Abstracts:This letter proposes a novel cluster-driven bilevel Load Redistribution (LR) attack model in cyber physical power systems. The main goal is on overloading multiple transmission lines while maintaining a high level of stealth. The proposed model employs cluster-based approach to form distinct clusters, each representing different load patterns within the power system. This enables the design of attack vectors that seamlessly blend with these load patterns, thereby challenging advanced anomaly detection systems. The stealth of the attack is quantified by the Stealth Distance Index (SDI), ensuring that injected false data remain within the maximum permissible distance from the centroid of its corresponding cluster. The proposed model is validated on the modified IEEE 14-bus and 30-bus systems, demonstrating enhanced stealth capabilities in LR attacks.

Abstracts:The multirobot coverage search problem in unknown environments has attracted significant attention. However, the existing methods are inefficient in the search process. The aim of the present study is to improve the search efficiency through an enhanced bioinspired neural network method. In this work, a connected Glasius bioinspired neural network (CGBNN) model is introduced to address the lack of consideration for neuronal connectivity and transmission properties in existing studies. The dynamic search environment is represented by the changes in neurons' activity values, which guide the robots in performing the search task. Each robot automatically plans its search path according to the principle of the decreasing gradient of CGBNN activity values until the task is completed. Experimental results demonstrate that the robots can avoid different types of obstacles to complete the coverage search, confirming the effectiveness of the proposed method. Meanwhile, it indicates that the proposed method outperforms others, the coverage rate is improved by 6.90%, 6.22%, and 4.02% compared to the GBNN, A-RPSO, and DMPC algorithms, respectively. In adition, the decision time is less affected by the complexity of the environment, which fulfills the practical demands of real-time decision-making in a large-scale complex environment.

Abstracts:The current Internet struggles to meet the deterministic transmission requirements in terms of end-to-end delay. Time-sensitive networking (TSN) provides a solution by offering deterministic forwarding services for critical flows, ensuring strict latency requirements. However, the costs and complexity of hardware associated with TSN increases the barriers for researchers to build prototype for validating newly proposed queue scheduling algorithms. To address this dilemma, software-based simulation platforms are widely used for reduction of simulation expenses. However, these platforms cannot flexibly simulate various queue scheduling algorithms. Although existing programmable packet scheduling methods can adapt to TSN queue scheduling algorithms for common hardware base, they cannot be directly applied to software-based TSN simulation platforms. In response, we propose a novel lightweight software-defined packet scheduling primitive—first-in-pick-out (FIPO), based on the programmable switch behavior-model-version-2 (BMV2). FIPO is capable of expressing customized queue scheduling algorithms to support current TSN algorithms and can be flexibly extended to future algorithms. Particularly, FIPO consists only of multipriority queues and eligible time comparator to implement TSN queue scheduling with minimal computational and management overhead. We also propose a fine-grained logical queue-based flow queue mechanism to enhance FIPO. Finally, a lightweight prototype system for the FIPO is established, incorporating four customized deterministic scheduling algorithms. Extensive experimental results show that FIPO can quickly implement customized queue scheduling algorithms and simulate network conditions that closely resemble real environments. It also demonstrates increased implementation flexibility, achieving millisecond-level configuration times with only a moderate increase in CPU utilization (less than 10%).

Abstracts:The article investigates the leader–followers consensus problem of nonlinear multiagent systems with nonlinear dynamics and uncertain exogenous disturbances, employing dynamic triggered strategies (DTSs). An active disturbance rejection control is utilized to design a disturbance observer for estimating the disturbances in the input channel. To enhance communication efficiency and reduce energy consumption during information interaction between agents, the disturbances estimator is integrated into the consensus controller to suppress these disturbances. The convergence condition and lower bound of DTSs are derived. Finally, numerical simulations validate the effectiveness of the proposed algorithm. Note to Practitioners— The complete system composed of multiple controlled objects in industrial production, transportation, aerospace, and other fields is inevitably influenced by external uncertain signals to varying degrees. The disturbance observer-based control technique effectively addresses the robust consensus problem of MASs. This significantly enhances the stability of most controlled systems operating in complex environments. Simultaneously, the distributed DTSs efficiently reduce communication burden between devices, minimize microprocessor computing power requirements, and save substantial economic costs. Undoubtedly, this offers a novel approach for the development of MASs.

Abstracts:This article is considered with the co-design problem of jump-like FlexRay protocol (FRP) and dynamic control for a class of discrete-time networked systems. A jump-like FRP is proposed to address the constraints of communication resources as well as nonperiodic denial of service (DoS) attacks in the sensor-to-controller communication network. The proposed novel protocol has the characteristics of traditional FRP time-triggered and event-triggered mechanisms. In addition, such protocol is able to avoid selecting sensors affected by DoS attacks. Subsequently, a set of dynamic output feedback controllers related to the selection of sensor nodes is designed to guarantee the finite-time boundedness of the closed-loop system with the prescribed $H_\infty$ performance. However, the co-design problem of protocol and dynamic control includes more nonlinear terms, making the problem more challenging to be solved. In order to address the co-design problem and enhance system performance, a genetic-algorithm-based controller design approach has been proposed. Finally, a numerical example and a two-area power system example are given to illustrate the effectiveness of the proposed method.

