Researh and Design of Heritable Clustering Algorithm in Wireless Sensor Network

Lihui Xie College of Information Science and Technology

Xiamen University Xiamen, Fujian Province, China

Xielihui1985@126.com

Biyu Tang College of Information Science and Technology

Xiamen University Xiamen, Fujian Province, China

tby@xmu.edu.cn

Abstract—The recent interest in Wireless Sensor Networks has led to a number of clustering algorithms that use the limited energy available at sensors more efficiently. In this article, we present a heritable clustering algorithm based on HEED for low energy wireless sensor network. It brings in appointment mechanism to reduce the cluster head reelect overhead efficiently. In the cluster, TDMA communication mechanism base on the idea of interlocking schedule and multi-level structure are adopted. Simulation results indicate that compare with HEED, this clustering algorithm enhance the energy efficiency and equalize the energy consume, which ultimately prolong the network lifetime. .

Keywords-Wireless Sensor Networks; clustering algorithm; multi-level

I. INTRODUCTION

Wireless Sensor Networks (WSNs) consists of large number of cooperating small-scale disposable low-powered nodes capable of limited computation, wireless communication, and sensing. In a wide variety of application areas including geophysical monitoring, precision agriculture, habitat monitoring, transportation, military systems and business processes[1].

There are two main research directions about topology control. Power control and hierarchical structure control[2]. The idea of hierarchical structure control is to select some nodes in the network to be cluster heads through clustering algorithm. These cluster heads form a higher level transmission backbone, responsible for intra-cluster data collection and inter-cluster data forwarding. Clustering algorithm can simplify the routing, reduce data transmission delay, make network extendible and facilitate the technology of data aggregation, so it greatly raising energy efficiency. Clustering algorithm is currently a hot research areas in WSNs.

The remainder of this paper is organized as follows, Section 2 gives an overview of current primary WSN clustering algorithms, HEED is detailed introduced, which is the original algorithm of the heritable clustering algorithm. In section 3, a new clustering algorithm is detailed described. Section 4 prevents the simulation results.

II. CLUSTERING ALGORITHM IN WSNS

A. Classical clustering algorithm 1) LEACH

LEACH (Low Energy Adaptive Clustering Hierarchy) [3] is a hierarchical clustering-based algorithm, which includes distributed cluster formation. The main idea is to form clusters of the sensor nodes based on the received signal strength and use local cluster heads as router to the sink. Periodically and randomly, cluster heads are changed in order to balance the energy dissipation of the nodes.

2) TEEN In TEEN(Threshold-Sensitive Energy Efficient Sensor

Network Protocol)[4], sensor nodes sense the medium continuously, but data transmission is done less frequently. It adopts two thresholds that are employed to limit the frequency that the nodes report its information. Important features of TEEN include its suitability for time-critical sensing applications. Since message transmission consumes more energy than data sensing, the energy consumption in this scheme is less than in proactive networks [5].

3) PEGAGIS PEGASIS (Power-Efficient Gathering in Sensor

Information Systems)[6] is a near optimal chain-based power efficient protocol enhanced over the LEACH protocol. Each node uses the signal strength to measure the distance to all neighbor nodes and then adjusts its signal strength so that only one neighbor can be heard. The chain will consist of those nodes that are closest to each other and form a route to the BS. Thus, nodes only communicate with their closest neighbors and take turns to communicate with the BS, so that network lifetime can be prolonged.

4) HEEDHybrid Energy-Efficient Distributed Clustering (HEED):

HEED [7] is a distributed clustering scheme in which CH nodes are picked from the deployed sensors. HEED considers a hybrid of energy and communication cost when selecting CHs. Unlike LEACH, it does not select cell-head nodes randomly. Only sensors that have a high residual energy can become cell-head nodes.

978-1-4244-7161-4/10/$26.00 ©2010 IEEE

B. HEEDIn HEED, each node is mapped to exactly one cluster and

can directly communicate with its cluster head. The algorithm is divided into three phases:

1) Initialization phase: The algorithm sets an initial percentage of cluster heads among all sensors. This percentage value, Cprob, is used to limit the initial cluster head announcements to the other sensors. Each sensor sets its probability of becoming a cluster head, CHprob, as formula (1), where Eresidual is the current energy in the sensor, and Emax is the maximum energy, which corresponds to a fully charged battery. CHprob is not allowed to fall below a certain threshold pmin, which is selected to be inversely proportional to Emax.

max

* residualprob prob

ECH CE

= (1)

Let MinPowri denote the minimum power level required by a node vi, 1 i M, to communicate with a cluster head u, where M is the number of nodes within the cluster range. We define the average minimum reachability power(AMRP) as the mean of the minimum power levels required by all M nodes within the cluster range to reach u, i.e.

