Energy efficient region based clustering algorithm for WSN using fuzzy logic

Ahamad, Firoj; Kumar, Rakesh

doi:10.1109/rteict.2016.7807984

Cited by 12 publications

(9 citation statements)

References 12 publications

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“…One cluster Head (CH) has the authority to communicate with the other CH and also with the BS. Simulation result verifies the proposed approach in prolonging the WSNs network lifetime [6].…”

Section: Fig3 Fuzzy Logic Controller Structuresupporting

confidence: 57%

Fuzzy C-Means Clustering for Enhancement of Energy in Wireless Sensor Network

Jain¹

2017

IJRASET

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Wireless Sensor Networks (WSNs) consists of a set of sensor nodes that are deployed in a field and interconnected with a wireless communication network. Each of these scattered sensor nodes have the talents to collect information, fuse that data and path the data again to the sink/base station. To collect data, each of these sensor nodes makes decision based on its observation of a part of the environment and on partial a-priori information. In existing work, they used fuzzy logic technique for the cluster head selection to increase lifetime of wireless sensor network. But they used fuzzy based technique like distance of base station and residual energy for selection of cluster head and sometimes one or more cluster heads are formed in the grid. This increase the energy consumption of nodes as nodes has to send data from multiple cluster heads. In proposed work, we used fuzzy-c means for the formation of clusters. Initially deployed the cluster head in the centre of every cluster in every grid and then we select another cluster head on the basis of residual energy and distance with base station. This improves the network lifetime and improvement in the energy consumption of each node to communicate efficiently. We used MATLAB for the simulation to show that our proposed work is much better than the existing techniques. Keywords: Wireless Sensor Network, Clustering, Fuzzy logic, Membership function. I. INTRODUCTION Wireless Network is a type of Computer Network that provides communication between a numbers of nodes without having aPhysical Connectivity between these nodes. No nodes are connected through a Physical Medium to communicate with each other. Rather they use wireless mediums such as air / atmosphere to transmit the data from one node to another. Commonly used wireless transmission mediums encompass Microwave Communication, Radio Wave Communication, Satellite Communication, and many others. WSN is a sub magnificence of Wireless Networks which have the same working principle but are slightly clever or better compared to the conventional Wireless Networks. A Wireless Sensor Network includes spatially disbursed sensors referred to as Sensor Nodes that senses and monitors the environmental situations at the side of communicating with the other nodes or sharing the data between a numbers of nodes. Wireless Sensor Networks (WSNs) consists of a set of sensor nodes that are deployed in a field and interconnected with a wireless communication network. Each of those scattered sensor nodes have the capabilities to acquire statistics, fuse that data and course the data back to the sink/base station. To collect data, each of these sensor nodes makes decision based on its observation of a part of the environment and on partial a-priori information. As large amount of sensors are deployed in harsher environment, it is vital that the distributed computation need to be robust and fault-tolerant. The identification of event in a wireless sensor network should be done as fast as possible, thus the computations ar...

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Section: Fig3 Fuzzy Logic Controller Structuresupporting

confidence: 57%

Fuzzy C-Means Clustering for Enhancement of Energy in Wireless Sensor Network

Jain¹

2017

IJRASET

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“…In [22][23][24][25][26][27], fuzzy logic was employed in clustering algorithms to handle the uncertainties in WSNs. Bezdek [22] proposed a fuzzy logic-based clustering and data processing in WSNs.…”

Section: Related Workmentioning

confidence: 99%

Spectral partitioning and fuzzy C-means based clustering algorithm for big data wireless sensor networks

Wang

Guo

et al. 2018

J Wireless Com Network

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In wireless sensor networks, sensor nodes are usually powered by battery and thus have very limited energy. Saving energy is an important goal in designing a WSN. It is known that clustering is an effective method to prolong network lifetime. Due to the development of big data, there are more sensor nodes and data needed to process. So how to cluster sensor nodes cooperatively and achieve an optimal number of clusters in a big data WSN is an open issue. In this paper, we first propose an analytical model to give the optimal number of clusters in a wireless sensor network. We then propose a centralized cluster algorithm based on spectral partitioning method. After that, we present a distributed implementation of the clustering algorithm based on fuzzy C-means method. Finally, we conduct extensive simulations, and the results show that the proposed algorithms outperform the hybrid energy-efficient distributed (HEED) clustering algorithm in terms of energy cost and network lifetime.

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“…Firoj Ahamad et al [12], proposed a method to extend the WSN lifetime by means of fuzzy logic based selection of CH which provides an absolutely non-conventional probabilistic methodology. This methodology uses the residual energy of the node and the distance from the sink, as two fuzzy variables.…”

Section: Literature Reviewmentioning

confidence: 99%

Pil-Leach Model for Increasing Energy Efficiency in Wireless Sensor Network

Ponnada¹

2018

ijarcs

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Wireless Sensor Networks (WSN) play a vital role in the modern arena. WSN is deployed in the various vital areas such as military operations, health care, surveillance, home application and environmental applications. WSN mainly works on battery power, whose capacity is limited. Energy efficiency is one of the main issues in wireless sensor networks. Various routing techniques are developed to achieve maximum reliability, network lifetime and scalability. Leach is one of the clustering convention used in wireless sensor networks. Even, after its existence of more than 17 years, Leach protocol grabs attention and interest of research community. Our research area is confined to power management. This paper proposes a technique called as Improved Lifetime -Leach with Particle Swarm Optimization (PIL-LEACH) to smoothen the associated information transmission inside the group and, to control the problems such as correlated data, cluster head selection and large distance transmission of the packet in Leach. Particle Swarm Optimization (PSO) algorithm selects the cluster head based on various parameters of sensor nodes such as residual energy, distance from base sink etc. This approach admits that we know the area of the sensor hubs. It uses the two-tier architecture of clusters heads which are far away from the sink in order to mitigate the energy losses.

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Energy efficient region based clustering algorithm for WSN using fuzzy logic

Cited by 12 publications

References 12 publications

Fuzzy C-Means Clustering for Enhancement of Energy in Wireless Sensor Network

Fuzzy C-Means Clustering for Enhancement of Energy in Wireless Sensor Network

Spectral partitioning and fuzzy C-means based clustering algorithm for big data wireless sensor networks

Pil-Leach Model for Increasing Energy Efficiency in Wireless Sensor Network

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