Abstracts:This article addresses the consensus control problem for a specific class of discrete-time stochastic multiagent systems (MASs). When transmitting the local measurement output to the local controller and neighboring agents, a multiple description coding (MDC) scheme is introduced to reduce the communication burden and enhance data transmission reliability in a resource-constrained environment. The MDC scheme encodes each signal into two descriptions, which are then transmitted through separate channels, and decoding schemes are employed to address the different characteristics of the arrival of the two descriptions, ensuring the boundedness of the decoding error. The proposed consensus control scheme uses the relative decoded measurement errors between local agents and their neighbors. The aim is to design an output-feedback control scheme that ensures the error dynamics of the controlled MAS reach exponentially mean-square boundedness. Sufficient conditions are established for the existence of the controllers through stochastic analysis techniques, and the desired controller gains are parameterized using the feasibility of certain matrix inequalities. The effectiveness of the proposed coding-decoding-based consensus control scheme is verified through a numerical simulation.

Abstracts:The healthcare delivery system exhibits inherent complexities stemming from its composition of multiple autonomous constituent systems with intricate interdependencies, characterized by dynamic and often decentralized operations. In this context, systems of systems (SoS) governance emerges as a promising paradigm to address these multifaceted challenges and optimize system-wide performance. This article conducts a comprehensive scoping review of SoS governance frameworks, focusing on their applicability to healthcare systems. Through a rigorous review process, 45 studies were selected from three major databases, yielding 37 distinct governance frameworks. The review analyzes publication trends, research objectives, applied frameworks, domains of application, and methodologies, providing a multifaceted understanding of the SoS governance landscape. The study highlights one framework that aligns closely with our proposed construct for healthcare system governance, and the alignment is discussed. In addition, technical components and variations across frameworks are identified and discussed, revealing the complexity and diversity of approaches in SoS governance.

Abstracts:Architecture design for system-of-systems (SoSs) is a complex challenge due to interdependencies, uncertainties, and the large design space. The evolutionary nature of SoSs necessitates a multistage architecting process, adding further complexity. This article, thus, proposes a deep reinforcement learning based evolutionary architecture path selection method that considers uncertainties and interdependency. The approach employs an architecture framework to guide the design and defines SoS architecture decisions as the addition of systems and the allocation of operational architecture to physical architecture across sequential stages. Capability evaluation leverages a capability-activity-system structure, supported by a functional dependency network analysis method. Utilizing a deep neural network as a functional approximator to predict future SoS capability, the article develops a proximal policy optimization (PPO) algorithm that balances immediate and future needs. Applied to a mosaic warfare-oriented naval antisubmarine SoS, the proposed method outperforms heuristic optimization techniques by achieving higher SoS capability, reduced instability, and fewer violations of budget and intermediate requirements constraints in both deterministic and stochastic scenarios. These results highlight the PPO method's effectiveness in addressing SoS architecting path selection challenges under uncertainty.

Abstracts:The weighted $k$-path vertex cover (WVCP$_{k}$) problem is a main branch of covering problems on dynamic networks with many numerous instances in real-world complex systems. A pivotal challenge in distributed systems for network covering optimization is designing decentralized schemes for autonomous decision-making agents. This article focuses on the distributed optimization of the WVCP$_{k}$ problem, where individual vertices, acting as rational agents, make decisions independently based on incomplete information. We formulate a Bayesian game model to capture the interactions among agents, who face the uncertainty to the statuses of their $k$-path neighbors and rely on communications to enhance their individual beliefs on the actual cover state. Our analysis delves into the Bayesian Nash equilibrium and the ex-post Pareto optimal cover state (POCS) within this framework. In addition, a Bayesian game-based perturbation parallel algorithm (BGPPA) is developed and shown to converge to the ex-post POCS set, even when agents are restricted to using only estimated expected utility. A series of numerical simulations indicate that the BGPPA delivers superior performance with rapid convergence across various networks.

Abstracts:In this article, the vehicular platoons under aperiodic sampled-data information exchange between connected neighbors are implemented with an average output consensus while achieving differential privacy protection. Initially, the complex vehicle dynamics system is simplified into a heterogeneous linear system interconnected through a communication graph. Subsequently, a distributed hybrid controller is deployed, specifically tailored for handling the intermittent sampled-data information, and is augmented with a dynamic noise generator. This framework restricts information exchange to a predefined neighborhood set of vehicles. To ensure differential privacy, design and incorporate random noise adhering to a Laplace distribution, where the decay index and control gain are adjustable parameters corresponding to the desired privacy level and system accuracy, respectively. This noise injection is guided by a differential privacy noise utilization algorithm. The controller design is then formulated and solved using a combination of the pole placement method and the generalized inverse concept, enabling effective networked control. Lastly, simulation examples are provided to rigorously validate the proposed theoretical framework, demonstrating its efficacy in maintaining platoon coherence while preserving the privacy of individual vehicle data.