A wireless sensor network for substationmonitoring and control in the smart grid

Natalie Matta∗†, Rana Rahim-Amoud†, Leila Merghem-Boulahia∗ and Akil Jrad†∗ICD/ERA (UMR CNRS 6279), Troyes University of Technology, France

Email: {natalie.matta, leila.merghem boulahia}@utt.fr†EDST, Centre-Azm, Laboratoire des Systemes electroniques, Telecommunications et Reseaux (LaSTRe),

Lebanese University, Lebanon, Email: {rana.rahim, ajrad}@ul.edu.lb

Abstract—A substation is an essential part of an electricsystem. It is where important functions such as trans-forming voltage from high to low take place. Substationmonitoring has become a necessity for the next generationpower grid. The information collected in an electric sub-station about the state of its components and the flow ofelectricity will give the electrical utility provider a clearerview and more control over that substation. A wirelesssensor network, with its sink able to communicate with theutility’s control system, constitutes an attractive technologyto be deployed in a substation in order to collect theneeded information. However, intelligent management ofthis data, to allow the detection of faults and to prolongthe sensor nodes’ lifetime, is yet to be provided andcan be introduced with the use of a multi-agent system.That is why in this paper an agent-based algorithm isproposed to achieve these goals. Simulation results showimproved performances, in terms of the nodes’ energyconsumption and the number of sent messages in thenetwork, compared to a value reporting approach whereeach detected information is directly sent to the sinkwithout any processing.

Index Terms—Wireless sensor networks, smart grids,substation monitoring, multi-agent systems, informationmanagement.

I. INTRODUCTION

The electric power network consists of three main

subsystems, i.e. power generation, transmission & dis-

tribution (T&D), and consumption. In the next gen-

eration electricity grid, i.e. the smart grid, the basic

power network is supplemented with an information

and communication technology (ICT) layer [1]. ICT

provides connectivity between the different components

of the grid, thus allowing for two-way communication

within the power network. From the generation station,

to the transmission lines and substations, then to the

distribution lines and substations, the electricity flow

makes its way to the consumers, supplemented by a

two-way information flow, as shown in Fig. 1. Some

of the objectives behind this concept are to make the

grid less centralized and more consumer-interactive, and

to improve its efficiency, reliability, and safety [2]. To

achieve the latter, monitoring and control systems have

become a major part of the smart grid. In particular,

T&D substation monitoring plays an essential role, since

important electric functions take place at this level of the

power grid, such as passing from high to low voltage.

Fig. 1. Illustration of the electricity and information flows from thegeneration station to the consumers

The benefits of the smart grid (e.g. improved reliability

and operation efficiency) will be delivered by a set of

functionalities and applications, which rely on two-way

communication between the different entities of the grid.

These communications may be wired (e.g. power line

communication PLC), or wireless (e.g. cellular networks,

sensor networks). Sensor networks constitute a suitable

solution to the needs of real time, continuous or event-

based, flow of information in the smart grid, since their

basic role is to collect information from their environ-

ment. Wireless sensor networks (WSNs) are pervasive,

can provide a relatively wide coverage, and are not as

2012 IEEE International Conference on Green Computing and Communications, Conference on Internet of Things, and Conference

on Cyber, Physical and Social Computing

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DOI 10.1109/GreenCom.2012.39

203

expensive to install and maintain as wired infrastructures

are.

Applications of WSNs in the smart grid span a wide

range, whether on the electric power generation side,

the transmission & distribution systems, or on customer

premises [3]–[5]. In this work, we focus on WSNs for

the monitoring and control systems of the power grid.

These systems are needed in order to achieve some of

the key functional elements of a smart grid, such as

fault and stability diagnosis and prevention, control of

reactive power, and voltage and frequency regulation

[6]. This includes the use of a WSN for the constant

monitoring of T&D substations which contain important

electric components.

In this context, we are looking to monitor the substa-

tion’s equipments (e.g. transformers and circuit breakers)

in order to detect and locate faults. The surveillance of

the substation will thus require a smart network that

is capable of local and decentralized decision-making,

specially when it comes to urgent situations. This can be

achieved with the use of a multi-agent system (MAS). In

fact, the WSN’s nodes are embodied with agents which

are responsible of locally processing sensed data.

In the remainder of this paper, some related works are

presented in section II. Section III exposes our agent-

based approach for data management in a substation’s

WSN. Simulation results are shown in section IV. Fi-

nally, we conclude and suggest future work in section

V.

