Federated Learning (FL), allowing data owners to conduct model training without sending their raw data to third-party servers, can enhance data privacy in Mobile Edge Computing (MEC) which brings data processing closer to the data sources. However, the heterogeneity of local data and constrained local resources in MEC bring new challenges hindering the development of FL. To this end, we propose an Auction-based Cluster Federated Learning scheme, called ACFL, comprising a clustered FL framework and an auction-based client selection strategy. Our clustered FL framework first introduces a mean-shift clustering algorithm to FL, which can intelligently cluster clients according to their local data distribution. Then, we select clients from each cluster using an auction mechanism to participate in FL training, which can mitigate the impact of data heterogeneity on model convergence and balance energy consumption. Moreover, we prove the proposed clustered FL framework converges at a sublinear rate. Extensive experiments conducted on real-world datasets demonstrate that the proposed FL scheme outperforms the conventional FL schemes in terms of convergence rate and energy balance.

Device-to-device communications underlying cellular networks is a novel approach for providing IoT services, in which D2D communication links and cellular communication links share the same channels. Unfortunately, various kinds of devices are randomly distributed in cellular networks and potentially result in critical interferences. To solve the interference problem, we propose a novel greedy based channel assignment algorithm in this paper. First, we construct a novel interference graph that describes the interferences between pairs of communication links when they simultaneously share the same channel. Then, a novel channel assignment algorithm is proposed to alleviate system interferences by greedily assigning the best channel to each communication link, which is simple and feasible to implement. Moreover, we convert the channel assignment problem into a robust graph coloring problem and search for a near-optimal solution to minimize the system interferences. Numerical results indicate that the proposed algorithm not only dramatically improves the network capacity, but also enhances the fairness among devices.

Mobile edge computing (MEC) is envisioned as a new paradigm by integrating the mobile computing functionality into 5G wireless networks, aiming at empowering communication networks with low-latency services. In general, mobile devices have finite battery lifetime (e.g., machine-type devices) and the energy harvesting is advocated to provide perpetual energy supply for achieving sustainable operation, which is very important for facilitating sustainable computing in future applications. In this paper, we propose a wireless powered MEC network architecture that employs device-to-device (D2D) communications underlaying heterogeneous networks (HetNets) to enable the computational tasks offloading to resource-rich edge servers. A dynamic offloading decision is made to execute the computation tasks. Then, we focus on the energy-efficient offloading scheme and joint offloading and user association scheme. From the illustrative results, we provide insights for the design of this new network architecture. Furthermore, several open research topics are discussed.

Routing is a challenging issue in opportunistic networks (OPPNETs). Existing routing schemes in OPPNETs can be classified into two categories: single-copy schemes and multicopy schemes. Single-copy schemes can save network resources but cannot guarantee transmission delay and packet delivery ratio. Multicopy schemes can increase network robustness and reduce average delay and, at the same time, consume a large amount of network resources and induce large overhead. Therefore, it is critical to design an efficient routing scheme that can make a better tradeoff between the performance of multicopy and single-copy schemes in resource-constraint OPPNETs. Inspired by this, in this paper, we propose a novel routing scheme based on a Nash bargaining solution in resource-constraint OPPNETs called GameR. In GameR, we map the distributed message dissemination to a multiplayer bargaining model by constructing a proper utility function based on estimated resource utilization ratio and history delivery predictability. GameR can efficiently utilize the network resources, achieving higher delivery ratio and lower overhead in resource-constraint OPPNETs. Extensive simulation results show that GameR performs better compared with existing classical routing schemes in terms of message delivery ratio, average delay, overhead, goodput, and overall performance.

Design an optimization model for task management among Mobile Terminal (MT), Macro cell Base Station (MBS), and multiple Small cell Base Stations (SBS) for the large-scale Mobile Edge Computing (MEC) system, is a challenging issue due to the large number of tasks and SBSs. Inspired by this, we propose a Parallel Optimal Task Allocation Mechanism (POTAM) framework for MEC, which includes Device to Device (D2D)-enabled computing, MBS computing and Edge Computation Resource Distribution (ECRD) computing. In POTAM, we exploit a parallel multi-block Alternating Direction Method of Multipliers (ADMM) based method to model both requirements of delay and energy consumptions, which formulates the task allocation under these requirements as a nonlinear 0–1 integer programming problem. To solve this problem, we develop an efficient combination of conjugate gradient, Newton and linear search techniques based algorithm with Logarithmic Smoothing and Cyclic Block coordinate Gradient Projection (CBGP) methods, which can guarantee convergence and reduce computational complexity with a good scalability. In order to allocate task cooperatively, an optimal approach is proposed, ECRD-A, which is used to find the shortest path among each node. Numerical results demonstrate the effectiveness of the POTAM and it can effectively reduce delay and energy consumption for a large-scale MEC system.