Predicting-Scheduling-Tracking: Charging Nodes With Non-Deterministic Mobility

doi:10.1109/ACCESS.2020.3046857

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Predicting-Scheduling-Tracking: Charging Nodes With Non-Deterministic Mobility

2021-01-01

Yuanhao Li

Lingyun Zhong

Feng Lin *

DOI：10.1109/ACCESS.2020.3046857

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Abstract

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In Wireless Sensor Networks (WSN), the energy problem plays the critical role in network performance and lifetime, because of the limited battery capacities of sensor nodes. Recently, emerging wireless charging technologies provide a promising approach to address the energy problem in WSN. Researchers construct Wireless Rechargeable Sensor Networks (WRSN), which introduce mobile chargers with high capacity batteries to charge sensor nodes. Most studies in WRSN have paid attention to charging static nodes or mobile nodes with deterministic trajectories. In this work, we explore how to charge nodes with non-deterministic mobility. We propose a novel approach, named Predicting-Scheduling-Tracking (PST), to perform charging tasks in this case. In the proposed scheme, different from the existing work, we guide the mobile charger to chase the sensor and recharge it. In our work, the base station runs an improved LSTM to predict the future locations of nodes periodically. Then, the mobile charger can select an appropriate node as the charging target by a charging scheduling algorithm. During the energy transferring, a Kalman-filter-based tracking algorithm is used to ensure the charging-required distance between the mobile charger and the target node. The simulation results show that the proposed charging scheme can fulfil the charging tasks in WRSN of nodes with non-deterministic mobility.

Keywords:

Wireless sensor networks

Robot sensing systems

Wireless communication

Trajectory

Task analysis

Target tracking

Batteries

Wireless rechargeable sensor networks (WRSN)

non-deterministic mobility

trajectory prediction

charging scheduling

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