each time it is powered on onlyto roll-back to the same checkpoint repeatedly, makingthe programming task impossible to complete4. Figure 9illustrates the problem.

• State inconsistency. Checkpointing can guarantee 100%state preservation only if all variables are exclusivelymaintained either in volatile memory or in non-volatilememory, but not split across both of them. When allvariables are maintained in volatile memory, they all aresaved in non-volatile memory at the same time at a givencheckpoint, and hence the system can roll back to aconsistent global state after power failure. This is also thesame when all variables are maintained in non-volatilememory. However, neither is practical because volatilememory is fast but expensive, but non-volatile memoryis slow and less expensive. As a result, to achieve fastexecution at a reasonable cost, most commodity hardwareuses a small volatile memory to save the most frequentlyused variables and stores the rest in non-volatile memory.Thus the state of a program is split over volatile and

4This problem is referred to as Sisyphean because in Greek mythology,Sisyphus, a king who annoyed the gods, was condemned for eternity to rolla huge rock up a steep hill, only to watch it roll back down.

non-volatile memories. When the system rolls back to theprevious checkpoint, all variables in volatile memory rollsback to their previous values, but those in the non-volatilememory preserve their new values. As a result, datainconsistencies can arise in certain cases as illustratedin the RSA encryption example in Figure 10.

• Timing inconsistency. A wearable IoT may be taskedwith monitoring a range of physiological data, such asblood pressure, heart rate, glucose level, and so on, andreporting them with timestamps to a cloud-based analyticfor further processing. However, if there is a powerfailure after collecting data from a limited set of sensors,the device can only read the remaining sensors whenthe power comes back. In this case, the timestamps fordifferent set of sensors should be different, which couldbe reported accurately only if the device had persistenttimekeeping across power failures. In battery powereddevices, a real-time clock (RTK) is always maintainedto avoid this problem. However, RTK requires a powersupply and hence persistent RTK cannot be supported inEH-IoTs which experience frequent power failure [99].As a result, The device may report inconsistent timestamps for the sensor data.

To address the aforementioned issues, researchers haveproposed many strategies to optimize checkpointing for bat-teryless EH-IoTs as well as solutions to maintain timing acrosspower failures (see Table VI for a summary). We survey theseworks in this section.

14

TABLE VI: Summary of recent research on computing optimizations for batteryless EH-IoTs.

Literature Technique Checkpoint overhead Sisyphean execution State inconsistency Time inconsistency

Mementos [100]CFG-based checkpoint placement,

X X × ×voltage polling checkpoint activation

Idetic [101]CDFG-based checkpoint placement,

X X × ×voltage polling checkpoint activation

QUICKRECALL [102] Run time checkpointing and × X X ×Hibernus [103], [104] sleeping, advanced ADC interrupt

DINO [105]Task-based checkpointing-versioning,

X × X ×manual idempotency analysis

Ratchet [106]Task-based checkpointing,

X × X ×automatic idempotency analysis

Chain [107]Idempotency task programming,

X X X ×channel-based data exchange

HarvOS [108]CFG-based checkpoint placement,

X × × ×advanced ADC interrupt

Clank [109]Dynamic idempotency task decomposition,

X X X ×checkpointing and versioning

Alpaca [110]Idempotency task programming,

X X X ×privatization data exchange

Mayfly [111]Idempotency task programming,

X X X Xremanence timekeeping

CleanCut [112] Automatic idempotency task decomposition X X X ×

TARDIS [113] SRAM decay timekeeping NA NA NA X

CusTARD [99] Capacitor voltage decay timekeeping NA NA NA X

‘X’ represents the corresponding challenge was addressed in this work, while ‘×’ means not. ‘NA’ means not applicable.

A. Checkpointing Optimizations

1) Checkpoint placement and activation: refers to thescheme of inserting potential checkpoints to the program atcompile-time and activate checkpointing processes at run-time.Inserting checkpoints at different locations of the programleads to distinct checkpointing overhead (including energyand memory) as the number of variables to be saved varies.In addition, checkpointing too early before a power failureresults in a waste of energy that can be used to performmore computations, while the checkpointing process cannot becompleted if it is too late as the residual energy is not enough.Thus, checkpoint placement should take the checkpointingoverhead as well as device residual energy into consideration.Due to the unpredictable power failures, however, optimalreasoning about the potential checkpoints is extremely difficultand imposing a heavy burden on the programmer.

Momentos [100] was the first work that supports the ex-ecution of long-running programs on intermittently powereddevices, using checkpointing approach. It required the pro-grammer to manually place trigger points (i.e., potential check-points) in the code. The authors proposed three checkpointplacement strategies based on the Control Flow Graph (CFG),which is a graphical representation of a program includingbasic constructs (e.g., branching statements, loops, and etc.)and the edges connecting these constructs. Specifically, Me-mentos placed a trigger point at each loop latch (loop-latch

mode) or after each call instruction (function-return mode),which allows an energy check after each loop iteration orat each time a function returns, respectively. In timer-aidedmode, a timer interrupt is triggered to periodically measure thesupply voltage and activate the checkpoint if necessary. Theunderlying placement rationale is that loop ends and functionreturns are locations where one may expect the stack to storefewer data, thereby less checkpointing overhead. Similarly,HarvOS [108] located lightweight checkpoints based on theinformation provided by the CFG of a program. The minordifference is that HarvOS also exploited the memory allocationpattern derived from static code analysis techniques [114] toaccurately calculate the amount of memory allocated duringthe program.

