Study of impacts of duty-cycle on overlapping multi-hop clustering in wireless sensor networks

The Journal of China Universities of Posts and Telecommunications October 2012, 19(Suppl. 2): 19–22 www.sciencedirect.com/science/journal/10058885

http://jcupt.xsw.bupt.cn

Study of impacts of duty-cycle on overlapping multi-hop clustering in wireless sensor networks XU Jia-qi1, WANG Lei1, MA Can1, SHU Lei3 ( ) 1. School of Software, Dalian University of Technology, Dalian 116020, China 2. Faculty of Computing and Information Technology, King Abdulaziz University, Saudi Arabia 3. Multimedia Engineering, Osaka University, Japanese

Abstract Clustering is a standard approach for achieving efficient and scalable performance in wireless sensor networks (WSNs). Especially, some sensor network applications need some nodes to affiliate to more than one cluster. This paper focused on studying the impacts of a duty-cycle based connected k-neighborhood (CKN) sleep scheduling algorithm for a multi-hop overlapping clustering algorithm in WSNs. It reveals the fact that waking up more sensor nodes cannot always increase the average cluster size but can always increase the cluster overlapping degree in a given duty-cycle based WSN. Keywords overlapping multi-hop clustering, sleep scheduling, energy harvesting, wireless sensor networks

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Introduction 

Organizing sensor networks into cluster-based architectures has been extensively studied to achieve the network scalability and energy efficiency [1–2]. Each cluster has a central cluster head (CH) to gather sensory data, then forwards to the desired recipient. Traditional clustering algorithms widely vary according to the bootstrapping schemes and the characteristics of the CHs, e.g., LEACH [3], ACE [4], HEED [5], RCC [6], LCA [7], which mostly concentrated on generating the minimum number of disjoint clusters, without much concern about critical issues such as renewable energy supply or overlapping problem. Moustafa et al. [8] has proposed a multi-hop clustering algorithm KOCA which can generate connected overlapping clusters that cover the entire sensor network with a specific average overlapping degree, and emphasized that overlapping clusters can facilitate many applications, such as node localization, inter-cluster routing and promotion of network robustness [9]. A key drawback of this research work is that all sensor Received date: 29-06-2012 Corresponding author: SHU Lei, E-mail: [email protected] DOI: 10.1016/S1005-8885(11)60449-4

nodes are assumed to be always awake during the clustering process. However, in most realistic situations, sensor nodes should be scheduled to dynamically sleep to conserve energy. In Ref. [10], Nath et al. proposed a connected k-neighborhood sleep scheduling algorithm, named as CKN, for duty-cycle based WSNs. CKN algorithm aims at allowing a portion of sensor nodes in the WSN to go to sleep but still keeping all the awoken sensor nodes connected. The number of sleeping nodes in the WSN when applying CKN algorithm can be adjusted when changing the value of k. Fig. 1 shows an illustrative example of overlapping clusters in a sleeping scheduled sensor network. In this paper, we are extremely interested in seeing the executing performance of KOCA in duty-cycled WSNs as shown in Fig. 1. Particularly, our interests fall into the following two aspects: 1) Will waking up more sensor nodes increase the average cluster size for any given WSN? 2) Will waking up more sensor nodes increase the cluster overlapping degree for any given WSN? The rest of this paper is organized as follows. Sect. 2 briefly surveys related work on clustering algorithms and overlapping problems. Sect. 3 describes the k-connected

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overlapping problem, and defines system model. In Sect. 4, we further research the problem through simulation experiments, and obtain meaningful results through analysis. Finally, Sect. 5 concludes the paper.

Fig. 1 An example of overlapped clusters in a duty-cycled WSN

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Related work

Clustering plays an important role in energy saving, lifetime extending and better scalability in WSNs. These goals can be achieved by various clustering algorithms proposed in recent decades, and they can be classified into several subsections according to their methodology of selecting CH as well as generating clusters. 2.1 Heuristic algorithms Heuristic algorithms like LCA [6] and its descendants [11], highest-connectivity cluster algorithm [12], are not based on particular metrics, they deal with only some certain parameters. In LCA, each node uses its unique ID to compete for becoming a CH as it either has the highest ID among all neighbor nodes or assumes none of its neighbors are CHs. Similar to LCA, highest-connectivity cluster algorithm chooses the node which is connected more number of nodes as a CH instead of considering ID number in LCA. However, LCA imposes greater communication delay while being applied in larger network, while highest-connectivity cluster algorithm suffers from additional overhead associated with more frequent topology changes. 2.2 Hierarchical clustering Hierarchical schemes are designed to form clusters

