t.

t1. Introduction

Advances in micro-electro-mechanical system (MEMS) tech-nology and wireless communications have enabled the devel-opment of small size, low-cost, low-power and multi-functionaldevices that are typically equipped with sensors such as seis-mic, magnetic, thermal, visual, infrared, acoustic and radar.Consequently, they can be used to measure tremor, distance, di-rection, speed, load and pressure, temperature, humidity, light,vibration, motion and acoustic parameters. Fig. 1 shows a block di-agram of a sensor node. Typically, a sensor node is equipped witha power supply, transceiver, memory, and processing and sensingunit. There is also an analog to digital converter (ADC) unit. Theprocessor collects and processes the signal taken from the sensorunit and sends it to the transmission unit [3,6].

Sensor nodes are able to self-organize and self-configure intoa Wireless Sensor Network (WSN), which has wide rangingapplications, including precision agriculture [8,54,42], monitoringof pests [20], and volcanoes [27] to name a few. Fig. 2 shows atypicalWSN, comprising of one sink (or base station) and a numberof sensor nodes scattered randomly or according to a predefinedmethodology in a physical space. A key characteristic of WSNs isthat nodes help each other forward data to the sink node, which isusually connected to the Internet via a gateway. Interested usersobtain information about the state of the field via the gateway andbase station.

Recently, a number of researchers have considered equippingsensor nodes with an actuator. Briefly, an actuator is a transducer

Corresponding author.E-mail addresses: hs528@uow.edu.au, hs528@uowmail.edu.au (H. Salarian),

kwanwu@uow.edu.au (K.-W. Chin), fazel@uow.edu.au (F. Naghdy).

that converts an electric signal to a physical movement. Exampleactuators include valves that control the water or gas outflowfrom a pipe, electrical motors that open/close doors and windows,and switches that turn on/off heaters or lights. Fig. 3 shows thekey components of an actuator node, i.e., transmission, processor,storage, controller (decision), Digital to Analog Converter (DAC)and actuation. The decision unit functions as an entity that takessensor readings and generates action commands. An exampleactuator node is the ME8300 Wireless Zone Valve actuator fromSpartan [48], which includes a TransCeiver Module (TCM) [16],16 MHz 8051 CPU, 32 kB Flash and 2 kB RAM, 8bit DAC, and868 MHz/315 MHz transceiver, Alternating Current (AC) motor asan actuation unit and 24 V AC power supply.

The resulting Wireless Sensor/Actuator Network (WSAN) notonly consists of sensor nodes that measure specific environmentalparameters, but some of them have the capability to act onthe environment through actuators. A distinguishing feature ofactuator nodes is that they are usually resource-rich, i.e., theyhave ample power supply, higher computational power and longercommunication range than sensor nodes. For these reasons, thereare fewer actuators deployed in a given environment [40].

Fig. 4 shows a typical WSAN, comprising of geographicallydistributed network of wireless sensor and actuator nodes. Similarto WSNs, sensor nodes gather information, but instead of a sinknode, they transmit collected data to actuator nodes via single-hopor multi-hop transmissions. These actuator nodes then decide theappropriate responses to effect the sensor field. Note that the sinknode is used to provide monitoring and management ability forend users by communicating with sensor and actuator nodes.

Table 1 outlines the various applications of WSANs. Forexample, home automation [37,18,25], animal control [55], mon-itoring the health of an infrastructure [44,65,28,29,23], preci-sion agriculture [32,42,17,61], intelligent buildings [35,11,26,39],J. Parallel Distrib. Comp

Contents lists available a

J. Parallel Dis

journal homepage: www

Coordination in wireless sensoractuatorHamidreza Salarian , Kwan-Wu Chin, Fazel NaghdySchool of Electrical, Computer, and Telecommunications Engineering, University of Wollon

a r t i c l e i n f o

Article history:Received 21 March 2011Received in revised form16 August 2011Accepted 19 February 2012Available online 28 March 2012

Keywords:Wireless sensor and actuator networksCoordinationEnergy efficiencyActuators

a b s t r a c t

Wireless SensorActuator Neto controlling light intensityUnlike conventional Wireleshand to collect and forward dend, this paper reviews currespecifically, we review technprotocols, (iii) transport proextensive qualitative comparunresolved problems and fut0743-7315/$ see front matter 2012 Elsevier Inc. All rights reserved.doi:10.1016/j.jpdc.2012.02.013ut. 72 (2012) 856867

t SciVerse ScienceDirect

rib. Comput.

elsevier.com/locate/jpdc

networks: A survey

gong, Northfields Avenue, Australia 2522, Australia

tworks (WSANs) have a myriad of applications, ranging from pacifying bullsin homes automatically. An important aspect of WSANs is coordination.

s Sensor Networks (WSNs), sensor and actuator nodes must work hand-in-ata, and act on any sensed data collaboratively, promptly and reliably. To thisnt state-of-the-art techniques that address this fundamental problem. Moreiques in the following areas: (i) sensoractuator coordination, (ii) routingocols, and (iv) actuator-to-actuator coordination protocols. We provide anison of their key features, advantages and disadvantages. Finally, we presenture research directions.

2012 Elsevier Inc. All rights reserved.

few nodes when they are used to monitor buildings. In home au-tomation and precision agriculture, there are more sensor nodesthan actuator nodes due to the coverage area. In animal control andinfrastructure health monitoring applications, because sensor andactuator nodes are integrated together, there are equal number ofsensors and actuators.

A key design parameter is the delay tolerance of applications.Here, delay tolerance is defined as the allowed time delay betweensensing and acting. This parameter is important because it governsthe time frame before actuators respond to sensed data. Forexample,when aWSAN is used tomonitor an infrastructure, sensornodes have an extremely low duty cycle, and hence, are asleepmost of the time. They either wake up periodically to test thestructure, say once a day at midnight, or may be woken to testthe structure immediately after a catastrophic event such as anearthquake, a collision, or a blast [23]. In precision agriculture [17]and sewer management system [52], sampling rates of 10 and14 min are sufficient to control and monitor key parameters.In lighting system applications, a sampling rate of two secondsis sufficient as light intensity follows an analogue pattern [18].Lastly, animal control applications require a sampling interval of500 ms [55].

In WSANs, guaranteeing that actions are carried out as perthe delay tolerance of an application is highly dependent onthree processes: distributed collection of data by sensor nodes,forwarding said data to actuator nodes, and cooperative decisionby actuator nodes on how to perform the required action. Thesethree processes are referred to as coordination in WSANs. Inaddition to delay tolerance, energy efficiency is important becauseeach sensor and actuator node has finite battery capacity.

