Structural Properties of Optimal Transmission Policies for Delay-Sensitive Energy Harvesting Wireless Sensors

TL;DR

This paper analyzes the structural properties of optimal transmission policies for energy harvesting sensors transmitting delay-sensitive data, revealing key monotonicity and submodularity characteristics that improve scheduling performance.

Contribution

It characterizes the structural properties of the optimal value function in a Markov decision process for energy harvesting sensors, guiding better scheduling policies.

Findings

Optimal value function is non-decreasing in queue backlog.

Optimal value function is non-increasing in battery state.

Optimal scheduling policy outperforms greedy policy in key metrics.

Abstract

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. Our numerical results confirm these properties and demonstrate that the optimal scheduling policy outperforms a so-called greedy policy in terms of sensor outages, buffer overflows, energy efficiency, and queuing delay.