Decentralized RL-Based Data Transmission Scheme for Energy Efficient Harvesting
Rafaela Scaciota, Glauber Brante, Richard Souza, Onel Lopez, Septimia, Sarbu, Mehdi Bennis, Sumudu Samarakoon

TL;DR
This paper introduces a decentralized reinforcement learning-based data transmission scheme for energy harvesting IoT devices, optimizing energy use and transmission strategies to improve efficiency and scalability in wireless IoT networks.
Contribution
It presents a novel RL-based approach for energy-aware data transmission in EH IoT systems, including a closed-form energy consumption model and deployment insights.
Findings
RL approach improves transmission efficiency
Optimal PB placement enhances line-of-sight and energy transfer
Model effectively captures energy consumption dynamics
Abstract
The evolving landscape of the Internet of Things (IoT) has given rise to a pressing need for an efficient communication scheme. As the IoT user ecosystem continues to expand, traditional communication protocols grapple with substantial challenges in meeting its burgeoning demands, including energy consumption, scalability, data management, and interference. In response to this, the integration of wireless power transfer and data transmission has emerged as a promising solution. This paper considers an energy harvesting (EH)-oriented data transmission scheme, where a set of users are charged by their own multi-antenna power beacon (PB) and subsequently transmits data to a base station (BS) using an irregular slotted aloha (IRSA) channel access protocol. We propose a closed-form expression to model energy consumption for the present scheme, employing average channel state information…
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Taxonomy
TopicsEnergy Harvesting in Wireless Networks · Wireless Communication Security Techniques · Opportunistic and Delay-Tolerant Networks
MethodsSparse Evolutionary Training · Balanced Selection
