Energy Aware Irregular Slotted Aloha Methods for Wireless Powered IoT Networks

Publication Name

IEEE Internet of Things Journal


This paper considers Radio Frequency (RF) energy harvesting devices that use an Irregular Slotted Aloha (IRSA) channel access protocol to transmit their data to a Hybrid Access Point (HAP). Specifically, it addresses the fundamental problem of optimizing the number of packet replicas transmitted by each device in each time frame. Unlike prior works, it considers a learning approach to optimize the number of replicas according to the energy level of devices. This paper first uses a model-based Markov Decision Process (MDP) to study the problem at hand. Then it proposes a model-free, centralized and a distributed Q-learning-based solution that aim to maximize the number of successful transmissions in each time frame. Our simulation results show that our centralized and distributed solutions respectively achieve up to 38% and 29% more successful transmissions than conventional Aloha.

Open Access Status

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Link to publisher version (DOI)