Research Article Trail-Using Ant Behavior Based Energy ...downloads.hindawi.com/journals/ijdsn/2016/7350427.pdf · (vi) ere is only one sink node in the entire network. 4. Ant-Based

Research ArticleTrail-Using Ant Behavior Based Energy-EfficientRouting Protocol in Wireless Sensor Networks

Soon-gyo Jung Byungseok Kang Sanggil Yeoum and Hyunseung Choo

College of Information and Communication Engineering Sungkyunkwan University Suwon 16419 Republic of Korea

Correspondence should be addressed to Hyunseung Choo chooskkuedu

Received 24 November 2015 Revised 29 February 2016 Accepted 15 March 2016

Academic Editor Li-Minn Ang

Copyright copy 2016 Soon-gyo Jung et alThis is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Swarm Intelligence (SI) observes the collective behavior of social insects and other animal societies Ant Colony Optimization(ACO) algorithm is one of the popular algorithms in SI In the last decade several routing protocols based on ACO algorithm havebeen developed for Wireless Sensor Networks (WSNs) Such routing protocols are very flexible in distributed system but generatea lot of additional traffic and thus increase communication overhead This paper proposes a new routing protocol reducing theoverhead to provide energy efficiencyThe proposed protocol adopts not only the foraging behavior of ant colony but also the trail-using behavior which has never been adopted in routing By employing the behaviors the protocol establishes and manages therouting trails energy efficiently in the whole network Simulation results show that the proposed protocol has low communicationoverhead and reduces up to 55 energy consumption compared to the existing ACO algorithm

1 Introduction

Swarm Intelligence (SI) is one of the Artificial Intelligence(AI) techniques and is a relatively novel field [1ndash3] It isstudied based on the observation of the collective behaviorin biological activities such as antbee foraging division oflabor larval sorting nest building and cooperative transportAnt Colony Optimization (ACO) algorithm is a well-knownalgorithm in SI [4] This algorithm is inspired by the actualbehavior of ants in which the ants communicate with eachother by a mediator called pheromone Regarding the ACOalgorithm a number of studies have been done on the prac-tical problems such as adaptive routing resource allocationand collaborative robotics Moreover ACO algorithm hasalready been applied to solve the routing problem in mobilead hoc networks and Wireless Sensor Networks (WSNs) [1ndash3 5ndash10]

Existing routing protocols based on ACO algorithm forWSNs use specific packet called ants The packet is to finddestination node from every node at regular intervals Thedestination can be found using the ants but it generates alot of additional traffic and consumes considerable energy

[2 3] Moreover the energy consumption by ants notice-ably increases when the number of nodes increases WSNstypically consist of resource constrained sensor nodes andthe sensor nodes are with limited energy resource So themost important feature of a routing protocol is the energyconsumption and the extension of the networkrsquos lifetimeDuring the recent years there are ACO-based energy-efficient routing protocols proposed for WSNs [7ndash10] Theprotocols relieve the additional traffic but still consumeconsiderable energy

The main goal of this research is to reduce the additionalenergy consumption which can be observed in the ACO-based routing protocols and to prolong the network lifetimeThe routing protocol proposed in this paper adopts the trail-using ants behavior which forms a large network of foragingtrails and engages the trails in leaf transport By adoptingthe behavior a routing trail can be formed in an energy-efficient way and reduce the energy consumption for routingIn addition the network lifetime can be prolonged with trailreestablishment Simulation results show that the proposedprotocol saves up to 55 energy for routing compared toexisting routing protocols based on ACO algorithm

Hindawi Publishing CorporationInternational Journal of Distributed Sensor NetworksVolume 2016 Article ID 7350427 9 pageshttpdxdoiorg10115520167350427

2 International Journal of Distributed Sensor Networks

The key contributions are as follows

(i) We propose a novel energy-efficient routing protocolfor WSNs

(ii) The protocol adopts the trail-using ants behaviorwhich reduces up to 55 energy consumption

(iii) An extensive simulation is conducted to demonstratethe improvement of the proposed protocol

The rest of this paper is organized as follows In Section 2 wereview the basic concept of ACO algorithm and several rout-ing protocols based on ACO algorithm Section 3 presentsa network energy model and assumptions In Section 4 thedetails of proposed routing protocol with a novel ant behaviorare described Section 5 explains the performance evaluationenvironment and shows the results Finally we conclude ourwork in Section 6

2 Related Work

ACO algorithm [4] originates from the actual behavior ofants in which ants communicate indirectly using pheromoneAnts lay pheromone on the ground and moving direction isdetermined according to the strength of pheromone Oncethe shortest path is found it will receive more pheromonewith higher rate than longer one It seems that this mecha-nism only gets a local shortest path but in fact it approachesthe global shortest path [3] ACO algorithm has been stud-ied to address the problems such as asymmetric travelingsalesman vehicle routing and WSNs routing [1ndash3 5] Inparticular a number of studies for routing have been donein WSNs [6ndash10]

AntNet [6] proposed by Di Caro and Dorigo in 1998is a routing technique inspired by the foraging behaviorof ants and is applied for wired networks The aim of thisrouting technique is for optimizing the performance of theentire network AntNet is based on a greedy stochastic policyaccording to the following rules (1) forward ants randomlysearch for destination (2) after arriving at the destinationthe ants travel backwards on the path they used before(3) all visited nodes are updated with the most adjectiveinformation for the destination Since the goodness of a pathis influenced by the travel time of forwards ants the selectionsprobabilities of path updated by backward ants can influenceants travelling in the forward rather than backward direction

Sensor-Driven Cost-Aware Ant Routing (SC) FloodedForward Ant Routing (FF) and Flooded Piggybacked AntRouting (FP) [7] presented by Zhang et al in 2004 are threeant-based routing algorithms derived fromAntNet forWSNsThe authors lead a good start-up of network using the initialpheromone settings In SC ants are launched from the sensornodes to sense the best direction to go Each sensor node hasa routing table and stores the probability distribution of linksand the estimated cost to the destination from each neighborin the table In FF forward ants are flooded to the destinationOnce forward ants arrive at the destination backward antsare created and traverse back to the source The floodingstops if the probability distribution is good enough for thedestination When a shortest path is found the rate of ants

launched is decreased FP also uses flooding mechanism tolaunch ants But the difference is that it combines forward antswith data ants

Energy-Efficient Ant-Based Routing (EEABR) algorithm[8] is proposed by Camilo et al in 2006 This algorithmis for maximizing the network lifetime of WSNs which isbased on ant behaviors EEABR algorithm aims to reduce theenergy consumption related to the ants In addition it takesinto account the size of ant packet to update the pheromonetrail In the typical ant-based algorithm each ant carriesthe information of all the visited nodes Then in a networkwith a number of sensor nodes the size of informationwould be so big that it consumes considerable energy to sendants through the network To solve such a problem eachant in EEABR algorithm carries only the last visited nodesrsquoinformation Each node keeps the information of the receivedand sent ants in its memory Each memory record containsthe previous node the forward node the antrsquos identificationand a timeout value When a forward ant arrived a nodelooks up in its memory If there is no record the node recordsthe information restarts the timer and forwards the ant tonext hop node Otherwise the ant is eliminated Once a nodereceives a backward ant it sends the ant to the next node towhere the ant must be sent