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combining both energy reserve and sensors proximity to the CH. LEACH [2] forms clusters according to the received signal strength and uses the CH nodes as routers to the base station. But it does not guarantee good CH distribution and assumes uniform energy consumption for CHs. In contrast, HEED [4] selects well-distributed CHs for WSN, and forms single-hop non-overlapping clusters to extend network lifetime. Unlike LEACH, it does not select CH randomly; only sensors having a high level of residual energy can become CH. Both HEED and LEACH form single-hop non-overlapping clusters with the objective of prolonging network lifetime. If the WSN is densely deployed, single hop clustering may generate so many clusters that may lead to the same condition as if there was no clustering. However, k-OCHE avoids such problems by generating c-hop clusters where c is the upper bound of cluster range associated with the CH's EA. 2.3 Overlapping clustering Although many clustering algorithms have been proposed for WSNs, there is few researchers focus on the overlapping clustering problem. It is desirable and beneficial to have some degree of overlapping clusters in a sensor network. For example, the nodes that lies in overlapping area of clusters which are called boundary nodes here can work as gateways for inter-CHs communication when needed. Moreover, overlapping clusters can not only boost the network robustness against cluster-based routing protocols, but also find their wide usage as node localization and recovery from CH failure and so on. To the best of our knowledge, there is only one clustering algorithm that specifically controls overlapping in the formation of clusters, i.e., KOCA [8]. Goal of KOCA is to ensure that the entire network is covered with connected overlapping clusters considering a specific average overlapping degree. KOCA is still a static clustering which the cluster formation is not changed for all the time. This condition causes an unbalance-load among all nodes. A node that roles as CH will get more load than a non-CH and so that it will die faster. The death of CHs will break the whole network because the link between nodes and center will be broken. k-OCHE allows a node go to sleep while keeping its neighbors k-connected and dynamically selects CHs based on EA, thus the role of CH can be rotated and reduce CHs’ load.

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3.4 An illustrative example

Problem statement

3.1 Network model The WSN can be modeled as a graph G represents a series of sensor nodes, and E Ž V

(V , E ) . V 2

denotes

the aggregation of valid links (edges) among nodes. Two sensor nodes are connected by an edge if they can communicate with each other. We only consider bidirectional links between nodes, and that means the communication is symmetric. k-OCHE operates in quasi-stationary networks where nodes do not move and location-unaware. All nodes transmit at the same fixed transmission power levels, hence nodes have the same radio range r. After the execution of k-OCHE, no nodes are left unattended after deployment. 3.2

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Sleeping scheduling model

To ensure the network connectivity and prolong its lifetime, we assume all nodes operating with CKN [10] based sleep/awake duty-cycling. Time is divided into epochs, and each epoch is T. On each epoch, nodes run CKN to decide whether to be awake. A node can go to sleep assuming that at least k of its neighbors will remain awake to keep it k-connected. And nodes reach a consensus to take turns to sleep while the whole network is globally connected. Therefore, by tuning the value of k, we can manipulate the sleep rate (s), so that to proceed our further work with clustering. 3.3 Notations 1) Network size (n): The number of nodes in the network. Sensor nodes are deployed randomly in a square area with side length of l. Then the node density ( P n / l 2 )

varies with the changing of the network size. 2) Minimum number of awake neighbors in an epoch for each node (k): Through tuning the value of k, we can keep the network k-connected, and optimized the geographic routing performance [1]. 3) Average node degree (d): the average degree of a node u, is the number of its neighbor nodes. The relation between the average node degree (d) and the radio range (r) of a node is given by Ref. [14]: nr 2 (1) d l2

We demonstrate our algorithm with Fig. 1, which is selected from the simulation results optionally. In Fig. 1, there are three clusters with three corresponding CHs A, B, C, nodes with black frame in the overlapping area are boundary nodes. Note that no two CHs are immediate neighbors. Since boundary nodes belong to multiple clusters, their table contains CHs of those clusters. A, B and C can communicate with each other through their common boundary nodes in their CH tables.

