a novel energy efficient beaconless geographic

8/22/2019 A Novel Energy Efficient Beaconless Geographic

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A Novel Energy Efficient Beaconless Geographic

Routing Algorithm for Wireless Sensor Networks

Oswald Jumira and Riaan WolhuterMIH Media Lab

Department of Electrical and Electronic Engineering

University of Stellenbosch, South Africa

Email: [email protected], [email protected]

Sherali ZeadallyDepartment of Computer Science and Information Technology

University of the District of Columbia

Washington, DC 2008, USA

Email: [email protected]

AbstractIn recent years, energy-efficient routing for WirelessSensor Networks (WSNs) has become an intensive area ofresearch and continues to attract the attention of researchers. Webriefly describe a novel energy-efficient beaconless geographicrouting protocol called EBGRES for WSNs with sensor nodespowered by ambient energy. In EBGRES, both geographicinformation and transceiver power characteristics are employedto make forwarding decisions - thereby enabling efficient, energy-aware localized routing in WSNs. We present a performance eval-uation of EBGRES using metrics such as energy consumption,network lifetime and node density to demonstrate the superiorperformance delivered by EBGRES over other recently proposedbeaconless geographic routing protocols.

Index Termscommunication; energy harvesting; sensor net-works; routing; wireless

I. INTRODUCTION

The rapid development of wireless short range commu-

nication technologies, embedded micro-sensing devices and

ubiquitous applications has lead to the increased attention on

wireless sensor networks (WSNs). Research work has been

dedicated to WSNs, including power management [1], routingand transportation [2], sensor deployment and coverage issues

[3], and localization [4]. A number of research projects on

data routing strategies and protocols for WSNs were focused

on energy-efficiency, with interest increasingly centered on

real-time applications which involve time-critical data, highly

scalable networks and increased network lifetime [5]. WSNs

require new types of power sources and low-latency routing

schemes which can accommodate energy supply challenges

from battery based energy sources.

In this paper, we focus on evaluation of geographic routing

protocols that are scalable, energy efficient and that guaran-

tee delivery in wireless sensor networks. We consider only

localized geographic algorithms where nodes do not needthe dissemination of route discovery information nor need

to maintain routing tables. Only local information such as

the position of the current node holding a packet, the one

of its neighbors and the one of the destination are required.

Various localized routing protocols [6][2][7] with hop count as

metric have been proposed to improve scalability. Each node

has position information by using a GPS or other localization

means [7]; routing decisions are made at each node using

only local information. Most energy-aware localized routing

schemes [7][3][8]use power consumption as metric and are

battery based. But most of these routing schemes do not

guarantee delivery especially in WSNs with obstacles such as

holes and buildings. Localized power-aware routing algorithms

that also guarantee end-to-end delivery were proposed in[7].

Previously proposed energy aware routing schemes [7][8][9]

are not localized, are not scalable, rely on the disseminationof route discovery information and routing tables, possess

limited lifetime and they rely on beacons for dynamic network

changes.

In WSNs network topology does not change much, main-

taining neighbor information can greatly improve the perfor-

mance because of the reusability of the stored information and

the low maintenance cost. First, the maintenance of neighbor

information incurs too much communication overhead and

results in significant energy expenditure. Second, the collected

neighbor information can quickly get outdated, which, in turn,

leads to frequent packet drops. Third, the maintenance of

neighbor information consumes memory which is also a scare

resource in WSNs. To overcome the challenges of conventionalgeographic routing schemes in scenarios with dynamic net-

work changes, beaconless geographic routing protocols have

been proposed [10][11][6],. Beaconless routing schemes, in

which each node forwards packets without the help of beacons

and without the maintenance of neighbor information, are fully

reactive.

Ambient energy harvesting as a power solution has become

popular in recent years, especially with significant progress in

the functionality of low power embedded electronics such as

wireless sensor nodes. We define an energy harvesting node as

any system which draws part or all of its energy from the en-

vironment such as solar energy, temperature variations, kinetic

energy or vibrations. A key distinction of this energy from thatstored in the battery is that this energy is potentially infinite,

though there may be a limit on the rate at which it can be

used. Knowledge of energy-harvesting devices characteristics

should be incorporated in WSN routing schemes. Research has

been carried out in this field before [12][13]. The proposed

energy aware routing schemes are not localized, not scalable,

rely on dissemination of route discovery information and

routing tables, limited lifetime and they rely on beacons for

dynamic networks changes. Knowledge of energy-harvesting

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devices characteristics should be incorporated in WSN routing

schemes.