This paper focuses on the coordination problem in WSANs.Specifically, we review past studies that improve communicationsbetween sensors and actuators, and also between actuators. Thecritical problems include routing and transport of sensed data andcommandsboth of which play a crucial role in the operation ofa WSAN as they govern how actuators respond to one or moreevents. Moreover, any solutions must ensure packet delays areFig. 2. Typical Wcategorize them according to their requirements in termsof bounded delay, reliability, bounded task completion time,service differentiation and energy efficiency.

Unlike a prior work [50], it reviews four newly publishedsensor-to-actuator coordination protocols. Also, we compareeach protocol in terms of coordination model, localizationprocedure, actuator mobility and energy efficiency. We like topoint out that [50] is only a categorization of papers as opposedto a proper literature review. Hence, it lacks analysis on thestrengths and weaknesses of WSAN protocols.

We compare six routing protocols designed forWSANs in termsof route discovery and maintenance, delay-bound and energyconsumption. In addition, we review five transport protocolsand analyze their ability to provide reliability and servicedifferentiation.

We highlight the advantages and drawbacks of six actuator-to-actuator protocols. We outline their ability in terms of handlingmultiple events and guaranteeing task completion time.

Lastly, our paper highlights key challenges and problemsassociated with coordination inWSANs, and present key futureworks.

In the next section, we give an overview of two main WSANarchitectures. The type of architecture will have a significantimpact on the coordination protocol used to meet application re-quirements. With this in mind, we present an overview of the co-ordination problem in Section 3, followed by prior solutions tosensoractuator, actuator-to-actuator, routing and transport prob-lems in Section 4. Then in Section 5, we present future trendsand open research problems. Finally, Section 6 presents ourconclusions.

2. Network architecture

The network architecture of WSANs can be categorized aseither fully-automated or semi-automated [3]; see Fig. 5. In thefully automated architecture, actuator nodes coordinate amongstthemselves and decide on a plan of action based on sensed data.H. Salarian et al. / J. Parallel Distrib. Comput. 72 (2012) 856867 857

Fig. 1. A block diagram of a typical sensor architecture.

reducing sewer overflow [52,31] and traffic light control [68,46].The number of sensor and actuator nodes varies according to appli-cation types. For example, in sewermanagement systemand trafficlight control, due to the large coverage area, the number of sensorsand actuators may be in the order of hundreds as compared to a

Fig. 3. A block diagram of a typical actuator architecture.

bounded, and packets are delivered reliably as they may containinformation used to locate actuator nodes, and also control themovement and responses of actuator nodes. Given the importanceof these problems, this paper makes the following contributions:

It presents an extensive review of WSAN applications. WeSN architecture.

rs

e

e

sthe sink will deplete their energy quicker than those furtheraway [33]. On the other hand, in the fully-automated architecture,sensed data is sent to different actuator nodes. Hence, thecommunication load can be distributed evenly amongst actuatornodes, and thereby, extend the lifetime of a WSAN [33].

We like to point out that WSNs are passive, where sensornodes simply record data and send them to one or more sinksfor processing, which may then issue new commands to changethe sample rate. However, WSANs are active in that sensed datagoverns the behaviors of actuators, which in turn causes theseactuator nodes to effect the environment, and hence change datacollected by sensors. In short, there is a close coupling betweensensor and actuator nodes. As we will elaborate in Section 3, thiscoupling yields a number of novel challenges.

3. Coordination

bIntelligent Buildings [35,11,26,39] Smoke detectors, glassbreak and motion sensors

Water sprinklers andcameras

2 s 2050 sensornodes

2030 actuator nodes

Sewer management [52,31] Water level Valves or city sewers 14 min More than 100sensor nodes

More than 100actuator nodes

Traffic light control [68,46] Magnetic sensor Relays of traffic lamp 10 s More than 100sensor nodes

More than 100actuator nodes

In the semi-automated architecture, however, sensor nodes routetheir data to actuators via a sink. Note, similar to WSNs, a centralentity or sink may be used to collect and process sensed data, andsend commands to actuator nodes. Alternatively, the sink nodemaybeused just formonitoring andmanaging the overall network.

Both architectures have their advantages and disadvantages.A semi-automated architecture is similar to WSNs. In addition tousing existing WSNs protocols, due to its centralized property,there is no need for a distributed communication and coordinationprotocol between sensor and actuator nodes. However, sincesensed data is routed through a sink rather than a nearby actuator,communication latency can be significant. Moreover, nodes near

a858 H. Salarian et al. / J. Parallel Dist

Fig. 4. A wireless sen

Table 1Summary of WSAN applications.

Applications Sensor types Actuator typ

Home automation [37,18,25] Temperature, humidity,light, acoustic andoccupancy sensors.

Relays, light,motors, noissystem adjusvalves.

Animal control [55] Location and velocitymeter sensors

Stimuli board

Infrastructure healthmonitoring [44,65,28,29,23]

Velocity meter andpiezoelectric sensors

Hydraulic orshakers

Precision agriculture [32,42,17,61] Temperature, humidity,light and soil moisturesensors

Valves, relayswitchesCoordination is a fundamental problem in WSANs. The exactcoordination used, however, is dependent on the networkib. Comput. 72 (2012) 856867

oractuator network.

s Delay tolerance Number of sensors Number of actuators

switches,-maskingting and

2 s More than 100sensor nodes

2050 actuator nodes

500 ms 2030 sensornodes

2030 actuator nodes

piezoelectric 1 Day 20100 sensornodes

20100 actuatornodes

light 10 min More than 100sensor nodes

2030 actuator nodesFig. 5. Two architectures of WSANs: (a) fully-automated and (b) semi-automatedarchitectures.

awith each other to find the nearest actuator node(s) that coversthe said area and has sufficient energy to carry out the requiredtask. The advantage here is that actuator nodes are excluded fromthis coordination effort. Instead, coordination is carried out bysensor nodes. As a result, sensor nodes around the event areawill deplete their energy quicker. In the second case, each sensornode independently selects an actuator node [62]. However, thereis no sensor-to-sensor coordination, which may overload a givenactuator or cause sensor nodes around an actuator node to depletetheir energy quicker.

Once sensor nodes decide on one or more actuators, their nexttask is to locate and select suitable actuator nodes. This task ismadedifficult in some applications where actuator nodes are mobile.Hence, there needs to be mechanisms that enable actuator nodesto update sensor nodes of their location, and vice-versa. Moreover,these mechanismsmust be energy efficient as continuous trackingof actuator nodes increases the duty cycle of resource constrainedsensor nodes.