II. RELATED WORK

Improving the reliability of the power network is

a key objective for the smart grid. Today’s electricity

system still suffers from power outages and blackouts

due to the lack of automated analysis and poor visibility

of the utility over the grid [2]. Deploying WSNs will

give the utility provider the needed view by collecting

information from the different subsystems of the grid.

The opportunities and importance of WSNs in the

smart grid were put forward in many research studies

such as [3]–[5], [7]–[9]. In fact, traditional wire-based

monitoring of electrical systems is expensive to install

and requires constant maintenance of communication

cables; whereas smart grid communications need to be

scalable and pervasive. Sensor networks present the

advantages of being low-cost, pervasive, flexible, and

rapidly deployed. Indeed, the use of WSNs can insure

a wide coverage, even of remote sites, as well as a dis-

tributed and decentralized architecture which overcomes

the issue of a single point of failure.

In [10], a decentralized voltage quality monitoring

architecture that employs sensor networks is introduced.

The advantages of such an architecture over client/server

ones, are its scalability and task distribution which takes

the load off the remote server. The authors consider a

scenario where each part of the power grid is monitored

by a sensor network, and propose a cooperation mecha-

nism between these networks in order to propagate their

respective quality indices. However, how the nodes in a

single network communicate to evaluate the local index

is not treated in [10]. In addition, the sensors’ power

consumption issue was not addressed by the authors.

A large-scale WSN for substation monitoring is con-

sidered in [11]. The paper describes a practical imple-

mentation of a WSN scaled to 122 nodes in a substation

in Kentucky, U.S. The authors address the issue of the

energy consumption of the nodes by applying a level-

crossing sampling scheme to reduce the frequency of

packet transmissions. This signal sampling mechanism

makes the sensor transmit data only when the sensor

values change by a predetermined amount. However,

they point out that this method is not enough to reduce

the energy consumption of the nodes, and should be

coupled with other power control mechanisms (such as

using multiple orthogonal channels on the physical layer,

or reducing route updates messages on the routing level).

The value of an MAS approach in the power industry

has been asserted in many settings, notably by the IEEE

Power Engineering Society [12]. The contributions of

MASs to this field span a variety of applications, such

as monitoring and diagnostics, and distributed control.

MASs have been applied to power quality monitoring.

In [13], a conceptual MAS is proposed where agents are

used to locally compute quality indices. The advantages

of the provided agent architecture include adaptability,

reconfiguration in case of failure, and the ability to assist

operators in decisions related to power quality events.

Nevertheless, the paper does not consider the underlying

communication network, and only presents an MAS con-

cept. The same issue is found in [14] where the authors

apply an MAS in order to control in a decentralized

manner a low voltage distribution network in which

distributed generation and demand side management

are included. Communications between the agents are

not simulated over any networking technology, and the

authors point out that they might become an issue when

the system is scaled up.

WSN communications based on the importance of

information have been previously studied in [15]. The

authors employ a cooperative MAS-based approach with

the objective of reducing communication cost in a WSN

in order to minimize the energy consumption of the

nodes.

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Although various energy harvesting techniques for

sensors in electric applications are becoming more avail-

able, the output of an energy harvesting module is

low and variable [16]. A combination of both energy

harvesting techniques and low energy consumption of

the sensor nodes can be implemented to achieve an

energy-autonomous WSN. Furthermore, in an electric

substation, we not only aim to prolong the WSN’s

lifetime, but we are also interested in analysing data and

transform it into actions in order to be able to control the

substation’s components based on electric characteristics

such as voltage levels.

In this paper, we consider a WSN deployed in an

electric substation. Each node is embodied with an

agent, thus forming an MAS. Our main contribution is

enabling the decentralized analysis of collected data and

substation control using a WSN. Our solution aims at

monitoring electric components and characteristics so

that faults can be predicted. Fault detection can be done

in a precise and timely manner, thus reducing recovery

delays. In addition, when a fault occurs, it is important to

take action as fast as possible in order to limit its extent

(e.g. activating a circuit breaker to isolate a certain area

so that the problem does not propagate). These points are

exposed and further discussed in the following section.

III. COOPERATIVE ENERGY-EFFICIENT WSN

COMMUNICATIONS FOR SUBSTATION MONITORING

A wireless sensor network deployed at a substation

monitors the different electric components present in a

substation, such as transformers and circuit breakers. The

sensor nodes in our scenario are embodied with agents

forming an MAS. The sink is usually placed at one end

of the substation’s site. Fig. 2 shows an example of such

a network.

