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Research ArticleDevelopment of Energy-Efficient Routing Protocol inWireless Sensor Networks Using Optimal Gradient Routing withOn Demand Neighborhood Information

K. Nattar Kannan1 and B. Paramasivan2

1 Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu 627012, India2Department of Computer Science and Engineering, National Engineering College, Kovilpatti, Tamil Nadu 628503, India

Correspondence should be addressed to K. Nattar Kannan; [email protected]

Received 23 January 2014; Revised 18 April 2014; Accepted 27 April 2014; Published 21 May 2014

Academic Editor: Ki-Il Kim

Copyright © 2014 K. N. Kannan and B. Paramasivan. This is an open access article distributed under the Creative CommonsAttribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work isproperly cited.

Wireless sensor networks (WSNs) consist of a number of autonomous sensor nodes which have limited battery power andcomputation capabilities with sensing of various physical and environmental conditions. In recent days, WSNs adequately needeffective mechanisms for data forwarding to enhance the energy efficiency in networks. In WSNs, the optimization of energyconsumption is a crucial issue for real-time application. Network topology of WSNs also is changed dynamically by anonymousnodes. Routing protocols play a major role in WSNs for maintaining the routes and for ensuring reliable communication. In thispaper, on demand acquisitions of neighborhood information are used to find the optimal routing paths that reduce the messageexchange overhead. It optimizes the number of hops for packet forwarding to the sink node which gives a better solution for energyconsumption and delay.The proposed protocol combines the on demandmultihop information basedmultipath routing (OMLRP)and the gradient-based network for achieving the optimal path which reduces energy consumption of sensor nodes. The proposedrouting protocol provides the least deadline miss ratio which is most suitable to real-time data delivery. Simulation results showthat the proposed routing protocol has achieved good performance with respect to the reduction in energy efficiency and deadlinemiss ratio.

1. Introduction

WSNs [1] are consisting of sensor nodes that are connectedthrough wireless media. Multihop transmission is occurringto route the data from the source to destination. Large num-ber of sensor nodes can deploy in environment that assembleand configure themselves. A tiny sensor device has thecapability of sensing, computation, and communication intoother devices. WSNs are used to monitor and measure theenvironmental conditions like temperature, humidity, sound,pollution levels, pressure, and so forth. The energy efficiencyis a challenging [2] issue in multimedia communicationdue to the resource constraints, efficient channel access, andlow transmission delay. The energy-efficient routing is akey research area in wireless sensor networks for dynamictopology nature property. Therefore we need to design the

effective routing protocols. In recent days, various energy-efficient routing protocols have been proposed for WSNs[3, 4]. The energy-efficient routing protocols are based onthe node uniformity which is considered to be deployeduniformly in the field.Thedesign of routing protocols is basedon the network topology which might change dynamically.Data delivery ratio is a key challenge in WSNs. Data canbe delivered effectively if the energy efficiency is achievedthrough effective communication among all nodes. It alsoprovides optimal communication in both point-to-point andbroadcast networks in terms of energy usage. Topologyinformation of the networks is key challenge for energy-efficient routing. It provides position information [5] inorder to relay the received packets to certain regions inthe networks. Nodes can enable us to learn the locationof neighboring nodes to find optimal path from a source

Hindawi Publishing CorporationInternational Journal of Distributed Sensor NetworksVolume 2014, Article ID 208023, 7 pageshttp://dx.doi.org/10.1155/2014/208023

2 International Journal of Distributed Sensor Networks

to destination for minimum energy consumption. Reliablerouting has achieved the load-balancing and has satisfiedcertainQoSmetrics [6] including the jitter, delay, latency, andbandwidth. Fault tolerance routing protocols are designedbased on the reliable routing structure with QoS metrics.Gradient-based routing (GBR) protocols are suitable for theWSNs in which a node maintains its gradient and representsthe direction toward a neighboring node to reach a destina-tion. Existing protocols [7] allow a sensor node to constructits gradient using the cumulative traffic load of a path forload-balancing. However, they have a critical drawback thata sensor node cannot efficiently avoid using the path withthe most overloaded node. The natural information gradientcan be used to design efficient information driven routingprotocols for WSNs.

