Research Article Energy Aware Simple Ant Routing … · Similarly,AntHocNet[ ]isanotherroutingmechanism that e ectively deals with network dynamics. It is comprised of four parts,

Research ArticleEnergy Aware Simple Ant Routing Algorithm forWireless Sensor Networks

Sohail Jabbar,1,2 Rabia Iram,1 Muhammad Imran,3 Awais Ahmad,4 Anand Paul,4

Abid Ali Minhas,1,5 and Mohsin Iftikhar6

1Department of Computer Science, Bahria University, Islamabad 44000, Pakistan2Department of Computer Science, COMSATS Institute of Information Technology, Sahiwal 57000, Pakistan3King Saud University, P.O. Box 92144, Riyadh 11451, Saudi Arabia4School of Computer Science and Engineering, Kyungpook National University, Daegu 702-701, Republic of Korea5College of Computer and Information Systems, Al Yamamah University, P.O. Box 45180, Riyadh 11512, Saudi Arabia6School of Computing and Mathematics, Charles Sturt University, Boorooma Street, Locked Bag 588, Wagga Wagga,NSW 2678, Australia

Correspondence should be addressed to Anand Paul; [email protected]

Received 1 October 2014; Accepted 23 December 2014

Academic Editor: Bo-Wei Chen

Copyright © 2015 Sohail Jabbar et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Network lifetime is one of the most prominent barriers in deploying wireless sensor networks for large-scale applications becausethese networks employ sensors with nonrenewable scarce energy resources. Sensor nodes dissipate most of their energy incomplex routing mechanisms. To cope with limited energy problem, we present EASARA, an energy aware simple ant routingalgorithm based on ant colony optimization. Unlike most algorithms, EASARA strives to avoid low energy routes and optimizesthe routing process through selection of least hop count path with more energy. It consists of three phases, that is, route discovery,forwarding node, and route selection. We have improved the route discovery procedure and mainly concentrate on energy efficientforwarding node and route selection, so that the network lifetime can be prolonged. The four possible cases of forwarding nodeand route selection are presented. The performance of EASARA is validated through simulation. Simulation results demonstratethe performance supremacy of EASARA over contemporary scheme in terms of various metrics.

1. Introduction

Wireless sensor networks (WSNs) are captivating growingattention because of their merit and aptness for autonomousand unattended monitoring in large-scale applications.Examples of such applications range from terrestrial toaquatic, from earth to cosmic, from agriculture to jungle,and from home and offices to international borders [1]. Mostof these applications require deployment of thousands oflow-cost battery-operated tiny sensors which are expectedto stay operational for a longer period. Despite considerableadvancements in different aspects [2–4], network lifetime, asyet, is one of the most prominent barriers in deployingWSNsfor large-scale applications because sensor nodes dissipatemost of their energy in complex routing mechanisms.

Routing has been widely investigated in the context ofmobile ad hoc network (MANET); however, due to uniquecharacteristics, it is still a challenging issue in WSNs [5].Most of the existing routing protocols either employ conven-tional [6–8] or nonconventional approaches [9–11]. Noncon-ventional approaches are based on natural systems such asbioinspired computing that is the focus of this work. In bioin-spired computing, ant colony optimization (ACO) has beenwidely used to develop metaheuristic algorithms for combi-natorial optimization problems such as asymmetric travel-ling salesman [12], graph coloring problem [13], and vehiclerouting problem [14]. Metaheuristic algorithms such as tabusearch [15], simulated annealing [16], iterative local search[17], evolutionary computation [18], and ant colony opti-mization [19] provide computational methods for heuristic

Hindawi Publishing CorporationMathematical Problems in EngineeringVolume 2015, Article ID 194532, 11 pageshttp://dx.doi.org/10.1155/2015/194532

2 Mathematical Problems in Engineering

optimization of a problem. These algorithms must be cus-tomized according to the application to find the optimal solu-tion. For example, authors in [20] have advocated the adop-tion of ACO for MANETs. WSNs share some common cha-racteristics with MANET (e.g., infrastructure less and dy-namic topology); however, differences such as high nodedensity and severe energy constraints [21] instigated us toinvestigate adoption of ACO for WSNs.

