Opportunities and Challenges of Wireless SensorNetworks Using Cloud Services

Ruoshui LiuComputer Lab

University of CambridgeCambridge, CB3 0FD, UK

rl348@cam.ac.uk

Ian J. WassellComputer Lab

University of CambridgeCambridge, CB3 0FD, UK

ijw24@cam.ac.uk

ABSTRACTTechnological advance in integrated, low-power microcon-troller and wireless communication devices as well as Micro-Electro Mechanical Systems (MEMS) sensors make it vi-able to embed tiny networked objects deeply into the phys-ical world. The unique characteristics of Wireless SensorNetworks (WSNs) make them attractive for application to avariety of challenging problems, e.g., the global energy cri-sis, climate change and population ageing. As WSNs be-come ubiquitous, the technology itself gives rise to sometechnical challenges to which Cloud Computing may offersolutions. In this position paper, we try to establish whatWSNs and Cloud Computing mean from a practical per-spective, and how they can help us achieve various objec-tives, e.g., to monitor civil infrastructure or public health,by building a new computing, communication, and manage-ment system architecture for sensing, processing, and stor-ing physical data. In addition, we present the opportunitiesand challenges of jointly applying WSN and Cloud Comput-ing technologies. Hopefully, this can help researchers realisethe potential gains available from the combination of WSNand Cloud Computing.

Categories and Subject DescriptorsA [General Literature]: Introductory and Survey;H.4 [Information Systems Applications]: General;J [Computer Applications]: Miscellaneous

General TermsDesign, Human Factors, Legal Aspects, Management,Performance, Reliability, Security, Standardisation.

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KeywordsWireless Sensor Networks, Internet of Things, CloudComputing, Energy Crisis, Smart Infrastructure, IPv6.

1. INTRODUCTIONWe are currently observing and experiencing the his-

torical transition in which the analog world is trans-forming into its digital counterpart. The Internet hasdriven significant changes in all consumer industries,including books, music, shopping and communicationbetween human beings. Today people use Facebook tokeep connected with friends as a social utility, LinkedInto build up their own professional network, chatter forreal-time communications and to access news real-timethrough a myriad of devices, for example, iPad runningiOS and Android based devices [6].

Now we have a more ambitious goal than ever be-fore to build a smarter, greener planet [12] in whichevery piece of information around us can be sampled,collected, transmitted, and analysed in order to provideus with solutions to various problems, such as the en-ergy crisis, safety issues, health improvement, and lifecomfort. The basic premise is the access to real-timeinformation concerning the environments around us.

2. WIRELESS SENSOR NETWORKSThe Internet of Things (IoT) is a concept that was

originally proposed in 1999 by Kevin Ashton [22] andwas the first to consider how the previously outlinedgoal could be achieved. The WSN technology emergedsoon after as one of its incarnations. A WSN is a col-lection of self-contained devices often utilising MEMSbased sensors. These nodes are colloquially referredto as motes and we note that mote is a commonlyused name in MEMS sensor literature [15]. Each motecontains a computational unit (e.g., microcontroller), amemory unit for data and program storage (e.g., StaticRandom Access Memory (SRAM)), Electrically ErasableProgrammable Read-Only Memory (EEPROM), andflash), an integrated low-power radio (e.g., transceiver)and a power supply (e.g., batteries). Sensor nodes can

Figure 1: An example of WSNs.

be deeply embedded in the physical world and spreadthroughout our environment, e.g., like smart dust [11],to spatially and temporally sample physical phenomena,such as temperature, humidity, light, inclination, ac-celeration, chemical substance, physiological measure-ments and environmental change. This data is theneither stored temporarily in local nodes or wirelesslytransmitted immediately to the network gateway/sink,as is shown in Figure 1. Then the clients can retrievethe data from the sink either using a wired or wirelesslink.

The actual deployment of WSNs to monitor ageingcivil infrastructure in order to assess its condition is dis-played in Figure 2. We have deployed a WSN in the sec-tion of a London Tube line between Baker Street Stationand Bond Street Station to sense a number of physicalparameters of the tunnel environment and its structure,i.e., temperature, humidity, crack displacement, and in-clination. It is hoped to observe how changes in theenvironment could affect the tunnel deformation as afunction of time [5] in a real-time manner, so that inthe future infrastructure can become smart/intelligentby having sensor nodes embedded within it.

This new network of physical objects can deliver ap-plications that create value for consumers and businessalike. The nature of millimetre-scale, nonintrusive, lowpower, self-organising, and cost efficient WSN is show-ing itself to be the best option for sourcing data from thephysical infrastructure around us. Moreover, this willlead to a reduced usage of resources, lowered installa-tion and maintenance cost, and rapid service upgrade.Figure 3 depicts a variety of applications of WSNs.

