Research Article An Improved GPSR Algorithm Based on ...

Research ArticleAn Improved GPSR Algorithm Based on Energy Gradient andAPIT Grid

Zhuang Liu, Xin Feng, Jingjing Zhang, Teng Li, and Yanlong Wang

College of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130012, China

Correspondence should be addressed to Xin Feng; [email protected]

Received 31 August 2015; Revised 16 December 2015; Accepted 21 December 2015

Academic Editor: Sara Casciati

Copyright © 2016 Zhuang Liu 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.

We investigate GPSR algorithms of WSN and propose an improved routing algorithm based on energy gradient and APIT gridto solve the problem of high and unbalanced energy consumption of GPSR. In GPSR, network uses greedy algorithm and right-hand rule to establish routing paths, and the path keeps running till some nodes within the path are invalid because energy isexhausted, which would lead to the high energy consumption of some nodes in the path and the low energy consumption of othersnearby the nodes not in the path as well as bringing high and unbalanced energy consumption of the network. Regarding theseproblems, we use APIT localization algorithm and APIT grid to query and establish routing paths and establish the correspondingenergy gradient when messages are transmitted along the routing paths. When some nodes are approaching the threshold status,we use right-hand rule and recursion greedy algorithm in advance to plan a new routing path towards the target area. When querymessages arrive at the event area, the network uses a different method to transmit data according to the density of sensor nodes.Simulation experiments show that the improved routing algorithm is capable of reducing the energy consumption of network andextending the lifecycle of network.

1. Introduction

WSN is composed of abundant cheap sensor nodes, andthese sensor nodes are self-organized by wireless commu-nication way in monitoring area to perceive, collect, andprocess information, furthermore, to transmit data to usersby multihop way [1]. WSN aggregates many technologiesincluding sensor technology, information fusion technology,and information processing technology and is widely usedin environmentmonitoring,military investigation, industrialproduction, medical health, rescue and relief, and so on.WSN has features of networking flexible, expandability, easydeployment, mobility, and low cost [2].

In the application of WSN, the viability of node is a keyissue related to the lifecycle of network. High energy con-sumption of sensor nodes will reduce the lifecycle of network.Moreover, the locations of sensor nodes decide the energyconsumption of communication between two sensor nodes.In consequence, routing protocols based on accurate localiza-tion technology are widely used in some critical applicationssuch as forest fire prevention. In routing protocols based on

geography, each node should be aware of the location of itselfand its neighbor nodes. When transmitting messages, thenetwork packages location information in data and transmitsit to the next hop by greedy algorithm [3].

2. Related Works

GPSR is a classical routing algorithm which is applied inad hoc and WSN geography-based routing protocol, andthis algorithm includes two types of data transmissionmethods: greedy transmission and border transmission [4].When investigating and improving a GPSR-based algorithm,three parts of knowledge are necessary including greedytransmission, border transmission, and APIT localizationalgorithm. In this section, we introduce the basic knowledgeand research status of GPSR-based algorithms.

2.1. Greedy Transmission. In GPSR, source node packageslocation information of target node and transmits data toneighbor node with location information, and the optimal

Hindawi Publishing CorporationJournal of SensorsVolume 2016, Article ID 2519714, 7 pageshttp://dx.doi.org/10.1155/2016/2519714

2 Journal of Sensors

xy

D

Figure 1: Greedy forwarding.

Void

D

yx

w

v z

Figure 2: Greedy forwarding failing.

next hop should be the nearest neighbor node. Accordingto this principle, data can be transmitted to sink node inmultihop way. The detailed process of greedy transmission isshown in Figure 1 [4]. The little dotted circle represents thecommunication radius of node x, and node D is sink node.Node x will find node y as its next hop which is the one thatis nearest to node D by greedy method. In this way, data willarrive at node D.