In dynamic changing conditions, routing in WSNs aimsto deliver data from sensors to the sink in reliable and energyefficient manner. The focus is on finding the optimal pathbetween source and destination. In our previous work [22],we have developed an analogy between foraging behavior ofants and routing process in WSNs. Routing can be handledwith the help of ACO utilizing the information of neighbordiscovery, routing and data transmission, and route mainte-nance. ACO mimics the real ant colony’s foraging behaviorto address the combinatorial optimization problem of findingthe best path on aweighted graph.The resulting shortest routemapping determined by the artificial ants can be applied tothe optimization problem of energy aware routing. Indirectcommunication used by social insects to coordinate theiractivities is called stigmergy, directing towards pheromonedeposition. By exploiting the stigmergy approach (respondsto new and modifies the existing environment), a numberof successful algorithms in such diverse application fieldsof combinatorial optimization are introduced for routing incommunication networks. Initially, ant agents (packet) moveby following a random decision based on link and nodeposition. Routing table is updated based on the stochasticdecisions made for pheromone deposition after they reachedthe sink/destination node, and their decision is based onsampling the complete paths. Proactive path is selected by theant agents so that it could affect the topology change of thenodes in a sensor network and similarly the routing. Reactivepaths are then used once the routing table is updated foroptimal routing of the shortest path. The resulting optimalroute can be determined by the ant agents to obtain a solutionof the optimization. Forward ants maintain memory of eachvisited node while moving towards the sink thus avoidingloops formation.The ant’s internalmemory helps to constructthe goodness of a solution, contribute in each move, andmanage feasibility of solutions. The backward moving antsretrace the path towards the source node. Figure 1 elucidatesthe phenomena.

The local and global pheromone are sharing help tobuild up the optimal solution. As far as the local pheromoneinformation is concerned it involves the problem specificheuristic information, which is knowledge embedded in thepheromone trails involved at the beginning of the stochasticsearch process (ants generation, activity in search of paths,and pheromone evaporation).

However, the global pheromone concentration informa-tion deals with the shared local pheromone value that ulti-mately effect the ant’s decision while construction of a goodsolution to deposit additional pheromone information tocollect useful global information. Reactive approach developsthe multipath between source and destination in order toavoid link failure and robustness, but it becomes difficult

when mobility among nodes causes frequent topologicalchanges. Whereas the proactive approach is reducing therouting, overhead becomes infeasible in disruptive situations.The hybrid approach is combining both the reactive and theproactive approaches and utilizing their advantages helps todevelop an optimized energy aware routing algorithm basedon ACO.

The remainder of this paper is organized as follows. Sec-tion 2 describes some of the closely relatedworks to our work.The working details of EASARA are described in Section 3.Section 4 proposed solution is discussed in Section 3 withdetailed elaboration of proposed improvements in routediscovery part of the baseline algorithm, simple ant routingalgorithm. Also, the proficiency of EASARA algorithm overSARA algorithm in four scenarios that may arise during theforwarding node selection which are discussed is detailed inthis section. Results and discussion are explained in Section4 proceeded by conclusion in Section 5.

2. Related Work

Energy aware routing based on ACO has been widely inves-tigated in the context of MANET [23, 24]. For example, ARA[25] is a reactive algorithm based on ACO that strives toreduce routing overhead. ARA consists of following threephases, that is, route discovery, route maintenance, androute failure handling. In route discovery, new paths arediscovered with the help of FANT (forward ant) and BANT(backward ant). Each FANT containing a unique sequencenumber passes through intermediate nodes while movingdata packets from a source towards the destination. Routingtable entries consist of destination address, next hop, andpheromone value which is maintained by each node inde-pendently. Choosing a route among multiple paths dependson the link quality and usability. Routing paths establishedshould be maintained (i.e., route maintenance) with theevaporation of pheromone value that indicates the qualityof the link. Missing successive number of acknowledgmentsindicates nonavailability of a path thatmight be caused due toseveral factors such as node failure or mobility. Route failurehandling deals with such situations that may call for reroutediscovery, for example, finding alternative path. To conserveenergy, availability of pheromone is considered while rerouteis discoverd.This significantly reduces the overhead as it doesnot require an exchange of routing tables. However, the crite-rion for choosing a neighbor is not clear during route selec-tion. Moreover, triggering a route recovery procedure basedonmissing anACKmay unnecessarily call for path discovery.

Similarly, AntHocNet [26] is another routing mechanismthat effectively deals with network dynamics. It is comprisedof four parts, that is, reactive path setup, proactive pathmaintenance and explorations, stochastic data routing, andlink failures. In reactive path setup, each receiving nodefurther unicasts or broadcasts the forward ants dependingupon the pheromone table maintained at each node. A nodeonly considers the first arrived forward ant that keeps theinformation of visited nodes on the path which helps thebackward ant in backtracking. Proactive path maintenanceupdates exist and look for better routes. Data routing is

Mathematical Problems in Engineering 3

Food

Node i, Link j

Source

Sink

Nest

(a)

Source

Nest

Food

Sink

(b)

Figure 1: Forward and backward ants. (a) Forward ant stochastically selects the path. (b) Backward ant stochastically selects the path.

Table 1: Literature based performance parameters evaluation.

