Due to the highly dynamic nature of UAV swarms, the ad-hoc network of UAV swarms usually experiences frequent changes, which poses significant challenges to maintaining persistent connectivity. Especially, the vulnerable nodes with a topological degree of one in the network topology of a UAV swarm, have the weakest network connectivity and are most susceptible to disconnection. To eliminate the vulnerable nodes, this paper proposes a midpoint guided topology control optimization method. This approach maintains the swarm’s steady-state operation while dynamically adjusting the positions of vulnerable nodes to increase their network topology degrees, thereby eliminating vulnerable nodes and enhancing the robustness and fault tolerance of the ad-hoc network. Furthermore, the validity of the proposed method is proved to be efficient through mathematical analysis, and the experimental results in three-dimensional space consistently demonstrate the superiority of the proposed approach.

Real-time systems involve tasks that may voluntarily suspend their execution as they await specific events or resources. Such self-suspension can introduce further delays and unpredictability in scheduling, making the analysis more challenging. Most current schedulability analysis methods of self-suspending tasks focus on fixed-priority scheduling or tasks with constrained deadlines. This paper proposes two schedulability analysis methods for self-suspending tasks with arbitrary deadlines under earliest-deadline-first-like (EDF-like) scheduling. Both methods are designed for preemptive uniprocessor systems. We first present a jitter-based response time analysis (JRTA) method. JRTA is designed based on a self-suspending response time analysis (SS-RTA) method under earliest-deadline-first (EDF) scheduling. We first convert self-suspensions to release jitters and then present a response time analysis (RTA) method of tasks with release jitters under EDF-like scheduling. To address the complexity of JRTA, we propose an improved schedulability analysis (ISA), a sufficiency blocking-based method. Finally, we provide many simulation experiments under some EDF-like scheduling algorithms. The results verify the effectiveness and efficiency of both proposed methods.

This article presents a novel knowledge-based genetic algorithm (GA) to generate a collision-free path in complex environments. The proposed algorithm infuses specific domain knowledge into robot path planning through the development of five problem-specific operators that integrate a local search technique to improve efficiency. In addition, the proposed algorithm introduces a unique and straightforward representation of the robot path and an effective method for evaluating the path quality and accurately detecting collisions. The proposed algorithm is capable of finding optimal or suboptimal robot paths in both static and dynamic environments. Simulation and experimental studies are conducted to showcase the effectiveness and efficiency of the proposed algorithm. Furthermore, a comparative study is performed to highlight the indispensable role of specialized genetic operators within the proposed algorithm in solving the path planning problem.

Fish schools present high-efficiency group behaviors to collective migration and dynamic escape from the predator through simple individual interactions. The purpose of this research is to infuse swarm robots with "fish-like" intelligence that will enable safe navigation and efficient cooperation, and successful completion of escape tasks in changing environments. In this paper, a novel fish-inspired self-adaptive approach is proposed for the collective escape of swarm robots. A bio-inspired neural network (BINN) is introduced to generate collision-free escape trajectories through the dynamics of neural activity and the combination of attractive and repulsive forces. In addition, a neurodynamics-based self-adaptive mechanism is proposed to improve the self-adaptive performance of the swarm robots in dynamic environments. Similar to fish escape maneuvers, simulations and real-robot experiments show that the swarm robots can collectively leave away from the threat and respond to sudden environmental changes. Several comparison studies demonstrated that the proposed approach can significantly improve the effectiveness, efficiency, and flexibility of swarm robots in complex environments.

Abstract Routing optimization is a well‐known and established topic with the fundamental goal of operating networks efficiently. Traditional optimization heuristics may suffer from performance penalty as it mismatches actual traffic, while artificial intelligence (AI) which has undergone a renaissance recently is gradually being applied to the network optimization and has shown excellent advantages. Especially deep reinforcement learning (DRL) is investigated as a key technology for routing optimization with the goal of enabling networks self‐driving. Therefore, we contributed in this paper a novel approach for practical intelligent routing method using DRL with GNN, which could be easily implemented as a northbound application on the SDN controller. Our method can not only output continuous control actions for routing optimization but also learn from some networks and generalize to other unseen ones. In order to emphasize the generalization and practicality of the intelligent routing method, we deploy it in a real SDN network for experimentation rather than simulation. The results show that the method can keep on optimizing the routing of traffic in other networks of different topologies after the training is stable. And compared with hop‐based OSPF, the optimal load‐balancing algorithm and the recent intelligent routing DROM, it reduces network delay by 16.1%, 19.6% and 14.3%, respectively, but at the expense of flow‐table space within the acceptable range.

