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A vehicular cloud (VC) can reduce latency and improve resource utilization of the Internet of vehicles by effectively using the underutilized computing resources of nearby vehicles. Although the task offloading of the VC enhances road safety and traffic management on the Internet of vehicles and meets the low-latency requirements for driving safety services on the Internet of vehicles business, there are still some key challenges such as the resource allocation mechanism of differentiated services (DiffServ) and task offloading mechanism of improving user experience. To address these issues, we study the task offloading and resource allocation strategy of the VC system where tasks generated by vehicles can be offloaded and executed cooperatively by vehicles in VC. Specifically, the computing task is further divided into independent subtasks and executed in different vehicles in VC to maximize the offloading utility. Considering the mobility of vehicles, the deadline of tasks, and the limited computing resources, we propose the optimization problem of task offloading in the VC system in the cause of improved user experience. To characterize the difference in service requirements resulting from the diversity of tasks, a DiffServ model focusing on the pricing of a task is utilized. The initial pricing of a task is tailored by the characteristics of the task and the uniqueness of the network status. In this model, tasks are sorted and processed in order according to task pricing, so as to optimize resource allocation. Numerical results show that the proposed scheme can effectively increase the resource utilization and task completion ratio. [ABSTRACT FROM AUTHOR]

In this correspondence, we jointly optimize the caching decision and radio and computation resource allocation for video service in mobile edge computing (MEC) enabled network to maximize the system revenue. To tolerate the uncertainty in the network traffic and avoid “hard constraint” based on constant content request rate, we employ robust optimization to obtain optimal caching decisions, and then accordingly allocate the radio and computation resources for video transcoding. Simulation results demonstrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Faced with multiple users with different delay sensitivities, how to effectively use transmission resources and computing resources in limited edge nodes to ensure the delay and energy requirements of users becomes a key issue. To this end, a joint optimization scheme based on mobile edge computing (MEC) for task offloading and resource allocation is proposed. Firstly, to minimize the total computation time of the offloading tasks at MEC, each user is assigned with the optimal MEC computing resource. Secondly, a resource block (RB) distribution algorithm based on the delay-sensitive, satisfaction degree and quality of RBs is introduced in a distributed manner. Finally, each user makes the offloading decision by comparing the local computational overhead with the offloading computational overhead. The simulation results show that the proposed algorithm achieves the minimum system overhead by effectively allocating transmission resources and computing resources under the premise of meeting the requirements of high-latency sensitive users. [ABSTRACT FROM AUTHOR]

Driven by the explosion transmission and computation requirement in 5G vehicular networks, mobile edge computing (MEC) attracts more attention than centralized cloud computing. The advantage of MEC is to provide a large amount of computation and storage resources to the edge of networks so as to offload computation-intensive and delay-sensitive applications from vehicle terminals. However, according to the mobility of vehicle terminals and the time varying traffic load, the optimal task offloading decisions is crucial. In this paper, we consider the uplink transmission from vehicles to road side units in the vehicular network. A dynamic task offloading decision for flexible subtasks is proposed to minimize the utility, which includes energy consumption and packet drop rate. Furthermore, a computation resource allocation scheme is introduced to allocate the computation resources of MEC server due to the differences in the computation intensity and the transmission queue of each vehicle. Consequently, a Lyapunov-based dynamic offloading decision algorithm is proposed, which combines the dynamic task offloading decision and computation resource allocation, to minimize the utility function while ensuring the stability of the queue. Finally, simulation results demonstrate that the proposed algorithm could achieve a significant improvement in the utility of vehicular networks compared with comparison algorithms. [ABSTRACT FROM AUTHOR]

Recently, with the evolutionary development of intelligent devices, the rapid increasing demand of mobile data traffic and heavy energy consumption in wireless networks become critical problems. Small cell network is considered as a promising solution to improve the communication quality of user equipments and increase the network transmission rate. Nevertheless, the increasing number of small base stations could extremely consume the extra electric energy and reduce network energy efficiency. To improve network energy efficiency, a clustering and geometric approach based power allocation algorithm is designed, which could guarantee the loading balance and the outage probability of heterogeneous cellular networks. Initially, a semi-definite programming based correlation clustering algorithm is introduced for femtocell base stations (FBSs) cluster formation to eliminate the intra-layer interference. Moreover, an energy efficiency and geometric approach based power allocation algorithm combines with the dynamic FBSs on/off strategy could further improve network energy efficiency. Thereby, the associated user equipments could be transferred to the intra-cluster FBSs by the dual connectivity method. Finally, numerical simulation results demonstrate that the proposed algorithm could achieve superior performance in terms of network energy efficiency compared with algorithms in literatures. [ABSTRACT FROM AUTHOR]

