Vehicular Edge Computing (VEC) has gained increasing interest due to its potential to provide low latency and reduce the load in backhaul networks. In order to meet drastically increasing computation demands from emerging ever-growing vehicular applications, e.g., autonomous driving, abundant computation resources of individual vehicles can play a crucial role in task execution in a VEC scenario, that can further contribute in considerably improving user experience. This is however an extremely challenging task due to high mobility of vehicles that can easily lead to intermittent connectivity, thereby disrupting on-going task processing. In this paper, we propose a task offloading scheme by exploiting multi-hop vehicle computation resources in VEC based on mobility analysis of vehicles. In addition to the vehicles within one hop from the task vehicle that generates computation tasks, certain multi-hop vehicles that meet the given requirements in terms of link connectivity and computation capacity, are also leveraged to carry out the tasks offloaded by the task vehicle. An optimization problem is formulated for the task vehicle to minimize the weighted sum of execution time and computation cost of all tasks. A semidefinite relaxation approach with an adaptive adjustment procedure is proposed to solve the formulated optimization problem for obtaining the corresponding offloading decisions. The simulation results show that our proposed offloading scheme can achieve significant improvement in terms of response delay by at least 34% compared with the other algorithms (e.g., local processing and random offloading).

Vehicular Edge Computing (VEC) is enjoying a surge in research interest due to the remarkable potential to reduce response delay and alleviate bandwidth pressure. Facing the ever-growing service applications in VEC, how to effectively aggregate and flexibly schedule ubiquitous network resources for implementing diverse tasks and meeting differentiated demands from numerous vehicular users remains haunting. Toward this end, we investigate collaborative task computing and on-demand resource allocation. The collaborative computing framework in VEC is provided to support deep collaboration and intelligent management of heterogeneous resources widely distributed in vehicles, edge servers and cloud. Based on this framework, the joint optimization problem of distributed task offloading and multi-resource management is formulated with the aim to maximize the system utility by making the optimal task and resource scheduling policy, the novelty of which lies in the exploration of available vehicle resources and the consideration of service migration. In view of the dynamics, randomness and time-variant of vehicular networks, the asynchronous deep reinforcement algorithm is leveraged to find the optimal solution. Extensive simulation experiments are implemented to demonstrate the superiority of our proposed algorithm in terms of response latency compared with full offloading and random offloading.

As the communication mode with the greatest attention and global network coverage, the Low Earth Orbit (LEO) satellite network has the characteristics of low propagation delay, low link loss and handheld terminal. However, with the increasing requirements of user terminals for network latency and bandwidth, the traditional central cloud computing mode no longer has advantages. Therefore, borrowing from the idea of edge computing in terrestrial networks, Orbital Edge Computing (OEC) technology deploys Multi-access Edge Computing (MEC) servers on LEO satellite constellations to meet the growing demand for real-time and reliability of various applications. Based on the above motivations, this paper proposes an OEC Task Allocation (OEC-TA) algorithm based on the greedy strategy in LEO satellite networks for the Walker Delta satellite constellation, which fully utilizes satellite computing resources to provide services to ground users. Then, we analyze the performance of our proposed algorithm in terms of computational cost. Finally, experimental results show that OEC-TA is better than DEC (Double Edge Computing) and random allocation model on average delay and energy consumption reduction. Compared with DEC, our OEC-TA can reduce the average delay and energy consumption up to 10% and 16.5%, respectively.

The Internet of Vehicles (IoV) is an emerging paradigm, driven by recent advancements in vehicular communications and networking. Meanwhile, the capability and intelligence of vehicles are being rapidly enhanced, and this will have the potential of supporting a plethora of new exciting applications, which will integrate fully autonomous vehicles, the Internet of Things (IoT), and the environment. In view of the delay-sensitive property of these promising applications, as well as the high expense by using infrastructures and roadside units (RSU), the task offloading among vehicles has gained enormous popularity considering its free-of-charge and timely response. In this paper, by utilizing the gathering period of vehicles in urban environment due to stopped by traffic lights or Area of Interest (AOI), a task offloading scheme merely relying on vehicle-to-vehicle (V2V) communication is proposed by fully exploring the idle resources of gathered vehicles for task execution. Through formulating the task execution as a Min-Max problem among one task and several cooperative vehicles, the task executing time is optimized with the Max-Min Fairness scheme, which is further solved by the Particle Swarm Optimization (PSO) Algorithm. Extensive simulation demonstrate that our model could well meet the delay requirement of delay-sensitive application by cooperative computing among vehicles.

Multiple unmanned aerial vehicles (UAVs) form a UAV cluster, which relies on wireless communication networks to facilitate information interaction among the UAVs. UAV clusters offer enhanced efficiency and fault tolerance, making reliable computing services possible. Compared to terrestrial networks, UAV computing network presents several desirable features, including mobility, availability, and flexibility, affording the potential for seamless global coverage. However, due to the inherent openness of wireless communication networks, UAV computing networks are susceptible to various security attacks. Existing access control mechanisms for UAV computing networks predominantly rely on base stations or central servers. This reliance elevates communication overhead for UAVs and exposes them to potential attacks from adversaries. Therefore, we propose a blockchain-aided distributed secure access control scheme specifically tailored for UAV computing networks, enabling UAVs to autonomously manage and determine identity, attributes, and access policies. To address the communication complexity and scalability concerns associated with blockchains, we integrate committee elections and clustering optimizations into the scheme. Security analysis and performance evaluation demonstrate that the proposed scheme can withstand common external and internal attacks in UAV clusters while ensuring lightweight energy consumption and scalability of UAV computing networks.