A Geographic Routing Algorithm inDuty-Cycled Sensor Networks with Mobile Sinks

Can Ma1, Lei Wang1, Jiaqi Xu1, Zhenquan Qin1, Ming Zhu1, Lei Shu2

1School of Software, Dalian University of Technology, China2Department of Multimedia Engineering, Osaka University, Japan

macan0711@gmail.com, lei.wang@dlut.edu.cn

ssdutjiaqi@gmail.com, qzq@dlut.edu.cn, zhuming@dlut.edu.cn, lei.shu@ieee.org

Abstract—In this paper, we focus on achieving better energyconservation for geographic routing algorithms in duty-cycledWSNs when there is a mobile sink. We simplify the problem asa topology coverage one, and propose a multi-metric geographicalgorithm (MMGR) which uses multi-metric candidates (MMCs)for geographic routing. The analysis and extensive simulationresults show that MMGR can achieve better energy conservationsthan McTPGF, while retaining good performance of end-to-enddelay and hop counts.

Keywords-Geographic Routing; formatting; Mobile Sink; Duty-Cycle; Energy Conservation

I. INTRODUCTION

Recent studies show that wireless sensor networks (WSNs)

with mobile sinks are more energy-effective than pure static

ones. Jae-Wan Kim et al. [1] propose an Intelligent Agent-

based Routing protocol that provides efficient data delivery

to mobile sink. Kun Wang et al. focus on performance of

GPSR [2] and TPGF [3] with single routing metric called

McTPGF [4] in random duty-cycled wireless multimedia sen-

sor networks.

Although existing researches either concern with duty-cycle

or with mobile sinks, MMGR considers the both aspects in

geographic routing, for energy conservation. We are interested

in two problems as follows.

First, Will MMGR be more energy-efficient than McTPGF

with a mobile sink, whereas a similar end-to-end delay and

number of hops compared with McTPGF? Second, Will

MMGR have shorted average end-to-end transmission delay

than that of GPSR?

II. PROPOSED MMGR ALGORITHM

A. The Definition of MMC

The principle for covering the topology is: a forwarding

node always chooses the next-hop nodes which are farthest rel-

atively and in different direction from the source node among

all neighbor nodes. Source node will divide its transmission

area into j part and choose the furthest neighbor in each part

of its transmission area as multi-metric candidates (MMCs).

In Fig. 1, there are 3 MMCs (the orange nodes) of the source

(the gray node), because each orange node is the furthest node

to source in each part of area.

Fig. 1. MMCs selection when the number of MMCs is 3.

B. MMGR Algorithm

The pseudocode for the SELECTION SORT is as follows.

The design of our algorithm is based on the McTPGF and

GPSR algorithms. MMGR uses two phases, MMC selection

and Duty-cycled routing.

Algorithm 1 THE MMGR ALGORITHM1: Get the number of MMC (j) and location of source node;

2: Set source node to be current node;

3: Divide current node transmission area into j parts;

4: Choose the farthest neighbor of every part to be MMCs;

5: if Not all the MMCs are nearer to source than current

node then6: the MMCs will be current nodes and goto 3;

7: end if8: Set all MMCs in Duty-Cycled mode and the others asleep;

9: if Base Station in 1-hop then10: Base Station return Acknowledgement;

11: End.

12: end if13: Get progress and sleeping delay of each neighbor node;

14: Compute routing metric of each neighbor;

15: Choose node with the smallest weight value;

16: if The next hop node already in the path then17: goto 14;

18: end if19: Forward the packet to the chosen node;

20: goto 10;

2011 Seventh International Conference on Mobile Ad-hoc and Sensor Networks

978-0-7695-4610-0/11 $26.00 © 2011 IEEE

DOI 10.1109/MSN.2011.60

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2011 Seventh International Conference on Mobile Ad-hoc and Sensor Networks

978-0-7695-4610-0/11 $26.00 © 2011 IEEE

DOI 10.1109/MSN.2011.60

343

2011 Seventh International Conference on Mobile Ad-hoc and Sensor Networks

978-0-7695-4610-0/11 $26.00 © 2011 IEEE

DOI 10.1109/MSN.2011.60

343

(a) Topology coverage when nodesnumber N=80.

(b) Topology coverage when nodesnumber N=130.

Fig. 2. Topology Coverage Condition

III. EVALUATION

To evaluate the MMGR algorithm and make comparisons

with McTPGF and GPSR, we use a novel sensor network

simulator NetTopo [5] to conduct experiments.

