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

Seung-Hee Choi,Sang-Jo Yoo

doi:10.1109/access.2021.3113298

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.

Highlights

As the Internet of Things (IoT) and wireless sensor networks (WSNs) are becoming a reality, their interconnections for smart devices are increasing [1], [2]
We propose a novel recurrent neural network (RNN)-based optimal duty cycle control method for WSNs, in which the optimal sensing schedule is dynamically determined for each sensor node
To derive the optimal duty cycle solution in the given environment for RNN supervised learning, we proposed an optimal solution derivation method based on a simulation model in the digital twin space

Summary

Introduction

As the Internet of Things (IoT) and wireless sensor networks (WSNs) are becoming a reality, their interconnections for smart devices are increasing [1], [2]. By enabling easy access and interaction with a wide variety of devices, such as home appliances, surveillance cameras, humidity sensors, actuators, forest fire detection sensors, vehicle trackers, and mobile phones, many objects surrounding us will be connected to networks in one form or another. An object tracking sensor network monitors both indoor and outdoor environments and tracks various objects, such as forest fires, polluted air, bio-chemical materials, automobiles, and animals [3]–[6]. Autonomous sensors equipped with video cameras enable the development of new security, surveillance, and military applications

Methods

Results

Conclusion