sustainable wireless

IEEE Wireless Communications • June 201230 1536-1284/12/$25.00 © 2012 IEEE

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INTRODUCTIONElectrical power grid is one of the largest andthe most complex man-made systems. The gen-eral principles of power generation and deliveryhave been determined more than a century agoand incremental updates have taken place asnew renewable energy generation techniquesand power electronic devices have become avail-able. A major upgrade in the power grid hasrecently been possible with the advances in

Information and Communication Technologies(ICT). The next-generation power grid empow-ered by ICT is known as the smart grid.

Smart grid is anticipated to employ variouscommunication technologies such as powerline,satellite, fiber optic, cellular, mesh and short-range wireless communications depending onthe location of the assets and the specific needsof the applications. Among those technologies,short-range wireless communications are promis-ing for smart grid monitoring and diagnosis taskswhen compared with the available wired sensorsand limited telemetry provided with RemoteTerminal Units (RTUs) of the Supervisory Con-trol And Data Acquisition (SCADA) systems. Inthe past, traditional measurement tools provedto be inadequate in several severe grid condi-tions. For instance, the actual reason of themajor blackout of 2003, in the eastern U.S.interconnection was later determined to be atree contacting the power lines in Ohio duringpeak demand, whereas at the time of the inci-dent, operators were not able to figure out thereason of the abnormal fluctuations in the fre-quency and act accordingly [1]. If robust moni-toring tools had existed, this blackout may havebeen avoided or at least, the outage could havebeen kept in a local region by isolating the faultylines before they caused the cascaded blackouts.Similar incidents emphasize that the absence ofadequate situational monitoring tools may causelarge-scale interruption of electrical services.Consequently this implies the timeliness ofrobust smart grid monitoring and diagnosistools.

WSNs have gained wide recognition as estab-lished monitoring tools and they are being usedin a wide variety of fields including defense andhealth that require high-confidence solutions.Smart grid can also take advantage from thislow-cost technology to cover large geographicalregions with high redundancy [2, 3]. However,before WSNs are deployed in the smart grid,several challenges need to be addressed. Twomajor challenges of WSNs have been the lowdata rate communications and limited lifetime of

MELIKE EROL-KANTARCI AND HUSSEIN T. MOUFTAH, UNIVERSITY OF OTTAWA

ABSTRACTThe electrical power grid has recently been

embracing the advances in Information andCommunication Technologies (ICT) for the sakeof improving efficiency, safety, reliability andsustainability of electrical services. For a reliablesmart grid, accurate, robust monitoring and diag-nosis tools are essential. Wireless Sensor Net-works (WSNs) are promising candidates formonitoring the smart grid, given their capabilityto cover large geographic regions at low-cost. Onthe other hand, limited battery lifetime of theconventional WSNs may create a performancebottleneck for the long-lasting smart grid moni-toring tasks, especially considering that the sen-sor nodes may be deployed in hard to reach,harsh environments. In this context, recentadvances in Radio Frequency (RF)-based wire-less energy transfer can increase sustainability ofWSNs and make them operationally ready forsmart grid monitoring missions. RF-based wire-less energy transfer uses Electromagnetic (EM)waves and it operates in the same medium as thedata communication protocols. In order toachieve timely and efficient charging of the sen-sor nodes, we propose the Sustainable wirelessRechargeable Sensor network (SuReSense).SuReSense employs mobile chargers that chargemultiple sensors from several landmark loca-tions. We propose an optimization model toselect the minimum number of landmarksaccording to the locations and energy replenish-ment requirements of the sensors.

SURESENSE: SUSTAINABLE WIRELESSRECHARGEABLE SENSOR NETWORKS FOR THE

SMART GRID

RE C E N T AD VA N C E S I NWI R E L E S S TE C H N O L O G I E S F O R SMART GR I D

For a reliable smartgrid, accurate, robustmonitoring and diagnosis tools areessential. WirelessSensor Networks arepromising candidatesfor monitoring thesmart grid, giventheir capability to cover large geographic regionsat low-cost.

