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Cooperative Vehicle-Infrastructure System (CVIS) can provide innovative services for traffic management and enable trips to be safer, more coordinated, and smarter. In the CVIS, the vehicles upload crowd sensing data to the Vehicular Edge Computing (VEC) server for quick data fusion and informed decision-making. However, with the ever-increasing number of vehicles, the VEC server cannot undertake massive computation-intensive tasks due to the limited edge computing capabilities. In this paper, we propose a reverse offloading framework that can fully utilize the vehicular computation resource to relieve the burden of the VEC server and further reduce the system latency. Under the proposed offloading framework, the binary reverse offloading (BRO) and partial reverse offloading (PRO) strategies are designed for two types of tasks, i.e., non-partitioned tasks and partitioned tasks. We formulate the system latency minimization problem by optimizing reverse offloading decisions, and the communication and computation resources allocation. Due to the non-convex and existing variables coupling, the original problem is transformed into the equivalent weighted-sum optimization problem. Based on the alternative optimization, we decouple the weighted-sum optimization problems into the two subproblems, and the closed-form expressions of transmission power and computation frequency of vehicles and RSU are derived. Low complexity greedy based efficient searching (GES) algorithm and joint alternative optimization based bi-section searching (JAOBSS) algorithm are proposed for BRO and PRO strategies, respectively. The algorithm complexity and performance bounds are analyzed. Simulation results show that the proposed GES algorithm can achieve optimal performance with low complexity. Besides, the proposed GES and JAOBSS algorithms can significantly improve the performance compared with other baseline schemes by 6.14% and 13.46% at least. [ABSTRACT FROM AUTHOR]

With powerful storage and computation capability, vehicular edge computing is considered as a promising paradigm to enhance the safety and quality-of-service of vehicles in intelligent transportation systems. In this work, we investigate a joint road side units (RSUs) selection and resource allocation problem, with the objective of minimizing the total task offloading delay subject to the bandwidth and computation resources constraints, in non-collaborative and collaborative RSU scenarios. For the former, as the formulated problem is a mixed-integer nonlinear programming problem, we re-formulate the original problem into a convex one and then decompose it into a distributed manner. By utilizing the alternating direction method of multipliers, we propose a joint RSUs selection and resource allocation (JRSRA) algorithm. For the collaborative RSU scenarios, we transform the problem into a convex optimization problem by linearization, and propose a joint resource allocation based branch and bound (JRABB) algorithm to solve the total task offloading delay minimization problem. Simulation results show that the proposed JRSRA and JRABB algorithms can reduce the total task offloading delay compared with other benchmark methods. [ABSTRACT FROM AUTHOR]

Vehicular edge computing (VEC) is considered as a novel paradigm to enhance the safety of automated vehicles and intelligent transportation systems (ITS). The computation offloading strategies are the key point of VEC, and the effect of time-varying channels cannot be ignored during the task transmission period. This paper investigates the utility maximization problem with task delay requirement constraints, in which the influence of time-varying channel on the task offloading strategies during the task offloading period is considered. The time-varying fading channel leads to the time-varying spectrum efficiency (SE), so the previous offloading strategies are questionable when the additional uncertain allocated bandwidth is taken into account. To deal with it, we first propose a linearization based Branch and Bound (LBB) algorithm to solve the fixed SE problem without considering the time-varying channel characteristics. Considering the complexity of the LBB algorithm, a closest rounding integer (CRI) algorithm is proposed to solve the fixed SE problem. Then, based on the resource allocation strategies of the fixed SE problem, we propose the LBB based computation offloading (LBBCO) algorithm and the CRI based computation offloading (CRICO) algorithm to solve the original problem for both the static tasks and dynamic tasks. The proposed LBBCO/CRICO algorithms are also applicable to multi-vehicle and multi-task scenarios. Furthermore, we analyze the effect of small-scale fading on the proposed offloading strategies. The simulation results show that the average utilities of LBBCO and CRICO algorithms have a small gap by 3.93% and 6.13% only to the upper bound, respectively. Meanwhile, the proposed LBBCO and CRICO algorithms can outperform the previous state-of-the-art solution by 4.52% and 2.38%, respectively. [ABSTRACT FROM AUTHOR]

The IoT is a novel platform for making objects more intelligent by connecting to the Internet. However, mass connections, big data processing, and huge power consumption restrict the development of IoT. In order to address these challenges, this article proposes a novel ECIoT architecture. To further enhance the system performance, radio resource and computational resource management in ECIoT are also investigated. According to the characteristics of the ECIoT, we mainly focus on admission control, computational resource allocation, and power control. To improve the performance of ECIoT, cross-layer dynamic stochastic network optimization is studied to maximize the system utility, based on the Lyapunov stochastic optimization approach. Evaluation results are provided which demonstrate that the proposed resource allocation scheme can improve throughput, reduce end-to-end delay, and also achieve an average throughput and delay trade-off. Finally, the future research topics of resource management in ECIoT are discussed. [ABSTRACT FROM AUTHOR]