TY - JOUR
T1 - A NOVEL REINFORCEMENT LEARNING BASED ROUTING ALGORITHM FOR ENERGY MANAGEMENT IN NETWORKS
AU - Çiğdem, Eriş
AU - Gül, Ömer Melih
AU - Bölük, Pinar Sarisaray
N1 - Publisher Copyright:
© (2024), (American Institute of Mathematical Sciences). All rights reserved.
PY - 2024/12
Y1 - 2024/12
N2 - Underwater wireless sensor networks (UWSNs) have the potential to provide environmental data for various applications, including studies related to environmental changes, early warning systems, and monitoring in industry. Continuous delivery of information in these contexts is paramount. UWSNs comprise the fundamental assets in these applications. However, the peculiar characteristics of underwater require sensor nodes to rely on their limited battery reserves. Consequently, energy management in these networks becomes a critical resource allocation problem within underwater sensor networks. To address this decision making problem, cluster-based network routing protocols have been extensively explored as a technology to minimize network energy consumption. Cluster heads (CHs) are employed to aggregate data and reduce overall energy usage, thus prolonging the network’s lifespan. On the other hand, the focus on harvesting energy from ambient resources underwater has gained attention as a means to extend the operational life of sensor nodes in the distributed communication network system. This paper considers the stochastic energy harvesting process at each sensor node, specifically addressing the energy-aware routing problem in underwater acoustic sensor networks (UASNs). The contribution of this work lies in proposing a novel reinforcement learning-based algorithm for determining cluster heads (CHs), which involves not only considering the nodes’ positions and residual energy but also accounting for the expected harvested energy. Numerical results validate that our introduced approach significantly decreases energy consumption and substantially extends the network’s operational lifetime considerably.
AB - Underwater wireless sensor networks (UWSNs) have the potential to provide environmental data for various applications, including studies related to environmental changes, early warning systems, and monitoring in industry. Continuous delivery of information in these contexts is paramount. UWSNs comprise the fundamental assets in these applications. However, the peculiar characteristics of underwater require sensor nodes to rely on their limited battery reserves. Consequently, energy management in these networks becomes a critical resource allocation problem within underwater sensor networks. To address this decision making problem, cluster-based network routing protocols have been extensively explored as a technology to minimize network energy consumption. Cluster heads (CHs) are employed to aggregate data and reduce overall energy usage, thus prolonging the network’s lifespan. On the other hand, the focus on harvesting energy from ambient resources underwater has gained attention as a means to extend the operational life of sensor nodes in the distributed communication network system. This paper considers the stochastic energy harvesting process at each sensor node, specifically addressing the energy-aware routing problem in underwater acoustic sensor networks (UASNs). The contribution of this work lies in proposing a novel reinforcement learning-based algorithm for determining cluster heads (CHs), which involves not only considering the nodes’ positions and residual energy but also accounting for the expected harvested energy. Numerical results validate that our introduced approach significantly decreases energy consumption and substantially extends the network’s operational lifetime considerably.
KW - Communication networks in operations research
KW - Network design and communication in computer systems
KW - Wireless sensor networks
KW - decision making
KW - distributed Systems
KW - energy management
KW - network protocols
KW - performance evaluation and scheduling
KW - resource and cost allocation
KW - underwater acoustic sensor networks
UR - http://www.scopus.com/inward/record.url?scp=85199556904&partnerID=8YFLogxK
U2 - 10.3934/jimo.2024049
DO - 10.3934/jimo.2024049
M3 - Article
AN - SCOPUS:85199556904
SN - 1547-5816
VL - 20
SP - 3678
EP - 3696
JO - Journal of Industrial and Management Optimization
JF - Journal of Industrial and Management Optimization
IS - 12
ER -