Dynamic online joint energy management and sampling rate control in energy harvesting aided IoT network

Abstract

Abstract Energy harvesting (EH) aided Internet of Things (IoT) network is a promising paradigm to librate IoT network from energy deficiency. Dynamic energy and traffic scheduling in such a scenario is challenging due to temporal correlation of energy constraints and delay requirements of IoT applications. In this paper, joint energy management and sampling rate control to explore the tradeoff between network utility and delay performance are studied while maintaining the energy causality constraint. Taking into account the dynamic characteristics of EH process, channel fading and traffic arrivals, a stochastic optimisation problem is formulated to maximise the network utility. Leveraging the Lyapunov optimisation approach, combined with the idea of weight perturbation, a framework is proposed to decompose the stochastic problem into several deterministic sub‐problems that can be solved separately. Based on the framework, an online resource allocation algorithm is developed to achieve two major goals: first, balancing energy consumption and energy harvesting to stabilise their data and energy queues; second, deriving the utility‐delay tradeoff by adjusting the control parameter. The stability of data buffer and energy buffer in the proposed network is theoretical verified with performance analysis.