In the rest of this section, a cooperation strategy

based on sensed values’ priorities, denoted as Priority-Based Cooperation (PBCoop), is presented. It can be

decomposed into two main phases which are detailed in

this section: (1) the evaluation of sensed data, and (2)

the selection of a communication policy. In summary,

according to the importance of the sensed data, a sensor

node will decide whether to immediately notify the

sink about this information, or to slightly delay this

notification. In fact, if the sensed value is not urgent,

then the node asks its neighbors about their data first. The

objective is to be able to identify a fault and its extent as

early as possible, namely at the substation’s level instead

of waiting for a response from the utility. In addition, a

lot of load can be taken off the sink and/or the utility’s

centralized control system (e.g. Supervisory Control and

Fig. 2. Representation of a substation’s WSN where the sensors areembodied with agents

Data Acquisition SCADA) by locally analyzing collected

data.

A. Data evaluation

In an electric substation, critical components’ moni-

toring applications can be deployed. An example would

be sensing a component’s temperature to evaluate its

state (if its temperature is too high, that might indicate

a problem) [11]. In addition, surveillance of electric

characteristics such as voltage, frequency, and reactive

power is needed. Whatever the collected information is

about, the need is common: evaluate the importance of a

sensed value and take the appropriate action accordingly.

To this end, we define three priority levels {0, 1, 2}, 0

being the least important. In the remainder of this paper,

we may use the terms pri-0, pri-1 and pri-2, referring to

the priority of a given value. For each sensed parameter,

we decompose its range of values into intervals, and

associate a priority to each of these intervals. When a

node senses a given value x (1)1, the embodied agent

will evaluate x by determining to which interval it

belongs to, and thus determine its associated priority

p(x) (2). When the first value is detected by a sensor,

an average is initialized, otherwise it is updated (3). The

next step of PBCoop consists in selecting the appropriate

communication policy depending on the result of the data

evaluation phase.

B. Communication policy selection

Having evaluated the priority of a detected value, the

priority p(x) is tested. If p(x) is equal to 0, then no

1The numbers between parentheses refer to the steps of the flowchartin Fig. 3.

205

further action is performed (no messages are sent). If

it is equal to 2, the value is sent to the sink (4), because

it is considered as urgent and a notification needs to

be directly sent. Alternatively, in case of priority 1, we

consider that the value should be reported because it may

signal a fault or a problem, but that it is less urgent than

pri-2 data. In that case, we aim at defining the extent of

the detected electric problem. To this end, a cooperation

phase is proposed ((5) to (10)), as explained in the next

paragraph.

1) The agent cooperation communication policy:When a sensor detects a value of priority 1, it will wait

for a pre-defined interval (represented by waitTimeBe-forePoll) before initiating its polling phase (5). During

this interval, if either a poll request or a cooperation

message is received by the agent, it will respond accord-

ingly. In the first case, when a poll request is received,

the agent will respond by sending its detected value (10).

In the second case, when getting a cooperation message,

it will concatenate its data by adding it to that message

(8), then forwarding the message to the next node on the

way to the sink (9).

If the pre-defined interval expires and neither of the

two previous actions take place, then the sensor agent

initiates the polling mechanism (6) and is referred to as

the poll-initiator. During this phase, its job is to send

poll requests to the sensors in its coverage area asking

them to cooperate, and to wait for their replies, if any.

Because neighbors may not always have data to send,

we have defined a waitTimeForPollResponses interval

during which the poll-initiator waits for its neighbors’

replies. If a neighbor has a pri-1 value that it has not sent

yet, it responds to the poll initiator by communicating

that value. The initiator then constructs a cooperation

message which contains its neighbors’ received values

and addresses along with its own data, and passes it to

its one-hop neighbor on the way to the sink (7). This

neighbor, having received a cooperation message, adds

its pri-1 value (which has not been sent yet) and its

address to the message, and passes it to the next node

on the way to the sink (8), (9). The same is done at each

node until the sink is reached.

When a cooperation message reaches the sink, it con-

tains the values and addresses of the participating nodes.

It will be able to identify each (value, node address)couple. In addition to that, and most importantly, it

will be capable of determining the first and last nodes

that participated in the cooperation message, and thus

of identifying the extent of a problem when it occurs.

This is crucial when attempting to locate a fault and

determining its extent and any damaged components.