E-D ANTS [9] proposed byWen et al in 2008 is designedto minimize the time delay in packet transfer for the energyconstrained Wireless Sensor Networks This protocol adoptsthe energy times delay model based on ACO algorithm Theprotocol aims at maximizing network lifetime and real timedata transmission service E-D ANTS is proactive and usesunicast transmission of multiple forward ants to discover theminimum energy consumption and delay paths Each antcarries the residual energy and the hop delay experiencedwhile hopping from node to node

Ladder Diffusion algorithm [10] is presented by Ho etal in 2012 to address the energy consumption and routingproblem in WSNs The algorithm focuses on reducing theenergy consumption and processing time to build the routingtable and avoiding the route loop This algorithm has twophases In the first phase called Ladder Diffusion phase eachnode records hop distance of neighbors in its routing tableto avoid the additional traffic of ACO algorithm After thatthe second phase is called route-choosing phase in whichthe algorithm selects the routes from sensor nodes to thesink node using the routing table created during the firstphase The path decision is based on the estimated energyconsumption of path and the pheromone

In WSNs energy efficiency is important because of theenergy constrained sensor nodesThe aforementioned relatedworks use the ants packet which is periodically sent with themission to find a path until the destination When the antsreach the destination the ants return to their source to updatethe pheromone This method creates additional traffic andincreases energy consumption Moreover there is no specificfinish time of the iteration

3 Network Model

In this paper the radio model is the same as described in [11ndash13] In this model both the free space and multipath fading

International Journal of Distributed Sensor Networks 3

channels are used When the distance is less than 1198890 the free

space (fs) model is applied Otherwise the multipath (mp)model is used 119864elec is the energy consumed by the electronicscircuit per bit 120576fs and 120576mp are the energy consumed by theamplifier in free space and multipath model respectivelyThen the energy consumed by the radio to transmit an 119897-bitmessage over a distance 119889 is given as follows

119864119879(119897 119889) =

119897119864elec + 119897120576fs1198892 for 119889 lt 119889

0

119897119864elec + 119897120576mp1198894 for 119889 ge 119889

0

(1)

The energy consumed by the radio to receive an 119897-bit messageis given as follows

119864119877(119897) = 119897119864elec (2)

In these models a radio dissipates 119864elec = 50 nJbit 120576fs =10 pJbitm2 and 120576mp = 00013 pJbitm

4 for the transmitteramplifier For simplicity of calculation we only use the freespace model 120576fs

In this paper we considerWSNs with a number of sensornodes distributed randomly Our assumption is as follows

(i) All nodes are stationary(ii) All links are symmetric(iii) All nodes can measure the distance between nodes

using RSSI [14 15](iv) All nodes are identical in terms of battery capacity

processing and communication capability(v) All nodes are left unattended after deployment and

battery recharging is not available(vi) There is only one sink node in the entire network

4 Ant-Based Routing Trail Protocol

Since the sensor nodes in WSNs have constrained energyresource energy efficiency must be considered in the designof routing protocol [16ndash18] As we introduced in Section 2EEABR algorithm considers energy consumption but it stilluses the agent of forward and backward ants so it generatesadditional traffic for finding a path to destination [2] Inaddition Ladder Diffusion algorithm still consumes exces-sive energy because the routing table building requires a largenumber of messages The study in [19] shows that trail-usingants form a large network of foraging trails and maintain thetrails network connectivity In the society of trail-using antsthe special worker makes frequent U-turns while markingpheromone trails intensively Using the marked pheromonethe other workers transport food to their nest Inspired bythe behavior of trail-using ants we use the concept of relaynodes [17] With the behaviors and concept the relay nodescorrespond to the marked pheromones and the links of relaynodes represent the pheromone trails

In the proposed protocol sensor nodes are one of thethree types

(i) Sink A node that is interested in receiving data packetfrom a set of relay nodes

(ii) Ordinary A node that detects an event and sends thegenerated data to a relay node

(iii) Relay A node that detects an event and is responsiblefor forwarding the data packet toward sink node

The protocol consists of three phases as follows

(i) Trail Establishing Phase The hop distance from thesink node to each node is computed and the routingtrails are established by selecting several relay nodesin a stochastic manner

(ii) Foraging PhaseThe node detecting an event sends thegenerated data packet to its relay node then the datapacket is relayed by some relay nodes toward to sinknode

(iii) Trail Reestablishing Phase When sensor node couldnot forward its data packet to its relay node the trailwithin one hop is reestablished by selecting new relaynodes

41 Trail Establishing Phase In this phase routing trails areestablished in the entire network for direction and routesto the sink node The direction is determined by the hopdistance calculated from the sink node and routes canconsist of the relay nodes which are selected in a stochasticmanner This phase involves forming the hop distance fromthe sink node broadcasting the trail establishing (ESTA)message and making the decision of node which receivedthe ESTA message Figure 1(a) shows the initial topologyand Figure 1(b) demonstrates the routing trail formed by thisphase

At the beginning all nodes initialize the hop distance asinfinity and the sink node sets its hop distance as zero Thisphase starts from sink node Sink node declares its role asa first relay node The new relay node creates a new ESTAmessage and broadcasts the message to its neighbors TheESTA message of relay node 119894 contains two fields ID 119894 Hopdistance Hop distance of relay node 119894 + 1 Upon receivingthe message the neighbors become candidate relay nodesset their waiting timer and wait for the expiration of thetimer Because the trail-using ants randomly make U-turnsand intensively deposit their pheromone the waiting timer isbased on probabilityThewaiting timer could be computed asfollows We refer to [20 21] for this equation

WT119894= rand(119879min + (119879max minus 119879min) sdot 2 sdot

100381610038161003816100381610038161003816100381610038161003816

119889119894119895

119889TXminus 05

100381610038161003816100381610038161003816100381610038161003816

sdotHD119894)

(3)

where WT119894is the waiting timer of the candidate relay node

119894 The function rand(119909) returns a random value between0 and 119909 119879min and 119879max are the minimum time and themaximum time respectively 119889

119894119895is the distance from the

candidate relay node 119894 to the sender node 119895 and 119889TX is thetransmission range of a node Since the distance estimatedby RSSI usually introduces error the normalized value of


Sink

Relay

Ordinary

i hop distance to sink

i

(a)

