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.

Space-air-ground integrated networks (SAGINs) have gained significant attention and become a promising architecture for ubiquitous connectivity for 5G-Advanced and 6G, enabling the integration of satellite networks, aerial networks, and terrestrial networks. This integration brings tremendous communication benefits, such as non-terrestrial networks, seamless global coverage, high flexibility, and augmented system capacity. Meanwhile, computing capability becomes an indispensable part of the SAGIN ecosystem. In SAGINs, limited and unbalanced computation and communication resources of different network segments make it challenging to provide strict quality-of-service (QoS) guarantees for specific traffic (e.g., delay-sensitive traffic and outage-sensitive traffic). To fully utilize available system resources in SAGINs, cooperative computing among different network segments is a promising technology. This article presents the fundamentals and applications of computing over SAGINs by introducing the system architecture and explaining the potential technical issues related to cooperative computing. Furthermore, the potential for cooperative computing over SAGINs is showcased with a preliminary performance evaluation. Finally, future research opportunities are discussed.

Unmanned aerial vehicles (UAVs) are attracting increasing attention for applications such as video streaming, surveillance, and delivery using reliable line-of-sight (LOS) links. Nevertheless, due to the large radio-frequency (RF) transmission footprint from a UAV transmitted to ground nodes, UAV communications may significantly deteriorate the performance of cochannel ground communication links. With the lack of a dedicated spectrum, researchers need to design efficient spectrum-sharing policies for UAV communications to enhance spectral efficiency (SE) and control interference-to-ground communications. One technique for spectrum sharing is spatial spectrum sensing (SSS), which enables devices to sense spatial spectrum opportunities and reuse them aggressively and efficiently by controlling the SSS radius. The goal of this article is to introduce the fundamentals, challenges, and applications of SSS for UAV spectrum access and discuss open research problems for realizing UAV spectrum sharing, including dynamic spectrum access for swarm UAV networks, artificial intelligence (AI)-enabled UAV spectrum access, blockchain-based UAV spectrum access, multichannel access for UAVs, and the integration of UAVs into cellular networks.

In this letter, we consider a multi-user wireless powered mobile edge computing (MEC) system, in which a base station (BS) integrated with an MEC server transfers energy to wireless devices (WDs) as an incentive to encourage them to offload computing tasks to the MEC server. We formulate an optimization problem to contemporaneously maximize the data utility and minimize the energy consumption of the operator under the offloaded delay constraint, by jointly controlling wireless-power allocation at the BS as well as offloaded data size and power allocation at the WDs. To solve this problem, the offloaded delay constraint is first transformed into an offloaded data rate constraint. Then an iterative algorithm is designed to obtain the optimal offloaded data size and power allocation at the WDs by using Lagrangian dual method. The results are applied to derive the optimal wireless-power allocation at the BS. Finally, simulation results show that our algorithm outperforms existing schemes in terms of operator’s reward.

Unmanned aerial vehicles (UAVs) are expected to be deployed as aerial base stations (BSs) in future wireless networks to provide extensive coverage and additional computational capabilities for user equipments (UEs). In this paper, we study mobile edge computing (MEC) in air-ground integrated wireless networks including ground computational access points (GCAPs), UAVs, and UEs, where UAVs and GCAPs cooperatively provide computing resources for UEs. Our goal is to minimize the total energy consumption of UEs by jointly optimizing users’ association, uplink power control, channel allocation, computation capacity allocation, and UAV three dimensional (3D) placement, subject to the constraints on deterministic binary offloading, UEs’ latency requirements, computation capacity, UAV power consumption, and available bandwidth. Due to the non-convexity of the primary problem and the coupling of variables, we introduce a coordinate descent algorithm that decomposes the UEs’ energy consumption minimization problem into several subproblems which can be efficiently solved. Simulation results demonstrate the advantages of the proposed algorithm in terms of the reduced total energy consumption of UEs.