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IEEE Wireless Communications • June 2012 31

the sensor nodes. Advanced data compressionand in-network aggregation techniques, andalternative wireless communication technologiessuch as the low-power Wi-Fi offer promisingsolutions for the former problem [4]. For solvingthe limited battery issue of the WSNs, energy-efficient protocols have been widely studied inthe literature where duty-cycling and variousenergy-efficient medium access and routing pro-tocols have been proposed. Moreover, by har-vesting energy from the ambient resources, it hasbeen possible to extend the lifetime of the sen-sor nodes. However duty-cycling and energy-effi-cient protocols are still only able to providelimited lifetime while energy-harvesting is gener-ally uncontrolled and may not be possible atevery circumstance due to unavailability of ambi-ent energy. For instance, in the smart grid,WSNs deployed at the overhead transmissionlines can harvest solar energy or vibration-induced energy but when WSNs are deployedfor indoor substation monitoring or under-ground vault or power line monitoring, it maynot be straightforward to harvest energy fromthe ambient resources. For this reason, therecent advances in the RF-based wireless energytransfer technology position wireless recharge-able sensor network as a strong candidate formonitoring the smart grid [5]. Figure 1 presentsan illustration of a wireless rechargeable sensornetwork inside a distribution substation of thesmart grid.

In this article, we propose a Sustainable wire-less Rechargeable Sensor network (SuReSense)for smart grid monitoring. SuReSense targetslong-term, reliable monitoring of the smart gridassets and it relies on Mobile wIreless ChargerRObots (MICRO) for replenishing the batteries

of the sensor nodes. At the first step, minimumnumber of landmarks are selected according tothe locations and energy replenishment require-ments of the sensors by the help of an IntegerLinear Programming (ILP) formulation. At thesecond step, landmarks are organized into clus-ters where each cluster is serviced by oneMICRO. MICROs visit the assigned landmarksin its cluster by following the shortest Hamiltoni-an cycle and disseminate wireless power to mul-tiple sensors from the determined locations. Thissustainable wireless charging scheme reduces thepath length of the MICROs, allows for a longerdocking time enabling the charger to top up itsown battery and further reduces the waiting timeof the sensors.

The rest of the article is organized as follows.We summarize power grid monitoring and diag-nosis tools, along with the studies that utilizeWSNs for smart grid monitoring. We briefly sur-vey the studies that use wireless charging tech-nology in WSNs. We present the SuReSensescheme which is a long-lasting smart grid moni-toring tool, and we discuss the advantages ofSuReSense by the help of illustrative results. Weconclude the article and provide future researchdirections.

SMART GRID MONITORING ANDDIAGNOSIS

Adopting the WSN technology for smart gridmonitoring has been recently considered in sev-eral studies [2, 3, 6]. In [2], the authors focus onthe wireless link quality of a WSN that isemployed in the power systems. The authorsimplement field tests in various locations, one of

Figure 1. Illustration of a smart grid with a distribution system employing the SuReSense.

Distribution substation

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IEEE Wireless Communications • June 201232

them being a 500-kV substation, and presentlink quality measurements from an IEEE802.15.4-based sensor network. In [6], theauthors implement a WSN-based monitoringtool for a test substation in Kentucky, and showthat WSNs are able collect data from the substa-tion using a dynamic link quality based routingalgorithm. The implemented WSN has been uti-lized for collecting data from circuit breakersand transformers in addition to ambient temper-ature and gas density measurements.

In [3], the authors explore the opportunitiesthat become available with multimedia sensornetworks and actor networks in the smart grid.The authors anticipate that WSN technologywill find diverse application areas includingpower plants, renewable energy generation sites,overhead transmission lines, transmission tow-ers, distribution transformers, feeder lines andfinally consumer premises. One such applicationof WSN technology at the consumer premiseshas been presented in [7]. An IEEE 802.15.4-based WSN has been employed as a part of anenergy management application that helpsreducing the electricity expenses of the con-sumers by utilizing the Time of Use (ToU)information as well as enabling efficient use ofrenewable energy that is supplied from the roof-top solar panels.

Besides the advantages of WSNs, limited life-time of the sensor nodes emerges as a significantperformance bottleneck that may delay theadoption of the WSNs in the long-lasting smartgrid monitoring tasks. In fact, extending the life-time of a WSN is a widely studied topic. Yet, thesolutions in this field rely on duty-cycling andenergy-efficient communication protocols thatcan only extend the lifetime of a WSN up to fewyears. For long-term operation, energy-harvest-ing have been considered in the literature, wherecoupling duty cycling with the slow energy-har-vesting process has led to promising results asreported in [8]. Yet, energy-harvesting relies on

the availability of ambient energy. Furthermore,coupling energy harvesting rate to energy con-sumption of a sensor node can be significantlychallenging for a sensor network with high datatraffic intensity.

