Lyapunov优化
计算机科学
数学优化
最优化问题
放松(心理学)
能量(信号处理)
李雅普诺夫函数
线性规划
任务(项目管理)
算法
李雅普诺夫方程
人工智能
数学
李雅普诺夫指数
统计
工程类
非线性系统
心理学
社会心理学
物理
系统工程
量子力学
混乱的
作者
Sha Chang,Shuiguang Deng,Yahui Wu,Wubin Ma,Haohao Zhou
出处
期刊:IEEE Transactions on Industrial Informatics
[Institute of Electrical and Electronics Engineers]
日期:2023-09-01
卷期号:19 (9): 9266-9279
标识
DOI:10.1109/tii.2022.3227618
摘要
In mobile crowdsensing (MCS), the battery of participants is often limited. When participants perform too many sensing tasks resulting in insufficient remaining energy, they will exit the MCS system. This article mainly addresses the energy balancing problem to prolong the system lifespan. By this means, it can ensure adequate participants and promote the completion of tasks. First, it formulates a discrete time optimization model, which transforms abovementioned problem into the online control of task admission and allocation. In addition, this model uses remaining energy variance of the participants to measure the degree of balance. Next, an online energy balancing strategy (OEBS) is proposed based on the Lyapunov optimization, which can realize energy balance without utility loss. Finally, an approximate optimal policy is presented based on the linear programming and genetic algorithm to solve abovementioned optimization problem. Experiments show that OEBS effectively maintains adequate participants, prolongs the lifespan of MCS system and maximizes the system utility even when there are few participants with multiple tasks. Specifically, the lifespan in OEBS is longer than that in utility optimization algorithm (UOA) and LP-relaxation algorithm significantly. The total utility in OEBS is more than that in UOA. OEBS can maximizes average utility of system by adjusting ${\bm{V}}$ . In addition, the energy balancing ability of OEBS is always effective as ${\bm{V}}$ changes.
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