Performance parameters ARA ACO SARA AntHocNetAverage energy consumption MNode operation time MPacket delivery rate MPacket drop ratio MThroughput M MEnd to end packet delayPacket size M MRouting overhead MMinimum energy MEnergy efficiencyLink cost M

based on stochastic decisions for load balancing. Link failuremechanism is triggered when nodes are unable to exchangeunicast or hello messages. Like AntHocNet, SARA [27] isanother mechanism that optimizes the routing procedure toreduce overhead. SARA employs a new controlled neighborbroadcast (CNB) mechanism in the route discovery whereone of the neighboring nodes only broadcasts the receivedFANT further. It further reduces overhead by employing datapackets to refresh the routes of active sessions. To limit thescope of recovery (i.e., route repair phase), SARA strives tofind a new link between the two end nodes of the failed pathinstead of establishing a new route from the source to desti-nation. SARA pursues a broadcast search in case the recoveryprocess remains successful. Unlike these mechanisms, thefocus of our work is WSNs where node energy is a primedesign consideration while making routing decisions.

Recently, a novel routing protocol based on ACO wasproposed in the context of WSNs [28]. To increase net-work lifetime through load balancing, the proposed protocolemploys a dynamic and adaptive mechanism that considersengagement of a node in data forwarding and residual energybesides data latency. The efficiency of the proposed protocolin terms of network lifetime improvement is also shown.

Table 1 summarizes the literature given in Section 2. Toreduce energy consumption and prolong network lifetime, a

multipath routing protocol (MRP) based on clustering andACO is proposed in [29]. MRP picks a cluster head (CH)among nodes based on residual energy and employs ACOto determine multiple paths between a CH and sink. Severalrouting factors such as energy consumption are considered bythe CH for data transfer while dynamically choosing a path.Most of these works do not consider WSNs dynamics suchas decay in pheromone, link failures due to node mobilityor dysfunction. Unlike these works, the focus of our work isdifferent.

3. Energy Aware Simple AntRouting Algorithm

As stated earlier, most applications of WSNs employ sensorswith limited nonrenewable energy and routing is a majorenergy guzzler that shorten network lifetime. To cope withthis problem, we present a reactive energy aware simple antrouting algorithm (EASARA)which is based onACO.Unlikemost algorithms, EASARA strives to avoid low energy routesand optimize the routing process through selection of theshortest path (number of hops) with more energy. EASARAmainly consists of three phases, that is, route discovery,forwarding node, and route selection. We have improved theroute discovery procedure of SARA and mainly concentrateon energy efficient forwarding node selection (FNS) androute selection so that the network lifetime can be prolonged.We have described four possible cases of forwarding nodeand route selection. The following describes the details ofEASARA.

3.1. Route Discovery in EASARA. Unlike traditional ACObased approaches, EASARA adopts a controlled neighborbroadcast mechanism [27] in route discovery to avoidflooding the network with FANTs. In CNB based routediscovery, each sensor node broadcasts the FANT to its directneighbors, and only one of them further rebroadcasts theFANT to its neighbors. Randomly choosing a neighbor for“rebroadcasting” may not be feasible. Therefore, EASARAadopts a probabilistic approach in picking a neighbor basedon energy and link cost. The probability of a neighborwith more energy and lower link cost is higher in FANT


Table 2: FANT packet format used by EASARA.

FANT ID Source Destination Node address Next N Hop pid Average energy threshold Ant Val

rebroadcast. First, it determines a threshold energy levelbased on average remaining energy of neighbor nodes thatis calculated as follows. Each node continuously updates itsenergy and exchanges heartbeat messages with neighbors toestablish and maintain a neighbor list containing updatedinformation such as ID and energy level [30]. Suppose thereare𝑁 nodes inWSNs; the average energy consumption of thenetwork is the sum of the Δ𝐸

𝑖divided by𝑁 as

𝐸avg =1

𝑁

𝑁

∑

𝑖=1

Δ𝐸𝑖, (1)

where Δ𝐸𝑖is the difference between initial and final energy

which can be calculated as

Δ𝐸𝑖= 𝐸initial − 𝐸final. (2)

Since data transmission is amajor energy consumer, thereforethe energy consumed by each node is the sumof transmission𝐸𝑇𝑥

and reception 𝐸𝑟𝑥:

Δ𝐸𝑖= 𝐸initial − (𝐸𝑇𝑥 + 𝐸𝑟𝑥) . (3)

The energy threshold can be calculated using (4) whichaverages the remaining energy of all the neighboring nodes(𝑀) of a source 𝑆:

Threshold energy = 𝐸avg =𝑀

∑

𝑖=1

Δ𝐸𝑖. (4)

