This paper addresses the challenges of achieving optimal quality of information (QoI) in non-dedicated vehicular mobile crowdsensing (NVMCS) system, where vehicles not originally designed for sensing are leveraged to collect real-time data as they traverse urban environments. These challenges are exacerbated by the interrelated issues of sensing coverage, sensing reliability, and the inherently dynamic nature of participating vehicles. To tackle these challenges, we propose QUIDS, a QUality-informed Incentive-driven multi-agent Dispatching System, which ensures high sensing coverage and sensing reliability under budget constraints in NVMCS systems. QUIDS improves QoI by introducing a novel metric, Aggregated Sensing Quality (ASQ), designed to quantitatively capture the concept of QoI by integrating both sensing coverage and sensing reliability. Moreover, we develop a Mutually Assisted Belief-aware Vehicle Dispatching algorithm that estimates sensing reliability and allocates monetary incentives under uncertain vehicle conditions, thereby further improving ASQ. Evaluation using real-world data collected from a deployed NVMCS system in a metropolitan area demonstrates the effectiveness of QUIDS. The ASQ metric shows a 38% improvement over non-dispatching scenarios and a 10% enhancement over state-of-the-art methods. Additionally, QUIDS reduces reconstruction map errors by 39–74% across various reconstruction algorithms, validating its efficacy in improving QoI within NVMCS systems. Addressing the often-overlooked issue of sensing reliability in existing studies, the QUIDS system leverages non-dedicated vehicles and incorporates a quality-informed incentive-driven dispatching system to jointly optimize sensing coverage and sensing reliability. This enables low-cost, high-quality, and scalable urban environmental monitoring without the need for dedicated sensing infrastructure, and makes the system applicable to diverse smart-city scenarios such as traffic monitoring and environmental sensing.

Urban particulate pollution presents considerable public health hazards, underscoring the need for effective control measures in various cities. This paper proposes SmartSpr, a physics-informed urban mobile sprinkler scheduling system designed for enhanced efficiency in reducing particulate pollution. SmartSpr incorporates a Physics-Informed Neural Network (PINN)-based model, enriched with Bayesian optimization, to accurately simulate the impact of mobile sprinklers on particulate matter (PM) dispersion. Building on this sprinkling effect model, a selective sprinkling strategy considering the replenish process is proposed. This strategy employs a sparsity-driven decoupling simulated annealing algorithm to refine sprinkler routes, prioritizing areas with substantial environmental benefits. Extensive field experiments and simulations have validated SmartSpr, demonstrating a 64.8% reduction in prediction error of SmartSpr's sprinkling model compared to the leading baseline and an 18% enhancement in pollutant reduction efficiency of the proposed scheduling algorithm.

We address the challenges in achieving optimal Quality of Information (QoI) for non-dedicated vehicular Mobile Crowdsensing (MCS) systems, by utilizing vehicles not originally designed for sensing purposes to provide real-time data while moving around the city. These challenges include the coupled sensing coverage and sensing reliability, as well as the uncertainty and time-varying vehicle status. To tackle these issues, we propose QUEST, a QUality-informed multi-agEnt diSpaTching system, that ensures high sensing coverage and sensing reliability in non-dedicated vehicular MCS. QUEST optimizes QoI by introducing a novel metric called ASQ (aggregated sensing quality), which considers both sensing coverage and sensing reliability jointly. Additionally, we design a mutual-aided truth discovery dispatching method to estimate sensing reliability and improve ASQ under uncertain vehicle statuses. Real-world data from our deployed MCS system in a metropolis is used for evaluation, demonstrating that QUEST achieves up to 26% higher ASQ improvement, leading to a reduction of reconstruction map errors by 32-65% for different reconstruction algorithms.

The swift expansion of the ride-hailing industry has given rise to pressing safety issues. This study investigates enhancing safety in the ride-hailing industry by developing an anomaly detection system for detecting abnormal vehicle trajectories. This system defines what constitutes an abnormal trajectory and establishes evaluation metrics for detection methods. Traditional methods for anomaly detection, such as real-time monitoring of speed and direction, are inadequate in the face of data heterogeneity and the sheer volume of information. And the proposed anomaly detection system is designed to overcome these limitations: We introduce a multi-tiered strategy, dividing the anomaly detection task into critical points (including origin and destination) analysis and path analyses to effectively identify and categorize abnormal patterns. By concentrating different features or patterns in each tier, the detection capability of this system is effectively enhanced. Combining our evaluation metrics, the system can provide risk assessment for abnormal critical points and trajectories, thus ensuring the safety of both drivers and passengers.

This article is concerned with the dissipative asynchronous filtering for a class of networked Takagi–Sugeno fuzzy Markov jumping systems subject to packet dropouts. The system measurements are quantized with a logarithmic quantizer and a Bernoulli model is introduced to deal with the packet dropouts occurred in the communication channel from the quantizer to the filter. Both the quantizer and the filter are mode dependent, and their modes are asynchronous with those of the physical plant, governed by two hidden Markov models. Based on the mode- and fuzzy-rule-dependent Lyapunov function method, a sufficient condition is derived such that the resultant filtering error system is stochastically stable in the mean-square sense and strictly dissipative. The slack-matrix approach and Finsler’s lemma are further exploited to design suitable dissipative filters if a set of linear matrix inequalities are feasible. Finally, two examples including a tunnel-diode circuit system are taken to demonstrate the efficiency of our proposed results.