However, placing checkpoints purely based on CFG maynot be energy efficient in some cases. For instance, whenpower loss happens right before a function return, the programwill restart from the previous checkpoint that is located beforethe function starts, therefore wastes the energy to re-executethe whole function. To address this problem, Mirhoseini etal. developed Idetic [101], which optimally insert checkpointsconsidering not only the checkpointing overhead but also there-computation energy cost. This is achieved by leveragingthe information provided by the Control Data Flow Graph(CDFG). CDFG is an intermediate representation of a programthat lies between the high-level behavioral specifications and

15

the low-level Hardware Description Language (HDL), whichmodels the connections and dependencies between processes.Different from CFG that only represents the control flow of aprogram, CDFG can present the data flow between differentconstructs as well. Idetic then formulated an optimizationproblem and derived optimal checkpoints using dynamic pro-gramming.

Actually, it is not necessary to initiate a checkpointingprocess at each checkpoint inserted in advance. For example,if the residual energy is enough to support the computationsuntil next checkpoint, checkpointing of current system state isa waste of energy. Thus, energy-aware checkpoint activationat run-time was proposed [100]–[102]. At each checkpoint,currently available energy of the device is estimated by mea-suring the storage capacitor voltage and calculating using theequation E = 1/2CV 2, where C is the capacitance of thecapacitor and V is the measured voltage. Then, a thresholdbased strategy is used to determine whether to activate acheckpoint. In detail, if the available energy is higher thana pre-defined threshold, current checkpoint would be skippedand execution continues; otherwise, the current system statewould be checkpointed.

However, since the number of computations between everytwo successive checkpoints may be different, the determina-tion of the pre-defined threshold is challenging. Usually, thethreshold is derived through complex offline emulation [100],[103]. In HarvOS [108], the decision on whether to activate acheckpoint not only depends on the currently available energybut also the worst-case estimation of the energy required toreach the next checkpoints, where the worst-case assumes thatno energy will be harvested before the next checkpoint. Thisstrategy ensures that checkpoints are activated much closerto the last practical point where the system should take acheckpointing process, thereby reducing the energy waste onuncheckpointed work and unnecessary checkpointing.

2) Checkpointing at run time: Some works, suchas QUICKRECALL [102], Hibernus [103], and Hiber-nus++ [104], leveraged the concept of checkpointing to ensureprogress of a long-running program, but does not requireinsertion of checkpoints in advance. The underlying idea isthat it initiates a checkpointing process only when a powerfailure is imminent, and then sleeps immediately. Otherwise,it continuously executes programs without interruption. Simi-larly to the basic checkpointing method, the decision to takea checkpointing (or the judgment that a power failure isabout to happen) is based on the measurement of currentlyavailable energy and the threshold based activation. As there isno pre-inserted checkpoint, continuously polling the capacitorvoltage is needed, which incurs heavy energy consumption.As a result, Hibernus and Hibernus++ exploited an advancedanalog-to-digital converter (ADC) that has interrupt function-ality to detect whether the voltage reaches a certain thresh-old, thereby significantly reducing the power consumption ofvoltage polling. However, how to determine the threshold stillremained a challenge and offline emulation was conducted inHibernus. To avoid the risk of power failure when saving thesystem states, Hibernus++ implemented a decoupling capacitorthat specially provides power supply to the device during the

checkpointing process.Run-time checkpointing eliminates the burden on program-

mers to reason and insert potential checkpoints at compile-time. Moreover, since it sleeps immediately after check-pointing, data inconsistency issue does not exist. However,it remains two drawbacks. First, it assumes that the closerthe checkpointing to a power failure, the better the programexecutes, which may not be the fact as it ignores the energyand memory overhead of checkpointing. For example, the lastmoment before power loss may have a large amount of volatiledata to be saved, which consumes more power during the storeand restore process, and occupies more non-volatile memory.Second, the size of the decoupling capacitor is limited andfixed, so the stored energy may not be enough to complete acheckpointing process.

3) Task-based checkpointing: To support intermittent pro-gram execution, there was another branch of works thatdecompose a long-running program into a sequence of shortand atomic tasks, such as sampling a sensor reading. Further-more, the idea of idempotency [115], [116] was employed toaddress the data inconsistency issue. Specifically, if a taskis idempotent, it can be executed multiple times withoutproducing different results. To ensure idempotency, the taskshould not contain any idempotency violation, which is awrite instruction to a non-volatile memory that was firstaccessed by a read instruction, termed as write-after-read orWAR. Recall the example in Figure 10, non-volatile variableEncrypted Glucose is first read after the checkpoint and thereis another line of code Encrypted Glucose = 1*2 assigning anew value to it before the next checkpoint, i.e., a WAR. Aslong as a power failure happens after the write instruction, apower failure before the next checkpoint would result in datainconsistency.