Pantonia and Brandao [8] proposed gradient-based rout-ing scheme for street lighting wireless sensor networks usinga confirmation mechanism for different neighbors. It isbased on the longest distance and has a satisfactory packagedelivery rate in severe conditions which has been specifiedto the application that agrees with point-to-point messageconfirmation.

Yu et al. [9] proposed energy aware temporarily orderedrouting algorithm (E-TORA). It is the extension ofTORA thatfocuses onminimizing the energy consumption of the nodes.The classic TORA made the least cost hops for selecting therouting paths in the networks. In this, same node repeatedlyinvolved in routing phase and also the repeated nodes are runout of its energy that makes routing over head and degradethe routing performance. This mechanism used shorter pathapproach which is not considering their power that decreasesentire network lifetime. E-TORA solved this problem thattakes into account power of each node. It also avoids nodeswith low energy for participating routing process. In this,node’s energy consumption is balanced in order to avoid thesame nodes drawn from their energy earlier that are usedfrequently for routing. E-TORA takes into account energy lefton the nodes in order to use nodes with more power thatincreases the lifetime of the network.

Jung et al. [10] proposed an on demand multihop lookahead based real-time routing protocol that offered ondemand acquisition of neighborhood information around thedata forwarding paths. It allows lighter message exchangeoverhead. In order to consume battery power evenly, OMLRPprovides load-balancing within the elliptical region. When asource and forwarding node forward the data to a destination,the source and the forwarding node select the optimal pathsthat satisfy a desired speed.

Two-hop velocity based routing (THVR) [11] is basedon two-hop neighborhood information based geographicrouting protocol to enhance the quality of a real-time packetdelivery forWSNs. It usedmapping packet deadline approachand the routing decision is made based on the two-hopvelocity integrated with the energy balancingmechanism. Anenergy-efficient packet drop control is incorporated whenkeeping less packet deadline miss ratio. THVR might leadto heavy message exchange overhead and high computingcomplexity. THVR does not optimize the number of hops

needed to relay the packet to the destination that consumesmore energy and delays than OMLRP.

Quang and Kim [12] proposed a two-hop neighborinformation based gradient routing (THVRG) for enhancingenergy efficiency and real-time performance in WSNs. Therouting decision was considered based on the two-hopinformation. They introduced control scheme that reducescomputational complexity and enhances energy efficiency ofthe sensor nodes. This method could be implemented onIEEE802.15.4 without major changes. They also suggestedmultichannel access mechanisms which can be combinedwith the gradient routing to enhance the real-time perfor-mance meantime.

Gao and Wang [13] proposed the algorithm of wirelesssensor network routing based on the gradient and the residualenergy ant algorithm. The ant algorithm integrates linkquality into pheromone based on the gradient to enhance thereal-time performance, whereas Yoo et al. [14] introduced anew gradient-based routing protocol for load-balancing witha new gradient method to prolong network lifetime in whichthe least loaded path is selected for data forwarding whichavoids the overloaded sensor node.

Ren et al. [15] introduced the packet attribute to identifydifferent packets generated by heterogeneous networks.Theyalso proposed an attribute aware data aggregation (ADA)scheme for making the data aggregation more efficient. Theyused a packet driven timing algorithm and a dynamic routingprotocol for energy efficiency. In the attribute aware dataaggregation scheme, packets are treated as ants for findingpaths and attracted the packets with the same attributeto gather them. They combined adaptive timing controlalgorithm with attribute aware data aggregation schemewhich make the packets with the same attribute spatiallyconvergent for improving the effective data aggregation.The ADA scheme provides various properties includingscalability and reliable routing with respect to the networksize. The following data aggregation schemes [16–21] havebeen proposed to save the limited energy on sensor nodes inWSNs.