2.2. Border Transmission. In greedy transmission, if a nodecannot find any other node in its neighborhood which isnearer than itself to target node, a routing void forms. GPSRuses surrounding stateless routing algorithms to solve thisproblem. In the algorithm, a plane graph is constructed todescribe network topology and make sure that any two edgesdo not cross each other [5]. As shown in Figure 2, x is nearerto target node D than its neighbor nodes w and y. Set D ascenter of the circle and the distance betweenD and x as radiusof the circle. There are two routing paths to target node D: (x→ y → z → D) and (x → w → v → D); however, node x

x z

y

2

31

Figure 3: Right-hand rule.

cannot transmit data to either node y or node w according togreedy algorithm.Thus, the network generates a routing void.

If relay node is aware of a routing void, it can use right-hand rule to find a new routing path. The right-hand rule isshown in Figure 3. From node y to node x, the next edge islinked x and y in the direction from x to y. The right-handrule selects another edge by anticlockwise rotation way andthen traverses the closed polygonal area [6]. In Figure 2, whennode x finds out a routing void, the routing path by right-hand rule is x → w → v → D → z → y → x, and then theborder transmission is finished.

In GPSR algorithm, when sensor node chooses routingpath it only needs to know the geography location of itself, theneighbor nodes, and the target node, and it is not necessaryto maintain global network topology [5].

2.3. APIT Localization. APIT (Approximate Perfect Point-In-Triangulation Test) algorithm is a classical range-freelocalization algorithm ofWSN.The basic idea of APIT is thatunknown node selects three beacon nodes randomly fromsurrounding area and makes them compose a triangle andthen judges the location relationship between this triangleand itself using PIT (Perfect Point-In-Triangulation Test) [7].If the unknown node is in this triangle, label the node. Usingthis method each sensor node will be tested by PIT and thealgorithm finds out the intersection area of all the trianglescontaining this unknown node and works out the centercoordinate of the area as its location [8].

2.4. Research Status of Improved Algorithms of GPSR. Most oftheGPSR-based algorithms are improved through the aspectssuch as optimizing routing path selection, avoiding routingvoid, using geography-based promotion and network status-based promotion, referencing moving nodes strategy, andenhancing transmission efficiency of special nodes.

Aiming at routing void problem, Karp and Kung pro-posed GPSR (greedy perimeter stateless routing), whichadopts surrounding stateless model to transfer data packageto target node through the set of neighbor nodes in planenetwork [9]. On the other hand, GPSR abandons the proce-dures of establishing, maintaining, and storing routing tablewith little delay of data transmission.However, there are some

Journal of Sensors 3

disadvantages of energy consumption due to data incon-sistency in GPSR. Nithyanandan et al. proposed optimalrouting algorithm based on energy option aiming at networkdata inconformity of GPSR, which ensures energy balancefeasibility in unsymmetrical WSN [10]. Shanmuga Raja etal. also adopted energy optimal routing method to ensurethe communication efficiency between nodes for reliablecommunication,which can prolongnetwork lifecycle [11]. Liuet al. proposed an improved algorithm with dynamic loadbalance ability based on awareness of neighbor nodes, whichcan solve the problem of reducing network lifecycle causedby routing void [12]. The authors of O-GPSR [13] proposeda new routing algorithm based on opportunistic forwardingprinciple. It uses three elements including distance, direction,and neighbor density to calculate each neighbor’s decisionmetric, by which next-hop node can be chosen. The newalgorithm can reduce end-to-end delay and routing load;moreover, it improves routing efficiency. The neighbor’sdecision metric is shown in

𝑚𝑔 = 𝑔𝑎

𝑑

𝑑0

+ 𝑔𝑏

𝜃

2Π

+ 𝑔𝑐 (1 −

𝑛

𝑁

) ,

𝑚𝑓 = 𝑓𝑎

𝑑0

𝑑

+ 𝑓𝑏

𝜃

2Π

+ 𝑓𝑐

𝑛

𝑁

.