01

1 1 2

1 2

2

2

11

1

1

21

21

11

1

1

2

2

2

1

1

1

2

2

22 3

2

2

22

2

2

3

2

2

2

23

32

3

2 2

33

333 3 33 3

Sink Ordinary

Relay i hop distance to sink

i

(b)

Figure 1 The example of topologies

distance is subtracted by 05Thus the nodes in the middle ofthe transmission range of sender node 119895 would have a smallwaiting time to the candidate relay nodes This subtractedvalue can be set by its environment HD

119894is hop distance of

the candidate relay node 119894 A node with higher HD valuewould have longer waiting time By considering the HD inthis calculation the nodes having smaller hop distance wouldhave smaller waiting timer and the hop distances would bewell distributed on the entire network

The candidate relay node whose timer expires firstbecomes a new relay node among the others The new relaynode initializes its pheromone and broadcasts a new ESTAmessage Depending on the status of nodes which receivedthe new ESTAmessage there are three reactions (1) the nodewhich determined its role as an ordinary node just records theinformation of the new relay node in its routing table (2) thenode which is waiting for its waiting timer to expire cancelsits timer and decides its role as an ordinary node and (3) thenode that has never received the ESTAmessage calculates andsets its timerThe steps described above occur repeatedly untilevery node declares its role and stop after expiration of trailestablishment timer

Relay nodesrsquo placement and connectivity are importantin the network performance [17] If the connectivity is notguaranteed the circle route might appear Furthermore theenergy balance between relay nodes can be destroyed whenthere are no enough relay nodes to forward data packetsWe suggest the following mechanism for providing theconnectivity In the first instance the transmission range isdivided into two zones on the basis of middle inside andoutside In our proposed protocol the candidate relay nodestypically cancel their waiting timer upon receiving the newESTA message If the candidate node is in the outside zonesof two or more relay nodes the node just waits until itstimer expires By adopting this mechanism it guarantees theconnectivity between relay nodes

Every node updates its hop distance upon receiving theESTAmessages by comparingwith its hop distance If the hopdistance included in the message is lower than that the nodeitself has the node employs the hop distance Simultaneouslythe hop distance and ID in the message are recorded in therouting table The routing table is used in the next phase forrouting trail selection In the next phase the information ofrelay node is shared by periodical HELLO messages

Figure 2 represents the status after broadcasting the newESTAmessage from relay node A Figure 2(a) shows the shiftafter broadcasting the new ESTA message from relay node Cwhich has determined its role by the ESTA message of relaynode A Node B sets its hop distance as three by comparingwith the hop distance which is the same as the message relaynode A received Node E sets its hop distance as four becausethe message is the same as the message relay node C receivedNodes F and G set their hop distance as five and wait fortheir waiting timer to expire Figure 2(b) demonstrates thevariation of nodesrsquo status after broadcasting the new ESTAmessage from relay node D Node F located in the outsidezone of relay node and inside zone of D cancels its waitingtimer Node G located in the two outside zones is waiting forits waiting timer to expire

42 Foraging Phase When an event is detected by a nodethe node generates a data packet and forwards it to its relaynode Each node manages the information of its neighborrelay nodes in their routing tableThe relay node periodicallybroadcasts a HELLO message including its hop distancepheromone and residual energy The nodes receiving themessage record the information in their routing table Fur-thermore if the hop distance included in themessage is lowerthan that of the receiver node the receiver node uses the hopdistance as a new one

The node having a data packet to be sent to the sinknode selects the next hop relay node referring to its routing


4

4

5

5

4

4

5

5

44

5

4 4

4

3

5

5

5

5

5

5 5

3

4

3 43

3

4

Inside zoneOutside zoneOverlapped outside zone

a

BC

DE

FG

(a)

4

4 5

5

5

4

4

5

44

5

4 4

4

3

5

5

55

5

5

55

5

5

5

5

5

5 5

3

4

3 43

3

4

5

a

BC

DE

F

G


(b)

Figure 2 The example of trail establishing phase

table The set of relay nodes to be selected is only the relaynodes which have lower hop distance than the node havingthe data packet Using the information in routing table thenode having higher selection probability can be selectedTheselection probability to choose next hop relay node can becalculated using the following equation [1ndash10 22]

119875119894119895=

120591119895

120572120578120573

119894119895120593120574

119895

sum119897isin119877(119894)

120591119897120572120578120573

119894119897120593120574

119897

(4)

Here 119875119894119895is the selection probability which node 119894 chooses to

forward its data packet to relay node 119895 119877(119894) is a set of therelay nodes having lower hop distance than node 119894 120591

119895is a

pheromone of relay node 119895 and 120578119894119895is the normalized distance

between node 119894 and relay node 119895 120593119895is the residual energy of

the relay node 119895 and it can be calculated using the followingequation 120572 120573 and 120574 are weight values

120593119895=

1

119862 minus 119890119895

(5)

where119862 is the initial energy of the nodes and 119890119895is the residual

energy of the relay node 119895After calculation of the selection probabilities for the

particular nodes the node having a data packet chooses therelay node which has the highest selection probability amongthe nodes as a next hop relay node The data packet is sentto the selected relay node and is relayed towards the sinknode Upon sending a data packet to a next hop relay node orthe sink node the relay nodes update their pheromone valueusing given equation as follows [1ndash10 22]

120591119894(119905) = 120591

119894(119905 minus Δ119905) + Δ120591 (6)

where 119905 is the current time Δ119905 is the gap of the last updatedtime Δ120591 is the amount of pheromone to be accumulated

120591119894(119905) = 119890

minus(119905minus1199051)sdot120588sdot 120591119894(1199051) (7)

01

1 1 2

1 2

2

2

11

1

1

21

21

11

1

1

2

2

2

1

1

1

2

2

22 3

2

2

22

2

2

3

2

2

2

23

32

3

2 2

33

333 3 33 3

A

Path 2

Path 1

Figure 3 The multiple paths in routing trail

Equation (7) is used to update the pheromone of node 119894where 120588 is the evaporation rate of pheromone and 1199051 is thelast updated time

The procedure finishes once the data packet reaches thesink node

Figure 3 represents the multiple paths formed by severalrelay nodes Ordinary node A can use one of the paths In thisfigure it is expected that there are many paths to select

43 Trail Reestablishing Phase In this phase the one hoptrail is reestablished when the node has a data packet and isnot available to select the next hop relay node This phase issimilar to the trail establishing phase but this phase just onlyconcerned one hop area toward the sink node

The node which has a data packet and no relay nodeto select becomes a trigger node and initializes this phaseThe trigger node broadcasts a trail reestablishment (RESTA)message to its neighbors RESTA message contains twofields the same as ESTA message The nodes having lower


Node having not enough energy or broken

01

1 1 2

1 2

2

2

11

1

1

21

21

1

1

1

2

2

2

1

1

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22 3

2

2

22

2

3

2

2

2

23

32

3

2 2

33

333 3 33 3

2

1

A

B

Relay

Ordinary with hop distance iiSink

(a)