In this work we focus on the performance evaluation of

an energy-efficient beaconless geographic routing protocols

(henceforth, this proposed routing protocol will be referred

to as EBGRES) for WSNs with sensor nodes powered by

ambient energy. EBGRES is scalable, energy-efficient, loop-

free and can guarantee end-to-end delivery in WSNs. We

consider only localized geographic algorithms where nodes do

not need to disseminate route discovery information nor need

to maintain routing tables. The rest of the paper is organized

as follows. We describe related works and our contributions in

Section II. We briefly present our proposed EBGRES protocol

in Section III. In Section IV, we present evaluation results

on the performance of EBGRES. We make some concluding

remarks in Section V.

II. RELATED WOR K

It is imperative to understand the sensor application routing

requirements and design a suitable energy harvesting product

that fits the application with minimal redesign and develop-ment. The improvement in energy-efficiency through localized

routing schemes and increased energy supply from the envi-

ronment through energy harvesting enable longer network and

node lifetime, increased data sampling, increased workload

and opens up other application areas for sensor networks.

In [14], Geographic and Energy Aware Routing (GEAR) a

geographic localized routing protocol which takes into account

a nodes residual energy information is proposed. GEAR

uses energy aware and geography-based neighbor heuristics

to route a packet towards the target region but it does not

take into account the realistic wireless channel conditions

and environmental energy supply. In [15] Geographical Power

Efficient Routing (GPER) protocol for sensor networks ispresented. Each sensor node makes local decisions as to how

far to transmit: therefore, the protocol is power efficient, highly

distributed, and scalable. GPER does not guarantee increased

lifetime, workload and network reliability.

In [9] a new adaptive algorithm, Distributed Energy Harvest-

ing Aware Routing Algorithm (DEHAR), for finding energy

efficient routes considering residual energy in a wireless sensor

network with energy harvesting is proposed. DEHAR has in-

creased overhead since it is a centralized solutions which gen-

erally needs global knowledge, including position and activity

status of all network nodes, thus nodes need the dissemination

of route discovery information and need to maintain routing

tables. In [8], an opportunistic routing protocol, EHOR isdesigned for routing in multi-hop WSNs powered solely using

Ambient Energy Harvesters (WSNHEAP). EHOR increases

goodput and efficiency but does not take into account a

realistic channel, is highly unreliable and it is not easily able

to select the appropriate forwarder list such that the expected

energy cost is minimized.

A geographic routing protocol called Energy-Efficient Geo-

graphic Routing (EEGR) for WSNs is proposed in [2]. EEGR

can provide near-optimal energy-efficient routing based only

on local information. EEGR makes use of beacons but does

not take into account the realistic channel conditions, does not

guarantee increased network lifetime and workload. Sanchez

et al. [10] proposed the Beacon-less On Demand Strategy for

Geographic Routing in Wireless Sensor Networks (BOSS).

BOSS uses a three-way handshake similar to IEEE802.11,

Request To Send/Clear To Send (RTS/CTS), handshake and a

timer-assignment function which divides the neighbor area into

sub-areas according to the progress towards the destination

and helps to reduce collisions. However, most of the proposed

beaconless schemes reported in the literature employ the hop-

count-based routing metric which is not efficient in terms

of energy consumption. In [6]an energy-efficient beacon-less

geographic routing for dynamic WSNs in which the network

topology frequently changes over time is proposed. The algo-

rithm assumes an unrealistic channel without any losses and

no failure in greedy forwarding. The energy supply is finite

leading to limited network lifetime and workload.

A. Contributions

In this work, we present a performance evaluation of the

proposed end-to-end (EtE) Energy-efficient Beaconless Ge-

ographic Routing with Energy Ssupply (EBGRES) protocol

which strives to guarantee packet delivery, reduces the energy

consumption of greedy forwarding and perimeter recovery

parts with an increase in energy supply (from energy harvest-

ing) in order to increase node and network lifetime, reliability

and workload. Some of the main characteristics of EBGRES

include:

Localization: a node has to be aware only of its location,

neighbors and of the final destination (no routing tables).

Scalable: the WSN is memoryless since no routing infor-

mation needs to be stored at the node.

Loop free: network is loop-free because the greedy stepalways chooses a node in the forward direction of the

destination.