The neighbors having remaining energy greater than thethreshold will become the candidates for FANT rebroadcast.The rationale is to engage high energy nodes in route dis-covery and hence improve network lifetime. Afterwards, thecandidate nodes determine the link cost based on the numberof times a node was previously selected for rebroadcast. Theselection time is inversely proportional to the probability ofa node to be picked in the route selection procedure. Theprobability 𝑝(𝑖, 𝑗

𝑖, 𝑑) to pick a neighbor for forwarding FANT

towards destination 𝑑 is given by (5), which is based onenergy, link cost, and usability:

𝑝 (𝑖, 𝑗𝑖, 𝑑) =

𝐶(𝑖,𝑗𝑖 ,𝑑)

∗ 𝑒 (𝑗𝑖)

∑𝑀

𝑘=0𝐶(𝑖,𝑗,𝑑)

∗ 𝑒 (𝑗𝑘)

∧𝐶(𝑖,𝑗,𝑑)

=1

1 + 𝑛, (5)

where 𝐶(𝑖,𝑗𝑖 ,𝑑)

represents the link cost between node 𝑖 to itsneighbor node 𝑗

𝑖towards destination 𝑑. This cost is based

on the number of times (𝑛) the link was previously used andon 𝑀, that is, the number of neighbors of node 𝑖; 𝑒(𝑗

𝑖) is

the energy of node 𝑗𝑖and 𝑒(𝑗

𝑘) is energy of node 𝑗

𝑘. This

rebroadcast of the FANTpacket continues in a similar fashionuntil it reaches the destination. Table 2 elucidates the FANTpacket used by EASARA. The rest of the route discoveryprocedure is similar to SARA.

Most of the fields are self-explanatory. FANT ID is aunique identifier for each session and is generated by thesource node. The addresses (source, destination, and node)represent the IDs of the source, destination, and address ofthe current node. The neighbor selected for rebroadcast islisted under Next. The next two fields indicate the numberof hops traversed from source towards the destination on aparticular path and path ID. Average energy threshold is theaverage remaining energy of neighbors.

3.2. Four Scenarios of FNS for Comparison of SARA andEASARA Algorithms. The depletion of node energy that hasbeen involved in various data and control sessions causesdecrease in the lifetime of network. The algorithm EASARAhelps to enhance the lifetime of the network by taking care ofnodes energy. If we consider nodes energy then four scena-rios occur where each shows a unique case in terms of energyutilized by a node and number of times a node is selected fordiscovering a route.The scenario is depicted clearly in Figure2 where a node energy usage in terms of energy units and thenumber of times a node is selected earlier. Nodes commu-nicate with each other through hello packets. The routes arediscovered by calculating the threshold level of energy of theneighboring nodes to select the nodes for controlled neighborbroadcast procedure where the probability of neighboringnodes is calculated.Thenodeswhich have greater energy leveland higher probability are selected.The four scenarios whicharise are as follows:

(i) nodes with equal energy but different number ofselection times,

(ii) nodes with variable energy and variable used numberof times with less number of nodes involved incontrolled neighbor broadcast process,

(iii) nodes with less energy and used less number ofselection times,

(iv) nodes with more energy and used less number oftimes.

The algorithm details of EASARA along with the four scena-rios are discussed below. Scenario depiction for the adapta-tion of EASARA is given in Figure 2.

3.2.1. Case 1: Nodes with Equal Energy but Different Numberof Selection Times. Considering Table 7, source 𝑆 has twoneighbors 𝐴 and 𝐵 and both are used with different numberof selection times (𝑛). Previously in SARA algorithm the nodewith less number of selection times has a higher probabilityto be selected. Random decision is made if a node is usedin equal number of times in SARA but in EASARA averageenergy is computed for node 𝐴 and node 𝐵. Average energycalculation enables to decrease the processing for nodeswhich are below the threshold calculated from the averageenergy consumption. A threshold of 6.5 units is calculated


Source

SARAEASARA

An = 2

n = 2

E = 6.5

n = 1E = 6.5

E = 5

n = 2E = 5

n = 3E = 6

n = 0E = 8

B

n = 1

E = 7

U

D

J1

J2

J3

J4

Figure 2: Scenarios depiction for adapting EASARA.

and obtained from average energy of the neighboring nodesso the node having energy greater than 6.5 is selected toparticipate in CNB broadcasting FANT to calculate the linkcost and probability. Link cost and probability are calculatedfrom (5). For node 𝐴, the link cost is 0.25 and link costfor node 𝐵 is 0.33 and so the probability for node 𝐴 is 0.43and the probability to select node 𝐵 is 0.56. So node 𝐵 hasprobability decision greater than node 𝐴; node 𝐵 is selectedto rebroadcast the FANT.

Table 3 shows a comparison calculation between the twoalgorithms.

