910 publications from this institution
This chapter is concerned with the cooperative target trackingTarget tracking of multiple UMVsUnmanned Marine Vehicles (UMVs) under switching network topologies. For the target to be tracked, only its position can be measured/received by some of the UMVsUnmanned Marine Vehicles (UMVs), and its velocity is unavailable to all the UMVsUnmanned Marine Vehicles (UMVs). A distributed extended state observer considering switching topologiesSwitching topologies is designed to integrally estimate unknown target dynamics and neighboring UMVs'Unmanned Marine Vehicles (UMVs) dynamics. Accordingly, a novel kinematic controller is designed, which takes full advantage of known information and avoids the approximation of some virtual control vectors. Moreover, a disturbance observer is presented to estimate unknown time-varying environmental disturbances. Furthermore, a distributed dynamic controller is designed to regulate the involved UMVsUnmanned Marine Vehicles (UMVs) to cooperatively track the target.
This paper deals with the distributed fault detection for discrete-time Markov jump linear systems over sensor networks with Markovian switching topologies. The sensors are scatteredly deployed in the sensor field and the fault detectors are physically distributed via a communication network. The system dynamics changes and sensing topology variations are modeled by a discrete-time Markov chain with incomplete mode transition probabilities. Each of these sensor nodes firstly collects measurement outputs from its all underlying neighboring nodes, processes these data in accordance with the Markovian switching topologies, and then transmits the processed data to the remote fault detector node. Network-induced delays and accumulated data packet dropouts are incorporated in the data transmission between the sensor nodes and the distributed fault detector nodes through the communication network. To generate localized residual signals, mode-independent distributed fault detection filters are proposed. By means of the stochastic Lyapunov functional approach, the residual system performance analysis is carried out such that the overall residual system is stochastically stable and the error between each residual signal and the fault signal is made as small as possible. Furthermore, a sufficient condition on the existence of the mode-independent distributed fault detection filters is derived in the simultaneous presence of incomplete mode transition probabilities, Markovian switching topologies, network-induced delays, and accumulated data packed dropouts. Finally, a stirred-tank reactor system is given to show the effectiveness of the developed theoretical results.
Identifying Internet-facing industrial control system (ICS) devices is important for asset inventory, vulnerability assessment, exposure measurement, and security monitoring. This dataset release supports research on network traffic fingerprinting for Internet-facing ICS devices under realistic measurement conditions. The dataset is constructed from Internet-scale ICS service discovery followed by protocol-specific probing across three commonly deployed ICS protocols: Modbus/TCP, EtherNet/IP, and S7comm. It contains anonymized network traffic, scanning logs, device information records, and protocol-specific scanner code used to document the measurement logic. The release covers 13,002 responsive Internet-facing ICS endpoints, including 2,205 labeled endpoints spanning 20 vendors, 8 device types, and 165 device models. The measurements are organized across three collections and 60 scanning rounds for each protocol. The dataset captures Internet-facing measurement characteristics that are rarely represented in testbed datasets, including long-tailed label distributions, response variability across scanning rounds, temporal variation, and scanner-location effects. These characteristics support empirical studies of device fingerprint generation, vendor/type/model identification, and robustness to Internet-facing measurement variability. All released IP addresses are anonymized using a consistent prefix-preserving transformation, allowing cross-file linkage across device information, packet captures, and scanning logs without exposing the original public endpoints.