Guaranteed delivery: the end-to-end network has two

routing phases: a localized greedy protocol prone to rout-

ing failure and a Perimeter routing phase that guarantees

delivery invoked when needed.

Energy efficient: every routing step taken in the network

is energy aware.

Lifetime: increased node and network lifetime due to

presence of energy harvesting source.

Beaconless: can withstand dynamic network changes and

reduced control message traffic.

III. PROPOSED APPROACH

A. Models

1) Network: Without loss of generality, it is assumed that

no two nodes are located at the same position. Throughout

this paper, if a node is u within a node vs transmission rangewe say that u is adjacent to v, or equivalently, that u is aneighbor of v. All nodes are equipped with the same radiotransceiver that enables a maximum transmission range . Each

node knows its own location as well as the location of the sink.

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The connectivity model we use is the unit disk graph (UDG)

[7]. In this model, any two nodes u and v can communicatewith each other reliably if and only if |uv| R, where |uv| isthe Euclidean distance between u and v. We define the densityof the network as the average number of neighbors per node.

Based on a realistic communication model in which data loss

is estimated by packets reception rate, we extend our scheme

to achieve localized energy-efficient beaconless routing in the

presence of unreliable communication links and environmental

energy supply. Even if the real positions (e.g. GPS coordinates

) are not available, virtual coordinates can be alternatively used

[7].

2) Energy Models: Each node u has its own energy levelEu such that 0 Eu Emax where Emax is the maxi-mum energy level that a node can have. This energy level

changes periodically because it can decrease as a result of

sending/receiving messages and increase as a result of the

energy it can harvest from its environment.

a) Energy for Transmission and Reception: The model

for the energy to transmit one bit of data over distance x

(distance between two nodes) is given in [2] as

Etx(x) = Eelec + Eampxk

where Eelec is the energy spent by transmitterelectronics,Eamp is the transmitting amplifier, and k(k 2)is the propagation loss exponent.

The energy consumed by the receiver electronics while

receiving one bit of data is Erx . We define the followingE = Eelec + Erx and the optimal transmission radius, do, thatminimizes the total power consumption for a routing task is

equal to:

do = k EEamp(12k1)b) Total Energy Consumed: Let x be the distance be-

tween the source node u and the destination node v, andEneeded(x) represents the energy consumed by delivering onebit data from u to v. Then,

Eneeded(x) = Eelec + Eampxk1

1st hop

+

Ni=2

Erelay(xi) relay

+

ERxsink

sink

=N

i=1

Erelay(xi)

where N denotes the hop count of the path from u to v,and xi represents the Euclidean distance of the ith hop.Minimizing the total energy consumption for delivering

each message is one of the most commonly used metrics for

routing in sensor networks.

c) Energy Harvesting: The amount of energy harvested

from the environment can be very different from node to node

due to the diversity of harvesters, the locations of the nodes,

the deployment policy, the rate of harvesting and the time

of the day. In our research we look at solar based energy

harvesting which has a diurnal characteristic (available readily

during the day and no sunlight at night). This actually has an

impact on the duty cycle and energy storage dimensioning.

In [16] a detailed account of energy harvesting architecture

and schemes is presented. Kansal et al. [1] suggested a

mathematical condition which would express the conditions

under which a sensor node can operate perpetually, through

an analysis of the relationship between the harvested energy

and the consumed energy. The energy model utilized in this

paper is for a solar based harvesting sensor node as defined in

[12]. Eharv (u , t) is the energy harvested by node u at time

t. Eharv (u , t) is a deterministic value issued from a previous

study of the environment. Node u can operate at all t whenthe energy available at node u at time t is greater than theenergy needed for sending or receiving at time t. If the energyharvested at time t, Eharv (

u , t) is greater than the energyrequired Eneeded(

u , t) , Eharv(u , t)Eneeded(u , t) couldbe wasted. In these cases we assume the use of a buffer (a

storage device such as capacitor or a rechargeable battery) to

store the energy for later use in case the harvested energy is

low or not available. This buffer has a maximal capacity ofBmax and can provide energy Bharv (t) as follows

Bharv (t) = Bharv (t1) +tt1 Eharv (

u , t) dt tt1 Eneeded(t) dt

Energy consumption of each active mode is fixed and the

Duty Cycle (DC) of a perpetually operating node completely

depends on the calculated residual value[17].