The next issue is establishing one or more routing paths toactuator nodes. The main challenge is constructing one or morepaths that meet the delay requirement of applications, and are

nodes need to send their data to a central station. On the otherhand, distributed actuatoractuator coordination reduces actuatorresponse time in comparison to the centralized model becauseeach actuator node is able tomake local decision based on receiveddata. On the other hand, the distributedmodel increases the energyconsumption of actuator nodes as they need to communicatewith each other after each event. Lastly, in both coordinationmodels, there needs to be a mechanism to handle the occurrenceof multiple events.

In summary, the close coupling between actuator and sensornodes poses a number of issues not found in conventional WSNs.These issues include [3,57,33,49,58]:

Bounded packet delayactuators have to act on sensed dataquickly. Otherwise, it would be detrimental to the operationof a WSAN. Specifically, any communication protocol mustensure packets do not exceed an applications delay tolerance.This means sensor nodes need to form low delay and reliablepaths to one or more actuator nodes, and actuator nodes mustcollaboratively reach a consensus on the best response to oneor more events. For example, intelligent building applicationsH. Salarian et al. / J. Parallel Distr

a

Fig. 6. Sensoractuator coordination mech

architecture. More specifically, coordination is carried out bythe sink in the semi-automated architecture. On the otherhand, in the fully-automated architecture, there are two modesof communication: sensor-to-actuator and actuator-to-actuatorcoordination. In the former, sensor nodes are required to find anappropriate actuator node to send their data. In the latter, theactuator nodes coordinate amongst themselves to dealwith senseddata [3,50,45]. In this study, the focus will be on fully-automatedarchitecture as existing WSNs protocols can be deployed in thesemi-automated architecture. Readers who are interested in theseWSNs protocols are referred to [56,25] and references therein.

In the fully-automated architecture, distributed protocols areneeded for sensor-to-actuator coordination. This is an importantfunction as a correct response from actuator nodes cannot beachieved unless sensed data arrives in a timely manner and isreceived correctly by the corresponding actuator nodes. This givesrise to the following fundamental question: how do sensor nodesdetermine the best actuator node(s) to send their data? Thisis important as it enables them to determine the location andintensity of an event before taking action.

Once an event occurs the data may be forwarded to an actuatornode (see Fig. 6(a)), or multiple actuator nodes (see Fig. 6(b)).In the first case, sensor nodes in the event area communicatesufficiently reliable to carry data between sensors and actuatornodes. A key consideration is congestion avoidance, especially inib. Comput. 72 (2012) 856867 859

b

nism: (a) single and (b) multiple actuators.

areas surrounding an actuator node. Otherwise, a congestion pathwill lead to packet loss and increased end-to-end delay. Anotherissue of importance is route construction and maintenance to oneormore actuator nodes. Moreover any unicast or multicast routingprotocols must consider the low duty cycle of nodes, and themobility of actuator nodes.

Actuators must coordinate amongst themselves to ensure atleast one of them will respond to any event that arises. Fig. 7shows two decision categories of actuatoractuator coordination.In the centralized decision category, whenever an actuator nodereceives an event from a sensor node, it sends the information toa predetermined, central actuator node or decision center, whichthen decides the best group of actuator nodes to perform therequired task; see Fig. 7(a). In the distributed decision category,after receiving event information, actuator nodes communicatewith each other and send sensed data, their residual energy,current position and action range to other actuator nodes in thenetwork. This information is then used by actuators to determinewhether to participate in an action; see Fig. 7(b) [62].

The advantage of centralized actuatoractuator coordination isthat the decision center is able to select the best actuator nodesto carry out a task, especially when there are multiple events. Thisadvantage, however, is the increased end-to-end delay as actuatorrequire the control of water sprinklers within two seconds; seeTable 1.

rand task completion time. MobilityThe mobility nature in WSAN is completely differentto WSNs. Mobile elements in WSN are designed to save theenergy of sensor nodes by collecting data from sensor nodesdirectly or via rendezvous or cache points [60,9,53]. On theother hand, inWSANs,mobile actuator nodes are used to reduceend-to-end packet delay and guarantee task completion times;e.g., in areas with high frequency of events.

We like to point out that protocols developed for WSNs aregenerally not applicable toWSANs for a number of reasons. Sensorsnodes are power constrained, and have limited transmissionrange and memory size, while actuator nodes are resource richand have better transmission capabilities as well as buffer size.Hence, WSAN protocols must consider the resource constraintsof both types of nodes. The information flow in WSNs is fromsensor nodes to a sink, which form amany-to-one communicationpattern. In a WSAN, the communication is many to many as itcan take place between any nodes. Moreover, sensed data mustbe routed to the corresponding actuators that are able to mountthe appropriate actions. Hence, localization, reliability and real-time communications are crucial to the operation of WSANs. We

Routing (CCR) protocol. Clustering is a standard approach used [1]extensively inWSNs to decrease the energy consumption of sensornodes. The authors construct a number of clusters in a givennetwork region based on a formula that considers the dimension ofan area, number of deployed sensor nodes, and transmission rangeof sensor nodes. Then for each cluster, a cluster head is selectedbased on the residual energy of sensor nodes, data transmissionrate and node density. A new cluster head is selected once itsresidual energy becomes the lowest in the cluster. The key rolesplayed by cluster heads are data aggregation, and forwardingpackets on paths thatmeet the following criteria: end-to-end delaythat is within a given delay bound, and least energy expenditure.If there are multiple paths, a cluster head selects the path withnodes having the most residual energy. CCR distributes energyconsumption uniformly amongst nodes and prevents the energyholes problem [59]. However, the most important issue is theoverheads required to form cluster. In [45], sensor nodes arerequired to collect position information from all nodes in thenetwork before they are able to construct the optimal number ofclusters for a given WSAN.

Melodia et al. [40] proposed a distributed and adaptive event-based partitioning method for sensoractuator coordination.860 H. Salarian et al. / J. Parallel Dist

a

Fig. 7. Actuatoractuator coordination m

Reliabilityto have correct execution of actions, actuator nodesneed to receive sensed data within a pre-determined timeperiod in order to re-construct an event, understand itsintensity, location and coverage, and lastly determine theappropriate number of actuators that are deployed in responseto the event. Unfortunately, sensed data and commandsmay belost due to congestion, bit error, or bad connectivity. Therefore,protocols must be developed to address one or more of theseissues such that sensed data and control information arecommunicated to sensor and actuator nodes reliably.

Bounded task completionapplications such as fire controlsystems require bounded task completion time. Specifically,they need a bound on the elapsed time from when sensornodes report the occurrence of a fire to when water sprinklerscompletely extinguish the fire.