Depending on how these tasks are connected, these workscan be further divided into two categories. First, the decom-posed tasks are connected by lightweight checkpoints, suchas DINO [105], Clank [109] and Ratchet [106], in which acheckpointing process would be launched at task boundaries.DINO and Ratchet analyzed the idempotency and decomposeda program at compile-time using static code analysis [114],which imposes heavy burdens on the programmers. Fortu-nately, Colin et al. developed CleanCut [112], a tool that canautomatically decompose programs into efficient, idempotenttasks at compile-time. At run time, executions were performedtask-by-task and a checkpointing process is launched aftera task. In contrast, Clank dynamically and automaticallydecomposed program executions into a stream of sections atrun-time.

Second, the execution just follows a task flow and nocheckpointing is needed between tasks, such as Chain [107]and Alpaca [110]. Instead of decomposing a long program thatis written line-by-line (or instruction-by-instruction), Chainand Alpaca designed a new programming model where aprogram is written at the granularity of a task, i.e., groups ofinstructions, and these tasks are connected through a controlflow graph. Figure 11 illustrates the comparison of conven-tional line-based coding model and task-based programmingmodel. In addition, Chain and Alpaca proposed a judicious

16

Start

Instruction 1

Instruction 2

Instruction N

…

End

Start

Task 1

Task 2

Task N

…

End

(a) (b)

Instruction 1_1Instruction 1_2…

Instruction 2_1Instruction 2_2…

Instruction N_1Instruction N_2…

Fig. 11: Comparison of (a) conventional line-based coding and(b) task-based programming model.

data management scheme that eliminates checkpointing aswell as data inconsistency. In detail, the variables are definedas either task-local or task-shared, where the former is limitedto use in individual tasks and stored in volatile memory, whilethe latter is defined in the global scope and stored in non-volatile memory. Since a task is idempotent and its inputsare stored in non-volatile memory, it is expected to producethe same results from power failures thereby preserving dataconsistency. For task-shared variables, Chain allocated a blockof non-volatile memory (termed as channel) to each pair oftasks, which is not memory efficient as it creates multipleversions of a task’s inputs/outputs. Alpaca solved this problemby discarding channels and linking the memory blocks ofdifferent tasks directly. Figure 12 presents the comparison ofthese two data exchange mechanisms.

4) Watchdog checkpointing: In checkpointing approach, ifthe maximum energy budget of the storage capacitor is notenough to support the executions to reach the next check-point, the instructions from last checkpoint would be executedrepeatedly and never completed. This is also applicable tothe task-based method when the maximum available energyis not enough to successfully accomplish a task, e.g., when atask is too long. To deal with the ‘Sysiphean’ problem andguarantee program progress, a watchdog timer was widelyadopted in the literature [100], [101], [106], [108], [109].Specifically, when the program re-executes from a checkpointor a task boundary, a watchdog timer is initiated. Once thetimer interrupt occurs, it launches a checkpointing process tomandatorily split previously uncompleted code group into twoor multiple smaller groups. Although watchdog checkpointingis not optimized in terms of the energy and memory overhead,it is the most effective way to address the ‘Sysiphean’ problem.

B. Timekeeping across power failures

Maintaining a reliable sense of time in battery-powereddevices can be easily achieved using an internal clock (e.g.,RTC, real-time clock). For the intermittently-powered devices,however, it is very challenging as the system clock is turned offwhen power fails. Thus, to execute a meaningful task, the firstchallenge is to track the time elapsed between power failures.

(a) (b)

Task T1

Outputs – T1

Task T2

Outputs – T1

Task T3

Task T1

Outputs – T1

Task T2 Task T3

Non-volatile Memory

Fig. 12: Comparison of data exchange mechanism in (a) Chainand (b) Alpaca.

In [99], [113], the authors exploit remanence decay of SRAM(Static Random-Access Memory, a type of volatile memory)and capacitor for timekeeping, respectively.

The principle of remanence decay on SRAM is that theSRAM cells lose their states (reset from 1 to 0) graduallywhen the power is cut off. The next time the device ispowered on, the elapsed time can be roughly estimated basedon the percentage of cells remaining 1. Similarly, the ideaof remanence decay on capacitor is based on the fact thata capacitor voltage dissipates slowly after disconnected froma power supply. Thus, the elapsed time can be estimated bymeasuring the capacitor voltage after reboot. TARDIS [113] isa timekeeper based on SRAM decay, which provides coarse-grained time tracking. CusTARD [99] is based on capacitorvoltage decay and allows finer-grained timekeeping, at thecost of equipping an additional capacitor on the device. Eitherof the timekeepers can accurately track time through powerfailures up to 45s. A detailed comparison of TARDIS andCusTARD can be found in [99].