(1)

In (1), 𝑚𝑔 represents measure of greedy forwarding;𝑚𝑓 represents measure of perimeter forwarding. 𝑑 is theEuclidean distance between source and destination. 𝑑0 isthe Euclidean distance between neighbors of source anddestination. 𝜃 is the deflection angle between node anddestination, 𝑛 is the amount of neighbors; 𝑁 is the totalnumber of nodes in the network. 𝑔𝑎, 𝑔𝑏, and 𝑔𝑐 are,respectively, the greedy forwarding weighting coefficients ofdistance, direction, and neighbor density. 𝑓𝑎, 𝑓𝑏, and 𝑓𝑐 are,respectively, the perimeter forwarding weighting coefficientsdistance factor, the angle factor, and the node density factor.From these two equations, we can draw a conclusion thatO-GPSR algorithm needs three extra parameters, distance,direction, and neighbor density, which are changed fre-quently depending on network topology, so the routingpath will be changed frequently. Therefore, this algorithmwill generate great energy consumption of calculation. AGPSR-based algorithmproposes a regional perimeter routingbased on left- and right-hand rules. When GPSR shows therouting void, there will be a line connecting the routingvoid node and destination node. The region is divided intotwo complementary subsets, and it can combine with left-and right-hand rule. When it uses the right (left) rule, thenetwork cannot find the corresponding node, so it shouldgo back to the source node and use the left (right) rule.According to the location of next hop node, the algorithmcompares the two distances from current node to target nodeand from greedy node to target node, and if the distancefrom the target node current node is shorter, network returnsto the greedy forwarding mode. The protocol solves thephenomenon that the sensor nodes are not easy to route thevoid in the low density of the sensor nodes [14]. From theabove analysis we can conclude that this algorithm will leadto network circuit because of using greedymode.The authors

of LQ-VV-GPSR propose a routing technology based onlink quality and velocity vector. When selecting next hop,a node does not select the neighbor node closest to targetnode by traditional greedy forwarding strategy but selectsa group of forward list based on measurement standardof link quality (LQ) information each time. After that, itselects optimal node depending on velocity vector (VV)information to improve the performance of the routing path[15]. According to these analyses, LQ-VV-GPSR does notconsider the energy consumption of normal nodes, whichwill produce uneven energy balance of network. Anotherrouting algorithm which can be referenced proposes a fuzzylogic dynamic beaconing (FLDB) strategy, which is based onthe correlation between node moving speed (NMS), numberof neighboring nodes (NoNNs), and beacon packet intervaltime (BPIT) using fuzzy logic control (FLC) mechanism. Inad hoc network, FLC mechanism suggests that high nodemovement and a small number of neighboring nodes needto increase the number of beacon packets dispatched byreducing BPIT, and infrequent node movements and largenumber of neighboring nodes need to decrease the numberof beacon packets dispatched by increasing BPIT. Improvingthe reliability of nodes’ neighbor list by optimizing timebetween transmissions of beacon packets can be feasible[16]. This paper researches the control strategy of beaconpacket overhead and transmission time for GPSR mobile adhoc network, which can be referenced in researching WSN.However, this algorithm does not consider protecting thenodes with large energy consumption which will reduce theuneven energy balance. A trust-based algorithm proposeda secure routing protocol named secured greedy perimeterstateless routing (S-GPSR) protocol. The trust level includesthe accuracy and sincerity of neighbor nodes and is ensuredby their contribution to packet transmission. Instead ofdefault minimal distance, a new trust distance parameteris created in neighbor table using trust levels conjunctwith geographical distance. It can reduce the overhead andimprove the delivery ratio of the networks [17]. The thesisconsiders the weight of nodes’ contribution to transferring toselect next hop which can enhance transmission efficiency;however, it increases energy consumption of being trustednodes continuously.

In this paper, we discuss the disadvantage of ignoringto protect the sensor nodes approaching invalid status forbig energy consumption and propose a new GPSR-basedalgorithm to reduce energy consumption of network, evenenergy balance, and prolong network lifecycle.The improvedalgorithm uses energy gradient and APIT grid to adjustthe working status of sensor nodes by an energy thresholdsettled in advance to reduce invalid nodes. We choosethree types of algorithms for comparison, including classicalGPSR, geography-based algorithm, and optimizing next hopselection algorithm.They areGPSR,O-GPSR, andGPSR-AD.