Fig. 3. Basic flowchart for a sensor node’s agent

Fig. 4. An example of message exchange between agents

The complete algorithm’s flowchart is depicted in Fig.

3, and further considerations are exposed in the next

paragraph.

2) Example: Fig. 4 gives an example of message

exchange between the agents when a value of priority

1 is sensed. When sensor A detects a pri-1 information,

it will send a poll request to the sensors in its coverage

area, namely sensors B and C (step a in Fig. 4). Sensor B

has a value of priority 1 which it has not sent before, and

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responds to A in a poll response message (step b). Sensor

C has no such value and will not take action. When

A receives B’s response, it will construct a cooperation

message (step c) containing B’s value and its address,

as well as its own value and address. The message is

then sent to C, A’s one-hop neighbor on the way to the

sink (step d). Not having any pri-1 value, C forwards the

message to its one-hop neighbor on the way to the sink

D (step e). D receives the cooperation message and has

an information of priority 1 that should be sent. It will

concatenate this value to the message (step f), and send

the latter to E (step g). Node E has no information to

send, it will forward the cooperation message to the sink

(step h). When it reaches the sink, (value, node address)couples are identified (step i).

C. Further considerations

The following considerations have also been taken into

account:

1) In case of urgent situations, and if the sensor has

actuation capabilities, or can directly communicate

with an actuator, an immediate action can be taken

via the actuator if needed (4). An example may be

activating a circuit breaker to cut the power flow.

2) In the case of a recurrent pri-1 event over a certain

period of time, a period ΔTe over which the same

detected value is not signaled can be defined. The

network administrator can configure the value of

this parameter to be greater than zero if he wishes

not to receive notifications about same pri-1 values

that occur repetitively over ΔTe sec.

3) For pri-0 values, the only action taken is the update

of the average of sensed values (3). This average is

also updated when any level of priority is detected.

This serves the purpose of having a WSN deployed

on site, as its job is to provide information about

the environment it surveys. In addition, computing

the average is for later extension of the algorithm

which may include evaluating and reporting qual-

ity indices of the monitored site.

4) PBCoop can be tuned via its three main parame-

ters: ΔTe, waitTimeBeforePoll, and waitTimeFor-

PollResponses.

The proposed solution is validated through simulation.

The results are exposed in the next section.

IV. PERFORMANCE EVALUATION

To validate our approach, simulations were conducted

using the Castalia simulator [17]. The main parameters

used in the simulations are presented in Table I. Each

simulation run lasts 600 seconds, and is repeated

4 times. In order to evaluate the scalability of the

algorithm, the density of the network is modified by

varying the number of nodes between 20 and 150 nodes

in a field of 30x12 meters2. Values are sampled each 6

seconds. Radio parameters are chosen as those of the

CC2420 transceiver [18] which equips a wide range of

sensor nodes, such as the Tmote Sky [19], and that are

available in Castalia. We have also used the T-MAC

protocol implementation provided by the simulator as

the MAC layer [20] [21].

TABLE IMAIN SIMULATION PARAMETERS

Parameter Value

Simulation time 600 seconds

Number of repetitions 4

Field size 30x12 meters

Number of Nodes 20-150 nodes

Sampling Interval 6 seconds

Radio CC2420

MAC T-MAC

Regarding PBCoop’s parameters, we have chosen ΔTe

to be equal to 0. After conducting several simulations

(which will not be detailed in this paper) with different

combinations of the waitTimeBeforePoll and waitTime-

ForPollResponses parameters, we have chosen these

parameters equal to 1.5 msec and 0.5 msec respectively.

These values constitute an acceptable trade-off between

the number of cooperation messages sent, their delay,

and the data accuracy. The first set of simulations are

conducted with 14% of important data (10% of data is

of priority 1, and 4% is of priority 2) in the network.

PBCoop is compared with a Value Reporting (VR)approach where each detected value is sent to the sink

without any processing. VR corresponds to the classic

client/server scheme. The average consumed energy of

the nodes and the average number of sent messages in

the network are studied and presented in Fig. 5 and

Fig. 6 respectively. A decrease in the number of sent

messages as well as in the consumed energy of the nodes

in PBCoop compared to the VR approach can be noted.