Service differentiationmultiple events with different urgencylevel will need to be treated accordingly. For instance, in homeautomation systems, therewill be sensors that are used to sensetemperature and lighting, while some will be responsible fortracking thewhereabouts of a person. These two types of sensornodes generate traffic flows which require different deliverynote that there are similar challenges and problems betweenWSNsand WSANs in the following areas: spectrum management [4,5],ib. Comput. 72 (2012) 856867

b

echanism: (a) centralized, (b) distributed.

MediumAccess Control (MAC) [15] and security [38,12]. Hence, wedo not consider them in this paper, and refer interested readersto the previously cited papers, and references therein for moreinformation.

4. Literature review

We divide the literature into four categories: sensor-to-actuator, routing, transport, and actuator-to-actuator coordinationeach of which encapsulates the problems and challengesdiscussed in Section 3.

4.1. Sensoractuator coordination

The fundamental problem addressed by the previous studies isto determine energy efficient methods/protocols that allow sensornodes to select and locate an appropriate actuator node. The mainissues to consider include the coordination model used, i.e., singleor multiple actuators, and how actuators update sensor nodes oftheir location.

Shah et al. [45] propose a Cluster-based Coordination andWhen an event happens, sensors in the event area independentlydetermine the nearest actuator node. More specifically, all sensor

cra method to reduce energy consumption when actuator nodesare mobile. Specifically, they use Kalman filtering to predict theposition of a mobile actuator at a given time instance. Actuatorssend update messages periodically, which sensor nodes then useto predict an actuators future location. This has the effect ofreducing location update messages, and as a result, there is lesscommunication overhead. In the Voronoi diagram and Kalmanfiltering method [39], each actuator node is aware of the positionof all sensor nodes in the network. The location information iseither set up manually, which is impractical, or communicatedby actuators. The drawback, however, is that as the number

In addition, actuator nodes play the role of mobile sinks, which hasa significant impact on how routing is carried out in a WSAN.

The authors of [51] analyzed the performance of three ad-hocnetwork routing protocols in terms of their ability to decreaseend-to-end packet delays. They analyzed Destination SequencedDistance Vector (DSDV) [10], Dynamic Source Routing (DSR) [30]and Ad Hoc On-Demand Distance Vector (AODV) [41]. The authorsuse these protocols for actuator-to-actuator communication andcoordination. DSDV is a proactive routing protocol that requiresnodes to periodically exchange routing tables. DSR, a reactiverouting protocol, embeds the route to be taken in the header ofH. Salarian et al. / J. Parallel Distr

Table 2Comparison of sensor-to-actuator proposals.

Prior works Coordination Actuator localization

Melodia et al. [40] Multiple actuators Dynamic clustering

Shah et al. [45] Single actuator Static clusteringZeng et al. [64] Multiple actuators Actuator nodes periodically broad

positionMelodia et al. [39] Multiple actuators Voronoi diagram and Kalman filte

predict position.

nodes in the event area that is sending data to the actuator forma cluster and a delivery tree that is rooted at the actuator node.The key advantage of this method is that sensors and actuatorwithin a given scope of the detected event are required to beactive. This means sensor nodes save energy when there are noevents in the network because there is no need to spend energyfor cluster maintenance. On the other hand, clusters are formedindependently by sensor nodes using only local information. Eachsensor node selects the nearest actuator node as a cluster head.This means sensor nodes are not required to communicate withother nodes to select cluster heads, as is the case in [45]. Thismethod, however, is not suitable for scenarioswith frequent eventsas constructing clusters and locating the nearest actuator nodewillconsume a significant amount of energy.

Zeng et al. [64] developed a real-time sensoractuator coor-dination protocol. Sensor and actuator nodes are aware of theirposition. In addition, actuator nodes aremobile. Actuator nodes pe-riodically broadcast their position, residual energy and load.Whensensor nodes receive the broadcast message, they select the near-est actuator node with the maximum residual energy and mini-mum load for event reporting. Interestingly, a sensor node is ableto request an actuator tomove closerwhenever the end-to-end de-lay of its current path exceeds a given threshold. This model, how-ever, is not energy efficient as it requires actuators to broadcastmessages periodically, which creates significant overheads. Also,the movement of an actuator node in response to a sensor nodesrequest may lead to higher end-to-end delays.

Melodia et al. [39] propose a sensor-to-actuator frameworkthat considers mobile actuator nodes, which comprises of a novellocation management scheme, where an actuator broadcasts amessage to inform all sensor nodes of its new position. Actuatornodes are aware of the location of all sensor nodes, and areequipped with two radios tuned to a distinct channel; one forcommunicating with sensor nodes, and other one to communicatewith other actuator nodes. In order to restrict the broadcastscope of an actuators messages to only relevant sensor nodes,the authors use Voronoi diagram to partition the network areainto a number of convex polygons or scope. Each sensor node isthen assigned to its nearest actuator node. Every time an actuatornode changes its position, it communicates with other actuatornodes to determine the polygon of each actuator node. As a result,energy consumption is reduced because there is no need for sensornodes to relay update messages. Melodia et al. [39] also proposedof actuators increases, the network will incur a non-negligibleamount of signaling overheads associated with localization.ib. Comput. 72 (2012) 856867 861

Actuator mobility Energy conservation techniques

No Rotate cluster head role and use alternatepaths

No Event-based clusteringast their Yes No consideration

ing to Yes Exploit Voronoi diagrams to scope broadcastmessages

Table 2 summarizes sensoractuator coordination protocolsaccording to their coordination, and actuator localization mecha-nism as well as whether actuators are mobile. All proposed mod-els are based on multiple actuator coordination except for theCCR protocol [45]. As mentioned in Section 3, multiple actuatorcoordination incurs communication overhead, in addition to in-creasing the energy consumption of actuator nodes. The differencebetween static [45] and dynamic [40] clustering is whether sensornodes communicate with each other to elect an actuator node asthe cluster head. More specifically, in dynamic clustering, sensornodes individually select an actuator node as a cluster head whilein static clustering, all sensors in a cluster communicate with eachother and choose an actuator node as their destination. This meansdynamic clustering can be categorized as a multiple actuator coor-dination mechanism while static clustering is a single actuator co-ordination mechanism. Actuator localization, mobility and energyconservation are three key challenges in sensoractuator coordina-tion. As Table 2 shows, all proposedmodels, except Zeng et al. [64],provide an energy efficient way for sensor nodes to locate actu-ator nodes. However, Zeng et al. [64]s model is not energy effi-cient as they only focus on actuator mobility. The use of Voronoidiagram and Kalman filtering, as proposed in [39,22], is promis-ing for static WSANs. However, it is unclear, how this methodsfare inWSANs with mobile actuators, especially when consideringthe non-negligible amount of signaling overheads associated withlocalization.

