在蜂窝物理系统中实现最优能量高效的包定期交付
Optimal Energy-Efficient Regular Delivery of Packets in Cyber-Physical Systems
AI总结：
本文提出了一种平衡能量效率与定期包交付的调度策略，通过马尔可夫决策过程建模并采用Whittle指数策略实现低复杂度优化。
摘要：
在蜂窝物理系统中，如车载无线传感器网络，大量传感器节点持续生成测量数据，需以定期方式由其他节点如执行器接收。同时，无线传感器网络中能量效率也至关重要。受此启发，我们开发了同时具备能量效率和定期包交付的调度策略。引入一个权衡参数以平衡这两个冲突目标。我们采用马尔可夫决策过程（MDP）模型，其中每个客户端的状态为其包的上次交付时间，将其转化为等效的有限状态MDP问题。尽管该等效问题可通过标准动态规划技术解决，但其计算复杂度较高。因此，我们将问题转化为 restless multi-armed bandit 问题，并采用低复杂度的 Whittle 指数策略。证明该问题可索引化，并推导出 Whittle 指数。此外，我们证明 Whittle 指数策略是渐近最优的，并通过大量仿真验证其最优性。
英文摘要：
In cyber-physical systems such as in-vehicle wireless sensor networks, a large number of sensor nodes continually generate measurements that should be received by other nodes such as actuators in a regular fashion. Meanwhile, energy-efficiency is also important in wireless sensor networks. Motivated by these, we develop scheduling policies which are energy efficient and simultaneously maintain "regular" deliveries of packets. A tradeoff parameter is introduced to balance these two conflicting objectives. We employ a Markov Decision Process (MDP) model where the state of each client is the time-since-last-delivery of its packet, and reduce it into an equivalent finite-state MDP problem. Although this equivalent problem can be solved by standard dynamic programming techniques, it suffers from a high-computational complexity. Thus we further pose the problem as a restless multi-armed bandit problem and employ the low-complexity Whittle Index policy. It is shown that this problem is indexable and the Whittle indexes are derived. Also, we prove the Whittle Index policy is asymptotically optimal and validate its optimality via extensive simulations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