After guaranteeing a sense of time, i.e., the time elapsedbetween power failures is obtained, it is critical to efficientlydetermine whether to execute from previous states or tolaunch a new task. In [111], the authors presented Mayfly,a programming language as well as a runtime that supportstimely execution on intermittently-powered devices. As a pro-gramming language, Mayfly programs consist of a sequenceof tasks, like Chain and Alpaca. The difference is that, to takethe timing information into consideration, it designs additionalattributes to each task, such as priority, expire time, and thecollect (a command that assigns the amount of data required).Mayfly exploits the CusTARD timekeeper to track the timeelapsed through power failures. After reboot, Mayfly runtimefirst estimates the elapsed time from last power outage andupdates the local system time. Then, according to currentsystem time, the lifetime of previous data can be calculated.If the data are expired, it discards the data and rollback to thebeginning of the task. Otherwise, the execution continues.

V. COMMUNICATIONS OPTIMIZATIONS FOR EH-IOTS

The main communication design challenge for EH-IoTs is tooptimize the power allocation for the transmitted packets overtime in order to make the most use of the energy being har-vested. More specifically, the goal is to solve for the optimum

17

Communication Optimization

Offline Online

Convex Optimization

Dynamic programming 

MDPPartially observedMDP

Statistics available?

Reinforcementlearning

N

Y

Fig. 13: Communication optimization taxonomy.

power allocation policy (or power level) as a function of thecurrent channel and energy harvesting states. This optimizationproblem has been extensively studied in the literature underdifferent networking contexts and comprehensively surveyedin several recent articles [117]–[121]. Figure 13 captures theoptimization taxonomy while Table VII summarizes some ex-amples from different networking contexts. A key observationis that, irrespective of the networking contexts, researchersbasically considered two different optimization approaches,offline or online:

Offline optimization: This method assumes that the trans-mitter has perfect a priori knowledge of the communicationchannel as well as the energy arrival process. With this knowl-edge, the optimization problem is formulated to maximize acertain short-term utility depending on the networking contextand solved by convex optimization techniques [121]. Thisapproach is usually not practical, but provides insight that canbe used to design a good practical solution.

Online optimization: In this case, the transmitter doesnot have to know the channel and the energy arrivals andmake power allocation decisions based only on currentlyobserved channel and energy states. Obviously, this is a morepractical approach, which does not require the transmitter toknow the exact channel state information, the exact arrivaltimes, and the amount of harvested energy. When energystates are fully observable, Markov Decision Process (MDP)is commonly used to solve the optimal power allocation policywhich maximizes long term reward. If energy states cannot beobserved precisely, Partially Observable MDP is used. TheseMDP problems can be solved using dynamic programmingif the statistical knowledge of the underlying system model,i.e., the transition probabilities, are known. However, for manypractical IoT applications, it is difficult to gather statisticsof energy harvesting prior to deployments. In such cases,reinforcement learning can be very effective to learn thesystem model online and derive the optimal power allocationpolicy without having to know the transition probabilities.

Previous work [119]–[121] surveyed only a few reinforce-ment learning based power allocation optimizations that werepublished until 2016. However, since then reinforcement learn-

ing has gained significant attention and many more paperswere published after 2016. We therefore provide a briefsurvey of these recently published works on reinforcementlearning in this section. Packet-less transmission is anothernovel design paradigm pursued by researchers to minimizeenergy consumption in energy harvesting devices. Given thatthese works have not been covered in the recent surveys [119]–[121], we summarize them as well in this section.

A. Reinforcement Learning based Communication Optimiza-tion in EH-IoTs

Application of reinforcement learning to communicationoptimization in EH-IoTs is receiving much interest lately asit requires minimal system state information and is suitable toimplement in practical scenarios. A variety of issues (suchas transmission power allocation, transmission policy, userscheduling, and so on) under different network contexts (e.g.,point-to-point, wireless sensor network (WSN), cellular net-work, and so on) have been investigated. Generally, basedon the number of considered system state, the optimizationproblem can be modeled as continuous MDP and discreteMDP if the system state is infinite and finite, respectively.When solving the problems, continuous MDP requires onemore step to handle the infinite states, and techniques likelinear approximation [135], [149], [152] and polynomial ap-proximation [138] are proposed. Table VIII summarizes theadvancements of reinforcement learning in EH-IoTs since2016.

Reinforcement learning in point-to-point EH communica-tion has been extensively explored. Transmission power al-location was investigated in [135]–[137]. In detail, formu-lating continuous MDP, Ortiz et al. [135] exploited linearapproximation and binary functions to handle infinite statesand State-action-reward-state-action (SARSA) algorithm tolearn the optimal policy. In contrast, Sakulkar et al. [136]considered discrete MDP and proposed the linear program ofsample means (LPSM) algorithm to learn the optimal powerallocation policy. Particularly, Kim et al. [137] proposed anaction bounding deep Q-learning algorithm to accelerate thelearning process. Wu et al. [138] investigated the transmissiondecision problem, i.e., whether to transmit a packet or not, andproposed an after-state SARSA learning algorithm. Ayatollahiet al. [139] considered a MIMO system where the transmittercan change the number of antennas during transmission andemployed Q-learning to learn the optimal transmission policy.Masadeh et al. [140] utilized SARSA algorithm to investi-gate the exploration and exploitation balancing problem anddemonstrated that convergence-based algorithm outperformsthe epsilon-greedy algorithm.

