Space-aerial-assisted computation offloading has been recognized as a promising technique to provide ubiquitous computing services for remote Internet of Things (IoT) applications, such as forest fire monitoring and disaster rescue. This article considers a space-aerial-assisted mixed cloud-edge computing framework, where the flying unmanned aerial vehicles (UAVs) provide IoT devices with low-delay edge computing service and satellites provide ubiquitous access to cloud computing. We aim to minimize the maximum computation delay among IoT devices with the joint scheduling for association control, computation task allocation, transmission power and bandwidth allocation, UAV computation resource, and deployment position optimization. Through exploiting block coordinate descent and successive convex approximation, we develop an alternating optimization algorithm with guaranteed convergence, to solve the formulated problem. Extensive simulation results are provided to demonstrate the remarkable delay reduction of the proposed scheme than existing benchmark methods.

Mobile edge computing (MEC) has been recognized as a viable technology to satisfy low-delay computation requirements for resource-constrained Internet of things (IoT) devices. Nevertheless, the broadcast feature of wireless electromagnetic communications may lead to the security threats to IoT devices. In order to enhance the task offloading security, this paper proposes a reconfigurable intelligent surface (RIS)-assisted secure MEC network framework. Furthermore, we investigate the max-min computation efficiency problem under the secure computation rate requirements, by jointly optimizing the local computing frequencies and transmission power of IoT devices, time-slot assignment, and phase beamforming of the RIS. To solve the formulated non-convex problem, we further develop an iterative algorithm, in which the Dinkelbach-type method and block coordinate descent (BCD) technique are utilized to tackle the fractional objective function and coupled optimization variables, respectively. In particular, the successive convex approximation (SCA) and penalty function-based methods are exploited to solve the transmit power control and reflecting beamforming optimization subproblems, respectively, and the closed-form expression for local computing frequencies optimization subproblem is derived. Numerical results quantify the performance gain achieved by the proposed RIS-assisted secure MEC networks, when compared to existing benchmark methods.

The combination of wireless energy transfer (WET) and mobile edge computing (MEC) has been proposed to satisfy the energy supply and computation requirements of resource-constrained Internet of Things (IoT) devices. However, the energy transfer efficiency and task offloading rate cannot be guaranteed when wireless links between the hybrid access point (HAP) and IoT devices are hostile. To address this problem, this paper aims at utilizing the intelligent reflecting surfaces (IRS) technique to improve the efficiency of WET and task offloading. In particular, we investigate the total computation bits maximization problem for IRS-enhanced wireless powered MEC networks, by jointly optimizing the downlink/uplink phase beamforming of IRS, transmission power and time slot assignment used for WET and task offloading, and local computing frequencies of IoT devices. Furthermore, an iterative algorithm is presented to solve the non-convex non-linear optimization problem, while the optimal transmission power and time allocation, uplink phase beamforming matrixes and local computing frequencies are derived in closed-form expressions. Finally, extensive simulation results validate that our proposed IRS-enhanced wireless powered MEC strategy can achieve higher total computation rate as compared to existing baseline schemes.

Resource sharing among vehicles can highly improve the capability and efficiency of Internet of Vehicles (IoV). However, it is challenging to establish trust and preserve privacy during the resource sharing process because of the high mobility and topological variability in IoV. Emerging blockchain technology expresses the excellent performance in handling distributed trust due to its verifiable and immutable ledger. In this paper, we first propose a consortium blockchain-based resource sharing paradigm in IoV, in which the resource sharing interactions are encapsulated as transactions and recorded by Road Side Units (RSUs). Moreover, a lightweight consensus mechanism named as Proof-of-Reputation is proposed to reduce computational power consumption and motivate vehicles involved in resource sharing. Finally a differentiated resource pricing scheme is proposed based on the dynamic match game of resource demand and supply. The reputation value is designed to indicate the trustworthy degree of vehicles, and the trust is established via the consensus procedure. We couple the resource sharing process and consensus together by utilizing the reputation value of each vehicle. The security and privacy analysis as well as simulation experiments on communication performance can verify the efficiency of the proposed blockchain system.