01

1 1 2

1 2

2

2

11

1

1

31

21

1

1

A

B

C

1

2

2

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1

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33 3

3

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33

3

3

2

2

2

23

33

3

2 2

33

333 3 33 3

2

1

Node having not enough energy or brokenRelay


(b)

Figure 4 The example for trail reestablishing phase

hop distance than that one in the message respond to themessage If the receiver node is a relay node the relaynode immediately replies to a trail reestablishment complete(CMP-RESTA) message If the receiver nodes are ordinarynodes they set their waiting timer and wait for its expirationWhen the waiting timer is expired the node becomes anew relay node and replies the CMP-RESTA message Uponreceiving the CMP-RESTA message the trigger node andother nodes record the information in the message In casethat the node is not the trigger node the node cancels itswaiting timer

Trigger node sets its reestablishing timer and waits forCMP-RESTA messages until the expiration of the timer Asthe timer expires trigger node updates its hop distance to thelower one among the values which are in the CMP-RESTAmessages If there is no CMP-RESTA message the triggernode updates its hop distance increase by 1 and returns backits foraging phase In case of no relay node to select this phaseis triggered again These steps are repeated until the relaynodes are founded From the aforementioned phase we cansay that it is available to rearrange the hop distances in thewhole network with dynamic

Figure 4 demonstrates an example of this phase InFigure 4(a) node A is a trigger node and node B is a newrelay node recently determined Relay node B notifies itsrole to other nodes with CMP-RESTA message Figure 4(b)shows the situation that some nodes deplete their energy andcannot be a new relay node Thus trigger node B could notreceive the CMP-RESTAmessages fromnew relay nodes afterbroadcasting the first RESTA message So the hop distanceof node B has been increased Node A notifies its decision tobe a new relay node using a CMP-RESTA message At thismoment node C cannot receive the message from node Cso the node should be a trigger node and initialize this phasewhen it is needed From these two examples we can say thatour proposed protocol is robust and adaptive in the dynamicsituation

5 Performance Evaluation

In this section we evaluate the proposed protocol and com-pare its performance to two other routing protocols basedon ACO algorithm the EEABR and Ladder Diffusion TheEEABR is well-known routing protocol for energy efficiencyLadder Diffusion is similar to our proposed protocol Thesetwo protocols are proactive and aim at the energy efficiencyfor WSNs Table 1 summarizes the comparison of threerouting protocols We used the following metrics to evaluatethe performance

(i) Total energy consumption the total consumed energyof all sensors

(ii) Average energy the average of energy of all sensornodes

(iii) Average hop distance the average hop distance of alldata packets passed from a source node randomlychosen to the sink node in the simulation

(iv) Energy efficiency the ratio between total consumedenergy and the number of packets received by the sinknode

51 Simulation Environment The performance evaluation isachieved through simulations using the well-known simula-tor NS-3 version 323 In the simulation sensor nodes arerandomly deployed The sensor-deployed area is 1000m times

1000mThe number of nodes varies from 100 to 1000 with afixed node number 100 For each case simulation is run 100times and we average them to get the mean value 1500 datapackets are generated by randomly chosen sensor nodes andsent to the sink node The data packet size is 512 bytes As weintroduced in Section 3 we used the same energymodel as in[11ndash13] Table 2 summarizes the parameters in the simulation

52 Simulation Results The results illustrated in Figure 5 arethe total energy consumption of all nodes in the simulation


Table 1 Comparison of three routing protocols

EEABR Ladder Diffusion Proposed ideaRoute selection Proactive Proactive ProactiveAdditionaltraffic

HighPeriodically

HighInitially and periodically

Very lowInitially

Ant behavior Foraging Foraging Exploring and foraging

Characteristics

(i) Forward ants(a) Iterative (periodically)(b) Finding the destination

(ii) Backward ants(a) Depositing pheromone(b) Returning to the source

(iii) Pheromone(a) On the links(b) Updating with backward ants

(i) Ladder table(a) Hop distance from sink

(ii) Artificial ants(a) Iterative (periodically)(b) Finding the destination using ladder table

(iii) Pheromone(a) On the links(b) Updating with artificial ants

(i) Relay node(a) Hop distance from sink

(ii) Pheromone(b) On the relay nodes(c) Updating with data packet

Table 2 Simulation parameters

Parameter Parameter valueArea size 1000m times 1000mNumber of sink nodes 1 center of target area

Number of nodes 100 to 1000 with 100increments

Node distribution Randomly distributedData packet size 512 bytesNumber of data packets 1500Transmission range (119889TX) 50mWeight parameters (120572 120573 120574) (033 033 033)Amount of pheromone (Δ120591) 1Pheromone decay rate (120588) 05Minimum waiting time (119879min) 30msMaximum waiting time (119879max) 300ms [21]Initial energy of node (C) 20 J [12]Electronics energy (119864elec) 50 nJbit [11ndash13]Amplifier factor for free-space model(120576fs)

10 pJbitm2 [11ndash13]

Iteration time of ants (EEABR andLadder Diffusion) 15

Ant packet size 32 bytes

Our proposed protocol saves up to 55 energy comparedto EEABR The Ladder Diffusion adopted the concept ofhop count to reduce the additional traffic of ants EEABRalgorithm considered the length of forward ant packet toaddress the energy consumption problem Even though theauthors proposed Ladder Diffusion and EEABR and intro-duced their solution the additional energy consumption stilloccurred Lower energy consumption of Ladder Diffusion inthe network with 100 nodes is due to the hop count basedrouting But after then the energy consumption increasesmore than proposed protocol because of the ant packets Asshown in the results it can be found that the additional trafficsare increased by the number of nodes simultaneously

Tota

l ene

rgy

cons

umpt

ion

(J)

Number of nodes

Ladder DiffusionEEABRProposed idea

16

14

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100 200 300 400 500 600 700 800 900 1000

Figure 5 Total energy consumption

As depicted in Figure 6 the sensor nodes in our proposedprotocol keep more energy compared to the other protocolsIn Ladder Diffusion and EEABR every node generatesadditional traffic to find path toward sink node Our protocoldid not employ the forwardbackward ants mechanism butemployed the concept of relay node to avoid the additionaltraffic Adopting the concept of relay node our proposedprotocol can keep the energy consumption more balancedthan Ladder Diffusion and EEABRThe energy consumptionincreases by the number of sensor nodes due to the nodeswhich participate in the data packet relay and generate antspacket From this result our proposed protocol reduced theadditional traffic in Ladder Diffusion and EEABR Moreoverit is expected that the network lifetime can be prolonged inour protocol than others

The results illustrated in Figure 7 correspond to theaverage hop distance required to reach the sink nodeThehopdistance is calculated by the nodes relaying the data packet