The smart grid infrastructure will cover alarge region including outdoors, indoors andunderground. Certain regions may be rich inambient energy, such as overhead power lines,while at some locations WSNs may not have thechance to scavenge energy at all. Therefore, inthis article, we consider utilizing the recentlyemerging RF-based wireless energy transfertechnology for a sustainable WSN. In the nextsection, we briefly survey the research on wire-less energy transfer and its applications in wire-less rechargeable sensor networks.

WIRELESS ENERGY TRANSFER FOR WSNS

Energy transfer from one device to anotherover several meters using electromagnetic (EM)waves is known as RF-based wireless charging.A wireless charging testbed using PowercastCo. [5] wireless power chargers have been pre-sented in a recent study [9]. The authors haveproposed a charging-aware routing protocoland an optimization framework in order todetermine optimal charging and transmissioncycles in a WSN. Since wireless charging mayutilize the same Industrial, Scientific and Medi-cal (ISM) band with the data communications,concurrent charging and data transmission maycorrupt data packets. The authors have modi-fied the existing Ad Hoc On-demand DistanceVector (AODV) routing protocol to include anadditional metric that represents the chargingduration. In this protocol, the path with mini-mum charging duration is selected as the for-warding path. The transmission and chargingdurations of the nodes that are on the forward-ing path are later determined with an optimiza-tion framework.

Utilizing a mobile charging vehicle for sup-plying energy for the WSN has been consideredin [10]. The authors assume that the mobilevehicle visits each and every sensor node forreplenishing sensor batteries. The batteries arecharged such that the minimum available energyis higher than a threshold within one cycle ofcharging. Shi et al. prove that the optimal trav-eling path for the vehicle is the shortest Hamil-tonian cycle when the objective is maximizingthe ratio of the docking time over cycle time. In[11], although the authors consider a differentwireless charging technology, i.e. energy trans-mission via magnetic resonance, they proposecombining the mobile charging ability with datacollection. The mobile charger is called SenCarand it houses a high-capacity rechargeable bat-tery, a DC/AC converter and a resonant coil.SenCar visits a subset of sensor nodes whichrequire urgent recharge, and in the meanwhile itcollects data from the sensor network.

In another recent work [12], the authorsstudy recharging of the RFID tags by the RFIDreaders using wireless energy transfer. Theauthors first consider stationary readers (charg-ers) and try to minimize the number of chargersin the network. Then, they assume that the tags

Figure 2. SuReSense for distribution substation monitoring.

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Landmark

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are mobile and they can receive power from dif-ferent readers as they move, and the problemturns into selecting reader locations that provideadequate charging for the tags along their path.In this article, following the promising results ofrecent wireless energy transfer studies, we pro-pose the SuReSense which utilizes wirelesscharging to address the need of long-term smartgrid monitoring tasks.

SUSTAINABLE WIRELESS RECHARGEABLESENSOR NETWORK (SURESENSE) FOR

SMART GRID MONITORINGSuReSense can be employed in various assets ofthe smart grid. In this article, we focus on distri-bution substation monitoring. A distribution sub-station includes a large number of equipment ina condensed environment. Accurate monitoringof those equipment can yield to early detectionof possible failures and aid in energy manage-ment decisions. As a sustainable monitoringsolution, wireless rechargeable sensor networktechnology is promising for the substations.However, given the remote locations and haz-ardous operation environments of the substa-tions, wireless charging cannot be done byhuman operators. To facilitate wireless chargingby devices, a straightforward approach would bemounting stationary chargers at certain locationsbut this approach would require a large numberof chargers due to the limited range of wirelesscharging; hence increase the deployment cost ofthe WSN. For this reason, we employ MobilewIreless Charger RObots (MICROs) to supplywireless power to the sensor nodes based ontheir energy replenishment requirements. Duringcharging process, MICROs park and emit radiosignals which have different frequencies than thesignals that are used for communication. In theliterature, the frequency band around 900MHz isutilized for wireless charging of the WSNswhereas sensors communicate in the 2.4Gz band[9]. MICROs wait until all of the sensor nodeswithin the communication range receive ade-quate power. This duration is denoted by h anda MICRO moves when it receives acknowledge-ment from the sensors indicating that they havefinished charging.