The network size in simulation is fixed as 600M*400M(1

pixel on the canvas is considered as 1 meter), each node has

a transmitting radius of 60m. A cycle-time is 50 seconds, and

each node has 10 slots, and each slot is 5 seconds, the duty-

cycle in our simulation scenario is 10. In terms of the impacts

of hop counts, we suppose that the delay caused by each hop

is one slot time long.

To evaluate the MMC mechanism for a size-fixed WSN, we

change the node number (from 50 to 200) to obtain different

values. Fig. 2 shows the topology coverage of different number

of nodes. Fig. 3 (a) is the simulation result on the average

coverage rate with j=3 in this fixed WSN. By comparing the

average coverage rates, we can clearly conclude that when

nodes number reaches 130, the coverage rate reaches 95%.

However, the growth of coverage rate will not increase with

nodes number obviously.

Fig. 3 (b) is the simulation result on average number of hops

that found by applying MMGR and McTPGF respectively.

By comparison, we can easily see that MMGR can get equal

performance to McTPGF when the number of node reaches

130 (coverage rate reaches 95%). Fig. 4 (a) is the simulation

result on average delay that found by applying MMGR,

McTPGF and GPSR respectively. It is easy to conclude that

the performance of MMGR and McTPGF are satisfactory and

better than GPSR. Fig. 4 (b) is the simulation result on average

energy of a transmission of MMGR and McTPGF. We can

easily find that MMGR is much more energy efficiency than

McTPGF, especially when the numbers of nodes are 110, 120

and 130. Comparing Figures 3, 4, MMGR cannot only get

energy efficiency, but also remain good performance on the

aspect of average hop counts and end-to-end delay.

IV. CONCLUSION

Energy efficiency of data transmission in duty-cycled WSNs

with a mobile sink is a fundamental requirement. In this paper,

a new Multi-Metric Geographic Routing (MMGR) algorithm

is proposed to facilitate the data transmission in duty-cycled

WSNs. By employing MMC, MMGR stands out from tradi-

tional geographic routing algorithms. Theoretical analysis as

well as simulation results show that MMGR has enhanced

(a) MMC: Average coverage ratesvs. number of nodes.

(b) MMGR and McTPGF: averagenumber of hops vs. number of nodes.

Fig. 3. Simulation Results I

(a) MMGR, McTPGF and GPSR:average delay vs. number of nodes.

(b) MMGR and McTPGF: averageenergy of a transmission vs. numberof nodes.

Fig. 4. Simulation Results II

performance on the aspect of energy efficiency compared with

McTPGF and GPSR in duty-cycled WSNs with mobile sinks.

MMGR can achieve high coverage of the network with quite

low energy consumption, which makes it efficient and practical

in real networks. We will explore the proposed method further

in a real testbed in future.

ACKNOWLEDGMENT

This work is partially supported by Natural Science Foun-

dation of China under Grant No. 61070181 and Grant No.

60903153, the Fundamental Research Funds for the Central

Universities No. DUT10ZD110, the SRF for ROCS, SEM, and

Natural Science Foundation of Liaoning Province under Grant

No. 20102021.

REFERENCES

[1] Jae-Wan Kim, Jeong-Sik In, Kyeong Hur, Jin-Woo Kim and Doo-SeopEom, An Intelligent Agent-based Routing Structure for Mobile Sinks inWSNs. Department of Electronics and Computer Engineering KoreaUniversity Seoul, Korea.

[2] B. Karp, H.T.Kung, GPSR: Greedy Perimeter Stateless Routing forWireless Network. Proc. of the Annual International Conference onMobile Computing and Networking, Boston, August, 2000.

[3] L. Shu, Y. Zhang, L. T. Yang, Y. Wang, M. Hauswirth, and N. Xiong,Tpgf: geographic routing in wireless multimedia sensor networks. InTelecommunication Systems,Volume 44, Numbers 1-2,79-95, 2009.

[4] Kun Wang, Lei Wang, Can Ma, Lei Shuy, Joel Rodriguesz, GeographicRouting in Random Duty-cycled Wireless Multimedia Sensor Networks.Software School, Dalian University of Technology, Dalian, China, 2010.

[5] L. Shu, C. Wu, Y. Zhang, J. Chen, L. Wang, and M. Hauswirth,Nettopo:beyond simulator and visualizer for wireless sensor networks.ACM SIGBED Review, Vol. 5, No. 3, October, 2008.

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