Reducing the energy consumption of Internet services requires knowledge about the specific traffic and energy consumption characteristics, as well as the associated end-to-end topology and the energy consumption of each network segment. Here, we propose a shift from segment-specific to service-specific end-to-end energy-efficiency modeling to align engineering with activity-based accounting principles. We use the model to assess a range of the most popular instant messaging and video play applications to emerging augmented reality and virtual reality applications. We demonstrate how measurements can be conducted and used in service-specific end-to-end energy consumption assessments. Since the energy consumption is dependent on user behavior, we then conduct a sensitivity analysis on different usage patterns and identify the root causes of service-specific energy consumption. Our main findings show that smartphones are the main energy consumers for web browsing and instant messaging applications, whereas the LTE wireless network is the main consumer for heavy data applications such as video play, video chat and virtual reality applications. By using small cell offloading and mobile edge caching, our results show that the energy consumption of popular and emerging applications could potentially be reduced by over 80%.

Unmanned aerial vehicle (UAV)-assisted multiaccess edge computing (MEC) technology has garnered significant attention and has been successfully implemented in specific scenarios. The optimization of the network energy consumption in the relevant scenarios is essential for the whole system performance due to the constrained energy capacity of UAVs. However, the dynamic changes in MEC network resources make the energy consumption optimization a challenge. In this paper, a multi-UAV-multiuser MEC model is established to assess the system energy consumption, and the optimization problem of multi-UAV cooperation strategies is formulated base on the model. Then, a multi-agent deep deterministic policy gradient (MADDPG) algorithm based on deep reinforcement learning (DRL) is employed to resolve the above optimization problem. Each UAV is equivalent to a single agent that cooperates with other agents to train actors and critic evaluation networks to accomplish collaborative decision-making. In addition, a prioritized experience replay (PER) scheme is used to improve the convergence of the training process. Simulation results show that the impact of changes in different network resources on the network energy consumption by comparing the performance of different algorithms. The findings presented in this paper serve as a valuable reference for future work on system performance optimization, specifically in terms of energy efficiency.

Unmanned aerial vehicle (UAV)-assisted multiaccess edge computing (MEC) networks can effectively broaden the application scope of the Internet of Things (IoT) in complex scenarios such as maritime operations, military communications, and emergency commands. However, uncertain factors, such as weather changes and temporary airspace control, pose great challenges to UAV flight safety. Obstacles resulting from these uncertain factors may intersect with UAVs with preplanned flight paths, leading to accidents. Therefore, generating the optimal flight trajectory to avoid these obstacles is key in the successful operation of this fuzzy system. In this paper, we present a heuristic trajectory generation scheme for complex offshore environments that can generate optimal trajectories according to complex terrain conditions and avoid uncertain obstacles. First, we build a complex terrain model based on a three-dimensional (3D) offshore environment to simulate the conditions in UAV-assisted MEC networks. Second, we propose a network performance optimization objective function that is based on UAV characteristics. Third, we improve the existing ant colony optimization (ACO) algorithm by introducing chaotic mapping, polarizing the pheromone recording rule and implementing a simulated annealing screening mechanism to efficiently generate trajectories. Finally, we design an efficient obstacle avoidance algorithm for different combinations of obstacle regions. The simulation results show that our proposed trajectory generation scheme can efficiently avoid obstacles and significantly improve the total trajectory loss rate compared with that of baseline schemes.