4.2. Routing protocols

Routing protocols play a critical role in WSANs as senseddata or control messages have pre-determined Quality of Service(QoS) requirements. The main issues are route discovery, routemaintenance, selecting a path that is within the required delaytolerance, and energy efficiency.Many routing protocols have beenproposed for WSNs in recent years. However, to the best of ourknowledge, none of the existing papers to date consider researchchallenges resulting from the coexistence of sensors and actuatornodes. That is, the communication path is between sensors andone or more actuator nodes. For example, a sensor node can senddata to multiple actuator nodes or select the best actuator nodeaccording to predefined parameters. Moreover, as actuator nodesare resource rich, they can bear the burden of the routing process.each packet. In addition, each node maintains a cache of routes,which they compile from packets they have processed, and from

r862 H. Salarian et al. / J. Parallel Dist

those transmitted by neighboring nodes. AODV, another reactiverouting protocol, works similarly to DSR where route requests aresent in an on-demand manner. However, it does not maintaincaches nor require source routes in packets. The results in [47]show that at startup time, packets routed using DSR and AODVarrive quicker than those forwarded by DSDV. This is becauserouters using DSDV require a non-negligible amount of time toachieve route convergence. However, after the network becomesstable, DSDV yields the lowest end-to-end delays as a sourcedoes not need to establish a route in an on-demand manner. Thedrawback of DSDV protocol is that it is not energy aware andwastes network bandwidth. To guarantee low end-to-end packetdelays, sensor nodes are required to update their routing tablesperiodically, which leads to an increased in power and bandwidthconsumption.

Hu et al. [22] propose to use anycast andmobile actuator nodesto minimize energy consumption in WSANs. The proposed modelis a variant of the Directed Diffusion (DD) [19] communicationparadigm, which was proposed for WSNs. An anycast tree is builtfrom a sensor to actuator nodes, where actuators form the leaves ofthe resulting tree. Every time an actuator node joins the network, itbroadcasts a route discovery message. This causes all sensor nodesto update their anycast tree. To decrease energy consumption andcommunication overheads, an actuator node floods an explicitleave message to remove itself from the anycast tree. This modelis suitable for applications that can tolerate minor packet loss.The main drawback is that actuator nodes may generate excessivesignaling overhead when they change their location given the useof flooding to update their location.

Boukerche et al. [7] developed a QoS aware routing protocol.Actuator nodes broadcast subscription messages with the energylevel and hop count field set to zero. Each sensor node that receivesthis message updates these fields and collects information aboutits hop level from actuator nodes and the energy level of itsneighboring nodes. Whenever a sensor node wants to send a datapacket, it updates the packets delivery time based on the elapsedtime from when the packet was generated. Sensors uniformlydistribute packets to actuators based on packets delivery timein order to normalize their energy usage. When paths have thesame length, sensors will select the one with the highest energylevel. The QoS-aware source routing protocol selects a path thatmeets a packets delivery time. In the scenario where no pathscan be found, the authors did not propose any solution. Onemechanism to alleviate this problem is for sensor nodes to increasetheir transmission power. However, this needs a coordinationmechanism that determines which nodes on a path are requiredto increase their transmission power. Moreover, such mechanismmust balance energy consumption due to higher transmissionpower and end-to-end delay.

Cayirci et al. [14] propose a Power Aware Many-to-manyRouting (PAMR) protocol. The basic idea is to create amulticast treerooted at a sensor node toward actuator nodes using a publish andsubscribe method. At start-up, actuators broadcast their interestto sensor nodes. A sensor node with the required data recordsthe number of hops, minimum energy of nodes on the route, andenergy that will be consumed by nodes when forwarding packets.A sensor node then selects the route with the smallest weight tominimize the formation of energy holes, and also provides end-to-end packet delay. In other words, each sensor node builds anenergy efficient delay aware multicast tree to actuator nodes thatare interested in its sensed data. The drawback of this model isthat PAMR cannot guarantee end-to-end packet delays. This isbecause sensor nodes have a fixed transmission power, and theydo not have any option when the delay on the minimum weight

path is larger than the required packet delivery time. To overcomethis problem, the authors also propose a different PAMR version,ib. Comput. 72 (2012) 856867

called Power Controlled PAMR (PCPAMR), where sensor nodesincrease their transmission powerwhen the required delivery timeof a packet is less than the delay of an existing path. In otherwords, PCPAMRbalances the required end-to-endpacket delay andenergy consumption of sensor nodes. The problemwith PCPAMR isthat when each sensor node individually changes its transmissionpower, it causes non-uniform energy consumption and energyholes to occur along the packet forwarding path.

The authors of [36] propose Delay-Energy Aware Routing Pro-tocol (DEAP). Sensor nodes wake up once in every predeterminedtime period, transmit data and then sleep. The time length of anactive period depends on the number of data packets in the trans-mission queue. A large queue size causes a long active time. Hence,sensor nodes make local decisions as to sleep or to be active basedon their queue length. Whenever a sensor node wants to send datapackets, it selects one that is closest to an actuator node. DEAP dis-tributes energy consumption between a source nodes forwardingset because each time a source node wants to send data packet,its forwarding set may change based on the active and sleep pe-riod of its neighbors. DEAP prolongs WSAN lifetime and reducesenergy consumption in sensor nodes but at the expense of end-to-endpacket delay. This happens because in normal scenarios, sourcenode builds the shortest forwarding path to the nearest actuatornode but when sensor nodes sleep according to their load, othernodes have to rebuild their routing path as nodes may decide toenter sleep mode, or new nodes may be available.

In applications such as the water irrigation system [17],actuators are in charge of opening or closing valves dependingon the need to water areas as determined by sensed dataprovided by sensors. Thismeans sensorsmay send data tomultipleactuators. Therefore, in order to save energy, Sanchez et al. [43]proposed an energy-efficient multicast routing protocol. As theproblem of finding an energy-efficient multicast tree is NP-complete, the authors used a heuristic that incorporates nodeslocation to build energy-efficient multicast paths. In particular,their heuristic used the destination and energy cost to eachdestination during tree construction. Moreover, they considermerging paths onto a common node, which then serves multipledestinations. The multicast problem proposed by Sanchez et al.however is not delay-aware. When the number of actuator nodesincreases, the computational power and storage requirementassociated with finding candidate sets and conducting a mergebecome significant for sensor nodes.

Table 3 summarizes the aforementioned routing modelsaccording to their route discovery and maintenance mechanism.We also compare the approach used to ensure the end-to-end packet delay of a path is bounded. A notable approach inthis respect involves actuator nodes issuing feedback to sensornodes on a given path to increase their transmission poweruntil the required end-to-end delay path is met. On the otherhand, protocols that place the responsibility of route discoveryand maintenance on sensor nodes have high communicationoverheads and energy consumption.

