Federated active anomaly detection on data streams becomes a crucial research problem, since it attempts to discover anomalous data with protecting data privacy and avoiding extensive data labeling. Although extensive work has been conducted on anomaly detection, distinguishing similar anomalies of different categories still remains quite a challenging issue. The requirement of privacy protection in federated settings aggravates the difficulties for instance query and scoring in active anomaly detection when solving this issue. To the best of our knowledge, limited work has focused on this research area. Therefore, we propose Density-aware and cluster-based Federated Active anomaly detection on data Streams, called DFAS. We design a novel lightweight federated anomaly detection clusters with density-aware hash cells, which successfully capture evolving data distribution. The federated anomaly detection clusters are incrementally updated with an acceptable theoretical reconstruction error guarantee. In addition, we propose a straightforward but effective metric divergences accompanied by a greedy search algorithm, which takes both global aggregation bias mitigation and efficiency into account. At last, DFAS detects anomalies and queries the instances for manual labels by measuring the density in hash cells of each cluster, effectively distinguishing closely distributed anomaly classes while maintaining data privacy in the federated setting. Comprehensive experiments on several real-world data sets show that DFAS outperforms previous methods, improving F1 scores by up to 26.7%.

In this paper, we propose micROS.BT, an event-driven behavior tree (BT) framework aiming at supporting swarm-robot coordination. Compared with other BT frame-works, micROS.BT implements the event-driven way under the multi-thread mode, which can effectively save computing resources. Moreover, in order to ensure swarm-robot coordination, we optimize the implementation of the traditional blackboard and propose the multi-mode blackboard, which supports inner-tree, inter-tree, and inter-robot data sharing. Furthermore, considering the limited modularity of a single tree, micROS.BT realizes a mechanism called hierarchical tree management which involves inter-tree notifying and waiting functionalities, while ensuring that each tree is independent and self-scheduled. The effectiveness of micROS.BT is verified by simulation and real-robot experiments for different system settings, showing that a substantial improvement is achieved in comparison with the traditional BT implementations.

This paper investigates the multi-channel access and rendezvous problem in unmanned aerial vehicle (UAV) relaying system in the absence of pre-allocated control channel. Both the geographical sensing range and the spectrum sensing bandwidth of each UAV are limited due to onboard payload constraints, hence it becomes challenging to design effective and efficient channel rendezvous mechanisms. To address the challenge, this paper first observes and analyzes the effects of UAV relaying network topology and geographical sensing range on the rendezvous, and it is found that a joint exploitation of motion and frequency control is essential to achieve efficient rendezvous. Based on the important insight, this paper formulates the UAV relaying rendezvous problem and proposes a novel joint space-frequency rendezvous (JSFR) method for multi-UAV networks, incorporating with distributed reinforcement learning techniques. The simulation results show that the JSFR method may significantly improve the effectiveness and efficiency of rendezvous in UAV relaying networks, in terms of rendezvous probabilities and convergence rates.

In this paper, we consider a surveillance scenario where a team of sensing robots survey a sensitive area and transmit the monitored data to a remote base station through a mobile relay. In this scenario, it is challenging to autonomously adjust the position of the mobile relay for the sake of minimizing the total communication-motion energy consumption of the system, while maintaining the communication quality of the mobile sensing robots. We first derive the asymptotically optimal transmit powers of the mobile relay and of the sensing robots according to the predefined end-to-end packet error rate (PER) requirement. Then, we propose a joint communication-motion planning (JCMP) method for minimizing the total communication-motion energy consumption in both: single- and multi-sensing-robot scenarios, where the trajectories of the sensing robots are rigorously defined. We further consider the scenario where the sensing robots' trajectories are not fixed but can be optimized in restrained areas. The effectiveness of the proposed JCMP is verified by analysis and numerical results for different system configurations, showing that a substantial energy-efficiency improvement may be achieved in comparison with the benchmark that only optimizes the communication energy consumption.

This paper considers a surveillance scenario where a mobile robot monitors an indoor environment and transmits the monitored data to a base station. Considering the indoor radio environment is complex, we first build the radio environment map (REM) with two different interpolation methods. Then, we combine REM with the structural blueprint of the building to build an integrated map called radio-structural map (RSM). Based on RSM, we propose an optimal surveillance path search (OSPS) method which minimizes the data transmission delay of the patrol robot under a communication reliability constraint. In OSPS, two optimization methods are adopted, which may sharply reduce the computation cost. Besides the numerical simulations, we further discuss the relationship between the communication reliability and data transmission delay. Finally, we test the applicability of OSPS in the stage simulator of ROS.