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This paper addresses the optimization problem of simple basic group communication topology (SBGCT) in multi-USV systems. In SBGCT, not only the fewer communication connections with lower communication costs are used to group USVs and establish intra-group and inter-group communication, but also each USV can obtain the data of all the other USVs without search algorithms. A nested-ring exact algorithm (NREA) is proposed to build the communication topology of minimizing the total cost based on minimizing the max cost with a nested-ring shape. The communication cost based on distance, weather, and occlusion impact is designed to evaluate the communication reliability of wireless data transceivers commonly used on USVs. In addition, a strategy of channel allocation is proposed to avoid interfering with the co-channel. Simulation results demonstrate the effectiveness of our algorithms. • An evaluation model of communication cost is established based on the attenuations of distance, weather, and occlusion to improve communication reliability. • The objectives of SBGCT are designed as minimizing the total communication cost based on minimizing the max communication cost. • A nested-ring exact algorithm is proposed to construct a strongly connected topology for the optimization problem of SBGCT [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we investigate the modeling and distributed control problems for the load frequency control (LFC) in a smart grid. In contrast with existing works, we consider more practical and real scenarios, where the communication topology of the smart grid changes because of either link failures or packet losses. These topology changes are modeled as a time-varying communication topology matrix. By using this matrix, a new closed-loop power system model is proposed to integrate the communication topology changes into the dynamics of a physical power system. The globally asymptotical stability of this closed-loop power system is analyzed. A distributed gain scheduling LFC strategy is proposed to compensate for the potential degradation of dynamic performance (mean square errors of state vectors) of the power system under communication topology changes. In comparison to conventional centralized control approaches, the proposed method can improve the robustness of the smart grid to the variation of the communication network as well as to reduce computation load. Simulation results show that the proposed distributed gain scheduling approach is capable to improve the robustness of the smart grid to communication topology changes. [Copyright &y& Elsevier]

• A fully distributed optimal control approach is proposed for DOASs. • Agents coordinate with connected peers directly without a coordinating agent. • Impacts of the adopted communication topologies are investigated. • It performs the best when adopting the fully connected topology. • It is fully compared with centralized and hierarchical distributed approaches. For heating, ventilation and air-conditioning (HVAC) systems, centralized optimal control approaches are widely investigated, while hierarchical distributed optimal control approaches are of increasing attention. Using both approaches, the central station and the coordinating agent play critical roles, resulting in low robustness. A novel fully distributed approach may offer higher robustness, but has not yet been investigated for HVAC systems. This paper therefore proposes a fully distributed optimal control approach for multi-zone dedicated outdoor air systems (DOASs) to be implemented in IoT-enabled building automation networks. Without the coordinating agent, information is exchanged directly between connected agents. The optimal solutions are found by coordinating these multiple agents. During iterations, the outdoor air volume of individual rooms and the PAU are optimized locally using the Incremental Cost Consensus (ICC) algorithm, and the outdoor air volume mismatch is estimated locally using the average consensus algorithm. Tests are conducted to validate the proposed approach by comparing it with existing approaches. The impacts of communication topology on the performance of the proposed approach are investigated. Results show that the proposed fully distributed optimal control approach with the fully connected topology performs better than the existing hierarchical distributed approach. Among different communication topologies, the proposed approach with the fully connected topology had the highest robustness, lowest computation complexity and highest optimization efficiency. It also guaranteed the best control performance when deployed over physical platforms (e.g. IoT-based smart sensors of limited capacity), which limit the maximum iteration number. [ABSTRACT FROM AUTHOR]

Cooperation is a central idea to the usage of swarm robotics. It enables the solution of complex problems with a coordinated execution of simple tasks by a large group of small robot, which together lead to the achievement of the swarm common goal. This coordination is only possible with an efficient task allocation. Inspired by the strategy of the particle swarm optimization algorithm, we propose a novel algorithm called the Clustered Dynamic Task Allocation. This algorithm performs task allocation in a swarm robotic system in a fully distributed manner. It performs a guided search of the allocation space using the concept of adaptive speed. This search process requires information exchange between robots. This robot communication process must be planned carefully so as to achieve two conflicting objectives: the knowledge acquired by a given robot must flow throughout the swarm so that the optimization process may converge yet this communication must be limited so it does not hinder the efficiency of the task allocation process regarding large swarms. This paper proposes the use of a clustered communication topology between the swarm robots, aiming to optimize the underlying communication process, and thus enabling efficient task allocation for large robotic swarms. The results obtained with the cluster-based topology are compared to those obtained with the full mesh-based topology. On average, the results show a clear improvement in terms of execution time and battery charge requirements. Moreover, the performance of the proposed algorithm and the stability of the produced allocation are compared to other well-known models, demonstrating its better applicability to real-world swarm robotics based systems. • We propose a novel approach to the Dynamic Task Allocation problem for Swarm Robotic based system. • The proposal is inspired by Particle Swarm Optimization. • We optimize the communication topology using a clustered configuration to reduce both execution time and battery charge usage. • We achieve a significant improvement both in terms of convergence time and battery charge requirement over the approach using a mesh topology. • We demonstrate that the proposed algorithm has real chances to be used in real-word applications based on swarm robotics when compared to existing well-known models. [ABSTRACT FROM AUTHOR]