3. Improved Algorithm

3.1. Energy Consumption Gradient. In WSN applications,sensor nodes are deployed in random way, and the energy


of sensor nodes is limited; therefore, it is a key issue toreduce network energy consumption and extend networklifecycle. In this paper, we propose an improved GPSR algo-rithm based on energy gradient and APIT grid to solve theproblems.

In the improved algorithm, APIT localization algorithmis used to locate all of the unknown sensor nodes, and the gridinformation and grid length are recorded when processinglocalization. When sink node sends message to interestedarea, a routing path from sink node to event area can be estab-lished by greedy algorithm. With processing routing pathrequirement, algorithm records energy gradient informationaccording to the energy data of each node in the queryingpath and establishes a routing path in reverse direction.Whenthe routing path is transporting data and the algorithm isrunning, right-hand rule with energy threshold is designedto optimize the routing maintain mechanism. After queryingto establish routing path, in process of data transmission, anenergy threshold, 𝐸threshold, is settled in advance accordingto data package energy consumption in the routing path andleft energy of the sensor node. When transmitting data alonga routing path, each sensor node should compare its leftenergy with 𝐸threshold. Along with consuming the energy ofa routing path, if one node’s left energy 𝐸rest is less than orequal to 𝐸threshold, algorithm uses right-hand rule to bypassthis node and modify the current routing path, and then thebypassed node still monitors and collects environment databut does not transmit data from other sensor nodes. So, thelifecycle of big energy load sensor nodes can be prolongedand the energy consumption of network can be distributedequally.

As shown in Figure 4, greedy routing path without rout-ing void is S → x → y → z → h → j → i → D.When 𝐸rest of node y is less than or equal to 𝐸threshold, drawtwo circles; the center of one circle is D and its radius isthe distance between node D and node y, and the center ofthe other circle is x and its radius is communication radius.Algorithmfinds optimal next hop, node a, in intersection areausing right-hand rule and greedy algorithm recursively untilthe routing path arrives at target node D. The new routingpath of improved algorithm is S → x → a → z →h → j → i → D. Furthermore, when showing routingvoid the improved algorithm uses right-hand rule to find outnew routing path.

When query message arrives at event area, proper sensornodes density should be set as 𝐷threshold in advance, andthen nodes density of event area should be figured out. Ifnodes density is more than or equal to 𝐷threshold, event areashould be divided into several subareas using grid length ofAPIT algorithm and each subarea has only one leader node.Moreover, leader node should be closest to the center ofsubarea. If one node is set as a leader, it sends message withits ID to all of nodes in this subarea so that each node in thissubarea transmits data through it. On the other hand, if nodesdensity is less than 𝐷threshold, sensor nodes of this subareause flooding routing algorithm [1] to transmit data outwardinstead of multihop way till all data packages are sent out andthe transmission process is finished. Transmission process isshown in Figure 5.

D

S

x

az

h

ji

y

Figure 4: Improved GPSR algorithm.

2

1

2 4310

0

LeaderNormal node

Packet forwarding

Figure 5: Segmentation of event area.

3.2. Energy Consumption Model. In this paper, we adoptthe wireless communication energy consumption modelproposed by Heinzelman et al. [18]. The energy consumptionof 𝑛 bit data transmission of wireless sensor node is 𝐸𝑇𝑥; it isshown in

𝐸𝑇𝑥 (𝑛, 𝑑) = 𝐸𝑇𝑥-elec (𝑛) + 𝐸𝑇𝑥-amp (𝑛, 𝑑)

=

{

{

{

𝐸elec ∗ 𝑛 + 𝑛 ∗ 𝜀fs ∗ 𝑑2, (𝑑 < 𝑅)

𝐸elec ∗ 𝑛 + 𝑛 ∗ 𝜀amp ∗ 𝑑4, (𝑑 ≥ 𝑅) .