Naturally, wireless energy transfer is limitedto a certain range, Rc, since the radio wavesattenuate as they travel from the transmitter tothe receiver. According to the free space model,the received power is inversely proportional tod2 and it is assumed to be 0 when d > Rc, whered denotes the distance between the transmitterand the receiver. For simplicity, we adopt a cir-cular disk model for wireless power propagation[12].

In Fig. 2, we present the SuReSense deployedin a rectangular field where the sensor nodes arereplenished by four MICROs. The star illus-trates one of the landmark locations and aMICRO is shown to wirelessly charge the sensornodes in its charging range while the otherMICROs are illustrated to be in their dockingstations. SuReSense employs two steps to mini-mize the number of the landmarks and the totallength of the traversed path:

• Landmark selection• Clustering and path formationThese steps are presented in Algorithm 1 andexplained in detail in the following subsections.

STEP I: LANDMARK SELECTIONLandmark selection is formulated with an ILPmodel whose objective is to minimize the num-ber of landmarks in order to serve as many aspossible sensor nodes from one location. Theobjective function of the ILP model is given inEq. 1 where lxy is a binary variable that is 1 ifthere is a landmark located at (x, y) and 0 other-wise.

(1)

We assume that sensor nodes receive powerfrom one and only one charger. This constraintis formulated by:

(2)

where Mixy is a binary variable which is 1 if the

sensor i is able to receive power from the land-mark located at (x, y) and 0 otherwise. Sxy is thenumber of sensors charged by the landmark withthe coordinates (x, y), and Sxy = SiMi

xy. Sxy shouldsatisfy Sxy > 0.

In a WSN, most of the energy is consumedduring packet transmission. Hence the frequencyof forwarding events to the sink and relaying thepackets of the neighbors, determine the amountof energy required for topping up the battery ofa sensor node in each charging cycle. Demandintensity refers to the energy replenishmentrequirement of a sensor node and it dependswhether the sensor is located on a busy pathtowards the sink or not. In the ILP formulation,we denote the demand intensity with Di. Theenergy supply of a MICRO is limited by its bat-tery capacity which is denoted by t. The totalsupply of the MICROs should be greater than orequal to the energy requirement of the sensors.This constraint is formulated by Eq. 3. The restof the ILP formulation involving the boundaryconstraints is not given since the key compo-nents have been defined above.

(3)

The landmarks that are selected by the aboveILP formulation are further processed in theclustering and path formation step.

minimize lxyyx∑∑

M ixyi

yx∑∑ = ∀1,

M x yxyi

ii∑ ≤ ∀δ τ , ,

Algorithm 1. SuReSense algorithm.

1: {Input: V (Set of sensor nodes), Rc (RF-based charging range)}2: {Output: Λ (Set of landmarks), Γ (Set of clusters of landmarks), Πi (Path of

mobile charger i)}3: Λ = SelectLandmarks(V, Rc)4: Π = ClusterLandmarks(Λ)5: for all ci ∈ Γ do6: Πi = SelectShortestPath(Λci)7: end for



STEP II: CLUSTERING AND PATH FORMATION

In this step, landmarks are grouped based ontheir proximity to the docking stations of theMICROs and each MICRO is assigned one clus-ter of landmarks. Sensors around the landmarklocations receive power from the MICRO that isin charge of their cluster. Since each MICROoperates in a smaller region that is close to itsdocking station, the length of the traversed pathreduces. Furthermore, sensor nodes wait less forenergy replenishment in comparison to the casewhen a single MICRO visits all of the nodes or

the landmarks. To further reduce the pathlength, optimum cycle to cover all of the verticesof the cluster, i.e. landmarks and the dockingstation, needs to be determined. For this pur-pose, MICROs follow the shortest Hamiltoniancycle which gives the optimum path length [10].

PERFORMANCE EVALUATION OF SURESENSE

SuReSense consists of 50 sensor nodes randomlydeployed in a rectangular field of 100m X 100m.Four MICROs are employed for wirelesslycharging the sensor nodes and their docking sta-tions are assumed to be at the corners of therectangular filed. We use CPLEX to determinethe landmark locations by solving the ILP for-mulation defined in the previous section. Thedemand intensity of a sensor node, i.e. theamount of the battery that needs to be charged,varies depending on several factors as explainedin the previous sections. We assume that thedemand intensity of the nodes vary between 1kJand 4kJ. The battery capacity of the MICROs isassumed to be 20kJ. The energy demand of thesensor nodes are forwarded to a sink node andthe sink node runs the ILP to determine thelandmarks, clusters and paths among the land-marks. In the first set of simulations, we set thewireless energy transfer range to Rc = 2m.