1

M

ii

MinPwrAMRP

M==

(2)

2) Repetition phase: During this phase, every sensor goes through several iterations until it finds the cluster head that it can transmit to with the least AMRP. If it hears from no CH, the sensor elects itself to be a cluster head and sends an announcement message to its neighbors informing them about the change of status. Finally, each sensor doubles its CHprob value and goes to the next iteration of this phase. It stops executing this phase when its CHprob reaches 1.

3) Finalization phase: During this phase, each sensor makes a final decision on its status. It either picks the least cost CH or pronounces itself as CH.

III. DESIGN OF THE CLUSTERING ALGORITHM

HEED produces a set of well distributed cluster heads, but the process of cluster head election consumes much energy. This article propose a new clustering algorithm HC-HEED(Heritable Clustering Algorithm Based on HEED). Before exploring HC-HEED, there are some premises here.

• Suppose the node is basically static. • All of the nodes is isomorphic except sink. • The link is symmetric. • The main task of the network is periodic data

collection. • Synchronization problem has been resolved.

A. Cluster head election mechanism HC-HEED is an improved algorithm based on HEED. In

HC-HEED, the first round of the cluster head election is the same as HEED, when the first set of cluster heads are elected,

the set of nodes in the same cluster will no change anymore. When next cluster head election comes, current cluster head pick out next cluster head from first-level child nodes according weight W, as in formula (2). First-level child node with the largest weight will become new cluster head of the next loop. We call this cluster head election mechanism as appointment mechanism.

i i iW Eresidual AMRPα β= × − × . (3)

B. Intra-cluster multi-level structure In HEED every cluster member is in the cluster range of the

cluster head, but in HC-HEED, especially after the appointment mechanism is carried out, not all the node in the cluster is in the cluster range of the new cluster head. HC-HEED brings in intra-cluster multi-level structure to make sure intra-cluster connectivity.

When a new cluster head is elected, it launch intra-cluster toppoligy establishment in the first instance, cluster member in its cluster range will become the first-level child node, other cluster member determine its own level according to the smallest level broadcast message it ever heard. Figure 1 respectively shows the intra-cluster topology of HEED and HC-HEED.

Figure 1. Intra-cluster topology of HEED and HC-HEED

C. Intra-cluster communication mechanism The core idea of D-MAC protocol is to use staggered

scheduling mechanism, nodes in the same multi-hop path wake up and transceiver messages in stagger time slots, like a chain reaction. In HC-HEED, similar mechanism is adopted, concrete steps are as follows:

Figure 2. Intra-cluster time slot scheduling

Cluster head allocates time slot segment to first-level cluster member according to the number of nodes in the branch carried by the first-level cluster member. The first-level cluster member sign the final time slot as its sending time slot and then transfer the remaining time slot segment to the lower level cluster member according to the branch node number. Lower level cluster member take the same mechanism until the whole cluster members get their time slot. Figure 2 illustrate intra-cluster time slot scheduling. Except for responsibility slot, a node can keep in lower power mode to reduce energy consume, which will greatly reduce energy consume.

IV. PERFORMANCE SIMULATION

This cluster algorithm is validated on a wireless sensor network simulator platform which is built on NS3 simulator platform. There are some important modules in this sensor network platform, the most important one is energy consume module. This section give a brief description of the simulation assumptions, then describe the energy consume module, finally reveal the simulation results.

A. Simulation assumptions 1) Simulation environment settings: 100 nodes randomly

distributed in the 200 200m m× region. Initial energy of each node is 587.52J, Cluster head is re-election every 40 data collection loops.

2) Communication mechanism of the whole network: Several multi-channel allocation mechanisms have been proposed, such as multi-channel DMAC[8], ARCH[9]. In ARCH, each cluster has an exclusive frequency which is got by a certain distributed algorithm before intra-cluster data transmission. So intra-cluster TDMA communication is free from interference of neighbor cluster. This section compares HC-HEED with HEED. The contrast scheme of this artical adopt distributed algorithm proposed by ARCH to get exclusive channel for each cluster, intra-cluster communication uses TDMA and inter-cluster communication uses CSMA/CA on a default public channel.

