The advent of 5G technologies enables highly anticipated prospect of the internet of things in smart cities. Smart terminals become a necessity with increasing number of applications and tasks. The limited computational resources and constrained battery energy at terminals compel tasks to be offloaded for execution sometimes. For time-sensitive and safety-critical tasks, fog computing is the preferred option instead of cloud computing, since fog computing brings the computing resources to the edge of networks and thus has relatively shorter response latency. In this context, Vehicular fog computing (VFC) that supplements fog computing by contributing vehicular computing resources has been attracting increasing attention in recent years. However, a few of challenging issues regarding task offloading in VFC still need to be addressed. For example, decision making for task allocation and response latency optimization are not straightforward, since on one hand the connection between vehicles and end users is usually short or intermittent; on the other hand the multi-hop inter-vehicle task forwarding is time-consuming and thus prone to packet loss. To address the latency related issue, we in this paper propose a three-layer architecture of VFC to schedule the tasks offloaded from MDs. RSU is endowed with computing facilities and storing resources such that it can act as not only a gateway to the remote cloud center but also a centralized infrastructure responsible for task scheduling among vehicles. A greedy heuristic based scheduling strategy and algorithms are proposed in the next. Last, an extensive series of experiments are carried out to investigate the proposed scheduling strategy. The results show that the proposed scheduling strategy outperforms other well known heuristics based scheduling strategies such as Min-Min and Max-Min and thus has wide prospects.

Unmanned aerial vehicle (UAV)-empowered edge computing has been widely investigated in obstacle-free scenarios, where a moving UAV is in charge of handling offloaded singleton tasks from mobile devices on the ground. However, little attention has been paid to the scenario, in which the UAV serves a complex area with multiple obstacles and dependent tasks . A dependent task can be formulated as a directed acyclic graph (DAG) that contains a number of subtasks; and each subtask can be executed by a corresponding service function (SF) deployed on the UAV. In this backdrop, the joint UAV trajectory planning, DAG task scheduling, and SF deployment is formulated as an optimization problem in this article. Afterwards, a deep reinforcement learning (DRL)-based algorithm is presented to tackle the NP-hard problem. The state space, action space, and the reward function of the agent, i.e., the UAV, are defined, respectively, under the DRL framework. To evaluate the effectiveness of the proposal, a series of experiments is conducted with different parameter settings. Results show that the DRL-based algorithm performs much better than three heuristic algorithms in success rate of trajectory planning, the number of executed tasks, and the average task response latency.

Various security threats and corresponding countermeasures has been a major concern of the maintainers of multi-hop wireless networks (MHWNs) in recent years. This is accompanied with background advances in wireless link bandwidth and rapid growth deployment of wireless network. As one of the main security threats MHWN faces, jamming attack has attracted much attention since it is easy to launch and can cause severe damages to the network. Accurate jammer localization plays an essential role in applying various anti-jamming countermeasures and it has been a hot topic in recent years. This survey overviews the major recent works done in the field of jammer localization for MHWN. The major contribution of this paper is to present all the typical jammer localization schemes in a single literature along with comprehensive performance comparison by simulation with respect to time complexity, location accuracy, and communication overhead in various MHWN deployment environments. Besides, directions for future research are outlined. We hope this survey can provide insight into different research directions to improve jammer localization accuracy for future developments.

Unmanned Aerial Vehicles (UAVs) are considered a promising solution for carrying communications and computational facilities to increase the flexibility of cloud-to-thing continuum, where short-range and long-range wireless links are adopted to connect mobile devices to the fog node and the fog node to the remote data center, respectively. Most existing UAV-involved resource allocation algorithms focus mainly on the radio resource allocation problem, and much less attention has been paid to the allocation of computational resources. Moreover, the dynamic arrival of tasks and the queueing delay at each computation entity is usually neglected. In this paper, a joint optimization problem is formulated that takes the weighted sum of energy consumption and delay experienced by tasks as the objective function. Processing frequencies and transmission powers of mobile devices and the fog node are the decision variables in the problem. To solve this problem, three decision-making algorithms are presented. The first one is used to decide the UAV’s position. The processing frequency, transmission power, and task assignment results at mobile devices are determined by the second algorithm. The last one is adopted by the fog node to optimize its processing frequency and transmission power. A series of simulation experiments are conducted to evaluate the effectiveness of the proposed algorithms. Compared with the random task assignment scheme with fixed parameters, the combination of our three algorithms always perform much better for a wide range of parameter settings.