Although the two approaches show approximately

similar results of consumed energy in Fig. 5 for a

network composed of a small number of nodes (20

and 30), the gap between PBCoop and VR increases

with a bigger number of nodes. This goes to show that

PBCoop is scalable. For example, in a network of 70

and 150 nodes respectively, results show an improvement

2This gives us higher densities compared with the deployment in[11]

207

Fig. 5. Average consumed energy per node for VR and PBCoop

Fig. 6. Average number of sent messages per node for VR andPBCoop

of nearly 23% and 60.7% respectively in terms of

energy consumption in PBCoop over VR. A reduction

of approximately 68% in terms of sent messages is

observed in PBCoop compared to VR. Having less sent

messages in the network is a direct consequence of the

fact that messages are sent only in case of values of

priority 1 and 2 in PBCoop, and the construction of a

cooperation message in case of pri-1 data also reduces

the number of exchanged messages. Even though polling

is done in that case, the gain in terms of decrease in

sent messages is still apparent. Having less messages

to send and receive, a node will consequently consume

less energy since radio communications require the most

of a node’s energy compared to sensing and processing.

Adopting the priority-based cooperation algorithm thus

achieves the objective of energy-efficiency. By reducing

the number of messages in the network, we also expect to

reduce congestion and insure that important and urgent

information is delivered to the sink.

The average transmission delay for VR messages

and PBCoop’s pri-2 and pri-1 messages is presented

in Fig.7. For pri-1 messages, this delay includes the

processing done by intermediate nodes along the way

to the sink. Since this processing is not done in VR and

for PBCoop’s pri-2 messages, the graph shows a greater

delay for pri-1 messages than VR and pri-2 messages.

Fig. 7. Average transmission delay of application messages

Fig. 8. Effect of the percentage of important data in the network onthe average consumed energy per node for PBCoop

PBCoop’s pri-2 messages have the lowest delay with an

average of 0.32 s, followed by VR messages with an

average of 0.34 s.

Furthermore, the percentage of important data in the

network in respect to the overall detected values plays

a role in the performance of the proposed algorithm.

In fact, communications between the sensor nodes in

PBCoop take place when data of priority 1 or 2 is

detected. In Fig. 8 and Fig. 9, the effect of the in-

crease of important data in the network is shown for

PBCoop. The percentages of important data considered

are 5%, 14%, and 37%. Although an increase in the

number of sent messages, and a slight increase in the

average consumed energy are shown, the results confirm

better performances for PBCoop compared to the value

reporting scenario. Also note that the close values of the

consumed energy for a given number of nodes (Fig. 8)

illustrate that the proposed approach is in fact scalable,

since an increase in the percentage of important data

does not drastically increase the energy consumption of

the nodes.

The primary performance evaluation of PBCoop has

shown its ability of reducing message exchange in the

network, while identifying important and critical infor-

mation for immediate action and fault identification in a

substation. A more advanced study on the effects of the

different parameters of PBCoop is currently being con-

208

Fig. 9. Effect of the percentage of important data in the network onthe average number of sent messages per node for PBCoop

ducted, along with a comparison with other approaches.

V. CONCLUSION

This paper has identified a key requirement of the next

generation power grid, which is the constant monitoring

of electric substations and their components via a WSN.

The needs for an energy-efficient and smart information

management of the data collected by that WSN were

addressed in this work. We proposed the PBCoop algo-

rithm to reduce energy consumption in sensor nodes, and

intelligently manage sensed data so that urgent values

are directly reported and that faulty components can be

identified. To achieve these goals, a cooperative agent-

based approach was adopted. Simulation results have

shown improved performances of the proposed algorithm

in terms of energy consumption and sent messages, in

comparison with a reporting scheme where each detected

value is sent to the sink without any processing. Since

it is priority-based, the performances of the algorithm

depend on the percentage of important data in the

network. Nonetheless, PBCoop showed better results and

proved to be scalable.

Future works include fine tuning the proposed al-

gorithm, as well as studying the success rate of fault

detection. Comparisons with state-of-the-art algorithms

are also planned.

ACKNOWLEDGMENT

This work was supported in part through grants from theTroyes University of Technology and the Lebanese University.

REFERENCES

[1] V. Environnement, “Smart grids,” Scientific Chronicles Magazine,vol. 18, Sept. 2010.

[2] L. S. C. for the U.S. Department of Energy, “The smart grid: anintroduction,” Technical report, Oct. 2008.

[3] M. Erol-Kantarci and H. Mouftah, “Wireless sensor networksfor smart grid applications,” in Electronics, Communications andPhotonics Conference (SIECPC), 2011 Saudi International, april2011, pp. 1 –6.