Ideally, we would like to have geographically isolatedWSN systems applied in different scenarios to be able tocommunicate with each other and thus work coopera-tively on demand so that a larger data set can be gener-ated and analysed thus enabling a comprehensive studyof objects that interest us, or allowing more sophisti-cated tasks to be accomplished. These required inter-

Figure 2: Tunnel monitoring using WSNs.

Figure 3: Applications of WSNs.

connections between different WSNs can be enabled bymaking use of Internet Protocol version 6 (IPv6) [7].To this end, a working group of TinyOS [14], which isa popular Operating System for WSNs, has released anIPv6 stack known as Berkeley Low power IP (BLIP)[13]. In addition IPv6 over Low power Wireless Per-sonal Area Networks (6LoWPAN) will enable a uniqueIPv6 address to be assigned to each of billions of sen-sor nodes. Therefore, addressing sensors becomes morestraightforward, enabling the IoT concept to becometangible to both researchers and consumers. It has beenpredicted that the IoT will expand to encompass around16 billion devices by 2020 [21]. These Internet-enableddevices can be hooked up to a network to communi-cate with other web-enabled gadgets and services usingIPv6.

3. CLOUD COMPUTINGIn contrast to the publically funded Grid Computing

initiative, Cloud Computing has since its conceptionbeen driven by commercial organisations. As the con-cept of Cloud Computing technology has emerged as

a very hot topic in both academia and consumer mar-kets, the question of what the Cloud actually means tous needs to be answered first. It is unfortunate thatClouds as yet do not have a clear and complete defi-nition in the literature [23]. Vaquero et al. proposeda more comprehensive definition after extracting andsummarising more than 20 experts’ definitions of theCloud [8], specifically “Clouds are a large pool of easilyusable and accessible virtualised resources (such as hard-ware, development platforms and/or services). Theseresources can be dynamically reconfigured to adjust toa variable load (scale), allowing also for an optimumresource utilisation. This pool of resources is typicallyexploited by a pay-per-use model in which guarantees areoffered by the Infrastructure Provider by means of cus-tomised Service Level Agreements (SLAs)”. In otherwords, Cloud Computing is a style of computing inwhich dynamically scalable resources are provided vir-tually to clients as a service via the Internet.

The definition and the practical deployment of theCloud require it to have the following key characteris-tics:

• Virtualisation: Virtualisation can hide a variety ofdifferent types of infrastructure, system platforms,and data at the back-end from clients for seamlesscontext switching and operation without worryingabout the underlying hardware implementation.

• Scalability : The Cloud needs to dynamically resizethe virtual resources if the number of services re-quired is increased or decreased in order to achieveoptimum usage while minimising resource waste orservice outage.

• Usability : A user-friendly interface to Cloud ser-vices is desirable for each user. Because CloudComputing is envisioned to serve clients across dif-ferent fields, from academia to industry, from busi-ness to government, the user interface needs to beas simple as web browsing.

• Reliability : As clients gradually start moving theirapplication and operation of computing servicesinto the Cloud, people will come to rely more heav-ily on the private and centrally managed Cloudservices than ever before. Providing the resilienceto single or multiple data centre failures is indis-pensable.

• Security : Sensitive data, e.g., customer informa-tion, breaches are undesirable, and virtualisationenables each user to have unique access to its indi-vidual virtualised environment having a completelevel of isolation.

• Cost : Because Cloud Computing has been drivenmainly by business use, it needs to have a business

model based on charging clients for using the ser-vices. Consequently, cloud service providers usu-ally adopt some form of pay-as-you-go model.

The services of Cloud Computing are broadly dividedinto the following three major categories and a stack ofthese services is shown in Figure 4:

• Infrastructure as a Service (IaaS): Service providersprovide virtualisation of computing resources (phys-ical processing and storage capability) for hostingclients chosen platforms. One example of IaaS isAmazon Elastic Compute Cloud (EC2) [2].

• Platform as a Service (PaaS): Platforms are builtupon infrastructure. This service provides devel-opment environments (e.g., Windows and variousversions of Linux), programming platforms (e.g.,Java VM, Python, and .Net) and APIs for buildingcloud-based applications and services. Examplesinclude Google App Engine [9], Amazon SimpleStorage Service (S3) [3], Azure Storage [16], andForce.com [18].