Mittal et al. [22] have proposed the improved LEACH(low-energy adaptive clustering hierarchy) communicationprotocol which is an extension of the classic LEACH becauseclassical LEACHdoes not dissipate energy evenly throughouta network. In this, cluster based mechanism is used forminimizing energy dissipation in sensor networks. ImprovedLEACH is performed better than classical clustering algo-rithms by using adaptive clustering mechanism and rotatingcluster heads for load-balancing.

TEEN (threshold sensitive energy efficient sensor net-work protocol) [23] utilized multilevel clustering mechanismto savemore energy. Cluster head broadcast three parametersincluding attribute, HT, and ST to its members when chang-ing the cell. Nodes in the networks are sensed informationcontinuous from environment. If the information value isbeyond HT or the varied range of characteristic value isbeyond ST, the node will send sensed information to clusterhead that reduces network traffic and increases networkslifetime.

International Journal of Distributed Sensor Networks 3

PEGASIS (power-efficient gathering in sensor informa-tion systems) [24] used greedy algorithm to form data chainfor gathering data. Each node aggregates the data fromdownstream node and sends it to upstream node along thechain. The distance between nodes on chain is shorter thanfrom member nodes to cluster head. PEGASIS save muchenergy because they reduce the routing overhead for dynamicformation of cluster. In this approach, distance between a pairof sensors is too long. In this condition, this pair of sensorswill consumemore energy than other sensors in transmittingdata phase.

2. Materials and Methods

In recent days researchers have proposed various routingschemes and challenges [25, 26] for enhancing real-timeproperties of WSNs to provide reliable transmission. In thatone-hop information is used to select forwarding nodes. GBRdoes not recognize the whole network topology of WSNs.In GBR, each node selects the next hop forwarding node,its neighbors, and destination based on its location. Thegradient of the cost field is established in GBR in whicheach node can find a minimum cost path to the destination.The cost metric is the hop count between two nodes. Thesource node finds the minimum hop count in routing path.The purpose of GBR is to maximize transmitting distanceof each hop and to reduce maintenance information andenergy consumption. GBR cannot optimize the number ofhops that is the main reason of energy expenditure and delay.OMLRP allows a minimum message exchange overheadwhen affording on demand acquisition of neighborhoodinformation among forwarding paths. In order to achieveoptimal reliable routing, OMLRP and GBR are combined interms of the number of hops and energy efficiency.

2.1. Gradient-Based Network Setup. Gradient-based networksetup takes the minimum hop count and remaining energyof a node while routing data from source node to the sink.The optimal route is established autonomously; the scheme iscomposed of three sections.

2.1.1. Gradient Setup. It can optimize the transmission energyand reduce the energy consumption of each node to prolongthe network lifetime. In this sink broadcasts a packet whichcontains a counter set to 1 initially. After receiving a packet,the receiving node sets its height equal to the counter in thepacket and increases the counter by 1 and then forwards thepacket.

2.1.2. Height Calculation. The sink sets its height to 0. Theheights of other nodes are equal to the smallest number ofhops to the sink which reduced the routing overhead becauseit selects the minimum hop to involve the routing.

2.1.3. Data Forwarding Approach. Each node calculates jointparameters for forwarding the packets to sink. A nodecompares with its joint parameters to neighboring nodesand selects a neighbor to relay its packets to the sink.

Source

Destination

X-axis

Helliptical

d(xs, y𝜑)

D(s, d)

𝜑s(x𝜑, ys)

Figure 1: Elliptical region of source and destination.

The semiminor axis of the elliptical region denoted by𝐻ellipse is considered and node energy is defined from for-mula (1) as follows:

𝐻ellipse =𝐷 (𝑠, 𝑑)

2× (1

ℎ) + 𝑐 ×

𝐸𝑗/𝐸𝑜𝑗

∑𝑗 (𝐸𝑗/𝐸𝑜𝑗)

, ℎ ≥ 1, (1)

where coefficient 𝐶 ∈ [0, 1]. The maximum value of 𝐶indicates end-to-end delay; the minimum value of 𝐶 leadsthe traffic to nodes with higher remaining energy. 𝐸0𝑗 is theinitial energy of node; 𝐸𝑗 is the remaining energy of node.Figure 1 shows elliptical region of source and destination toselect the neighboring node for forwarding packets.