Number of nodes100 200 300 400 500 600 700 800 900 1000

1995

1990

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age r

esid

ual e

nerg

y (J

)


Figure 6 Average residual energy

Aver

age h

op d

istan

ce

Number of nodes

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100 200 300 400 500 600 700 800 900 1000


Figure 7 Average hop distance

which is randomly generated in the random nodes LadderDiffusion provides the direction to the sink node by adoptinghop count calculation so the average hop distance is lowerthan others In the case of EEABR the reason why the hopdistance of EEABR algorithm is longer than others is thatthe protocol just considers energy and pheromone Becauseof the relay nodersquos selection in stochastic manner the hopdistance depends on the selected relay nodes So the hopdistance is a little higher than Ladder Diffusion Thus theproposed protocol is good in the energy efficiency becausethere is no additional traffic for finding the path toward sinknode

Figure 8 shows the energy efficiency of the three protocolsunder different network sizes Proposed protocol is the best

Number of nodes100 200 300 400 500 600 700 800 900 1000

Ener

gy effi

cien

cy (b

itsm

J)

2500

2000

1500

1000

500

0


Figure 8 Energy efficiency

at all network size except the first network size while EEABRis the worst Ladder Diffusion in the network size with 100nodes has good performance due to the hop distance Theenergy efficiency of the three protocols decreases when thenetwork size grows because the hop distance increases

6 Conclusion

In this paper we presented the energy-efficient routing proto-col based on the novel behavior of ants which has never beenadopted in routingThe proposed protocol aims at solving theadditional traffic problem in the routing protocols based onant behavior and making routing with energy efficient Forsolving the problem we studied the novel ants behavior andemployed the concept of relay node and form the routingtrail with several relay nodes selected in stochastic mannerinspired by the behavior Thereafter the additional trafficthat occurred in the ACO algorithm based routing protocolscould be reduced The simulation showed that our proposedprotocol not only decreases the total energy consumption upto 55 for routing but also guarantees the maximization ofnetwork lifetime

Competing Interests

The authors declare that they have no competing interests

Acknowledgments

This work was supported by Priority Research Centers Pro-gram through the National Research Foundation of Korea(NRF) funded by the Ministry of Education Science andTechnology (2010-0020210) and the ICT RampD program ofMSIPIITP Republic of Korea (B0101-15-1366 Developmentof Core Technology for Autonomous Network Control andManagement and 10041244 Smart TV 20 Software Platform)and the MSIP Korea under the G-ITRC support program(IITP-2015-R6812-15-0001) supervised by the IITP


References

[1] Z Zhang K Long J Wang and F Dressler ldquoOn swarm intel-ligence inspired self-organized networking its bionic mecha-nisms designing principles and optimization approachesrdquo IEEECommunications Surveys amp Tutorials vol 16 no 1 pp 513ndash5372014

[2] M Saleem G A Di Caro and M Farooq ldquoSwarm intelligencebased routing protocol for wireless sensor networks survey andfuture directionsrdquo Information Sciences vol 181 no 20 pp4597ndash4624 2011

[3] W Guo and W Zhang ldquoA survey on intelligent routing proto-cols in wireless sensor networksrdquo Journal of Network and Com-puter Applications vol 38 no 1 pp 185ndash201 2014

[4] M Dorigo M Birattari and T Stutzle ldquoAnt colony optimiza-tionrdquo IEEE Computational Intelligence Magazine vol 1 no 4pp 28ndash39 2006

[5] AM Zungeru L-MAng andK P Seng ldquoClassical and swarmintelligence based routing protocols for wireless sensor net-works a survey and comparisonrdquo Journal of Network andComputer Applications vol 35 no 5 pp 1508ndash1536 2012

[6] G Di Caro and M Dorigo ldquoAntNet distributed stigmergeticcontrol for communications networksrdquo Journal of ArtificialIntelligence Research vol 9 pp 317ndash365 1998

[7] Y Zhang L D Kuhn andM P J Fromherz ldquoImprovements onant routing for sensor networksrdquo in Ant Colony Optimizationand Swarm Intelligence M Dorigo M Birattari C Blum LM Gambardella F Mondada and T Stutzle Eds vol 3172 ofLecture Notes in Computer Science pp 154ndash165 Springer BerlinGermany 2004

[8] T Camilo C Carreto J S Silva and F Boavida ldquoAn energy-efficient ant-based routing algorithm for wireless sensor net-worksrdquo in Ant Colony Optimization and Swarm Intelligence pp49ndash59 Springer Berlin Germany 2006

[9] Y-FWen Y-Q Chen andM Pan ldquoAdaptive ant-based routingin wireless sensor networks using EnergylowastDelay metricsrdquo Jour-nal of Zhejiang University SCIENCE A vol 9 no 4 pp 531ndash5382008

[10] J-H Ho H-C Shih B-Y Liao and S-C Chu ldquoA ladder diffu-sion algorithmusing ant colony optimization forwireless sensornetworksrdquo Information Sciences vol 192 pp 204ndash212 2012

[11] W B Heinzelman A P Chandrakasan and H Balakrish-nan ldquoAn application-specific protocol architecture for wirelessmicrosensor networksrdquo IEEE Transactions onWireless Commu-nications vol 1 no 4 pp 660ndash670 2002

[12] A Ray and D De ldquoEnergy efficient clustering hierarchy proto-col for wireless sensor networkrdquo in Proceedings of the 1st IEEEInternational Conference on Communication and IndustrialApplication (ICCIA rsquo11) pp 1ndash4 Kolkata India December 2011

[13] M Azharuddin P Kuila P K Jana and S Thampi ldquoEnergyefficient fault tolerant clustering and routing algorithms forwireless sensor networksrdquoComputers and Electrical EngineeringC vol 41 pp 177ndash190 2015

[14] Y Wang I G Guardiola and X Wu ldquoRSSI and LQI dataclustering techniques to determine the number of nodes inwireless sensor networksrdquo International Journal of DistributedSensor Networks vol 2014 Article ID 380526 11 pages 2014

[15] H P Mistry and N H Mistry ldquoRSSI based localization schemein wireless sensor networks a surveyrdquo in Proceedings of the5th International Conference on Advanced Computing amp Com-munication Technologies (ACCT rsquo15) pp 647ndash652 HaryanaIndia Feburary 2015

[16] J N Al-Karaki andA E Kamal ldquoRouting techniques inwirelesssensor networks a surveyrdquo IEEEWireless Communications vol11 no 6 pp 6ndash28 2004

[17] T Rault A Bouabdallah and Y Challal ldquoEnergy efficiencyin wireless sensor networks a top-down surveyrdquo ComputerNetworks vol 67 pp 104ndash122 2014

[18] N A Pantazis S A Nikolidakis and D D Vergados ldquoEnergy-efficient routing protocols in wireless sensor networks a sur-veyrdquo IEEE Communications Surveys and Tutorials vol 15 no 2pp 551ndash591 2013

