Wireless sensor networks (WSNs) with ultra-dense sensors are crucial for several industries, such as smart agricultural systems deployed in the fifth generation (5G) and beyond 5G Open Radio Access Networks (O-RAN). The WSNs employ multiple unmanned aerial vehicles (UAVs) to collect data from multiple sensor nodes (SNs) and relay it to the central controller for processing. UAVs also provide resources to SNs and extend the network coverage over a vast geographical area. The O-RAN allows the use of open standards and interfaces to create a wireless network for communications between the UAVs and ground SNs. It enables real-time data transfer, remote control, and other applications that require a reliable and high-speed connection by providing flexibility and reliability for UAV-assisted WSNs to meet the requirements of smart agricultural applications. However, the limited battery life of UAVs, transmission power, and shortage of energy resources SNs make it difficult to collect all the data and relay it to the base station, resulting in inefficient task computation and resource management in smart agricultural systems. In this paper, we propose a joint UAV task scheduling, trajectory planning, and resource-sharing framework for multi-UAV-assisted WSNs for smart agricultural monitoring scenarios that schedule UAVs' charging, data collection, and landing times and allow UAVs to share energy with SNs. The main objective of our proposed framework is to minimize the UAV energy consumption and network latency for effective data collection within a specific time frame. We formulate the multi-objective, which is a non-convex optimization problem, and transform it into a Markov decision process (MDP) with a multi-agent deep reinforcement learning (MADRL) algorithm. The simulation results show that the proposed MADRL algorithm reduces the energy consumption cost when compared to deep Q-network, Greedy, and mixed-integer linear program (MILP) by 61.92%, 68.02%, and 69.9%, respectively.

Unmanned Aerial Vehicle (UAV)-powered 5G/6G networks integrated with rechargeable wireless sensor networks (RWSNs) offer promising solutions for extending system lifetime, collecting data, and providing computing services and power to sensor nodes (SNs). UAVs offer significant advantages, including exceptional mobility, cost-effective deployment, and the ability to be easily reprogrammed for a wide range of missions. However, the limited onboard power capacity of UAVs, coupled with the lack of dynamic and intelligent charging station (CS) management and inefficient path planning, can lead to SN failure in dynamic mobile environments. To address these challenges, we propose an energy-efficient laser-charged UAV (LCU)-enabled RWSN environment, wherein UAVs, powered by laser beams from ground-based stations, provide services, collect data, and transfer energy to SNs. We formulate a joint optimization problem involving power allocation, dynamic charging strategy (DCS), and path planning to minimize task completion time and sensor node death time. Given the NP-hard nature of the problem, we employ a stochastic game model based on a Markov decision process (MDP) for its solution. To solve this problem, we propose a deep reinforcement learning (DRL) based algorithm that enables real-time charging scheduling decisions while optimizing network performance. We introduce a multi-agent double deep Q-network (MA-DDQN) model to determine the optimal trajectories for all UAVs in large and complex environments. Simulation results demonstrate that the MA-DDQN approach outperforms state-of-the-art techniques, showing significant improvements in terms of average delay, energy consumption, and task completion time.

The Internet of Health Monitoring (IoHM) plays a vital role in emergency medical package delivery (EMPD) by enabling real-time monitoring and transmission of critical health data through interconnected devices in 6G networks. Autonomous aerial vehicles (UAVs) act as aerial base stations (ABSs), facilitating data collection and transmission between GAI-IoHM devices and edge servers. This is crucial for efficient communication in 6G-enabled space-air–ground integrated networks (SAGINs). However, UAVs supporting EMPD face challenges related to limited energy and computational capacity, especially during task offloading to edge servers. To address these constraints, this article proposes the integration of generative artificial intelligence (GAI) into UAVs for intelligent policy learning, enabling adaptive decision-making, real-time diagnostics, and efficient path planning under uncertainty. We present a novel multiagent deep reinforcement learning (MADRL)-based joint optimization framework for cooperative task allocation, trajectory planning, and power management (CTATP) in a 6G-enabled SAGIN architecture. The problem is modeled as a partially observable markov decision process (POMDP) to capture dynamic and uncertain operational conditions. To solve this, we introduce the GAI-based deep deterministic double policy gradient (GAI-DD3PG) algorithm, which leverages a generative actor-network to learn adaptive, energy-efficient control policies from latent action spaces. Simulations in urban emergency scenarios with 10–20 UAVs demonstrate GAI-DD3PG’s efficacy. Compared to proximal policy optimization (PPO), deep deterministic policy gradient (DDPG), multiagent federated reinforcement learning (MAFRL), and Greedy heuristics, GAI-DD3PG achieves a 20% energy reduction, 15% higher delivery success rate, 25% shorter trajectories, and 30% improved resource utilization. These results highlight its potential for reliable EMPD in complex 6G SAGIN environments.