Acknowledgment mechanism is applied to calculate thedelay of the packets. A node will stamp the time to identifythe delay of packets of each node when it receives the packetand then compare it with the time when the ACK packet isreceived.The delay estimation of 𝑇𝑖𝑗 for time instant (𝑇+1) iscalculated by

𝜏𝑖𝑗(𝑡 + 1) = 𝛼𝑀

𝑖𝑗 (𝑡) +

1 − 𝛼

𝑇

𝑡−1

∑

𝑘=max(1,𝑡−𝑇)𝜏𝑖𝑗(𝑘) , (2)

where 𝑇 is time window.0 < 𝛼 < 1 is the configurable weighting coefficient.𝑀𝑖𝑗 is

the newly measured delay which is defined by larger value of𝛼. This gradient-based network setup is then combined withOMLRP to make energy efficiency in WSNs which improvesthe network lifetime.

2.2. On Demand Multihop Information Based MultipathRouting. OMLRP considered the following assumptions forreal-time routing.

(i) Homogeneous sensor nodes are deployed in thenetwork.

(ii) Global positioning system (GPS) is used by eachsensor node to be aware of its location in the field.

(iii) One of them initiates generating packets that becamethe source node.

This approach performed multihop lookahead aroundthe paths from the source to destination within an elliptical


Source

Destination

Elliptical region

k1

k1k2

k2

k3

k4

p1

p2

Figure 2: On demand multihop information based multipathrouting within elliptical region with𝐾hop = 4.

region. It selects an optimal path amongmultiple pathswithinthe elliptical region. Figure 2 shows on demand multihopinformation based multipath routing within elliptical regionwith 𝐾hop = 4. A multipath algorithm is obtained from[27, 28] that selects multiple routes from source to desti-nation within elliptical region with high link quality andlow latency. In this, a node sends its packets to neighborsthrough multiple alternative paths. Optimal path is selectedfor data transmission from source to destination. If a problemoccurred in selected path, it selects the next available shortestpath for forwarding data to destination. The elliptical regionrestricted the lookahead around the packet forwarding pathfor reliable routing. The elliptical region is calculated usinglocation information of the source and the destination fromGPS systems [29]. When the source node starts forwardingthe packet to the destination, the multihop lookahead istriggered within the restricted elliptical region. The ellipticalregion is calculated by using location of the source node𝑠(𝑥𝑠, 𝑦𝑠) and destination node 𝑑(𝑥𝑑, 𝑦𝑑) from

𝐷(𝑠, 𝑑) = √(𝑥𝑑 − 𝑥𝑠)2+ (𝑦𝑑 − 𝑦𝑠)

2, (3)

where 𝐷(𝑠, 𝑑) is a distance between source node 𝑠 anddestination node 𝑑.

Lookahead Algorithm 1. As a sensor node can distinguishwhether it is within the elliptical region determined by thefunction (∗), lookahead algorithm is used to find out eachsensor node located within elliptical region.

This algorithm provides an effective way to retain theenergy efficiency and scalability. Hybrid metrics such aslink quality and latency are used as the criteria for optimalpath selection. Lookaheadmessagemechanism is used whichincludes five tuples (𝐾hop, 𝑠(𝑥𝑠, 𝑦𝑠), 𝑑(𝑥𝑑, 𝑦𝑑),𝐻ellipse, 𝑅),

where 𝐾hop indicates no lookahead hops. 𝑠(𝑥𝑠, 𝑦𝑠)and𝑑(𝑥𝑑, 𝑦𝑑) indicate location of source and destination nodes,respectively.𝐻ellipse indicateselliptical region from

𝐻ellipse =𝐷 (𝑠, 𝑑)

2×1

ℎ, ℎ ≥ 1, (4)

where 𝐷(𝑠, 𝑑)/2 is the semiaxis of the elliptical region and his the size of the elliptical region.