[19] S E F Evison A G Hart and D E Jackson ldquoMinor workershave a major role in the maintenance of leafcutter ant pherom-one trailsrdquo Animal Behaviour vol 75 no 3 pp 963ndash969 2008

[20] Y Yim H Park J Lee S Oh and S-H Kim ldquoDistributedforwarder selection for beaconless real-time routing in wirelesssensor networksrdquo in Proceedings of the IEEE 77th VehicularTechnology Conference (VTC Spring rsquo13) pp 1ndash5 DresdenGermany June 2013

[21] J Lee H Park S Kang and K-I Kim ldquoRegion-based collisionavoidance beaconless geographic routing protocol in wirelesssensor networksrdquo Sensors vol 15 no 6 pp 13222ndash13241 2015

[22] M Roth and S Wicker ldquoAsymptotic pheromone behavior inswarm intelligent MANETsrdquo in Mobile and Wireless Commu-nication Networks pp 335ndash346 Springer US 2005

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Active and Passive Electronic Components

Control Scienceand Engineering

Journal of



RotatingMachinery


Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design



Shock and Vibration


Civil EngineeringAdvances in

Acoustics and VibrationAdvances in



Electrical and Computer Engineering

Journal of

Advances inOptoElectronics


Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of


Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014


Chemical EngineeringInternational Journal of Antennas and

Propagation




Navigation and Observation



DistributedSensor Networks



The key contributions are as follows

(i) We propose a novel energy-efficient routing protocolfor WSNs

(ii) The protocol adopts the trail-using ants behaviorwhich reduces up to 55 energy consumption

(iii) An extensive simulation is conducted to demonstratethe improvement of the proposed protocol

The rest of this paper is organized as follows In Section 2 wereview the basic concept of ACO algorithm and several rout-ing protocols based on ACO algorithm Section 3 presentsa network energy model and assumptions In Section 4 thedetails of proposed routing protocol with a novel ant behaviorare described Section 5 explains the performance evaluationenvironment and shows the results Finally we conclude ourwork in Section 6

2 Related Work

ACO algorithm [4] originates from the actual behavior ofants in which ants communicate indirectly using pheromoneAnts lay pheromone on the ground and moving direction isdetermined according to the strength of pheromone Oncethe shortest path is found it will receive more pheromonewith higher rate than longer one It seems that this mecha-nism only gets a local shortest path but in fact it approachesthe global shortest path [3] ACO algorithm has been stud-ied to address the problems such as asymmetric travelingsalesman vehicle routing and WSNs routing [1ndash3 5] Inparticular a number of studies for routing have been donein WSNs [6ndash10]

AntNet [6] proposed by Di Caro and Dorigo in 1998is a routing technique inspired by the foraging behaviorof ants and is applied for wired networks The aim of thisrouting technique is for optimizing the performance of theentire network AntNet is based on a greedy stochastic policyaccording to the following rules (1) forward ants randomlysearch for destination (2) after arriving at the destinationthe ants travel backwards on the path they used before(3) all visited nodes are updated with the most adjectiveinformation for the destination Since the goodness of a pathis influenced by the travel time of forwards ants the selectionsprobabilities of path updated by backward ants can influenceants travelling in the forward rather than backward direction

Sensor-Driven Cost-Aware Ant Routing (SC) FloodedForward Ant Routing (FF) and Flooded Piggybacked AntRouting (FP) [7] presented by Zhang et al in 2004 are threeant-based routing algorithms derived fromAntNet forWSNsThe authors lead a good start-up of network using the initialpheromone settings In SC ants are launched from the sensornodes to sense the best direction to go Each sensor node hasa routing table and stores the probability distribution of linksand the estimated cost to the destination from each neighborin the table In FF forward ants are flooded to the destinationOnce forward ants arrive at the destination backward antsare created and traverse back to the source The floodingstops if the probability distribution is good enough for thedestination When a shortest path is found the rate of ants

launched is decreased FP also uses flooding mechanism tolaunch ants But the difference is that it combines forward antswith data ants

Energy-Efficient Ant-Based Routing (EEABR) algorithm[8] is proposed by Camilo et al in 2006 This algorithmis for maximizing the network lifetime of WSNs which isbased on ant behaviors EEABR algorithm aims to reduce theenergy consumption related to the ants In addition it takesinto account the size of ant packet to update the pheromonetrail In the typical ant-based algorithm each ant carriesthe information of all the visited nodes Then in a networkwith a number of sensor nodes the size of informationwould be so big that it consumes considerable energy to sendants through the network To solve such a problem eachant in EEABR algorithm carries only the last visited nodesrsquoinformation Each node keeps the information of the receivedand sent ants in its memory Each memory record containsthe previous node the forward node the antrsquos identificationand a timeout value When a forward ant arrived a nodelooks up in its memory If there is no record the node recordsthe information restarts the timer and forwards the ant tonext hop node Otherwise the ant is eliminated Once a nodereceives a backward ant it sends the ant to the next node towhere the ant must be sent

E-D ANTS [9] proposed byWen et al in 2008 is designedto minimize the time delay in packet transfer for the energyconstrained Wireless Sensor Networks This protocol adoptsthe energy times delay model based on ACO algorithm Theprotocol aims at maximizing network lifetime and real timedata transmission service E-D ANTS is proactive and usesunicast transmission of multiple forward ants to discover theminimum energy consumption and delay paths Each antcarries the residual energy and the hop delay experiencedwhile hopping from node to node

Ladder Diffusion algorithm [10] is presented by Ho etal in 2012 to address the energy consumption and routingproblem in WSNs The algorithm focuses on reducing theenergy consumption and processing time to build the routingtable and avoiding the route loop This algorithm has twophases In the first phase called Ladder Diffusion phase eachnode records hop distance of neighbors in its routing tableto avoid the additional traffic of ACO algorithm After thatthe second phase is called route-choosing phase in whichthe algorithm selects the routes from sensor nodes to thesink node using the routing table created during the firstphase The path decision is based on the estimated energyconsumption of path and the pheromone

In WSNs energy efficiency is important because of theenergy constrained sensor nodesThe aforementioned relatedworks use the ants packet which is periodically sent with themission to find a path until the destination When the antsreach the destination the ants return to their source to updatethe pheromone This method creates additional traffic andincreases energy consumption Moreover there is no specificfinish time of the iteration

3 Network Model

In this paper the radio model is the same as described in [11ndash13] In this model both the free space and multipath fading




119864119879(119897 119889) =

119897119864elec + 119897120576fs1198892 for 119889 lt 119889

0

119897119864elec + 119897120576mp1198894 for 119889 ge 119889

0

(1)


119864119877(119897) = 119897119864elec (2)





