In sixth-generation (6G) networks, unmanned aerial vehicles (UAVs) are expected to be widely used as aerial base stations (ABS) due to their adaptability, low deployment costs, and ultra-low latency responses. However, UAVs consume large amounts of power to collect data from multiple sensor nodes (SNs). This can limit their flight time and transmission efficiency, resulting in delays and low information freshness. In this paper, we present a multi-access edge computing (MEC)-integrated UAV-assisted wireless sensor network (WSN) with a laser technology-based energy harvesting (EH) system that makes the UAV act as a flying energy charger to address these issues. This work aims to minimize the age of information (AoI) and improve energy efficiency by jointly optimizing the UAV trajectories, EH, task scheduling, and data offloading. The joint optimization problem is formulated as a Markov decision process (MDP) and then transformed into a stochastic game model to handle the complexity and dynamics of the environment. We adopt a multi-agent deep Q-network (MADQN) algorithm to solve the formulated optimization problem. With the MADQN algorithm, UAVs can determine the best data collection and EH decisions to minimize their energy consumption and efficiently collect data from multiple SNs, leading to reduced AoI and improved energy efficiency. Compared to the benchmark algorithms such as deep deterministic policy gradient (DDPG), Dueling DQN, asynchronous advantage actor-critic (A3C) and Greedy, the MADQN algorithm has a lower average AoI and improves energy efficiency by 95.5%, 89.9%, 78.02% and 65.52% respectively.

In the fields of machine learning and artificial intelligence, recommendation systems (RS) or recommended engines are commonly used. In today's world, recommendation systems based on user preferences assist consumers in making the best decisions without depleting their cognitive resources. They can be applied to a variety of things, including search engines, travel, music, movies, literature, news, gadgets, and dining. A lot of people utilize RS on social media sites like Facebook, Twitter, and LinkedIn, and it has proven beneficial in corporate settings like those at Amazon, Netflix, Pandora, and Yahoo. There have been numerous proposals for recommender system variations. However, certain techniques result in unfairly recommended things due to biased data because there are no established connections between the items and consumers. In order to solve the challenges mentioned above for new users, we propose in this work to employ Content-based Filtering (CBF) and Collaborative Filtering (CF) with semantic relationships to capture the relationships as knowledge-based book recommendations to readers in a digital library. When proposing things, patterns are more discriminative than single phrases. To capture the similarity of the books that the new user had retrieved, the patterns were grouped in a semantically equivalent manner using the Clustering method. The effectiveness of the suggested model is examined through a series of extensive tests employing Information Retrieval (IR) evaluation criteria. Recall Precision and F-Measure, two of the three widely used performance measuring metrics, were employed. The findings demonstrate that the suggested model performs noticeably better than cutting-edge models.

The Internet of Everything (IoE) is accelerating the demand for intelligent, low-latency, and highly reliable communication systems to support automation and real-time decision-making. In dense urban and industrial environments, unmanned aerial vehicles (UAVs) are increasingly utilized to extend network coverage, improve connectivity, and enable dynamic data collection. However, managing RIS-assisted UAV-enabled IoE networks poses significant challenges, including accurate signal prediction, high computational complexity, and decentralized task assignment. To address these issues, we propose a novel RIS-empowered UAV-based Dynamic Area of Coverage (DAC) architecture. In this framework, UAVs equipped with reconfigurable intelligent surfaces (RIS) adaptively adjust the phase of reflected signals to optimize wireless channel conditions, suppress interference, and enhance signal quality.We formulate the Dynamic Area of Coverage with Location, Resource Allocation, and Trajectory Optimization (DAC-LRT) problem as a mixed-integer nonlinear programming (MINLP) model, aiming to jointly optimize UAV positioning, power distribution, and trajectory control to maximize real-time downlink capacity and ensure energy efficiency. To solve the DAC-LRT problem in dynamic and large-scale IoE environments, we design a Multi-Agent Distributed Deep Deterministic Policy Gradient (MAD3PG) algorithm. MAD3PG enables decentralized and adaptive policy learning by allowing UAVs to derive optimal actions directly from environmental observations. Simulation results demonstrate that our proposed approach significantly outperforms state-of-the-art methods, achieving improvements of 82.92%, 78.02%, and 71.9% in downlink capacity, coverage ratio, average throughput, and spectral efficiency over Deep Deterministic Policy Gradient (DDPG), Asynchronous Advantage Actor-Critic (A3C), and Greedy algorithms, respectively.