Traditional routing schemes usually use fixed models for routing policies and thus are not good at handling complicated and dynamic traffic, leading to performance degradation (e.g., poor quality of service). Emerging Deep Reinforcement Learning (DRL) coupled with Software-Defined Networking (SDN) provides new opportunities to improve network performance with automatic traffic analysis and policy generation. However, existing DRL-based routing solutions usually rely on all node information to make routing decisions for the network and hence are both hard to converge in large networks and vulnerable to topology changes. In this paper, we propose ScaleDeep, a scalable DRL-based routing scheme for SDN, which improves the routing performance and is resilient to topology changes. Essentially, ScaleDeep takes advantage of partial control on network nodes and DRL. We select a set of critical nodes from a network as driver nodes, which can simulate the entire network operation, based on the control theory. By observing the traffic variation on the driver nodes, DRL dynamically adjusts some link weights for a weighted shortest path algorithm to change the routing paths and improve the routing performance. Limiting the control on driver nodes improves the convergence ability of DRL and reduces the dependency of the DRL agent on the fixed network topology. To validate the performance of ScaleDeep, we conduct packet-level simulations on different topologies. The results show that ScaleDeep outperforms existing DRL-based schemes by reducing the average flow completion time by up to 36% and exhibiting better robustness against minor topology changes.

Research on tracking control has been on-going for many years. The accuracy and the practicality of the tracking control method has always been one of the most important aspects when designing the control strategy. Autonomous Underwater Vehicles are becoming increasingly important in the applications of ocean surveillance and military, etc. Therefore, this paper aims to develop a control method for autonomous underwater vehicles based on bioinspired neural dynamics. The proposed method practically solves the speed jump and chattering issues that are respectively in conventional backstepping and sliding mode controls with the aid of the bioinspired neural dynamics. In addition, the proposed control method also takes the dynamic uncertainties for the autonomous underwater vehicle into the consideration. The combined tracking method has relatively good overall performance for autonomous underwater vehicle against model uncertainties and disturbances.

To improve the processing efficiency of jobs in distributed computing, the concept of coflow is proposed. A coflow is a collection of flows that are semantically correlated in a multi-stage computation task. A job consists of multiple coflows and can be usually formulated as a Directed-Acyclic Graph (DAG). A proper scheduling of coflows can significantly reduce the completion time of jobs in distributed computing. However, this scheduling problem is proved to be NP-hard. Different from existing schemes that use hand-crafted heuristic algorithms to solve this problem, in this paper, we propose a Deep Reinforcement Learning (DRL) framework named DeepWeave to generate coflow scheduling policies. To improve the inter-coflow scheduling ability in the job DAG, DeepWeave employs a Graph Neural Network (GNN) to process the DAG information. DeepWeave learns from the history workload trace to train the neural networks of the DRL agent and encodes the scheduling policy in the neural networks, which make coflow scheduling decisions without expert knowledge or a pre-assumed model. The proposed scheme is evaluated with a simulator using real-life traces. Simulation results show that DeepWeave completes jobs at least 1.7X faster than the state-of-the-art solutions.

Despite the critical role that middleboxes play in introducing new network functionality, management and innovation of them are still severe challenges for network operators, since traditional middleboxes based on hardware lack service flexibility and scalability. Recently, though new networking technologies, such as network function virtualization (NFV) and software-defined networking (SDN), are considered as very promising drivers to design cost-efficient middlebox service architectures, how to guarantee transmission efficiency has drawn little attention under the condition of adding virtual service process for traffic. Therefore, we focus on the service deployment problem to reduce the transport delay in the network with a combination of NFV and SDN. First, a framework is designed for service placement decision, and an integer linear programming model is proposed to resolve the service placement and minimize the network transport delay. Then a heuristic solution is designed based on the improved quantum genetic algorithm. Experimental results show that our proposed method can calculate automatically the optimal placement schemes. Our scheme can achieve lower overall transport delay for a network compared with other schemes and reduce 30% of the average traffic transport delay compared with the random placement scheme.