100381610038161003816100381610038161003816100381610038161003816

119889119894119895

119889TXminus 05

100381610038161003816100381610038161003816100381610038161003816

sdotHD119894)

(3)






Sink

Relay

Ordinary


i

(a)

01

1 1 2

1 2

2

2

11

1

1

21

21

11

1

1

2

2

2

1

1

1

2

2

22 3

2

2

22

2

2

3

2

2

2

23

32

3

2 2

33

333 3 33 3

Sink Ordinary


i

(b)












4

4

5

5

4

4

5

5

44

5

4 4

4

3

5

5

5

5

5

5 5

3

4

3 43

3

4


a

BC

DE

FG

(a)

4

4 5

5

5

4

4

5

44

5

4 4

4

3

5

5

55

5

5

55

5

5

5

5

5

5 5

3

4

3 43

3

4

5

a

BC

DE

F

G


(b)



119875119894119895=

120591119895

120572120578120573

119894119895120593120574

119895

sum119897isin119877(119894)

120591119897120572120578120573

119894119897120593120574

119897

(4)



119895is a




120593119895=

1

119862 minus 119890119895

(5)




120591119894(119905) = 120591

119894(119905 minus Δ119905) + Δ120591 (6)


120591119894(119905) = 119890


01

1 1 2

1 2

2

2

11

1

1

21

21

11

1

1

2

2

2

1

1

1

2

2

22 3

2

2

22

2

2

3

2

2

2

23

32

3

2 2

33

333 3 33 3

A

Path 2

Path 1









01

1 1 2

1 2

2

2

11

1

1

21

21

1

1

1

2

2

2

1

1

1

2

2

22 3

2

2

22

2

3

2

2

2

23

32

3

2 2

33

333 3 33 3

2

1

A

B

Relay


(a)

01

1 1 2

1 2

2

2

11

1

1

31

21

1

1

A

B

C

1

2

2

2

1

1

1

2

3

33 3

3

2

33

3

3

2

2

2

23

33

3

2 2

33

333 3 33 3

2

1



(b)

















HighPeriodically


Very lowInitially


Characteristics

















Tota

l ene

rgy

cons

umpt

ion

(J)

Number of nodes


16

14

12

10

8

6

4

2

100 200 300 400 500 600 700 800 900 1000





Number of nodes100 200 300 400 500 600 700 800 900 1000

1995

1990

1985

1980

1975

1970

1965

1960

1955

Aver

age r

esid

ual e

nerg

y (J

)



Aver

age h

op d

istan

ce

Number of nodes

16

14

12

10

8

6

4

2

100 200 300 400 500 600 700 800 900 1000





Number of nodes100 200 300 400 500 600 700 800 900 1000

Ener

gy effi

cien

cy (b

itsm

J)

2500

2000

1500

1000

500

0




6 Conclusion


Competing Interests


Acknowledgments



References

























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation












119864119879(119897 119889) =

119897119864elec + 119897120576fs1198892 for 119889 lt 119889

0

119897119864elec + 119897120576mp1198894 for 119889 ge 119889

0

(1)


119864119877(119897) = 119897119864elec (2)





















100381610038161003816100381610038161003816100381610038161003816

119889119894119895

119889TXminus 05

100381610038161003816100381610038161003816100381610038161003816

sdotHD119894)

(3)






Sink

Relay

Ordinary


i

(a)

01

1 1 2

1 2

2

2

11

1

1

21

21

11

1

1

2

2

2

1

1

1

2

2

22 3

2

2

22

2

2

3

2

2

2

23

32

3

2 2

33

333 3 33 3

Sink Ordinary


i

(b)












4

4

5

5

4

4

5

5

44

5

4 4

4

3

5

5

5

5

5

5 5

3

4

3 43

3

4


a

BC

DE

FG

(a)

4

4 5

5

5

4

4

5

44

5

4 4

4

3

5

5

55

5

5

55

5

5

5

5

5

5 5

3

4

3 43

3

4

5

a

BC

DE

F

G


(b)



119875119894119895=

120591119895

120572120578120573

119894119895120593120574

119895

sum119897isin119877(119894)

120591119897120572120578120573

119894119897120593120574

119897

(4)



119895is a




120593119895=

1

119862 minus 119890119895

(5)




120591119894(119905) = 120591

119894(119905 minus Δ119905) + Δ120591 (6)


120591119894(119905) = 119890


01

1 1 2

1 2

2

2

11

1

1

21

21

11

1

1

2

2

2

1

1

1

2

2

22 3

2

2

22

2

2

3

2

2

2

23

32

3

2 2

33

333 3 33 3

A

Path 2

Path 1









01

1 1 2

1 2

2

2

11

1

1

21

21

1

1

1

2

2

2

1

1

1

2

2

22 3

2

2

22

2

3

2

2

2

23

32

3

2 2

33

333 3 33 3

2

1

A

B

Relay


(a)

01

1 1 2

1 2

2

2

11

1

1

31

21

1

1

A

B

C

1

2

2

2

1

1

1

2

3

33 3

3

2

33

3

3

2

2

2

23

33

3

2 2

33

333 3 33 3

2

1



(b)

















HighPeriodically


Very lowInitially


Characteristics

















Tota

l ene

rgy

cons

umpt

ion

(J)

Number of nodes


16

14

12

10

8

6

4

2

100 200 300 400 500 600 700 800 900 1000





Number of nodes100 200 300 400 500 600 700 800 900 1000

1995

1990

1985

1980

1975

1970

1965

1960

1955

Aver

age r

esid

ual e

nerg

y (J

)



Aver

age h

op d

istan

ce

Number of nodes

16

14

12

10

8

6

4

2

100 200 300 400 500 600 700 800 900 1000





Number of nodes100 200 300 400 500 600 700 800 900 1000

Ener

gy effi

cien

cy (b

itsm

J)

2500

2000

1500

1000

500

0




6 Conclusion


Competing Interests


Acknowledgments



References

























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










Sink

Relay

Ordinary


i

(a)

01

1 1 2

1 2

2

2

11

1

1

21

21

11

1

1

2

2

2

1

1

1

2

2

22 3

2

2

22

2

2

3

2

2

2

23

32

3

2 2

33

333 3 33 3

Sink Ordinary


i

(b)












4

4

5

5

4

4

5

5

44

5

4 4

4

3

5

5

5

5

5

5 5

3

4

3 43

3

4


a

BC

DE

FG

(a)

4

4 5

5

5

4

4

5

44

5

4 4

4

3

5

5

55

5

5

55

5

5

5

5

5

5 5

3

4

3 43

3

4

5

a

BC

DE

F

G


(b)



119875119894119895=

120591119895

120572120578120573

119894119895120593120574

119895

sum119897isin119877(119894)

120591119897120572120578120573

119894119897120593120574

119897

(4)



119895is a




120593119895=

1

119862 minus 119890119895

(5)




