In modern mobile edge computing (MEC) systems, effectively integrating service caching, task offloading, and service delivery is crucial for ensuring high network quality and enhanced user experience. In this article, we introduce a novel joint optimization framework that employs Uncrewed aerial vehicles (UAVs) as dynamic edge computing nodes, collaborating with service collection units (SCUs) that aggregate service requests from Internet of Things (IoT) devices. In the proposed system, UAVs equipped with small-capacity edge servers can process computational tasks locally or forward them to a base station (BS) via the backhaul when necessary. Both user layers (UEs) and UAVs dynamically cache programs using an adaptive algorithm based on task frequency and program weight, which maximizes cache hit rates and expedites service execution. A joint optimization problem is formulated to minimize the overall end-to-end latency through coordinated task offloading, CPU allocation, caching, and UAV scheduling, subject to energy, storage, and mobility constraints. Our resource-aware task offloading and UAV trajectory planning further enable efficient service responsiveness and minimize reliance on the backhaul link. The overall objective is to reduce maximum task latency and backhaul congestion across the network. Extensive simulation results show that our framework significantly outperforms standard baselines, including random caching, most caching, and calculation delay and energy consumption multiobjective optimization problem (CDECMOP), achieving up to$18.5\%$lower energy consumption and$23.6\%$lower latency across diverse network scenarios. These findings underscore the effectiveness of the proposed UE-UAV cooperative caching and computation strategy for scalable, energy-efficient, and low-latency UAV-assisted MEC systems.

Unmanned Aerial Vehicles (UAVs) have become increasingly pivotal in various Internet of Things (IoT) implementations due to their dynamic mobility and adaptability. This article examines the deployment of a rotary-wing UAV to efficiently manage data retrieval from dispersed IoT nodes within a UAV-assisted IoT framework. To support energy-efficient data collection, we incorporate uplink Non-Orthogonal Multiple Access (NOMA), enabling multiple IoT nodes to transmit data simultaneously. The core objectives of our research include improving the UAV navigational path, reducing energy consumption, reducing mission duration, and improving overall data collection efficiency. However, these goals present considerable difficulties, as conventional optimization techniques often fall short when faced with unpredictable time slots, resulting in a vast range of decision variables and the complexity of non-convex functions. We approach these challenges by framing the optimization tasks within a Markov Decision Process (MDP), employing the Deep Deterministic Policy Gradient (DDPG) algorithm to develop adaptive, robust UAV control strategies. This methodology facilitates dynamic adjustment of UAV operations, ensuring effective data collection and management. Numerical analysis validates the effectiveness of our method, showcasing significant improvements over existing relevant works.

Unmanned Aerial Vehicles (UAVs) have become vital tools for data collection in Internet of Things (IoT) networks, enabling efficient monitoring and information acquisition across various domains. However, UAV-assisted IoT networks often face significant challenges such as high data loss, latency, and resource inefficiency due to inadequate buffer management and dynamic service time (DST) allocation for IoT nodes. Existing approaches frequently overlook critical factors such as IoT nodes' energy levels, UAVs' energy constraints, and the dynamic data generation rates of IoT devices. To address these challenges, this article introduces a novel strategy for dynamically optimizing UAV trajectories by integrating real-time data from ground nodes and UAV energy levels. Given the DST and buffer constraints (BC) of IoT devices, optimizing UAV trajectories for data collection is a complex problem. We propose an optimization framework that strategically plans UAV trajectories to minimize service time at designated Rendezvous Points (RPs) and bypasses RPs when necessary to reduce the overall trajectory path, taking into account buffer status and dynamic service time adjustments. To efficiently solve this optimization problem, we employ a meta-heuristic technique known as Path Cheapest Arc with Guided Local Search (PCA-GLS) for UAV route planning within predefined time windows. Extensive simulations demonstrate the effectiveness of our proposed solution in optimizing UAV trajectories and improving data collection performance compared to existing algorithms such as BA-UAV, ACO-MS, and NSGA-II.

The Internet of Things (IoT) has been increasingly applied in various applications in recent years. In IoT, many tasks are performed for a load operation, such as creating a cluster, preserving convergence/connectivity issues, etc. However, the energy consumption rate is also high due to more traffic in dense networks. Generally, a traditional IoT node's battery power capacity is limited due to a short-range cycle. To address the energy shortage problem, researchers have tackled it through the Solar Powered (SP) energy harvesting technique. This method provides abundant energy to the IoT nodes at a lower cost. One issue arises from the target coverage area, which requires that each target must have at least one node for continuous monitoring in a given area. To address these issues, we have designed an effective solution called the Efficient Scheduling Target Coverage (ESTC) algorithm. This approach consists of various cover sets that work in an interleaving way. If only some node sets need to be active to satisfy coverage constraints, then there is no need to activate all sets simultaneously. ESTC provides robust coverage awareness with a perpetual network lifetime using scheduling techniques. Furthermore, the proposed work also promotes a green IoT network.