Recurrent Neural Network-Based Optimal Sensing Duty Cycle Control Method for Wireless Sensor Networks

Abstract

With the development of Internet of Things (IoT) technologies, environmental monitoring systems using wireless sensor networks (WSNs) have received considerable attention. Reliable object detection and tracking is an important research issue in various WSN applications, such as environment and disaster monitoring, disaster propagation tracking, and intruder monitoring and tracking. Generally, because batteries are used as energy sources for sensors in WSNs, a highly energy-efficient operation is needed to prolong the life of the sensors and networks. To save energy, sensors usually manage multi-mode sensing operations, in which they periodically switch between active and inactive periods. A tradeoff exists between object detection accuracy and energy efficiency when we select a sensing schedule. Depending on the object speed, direction, and sensor deployment topology, different sensing schedules should be dynamically applied to individual sensors. In this paper, we propose a novel recurrent neural network (RNN)-based dynamic duty cycle control method for sensor nodes. For RNN training, a target optimal duty cycle for a given network condition is derived from the proposed digital twin-space analytic solution. Simulation results show that the proposed model provides accurate object detection performance and achieves high energy efficiency.INDEX TERMS Duty cycle control, machine learning, object tracking, recurrent neural network, wireless sensor networks.

Wireless Sensors Networks Energy Optimization using LEACH and ANNs

“…[16] Introduced a Machine Learning-based routing system that dynamically adapts the routing pathways to balance energy use based on past data on node energy consumption. [17] Used AI algorithms to intelligently regulate the transmission power and duty cycle of nodes in order to maximize the WSNs' energy efficiency. Energy efficiency may be greatly improved with the use of effective topology management and routing protocols.…”

A review on evolving domains of Internet of Things: Architecture, applications, and technical challenges

“…Further, long short‐term memory (LSTM)‐based RNN‐enabled learning model trained with the output (labels, sequential datasets) is generated from the digital twin model. Finally, the proposed model provides better results as it is accurate and obtains high efficiency 15 . A multiobject tracking model is constructed with deep learning (DL) and a wireless multimedia sensor network.…”

“…Finally, the proposed model provides better results as it is accurate and obtains high efficiency. 15 A multiobject tracking model is constructed with deep learning (DL) and a wireless multimedia sensor network. An efficient RNN with tumbling effect (RNN-T) helps to tracks the accurate location of animals.…”