Energy-Efficient Mobile Device-Assisted Schemes In Wireless Sensor Networks

Abstract

Recently, wireless sensor networks (WSNs), consisted of battery-powered sensor nodes, are widely adopted by various civilian/military applications for implementing real-time monitoring or long-term surveillance tasks. One of the critical issues of WSNs is energy efficiency. Due to the limited battery capacity, the network lifetime and performance of WSNs are constrained. Also, once the sensor is deployed into a risky/remote environment, the replacement of its battery is hard. Therefore, how to improve the energy efficiency of the WSN is a critical issue and has gained tremendous attention from researchers around the world.

To address this problem, by taking advantage of the emerging high-mobility devices (e.g., unmanned aerial vehicle (UAV)), we propose energy-efficient mobile device-assisted schemes in different-scale WSNs. Thanks to the rapid development of wireless techniques, two emerging approaches, i.e., data gathering technique and wireless charging technique, are beneficial to balance the workloads among all sensors or replenish energy to achieve the semi-permanent WSN. First, we design data gathering schemes using the mobile data collector. In order to meet the performance requirements of systems with different scales, our algorithms have two working modes: single- and multiple-data-collector scenarios. For the small-scale system, a single data collector is adopted to access and collect data from the deployed node, and we propose single mobile data collector-assisted (SDCA) data collection schemes for small-scale WSNs. For the large-scale system, multiple data collectors are utilized to gather sensed data from deployed nodes, and two-mode multiple mobile data collector-assisted (MDCA) data collection scheme is designed for balancing between the system energy consumption and the data forwarding latency. Second, the joint data collection and energy charging scheme is developed by adopting mobile chargers (MCs) as mobile devices that are responsible for energy charging and data collection simultaneously. For facing the different performance requirements of systems, a two-mode MC scheduling algorithm is presented. To evaluate our works, extensive simulation experiments are conducted on the OMNeT++ simulator. The results demonstrate that the proposed algorithms achieve better performance than the control group regarding system-wide energy efficiency, network lifetime and average end-to-end delay.