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Optimisation of data reliability in UASN using adaptive Buffalo algorithm - 2021

Research Area:  Wireless Sensor Networks

Abstract:

Underwater acoustic network is introduced in recent times to explore the resources available in an underwater environment. The scientific data are collected, and the data packets are transmitted to the onshore base station through the acoustic network. The underwater acoustic network faces several challenges like higher propagation delay, increased energy consumption and limited bandwidth availability due to harsh environmental condition. The most notable limitation is to forward the data packets efficiently from the underwater sensor node to the onshore sink node. Identifying energy-efficient path is not enough to achieve maximum efficiency. The Routing protocol developed for the underwater environment must overcome link failure due to congestion, harsh environment or due to energy drop out. A new optimisation protocol is developed based on biological inspiration over the characteristics of African Buffalo, and it is named as Buffalo optimisation algorithm (BOA). In underwater acoustic network, most of the energy is wasted due to the occurrence of collision in the intermediate routing nodes. And uneven traffic generation is also increasing the possibilities of occurrence of early dead nodes. To overcome all these limitations, BOA utilises lookup table based multipath communication. The algorithm is implemented in NS2 simulator, and the performance was compared with the conventional algorithms like vector based flooding protocol, depth-based forwarding protocol and channel aware routing protocol. The simulation results show throughput, packet delivery ratio increased by 10% and 12% respectively. The end-to-end delay and overhead reduced by 8% and 4% respectively. Furthermore, the test bed is developed to identify the performance of the buffalo optimisation protocol in a hardware environment. Comparative analysis of BOA with fore mentioned algorithms shows, energy consumption reduced by13% and response time improved by 3%.

Author(s) Name:  M. Ayyadurai & S. Selvakumar Raja

Journal name:  Journal of Ambient Intelligence and Humanized Computing

Conferrence name:  

Publisher name:  Springer

DOI:  10.1007/s12652-020-02018-7

Volume Information:  volume 12, pages 5359–5370 (2021)