In dense traffic unmanned aerial vehicle (UAV) ad-hoc networks, traffic congestion can cause increased delay and packet loss, which limit the performance o

As a decentralized distributed ledger, blockchain is endowed with immutability, traceability, anonymity, and transparency, which has got rapid development in cryptocurrency and production as a new trend. In the meantime, the occurrence of Blockchain-as-a-Service (BaaS) helps in mitigation of the complexity and difficulty in deployment and management of blockchain systems and makes it easier to concentrate on business logic implementation for developers. However, most existing BaaS systems are hosted in the environment of cloud vendors, which has also incurred vendor lock-in risk and impairs the inherent trustless characteristic of blockchain. Though present BaaS systems own a level of availability by using cloud computing or edge computing as infrastructure, the availability is limited, considering the availability of network connections and the data center itself. In this article, a novel cloud-edge collaborative BaaS paradigm is proposed. With the assistance of redundant blockchain node candidates, leader election, and edge network self-healing components, the proposed BaaS system is equipped to extend BaaS to the on-premises edge or private cloud, and realize high availability of blockchain systems in edge-autonomy and cross-data-center scenarios. Through testing a simplified system in a simulated environment in cloud servers, this proposed BaaS system has an acceptable throughput under specific transaction sending rates without much performance degradation due to containerization and data synchronization compared with the original blockchain on-premises deployment method.

In an unmanned aerial vehicle ad-hoc network (UANET), sparse and rapidly mobile unmanned aerial vehicles (UAVs)/nodes can dynamically change the UANET topo

Multi-Agent Reinforcement Learning (MARL) is widely used to solve various problems in real life. In the multi-agent reinforcement learning tasks, there are multiple agents in the environment, the existing Proximal Policy Optimization (PPO) algorithm can be applied to multi-agent reinforcement learning. However, it cannot deal with the communication problem between agents. In order to resolve this issue, we propose a Graph-based PPO algorithm, this approach can solve the communication problem between agents and it can enhance the exploration efficiency of agents in the environment and speed up the learning process. We apply our algorithms to the task of multi-UAV navigation for communication coverage to verify the functionality and performance of our proposed algorithms.

In the UAV swarm, the degradation in the health status of some UAVs often brings negative effects to the system. To compensate for the negative effect, we present a fault-tolerant Multi-Agent Reinforcement Learning Algorithm that can control an unstable Multiple Unmanned Aerial Vehicle (Multi-UAV) system to perform exploration tasks. Different from traditional multi-agent methods that require the agents to remain healthy during task execution, our approach breaks this limitation and allows the agents to change status during the task. In our algorithm, the agent can accept both the adjacency state matrix about the neighboring agents and a kind of healthy status vector to integrate both and generate the communication topology. During this process, the agents with poor health status are given more attention for returning to normal status. In addition, we integrate a temporal convolution module into our algorithm and enable the agent to capture the temporal information during the task. We introduce a scenario regarding Multi-UAV ground exploration, where the health status of UAVs gradually weakens over time before dropping into a fault status; the UAVs require rescues from time to time. We conduct some experiments in this scenario and verify our algorithm. Our algorithm can increase the drone’s survival rate and make the swarm perform better.