4.3. Transport protocols

Reliability is a critical issue as actuators use sensed data toreconstruct events before launching the corresponding action(s)for a given event. Therefore, it is important for actuators toascertain the type, location and intensity of events reliably.The main approaches taken in prior works include varying thetransmission power of nodes, and number of retransmissions.Apart from that, service differentiation is a key concern inscenarios with multiple events. In general, transport protocolsshould provide both reliability and real-time data communication

between sensor and actuator nodes. In addition, they must alsoguarantee event reliability; defined as the amount of data that is

hNgai et al. [67,66] propose a latency-oriented fault tolerant(LOFT) transport protocol for WSANs. Sensors and actuators areaware of their location. Each sensor node maintains and schedulesqueues according to the urgency of events. When a sensor nodewants to send a data packet to a destination, it selects the paththat meets the required delivery time. In scenarios where thereare multiple paths, the source node uses the least congested path.The authors also propose to use mobile actuators to service areaswith high frequency of events. The drawback of [67,66] is thatsensor nodes are required to send the status of their queue totheir neighbors,which increases signaling overhead.Moreover, theauthors assume nodes are aware of their location. For example,they suggest the use of the Geographic Positioning System (GPS),which consumes non-negligible amount of energy. Ngai et al.

routed to the selected actuator. The limitation with their approachis that the new actuator is chosen with respect to the old actu-ator. Hence, the new actuator may not be close to sensor nodes.As a result, the redirected traffic may unnecessarily consumemoreenergy.

Table 4 summarizes transport protocols developed for use inWSANs. Most of them rely on the existence of multiple paths,and the use of varying transmission range to shorten forwardingpaths. The former technique means more nodes are required toparticipate in the forwarding of packets, whereas the lattermethodleads to increased interference.Moreover, the use ofmultiple pathsand increased transmission range is likely to degrade networkcapacity as nodes on different paths are likely to interfere andincrease contention delay. Apart from that, transport protocolsH. Salarian et al. / J. Parallel Distr

Table 3Comparison of routing protocols.

Prior works Route discovery Route maintenance

Dinh et al. [51] Sensor nodes Sensor nodes periodically excrouting tables

Hu et al. [22] Actuator nodes Actuator nodesBoukerche et al. [7] Actuator nodes Actuator nodes periodically br

subscription messages

Cayirci et al. [14] Actuator nodes Actuator nodes periodically brsubscription messages

Durresi et al. [36] Sensor nodes On demandSanchez et al. [43] Sensor nodes None.

reliably delivered to an actuator node within a given time bound.In addition, anycast communication is used frequently as senseddata needs to be routed to the closest actuator node(s). For thesereasons, transport protocols developed for WSNs are generally notsuitable for use in WSANs.

Zhou et al. [34] propose a real-time data transport protocolcalled POWER-SPEED. Sensor nodes calculate the hop-by-hopdelay that a packet will experience on a given path. They then usethe expiration time of each packet to determine their transmissionpower. A packet that has a higher delay tolerance will traversemore hops as sensor nodes will use a lower transmission power,which increases the number of hops on a given path. Otherwise,the packet will be forwarded over a route with fewer hops. Thelimitation of POWER-SPEED is that it does not consider congestion.In other words, increasing or decreasing transmission power doesnot necessarily alleviate or avoid congestion on a given route. Inaddition, POWER-SPEED cannot handle multiple events.

Gungor et al. [2] propose RT2, an energy efficient, reliable andreal-time transport protocol. The authors define event reliabilityas the percentage of event data received by actuator nodes withina given time bound. To provide event reliability, RT2 uses theTime-Critical Event First (TCEF) [2] scheduling algorithm, wheresensor nodes service packets according to their deadline. Aninteresting feature is that actuator nodes will send feedbackmessages to sensor nodes in order to decrease their sampling ortransmission rate if the observed event reliability is above a givenpercentage. Sensor nodes are notified of impending congestionwhen their buffer overflows or when the average packet delayexceeds a threshold. In this situation, sensor nodes set a CongestionNotification (CN) bit in their packets to inform actuators of theimpending congestion [21]. Upon receiving packets with theCN bit set, and observing that the event reliability is below agiven threshold, actuators inform sensor nodes to decrease theirsampling or transmission rate. The advantage of RT2 is that it savesthe energy of sensor nodes when there is congestion. On the otherhand, its drawback is that there is no mechanism to overcomecongestion and guarantee event reliability.[67,66] propose a novel replication method to increase reliability.Nodes maintain the link loss rate to each of their respectiveib. Comput. 72 (2012) 856867 863

Delay bound Energy conservation techniques

ange No None

Select the nearest actuator node Anycast treeoadcast Select the path that meets packet

delivery timeDistributes packets betweenexisting paths

oadcast Vary transmission range Multicast tree

No Sleep and wake-upNo Multicast tree

neighbor, and use this information to determine their transmissionstrategy. If the loss rate is high, a sensor node sends its packets tomultiple neighbors that have a path to the actuator. If all neighborsfail tomeet the required loss rate, the sensor node sends a feedbackmessage to a packets previous hop. The process is repeated untilanother path is found or the feedback message is received bythe source node, which then decides whether to terminate thetransmission.

Melodia et al. [40] propose a transport protocol that providesevent reliability to applications. Here, event reliability is definedas the percentage of data that arrived within a given time bound.Whenever a sensor node needs to transmit a data packet, itbuilds a routing path to its nearest actuator using the minimumtransmission power.When an actuator receives a data packet froma sensor node, it computes its event reliability and broadcasts theresult to sensor nodes. If an applications event reliability is notmet, sensor nodes adjust their transmission power according to agiven probability. For example, if the calculated event reliabilityis 0.1 of the required event reliability, more sensor nodes in thepacket forwarding path will increase their transmission power. Onthe other hand, if the calculated event reliability is 0.9, sensorsreduce their transmission power. This mechanism, however, doesnot consider congestion at nodes. Hence, increasing transmissionpower may not necessarily alleviate the low event reliabilityreported by actuators. In addition, the authors assume that in eachnetwork area, only one event will occur, and do not consider thepossibility of multiple events requiring different reliability boundswithin said area. For example, in some applications such as homeautomation, light or vision sensors will generate different senseddata with different event reliability requirements.