Sensor node𝑁𝑎 determine its location (𝑥𝑎, 𝑦𝑎)𝑋 = 𝑋𝑎 cosΦ + 𝑌𝑎 sinΦ ∗

𝑌 = −𝑋𝑎 sinΦ + 𝑌𝑎 cosΦ ∗∗

𝑁𝑎 calculates 𝑓(𝑥, 𝑦) from ∗ and ∗∗If the 𝑓(𝑥, 𝑦) > 0𝑁𝑎 is located at out of the elliptical region.

If the 𝑓(𝑥, 𝑦) ≤ 0,𝑁𝑎 is located within the elliptical region.

Algorithm 1

It calculates average speed of every path from the sourceto the destination until 𝐾hop for selecting optimal path. 𝑅 isa selected optimal path among multiple paths from sourceto destination within the elliptical region based on multihoplookaheadmechanism. Figure 2 showed the on demandmul-tihop information based multipath routing within ellipticalregion with 𝐾hop = 4. If 𝐾hop = 4, every node in anelliptical region maintains location until four-hop neighbor-hoods. From (4), if ℎ is 1, the elliptical region is a circularregion between 𝑠(𝑥𝑠, 𝑦𝑠) and 𝑑(𝑥𝑑, 𝑦𝑑) and ℎ value may bedynamically determined by system. Source and forwardingnode can deliver data to a destination using selected optimalpath for making even energy consumption by all nodes.

OMLRPmaintains the multihop neighborhood informa-tion in the linked tables that restricts flooding of themultihoplookahead termination message within the elliptical region.OMLRP also calculates multihop transmission speed andmaintains the location and the speed of the information ofmultihop neighborhoods into linked tables. All the columnsin the linked tables having information of multihop neigh-borhoods would be deleted except columns of optimal pathselection. OMLRP provides optimal paths from a source to adestination based on the multihop information. It calculatesaverage speed 𝑆𝑘 of every path from the source to thedestination until 𝐾hop for selecting optimal paths. The 𝑆𝑘 iscalculated from

𝑆𝑘 =∑𝑑

∑ 𝑡, (5)

where ∑𝑑 and ∑ 𝑡 mean the sum of distances and the sumof estimated delay times of the neighborhoods until 𝐾hop,respectively; both are calculated from

∑𝑑 =

𝑘−1

∑

𝑗=0

𝑑 (𝑛𝑖, 𝑑) − 𝑑 (𝑛𝑖+1, 𝑑) , (6)

∑𝑡 =

𝑘−1

∑

𝑗=0

delay𝑖+1𝑖 , (7)

where 𝑛𝑖 is the coordinates (𝑥𝑖, 𝑦𝑖) of 𝑁𝑖. The forwardingnodes 𝑁0 and 𝑁𝑘 are maintained when they are involvedin routing path from the source to destination. Hence theoptimal paths are selected by source and forwarding nodesthemselves to𝐾hop neighborhoods toward the destination.


Table 1: Simulation parameter.

Parameter ValueNumber of nodes (𝑁) 50, 150, 300Area 200m × 200mSource location 175m, 175mSink location 20m, 20mPause time 300msConstant bit rate 1 packet/sPacket frame size 30 bytesInitial energy 2 joules

Table 2: Average energy consumption per packet.

Routing protocol Energy per packet (mj/packet)OMLRP-4hop 51.43TVRP 43.15EEOGRP 43.02

3. Results and Discussions

The proposed energy-efficient optimal gradient-based rout-ing protocol (EEOGRP) is evaluated and simulated in net-work simulator (NS2). In this evaluation, the EEOGRP hasbeen compared with two existing real-time routing protocolsin sensor networks such as OMLRP-4hop and THVR. Thesimulation parameters for WSNs are shown in Table 1.