3.2.2. Case 2: Nodes with Variable Energy and Variable UsedNumber of Times with Less Number of Nodes Involved inControlled Neighbor Broadcast Process. Node 𝐵 is selectedpreviously on the basis of higher probability and node energy.𝐵 gets the energy information of its neighboring nodes;it calculates the energy threshold to be 6.5 with its threeneighbors𝐴, 𝑗

2, and 𝑗

4. So node𝐴 and node 𝑗

4can participate

in CNB selected by the node 𝐵. The link cost of the node 𝑗4

is 0.5; for node 𝐴 the link cost is calculated to be 0.25 andprobabilitywill be 0.28 for node𝐴, and for node 𝑗

4probability

will be 0.71, so node 𝑗4is selected to rebroadcast the FANT

among its neighbors, whereas when we compare SARA withproposed algorithm node 𝐵 calculates the link cost andprobability for all its neighbors and only the neighbor withgreatest probability will be selected to rebroadcast. EASARAmakes the decision based on the average energy thresholdvalue and calculates the probability of those nodes which areabove a threshold energy level. Table 4 explains case 2.

3.2.3. Case 3: Nodes with Less Energy and Used Less Number ofSelection Times. 𝐽

4calculates the energy threshold to be 5.5; it

selects 𝑗3node for calculating its link cost only. The link cost

and probability is 0.2 and 1, respectively, whereas in SARAalgorithm the nodes with less number of selection times areselected due to their higher probability among their neigh-bors irrespective of the fact that the node has less energy, anddue to its number of less selection times it participates in theroute discovery procedure without knowing the left energylevel of the node. Itmay cause the network to have dead nodeswhich affects the network lifetime.

As themobility in the network and four sessions are underconsideration, this case occurs as a node is involved in anumber of processes. Similarly in EASARA, only the node(s)in neighbor that will have energy above the threshold level isselected, so node 𝐽

4only calculates the probability and link

cost of that node whereas in SARA algorithm all neighborslink cost and probability will be calculated so processingenergy will be consumed for each node. Table 5 gives anextended understanding to this scenario.

3.2.4. Case 4: Nodes with More Energy and Used Less Numberof Times. This case resembles SARA algorithm initial sessioncase when all the nodes have equal energy and link costand probability will be calculated to have a load balanceamong the network nodes. Now 𝑗

3node calculates the energy

threshold to be 6 nodes for its two neighboring nodes 𝑢and 𝑗

2; node 𝑢 is selected to calculate link cost to be 0.25

with probability 1, which ultimately reaches the destination;dissimilarly it reaches destination node. But in EASARA as


Table 3: Case 1 route discovery comparison for SARA and EASARA.

EASARA SARA

Neighbor set 𝑆 → 𝐴 (𝐸 = 6.5) 𝑆 → 𝐴

𝑆 → 𝐵 (𝐸 = 6.5) 𝑆 → 𝐵

Link cost

𝐴: 𝑛 = 2; 𝐴: 𝑛 = 2;𝐶(𝑠, 𝐴, 𝑑) = 0.25 𝐶(𝑠, 𝐴, 𝑑) = 0.25

𝐵: 𝑛 = 2; 𝐵: 𝑛 = 2;𝐶(𝑠, 𝐵, 𝑑) = 0.33 𝐶(𝑠, 𝐵, 𝑑) = 0.33

Probability 𝑃(𝑠, 𝐴, 𝑑) = 0.43 𝑃(𝑠, 𝐴, 𝑑) = 0.43𝑃(𝑠, 𝐵, 𝑑) = 0.56 𝑃(𝑠, 𝐵, 𝑑) = 0.56

Average energy threshold 6.5 —Number of nodes in CBR 2 (𝐴, 𝐵) 2 (𝐴, 𝐵)

ConclusionNode 𝐵 is selected with greater probability based onenergy and the less number of times it is used. Node 𝐵

energy is 6.5.

Node 𝐵 is selected based onless number of time used


EASARA SARA

Neighbor set𝐵 → 𝐴 (6.5) 𝐵 → 𝐴

𝐵 → 𝑗2(5) 𝐵 → 𝑗

2

𝐵 → 𝑗4(8) 𝐵 → 𝑗

4

Link cost

𝐴: 𝑛 = 2; 𝐴: 𝑛 = 2;𝐶(𝐵, 𝐴, 𝑑) = 0.25 𝐶(𝐵, 𝐴, 𝑑) = 0.25

𝑗2: 𝑛 = 2; 𝑗

2: 𝑛 = 2;

𝐶(𝐵, 𝑗2, 𝑑) = 0.25 𝐶(𝐵, 𝑗

2, 𝑑) = 0.25

𝑗4: 𝑛 = 2; 𝑗

4: 𝑛 = 2;

