Fuzzy Logic Implemented Routing Techniques for Underwater Wireless Sensor Networks

Bhaskarwar, Roshani V.; Pete, Dynandeo J.

doi:10.1109/incet51464.2021.9456144

Cited by 3 publications

(2 citation statements)

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“…The fuzzy logic system (FLS) is depicted in Figure 1. FLS is divided into four parts as explained below 18 Fuzzifier: Nonfuzzified inputs get fuzzified using fuzzifier.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy‐based unequal clustering protocol for underwater wireless sensor networks

Panchal,

Gajjar

2023

Int J Communication

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SummaryUnderwater wireless sensor network (UWSN) is a network made up of underwater sensor nodes, anchor nodes, surface sink nodes or surface stations, and the offshore sink node. Energy consumption, limited bandwidth, propagation delay, high bit error rate, stability, scalability, and network lifetime are the key challenges related to underwater wireless sensor networks. Clustering is used to mitigate these issues. In this work, fuzzy‐based unequal clustering protocol (FBUCP) is proposed that does cluster head selection using fuzzy logic as it can deal with the uncertainties of the harsh atmosphere in the water. Cluster heads are selected using linguistic input variables like distance to the surface sink node, residual energy, and node density and linguistic output variables like cluster head advertisement radius and rank of underwater sensor nodes. Unequal clustering is used to have an unequal size of the cluster which deals with the problem of excess energy usage of the underwater sensor nodes near the surface sink node, called the hot spot problem. Data gathered by the cluster heads are transmitted to the surface sink node using neighboring cluster heads in the direction of the surface sink node. Dijkstra's shortest path algorithm is used for multi‐hop and inter‐cluster routing. The FBUCP is compared with the LEACH‐UWSN, CDBR, and FBCA protocols for underwater wireless sensor networks. A comparative analysis shows that in first node dies, the FBUCP is up to 80% better, has 64.86% more network lifetime, has 91% more number of packets transmitted to the surface sink node, and is up to 58.81% more energy efficient than LEACH‐UWSN, CDBR, and FBCA.

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“…The fuzzy logic system (FLS) is depicted in Figure 1. FLS is divided into four parts as explained below 18 Fuzzifier: Nonfuzzified inputs get fuzzified using fuzzifier.…”

Section: Introductionmentioning

confidence: 99%

“…FLS is divided into four parts as explained below. 18 1. Fuzzifier: Nonfuzzified inputs get fuzzified using fuzzifier.…”

mentioning

confidence: 99%