3.1. Energy Consumption. Each node is initialized with initialenergy source of 2J to evaluate the energy balancing per-formance. Figure 3 shows total amount of energy consump-tion in which EEOGRP consumes less energy. Each nodeexchanges beacon signals with its neighbors every 50ms. Asource node transmits data at 50ms. THVR and OMLRP-4hop show higher energy consumption of the all nodes interms of two-hop lookahead approach, because THVR hasheavy message exchange overhead and high computationalcomplexity. The EEOGRP has selected the minimum hopfor end-to-end data delivery compared to other two proto-cols. EEOGRP consumes lower energy compared to others.Table 2 showed average energy consumption per packet forthree protocols.

EEOGRP used the lookahead algorithm to make thenetwork energy-efficient. EEOGRP shares large number ofpaths between a source and a destination. Figure 4 showsoptimal path selection and average energy consumption forselecting an optimal path to route data since sensor nodes inthe elliptical region between the source and the destinationconsumed energy evenly.

3.2. Deadline Delivery Success Ratio. Deadline delivery suc-cess ratio (DDSR) is the ratio of successful received packetsat the sink by the deadline. DDSR is evaluated for threerouting algorithms with varied deadlines. When the deadlineincreases, more packets are received prior to the deadline.EEOGRP optimizes the number of relaying hops in routingphase. Figure 5 shows deadline delivery ratio in case of one

0

5

10

15

20

25

30

35

40

45

50

0 50 100 150 200 250 300Simulation time (s)

Tota

l am

ount

of e

nerg

y co

nsum

ptio

n (m

J)

EEOGROMLRP-4hopTHVR

Figure 3: Total amount of energy consumption of sensor networks.

Simulation time (s)

Optimal path selection

0

5

10

15

20

25

30

35

40

45

50

0 50 100 150 200 250 300

Tota

l am

ount

of e

nerg

y co

nsum

ptio

n (m

J)

EEOGROMLRP-4hopTHVR

Figure 4: Average energy consumption of sensor networks.

source. EEOGRP has the highest DDSR as compared toother two protocols and provides better performance thanTHVR and OMLRP-4hop because it uses more relayingneighborhood’s information. EEOGRP also used the selectiveforwarding ACK scheme that leads to the reduction of thenetwork traffic to make a reliable routing. Figure 6 showeddeadline delivery ratio in case of multiple sources. In this,EEOGRP achieved more than 89% of DDSR because ithas forwarded the packets to destination with less messageexchange overhead, whereas THVR and OMLRP achievedless DDSR compared to EEOGRP because THVR usesproactive updating ACK scheme that makes the overhead


0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1000 1050 1100 1150 1200 1250 1300 1350Deadline (ms)

Dea

dlin

e del

iver

y ra

tio (%

)

EEOGRPOMLRP-4hopTHVR

Figure 5: Deadline delivery ratio in case of one source.

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1 2 3 4 5 6Number of sources

Dea

dlin

e del

iver

y ra

tio (%

)

EEOGRPOMLRP-4hopTHVR

Figure 6: Deadline delivery ratio in case of multiple sources.

traffic for updating two-hop information and OMLRP-4hophas longer waiting time for retrieving multihop informationto route the packets to destination.

4. Conclusion

In this paper, an energy-efficient optimal gradient-basedrouting protocol is proposed which is in combination withOMLRP and a gradient-based routing. Optimal routing pathand reduced energy consumption of senor nodes are achieved

through lookahead mechanism within elliptic region. Simu-lation results have shown that the EEOGRP used gradient-based routing and lookahead algorithm within an ellipticregion for achieving better performance with respect toenergy efficiency andDDSR compared to other two protocolslike OMLRP-4hop and THVR. In addition, the EEOGRPreduced the computational complexity and enhances theenergy efficiency of the sensor nodes by selecting optimalpath and also provides effective routing that increases net-work lifetime.

Conflict of Interests

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

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