Some literature investigated the implementation of rein-forcement learning in EH WSN, using real-world experimentdata. Wireless sensor nodes equipped with a solar cell performsensing tasks and transmit the information to a remote server.Kosunalp et al. [141] utilized Q-learning to predict the so-lar energy arrival and the experimental results demonstratedthat a prediction error ratio of 0.3 was achieved. Chen etal. [142] considered the optimal policy that schedules the

18

TABLE VII: Examples of EH-communication optimization problems.

Network Context Problem/Objective Strategy

Point-to-point

Optimal power allocation to maximize throughput [122] Offline/Convex optimization

Optimal energy management to minimize energy consumption [123] Online/MDP/Reinforcement learning

Optimal transmission policy to maximize throughput [124] Online/MDP/Reinforcement learning

Cooperative Network

Optimal power and rate allocation at Tx/relay to maximize throughput [125] Offline/Convex optimization

Optimal relay selection to maximize throughput [126] Offline/Convex optimization

Optimal relay scheduling to maximize throughput [127] Online/PO MDP/Dynamic programming

Cognitive NetworkOptimal SU channel access policy to maximize throughput [128] Offline/Convex optimization

Optimal power allocation for cognitive relay to maximize throughput [129] Offline/Convex optimization

Optimal SU channel access policy to maximize throughput [130] Online/MDP/Reinforcement learning

Cellular NetworkOptimal user scheduling to maximize throughput [131] Online/PO MDP/Dynamic programming

Optimal BS ON/OFF decision to maximize its availability region [132] Offline/Convex optimization

WIPTOptimal transmission beamforming to minimize power consumption [133] Offline/Convex optimization

Optimal power splitting scheme to maximize power transfer and throughput [134] Offline/Convex optimization

*MDP - Markov Decision Process, PO MDP - Partially Observed Markov Decision Process, WIPT - Wireless Information & Power Transfer.

state (active or sleep) of sensor nodes to maintain effectivearea coverage. Q-learning algorithm was implemented and theexperiment results suggested that a coverage ratio of 0.99can be obtained. Paper [144], [143] and [145] considered thepower management policy at the sensor nodes, i.e., how toschedule the energy for sensing, transmission and sleeping.Hsu et al. [144] proposed and implemented a fuzzy Q-learningalgorithm, while Shresthamali et al. [143] implemented the Q-learning algorithm. Without real-world experiments, Aoudia etal. [145] modeled the problem as a discrete MDP and proposedan actor-critic learning algorithm.

Future cellular networks (or heterogeneous networks) areexpected to densely deploy small-cell base stations (BSs) toincrease capacity and it is possible to power these BSs withenergy harvested from the environment, like solar and wind.However, incorporation of energy harvesting into both BS anduser poses more challenges on managing the network, such asuser access control [146], [147], BS ON/OFF switching [148],and resource allocation [149], due to the stochastic natureof energy source. Chu et al. [146] proposed a long short-term memory (LSTM) deep Q-learning algorithm to learnthe optimal multi-access control policy at the BS. Using theproposed method, their following work [147] investigated jointaccess control and battery prediction problem and developeda two-layer reinforcement learning network to maximize thesum rate and minimize the prediction loss. Assuming the trafficof small cell BS can be offloaded to a macro BS, Miozzo etal. [148] investigated the optimal ON/OFF switching policyof small cell BSs to improve energy efficiency of the overallnetwork. Specifically, distributed multi-agent Q-learning wasproposed to learn the energy income and traffic demandpatterns at each small cell BS and determine its ON/OFFstate. Similarly, to improve network energy efficiency, Wei etal. [149] utilized the actor-critic algorithm to investigates theoptimal policy for user scheduling and resource allocation.

Wireless information and power transfer (WIPT) allows thetransmitter and receiver to exchange energy and informationin a time-devision manner. To achieve the optimal systemperformance, time slot allocation for information and energytransmission is critical. Recent works [150], [151] attempted tosolve this problem using reinforcement learning. Specifically,[150] consider the Tx transmits energy to the Rx and Rx usesthe harvested energy to send data back to Tx. The Q-learningalgorithm was exploited to find the optimal data transmissionrate at the receiver and the results domonstrated that thealgorithm can reduce packet loos rate by 60%, comparedto random selection scheme. Differently, Chun et al. [151]considered the Tx transmits both information and power toRx. To maximize the receiption rate at the Rx, the Q-learningalgorithm was utilized to learn the optimal data transmissionrate at the Tx and energy harvesting interval at the Rx.

