Robust Mechanism of Trap Coverage and Target Tracking in Mobile Sensor Networks
Chia-Hsu Kuo, Tzung-Shi Chen, Siou-Ci Syu · IEEE Internet of Things Journal · 2018
In this paper, we propose an adaptive mechanism of trap coverage with a robust area coverage model, which employs mobile sensors for applications in mobile sensor networks (MSNs) and in the Internet of Things (IoT). Many promising applications including the target tracking and mobile sensing can be reasonably realized and improved after incorporating the characteristics of trap coverage mechanism on the basis of adaptively adjusting the trap size and sensor mobility. The trap evidently exists throughout the deployment of sensors in wireless sensor networks, in which the target has predictably vanished or application service remains undetected. The properties of the trap in the trap coverage mechanism are contrary to the purpose of target tracking and services detection. This creates a serious problem for target tracking and services detection in MSNs. This paper proposes a robust mechanism of trap coverage involving the use of mobile sensors in target tracking and services detection for applications in MSNs. The experimental results revealed that the proposed method efficiently reduces the target-missing time and the total number of unavailable sensors, and also enhances the maintenance of trap coverage through the movement of mobile sensors in MSNs based on the simulation results and analysis. Performance comparison is conducted by adjusting the total amount of sensors in an adaptively trap coverage. The novel mechanism makes MSNs a much more flexible and provides a cost-effective solution in IoT than static sensor networks.