Joint Power Charging and Routing in Wireless Rechargeable Sensor Networks

Abstract

The development of wireless power transfer (WPT) technology has inspired the transition from traditional battery-based wireless sensor networks (WSNs) towards wireless rechargeable sensor networks (WRSNs). While extensive efforts have been made to improve charging efficiency, little has been done for routing optimization. In this work, we present a joint optimization model to maximize both charging efficiency and routing structure. By analyzing the structure of the optimization model, we first decompose the problem and propose a heuristic algorithm to find the optimal charging efficiency for the predefined routing tree. Furthermore, by coding the many-to-one communication topology as an individual, we further propose to apply a genetic algorithm (GA) for the joint optimization of both routing and charging. The genetic operations, including tree-based recombination and mutation, are proposed to obtain a fast convergence. Our simulation results show that the heuristic algorithm reduces the number of resident locations and the total moving distance. We also show that our proposed algorithm achieves a higher charging efficiency compared with existing algorithms.

Event-Driven Joint Mobile Actuators Scheduling and Control in Cyber-Physical Systems

“…In [14], considering the energy consumed by the actuators to move and perform action, the actuator scheduling and control problem is formulated by Mixed-Integer Non-Linear Program (MINLP). In [6], [8], the actuator is scheduled to visit the sensors in sequence and changes their energy to some certain levels. However, the dynamics of the system states is not taken into account in [6], [8], [14].…”

“…In [6], [8], the actuator is scheduled to visit the sensors in sequence and changes their energy to some certain levels. However, the dynamics of the system states is not taken into account in [6], [8], [14]. During the movement of the actuators, the system states will change, and, thus, the actuator control decision should be updated accordingly.…”

“…The CPS are characterized by the sensoractuator coordination, the heterogeneous information communication, and the intelligent decision/actuation [1]. These characteristics enable the use of CPS to several domains such as smart building [2], [3], power grid [4], mobile charging [5]- [8], industrial and environment control [9]- [12].…”

“…The design of the scheduling of the mobile actuators and the control of their outputs should meet: 1) control accuracy requirement [6], e.g., to ensure the sensors have enough energy to work until being charged again, the replenished energy of the sensors should reach to some certain levels; 2) realtime requirement [8], e.g., the tasks assigned to the actuators (relocating and controlling) should be finished within a predefined threshold so as to generate a correct result; and 3) energy efficiency requirement [14]. Although the actuators are more powerful than the sensors, they still have limited energy budgets: 1) the actuators cannot be easily recharged after the deployment, and 2) the relocating and the controlling tasks consume much more energy than the computation and the communication tasks.…”

“…When taking multi-objective such as the multi-actuator scheduling and control, the realtime performance and the energy efficiency into account, the optimization variables are coupled with each other nonlinearly. Heuristics [5], [14] and evolutionary approaches [8] are popular methods to solve complex optimization problems. However, the solution qualities of these methods are hard to guarantee.…”