𝐶(𝐵, 𝑗4, 𝑑) = 0.5 𝐶(𝐵, 𝑗

4, 𝑑) = 0.5

Probability𝑃(𝐵, 𝐴, 𝑑) = 0.28 𝑃(𝐵, 𝐴, 𝑑) = 0.25𝑃(𝐵, 𝑗

4, 𝑑) = 0.71 𝑃(𝐵, 𝑗

2, 𝑑) = 0.25

𝑃(𝐵, 𝑗4, 𝑑) = 0.5

Average energy threshold 6.5 —Number of nodes in CBR 2 (𝐴, 𝑗

4) 3 (𝐴, 𝑗

2, 𝑗4)

ConclusionNode 𝑗

4is selected due to the less number of times it is selected

but more processing nodes are involved in calculating theprobability of the link.

Node 𝑗4is selected based on the number of

times a node is selected and the energy of thenode energy of 𝑗

4is 8.

shown in Table 6, only one node is selected based on energythreshold value for being further selected as the forwardingnode as SARA calculates link cost and probability for eachneighboring node.

Four cases have been discussed in it: firstly, if the node’senergy in neighbors is used equally but with variations intheir link cost, that is, number of times a node is used inthe network. Secondly, the nodes are used more but energyconsumption is less as compared to neighboring nodes;thirdly the weaker neighbor cannot participate in calculatingthe link cost and probability to reach destination and thefourth case is the less the link cost is and the less the energy

consumption will be. This will help to discover the pathswhere more energy efficient nodes update their energy levelat each step to enhance the lifetime of the network.

3.3. Route Selection Criteria and Procedure. Once the routesare discovered, they are selected on the basis of a numberof hops, energy of the node, and pheromone concentration.When multiple routes are discovered using a controlledneighbor broadcast then the route is selected on the basis ofprobability procedure.The route selection in EASARA differsfrom SARA algorithm. Two different metrics are used for thebest shortest path selection.The only difference lies in the fact



EASARA SARA

Neighbor set 𝑗4→ 𝑗2(5) 𝑗

4→ 𝑗2

𝑗4→ 𝑗3(6) 𝑗

4→ 𝑗3

Link cost

𝑗2: 𝑛 = 2; 𝑗

2: 𝑛 = 2;

𝐶(𝑗4, 𝑗2, 𝑑) = 0.25 𝐶(𝑗

4, 𝑗2, 𝑑) = 0.25

𝑗3: 𝑛 = 3; 𝑗

3: 𝑛 = 3;

𝐶(𝑗4, 𝑗3, 𝑑) = 0.2 𝐶(𝑗

4, 𝑗3, 𝑑) = 0.2

Probability

𝑗2: 𝑛 = 2;

𝑃(𝑗4, 𝑗2, 𝑑) = 0.55

𝑃(𝑗4, 𝑗3, 𝑑) = 0.44

𝐶(𝑗4, 𝑗2, 𝑑) = 0.25

𝑗3: 𝑛 = 3;

𝐶(𝑗4, 𝑗3, 𝑑) = 0.2

Average energy threshold 5.5 —Number of nodes in CBR 1 (𝑗

2) 2 (𝑗

2, 𝑗3)

ConclusionNode 𝑗

2is selected on the basis of

the less number of times it isused.

Node 𝑗3is used due to less energy consumption and the fact that

the processing energy consumption of other nodes decreases.Energy of 𝑗

3is 6.


EASARA SARA

Neighbor set 𝑗3→ 𝑢 (7)

𝑗3→ 𝑗2(5)

𝑗2→ 𝑗1

𝑗2→ 𝑈

𝑗2→ 𝑗3

Link cost 𝑈: 𝑛 = 1; 𝐶(𝑗3, 𝑢, 𝑑) = 0.5

𝑗3: 𝑛 = 2; 𝐶(𝑗

3, 𝑗2, 𝑑) = 0.25

𝑗1: 𝑛 = 2;

𝐶(𝑗2, 𝑗1, 𝑑) = 0.33

𝑗3: 𝑛 = 3;

𝐶(𝐵, 𝑗4, 𝑑) = 0.25

𝑈: 𝑛 = 1;𝐶(𝑗2, 𝑢, 𝑑) = 0.2

Probability 𝑃(𝑗3, 𝑢, 𝑑) = 1

𝑃(𝑗2, 𝑢, 𝑑) = 0.42

𝑃(𝑗2, 𝑗1, 𝑑) = 0.32

𝑃(𝑗2, 𝑗3, 𝑑) = 0.25

Average energy threshold 6 —Number of nodes in CBR 1 3

ConclusionNode 𝑢 is selected on the basis ofthe less number of times it isused.