(2)

In (2), 𝑅 is communication radius, and the energyconsumption of receiving 𝑛 bit data is 𝐸𝑅𝑥, which is shownin

𝐸𝑅𝑥 (𝑛) = 𝐸𝑅𝑥-elec (𝑛) ,

𝐸𝑅𝑥 (𝑛) = 𝐸elec ∗ 𝑛.(3)

In (3), 𝐸amp is amplification factor of signal amplifier,𝜀fs is energy consumption of free space, 𝐸elec is energyconsumption of emission and receiving electronic circle, and𝑑 is distance between two sensor nodes requiring data trans-mitting.


0 100 200 300 400 500 600 700 800 900 10000

100

200

300

400

500

600

700

800

900

1000 The rout

Border length

Bord

er le

ngth

Figure 6: Routing path of GPSR.

4. Simulation Experiment

4.1. Simulation Experiment Environment and Parameters.In this paper, simulation experiments are performed inMATLAB R2011b to compare this improved algorithm withother three GPSR-based algorithms on network energy con-sumption and valid sensor nodes amount. Furthermore, weconduct experiments about the left energy of each node inevent area ranked by experimental rounds and the left energyvariance analysis of different energy thresholds ranked byexperimental rounds.

The monitoring area of experiments is 1000m ∗ 1000m.There are 300 sensor nodes being deployed randomly. Com-munication radius is 250m.The proportion of beacon nodesis 30% and initial energy is 0.5 J. Node (1000, 1000) is thesink node. The lower left corner of the monitoring area isevent area, and its length is 1.6 times of the grid length ofAPIT (25m ∗ 1.6 = 400m). In experiments, each sensor nodeknows the location of itself using APIT algorithm.

4.2. Simulation Experiment Result Analysis. The initial rout-ing path being established is shown in Figure 6. Blue starnodes represent 300 sensor nodes and red star node rep-resents sink node. Left bottom corner area surrounded bydotted black lines represents event area and the solid blackline represents routing path. Sink node sends query messageto event area and the routing path is established according tothe reversed direction of query message path.

Left energy of network is a key issue to evaluate perfor-mance ofWSN, so experiments are performed to compare thefour algorithms on the left energy of each round. As shownin Figure 7, horizontal coordinate represents the numberof rounds and vertical coordinate represents the total leftenergy of all of sensor nodes in network. The experimentsare about the left energy of network in each round beforenodes of event area are invalid because of energy exhausted.Red line represents the curve reflecting of using this improvedGPSR algorithm. Purple line represents that of using O-GPSR algorithm. Blue line represents that of using GPSR-AD algorithm. Green line represents that of using GPSR

100 200 300 400 500 600 700 800 900 10000The rounds

0

50

100

150

The l

eft en

ergy

Improve GPSRO-GPSR

GPSR-ADGPSR

Figure 7: Comparison of left energy.

100 200 300 400 500 600 700 800 900 10000The rounds

0

50

100

150

200

250

300Th

e num

ber o

f aliv

e nod

es

Improve GPSRO-GPSR

GPSR-ADGPSR

Figure 8: Comparison of amount of alive nodes.

algorithm. From Figure 7 it can be concluded that in eachround this improved GPSR algorithm has more left energythan the other three algorithms, and this improved algorithmcan reduce network energy consumption and extend networklifecycle.

When the nodes in event area are invalid because energyis exhausted, the amount of valid sensor nodes of networkis another key factor to evaluate the performance of routingalgorithm.As shown in Figure 8, horizontal coordinate repre-sents experiments rounds and vertical coordinate representsthe amount of live nodes. The experiments are about theamount of live sensor nodes of each round before all sensornodes in event area are invalid because energy is exhausted.Red line represents the condition of using this improvedGPSR algorithm. Purple line represents that of using O-GPSR algorithm. Blue line represents that of using GPSR-AD algorithm. Green line represents that of using GPSR


5 10 15 20 25 30 35 40 450The nodes of event area

0

0.1

0.2

0.3

0.4

0.5

0.6

The l

eft en

ergy

100 rounds200 rounds300 rounds

400 rounds500 rounds

Figure 9: Left energy of nodes in event area.

