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,energyefficiency,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 energyefficiency 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 energyefficiency and throughput maximization.
Shabeer AhmadJinling ZhangAli NaumanAdil KhanKhizar AbbasBabar Hayat
Wireless sensor network(WSN)technologies have advanced significantly in recent years.With in WSNs,machine learning algorithms are crucial in selecting cluster heads(CHs)based on various quality of service(QoS)metrics.This paper proposes a new clustering routing protocol employing the Traveling Salesman Problem(TSP)to locate the optimal path traversed by the Mobile Data Collector(MDC),in terms of energy and QoS efficiency.To bemore specific,to minimize energy consumption in the CH election stage,we have developed the M-T protocol using the K-Means and the grid clustering algorithms.In addition,to improve the transmission phase of the Low Energy Adaptive Clustering-Grid-KMeans(LEACH-G-K)protocol,the MDC is employed as an intermediary between the CH and the sink to improve the wireless sensor network(WSN)QoS.The results of the experiment demonstrate that the M-T protocol enhances various Low Energy Adaptive Clustering protocol(LEACH)improvements such as the LEACH-G-K,LEACH-C,Threshold sensitive Energy Efficient Sensor Networks(TEEN),MDC maximum residual energy leach protocol.