Node 𝑢 is selected based on the energy consumption andnumber of times a node is used with energy. Energy of 𝑢 is 7.

that now the routes are selected on the basis of nodes that havea particular energy threshold plus the number of time a nodeis selected. Number of hops from source to destination andthe other pheromone level present in the link between nodes.SARA uses the hop distance, and the pheromone value asmetrics whereas EASARA introduces anothermetric in routeselection criteria which are node energy and number of hops:

𝑝 (𝑢, 𝑗𝑖, 𝑑) =𝜑 (𝑢, 𝑗

𝑖, 𝑑)

∑𝑘=𝑀

𝑘=0𝜑 (𝑢, 𝑗

𝑘, 𝑑)

,

𝜑 (𝑢, 𝑗𝑖, 𝑑) =

(ph (𝑢, 𝑗𝑖, 𝑑) + 1)

𝐹

𝑒nh(𝑗𝑖 ,𝑑),

𝜑 (𝑢, 𝑗𝑖, 𝑑) =

(Energy (𝑢, 𝑗𝑖, 𝑑))1/2

𝑒nh(𝑗𝑖 ,𝑑),

(6)

where the probability of the route selection between node𝑢 and 𝑗

𝑖is dependent on the link cost 𝜑 where 𝑀 is the

number of adjacent nodes. The link cost is calculated basedon pheromone value between the nodes and number of hops.The pheromone value tells the fact that each node tracks theamount of pheromone on each neighbor link. A packet passesthrough the link between the nodes 𝑢 and 𝑗; the pheromoneintensity ph increases by a factor 𝛼. 𝑇

𝑖is the time when a

packet crosses the link and increases pheromone level and


Table 7: Pheromone description.

Pheromone intensity and its effects 𝛼 𝜁

Pheromone intensity will increase slowly, and paths become unstable Too small Too largePheromone intensity will decrease slowly, and paths become stable Too large Too small

Table 8: Simulation parameters.

Parameters ValuesSimulation time 100 sSimulation area 1000m2

Number of nodes 104Packet size 512 bitsMAC type 802.11 bPacket rate 16 Packets/secNode speed 0ms−1–10ms−1

Receiving range 100mPropagation model Two ray groundMobility model Random way pointTraffic FTPNode initial energy 20 J𝑇𝑥energy 0.003 J

𝑅𝑥energy 0.001 J

𝑡 is the time in which a packet crosses the link recently. 𝜁is time when the pheromone decreases. 𝛾 is the pheromonedecrease intensity. The 𝛼 and 𝜁 are used to optimize the linkpheromone availability. The pheromone increasing intensity𝛼 and decreasing intensity 𝜁 values are adjusted to tune theroute availability based on the pheromone intensity. Table 7gives the summary of this pheromone description.

4. Results and Discussion

For performance evaluation of our proposed algorithm,EASARA, with existing protocol SARA. We used NS-2.31simulator by considering simulation parameters as shown inTable 8. All the environmental parameters of both algorithmsare same.Theperformancemetricswhich are used to evaluatethe performance of both algorithms in route discovery androute recovery phenomena are as follows:

(i) transmitted packets per node,(ii) received packets per node,(iii) total number of packets per node,(iv) energy consumption:

(a) per node,(b) per selected path,(c) total network,(d) route recovery.

Initial results during simulations for randomly selected pathsfor the proposed route discovery and route recovery proce-dure are presented. We have started the analysis based on the

101520253035

Num

ber o

f pac

kets

Node Id

SARAEASARA

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101

Figure 3: Number of packets transmitted per node.

1030507090

110130150170

Num

ber o

f pac

kets

Node ID

SARAEASARA

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101

Figure 4: Number of packets received per node.

characteristics of typical sensor node architectures such as inreceiving and transmitting the control packets during routediscovery procedure. It helps to identify the various factorsthat affect system lifetime.

Figure 3 shows the packets transmitted per node whereSARAhasmore value compared to EASARA.When any nodein route discovery procedure communicates with its neigh-boring nodes for FNS, it generates packets called controlpackets. The average energy calculation of the neighboringnode before controlled neighbor broadcast procedure helpsto involve less number of nodes in link cost and probabil-ity calculations. This reduces the number of transmissionpackets among the nodes. The number of control packetsthat are transmitted during the route discovery procedure isanalyzed in Figure 3. Also, the comparison of both competingalgorithms in the number of received control packets duringthe route discovery procedure is depicted in Figure 4. Thatshows almost the same result of better performance ofEASARA over SARA as shown in Figure 3.

Route discovery packets, route recovery, and route main-tenance packets in the routing algorithm depict more over-head of the protocol. Overhead is exponentially related tothe number of interacting nodes. Increase in control packets


10

60

110

160

2101 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103

Node ID

SARAEASARA

Num

ber o

f pac

kets

Figure 5: Total number of packets entertained per node.