[4] B. Bilgin and V. Gungor, “On the performance of multi-channelwireless sensor networks in smart grid environments,” in Com-puter Communications and Networks (ICCCN), 2011 Proceed-ings of 20th International Conference on, jul. 31-aug. 4 2011,pp. 1 –6.

[5] S. Isaac, G. Hancke, H. Madhoo, and A. Khatri, “A surveyof wireless sensor network applications from a power utility’sdistribution perspective,” in AFRICON, sept. 2011, pp. 1 –5.

[6] J. Momoh, “Smart grid design for efficient and flexible powernetworks operation and control,” in Power Systems Conferenceand Exposition, 2009. PSCE ’09. IEEE/PES, march 2009, pp. 1–8.

[7] V. Gungor, B. Lu, and G. Hancke, “Opportunities and challengesof wireless sensor networks in smart grid,” Industrial Electronics,IEEE Transactions on, vol. 57, no. 10, pp. 3557 –3564, oct. 2010.

[8] S. Ullo, A. Vaccaro, and G. Velotto, “The role of pervasive andcooperative sensor networks in smart grids communication,” inMELECON 2010 - 2010 15th IEEE Mediterranean Electrotech-nical Conference, april 2010, pp. 443 –447.

[9] M. Erol-Kantarci and H. T. Mouftah, “Wireless multimediasensor and actor networks for the next generation power grid,”Ad Hoc Networks, vol. 9, no. 4, pp. 542 – 551, 2011.

[10] M. di Bisceglie, C. Galdi, A. Vaccaro, and D. Villacci, “Coop-erative sensor networks for voltage quality monitoring in smartgrids,” in PowerTech, 2009 IEEE Bucharest, 2009.

[11] A. Nasipuri, R. Cox, J. Conrad, L. Van der Zel, B. Rodriguez, andR. McKosky, “Design considerations for a large-scale wirelesssensor network for substation monitoring,” in Local ComputerNetworks (LCN), 2010 IEEE 35th Conference on, oct. 2010, pp.866 –873.

[12] S. McArthur, E. Davidson, V. Catterson, A. Dimeas, N. Hatziar-gyriou, F. Ponci, and T. Funabashi, “Multi-agent systems forpower engineering applications;part i: Concepts, approaches, andtechnical challenges,” Power Systems, IEEE Transactions on,vol. 22, no. 4, pp. 1743 –1752, nov. 2007.

[13] S. Srivastava, S. Suryanarayanan, P. Ribeiro, D. Cartes, andM. Stcurer, “A conceptual power quality monitoring techniquebased on multi-agent systems,” in Proceedings of the 37th AnnualNorth American Power Symposium, oct. 2005, pp. 358 – 363.

[14] G. Hutchinson, P. Taylor, G. Coates, and R. Hair, “A multi-agentsystem for decentralised control of low voltage distribution net-works,” in Universities Power Engineering Conference (UPEC),2010 45th International, 31 2010-sept. 3 2010, pp. 1 –6.

[15] A. Sardouk, R. Rahim-Amoud, L. Merghem-Boulahia, andD. Gaiti, “A multi-criterion data aggregation scheme for wsn,”in Proceedings of the 2009 IEEE International Conference onWireless and Mobile Computing, Networking and Communica-tions. IEEE Computer Society, 2009, pp. 30–35.

[16] R. Moghe, Y. Yang, F. Lambert, and D. Divan, “A scoping studyof electric and magnetic field energy harvesting for wirelesssensor networks in power system applications,” in Energy Con-version Congress and Exposition, 2009. ECCE 2009. IEEE, sept.2009, pp. 3550 –3557.

[17] N. I. A. (NICTA), “Castalia simulator website,” http://castalia.npc.nicta.com.au/.

[18] T. Instruments. cc2420 product description. http://www.ti.com/product/cc2420#description.

[19] Moteiv, “Tmote sky brochure,” http://sentilla.com/files/pdf/eol/tmote-sky-brochure.pdf.

[20] T. van Dam and K. Langendoen, “An adaptive energy-efficientmac protocol for wireless sensor networks,” in Proceedings ofthe 1st international conference on Embedded networked sensorsystems, ser. SenSys ’03, 2003.

[21] Y. Tselishchev, A. Boulis, and L. Libman, “Experiences andlessons from implementing a wireless sensor network mac pro-tocol in the castalia simulator,” in Wireless Communications andNetworking Conference (WCNC), 2010 IEEE, april 2010, pp. 1–6.

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