Flying ad hoc networks (FANETs) that consists of multiple unmanned aerial vehicles (UAVs) are promising technologies for future networked systems due to the versatility of UAVs. One of the most distinguishing features of FANET is frequent and rapid topological fluctuations due to the high mobility of UAVs. Hence, the topology management adapting to the movements of UAVs is one of the most important issues in FANET. In this paper, we study a FANET topology management problem that optimizes the locations and movements of UAVs to maximize the network performance, adapting to the topological changes while UAVs carry out their missions. When formulating the problem, we take into account the routing protocol as an arbitrary function since the network performance is inseparably linked with the routing protocol in use. We first develop two algorithms. One is the topology construction algorithm, which constructs a FANET topology from the scratch without any given initial topology, based on particle swarm optimization (PSO). The other is the topology adjustment algorithm, which incrementally adjusts the FANET topology adapting to the movements of UAVs with low computational costs, based on gradient descent. Then, by defining a logical distance (the so-called topology edit distance) that measures the degree of changes in FANET topology, we develop an integrated topology management algorithm that contains the topology construction and adjustment algorithms. The simulation results show that our algorithm achieves a good network performance with low computational overhead, which is one of the most important virtues in FANETs with rapidly varying topology.

In recent years, the emergence of flying ad hoc networks (FANETs) with multiple unmanned aerial vehicles (UAVs) has made it possible to effectively perform not only the far-off missions but also assorted complex missions. In this article, we consider a mission-critical FANET to perform given missions using multiple UAVs, taking into account a dynamic environment with a time-varying network topology. To effectively operate the mission-critical FANET, we study the joint mission assignment and topology management problem aiming at maximizing the weighted sum of mission and network performances, while guaranteeing end-to-end communications between mission-performing UAVs and their corresponding ground control stations, inter-UAV safety distance maintenance, and other mission-related constraints. To address this problem, we first develop three algorithms: one is to construct a mission-critical FANET from scratch, and the others are to manage the network topology and to switch UAV roles between mission performing and data relaying in response to the changes in the network topology. Then, we develop a dynamic mission-critical FANET operation algorithm incorporating the three algorithms with a few rules, by which the mission-critical FANET can be effectively managed and operated with reasonable computational complexity in the dynamic environment. Through simulation results, we show that our proposed algorithm works well in the dynamic environment while satisfying the constraints, and that its performance is not only superior to the existing algorithms but also close to the optimal performance.

In this paper, we study radio and energy resource management in renewable energy-powered wireless networks, where base stations (BSs) are powered by both on-grid and renewable energy sources and can share their harvested energy with each other. To efficiently manage those resources, we propose a hierarchical and distributed resource management framework based on deep reinforcement learning. The proposed framework minimizes the on-grid energy consumption while satisfying the data rate requirement of each user. It is composed of three different policies in a distributed and hierarchical way. An intercell interference coordination policy constrains the transmission power at each BS to coordinate the intercell interference among the BSs. Under the power constraints, a distributed radio resource allocation policy of each BS determines its own user scheduling and power control. Lastly, an energy sharing policy manages the energy resources of the BSs by sharing the harvested energy via power lines between them. Through the simulation, we demonstrate that the proposed framework can effectively reduce the on-grid energy consumption while satisfying the data rate requirements.

This paper presents a novel reference architecture for blockchain-enabled federated learning (BCFL), a state-of-the-art approach that amalgamates the strengths of federated learning and blockchain technology. We define smart contract functions, stakeholders and their roles, and the use of interplanetary file system (IPFS) as key components of BCFL and conduct a comprehensive analysis. In traditional centralized federated learning, the selection of local nodes and the collection of learning results for each round are merged under the control of a central server. In contrast, in BCFL, all these processes are monitored and managed via smart contracts. Additionally, we propose an extension architecture to support both cross-device and cross-silo federated learning scenarios. Furthermore, we implement and verify the architecture in a practical real-world Ethereum development environment. Our BCFL reference architecture provides significant flexibility and extensibility, accommodating the integration of various additional elements, as per specific requirements and use cases, thereby rendering it an adaptable solution for a wide range of BCFL applications. As a prominent example of extensibility, decentralized identifiers (DIDs) have been employed as an authentication method to introduce practical utilization within BCFL. This study not only bridges a crucial gap between research and practical deployment but also lays a solid foundation for future explorations in the realm of BCFL. The pivotal contribution of this study is the successful implementation and verification of a realistic BCFL reference architecture. We intend to make the source code publicly accessible shortly, fostering further advancements and adaptations within the community.

In this article, we examine cloud–edge–terminal Internet of Things (IoT) networks, where edges undertake a range of typical dynamic scheduling tasks. In these IoT networks, a central policy for each task can be constructed at a cloud server. The central policy can be then used by the edges conducting the task, thereby mitigating the need for them to learn their own policy from scratch. Furthermore, this central policy can be collaboratively learned at the cloud server by aggregating local experiences from the edges, thanks to the hierarchical architecture of the IoT networks. To this end, we propose a novel collaborative policy learning framework for dynamic scheduling tasks using federated reinforcement learning. For effective learning, our framework adaptively selects the tasks for collaborative learning in each round, taking into account the need for fairness among tasks. In addition, as a key enabler of the framework, we propose an edge-agnostic policy structure that enables the aggregation of local policies from different edges. We then provide the convergence analysis of the framework. Through simulations, we demonstrate that our proposed framework significantly outperforms the approaches without collaborative policy learning. Notably, it accelerates the learning speed of the policies and allows newly arrived edges to adapt to their tasks more easily.