CA-MWSN: Clustering Algorithm for Mobile Wireless Sensor Network

CA-MWSN: Clustering algorithm for mobile wireless sensor network

Priti Kumari M. P Singh School of Computing Computer Science Engineering IIT, Mandi NIT, Patna

Abstract-This paper proposes a network capacity, outage probability, centralized algorithm for cluster-head performance analysis, and optimization, selection in mobile wireless sensor MAC/network layer cooperative network using fuzzy logic. Before communications, security and privacy execution of algorithm in each round, [2].Among those research areas, energy sensor nodes update their locations to conservation is one of the prominent base station. Base station runs CA- challenges as network lacks the battery MWSN, the proposed algorithm using recharging option. Cluster-based sensor three fuzzy inputs Residual energy, network is considered here among various Expected Residual Energy and Mobility wireless sensor network topology like to find Chance of nodes to be elected as cluster-based sensor network, sensor Cluster-head. The node with highest network with a fusion centre, and chance is declared as a Cluster-head for concatenated sensor network as earlier particular sub-divisions. experiments show that Clustering of sensor nodes saves energy by 8 times [3].

This paper considers design and Keywords- Mobile wireless sensor implementation of a Clustering Algorithm network, fuzzy logic, cluster-head, CA- in Mobile Wireless Sensor Network (CA- MWSN, centralized algorithm MWSN). There are numerous issues which need to be pondered before designing any clustering algorithm and these issues differ 1. INTRODUCTION for underlying network. In homogenous network, the role of cluster-head certainly Due to omnipresence impact of is transferred to avoid energy shortage of networks in our day-to-day activities, an individual node. In case of mobile Mobile wireless sensor network has wireless sensor networks, topology of distinct hot research areas these days like network are supposed to change as in our node localization and discovery algorithms case all the sensor nodes including cluster- [1], techniques for energy utilization and heads are movable. In such scenario, conservation, resource allocation and cluster-radius and location of cluster-head optimization, distributed deployment are thought to be decided dynamically strategies, cognitive radio looking over current positions of sensor techniques ,Dynamic adjusting strategies, nodes. And this gives uniform distribution network coding and its applications, of cluster-heads and better coverage either Evolving needs of human being has locally and globally. speed up researches in the area of mobile wireless sensor network. Though In case of Leach [3], if randomly techniques and yield grew up but generated probability did not lie below challenges also multiplied as mobility threshold, no cluster-head would be introduced. In the context of clustering chosen. This vagueness is overcome in algorithms, some algorithms are CA-MWSN by definite selection of advancement of LEACH [3], like LEACH- cluster-head in each round until expected M (Leach-Mobile) [4] and LEACH-ME residual energy goes below to ‘low’. (Leach-Mobile-Enhanced) [5]. LEACH-M The rest of this paper is organized as [4] is similar to LEACH [3] except the fact follows. Section 2 gives the idea of that sensor nodes are mobile and due to working of algorithms previously designed mobility sensor nodes change their for mobile wireless sensor network. position plus cluster-heads sometimes. Section 3 tells about pre-conditions Hence before transmitting data, each required before executing CA-MWSN sensor node confirms about its cluster- algorithm. Section 4 describes the premise head firstly. And cluster heads removes of algorithm CA-MWSN. Section 5 mobile nodes from their node list if no concludes the paper and gives some data is received in continuous two time prospects for future work. slots. In Leach-ME [5], before election of cluster-head mobility factor of node is 2. RELATED WORK seen. Mobility factor is calculated in terms of remoteness where remoteness is distance between two nodes. Mobility factor, energy level and transition count are decidability causes for selection of cluster-head. Apart from Leach-based protocols, there is LUDC (Location- Unaware distributed clustering algorithm for mobile wireless sensor networks using fuzzy logic) [6] in which cluster-head of a node is selected on the basis of four fuzzy descriptors: energy level, mobility, quality of link and received signal strength. There is another algorithm ACE-L (Algorithm for Cluster-head Election with Location) [7] which are suitable for pre-defined mobile wireless sensor network. For the selection of cluster-head, location of node is considered as it can be anticipated by name. There are some fixed reference points corresponding to those cluster-heads are chosen. But in case of gathering of judging the location of all nodes in sensor nodes at one place which is possible network, base station divides the network be as nodes are movable, one cluster-head into certain parts dynamically. For each corresponding to one reference point may part, a cluster-head is decided. Dynamic give lower performance. division of network into certain parts provides better coverage and energy Hitherto, algorithms are not discerning balance because base station keeps global the real problem or property of mobile knowledge of network and if there is wireless sensor network i.e. movement of accumulation of nodes cluster-radius is sensor nodes which should be considered made sort. Once network gets divided into firmly during design of an algorithm sub-parts, nodes belonging to a particular because logistics changes holistically in sub-part send information of their Residual case of random motion probability. Energy (RE) and Mobility as shown in Scenarios changes in following respects: Figure 1. 1. If sensor nodes become dense in particular area, more number of cluster-heads is needed than earlier.

2. If sensor nodes become sparse in particular area, one cluster-head is Figure 1. Round phases of cluster-head able to cover larger area than selection earlier.

