Date of Award

4-4-2024

Document Type

Thesis

School

School of Computing

Programme

Ph.D.-Doctoral of Philosophy

First Advisor

Dr.B.Santhi

Keywords

Sensor Network, Clustering, Routing, Multi Base Station, Load Balancing, Sensor Cloud Gateway, Data Compression, Cloud Storage, Data Decompression, Lifespan

Abstract

Wireless Sensor Networks (WSNs) is created, stemming from their applications in distinct areas. This research focuses on implementing an efficient clustering and routing protocols to maximize the lifespan of the WSN by proposing a novel method known as the Energy Efficient Cluster-aware Routing Protocol (EECR). The proposed method comprises of three steps: cluster formation, cluster head (CH) selection, and multi-hop data transmission. The factors needed are residual energy, the minimum distance to the base station (BS), and the minimum Load Count as given in the Energy and Distance CH selection algorithm. The shortest pathway is estimated by the Energy Route Request Adhoc On demand Distance Vector (ERRAODV) algorithm.

In Multi Base Station Energy Efficient Cluster-aware Routing (MBS-EECR) algorithm is developed to overcome the problem that occurs due to the relay transmission over a period of time, if all the clusters nearer to the BS reach the maximum threshold value of load count, then the network doesn't permit the data transmission further to take an alternate route of data packets. The result is data loss, and its always reduces the network performance. In the multi-base station load balancing mechanism (MBSLBM), an algorithm is used to dynamically assign the work load to other base stations.

Cloud preserving real time data efficiently, since local servers cannot maintain the immense volumes of data. The middleware named as sensor cloud gateway (SCG) compress the data from WSN, it procures better compression. The proposed Novel lossless Advanced Neighborhood Indexing Scheme (ANIS) compresses the data efficiently, which minimizes data size with storage requisites.

The ANIS technique introduced here amends the compression ratio to 80.73% from 78.31%, which is obtained from an existing New Lossless Neighborhood Indexing scheme (NIS) algorithm. The proposed algorithms are achieved the objective of maximizing the life span, reducing energy consumption and identify the node failure, and then handling the data communication problem in WSNs.

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