Melodia et al. [39] propose a transport protocol for sen-soractuator network similar to the distributed heuristic modelin [40], which trade-offs the energy consumption of sensor nodesand providing minimum event reliability. The only difference isthat whenever the reliability is low, even after an increase in trans-mission power, the actuator node detects congestion occurrences.In this scenario, a new actuator is chosen, and half of the traffic isthat rely on retransmissions may cause unwanted responses. Forexample, in a fire system, a temperature sensor node may send

rn

lminimize task completion time. Each actuator node is assignedto a given area. As actuators may have overlapping areas, thealgorithm selects one that can complete the task with minimumenergy expenditure and within a given delay bound. The selectionis carried out according to actuator nodes residual energy andminimum load. This approach can only be used when there aremultiple static actuators in an overlapping area. The proposedalgorithm is not capable of handling the occurrence of multipleevents with different task completion times because it does notprioritize tasks according to their urgency.

Zeng et al. [64] proposed an approach that uses mobile actuatornodes. The first actuator node that receives an event occurrencereport starts collecting information such as residual energy andcurrent position from other actuators. Actuators also indicatewhether theyhave received the same report. Based on the collectedinformation, an actuator that has the highest residual energy andlocated in range of the event area is then assigned to completethe task. A key limitation of this work is that the authors havenot considered multiple events. In particular, an actuator may be

Shah et al. [45] also developed an actuator relocation method.The network area is divided into clusters, where cluster heads areresponsible for collecting sensed data from their respective cluster,and for sending them to actuator nodes. An actuatoractuatorcoordinating procedure is triggered when a cluster head is notin the action range of any actuator nodes. This cluster head thencauses a relocation message to be broadcasted to other actuatornodes, which then determine the possibility of covering saidcluster head whilst remaining in range to cover their existingcluster heads. If an actuator node can move toward the clusterhead that issued the relocation message, it sends back a replymessage that contains the residual energy andnumber of clusters itis responsible for. The actuator nodewith themaximum amount ofresidual energy andminimumnumber of attending cluster head(s)is then selected to help the cluster head. The disadvantage of thisactuator relocation model is that while actuator nodes are mobile,the authors have not proposed any solution when there are noactuators that could move toward uncovered cluster heads.

Table 5 summarizes actuator-to-actuator coordination mod-864 H. Salarian et al. / J. Parallel Dist

Table 4Comparison of transport protocols.

Prior works Delay bound Reliability

Zhou et al. [34] By changing transmission range ofsensor nodes to shorten or elongate aforwarding path

No consideration

Gungor et al. [2] Scheduling packets according to theirdeadlines and dynamic adjustment ofsampling rate

TCEF schedulingchanging sampli

Ngai et al. [67,66] Forwards packets on the leastcongested path. They also considerthe use of mobile actuators

Measuring linksmultiple copies omany paths

Melodia et al. [40] Probabilistic transmission schedule,and increasing nodes transmissionrange to reduce route length

Probabilistic recrnodes

Melodia et al. [39] Changing sensor nodes transmissionrange and shifting load to lesscongestion actuator nodes

Changing sensorrange and shiftincongested paths

a data packet on two different paths, and as a result, they havedifferent arrival times. Upon receiving the first packet, the actuatorstarts the water sprinklers. However, when the second packetarrives, it may conclude that more water is required to extinguishthe fire. It thus increases the water flow to the sprinklers, whichunfortunately leads to flooding.

4.4. Actuator-to-actuator coordination

In actuator-to-actuator coordination, it is critical that taskscompletes on time, so bounding this time is one of the most im-portant application requirement thatmust bemet. Vassis et al. [24]propose amulti-channel actuatoractuator communication proto-col to decrease task completion time. Each actuator uses two inde-pendent radio transceivers, with one used for communicatingwithsingle-hop or neighboring actuators, and the other for multi-hopor remote actuators. The transceiver used for multi-hop commu-nications has a longer range, and lower data rate. As each node hastwo transceivers, the network is less likely to be congested due tothe separate collision domain used for local and remote communi-cations. As a result, nodes experience shorter medium access de-lay. On the other hand, the multi-hop transceiver with its longertransmission range reduces end-to-end delay as packets traversefewer hops to their destination actuator. The downside with thisapproach is that it incurs additional cost and energy due to the useof two radio transceivers.

The authors of [40] developed a localized auction algorithm toselected to carry outmultiple tasks but its residual energymayonlybe sufficient to complete one task.ib. Comput. 72 (2012) 856867

Service priority? Energy efficiency

Yes Forwarding paths are selectedbased on required delivery time ofpackets

algorithm and theg rate

Yes During congestion, sampling rate ofsensor nodes is reduced

oss rate and sendingf a packet along

Yes No

uitment of sensor No Adjust sensor nodes transmissionrange

nodes transmissiong packets onto lessor actuators

No Reduce sensor nodes transmissionrange if event reliability is above agiven threshold

Melodia et al. [39] propose a novelmechanism to controlmobileactuators in scenarios where events occur in partially overlappedspace and/or time. They formulated a Mixed Integer Non-LinearProgram (MINLP) that aims to minimize energy consumptionwhilst ensuring tasks complete within the given time bound. Inthe presence of multiple events, the proposed mechanism aimsto guarantee task completion time for high priority events. Foreach task, the proposedmodel selects an ideal number of actuatorsand determines the actuator velocity required to move into anevent area using minimal energy whilst adhering to a given delaybound. The key limitation of this work is that the MINLP modelis a centralized method, and the authors have not proposed anyheuristics or distributed models for the problem.

Ngai et al. [67] developed a relocation method where actuatorsare mobile and visit areas that have a high frequency of events.The network area is divided into cells, and a selected actuatorperiodically collects the event frequency of all cells in thenetwork. The selected actuator node runs a relocation algorithm tobalance load and move more actuators to areas with higher eventfrequencies. This method requires actuator nodes to periodicallycommunicate with selected actuator nodes, which increasesenergy consumption. To reduce power consumption, the authorsassume fixed event frequency. This means after an actuator nodeis assigned to a given area, there is no longer any need forcommunication between actuator nodes. Unfortunately, in someapplications, different areas will have varying number of eventsoccurring within a given time period.els. As we can see, only a handful of works exist in this cate-gory. The main theme of these works is developing protocols to

applications deadline and ensuring reliability. Designing actuator-to-actuator protocols that guarantee taskcompletion time in scenarios with multiple events. Centralizedcoordination models are well equipped to handle multipleevents but are unable to guarantee the task completion timeof each event. On the other hand, distributed coordinationmodels incur a non-negligible amount of overheads when usedto guarantee the task completion time of multiple events. Apromising approach is to utilize clustering, where an actuatornode is designated as the cluster head. When multiple eventsoccur in a cluster, the cluster head is then responsible for

August 1994.[11] J. Capella, A. Bonastre, J. Serrano, R. Ors, A new robust, energy-efficient

and scalable wireless sensor networks architecture applied to a wirelessfire detection system, in: Intl. Conf. on Wireless Networks and InformationSystems, Shanghai, China, December 2009.

