In a distributed edge computing environment, Internet of Things (IoT) and Vehicular-IoT (V-IoT) devices communicate through Wireless Sensor Networks (WSNs) by collecting and transmitting data from different environments. Although energy efficiency is always a critical challenge in WSN due to limited battery power, along with the demand for fast communication over edge devices in 5G and beyond 5G scenarios. Therefore, to overcome the challenges, an advanced hierarchical agent-based power management scheme is proposed for WSNs that optimizes energy distribution while maintaining reliable communication. The proposed model employs Master Agents (MAs), Coordination Agents (CoAs), and Task Agents (TAs) to manage power allocation by following a specific order of selection of nodes. The system dynamically adjusts power distribution based on node requirements, trust values, and communication demands over an optimally covered area of nodes, distributed with a 2D Poisson Point Process. Simulation results demonstrate improved energy efficiency, extended network lifetime, and enhanced communication reliability in edge computing scenarios.

The rise of innovative applications, like online gaming, smart healthcare, and Internet of Things (IoT) services, has increased demand for high data rates and seamless connectivity, posing challenges for Beyond 5G (B5G) networks. There is a need for cost-effective solutions to enhance spectral efficiency in densely populated areas, ensuring higher data rates and uninterrupted connectivity while minimizing costs. Unmanned Aerial Vehicles (UAVs) as Aerial Base Stations (ABSs) offer a promising and cost-effective solution to boost network capacity, especially during emergencies and high-data-rate demands. Nevertheless, integrating UAVs into the B5G networks presents new challenges, including resource scarcity, energy efficiency, resource allocation, optimal power transmission control, and maximizing overall throughput. This paper presents a UAV-assisted B5G communication system where UAVs act as ABSs, and introduces the Deep Reinforcement Learning (DRL) based Energy Efficient Resource Allocation (Deep-EERA) mechanism. An efficient DRL-based Deep Deterministic Policy Gradient (DDPG) mechanism is introduced for optimal resource allocation with the twin goals of energy efficiency and average throughput maximization. The proposed Deep-EERA method learns optimal policies to conserve energy and enhance throughput within the dynamic and complex UAV-empowered B5G environment. Through extensive simulations, we validate the performance of the proposed approach, demonstrating that it outperforms other baseline methods in energy efficiency and throughput maximization.