B. Proposed EBGRES protocol

The main mechanism for EBGRES uses position informa-

tion and the optimal forwarding distance to support localized

packet forwarding with perpetual energy supply. Instead of

forwarding the packets to the neighbor closest to the sink or theneighbor that has the maximum progress, the packets are trans-

mitted to the neighbor which is closest to the energy optimal

relay position. EBGRES[18] makes use of three different types

of messages, DATA, ACK and SELECT. In beaconless greedy

forwarding, only the nodes in the relay search region (fig. 1)

of the forwarder are candidates which can forward the packet.

The forwarder chooses the neighbor closest to its optimal

relay position as its next-hop relay using the DATA/ACK

handshaking mechanism. In this way, each packet is expected

to be delivered along the minimum energy route from the

source to the sink. If there is no node in the relay search

region, the forwarder enters the beaconless recovery mode, and

the beaconless perimeter relay is employed to recover from thelocal minimum. Duty cycle management together with energy

harvesting help to preserve energy and increase the nodes

lifetime.

1) Relay Search Region: In EGBRES each nodes relay

search region is based on the energy available at the power

adjusted transmitter. The best next-hop neighbor for any node

u is the neighbor closest to its ideal relay position fu. Thereis no need for all neighbors of node u to contend with eachother in order to be the next relay node. For any node only the

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neighbors in its relay search region are eligible candidates that

can forward the packets transmitted from node u. The conceptof relay search region is introduced to prohibit the unsuitable

neighbors from participating in the relay contention procedure.

For a node u, its next-hop relay search region, denoted byRu, is defined as the disk centered at us ideal next-hop relayposition with radius rs(u) where rs(u) |ufu| = do.

Figure 1. Relay search region

2) Forwarding Scheme: For node u where the distance

between u and the sink s is |us|. If |us| k EEamp(121k)

and |us| < R, u directly sends its packets to the sink sincerelaying the packets by other nodes is not more energy-

efficient than direct transmission. Otherwise, u selects itsneighbor that is closest to fu to relay its packets. Let (xu, yu)and (xs, ys) be the coordinates of node u and the sink srespectively. The location of fu, denoted by (xuo, yuo), canbe computed as follows:

xuo = xu do|us|(xu xs)yuo = yu do|us|(yu ys)

.

In the beaconless greedy mode for EGBRES, when the

forwarding node has a data packet to transmit, it broadcastsa DATA message and waits for responses for a predefined

maximum time, Tmax seconds. The DATA message contains:the original message, the energy level status, the energy

harvesting rate, the energy consuming rate, the duty cycle,

the position of the forwarding node, the location of the ideal

next-hop relay position as well as the radius of its relay

search region and the position of the final destination. For any

neighbor w of node u that receives the DATA message fromu, it first checks whether it falls in Ru. Ifw = Ru, the requestmessage is simply discarded. Otherwise, node w generatesan acknowledgment (ACK) message which also contains its

own location, duty cycle, energy status and sets a proper

delay, denoted by wu, for broadcasting the ACK messagebased on a Discrete Dynamic Forward Delay function. When

node u receives an ACK message (within time, t Tmax) from its neighbor w, the next-hop forwarding node foru, denoted by relay(u) , is updated if relay(u) is null or|wfu| < relay(u)fu. Finally, node u sends out a SELECTmessages to w (its next-hop relay ). Node u then enters intoa sleep or idle mode after this confirmation.

3) Delay Algorithm: The time for the relay node to wait be-

fore responding to the forwarding node is related to its position

and this behavior has two important goals: avoiding collisions

and determining the forwarding strategy. In EGBRES, the

forwarding node selects as next forwarder the neighbor which

is in the relay search region and replies first while other

neighbors are suppressed. Therefore, the forwarding strategy

is clearly controlled by timers. By forcing some neighbors to

wait more than others we can reduce the number of possible

answers and thus, the bandwidth consumption. Each ACK

message is associated with a label which records the delay

for broadcasting the message. When node w receives a DATAmessage from node u, instead of generating and broadcastingthe ACK message immediately, node w broadcasts the ACKmessage after a delay wu.The delay is computed by thefollowing function, wv = |wfu| where |wfu| is thedistance between w and fp, and is a constant which can bedetermined empirically. wu is proportional to |wfu| whichcan ensure that the node closest to the next hop optimal relay

position broadcasts the ACK message first. On the other hand,

if node w receives an ACK message from another neighbor

v of u, it just removes the ACK message that was destined

to node u from its broadcast queue because it must satisfythat |wfu| |vfu|. Given a suitable , this scheme cansignificantly reduce the number of message send to node u.

