Visible to the public A Bi-objective Routing Model for Underwater Wireless Sensor Network

TitleA Bi-objective Routing Model for Underwater Wireless Sensor Network
Publication TypeConference Paper
Year of Publication2019
AuthorsPersis, D. Jinil
Conference NameProceedings of the 2019 3rd International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence
Date Publishedmar
PublisherAssociation for Computing Machinery
Conference LocationMale, Maldives
ISBN Number978-1-4503-7211-4
Keywordsacoustic signal, composability, compositionality, pubcrawl, Routing, sensor, swarm intelligence, UWSN
AbstractUnderwater wireless communication is a critical and challenging research area wherein acoustic signals are used to transfer data. The Underwater Wireless Sensor Network (UWSN) is used to transmit data sensed by the sensors in the sea bed to the surface sinks through intermediate nodes for seismic surveillance, border security and underwater environment monitoring applications. The nodes comprising of UWSN are battery operated and are subjected to failures leading to connectivity loss. And the propagation delay in sending the data in the form of acoustic signals is found to be high and as the depth increases the transmission delay also increases. Hence, routing in UWSN is a complex problem. The simulation experiments of the delay sensitive protocols are found to minimize the delay at the expense of network throughput which is not acceptable. The energy aware routing protocols on the other hand reduces energy consumption and routing overhead but has high delay involved in transmission. In this study, transmission delay and reliability estimation models are developed using which bi-objective routing model is proposed considering both delay and reliability in route selection. In the simulation studies, the bi-objective model reduced delay on an average by 9% and the reliability of the network is improved by 34% when compared to the delay sensitive and reliable routing strategies.
URLhttps://doi.org/10.1145/3325773.3325786
DOI10.1145/3325773.3325786
Citation Keypersis_bi-objective_2019