In Fig. 3, we present a sensor network topol-ogy with 50 nodes and 18 landmark locations.After the landmarks are clustered and theHamiltonian cycle is computed, one of theMICROs, as shown in the upper right side of thefigure, visits the assigned landmarks and returnsback to its docking station to replenish its bat-tery from mains. Note that, only one of theMICROs and the associated Hamiltonian cycleis shown to increase the readability of the figure.

In Fig. 4, we present the number of land-marks required for varying demand intensities.Higher demand corresponds to higher networkactivity since packet forwarding/transmissionconsumes most of the sensor energy, as men-tioned before. As seen from the figure, fordemand intensity of 1kJ, 15 landmarks are deter-mined. Naturally, the number of landmarksincreases as the demand intensity increases. Theextreme case would be reserving a landmark foreach sensor and this would be the equivalent tovisiting each sensor node for charging. To avoidthis approach, the battery size of the charger canbe extended.

Figure 5 illustrates the total path lengths offour MICROs when the landmark selection andclustering of SuReSense is employed, in compar-ison to the case where each sensor node is indi-vidually visited using the shortest Hamiltoniancycle. Even for high demand intensities, SuRe-Sense is able to produce paths with almost 150mshorter length. Reduced path length implies thatless time is spent away from the docking station,allowing more time to top up the battery of theMICROs since the charging process is repeatedperiodically. Furthermore, it reduces the waitingtime of the sensor nodes. We also evaluateSuReSense for varying wireless power transferranges. For these set of simulations, the demandintensity is fixed to 2kJ.

In Fig. 6, we present the path length for

Figure 3. Sensor nodes, landmarks and the hamiltonian path in one of theSuReSense clusters.

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ranges varying between 2m to 5m. As seen fromthe figure, for larger ranges, once again SuRe-Sense is able to reduce the path length whencompared to the case that each node is visitedwith regards to the shortest Hamiltonian cycle.

CONCLUSIONS AND FUTURE DIRECTIONS

A wide range of smart grid assets can be moni-tored and diagnosed by the low-cost WSN tech-nology. Besides the numerous advantages ofWSNs, limited lifetime of the sensor nodes havebeen considered as a significant performancebottleneck. Although, duty cycling, energy-effi-cient network protocols and energy-harvestingtechniques offer solutions to extend the lifetimeof the WSNs, still the maximum lifetime of atypical sensor node is limited while the smartgrid requires long-lasting monitoring tools.

In this article, we discuss the employment ofwireless rechargeable sensor networks to answerthe large-scale, low-cost and long-term monitor-ing needs of the smart grid. We propose a Sus-tainable wireless Rechargeable sensor network(SuReSense) where Mobile wIreless ChargerRObots (MICRO) are used to replenish the bat-teries of the sensor nodes by visiting severallocations in the field. These locations are calledas landmarks, and they are determined periodi-cally based on the energy requirements of thesensors. To further reduce the path length andwaiting time, the landmarks are organized intoclusters according to the geographical proximitycriterion, and a Hamiltonian cycle is formedbetween the docking stations of the MICROsand the associated landmarks. SuReSensereduces the length of the path traversed by theMICROs compared to the case when each sen-sor is individually visited. This approach increas-es the duration spent at the docking station;hence it provides more time for replenishing thebatteries of MICROs. Furthermore, it allowsreducing the waiting time of each sensor forenergy replenishment.

Utilizing wireless rechargeable sensor net-works in the smart grid is a novel concept, and itis anticipated to increase the adoption rate of sen-sor networks by increasing their credibility andsustainability. As a future research direction,sleep scheduling, energy-efficient communicationprotocols and energy-harvesting technologies canbe utilized along with SuReSense. Furthermore,selection of MICROs and their dispatch frequen-cy are left as future work. Another research direc-tion is combining mobile charging with mobiledata collection solutions which can further reduceenergy consumption of the sensor nodes.

REFERENCES[1] G. Andersson et al. , “Causes of the 2003 Major Grid

Blackouts in North America and Europe, and Recom-mended Means to Improve System Dynamic Perfor-mance,” IEEE Trans. Power Systems, vol. 20, no. 4, Nov.2005, pp. 1922–28.

