Clustering Architectures for Dense Wireless Sensor Networks

International Journal for Research in Applied Science & Engineering Technology (IJRASET), Volume 5, Issue 1, January 2017

122 Pages Posted: 3 Feb 2017

Date Written: January 1, 2017

Abstract

A wireless sensor is a miniature component which measure physical parameters from the environment and transmit them to the monitoring station by wireless medium. In wireless medium, the sensor and its associated components are called as node. A node is self-possessed by a sensor, processor, local memory, transceiver and a low-powered battery.

To diminish the data transmission time and energy consumption, the sensor nodes are assembled into a number of little groups referred as clusters and the phenomenon is referred as clustering. Every cluster comprise of a leader which is known as cluster head. The cluster head will be chosen by the sensor nodes in the individual cluster or be pre-assigned by the user. The main advantages of clustering are the transmission of aggregated data to the base station, offers scalability for huge number of nodes and trims down energy consumption. Fundamentally, clustering could be classified into centralized clustering, distributed clustering and hybrid clustering. In centralized clustering, the cluster head is fixed. The rest of the nodes in the cluster act as member nodes. In distributed clustering, the cluster head is not fixed. The cluster head keeps on shifting form node to node within the cluster on the basis of some parameters. Hybrid clustering is the combination of both centralized clustering and distributed clustering mechanisms.

A distributed clustering methodology, the hybrid energy efficient clustering algorithm (HEECA) has been investigated. The proposed methodology is a well-distributed and energy-efficient clustering algorithm which employs three novel techniques: zone based transmission power (ZBTP), routing using distributed relay nodes (DRN) and rapid cluster formation (RCF). The proposed methodology is compared with the two well-evaluated existing distributed clustering algorithms O-LEACH and hybrid energy efficient distributed clustering (HEED). The proposed methodology shows an improvement in residual energy, throughput and energy efficiency of the wireless sensor system. The clustering process could be effectively controlled, thereby the number of cluster head selection and the number of packets delivered to the base station shall be carried out effectively. Ultimately, the overall lifetime of the wireless sensor network is much improved. The distributed relay nodes employed in the proposed methodology could effectively connect two separate wireless sensor network fields with reduced packet loss and forms a better alternate to optical fiber link.

A distributed clustering methodology, the variable power energy efficient clustering (VEEC) mechanism has been proposed. The proposed algorithm is a well distributed and energy efficient clustering algorithm which employs relay nodes, variable transmission power and single message transmission per node for cluster set-up. The performance of the proposed methodology is compared with two existing distributed clustering algorithms LEACH and HEED. The proposed methodology depicts an improvement in average communication energy and total system energy consumption. Ultimately, the overall network lifetime is much prolonged in VEEC methodology.

A distributed clustering methodology, the energy efficient hierarchical distributed clustering algorithm (EHDCA) has been proposed. It is a well-distributed clustering mechanism and the cluster head selection is based on residual energy, communication cost and the distance to the base station. The main characteristic feature of the proposed methodology is the cluster head selection is carried out in just few steps. The performances of the proposed clustering methodology have been compared with LEACH. Its hierarchical nature shall be effectively employed for reduction in total energy consumption and backbone energy consumption. The energy efficiency and overall network lifetime shall be greatly improved.

A dynamic clustering algorithm for mobile wireless sensor networks, the mobility assisted dynamic clustering algorithm (MADCA) has been investigated and analysed properly. The proposed methodology is hierarchical, dynamic and energy efficient algorithm. This technique exhibits multiple clusters, with each cluster having one cluster head and two deputy cluster heads. The sensors start collecting the data only when the base station comes in range with the cluster head. The performance of the proposed algorithm has been evaluated against the existing LEACH-Mobile (LEACH-M) algorithm. This methodology shows a large reduction in average communication energy and node death rate. The network lifetime has been prolonged by integrating the novel concepts to the proposed methodology, thereby finds useful when both the sensor nodes as well as the base station are mobile. The research works reported in this monograph gives a clear view on the proposal of few distributed clustering methodologies and the manner by which the clustering parameters could be improved for both static and mobile wireless sensor networks. All the simulation works have been carried out using the network simulator (NS-2) and the results have been compared with the existing distributed clustering methodologies. Every modules concentrates mainly on the betterment of energy efficiency, throughput, clustering efficiency and network lifetime. Also, few future enhancements to this work have been entailed for giving further progression to these proposed distributed clustering methodologies.

Suggested Citation

Prabhu, S.R.Boselin, Clustering Architectures for Dense Wireless Sensor Networks (January 1, 2017). International Journal for Research in Applied Science & Engineering Technology (IJRASET), Volume 5, Issue 1, January 2017. Available at SSRN: https://ssrn.com/abstract=2909558

S.R.Boselin Prabhu (Contact Author)

VSB College of Engineering ( email )

Coimbatore, Tamil Nadu
India

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