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Title: | Realistic Cluster-Based Energy-Efficient and Fault-Tolerant (RCEEFT) Routing Protocol for Wireless Sensor Networks (WSNs) |
Authors: | Effah, Emmanuel Thiare, Ousmane |
Keywords: | Spatial correlation model (SCM), Mass measurement pattern and accuracy concept (MMP&AC), Event reporting (ER) |
Issue Date: | Feb-2020 |
Publisher: | Springer Link |
Abstract: | Despite the numerous research advances in multichannel event reporting (ER) protocols in Wireless Sensor Networks (WSNs), the core issues such as effective energy consumption management, balanced network-wide energy depletion rates, fault-avoidance-based fault tolerance (FT) for network reliability and elimination of needless data redundancies in ER remain challenges that have not received adequate and holistic research considerations commensurable with the recent technological advancements and the skyrocketing demands and permeance of WSN�s applications in all fields of life. RCEEFT is an adaptive cluster-based data acquisition and multichannel routing protocol for WSNs which integrates unique measures to ensure peerless improvement in WSNs� energy consumption, balanced network-wide energy depletion rates (improved sensor field coverage), fault-avoidance-motivated FT and elimination of needless data redundancies in ER (enhanced accuracy and precision in ER) to extend the WSNs� lifespan. The experimental results obtained through simulations established RCEEFT as a more realistic protocol and also performs better than EESAA, DEEC, E-DECC, SEP, M-GEAR, D-DEEC, T-DEEC and LEACH in terms of energy efficiency and WSN lifetime, wider coverage sensitivity, balanced network-wide energy depletion rates, FT and elimination of needless data redundancies in ER. |
Description: | Conference paper & Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1129): https://link.springer.com/chapter/10.1007/978-3-030-39445-5_25 |
URI: | http://52.157.139.19:8080/xmlui/handle/123456789/86 |
Appears in Collections: | ICTs including Big Data and Artificial Intelligence |
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