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Study method and observations

4.1 Experiment setup To check the impacts of duty-cycle on the KOCA algorithm, we implemented it into our NetTopo WSNs simulator [15, 17], in which the CKN algorithm was implemented. In our work, the number of deployed sensor nodes representing different network sizes (n) are increased from 100 to 1 000 (each time increased by 100). The value of k in CKN algorithm is changed from 1 to 10 (each time increased by 1). The nodes were randomly placed according to a uniform distribution on a 700 m u 700 m area. For every number of deployed sensor nodes, we use 100 different seeds to generate 100 different network deployments. The transmission radius for each node is 60 m. The consumed energy to transmit a l-bit message over distance d is ETX ( L, d ) Eelec u L 

H amp u L u d 2 : and we assume that the radio dissipates Eelec

50 nJ bit to the transmitter or receiver circuity,

H amp

100 pJ bitm 2 , and the data rate is 20 kbit/s.

Through repeated experiments, we assure that each point in the plotted results represents an average of ten simulation runs each with 100 different seeds. 4.2 Result analysis As shown in Fig. 2, nine snapshots of executing the KOCA algorithm in CKN based WSNs are provided. The sleeping rate decreases when k gets bigger which is the result of applying the CKN algorithm. The number of CHs produced by the adding CN algorithm will increase with the growth of k (from 1 to 3) when the network size n remains the same.

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in the KOCA algorithm. It clearly reflects that: for a certain WSN with a particular network density, waking up more sensor nodes can always increase the cluster overlapping degree.

Fig. 4 Overlapping degree vs. the value of k

This research result is meaningful since it provides a new method to further control the average cluster size and cluster overlapping degree in KOCA algorithm.

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Fig. 2 Impact of network connectivity for executing KOCA in a CKN based WSN in different network size n with changed k values

Fig. 3 shows the simulation results of the average cluster size (average number of nodes in a cluster) when the value of k changes for different number of deployed sensor nodes. The observation that we can gain from this simulation result is that: for a certain WSN with a particular network density, waking up more sensor nodes cannot always increase the average cluster size. Instead of that, the average cluster size will reach a relative stable value, and we will work further on it in our future work.

Conclusions

Multi-hop overlapping clustering algorithm (KOCA) is one of the earliest researches that specifically aims at controlling the overlapping degree among clusters, which are generated to cover the entire WSN. A key drawback of the research work is that all sensor nodes are assumed to be always awake during the clustering process, and in this circumstance the sensor network will become unreliable with great responsibility. To balance the unreliability, the sleep scheduling strategy should be applied in WSNs with energy harvesting. In this paper, we study the impacts of duty-cycle on overlapping multi-hop clustering in sensor networks. We find that the average cluster size will reach a relative stable value, and wake up more sensor nodes can always increase the cluster overlapping degree. Acknowledgements This work was supported by the National Natural Science Foundation of China (61070181).

References

Fig. 3 Average cluster size vs. the value of k

Fig. 4 shows the three-dimensional relationship of network size n, k in CKN algorithm, and the cluster overlapping degree (the total number of boundary nodes)

1. Wu X, Shu L, Meng M, et al. Coverage-driven self-deployment for cluster based mobile sensor networks. CIT, Seoul, Korea, 2006 2. Wu X, Niu Y, Shu L, et al. Relay shift based self-deployment for mobility limited sensor networks. UIC, Wuhan, China, 2006 3. Heinzelman W R, Chandrakasan A, Balakrishnan H. Energy efficient communication protocol for wireless sensor networks. HICSS 2000 4. Chan H, Perrig A. ACE: an emergent algorithm for highly uniform cluster formation. EWSN 2004

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each requirement in terms of the degree of beliefs, and the combination of the judgments from multiple requirements with the joint degree of beliefs. The application of DST in other two phases of network selection, i.e. weighting and updating in Fig. 1, were also discussed, which could dynamically update the weight parameters of different requirements and the network conditions from multiple different resources. The simulations in comparison to the latest hybrid MDAM approach verified the proposed DST-based method’s consistence and effectiveness. The work in this paper abstracted the several problems encountered during the different phases of network selection into one common problem, i.e. converging different masses (degrees of beliefs) collected from multiple resources to one joint mass. The application framework, the implementation and the verification are conducted in this paper. Next we’ll further investigate different judgment initiation methods (e.g. the rule-based method [11]) and evaluate their influences on the network selection performance. Also, more detailed analyses will be conducted to evaluate the more inherent features of this method. Acknowledgements This work was supported by the Chinese Universities Scientific Fund, and the Fundamental Research Funds for the Central Universities (BUPT 2012RC0134).

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