[2] V. C. Gungor, B. Lu, and G. P. Hancke, “Opportunitiesand Challenges of Wireless Sensor Networks in SmartGrid,” IEEE Trans. Industrial Electronics, vol. 57, no. 10,Oct 2011, pp. 3557–64.

[3] M. Erol-Kantarci and H. T. Mouftah, “Wireless Multime-dia Sensor and Actor Networks for the Next-GenerationPower Grid,” Elsevier Ad Hoc Networks J., vol. 9 no. 4,2011, pp. 542–11.

[4] S. Tozlu, “Feasibility of Wi-Fi Enabled Sensors for Inter-net of Things,” 7th Int’l. Wireless Communications andMobile Computing Conf. (IWCMC), 4–8 July 2011, pp.291–96.

[5] Powercast Corporation, http://www.powercastco.com/.[6] A. Nasipuri et al. , “Design Considerations for A Large-

Scale Wireless Sensor Network for Substation Monitor-ing,” Proc. IEEE 35th Conf. Local Computer Networks(LCN), Oct. 2010, pp. 866–73.

[7] M. Erol-Kantarci and H. T. Mouftah, “Wireless SensorNetworks for Cost- Efficient Residential Energy Manage-ment in the Smart Grid,” IEEE Trans. Smart Grid, vol.2,no.2, June 2011, pp. 314–25.

[8] V. Pryyma, D. Turgut, and L. Boloni, “Active TimeScheduling for Rechargeable Sensor Networks,” ElsevierComputer Networks, vol. 54, no. 4, 2010, pp. 631–40.

Figure 5. Total length of path traversed by MICROs for varying demand intensity.

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Figure 6. Total length of path traversed by MICROs for varying range (Rc).

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[9] R. D. Mohammady, K. Chowdhury, and M. Di Felice,“Routing and Link Layer Protocol Design for SensorNetworks with Wireless Energy Transfer,” IEEE GLOBE-COM, Miami, Dec. 2010.

[10] L. Shi et al. , “On Renewable Sensor Networks withWireless Energy Transfer,” Pro. IEEE INFOCOM, Shang-hai, China, Apr. 10–15, 2011, pp. 1350–58.

[11] M. Zhao, J. Li, and Y. Yang, “Joint Mobile EnergyReplenishment and Data Gathering in WirelessRechargeable Sensor Networks,” Proc. 23rd Int’l. Tele-traffic Congress, Sept. 6–8, 2011, San Francisco, USA.

[12] S. He et al. , “Energy Provisioning in Wireless Recharge-able Sensor Networks,” Proc. IEEE INFOCOM, Shanghai,China, Apr. 10–15, 2011, pp. 2006–14.

BIOGRAPHIESMELIKE EROL-KANTARCI [M’08] ([email protected])is a postdoctoral fellow at the School of Electrical Engineer-ing and Computer Science, University of Ottawa. She receivedthe M.Sc. and Ph.D. degrees from the Department of Com-puter Engineering, Istanbul Technical University, Turkey, in2004 and 2009, respectively. During her Ph.D., she was a Ful-bright visiting researcher at the Department of Computer Sci-ence, University of California Los Angeles. She received the

B.Sc. degree from the Department of Control and ComputerEngineering of the Istanbul Technical University, in 2001. Hermain research interests are wireless sensor networks, smartgrid communications, cyber-physical systems and underwatersensor networks. She has over 40 referred journal articles andconference papers.

HUSSEIN MOUFTAH [F] ([email protected]) joined theSchool of Information Technology and Engineering, Univer-sity of Ottawa in September 2002 as a Canada ResearchChair Professor. He has been with the ECE Department atQueen’s University (1979–2002), where he was prior to hisdeparture a Full Professor and the Department AssociateHead. He has three years of industrial experience mainly atBNR of Ottawa, now Nortel Networks (1977–79). He servedas Editor-in-Chief of the IEEE Communications Magazineand IEEE ComSoc Director of Magazines, Chair of theAwards Committee and Director of Education. He has beena Distinguished Speaker of the IEEE ComSoc (2000–07). Heis the author or coauthor of six books, 40 book chaptersand more than 1000 technical papers and 10 patents inthis area. He is a Fellow of the Canadian Academy of Engi-neering, the Engineering Institute of Canada and the RoyalSociety of Canada RSC: The Academy of Science.


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