The rapid proliferation of new applications poses great challenges to the computation capability of mobile devices. To tackle this problem, computation offloading was proposed as a promising paradigm. In this paper, we consider a cellular device-to-device (D2D) mobile edge computing (MEC) system consisting of one base station (BS) deployed with an MEC server and users. We assume that a portion of users have task computation requirements and may perform local computing, MEC offloading, or D2D offloading. Further assuming that tasks can be partitioned into small-sized data and can be executed simultaneously via various computation modes, we jointly study computation offloading and resource allocation problem. To achieve efficient information interaction and task management, we first propose a joint task management architecture. Defining task execution cost as the weighted sum of task execution latency and energy consumption, we then formulate the joint optimization problem as a task execution cost minimization problem. As the formulated problem is a mixed integer nonlinear problem, which cannot be solved conveniently, we propose a heuristic algorithm that successively solves computation offloading subproblem and resource allocation subproblem by Kuhn–Munkres algorithm and Lagrangian dual method, respectively. Numerical results demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Heterogeneous machine-to-machine communication networks (HMCNs) are expected to provide ubiquitous connectivity for machine type communication devices (MTCDs) with reduced human intervention. Although various types of services will be supported for MTCDs, diverse quality of service requirements and network characteristics pose challenges and difficulties to the resource allocation, cell selection, and random access schemes of the HMCNs. In this paper, we propose a joint access class barring (ACB), cell selection, and resource allocation algorithm for HMCNs. To achieve efficient utilization of the resource blocks (RBs) in heterogeneous cells, we first propose a virtual clustering-based joint ACB and RB allocation sub-algorithm. Partitioning the MTCDs into virtual clusters, we formulate the joint ACB and RB allocation problem of the virtual clusters as RB utilization maximization problem. Through solving the optimization problem, we obtain the optimal ACB factor and RB allocation strategy. Given the optimal strategy, we then formulate the joint cell selection and power allocation problem of the MTCDs as a utility function maximization problem. As the formulated problem is a mixed nonlinear optimization problem, which cannot be solved conveniently, we transform the problem using relaxation method and solve it through applying an iterative algorithm and the Lagrange dual method. Simulation results demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

To meet the ever-increasing demands of mobile traffic, femtocells are considered as one of the promising solutions. In this paper, we study a sensing-based resource allocation scenario in cognitive femtocell networks and present an efficient distributed imperfect-spectrum-sensing-based resource allocation (DIRA) algorithm while considering the channel uncertainty to maximize the total data rate of cognitive femtocell networks by jointly optimizing both subchannel assignment and power allocation taking into account the influence of the sensing accuracy. However, the general optimization problem turns out to be a mixed integer programming problem. In order to make it tractable, the original optimization problem is divided into two sub-optimization problems,namely,optimal subchannel allocation and optimal power allocation. Specifically, the proposed distributed fairness-based subchannel allocation (DFSA) algorithm guarantees fairness by introducing channel condition difference and satisfaction degree as the indicators of subchannel allocation. Additionally, optimal power allocation with the consideration of imperfect spectrum sensing and interference uncertainty is performed using the proposed chance-constrained power optimization (CPO) algorithm. Bernstein's approximation is conducted to make the chance constraint tractable. Simulation results illustrate that the distributed imperfect-spectrum-sensing-based resource allocation (DIRA) algorithm can provide considerable fairness among femtocells and at the same time maximize the total data rate of the cognitive femtocell network. [ABSTRACT FROM AUTHOR]

Mobile edge computing has risen as a promising technology for augmenting the computational capabilities of mobile devices. Meanwhile, in-network caching has become a natural trend of the solution of handling exponentially increasing Internet traffic. The important issues in these two networking paradigms are computation offloading and content caching strategies, respectively. In order to jointly tackle these issues in wireless cellular networks with mobile edge computing, we formulate the computation offloading decision, resource allocation and content caching strategy as an optimization problem, considering the total revenue of the network. Furthermore, we transform the original problem into a convex problem and then decompose it in order to solve it in a distributed and efficient way. Finally, with recent advances in distributed convex optimization, we develop an alternating direction method of multipliers-based algorithm to solve the optimization problem. The effectiveness of the proposed scheme is demonstrated by simulation results with different system parameters. [ABSTRACT FROM PUBLISHER]

Mobile edge computing (MEC) has attracted great interests as a promising approach to augment computational capabilities of mobile devices. An important issue in the MEC paradigm is computation offloading. In this paper, we propose an integrated framework for computation offloading and interference management in wireless cellular networks with MEC. In this integrated framework, we formulate the computation offloading decision, physical resource block (PRB) allocation, and MEC computation resource allocation as optimization problems. The MEC server makes the offloading decision according to the local computation overhead estimated by all user equipments (UEs) and the offloading overhead estimated by the MEC server itself. Then, the MEC server performs the PRB allocation using the graph coloring method. The outcomes of the offloading decision and PRB allocation are then used to distribute the computation resource of the MEC server to the UEs. Simulation results are presented to show the effectiveness of the proposed scheme with different system parameters. [ABSTRACT FROM AUTHOR]