Algorithm 1 EBGRES Routing performed at node u

1: If

|us| k

E

Eamp(12k)

and (|u| R)then

2: n(u) = s; /*direct transmission from source to sink3: Else calculate (xuo, yuo); /*use intermediate node

/*broadcasting the DATA Message */4. Broadcast DATA message;5. If M is a DATA then6. If RBbit = P and u / RW then7. Discard message M;8. Else Set uv /*according to delay algorithm

/* on receiving message from node w */9. If M is an ACK message ((xw, yw), (xv, yv)) then10. If (xv = xu)and (yv = yu)then10. If (n(u) = NULL) or (|wfu| < |n(u)fv|) then11. n(u) = w;12. Else If u received DATA message from v and uv is not duethen13. C ancel broadcasting ACK message to v;

/* Broadcasting reply14. For each delay label uv do15. If uv is due then16. Generate ACK message ((xu, yu), (xw, yw))17. Broadcast ((xu, yu), (xw, yw))

/*Broadcasting SELECT message18. If u receives ACK message then19. Broadcast SELECT message to w

/* node v becomes the next forwarding node/* Recovery mode

20. Else If Tmax due then

21. Employ beaconless perimeter routing recovert from local minimum.

4) Perimeter Routing: When node u broadcasts a DATAmessage to its best next-hop relay, it sets its timer to Tmax andstarts the timer. Tmax is large enough to guarantee that nodeu can receive the ACK message from the furthest neighbor inRu before the timer is expired. If node u receives no ACKmessage till the timer is expired, it assumes that there is no

neighbor in its relay search region and activates the beaconless

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Perimeter relaying [18]. The perimeter relaying algorithm

works in two phases: the selection phase and the protest

phase. In the selection phase, the forwarder u broadcasts aDATA message to its neighbors, and the neighbors answer

with ACK messages in counterclockwise order according to

a perimeter-based delay function. After the first candidate wanswers with a valid ACK, the protest phase begins. First,

only the nodes in NGG(v, w) (i.e., the Gabriel Graph circlehaving uw as diameter) are allowed to protest. If a node vprotests it automatically becomes the next-hop relay. After

that, only nodes in NGG(v, v) are allowed to protest. Finally,the forwarder sends the packet to the selected (first valid or last

protesting) candidate. A more detailed description of EBGRES

can be found in [18].

IV. PERFORMANCE EVALUATION OF PROPOSED

APPROACH (EBGRES)

In our simulation tests we use OMNET++ simulator to carry

out the tests. We simulate sensor nodes which are uniformly

spread throughout a square area of 1000m 1000m. TheDATA message payload is 128 bytes, the size of the ACK

message 25 bytes and the SELECT message is 20 bytes.Similar to [2] the energy spent by the transmitter electronics

on transmitting or receiving 1 bit data (i.e., Erx and Eelec) isset to 50 nJ/bit, the transmitting amplifier (Eamp) is set to100 pJ/bit/m2, and the propagation loss exponent (k) is set to2. Each sensor node was initialised with 1 J of battery energy.

Two nodal energy harvesting rates are assumed in Table 1.

Each nodes harvesting rate is randomly chosen to be one of

the two levels and is fixed on the level in one simulation run.

Table IENERGY HARVESTING RATE

high low

Min(mW) 0.1 0.01Max(mW) 0.5 0.05

The transmission range for the power adjusted transmitter

depends on the energy available (i.e., do =

1000 m) andthe transmission delay (i.e., is 106 s/m) . We are mainlyinterested in evaluating the total energy consumption (trans-

mitter and receiver energy) for sensor-to-sink packet delivery

independent of the MAC layer used. For each simulation run,

20 nodes are selected as sources and each source generates

40 data packets. The simulation is terminated until the sink

receives all the data packets generated in the network, and the

simulation results are the average of 50 independent runs.