0.040.050.060.070.080.09

0.1

Ener

gy (J

)

Node ID

SARAEASARA

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101

Figure 6: Energy consumption per node during packet transmis-sion.

reflects in the form of decrease in the bandwidth available forthe data packets to transfer among the nodes and to reachfrom the source to destination. This results in less reliabilityof data transmission. Same is the case that happened inthe working of SARA which is using more control packetsin route discovery, route recovery, and route maintenanceprocesses. Hence, more energy is consumed per networknode as is shown in the Figure 5.

Control packets are the primary source of energy con-sumption. Energy consumption by the node during the trans-mission of control packets is shown in Figure 6which justifiesthe above facts that less number of packets transmittedbetween intermediate nodes for route discovery procedure isan effective way to save energy.

This is favored most in the situations where the nodes aremobile and distributed in the network area. SARA utilizedmore control packets for route discovery, so more energy isconsumed per node as compared to EASARA by involvingless number of nodes.

Similarly, Figure 7 shows performance of the node onthe energy consumption while receiving control packets. TheEASARAoutperforms SARA algorithm, that is, the reflectionofmore entertainment of control packets by SARA comparedto the proposed solution.

This is again demonstrated in Figures 8 and 9 in anotherway by figuring out the total energy consumption per nodeand of the overall network during the route discovery phe-nomenon, respectively.

00.020.040.060.08

0.10.120.140.16

Ener

gy (J

)

Node ID

SARAEASARA

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101

Figure 7: Energy consumption per node during packet reception.

0

0.05

0.1

0.15

0.2

0.25

0.3

Ener

gy (J

)

Node ID

SARAEASARA

1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103

Figure 8: Total energy consumption per node during entertainmentof packet.

Forwarding node energy consumption is calculatedagainst five randomly selected paths during the simulation.From Figure 10 it is intuited that EASARA gives a betterresult than SARA. The reason of this improved performanceis the reduction of energy consumption at each node duringthe set up phase as fewer transmissions occur in EASARAas compared to SARA, less transient nodes are involved incontrolled neighbor broadcast and probability calculation,and less node to node delay from the overall packet energyfrom source to destination occurs.

In SARA, the route recovery process involves two phases.First the nodes recovery process is initiated locally by lookingat the neighbor table entry. If it is not present then the nodeinitiates the forward node selection. In another case if it failsthen the source node initiates a route discovery procedure.SARA solves the link congestion problem already. We havetaken the case of 5 randomly selected paths where a linkbreak occurs and the node cannot find a node in its neighbortable and it calculates the probability of the next forwardingnode to be selected. The path selection using the probabilityfunction that is based on the link cost and energy helps inreduction of overall energy consumption. That appears inthe form of betterment in EASARA as compared to that ofSARA.Our aim is tomake the route discovered energy-awareso that the lifetime of the network can be increased, whose


0

5

10

15

20

SARA

SARA

EASARA

EASARA

Ener

gy (J

)

Figure 9: Total energy consumed.

0

0.5

1

1.5

2

1 2 3 4 5Path number

Ener

gy (J

)

SARAEASARA

Figure 10: Energy consumption during selection of forwardingnode.

achievement is depicted based on empirical results in Figure11.

5. Conclusion

This paper has presented EASARA, an energy aware simpleant routing algorithm, to improve WSNs lifetime. EASARAis a reactive scheme based on ant colony optimization.To cope with limited energy problem, EASARA strives toavoid low energy routes and optimizes the routing process.EASARA mainly consists of three phases, that is, routediscovery, forwarding node, and route selection. EASARAstrives to avoid low energy nodes in route discovery andengages nodes with higher energy as forwarders. Moreover,it prefers to choose high energy routes with least hop countpath to improve network lifetime and minimize delay. Thefour possible cases of forwarding node and route selectionwere also discussed. The performance of the EASARA wasvalidated through simulation experiments.The experimentalresults validated the effectiveness and efficiency of EASARAcompared to contemporary scheme in terms of variousperformance metrics. This scheme can also be used in M2M,in IoT, and in extension various cyber physical systems (CPS)

1 2 3 4 5Path number

SARAEASARA

0

0.5

1

1.5

2

Ener

gy (J

)

Figure 11: Energy consumption during route discovery process.

[31–35], apart from the fact that a sensor based Big data willalso benefit from such energy aware technique.

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper.

Acknowledgments

The authors would like to extend their sincere appreciation tothe Deanship of Scientific Research at King Saud Universityfor funding this research through Research Group Project no.(RG#1435-051).

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Research Article Energy Aware Simple Ant Routing … · Similarly,AntHocNet[ ]isanotherroutingmechanism that e ectively deals with network dynamics. It is comprised of four parts,