This paper considers computation offloading for mobile applications with task-dependency requirements in mobile edge computing (MEC) systems. Based on the online arrival patterns and various delay constraints of practical applications, we focus on minimizing the system deadline violation ratio (DVR) to improve the overall reliability performance. Specifically, we propose a DVR minimization computation offloading scheme with task migration and merging, in which the task migration and merging model is designed to construct an overall directed acyclic graph (DAG) for all currently dependent tasks. We consider a multi-slot MEC system where applications arrive slot-by-slot without prior knowledge of future arrivals. Then given the number of application arrivals at each time slot, we equivalently transform the DVR minimization problem into a problem that maximizes the number of completed applications in a finite time horizon. The above problem is challenging to determine the optimal task execution order for different applications with various task dependencies and delay constraints. To address this, we develop a migration-enabled multi-priority task sequencing algorithm, which creatively introduces several task priority metrics and determines the optimal task execution order. Then, a deep deterministic policy gradient (DDPG)-based learning algorithm is developed to find the optimal offloading policy. Experimental results demonstrate that the proposed scheme can reduce the system DVR by 60.34%~70.3% compared with existing benchmark schemes under various network scenarios.

Coverage path planning (CPP) for unmanned aerial vehicles (UAVs) plays a significant role in intelligent distributed surveillance systems. However, due to poor cooperation, most existing CPP methods may cause strongly overlapped trajectories, missing areas, or even collisions in uncertain and complex environments, leading to long task completion time and low coverage efficiency. To this end, in this paper we propose a novel multi-UAV distributed online cooperation (MDOC) CPP method that aims to minimize task completion time. Moreover, this method allows UAVs to quickly respond to unknown obstacles and complex emergencies, such as UAV breakdown or communication interruption. To establish close cooperation between UAVs, we propose an efficient environmental information map (EI-map) fusion technique that enables them to obtain global exploration in real-time in a cooperative manner. Then we innovatively develop a distributed cooperative deep Q-learning (DCDQN) algorithm to obtain UAVs' coverage paths online that are determined by minimizing task time and avoiding overlaps, missing areas, and collisions. Specifically, attributing to the fused EI-map, we expand the state space of DCDQN to collect sufficient observations and design a novel cooperative learning pattern to efficiently plan the path for global optimization. Simulation results show that our method outperforms the state-of-the-art in task completion time and coverage efficiency, especially in uncertain and complex environments. In addition, we validate that our method can efficiently complete full coverage even in emergencies.

With the prosperity of wireless networks, vehicular networks (VNs) have been extensively studied in recent years. It is deployed to ensure road safety, enhance the driving experience, and reduce traffic congestion. However, VNs are vulnerable to various attacks, especially Distributed Denial of Service (DDoS) that attackers control a large number of compromise nodes inside the networks to occupy the network resources of legitimate users and impact the communication among vehicles and between vehicles and infrastructure. In this paper, we design a platform to efficiently detect and rapidly respond to the DDoS attack in VNs based on software-defined networking (SDN). The proposed platform not only contains the trigger mechanism based on the message of OpenFlow protocol (i.e., PACKET_IN message) for a response not timely but also involves a flow feature extraction strategy based on the multi-dimensional information. Moreover, we construct an effective global network flow table feature values based on OpenFlow flow table feature and the entropy feature of flow table entry. We determine all flow table entry by the trained SVM. By analyzing the simulation results, we verify that the detection scheme effectively reduces the time for starting attack detection and classification recognition and has a lower false alarm rate.

Multi-agent cooperative target tracking plays a significant role in distributed artificial intelligence for unmanned aerial vehicle (UAV) systems. Most existing tracking solutions using online path planning focus on general target trajectories. Such trajectories have strong regularity and are smooth with minimal fluctuations. However, in real UAV applications a target will change its trajectory frequently to prevent from being tracked, and thus its trajectory will fluctuate greatly and randomly. To this end, in this paper we consider a realistic scenario where the UAV target trajectory is changeable and random. For real-time target tracking, we formulate the tracking task as a distributed model predictive control (DMPC) problem, the objective of which is to optimize tracking performance under various constraints. To do so, we innovatively combine the adaptive differential evolution (ADE) algorithm with Nash optimization, and then propose a Nash-combined ADE method. Specifically, we use ADE to adaptively adjust the predicted trajectory of each UAV agent and Nash optimization to efficiently solve DMPC formulation for global optimization, which can improve the tracking accuracy and simultaneously reduce the computational complexity. The simulation results show that the proposed method performs well in terms of tracking accuracy, UAV collision avoidance and tracking stability for realistic target trajectories.

Mobile crowdsourcing is a promising strategy for trusted data collection in Internet-of-Things (IoT) applications. In this article, we propose a new fog-blockchain distributed approach for crowdsourcing reputation management to prevent user's privacy leakage, malicious users' participation, and reputation tampering in wireless IoT systems. To protect the user's privacy, we design a cross-layer privacy protection model to separate the user's identity and tasks flexibly by means of a hierarchical structure based on fog computing. Moreover, considering the multiconstraint requirement of crowdsourcing tasks, we present a multifactor reputation evaluation method to accurately identify malicious users. Furthermore, to solve the multi-identity problem of users on multiple fog nodes, we propose an adaptive fog-blockchain reputation storage method, which efficiently reduces the system resource consumption by analyzing the adaptive classification of fog nodes. Exhaustive experimental simulation results validate the security and efficiency of our proposed reputation management system.

The combination of pervasive edge computing and blockchain technologies opens up significant possibilities for Industrial Internet of Things (IIoT) applications, but there are several critical limitations regarding efficient storage and rapid response for large-scale low-delay IIoT scenarios. To address these limitations, we propose a hierarchical edge-cloud blockchain called LayerChain. Specifically, to promote scalability, we design a layered structure to hierarchically store the blockchain data in multiple distributed clouds and edge nodes. Next, we propose a node classification method to accommodate differences between the edge nodes when deploying the blockchain. Moreover, to mitigate lengthy delays during block propagation, we propose a tree-based clustering algorithm where blocks are propagated through different clusters with a compressed tree depth. Simulation results show that our LayerChain efficiently reduces the system's resource requirements and block propagation time, making it well-suited for large-scale low-delay IIoT applications.