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Charging Path Optimization in Mobile Networks
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AI
DOI：10.1109/TNET.2022.3167781.png)
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Abstract
En 中文
We study a class of generic charging path optimization problems arising from emerging networking applications, where mobile chargers are dispatched to deliver energy to mobile agents (e.g., robots, drones, vehicles), which have specified tasks and mobility patterns. We instantiate our work by focusing on finding the charging path maximizing the number of nodes charged within a fixed time horizon. We show that this problem is APX-hard. By recursively decomposing the problem into sub-problems of searching sub-paths, we design quasi-polynomial-time algorithms achieving logarithmic approximation to the optimum charging path. Our approximation algorithms can be further adapted and extended to solve a variety of charging path optimization and scheduling problems with realistic constraints, such as limited time and energy budget.
Keywords:
Optimization
Approximation algorithms
Sensors
Scheduling
Heuristic algorithms
Trajectory
Task analysis
Charging path optimization
mobile charger scheduling
mobile networks
approximation algorithm
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