120591119894(119905) = 120591

119894(119905 minus Δ119905) + Δ120591 (6)


120591119894(119905) = 119890


01

1 1 2

1 2

2

2

11

1

1

21

21

11

1

1

2

2

2

1

1

1

2

2

22 3

2

2

22

2

2

3

2

2

2

23

32

3

2 2

33

333 3 33 3

A

Path 2

Path 1









01

1 1 2

1 2

2

2

11

1

1

21

21

1

1

1

2

2

2

1

1

1

2

2

22 3

2

2

22

2

3

2

2

2

23

32

3

2 2

33

333 3 33 3

2

1

A

B

Relay


(a)

01

1 1 2

1 2

2

2

11

1

1

31

21

1

1

A

B

C

1

2

2

2

1

1

1

2

3

33 3

3

2

33

3

3

2

2

2

23

33

3

2 2

33

333 3 33 3

2

1



(b)

















HighPeriodically


Very lowInitially


Characteristics

















Tota

l ene

rgy

cons

umpt

ion

(J)

Number of nodes


16

14

12

10

8

6

4

2

100 200 300 400 500 600 700 800 900 1000





Number of nodes100 200 300 400 500 600 700 800 900 1000

1995

1990

1985

1980

1975

1970

1965

1960

1955

Aver

age r

esid

ual e

nerg

y (J

)



Aver

age h

op d

istan

ce

Number of nodes

16

14

12

10

8

6

4

2

100 200 300 400 500 600 700 800 900 1000





Number of nodes100 200 300 400 500 600 700 800 900 1000

Ener

gy effi

cien

cy (b

itsm

J)

2500

2000

1500

1000

500

0




6 Conclusion


Competing Interests


Acknowledgments



References

























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










4

4

5

5

4

4

5

5

44

5

4 4

4

3

5

5

5

5

5

5 5

3

4

3 43

3

4


a

BC

DE

FG

(a)

4

4 5

5

5

4

4

5

44

5

4 4

4

3

5

5

55

5

5

55

5

5

5

5

5

5 5

3

4

3 43

3

4

5

a

BC

DE

F

G


(b)



119875119894119895=

120591119895

120572120578120573

119894119895120593120574

119895

sum119897isin119877(119894)

120591119897120572120578120573

119894119897120593120574

119897

(4)



119895is a




120593119895=

1

119862 minus 119890119895

(5)




120591119894(119905) = 120591

119894(119905 minus Δ119905) + Δ120591 (6)


120591119894(119905) = 119890


01

1 1 2

1 2

2

2

11

1

1

21

21

11

1

1

2

2

2

1

1

1

2

2

22 3

2

2

22

2

2

3

2

2

2

23

32

3

2 2

33

333 3 33 3

A

Path 2

Path 1









01

1 1 2

1 2

2

2

11

1

1

21

21

1

1

1

2

2

2

1

1

1

2

2

22 3

2

2

22

2

3

2

2

2

23

32

3

2 2

33

333 3 33 3

2

1

A

B

Relay


(a)

01

1 1 2

1 2

2

2

11

1

1

31

21

1

1

A

B

C

1

2

2

2

1

1

1

2

3

33 3

3

2

33

3

3

2

2

2

23

33

3

2 2

33

333 3 33 3

2

1



(b)

















HighPeriodically


Very lowInitially


Characteristics

















Tota

l ene

rgy

cons

umpt

ion

(J)

Number of nodes


16

14

12

10

8

6

4

2

100 200 300 400 500 600 700 800 900 1000





Number of nodes100 200 300 400 500 600 700 800 900 1000

1995

1990

1985

1980

1975

1970

1965

1960

1955

Aver

age r

esid

ual e

nerg

y (J

)



Aver

age h

op d

istan

ce

Number of nodes

16

14

12

10

8

6

4

2

100 200 300 400 500 600 700 800 900 1000





Number of nodes100 200 300 400 500 600 700 800 900 1000

Ener

gy effi

cien

cy (b

itsm

J)

2500

2000

1500

1000

500

0




6 Conclusion


Competing Interests


Acknowledgments



References

























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation











01

1 1 2

1 2

2

2

11

1

1

21

21

1

1

1

2

2

2

1

1

1

2

2

22 3

2

2

22

2

3

2

2

2

23

32

3

2 2

33

333 3 33 3

2

1

A

B

Relay


(a)

01

1 1 2

1 2

2

2

11

1

1

31

21

1

1

A

B

C

1

2

2

2

1

1

1

2

3

33 3

3

2

33

3

3

2

2

2

23

33

3

2 2

33

333 3 33 3

2

1



(b)

















HighPeriodically


Very lowInitially


Characteristics

















Tota

l ene

rgy

cons

umpt

ion

(J)

Number of nodes


16

14

12

10

8

6

4

2

100 200 300 400 500 600 700 800 900 1000





Number of nodes100 200 300 400 500 600 700 800 900 1000

1995

1990

1985

1980

1975

1970

1965

1960

1955

Aver

age r

esid

ual e

nerg

y (J

)



Aver

age h

op d

istan

ce

Number of nodes

16

14

12

10

8

6

4

2

100 200 300 400 500 600 700 800 900 1000





Number of nodes100 200 300 400 500 600 700 800 900 1000

Ener

gy effi

cien

cy (b

itsm

J)

2500

2000

1500

1000

500

0




6 Conclusion


Competing Interests


Acknowledgments



References

























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation












HighPeriodically


Very lowInitially


Characteristics

















Tota

l ene

rgy

cons

umpt

ion

(J)

Number of nodes


16

14

12

10

8

6

4

2

100 200 300 400 500 600 700 800 900 1000





Number of nodes100 200 300 400 500 600 700 800 900 1000

1995

1990

1985

1980

1975

1970

1965

1960

1955

Aver

age r

esid

ual e

nerg

y (J

)



Aver

age h

op d

istan

ce

Number of nodes

16

14

12

10

8

6

4

2

100 200 300 400 500 600 700 800 900 1000





Number of nodes100 200 300 400 500 600 700 800 900 1000

Ener

gy effi

cien

cy (b

itsm

J)

2500

2000

1500

1000

500

0




6 Conclusion


Competing Interests


Acknowledgments



References

























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










Number of nodes100 200 300 400 500 600 700 800 900 1000

1995

1990

1985

1980

1975

1970

1965

1960

1955

Aver

age r

esid

ual e

nerg

y (J

)



Aver

age h

op d

istan

ce

Number of nodes

16

14

12

10

8

6

4

2

100 200 300 400 500 600 700 800 900 1000





Number of nodes100 200 300 400 500 600 700 800 900 1000

Ener

gy effi

cien

cy (b

itsm

J)

2500

2000

1500

1000

500

0




6 Conclusion


Competing Interests


Acknowledgments



References

























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










References

























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation











RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation









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