Ortiz [152] employed reinforcement learning to find theoptimal power allocation policy for Tx and relay in cooperativeEH networks, with the purpose of maximizing the throughputat the receiver. Specifically, two-hop communication was splitinto two point-to-point problems (transmitter to relay and relayto receiver) and solved by the method proposed in [135]individually. In cognitive networks, the secondary user (SU)can perform either spectrum sensing (to detect whether thespectrum is free), channel probing (to acquire CSI), or datatransmission (need to configure transmission power) at eachtime slot. Assuming the SU is powered by the energy harvestedfrom the ambient environment, Wu et al. [153] proposed apolicy-based learning algorithm to determine SU’s action withthe goal of maximizing its throughput.

In mobile edge computing (MEC) systems, end IoT devicesoffload data to edge servers, and edge servers offload part of itsworkload or preprocessed data to a remote cloud. ConsideringEH MEC where both the IoT devices and edge servers canharvest energy from the environment, reinforcement learning

19

TABLE VIII: Summary of recent advancements using reinforcement learning in EH-IoTs.

Network Issue Reference Perform MethodologyContext RL at

Power allocation[135] Tx Continuous MDP, linear approximation, binary functions, and SARSA

[136], [137] Tx Discrete MDP, LPSM, action-bounding deep Q-learning

Point-to-point Transmission decision [138] Tx Continuous MDP, polynomial approximation, after-state SARSA

Transmission policy [139] Tx Discrete MDP, Q-learning

Exploration & exploitation [140] Tx Discrete MDP, SARSA

WSN

Energy prediction [141] Nodes Q-learning with real solar data

Sleep scheduling [142] Nodes Q-learning with real solar data

Power management[143], [144] Nodes Q-learning [143], Fuzzy Q-learning [144] with real solar data

[145] Nodes Discrete MDP, actor-critic learning algorithm

Cellular

Access control [146], [147] BS Discrete MDP, LSTM deep Q-learning

BS ON/OFF switching [148] BS Discrete MDP, distributed multi-agent Q-learning

Resource allocation [149] BS Continuous MDP, linear approximation, actor-critic learning algorithm

WIPT Transmission interval/rate[150] Rx Discrete MDP, Q-learning

[151] Tx/Rx Discrete MDP, Q-learning

Cooperative Power allocation [152] Tx/Relay Continuous MDP, linear approximation, SARSA

Cognitive SU action decision [153] SU Discrete MDP, after-state, policy-based learning algorithm

MEC Data offloading[154] Edge Discrete MDP, post-decision state based learning

[155], [156] IoTs Discrete MDP, post-decision state based learning, deep CNN

SU - Secondary User, MEC - Mobile Edge Computing.

has been applied to optimize the offloading policy at IoTdevices and edge servers. At edge servers, Xu et al. [154]proposed a post-decision state (PDS) based learning algorithmto obtain the optimal workload offloading (to the centralizedcloud) policy, which minimizes the long-term system cost.Instead, at the IoT devices, Min et al. [155], [156] combinedPDS based learning and deep convolutional neural network(CNN) to select the optimal edge device and offloading rate.

B. Packet-less Communication in EH-IoNT

In many IoT applications, the deployed sensor nodes onlyneed to detect an event, such as normal vs. faulty operationof the monitored structure, and transmit that information to anearby access point. Because the event information is eitherjust binary or event identification from a small set of possibleevent types, a single pulse could be used to convey thisinformation, thus eliminating the need to transmit a packetwith its associated overhead such as node identification ad-dress, preamble, synchronization, etc. In low energy harvestingenvironments, this can help maintain the energy consumptionof the device below the energy production rate.

Such packet-less pulse-based event notification has been in-vestigated by different research groups. Without packet headerand payload, the main issues to solve is how to localize theevent and how to convey the event type from a single pulse.For structural health monitoring of an airplane wing, Das etal., [157] proposed transmitting an ultrasonic pulse from a

sensor to a nearby access point through the metal substrate toconvey the detection of fault when vibration exceeds a giventhreshold. The sensor nodes are placed in a carefully designedcellular diagram, which allows the access point to detect theposition of the event by detecting the direction of arrival ofthe pulse. The vibrations are also used to harvest energy andpower the pulse, thus eliminating the need for batteries.

The idea of using a single pulse to convey event informationis particularly attractive for nanoscale IoT, also known asInternet of Nano Things (IoNT), where nanoscale sensorsmust conserve transmission energy as much as possible tomaintain energy consumption below the extremely limitedamount of energy it can possibly harvest from the environment.A detailed analysis in [163] revealed that even with 1 pJ energyconsumption per pulse, the transmission power would accountfor 68% of the total device power consumption, suggestingthat the number of pulses must be minimized to achieve abalance between power consumption and generation in IoNT.In our recent work [158], we have shown that if the events tobe detected emit distinct amounts of energies, it is possible toconvey both event type and location information using a singlepulse. This is possible if all harvested energy from the eventis used to transmit the pulse with a carefully allocated pulsewidth for each sensor node located at different locations. Inthis case, the amplitude of the pulse is influenced by both thetype of event and its location, thus a classifier at the accesspoint can uniquely detect the event type and its location by

20

TABLE IX: Summary of methods for packet-less event notification using a single pulse in EH-IoTs.

