Energy-Efficient Trajectory Optimization for UAV-Assisted IoT Networks
Abstract : In this paper, we propose and study an energy-efficient trajectory optimization scheme for unmanned aerial vehicle (UAV) assisted Internet of Things (IoT) networks. In such networks, a single UAV is powered by both solar energy and charging stations (CSs), resulting in sustainable communication services, while avoiding energy outage. In particular, we optimize the trajectory design of UAV by jointly considering the average data rate, the total energy consumption, and the fairness of coverage for the IoT terminals. A dynamic spatial-temporal configuration scheme is operated for terminals working in the discontinuous reception (DRX) mode. The module-free, action-confined on-policy and off-policy reinforcement learning approaches are proposed and jointly applied to solve the formulated optimization problem in this paper. We evaluate the effectiveness of the proposed strategy by comparing it with other dynamic benchmark algorithms. The extensive simulation results provided in this paper reveal that the proposed scheme outperforms the benchmarks in terms of data transmission, energy efficiency and adaptivity of avoiding battery depletion. By deploying the proposed trajectory scheme, the UAV is able to adapt itself according to the temporal and dynamic conditions of communication networks.
? We have considered some key metrics to evaluate the proposed MAC’s performance and compared it with existing protocols.
? The UAVs are also promising for different remote data collection disaster response, and Flash Crowds solutions in order to meet the demand of seamless wireless communication by enabling effective network load-balancing.
? In, a Q-learning based IoT task offloading service is provided by the UAVs along with network resource allocation solution considering the UAV path selection constraint.
? However, the core challenge of multiple UAV trajectory optimization with energy efficiency constraint is yet not well-investigated.
? We exploit the successive convex approximation (SCA) technique and Dinkelbach algorithm to transform the non-convex fractional programming problem into a solvable form.
? UAV and ground users solve the optimization problem cooperatively in a distributed manner.
? The SCA-based algorithm is adopted to achieve the local optimizer of the original problem.
? The original problem is decomposed into several sub-problems without losing optimality, and the UAV and users solve the optimization problem cooperatively.
? The non-convex and non-linear energy efficiency maximization problem has been solved in a distributed manner.
• In the proposed HPP scheme, the probabilistic roadmap (PRM) algorithm is used to design the shortest trajectory map and the optimized artificial bee colony (ABC) algorithm to improve different path constraints in a three-dimensional environment.
• A lightweight blockchain-based secure routing algorithm for swarm UAV networking is proposed in.
• The proposed hybrid algorithm aims to navigate the most acceptable flight route in a complex environment without colliding with environmental elements by combining the PRM and ABC algorithms.
• The proposed model uses an energy-efficient clustering technique which plays a significant role in reducing delay and energy of UWSNs.
? To improve the user experience on the computing service, UAVs should maximize their energy efficiency by optimizing their computing ability in the limited service time.
? To the best of our knowledge, the joint optimization of UAV trajectory, computation load allocation, and communication resource management considering energy efficiency has not been investigated in the UAV-assisted MEC system.
? Moreover, for both scenarios, with loose user offloading requirements, the energy efficiency is improved due to the expanded optimization feasible set.
? In contrast, with tight user offloading requirements, the energy efficiency is decreased significantly due to high energy consumption for the UAV to move closer to the users.
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