• Service as a Service (SaaS): Instead of running ap-plications on local personal PCs, applications areexposed as a service running in a cloud platformon top of the underlying infrastructure. Conse-quently, such services are delivered through vari-ous types of interface, e.g., web browsers and com-mand line terminal. Examples of SaaS are SalesCloud 2 [19], Service Cloud [20] from saleforce.comin which Customer Relationship Management(CRM) package is provided, Google Docs programs(e.g., word processing, spreadsheet, andPowerPoint-like presentation tools) [10], and Mi-crosoft Office Web Apps [17]. In addition, Adobehas also released their cloud version of the imageediting software Photoshop Express online apps[1].

4. OPPORTUNITIES FOR WSNS TO TAKETHE BENEFITS OF CLOUD COMPUT-ING

The promising applications of WSNs that we haveidentified so far have raised a few new problems. Theamount of data being generated and gathered by WSNswill increase exponentially as the number of sensor nodesrises in the near future. Even with only a few WSNs,occasional Event Bursts could trigger a chain reactionof Data Bursts. This phenomenon can be observed inthe application of monitoring natural disasters usingWSNs. One good example of a Data Burst is owing tothe earthquake and tsunami that hit Japan in March2011. A WSN that includes seismic sensors normallymonitors earth movement at infrequent intervals, e.g.,

Figure 4: Service stack of Cloud Computing.

once per hour, to reduce energy consumption and stor-age requirements. However, the WSN will begin sam-pling and collecting data at a much shorter interval,say in excess of once per minute, if it detects particu-lar events, such as movement of the earth surface or arise of sea level. The frequency of sampling and datatransfer rate can rise further if the situation becomesworse. This instantaneous peak in the data samplingand collecting process will result in what are known asEvent Bursts and Data Bursts.

These phenomena could also happen in the situationwhere the condition of patients being monitored getsworse and there is a great possibility of Data Avalanche.That said we do not wish to lose data in these criticalsituations. Therefore, it is not uncommon to measuredata volume in the order of exa- (1018) bytes, espe-cially in the era of eScience or eHealth. In addition, wehave observed a few difficulties when undertaking realdeployments of WSNs:

• Storage: Often a single standard PC server withmoderate storage size is left running remotely inan insecure location, e.g., inside a standard officeroom at a company. It will be highly likely to reachthe storage limit very quickly if used for long termmonitoring. This has been observed when the au-thors worked in a project with an industrial part-ner, namely AquaMW LLP [4]. In this applicationwireless sensors have been applied on industrialplant sites in order to provide SMART solutionsfor seamlessly connecting the physical and digital

worlds and improving efficiency and reducing en-ergy consumption and CO2 emissions. In the mis-sion critical tasks in which the health of the waterpump motors is tracked, it is necessary to sampledata once every few seconds, e.g., say 5 seconds.There are 4 different types of sensors, i.e., energymeter, flow sensor, pressure sensor, and temper-ature sensor, integrated onto each wireless node.Each analog channel requires 12-bit resolution, sothere are 60 bytes of information including metadata in each packet that is generated by a singlenode. Therefore, the total volume of data gener-ated by a single node for a year will amount tosome 0.4 GB. The deployment of WSNs for in-dustrial applications normally involves hundredsof nodes in one network, so the total quantity ofdata can easily reach several hundreds of GB peryear. It is clear that a conventional data storagestrategy will quickly become ineffective. Conse-quently, data loss is very likely in such situationsif timely elastic data storage mechanism are notavailable. In addition, automated efficient pro-cesses to build databases for several WSNs run-ning at the same time are lacking. Therefore, de-velopers are required to repeatedly and manuallybuild a database for every newly setup WSN eventhough most of the table attributes are common.

• Accessibility : The actual data created from themonitoring of critical civil infrastructure, e.g., rail-way underground tunnels, is extremely sensitive,

so there is a tight restriction on the different lev-els of data access to the server from the externalenvironment. For instance, infrastructure ownersshould have highest privilege to access the data incomparison with other users who have reduced ba-sic access. Currently, this is often not implementedor poorly implemented on standalone server ma-chines. This is mainly due to unfamiliarity andthe difficulty of configuration.

• Reliability : Because there is only one server in op-eration at any time, it is not resilient to singlepoint of failure problems owing to either hardwarebreakdown or software crash, e.g., hacker attack.

• Real-time Processing : The data gathered from thesite is often stored and processed off-line. Thereare no real-time signal processing or continuously-running algorithms for analysing data. Therefore,a timely alert cannot be sent out to end users whenunusual events occur.

Current technology is not able to provide sufficientdata storage and the powerful analytics capability re-quired within a reasonable response time, and havingwidespread accessibility. It is probable that satisfyingsuch requirements is beyond the scope of most small/medium companies and academic research teams.