The performance metrics used in our performance eval-uation tests include: energy consumption which is the total

amount of energy utilized for transmitting and receiving (per

every hop) the data and control messages from source to sink,

node density is the number of nodes per unit area and minimum

residual energy calculates the minimum residual energy at the

end of simulation for all the sensor nodes participating in the

routing. We compare EBGRES with BOSS and EBGR because

they are all beaconless and geographic routing based algo-

rithms. We include the energy optimal routing (referred to as

OPT) where each packet is delivered along the ideal minimum-

energy-path computed by the shortest-path algorithm. The goal

of the measurements is to evaluate the energy consumption

of the routing algorithms with an increase in node density

and transmission range. The minimum residual energy metric

determines the nodes lifetime and networks lifetime. The

test energy consumption versus transmission range (as shown

in Figure 2) is important because we have a power adjusted

transmitter meaning the more the residual energy on the node

the longer the transmission range leading to a lower number

of hops. The test energy consumption versus node density

(as shown in Figure 3) is important because it measures the

scalability of the routing algorithms and their performance

where there are many potential routes. The test minimum

residual energy versus node density (as shown in Figure 4)

evaluates the potential networks and nodes lifetime.

Figure 2. Energy consumption versus Transmission Range

It can be observed from Figure 2 that EBGRES performance

is very close to that of optimal routing and is more efficient

than BOSS and EBGR. EBGRES has increased energy avail-

able which means that the transmission range is always nearly

reaching the maximum and will follow the shortest route path

with a smaller number of hops.

For the set of simulations from Figure 3, the sink is placed

at the top-left corner of the simulation region, and there is

only source which is placed at the bottom-right corner. We

measure the total energy consumption to deliver a packet

from the source to the sink under different routing schemes,and compare with the theoretical results (lower and upper

bounds) we reported in [18]. As can be seen from Figure

3, when the node density increases the energy consumption

under EBGRES approaches the lower bound, demonstrating

the energy efficiency of EBGRES. It is also worth noting

that the energy consumption under EBGRES keeps within the

upper and lower bounds. BOSS is less efficient-energy in terms

of consumption and is above the upper bound. EBGRs energy

consumption is close to the upper bound as the node density

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Figure 3. Energy consumption versus Node Density

increases and less efficient than EBGRES.

We also compare the various routing schemes using the

minimum residual energy metric. The higher the value is, the

better the performance is and the higher the networks andnodes lifetime. In Figure 4 we show the comparison of the

routing algorithms over 100 simulation runs using an initial

energy of 1 Joule. The more densely the nodes are deployed

the more minimum energy remained on the nodes. Since we

fix transmission power of the nodes, when the nodes are closer

to each other, the number of hops needed to deliver the packet

from the source to the destination becomes smaller as a result

of which the required energy for delivering one packet from

the source to the destination is reduced. Furthermore, when the

network is denser, the number of paths between the commu-

nication pairs (source and sink) increases, and each node has

more choices for the next hop to distribute traffic load, and the

result is a decrease in the energy consumption variance amongall the nodes. EBGRES shows a high minimum residual energy

which is nearly constant for various node densities because of

the availability of perpetual energy.

Figure 4. Minimum Residual Energy versus Node Density

V. CONCLUSIONS

The performance results obtained demonstrate that our

proposed EBGRES protocol is more energy-efficient than

other recently proposed routing protocols such as BOSS and

EBGR in terms of energy consumption, transmission range and

minimum residual energy. The availability of perpetual energy

leads to increased energy in the nodes which is then utilized

to increase the transmission range for the power adjustedtransmitter leading to a reduction in the number of hops and

the total energy usage along a path (from source to sink). Our

future work will implement EBGRES routing algorithm for a

real life WSN application.

ACKNOWLEDGMENT

We would like to acknowledge the MIH Media Lab and the

University of Stellenbosch for funding our research.

REFERENCES

[1] A. Kansal, J. Hsu, S. Zahedi, and M. B. Srivastava, Power management

in energy harvesting sensor networks, ACM Transactions on EmbeddedComputing Systems, vol. 6, no. 4, pp. 135, Sep. 2007.

[2] H. Zhang, EEGR: Energy-Efficient Geographic Routing inWirelessSensor Networks, ICPP Parallel Processing, 2007, 2007.

[3] S. Zhang and A. Seyedi, Analysis and Design of Energy HarvestingWireless Sensor Networks with Linear Topology, IEEE InternationalConference onCommunications (ICC), pp. 15, 2011.

[4] E. H. Elhafsi, N. Mitton, and D. Simplot-Ryl, End-to-End energyefficient geographic path discovery with guaranteed delivery in ad hocand sensor networks, 2008 IEEE 19th International Symposium onPersonal, Indoor and Mobile Radio Communications, pp. 15, Sep.2008.

