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Delay-Sensitive Energy Harvesting Wireless Sensors: Optimal Scheduling, Structural Properties, and Approximation Analysis
IEEE Transactions on Communications ( IF 8.3 ) Pub Date : 2020-04-01 , DOI: 10.1109/tcomm.2019.2956510
Nikhilesh Sharma , Nicholas Mastronarde , Jacob Chakareski

We consider an energy harvesting sensor transmitting latency-sensitive data over a fading channel. We aim to find the optimal transmission scheduling policy that minimizes the packet queuing delay given the available harvested energy. We formulate the problem as a Markov decision process (MDP) over a state-space spanned by the transmitter’s buffer, battery, and channel states, and analyze the structural properties of the resulting optimal value function, which quantifies the long-run performance of the optimal scheduling policy. We show that the optimal value function (i) is non-decreasing and has increasing differences in the queue backlog; (ii) is non-increasing and has increasing differences in the battery state; and (iii) is submodular in the buffer and battery states. Taking advantage of these structural properties, we derive an approximate value iteration algorithm that provides a controllable tradeoff between approximation accuracy, computational complexity, and memory, and we prove that it converges to a near-optimal value function and policy. Our numerical results confirm these properties and demonstrate that the resulting scheduling policies outperform a greedy policy in terms of queuing delay, buffer overflows, energy efficiency, and sensor outages.

中文翻译:

延迟敏感能量收集无线传感器:优化调度、结构特性和近似分析

我们考虑通过衰落信道传输延迟敏感数据的能量收集传感器。我们的目标是找到最优传输调度策略,在给定可用能量的情况下最小化数据包排队延迟。我们将问题表述为一个马尔可夫决策过程 (MDP),在由发射机的缓冲器、电池和信道状态跨越的状态空间上进行,并分析所得最优值函数的结构特性,该函数量化了发射机的长期性能最优调度策略。我们表明最优值函数 (i) 是非递减的,并且在队列积压中具有递增的差异;(ii) 非增加且电池状态差异增加;(iii) 在缓冲和电池状态下是子模块的。利用这些结构特性,我们推导出一个近似值迭代算法,该算法在近似精度、计算复杂度和内存之间提供可控的权衡,并证明它收敛到接近最优的值函数和策略。我们的数值结果证实了这些特性,并证明由此产生的调度策略在排队延迟、缓冲区溢出、能源效率和传感器中断方面优于贪婪策略。
更新日期:2020-04-01
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