Ref Application Pulse medium Detectable #of event types Method of detection Energy

source

[157] SHM for aeroplane ultrasound throughmetal substrate binary DoA for location, pulse for event wing

vibrations

[81] IoNT for chemical re-action detection THz via air > binary pulse amplitude for classification at AP, pulse

width for location, pulse energy for event events

[158] IoNT event monitoring THz via air > binary pulse energy for event (location cannot be detected) chemicalreactions

[159], [160] IoNT event monitoring THz via air > binary DoA for location, derivative order for event anything

[161], [162] IoNT event monitoring mmWave/THz via air > binary DoA for location, central frequency for event anything

*SHM - Structure Health Monitoring.

simply detecting the amplitude of the pulse.The method proposed in [158] does not scale to large num-

ber of sensor nodes as the number of classes to classify usingonly the amplitude of the pulse increases. In our subsequentresearch [159]–[162], we have shown that if the access pointcan be configured with necessary hardware for detecting thedirection of arrival (DoA) of the pulse, then a given pulsefeature only has to classify the event type as the locationis already obtained by DoA. We have demonstrated that ashigher derivative of Gaussian pulses are often used for IoNT,we can use a range of pulse features for detecting event types,including the derivative order [159], [160] as well as the centerfrequency [161], [162] of the Gaussian pulse. We compare thevarious methods of conveying event notification using a singlepulse in Table IX.

C. Ambient Backscatter CommunicationIoT devices are supposed to communicate with each other

or the Internet. However, typical radio modules are powerhungry, therefore the overall power consumption of an IoT canbe dominated by communication depending on the amount ofdata transmitted. Such high energy consumption arises fromthe fact that conventional radio modules (e.g., Wi-Fi, Blue-tooth) require active generation of radio frequency signals andemission to the space. In recent years, fortunately, the emerg-ing ambient backscatter communication has demonstrated itspotential to achieve ultra-low-power communication, whichmakes it promising to be practically realized in future EHIoTs, such as RFID [164].

Instead of generating RF signals itself, a backscatter trans-mitter transmits data by simply re-modulating and reflectingsurrounding RF signals that are transmitted by ambient RFsources, such as TV towers, cellular base stations, and Wi-Fi access points [165]. Ambient backscattering mainly hasthree advantages. First, it significantly reduces the energyconsumption of data transmission as it does not generateRF signals and operates passively. Second, it simplifies thehardware design (RF components) by eliminating the RFemitter. Third, it alleviates the pressure on spectrum as itreuses the licensed spectrum without the need of expensivededicated spectrum.

The low energy consumption and simple hardware designmake ambient backscatter attractive and promising for EH

IoTs. However, practical realization of backscattering stillfaces some challenges. For example, how to improve the datarate and energy efficiency to meet the application requirement,how to guarantee robust communication without the control ofambient RF sources, and how to manage interference betweenbackscatter users and legitimate licensed users, how to ensuresecure communication to avoid attacking and eavesdropping,are still being investigated. As a recent survey comprehen-sively reviewed the advancements and remaining issues ofambient backscatter communication, we will not repeat themhere, but refer the reader to [166].

VI. FUTURE RESEARCH DIRECTIONS

A. Energy-aware debugging

During software development, program debugging is highlyrecommended to ensure an IoT device can operate correctlyin real deployments. For example, a Devpack is neededwhen debugging a TI SensorTag [167]. However, conventionaldebugging cannot fully address the concerns of EH-IoTs.First, it provides constant power to the target device, whichwould treat an EH device as a grid- or battery-powered de-vice. Therefore, bugs that may occur under energy harvestingconditions may not be detected. Second, developers usuallyinsert some additional codes (e.g., printf()) to monitor thestate of the program during debugging. But execution of thesecodes consumes extra energy and may incorrectly lead to theSisyphean problem that we learned from our survey in SectionIV. Work on designing new debugging concepts and tools forEH-IoTs is rare with the exception of [168], which attemptsto actively manipulate the amount of energy delivered to thetarget device to account for any additional energy consumptiondue to extra debugging codes. More research in this directionis required to realize highly efficient and reliable debuggingand development support for EH-IoTs.

B. Deep learning to improve context sensing from energyharvesting signals

The lesson that we have learned from Section III is thatenergy harvesters could potentially replace specialized sensorsin EH-IoTs saving significant power consumption, but at theprice of reduced context detection performance. Application ofdeep learning has shown great promise for many challenging

21

detection problems, such as speech detection, face recognition,natural language processing and so on. However, its applica-tion to context detection from energy harvesting signals havenot been explored yet. Once EH-IoTs are deployed widely,they will generate a huge amount of data, which will createthe opportunity to apply deep learning to improve contextdetection accuracy. In the meantime, simulations could be usedto generate the required data for training.