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

The v

aria

nce

100 200 300 400 500 6000The rounds

E ≤ 0.1

E ≤ 0.05

E ≤ 0.15

Figure 10: Left energy variance of nodes in event area.

algorithm. From Figure 8, it can be concluded that all ofthe sensor nodes in event area are invalid at more than600 rounds by these four algorithms, and the amount ofvalid nodes of network is about 230 in GPSR algorithm,220 in GPSR-AD algorithm, and 250 in both O-GPSR andthis improved algorithm. Furthermore, from 100 rounds to500 rounds, the amount of valid nodes using this improvedalgorithm is obviously more than that using the other GPSR-based algorithms.

As follows, we investigate the even energy balance ofthis improved algorithm. Figure 9 presents the left energyof each node in event area ranked by 100, 200, 300, 400,and 500 rounds. Figure 10 illustrates the left energy varianceanalysis of each sensor node in event area with differentenergy thresholds, and we can judge which energy thresholdcan make less left energy variance and achieve more evenenergy balance.

In Figure 9, horizontal coordinate represents node ID inevent area and vertical coordinate represents the left energyof nodes. Red line represents the left energy of nodes inevent area at 100 rounds. Blue line represents that at 200rounds. Light blue represents that at 300 rounds. Yellow linerepresents that at 400 rounds. Black line represents that at 500rounds. From Figure 9 we can draw a conclusion that alongwith rounds increasing energy exhaust of each node has thesame tendency and the differential section of nodes is increas-ing persistently. When close to invalid status, nodes in eventarea havemost difference of left energy anddissipative energy.

In Figure 10, horizontal coordinate represents experi-mental rounds and vertical coordinate represents the leftenergy variance of nodes in event area. Red line representsthe left energy variance when energy threshold is 0.1 J. Blueline represents that when energy threshold is 0.05 J. Blackline represents that when energy threshold is 0.15 J. FromFigure 10 we can draw a conclusion that before 200 roundsthe values of left energy variance at the three thresholds are alllocated between 0.015 and 0.02, and after 200 rounds variancedistribution of these three thresholds is different. Threshold0.1 J has the most stable variance distribution. However, boththreshold 0.05 J and threshold 0.15 J have large change intendency of variance. Threshold 0.15 can lead to less leftenergy variance so large value of energy threshold can leadto small variance value of left energy, which can enhance theenergy balance of the network.

5. Conclusion

In this paper, we propose an improvedGPSR algorithm basedon energy gradient and APIT grid to solve problems of highenergy consumption and uneven energy balance of GPSR.In the new algorithm, network establishes path gradientbased on energy consumption and energy remains throughquery message sending and sets energy threshold in advance.And then, if current sensor node energy is approaching thethreshold according to the energy gradient, network usesright-hand rule and iterative greedy algorithm to establishnew routing path.Moreover, this improved routing algorithmuses APIT localization algorithm to locate sensor nodes andrecords the grid distribution information to design themech-anism of query message transmission and event messagetransmission. Meanwhile, this improved routing algorithmuses right-hand rule and border transmission strategy topredict and avoid routing void. The new routing algorithmtakes the uneven energy consumption balance in routingpath into account and designs dynamic updating routingpath mechanism to avoid nodes failing quickly due to energyexcessive consumption, and hence energy consumption canbe shared in the whole network which can reduce networkenergy consumption. Simulation experiment results showthat improved algorithm can suspend the appearance ofinvalid sensor nodes, reduce energy consumption of network,and extend network lifecycle.

Conflict of Interests

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


Acknowledgment

This work was supported by the Natural Science Foundationof China (NSCF) (no. 61275080).

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