This paper tries to resolve this issue along with proper energy usage. Most of the On the basis of residual energy, what calculations are performed at the base will be Expected Residual energy (ERE) station which is considered as source of after next round is calculated. If ERE falls infinite energy. below Threshold energy, node is discarded 3. Pre-conditions from being competitor for cluster-head. The Threshold Energy is the minimum 1. All nodes in the network are energy required by a node to run a cluster- homogeneous and energy constrained. head round. Although, Threshold Energy is variable because it depends on number Nodes and cluster-heads are 2. of nodes forming a cluster and distance of movable. node from base station, however in the 3. Base station is static. interest of maintaining simplicity of algorithm it can be considered as fixed 4. Each node is equipped with amount of energy. Base station calculates location finding system. Expected Residual Energy by following formula for round ‘r’:

Expected Residual Energy (r) = Before execution of algorithm, it is Residual Energy (r) – Energy assumed that all nodes must send their location information to all nodes and on Consumption (r) …… (1) Where Energy Consumption can be 5 for Aj (j: =1 to k) calculated depending on either node is { going to work as cluster-head or as 6 for Ajk (k: =1 to P) stationary node. {

7 Send A1k (RE, M) -> BS

8 ERE (A1k):= RE (A1k) – EC

9 If (ERE (A1k)) < TH 4. The proposed algorithm CA- 10 k++ MWSN 11 else 12 FIE (ERE (A ), RE (A ), M (A )):= x[k] CA-MWSN is an algorithm designed for 1k 1k 1k 13 k++ mobile wireless sensor network which uses 14 Max x[k] fuzzy logic to find the cluster-head. There 15 k -> CH of A1 are three fuzzy descriptors Residual 16 j++ energy, Expected Residual Energy and } Mobility with which fuzzy inference } engine deals to decide ‘chance’ of a node } for being cluster-head. The node with highest ‘chance’ is selected as cluster- head. Cluster-head works for a round and 5. Conclusion and future work after completion of round becomes a This algorithm is able to satisfy the general node and again participate in the rational of proper coverage which is quite election for cluster-head. The CA-MWSN difficult in case of mobile sensor nodes. algorithm is depicted below: Location of base station and other sensor A- Total area nodes are considered before selection of

Ai- Sub-area cluster-heads whereby energy balance of N- Total number of nodes all alive nodes can be maintained. P- Total number of nodes in sub-area However, execution of this algorithm can RE- Residual Energy result in selection of two cluster-heads ERE- Expected Residual Energy which may reside quite nearer to each – EC- Energy Consumption other, though residing in different sub- TH- Threshold Energy M- Mobility areas. Ergo, removal of this drawback is BS- Base station subsequent prospective of this work. In X- x-coordinate of node future, this very algorithm can be Y- y-coordinate of node augmented with the selection of optimal FIE- Fuzzy Inference Engine fuzzy set and back-up cluster head. CH- Cluster head Choosing speed of nodes with random probability can be replaced with different ALGORITHM: CA-MWSN (A, N) walk models. Multi-hop architecture can 1 for (i: =1 to N) also be supported with CA-MWSN in { coming time. 2 Send i (X, Y) -> BS ACKNOWLEDGEMENTS 3 i++

4 BS divides A into Aj…………Ak The authors would like to thanks Ravi the 33rd Annual Hawaii International Conference on , vol., no., pp.10 pp. vol.2,, 4-7 Jan. 2000. Kumar Gupta, Sachin, Shashi Ranjan and Shashi Shekhar for their valuable [4]. Do-Seong Kim, Yeong-Jee Chung, “Self- assistance. Organization Routing Protocol Supporting Mobile Nodes for Wireless Sensor Network”, in Proc. Computational Sciences IMSCCS, 2006, pp.622-626.

[5]. G. Santhosh Kumar, Vinu Paul M V, K. Poulose Jacob, Mobility Metric based LEACH-Mobile Protocol, 16th International Conference on Advanced Computing and References Communications, 2008 IEEE.

[6]. C.-M. Liu, C.-H. Lee, L.-C. Wang, Power-efﬁcient Akcan, Hüseyin, et al. "GPS-Free node localization [1]. communication algorithms for wireless mobile sensor networks, in mobile wireless sensor networks." Proceedings of the 5th in: Proceedings of the 1st ACM International Workshop on ACM international workshop on Data engineering for wireless Performance Evaluation of Wireless Ad hoc, Sensor, and and mobile access. ACM, 2006. Ubiquitous Networks, 2004, pp. 121–122.

[2]. Ren, Yi, et al. "Security in mobile wireless sensor [7]. Chuan-Ming Liu, Chuan-Hsiu Lee, Li-Chun Wang, networks–a survey." Journal of Communications 6.2 (2011): Distributed clustering algorithms for data-gathering in wireless 128-142. mobile sensor networks, J. Parallel Distrib. Comput. 67 (2007) 1187 – 1200, Elsevier. [3]. Heinzelman, W.R.; Chandrakasan, A.; Balakrishnan, H., "Energy-efficient communication protocol for wireless microsensor networks," System Sciences, 2000. Proceedings of