Such hurdles to the success of WSNs (or IoT) lead usto believe that a Cloud based computing service mayoffer a solution to such stringent requirements. Thescalable computing capability for data processing, ag-ile application development tools and a virtually infinitecapacity for data storage make cloud computing techni-cally compelling. Let’s imagine, if wireless sensor nodesare sensing the micro-scale physical world individually,they can eventually function as the macro-probe to ourworld with a powerful brain when they work togetherin an intelligent and collaborative way using the Cloudas the backend infrastructure for data storage and pro-cessing. The Cloud also acts as a central library withexpandable capacity in which developers and users onlyneed to spend their efforts in developing the applica-tion features while other components such as security,scalability and shared data models have already beendeveloped and tested with millions of users. Further-more, geographically distributed data centres assure usof data safety by scheduling regular data backup to adifferent data centre. An overview of opportunities inthe applications of WSNs using Cloud is shown in Fig-ure 5.

5. CHALLENGES OF UTILISING WSNS ANDCLOUD COMPUTING

The Cloud brings us many opportunities to deployWSNs in various applications with the aim to resolve a

number of difficult problems, e.g., the global energy cri-sis, by optimising resource usage and reducing carbonemissions. It enables users to easily collect, access, pro-cess, visualise, archive, share and search large amountsof sensor data from different applications. However, thesynergy between these two innovative technologies ex-hibits some challenges that require answers in order tomove both forwards:

• Data Format and Event Processing : There is stilla lack of standard representation of data comingfrom different types of WSNs. The different dataabstraction models supported by diverse vendorsbecome barriers to the effective exchange of infor-mation and their retrieval. In the ideal case, thescalable API’s and adaptive data structure modelsshould make the parsing and processing data easyand user friendly. However, the data format andprocess is not forced to comply with a standard,so users tend to choose their favourite language ormodels to accomplish the job.

For instance, one user may use Python to collectthe data represented in an XML file, but the PaaSto which this user is tied may only support a de-velopment environment written in Ruby. A multi-tenant data architecture ensures data scalability,but the consistency is not 100% guaranteed whenclients switch from one service provider to another.

• Complex Event Query : Large real-time data feedsfrom heterogeneous sensors could require the de-sign of new Database mechanisms and query meth-ods, e.g., NoSQL, for efficient and transparent dataquery and retrieval, instead of using classical Re-lational Database Management Systems (RDBM-Ses), e.g., MySQL and Oracle RDBMS.

• Network Bandwidth: As the number of sensor nodesdramatically increases and with certain latency crit-ical applications (e.g., streaming video, voice andimages with real-time multimedia content), it isa challenge to accurately transfer such a huge vol-ume of data in a timely manner using conventionalnetworks, e.g., mobile, WLAN, MAN, and ADSL,in which the transmission is assumed to be asym-metrical for the uplink and downlink.

• Maintenance Dilemma: The Cloud cannot afforda service failure because service providers need tokeep their customers’ loyalty. Therefore, they haveimplemented redundancy techniques and regularmaintenance to ensure their service keeps running24 hours, 7 days a week. This can be done bydistributing their multiple data centres geograph-ically across the world, and backing up the dataregularly. The challenge is the efficient allocation

Figure 5: WSNs using Cloud Computing.

of computational resource (load balance) and stor-age. Furthermore, data migration becomes a seri-ous problem.

• Payment for Services: The method of billing cus-tomers is not standardised and in general is notregulated. One of the objectives of introducingCloud Computing to the market is to bring thecost of computing resource down significantly forclients. However, it becomes very difficult for cus-tomers to analyse or judge if the service they rentis over billed. Frequently, they are even confusedby the way they are billed. This will increase thebarrier for new users coming into the Cloud, orrisk losing current customers because of poor tar-iff plans.

6. CONCLUSIONSWSNs appear to be a promising realisation of the

IoT. It is also believed that the synergy between WSNand Cloud Computing will offer a potential solutionto various social, environmental, public problems, e.g.,the global energy crisis, population ageing, and secu-rity surveillance. In this paper, we have identified theunique characteristics of WSN and Cloud Computing.This helps us to clear up some of the confusion overthese buzz words, which in turn permits us to revealthe opportunities of applying one technology by leverag-ing another to tackle even more complex problems. Wehave also identified a number of challenges that we arefacing, and we hope this can inspire researchers to in-

vestigate the potentially powerful combination of WSNand Cloud Computing.

7. ACKNOWLEDGMENTSWe would like to thank Prof. Kenichi Soga, Dr. Pe-

ter Bennett, and Dr. Yusuke Kobayashi for their pro-fessional advice on Civil Engineering and for their jointwork on the deployment of Wireless Sensor Networks inunderground railway tunnels.

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