[5] N. Chilamkurti, S. Zeadally, A. Vasilakos, and V. Sharma, Cross-LayerSupport for Energy Efficient Routing in Wireless Sensor Networks,

Journal of Sensors, pp. 19, 2009.

[6] H. Zhang and H. Shen, Energy-Efficient Beaconless Geographic Rout-ing in Wireless Sensor Networks, IEEE Transactions on Parallel and

Distributed Systems, vol. 21, no. 6, pp. 881896, Jun. 2010.[7] E. H. Elhafsi, N. Mitton, and B. Pavkovic, Energy-aware Georouting

with Guaranteed Delivery in Wireless Sensor Networks with Obstacles,International Journal of Wireless Information Networks, vol. 3, pp. 121, 2009.

[8] Z. A. Eu, H.-p. Tan, and W. K. G. Seah, Opportunistic Routing inWireless Sensor Networks Powered by Ambient Energy Harvesting,Computer Networks (Elsevier), vol. Vol. 54, pp. 29432966, 2010.

[9] M. K. Jakobsen, J. Madsen, and M. R. Hansen, DEHAR : a DistributedEnergy Harvesting Aware Routing Algorithm for Ad-hoc Multi-hopWireless Sensor Networks, IEEE International Symposium on aWorld ofWireless Mobile and Multimedia Networks (WoWMoM), pp. 19, 2010.

[10] J. Sanchez, R. Marin-Perez, and P. Ruiz, BOSS: Beacon-less ondemand strategy for geographic routing inwireless sensor networks,2007 IEEE Internatonal Conference on Mobile Adhoc and SensorSystems, pp. 110, Oct. 2007. [Online]. Available: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4428612http://www.

computer.org/portal/web/csdl/doi/10.1109/MOBHOC.2007.4428612[11] R. Nawaz, S. A. Hussain, S. A. Abid, and J. Shafi, Beaconless Multihop

Routing Protocol for Wireless Sensor Networks, IEEE 3rd InternationalConference onCommunication Software and Networks (ICCSN), pp.721725, 2011.

[12] P. Lee, Z. A. Eu, M. Han, and H.-p. Tan, Empirical Modeling of a Solar-Powered Energy Harvesting Wireless Sensor Node for Time-SlottedOperation, IEEE Wireless Communications and Networking Conference(WCNC), pp. 179184, 2011.

[13] Z. G. Wan, Y. K. Tan, and C. Yuen, Review on Energy Harvesting andEnergy Management for Sustainable Wireless Sensor Networks, IEEE13th International Conference on Communication Technology (ICCT),pp. 362 367, 2011.

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http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4428612%20http://www.computer.org/portal/web/csdl/doi/10.1109/MOBHOC.2007.4428612http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4428612%20http://www.computer.org/portal/web/csdl/doi/10.1109/MOBHOC.2007.4428612http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4428612%20http://www.computer.org/portal/web/csdl/doi/10.1109/MOBHOC.2007.4428612http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4428612%20http://www.computer.org/portal/web/csdl/doi/10.1109/MOBHOC.2007.4428612http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4428612%20http://www.computer.org/portal/web/csdl/doi/10.1109/MOBHOC.2007.4428612http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4428612%20http://www.computer.org/portal/web/csdl/doi/10.1109/MOBHOC.2007.4428612


7/7

[14] Y. Yu, R. Govindan, and D. Estrin, Geographical and Energy AwareRouting : a recursive data dissemination protocol for wireless sensornetworks, Energy, vol. 463, pp. 23, 2001.

[15] S. Wu and K. Sel, GPER : Geographic Power Efficient Routing inSensor Networks LU, Proceedings of the 12th IEEE InternationalConference on Network Protocols, pp. 161172, 2004.

[16] J. Jessen and M. Venzke, Design considerations for a universal smartenergy module for energy harvesting in wireless sensor networks,

Intelligent Solutions in, 2011.[17] H. Yoo, M. Shim, and D. Kim, Dynamic Duty-Cycle Scheduling

Schemes for Energy-Harvesting Wireless Sensor Networks, IEEE Com-munications Letters, vol. 16, no. 2, pp. 202204, 2012.

[18] O. Jumira, R. Wolhuter, and S. Zeadally, Energy-efficient beaconlessgeographic routing in energy harvested wireless sensor networks,Concurrency and Computation: Practice and Experience, 2012.

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a novel energy efficient beaconless geographic

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