C. Exploring advanced energy harvesters for context sensing

Our survey in Section III revealed that most EH-basedcontext sensing research to date used the very basic KEHor solar cell hardware, which have certain limitations forcontext recognition. For example, the frequency responseband of most kinetic energy harvesters is narrow and well-tuned to maximize their energy harvesting efficiencies fortarget applications. As a result, this KEH hardware is moresensitive to capture the signal within its resonance band, whileresponding less to the signals outside their narrow responseband. On the contrary, specialized sensors like accelerometersand microphones are engineered with a wide and flat responsecurves ranging from 1Hz to several thousands of Hz overthe frequency of interests. Also, accelerometers can providemeasurements in three dimensions, while the popular KEHdevices, such as the ones used in existing context detectionresearch surveyed in Section III only harvest energy from asingle direction.

Clearly, a new research direction would be to explore thecontext detection opportunities with more advanced kineticenergy harvesting hardware, such as those that can harvestenergy from multiple axes [169], multiple elements within anarray [170], or multiple modes such as from both piezoelectricand electromagnetic effects [171]. Similarly, use of moreadvanced solar cells which can harvest energy from a widerrange of optical frequencies [85] may help detect contextsmore accurately.

Currently, energy harvesting hardware is designed purelywith the objective of improving the energy harvesting densityor efficiency of the product without any consideration ofits context detection capability. A future multidisciplinaryresearch direction would be to explore new materials andprocesses that can jointly optimize both energy harvestingcapacity as well as the context detection performance.

Finally, researchers so far have explored context sensingusing a single mode of energy harvesting. In future, IoTsmay combine multiple modes of energy harvesting, such ascombine both kinetic and solar, to boost the power supply.Such multi-modal EH-IoTs would provide richer EH signals,which could be exploited for more accurate context detection.Designing algorithms that can effectively fuse informationfrom multiple EH signals would be an interesting future work.

D. Context sensing from RF energy harvesting

Our survey in Section III revealed that, compared to ki-netic and solar EH based context sensing, research on RFEH based sensing is rare. Recently, received signal strength(RSS) and channel state information (CSI) of Wi-Fi signals

have been extensively exploited to perform a multitude ofsensing applications, such as activity recognition [172], [173],fall detection [174], occupancy counting [175], gait recogni-tion [176] and many more. Whether these sensing objectivescould be realized with the harvested energy signal from Wi-Fireceptions remains unexplored.

E. Secure communication for EH IoTs

Due to low and unpredictable energy supply, EH-IoTscan be more vulnerable to security attacks compared toconventional battery-powered IoTs. For example, as an EHIoT may suspend in the middle of secure communicationprotocols, it opens new attack horizons, e.g., DoS attacksto the gateway that communicates with EH-IoT device. Dueto its low power requirements, backscattering is consideredan attractive communication option for EH-IoTs. However,the passive operation makes backscatter communication vul-nerable to various security attacks like eavesdropping andjamming [166]. While most security attacks can be effectivelyaddressed by encryption, the overhead of sharing secret keysmake them challenging for EH-IoTs that must operate withminimal energy supply. A future research direction could beto exploit the energy harvesting signals for generating keysdynamically with minimal power consumption. For example,Lin et al. [177] has recently demonstrated that the tinyvibrations generated by human heartbeats can be by measuredby piezoelectric-based wearable KEH, which in turn can beused to generate symmetric keys for two IoTs worn by thesame user (both devices are subjected to the same heartbeats).However, whether EH signals, such as photocurrents fromsolar cells, can also be useful for key generation for non-wearable IoTs, remains unexplored.

F. Integration of embedded system research

As shown in Figure 3, current EH-IoT architecture simplyuses an energy harvesting module to replace the battery, whichcan enable wide and rapid adoption of energy harvestingwithout requiring significant hardware modification in IoTdesign. However, future research direction can be a moreholistic design, where the energy harvester, energy storage, aswell as microcontroller can be integrated together. Such designrequires research in both material science and electronics.

G. Identifying and addressing new systems challenges for EHIoTs

Powering IoTs using energy harvesting has been proposedfor many years and most of the research is based on simulationor a single prototype. Often, interesting systems challengesarise in large-scale practical deployments. Therefore, a futuredirection would be to study practical deployments of EH-IoTsto better understand the system challenges involved in thoseapplications.

VII. CONCLUSION

Energy harvesting is a promising new approach to per-petually power a growing number of IoT sensors. The key

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challenge for EH-IoTs is to ensure smooth operation over un-predictable power supply, which requires optimizations acrosssensing, computing, as well as communications. In recentyears, many researchers have worked on this problem andproposed various solutions to optimize the use of harvestedenergy. In this paper we have classified, compared, and ana-lyzed these solutions, identified lessons learned and discussedpotential future directions for research. We have also identifiedand summarized relevant standards activities that are currentlybeing undertaken to promote interoperability and acceleratethe deployment of future EH-IoTs.

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