[12] H. Chan, A. Perrig, Security and privacy in sensor networks, Computer 36 (10)(2003) 103105.

[13] I. Chatzigiannaki, A. Kinalis, S. Nikoletseas, Sink mobility protocols for datacollection in wireless sensor networks, in: ACM International Workshop onMobility Management and Wireless Acces, Torremolinos, Spain, 2006.

[14] T. Coplu, O. Emiroglu, E. Cayirc, Power awaremany tomany routing inwirelesssensor and actuator networks, in: 2nd EuropeanWorkshop onWireless SensorNetworks, Istanbul, Turkey, February 2005.

[15] I. Demirkol, C. Ersoy, F. Alagoz, MAC protocols for wireless sensor networks: asurvey, IEEE Communications Magazine 44 (4) (2006) 115121.H. Salarian et al. / J. Parallel Distr

Table 5Comparison of actuator-to-actuator protocols.

Prior works Coordination model Multiple events?

Vassis et al. [24] Distributed NoMelodia et al. [40] Distributed NoZeng et al. [40] Distributed NoMelodia et al. [40] Centralized YesNgai et al. [40] Centralized YesShah et al. [40] Distributed No

control mobile actuator nodes. A key observation is that guaran-teeing task completion time when there are multiple events iscurrently not possible with distributed actuator-to-actuator coor-dination mechanisms. Besides that, coordinating multiple actua-tors, especially in scenarios with varying event frequencies, incursa significant amount of communication overheads. On the otherhand, centralized coordination models have high delays and as aconsequence, they are not suitable for tasks that require fast com-pletion time.

5. Future research directions

The coordination problem cuts across different layers ofthe protocol stack. Moreover, unlike WSNs, protocols must bedeveloped according to a set of requirements that is distinct toWSANs. To this end, there are a number of open research issues.They include,

Developing techniques to distribute energy consumption andpreventing energy holes from forming around actuator nodes.In this regard, multiple actuator nodes can be distributeduniformly to balance power consumption. However, thisassumes uniform events occurrence and sampling rate. Oneapproach to alleviate these problems is to periodically changethe target actuator(s) which sensor nodes send their data to, orchange themobile actuator nodes that visit areas with frequentevents.

Designing energy efficient methods for delivering sensed datato actuator nodes in a timely manner. Current techniquesrely primarily on wireless communications. Unfortunately, thewireless channel is inherently lossy and shared by multiplecontending nodes. As a result, multiple techniques are requiredto overcome packet loss, ensure fair channel access, andminimize delay all of which require nodes to budget asignificant portion of their energy on addressing the vagariesof the wireless channel. To this end, a promising approach isto use mobile sinks [63,13,47]. As a result, communications canbe kept to a minimum as sensors are able to forward their datadirectly to a mobile sink or to a node, via a limited number ofhops, that is on a mobile sinks path. A key challenge is meetingassigning each task to different actuator nodes and ensuring alltasks are completed on time.ib. Comput. 72 (2012) 856867 865

Bounded task completion time? Minimize actuators energy?

Yes NoYes YesYes YesYes YesYes NoNo Yes

6. Conclusion

The potential of WSANs has attracted the attention ofresearchers from diverse disciplines. The key driving factor istheir wide range of applications as sensor nodes are now ableto interact with the environment they are in by effecting keyparameters. In this context, this paper has provided an extensivereviewof solutions pertaining to the coordination process betweensensor and actuator nodes, as well as between actuator nodes. Itis evident that addressing the coordination problem will have toinvolve energy efficient protocols fromanumber of protocol layers.Moreover, in comparison toWSNs, a significant amount of researchremains. In particular, those related to developing distributedprotocols to control multiple actuator nodes, and providing anupper bound on task completion time. To this end, we haveoutlined a number of open research problems and issues to guidefuture researchers.

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Hamidreza Salarian received the B.S. degree in computerengineering (hardware major) from Ferdowsi UniversityMashhad and the M.S. degree in computer architecturefrom Isfahan University of Technology, Iran in 2005and 2008. He is currently a Ph.D. student with theSchool of Electrical, Computer and TelecommunicationsEngineering (SECTE) at University of Wollongong. Hisresearch focused on energy consumption in wirelesssensor networks and wireless sensoractuator networks,

in particularly, the effect of mobility in increasing systemlifetime.

H. Salarian et al. / J. Parallel Distr

Kwan-WuChin obtained his Bachelor of Sciencewith FirstClass Honours from the Curtin University of Technology,Australia. He then pursuedhis Ph.D. at the sameuniversity,where he graduated with distinction and the vice-chancellors commendation. He then joined MotorolaResearch Lab as a senior research engineer, where hedeveloped zero configuration home networking protocolsand designed new medium access control protocolsfor wireless sensor networks. In 2004, he joined theUniversity of Wollongong as a Senior Lecturer. His currentresearch areas include medium access control protocols

for wireless networks, routing protocols for delay tolerant networks, RFID anti-collision protocols, and discrete optimization problems related to computercommunications. To date, he holds four US patents, and has published more than50 articles in numerous conferences and journals.ib. Comput. 72 (2012) 856867 867

Fazel Naghdy has a demonstrated track record and leader-ship in research, teaching, and management. His researchhas had its focus on machine intelligence and control par-ticularly in embeddedmechatronics and robotics systems.He has more than 250 publications in international jour-nals and conferences and as book chapters. He is alsocontributing editor to IEEE Transactions on MechatronicsEngineering, and International Journal of Intelligent Au-tomation and Soft Computing. He has served on a largenumber of International scientific committee of various in-ternational conferences. He is the Director of Centre for

Intelligent Mechatronics Research. His current research interests include hap-tic rendered virtual manipulation of clinical and mechanical systems, intelligentcontrol and learning in non-linear and non-structured systems. He is currently aProfessor at University of Wollongong, School of Electrical, Computer & Telecom-munication Engineering, Australia. Fazel Naghdy was born in Tehran, Iran. He re-ceived his first degree from Tehran University in 1976, M.Sc. and Ph.D. from thePostgraduate School of Control Engineering, University of Bradford, England, in1979, 1982, respectively.

Coordination in wireless sensor--actuator networks: A surveyIntroductionNetwork architectureCoordinationLiterature reviewSensor--actuator coordinationRouting protocolsTransport protocolsActuator-to-actuator coordination

Future research directionsConclusionReferences