B. Energy consume module This article adopts parameters provides by low-power RF

chip CC1100[10] and MCU MSP430. Table shows the main current consumptions of the node under several status. Note that IDLE is just a brief transition statu between two other status such as SYNC and TX.

TABLE I. CURRENT CONSUMPTION UNDER SEVERAL STATUS

Suatu of RF current[mA] RF MCU+PF

SYNC 16.5 18.75 IDLE 1.6 3.85

TX (10dBm) 28.9 31.15 TX ( 7dBm) 24.2 26.45 TX ( 5dBm) 19.0 21.25 TX ( 0dBm) 15.5 17.75 TX ( -5dBm) 13.7 15.95

TX ( -10dBm) 14.0 16.25 TX ( -15dBm) 12.7 14.95 TX ( -20dBm) 12.0 14.25 TX ( -30dBm) 11.5 13.75

MCU low power mode RF sleep

0.0009 0.0409

C. Comparison of node energy variance Table compare the variance of node energy between

HEED and HC-HEED at 200, 400, 600,800 data collection loops. It shows that HC-HEED can effectively balance the node energy consumption.

TABLE II. COMPARISON OF NODE ENERGY VARIANCE

Data collection loops 200 400 600 800 HEED 1.33 2.24 5.12 6.91

HC-HEED 0.62 0.93 2.01 3.60

D. Comparison of the whole network energy

0 200 400 600 800 1000 1200 1400 1600 1800 20000

1

2

3

4

5

6x 10

7

Transmission rounds

Who

le n

etw

ork

ene

rgy•

J•

HEEDHC-HEED

Figure 3. Total energy comparison between HEED and HC-HEED

Figure 3 is a network energy trend map, the line ending at the time when the first node die. It is easy to know that HC-HEED greatly extend the network lifetime compare with HEED. What’s more, the total network energy of HC-HEED is smaller than HEED when the first node die, which confirm that HC-HEED has better performance in energy utilization.

ACKNOWLEDGEMENTS

This work is supported by the key plan project in Fujian Province of China.

REFERENCES

[1] Heinzelman W R, Chandrakasan A, Balakrishnan H. Energy-efficient Communication Protocol for Wireless Micro-sensor Networks[R]. Proc. 33rd Annual Hawaii International Conference on System Sciences, 2000.3005-3014.

[2] Bao L, Garcia-Luna-Aceves J J. Topology Management in Ad Hoc Networks[C], In Proceeding of the 4th ACM International Symposium on Mobile Ad Hoc Networking & Computing, June 2003:129-140.J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68–73.

[3] Heinzelman W R, Chandrakasan A, Balakrishnan H. Energy-efficient Communication Protocol for Wireless Micro-sensor Networks[R]. Proc. 33rd Annual Hawaii International Conference on System Sciences, 2000. 3005-3014.

[4] Manjeshwar A, and Agarwal D P, “TEEN: a Routing Protocol for Enhanced Efficiency in Wireless Sensor Networks,” 1st Int’l. Wksp. on Parallel and Distrib. Comp. Issues in Wireless Networks and Mobile Comp., April 2001.

[5] Jamal N A, and Ahmed E K, "Routing Techniques in Wireless Sensor Networks: A Survey," IEEE Wireless Communicaitons, vol.11, no.6, December 2004.

[6] Lindsey S, and Raghavendra C, “PEGASIS: Power-Efficient Gathering in Sensor Information Systems,” IEEE Aerospace Conf. Proc., 2002, vol. 3, 9–16, pp. 1125–30.

[7] O. Younis, S. Fahmy, HEED: A Hybrid, Energy-E cient, Distrib-uted clustering approach for Ad Hoc sensor networks, IEEE Transactions on Mobile Computing 3 (4) (2004) 366–379.

[8] G. Lu, B. Krishnamachari and C. Raghavendra. An Adaptive Energy-efficient and Lo-w-latency MAC for Data Gathering in Wireless Sensor Networks [J]. Parallel and Distributed Processing Symposium, Proceedings of the 18th International, 2004.

[9] Mohamed Younis, Samuel Bushra, “Efficient Distributed Medium Access Arbitration for Multi-Channel Wireless Sensor Networks”, IEEE Communications Society, 2007

[10] CC1100 Low-Power Sub- 1 GHz RF Transceiver [EB/OL] .2009. http://focus.ti.com/lit/ds/symlink/cc1100.pdf