Result: AI-Driven Energy-Efficient Routing in IoT-Based Wireless Sensor Networks: A Comprehensive Review

Title:
AI-Driven Energy-Efficient Routing in IoT-Based Wireless Sensor Networks: A Comprehensive Review
Authors:
Thakur, Sumendra, Sarkar, Nurul I, Yongchareon, Sira
Publisher Information:
MDPI AG
Publication Year:
2025
Collection:
Auckland University of Technology: AUT Scholarly Commons
Subject Terms:
0301 Analytical Chemistry, 0502 Environmental Science and Management, 0602 Ecology, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering, Analytical Chemistry, 3103 Ecology, 4008 Electrical engineering, 4009 Electronics, sensors and digital hardware, 4104 Environmental management, 4606 Distributed computing and systems software
Document Type:
Academic journal article in journal/newspaper
File Description:
application/pdf
Language:
English
Relation:
https://doi.org/10.3390/s25247408; https://hdl.handle.net/10292/20335
DOI:
10.3390/s25247408
Availability:
https://hdl.handle.net/10292/20335
https://doi.org/10.3390/s25247408
Rights:
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). ; https://creativecommons.org/licenses/by/4.0/ ; OpenAccess
Accession Number:
edsbas.69EA28A1
Database:
BASE

Further information

<jats:p>Efficient routing remains the linchpin for achieving sustainable performance in Wireless Sensor Networks (WSNs) within the Internet of Things (IoT). However, traditional routing mechanisms increasingly struggle to cope with the growing complexity of network architectures, frequent changes in topology, and the dynamic behavior of mobile nodes. These issues contribute to data congestion, uneven energy consumption, and potential communication breakdowns, underscoring the urgency for optimized routing strategies. In this paper, we present a comprehensive review of over 100 studies of spanning conventional and AI-enhanced energy-efficient routing techniques. It covers diverse approaches, including metaheuristics, machine learning, reinforcement learning, and AI-based cross-layer methods aimed at improving the performance of WSN-IoT systems. The key limitations of existing solutions are discussed along with performance metrics such as scalability, energy efficiency, throughput, and packet delivery. We also highlight various research challenges and provide research directions for future exploration. By synthesizing current trends and gaps, we provide researchers and practitioners with a structured foundation for advancing intelligent, energy-conscious routing in next-generation IoT-enabled WSNs.</jats:p>