Mobile edge computing (MEC) is a promising paradigm to improve the quality of computation experience of mobile devices because it allows mobile devices to offload computing tasks to MEC servers, benefiting from the powerful computing resources of MEC servers. However, the existing computation-offloading works have also some open issues: 1) security and privacy issues, 2) cooperative computation offloading, and 3) dynamic optimization. To address the security and privacy issues, we employ blockchain technology that ensures the reliability and irreversibility of data in MEC systems. Meanwhile, we jointly design and optimize the performance of blockchain and MEC. In this paper, we develop a cooperative computation offloading and resource allocation framework for blockchain-enabled MEC systems. In the framework, we design a multi-objective function to maximize the computation rate of MEC systems and the transaction throughput of blockchain systems by jointly optimizing offloading decision, power allocation, block size and block interval. Due to the dynamic characteristics of the wireless fading channel and the processing queues at MEC servers, the joint optimization is formulated as a Markov decision process (MDP). To tackle the dynamics and complexity of the blockchain-enabled MEC system, we develop an A3C-based cooperation computation offloading and resource allocation algorithm to solve the MDP problem. In the algorithm, deep neural networks are optimized by utilizing asynchronous gradient descent and eliminating the correlation of data. Simulation results show that the proposed algorithm converges fast and achieves significant performance improvements over existing schemes in terms of total reward.

The proliferation of smart vehicular terminals (VTs) and their resource hungry applications imposes serious challenges to the processing capabilities of VTs and the delivery of vehicular services. Mobile Edge Computing (MEC) offers a promising paradigm to solve this problem by offloading VT applications to proximal MEC servers, while TV white space (TVWS) bands can be used to supplement the bandwidth for computation offloading. In this paper, we consider a cognitive vehicular network (CVN) that uses the TVWS band, and formulate a dual-side optimization problem, to minimize the cost of VTs and that of the MEC server at the same time. Specifically, the dual-side cost minimization is achieved by jointly optimizing the offloading decision and local CPU frequency on the VT side, and the radio resource allocation and server provisioning on the server side, while guaranteeing network stability. Based on Lyapunov optimization, we design an algorithm called DDORV to tackle the joint optimization problem, where only current system states, such as channel states and traffic arrivals, are needed. The closed-form solution to the VT-side problem is obtained easily by derivation and comparing two values. For MEC server side optimization, we first obtain server provisioning independently, and then devise a continuous relaxation and Lagrangian dual decomposition based iterative algorithm for joint radio resource and power allocation. Simulation results demonstrate that DDORV converges fast, can balance the cost-delay tradeoff flexibly, and can obtain more performance gains in cost reduction and as compared with existing schemes.

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 a distributed deep learning paradigm, federated learning (FL) provides a powerful tool for the accurate and efficient processing of on-board data in vehicular edge computing (VEC). However, FL involves the training and transmission of model parameters, which consumes the vehicles’ precious energy resources and takes up much time. It is a departure from many applications with severe real-time requirements in VEC. And the capabilities and data quality of each vehicle are distinct that will affect the performance of training the model. Therefore, it is crucial to select the appropriate vehicles to participate in learning tasks and optimize resource allocation under learning time and energy consumption constraints. In this paper, taking the vehicle position and velocity into consideration, we formulate a min-max optimization problem to jointly optimize the on-board computation capability, transmission power, and local model accuracy to achieve the minimum cost in the worst case of FL. Specifically, we propose a greedy algorithm to select vehicles with higher image quality dynamically, and it keeps the system’s overall cost to a minimum in FL. The formulated optimization problem is a nonlinear programming problem, so we decompose it into two subproblems. For the resource allocation problem, we use the Lagrangian dual problem and the subgradient projection method to approximate the optimal value iteratively. For the local model accuracy problem, we develop an adaptive harmony algorithm for heuristic search. The simulation results show that our proposed algorithms have well convergence and effectiveness and achieve a tradeoff between cost and fairness.

Mobile Edge Computing (MEC) plays a significant role in enabling diverse service applications by implementing efficient data sharing. However, the unique characteristics of MEC also bring data privacy and security problem, which impedes the development of MEC. Blockchain is viewed as a promising technology to guarantee the security and traceability of data sharing. Nonetheless, how to integrate blockchain into MEC system is quite challenging because of dynamic characteristics of channel conditions and network loads. To this end, we propose a secure data sharing scheme in blockchain-enabled MEC system using an asynchronous learning approach in this paper. First, a blockchain-enabled secure data sharing framework in MEC system is presented. Then, we present an adaptive privacy-preserving mechanism according to available system resources and privacy demands of users. Next, an optimization problem of secure data sharing is formulated in blockchain-enabled MEC system with the aim to maximize the system performance with respect to the decreased energy consumption of MEC system and the increased throughput of blockchain system. Specially, an asynchronous learning approach is employed to solve the formulated problem. Numerical results demonstrate the superiority of our proposed secure data sharing scheme when compared with some popular benchmark algorithms in terms of average throughput, average energy consumption and reward.

Federated learning (FL) is a popular edge learning approach that utilizes local data and computing resources of network edge devices to train machine learning (ML) models while preserving users’ privacy. Nevertheless, performing efficient learning tasks on the devices and achieving longer battery life are primary challenges faced by federated learning. In this paper, we are the first to study the application of heterogeneous computing (HC) and wireless power transfer (WPT) to federated learning to address these challenges. Especially, we propose a heterogeneous computation and resource allocation framework based on a heterogeneous mobile architecture to achieve effective implementation of FL. To minimize the energy consumption of smart devices and maximize their harvesting energy simultaneously, we formulate an optimization problem featuring multidimensional control, which jointly considers time splitting for WPT, dataset size allocation, transmit power allocation and subcarrier assignment during communications, and processor frequency of processing units (central processing unit (CPU) and graphics processing unit (GPU)). However, the major obstacle is how to design a proper algorithm to solve this optimization problem efficiently. For this purpose, we decouple the optimization variables so as to achieve high efficiency in deriving its solution. Particularly, we first compute the optimal processor frequency and dataset size allocation via employing the Lagrangian dual method, followed by finding the closed-form solution to the optimal time splitting allocation, and finally attain the optimal subcarrier assignment as well as transmit power for transmissions through an iteration algorithm. To evaluate the performance of our proposed scheme, we set up four baseline schemes as comparison, and simulation results show that the proposed scheme converges quite fast and better enhance the energy efficiency of the wireless powered FL system compared with the baseline schemes.