An intelligent fault detection approach based on reinforcement learning system in wireless sensor network
Abstract The Internet of Things (IoT) has developed a well-defined infrastructure due to
commercializing novel technologies. IoT networks enable smart devices to compile …
commercializing novel technologies. IoT networks enable smart devices to compile …
DRSIR: A deep reinforcement learning approach for routing in software-defined networking
DM Casas-Velasco, OMC Rendon… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Traditional routing protocols employ limited information to make routing decisions, which
leads to slow adaptation to traffic variability and restricted support to the quality of service …
leads to slow adaptation to traffic variability and restricted support to the quality of service …
Multi-agent deep reinforcement learning for packet routing in tactical mobile sensor networks
Tactical wireless sensor networks (T-WSNs) are used in critical data-gathering military
operations, such as battlefield surveillance, combat monitoring, and intrusion detection …
operations, such as battlefield surveillance, combat monitoring, and intrusion detection …
Intelligent routing algorithm for wireless sensor networks dynamically guided by distributed neural networks
Z Liu, Y Liu, X Wang - Computer Communications, 2023 - Elsevier
Using reinforcement learning to adjust the power balance of sensor nodes dynamically is an
essential approach for extending the lifetime of wireless sensor networks (WSNs), which …
essential approach for extending the lifetime of wireless sensor networks (WSNs), which …
A Q-learning-based routing approach for energy efficient information transmission in wireless sensor network
Nowadays, wireless sensor networks have played an important role in many applications. In
these applications, a large number of wireless sensors are deployed in an environment to …
these applications, a large number of wireless sensors are deployed in an environment to …
A centralized routing for lifetime and energy optimization in wsns using genetic algorithm and least-square policy iteration
Q-learning has been primarily used as one of the reinforcement learning (RL) techniques to
find the optimal routing path in wireless sensor networks (WSNs). However, for the …
find the optimal routing path in wireless sensor networks (WSNs). However, for the …
Reinforcement learning aided routing in tactical wireless sensor networks
A wireless sensor network (WSN) consists of a large number of sensor nodes with limited
battery lives that are dispersed geographically to monitor events and gather information from …
battery lives that are dispersed geographically to monitor events and gather information from …
Artificial Intelligence-Based Intrusion Detection and Prevention in Edge-Assisted SDWSN With Modified Honeycomb Structure
J Kipongo, TG Swart, E Esenogho - IEEE access, 2023 - ieeexplore.ieee.org
The software-defined wireless sensor network (SDWSN) has the potential to improve
flexibility, scalability, and network performance, but security and quality of service (QoS) are …
flexibility, scalability, and network performance, but security and quality of service (QoS) are …
Energy-balanced routing in wireless sensor networks with reinforcement learning using greedy action chains
Z Liu, X Wang - Soft Computing, 2023 - Springer
The challenge of routing in energy-constrained wireless sensor networks is to balance
energy and extend network lifetime, and reinforcement learning is an effective method to …
energy and extend network lifetime, and reinforcement learning is an effective method to …
A lifetime-aware centralized routing protocol for wireless sensor networks using reinforcement learning
This paper presents the design of a Lifetime-Aware Centralized Q-routing Protocol
(LACQRP) for Wireless Sensor Network (WSN) to maximize the network lifetime. This is …
(LACQRP) for Wireless Sensor Network (WSN) to maximize the network lifetime. This is …