The first step in the diagnosis of failure occurrences in discrete event systems is the verification of the system diagnosability. Several works have addressed this problem using either diagnosers or verifiers for both centralized and decentralized architectures. In this technical note, we propose a new algorithm to verify decentralized diagnosability of discrete event systems. The proposed algorithm requires polynomial time in the number of states and events of the system and has lower computational complexity than all other methods found in the literature. In addition, it can also be applied to the centralized case.

The rapid urbanization process in the last century has deeply changed the way we live and interact with each other. As most people now live in urban areas, cities are experiencing growing demands for more efficient and sustainable public services that may improve the perceived quality of life, specially with the anticipated impacts of climatic changes. In this already complex scenario with increasingly overcrowded urban areas, different types of emergency situations may happen anywhere and anytime, with unpredictable costs in human lives and economic losses. In order to cope with unexpected and potentially dangerous emergencies, smart cities initiatives have been developed in different cities, addressing multiple aspects of emergencies detection, alerting, and mitigation. In this context, this article surveys recent smart city solutions for crisis management, proposing definitions for emergencies-oriented systems and classifying them according to the employed technologies and provided services. Additionally, recent developments in the domains of Internet of Things, Artificial Intelligence and Big Data are also highlighted when associated to the management of urban emergencies, potentially paving the way for new developments while classifying and organizing them according to different criteria. Finally, open research challenges will be identified, indicating promising trends and research directions for the coming years.

In recent years, the concept of smart cities has become increasingly important in the pursuit of sustainable development goals. In general, common urban challenges have been addressed through smart-city services, and new perspectives for more sustainable cities have emerged. To realize the full potential of such smart urban environments, geospatial approaches have been used as a focal point, offering a plethora of applications that contribute to a better understanding of urban challenges and innovation potentials. Nevertheless, although significant progress has been made, different problems may arise when the available technologies and resources are not understood or even when their potentialities are not properly capitalized. This article reviews the state of the art in the field, highlighting success cases and remaining challenges in exploiting geospatial data-driven strategies, particularly when leveraging geographic information systems, satellites, and distributed sensors to produce and process geospatial data and datasets in urban scenarios. Moreover, a more organized perspective of the area is provided in this article, as well as future development trends, supporting new research efforts in this area when empowering smart cities for a more sustainable future.

The smart-city revolution has been promoted as the next step in urban development, leveraging technology to achieve enhanced development standards amid the increasingly complex challenges of urbanization. However, despite the implementation of more efficient urban services, issues regarding their tangible effects and impact on people’s lives remain unresolved. In this context, the concept of intelligent cities is seen as a necessary evolution of the smart-city paradigm, positioning human factors as the driving forces behind urban technological evolution. This integrative concept embodies advanced technology to enhance essential urban functions, with sustainability, equity, and resilience as macro-development goals. This study reviews the multifaceted dimensions of intelligent cities, from designing and deploying smart infrastructure to implementing citizen-centric decision-making processes. Additionally, it critically examines the digital divide and highlights the importance of equitable development policies as essential for enabling transformative urban change. By linking technological advancement to social issues, this article provides practical insights and case studies from the cities of Helsinki, Barcelona, and Buenos Aires, demonstrating that smart-city initiatives are still failing to bridge the equity service distribution gap. This comprehensive assessment approach ultimately serves as a reference for future evaluations of intelligent urban transformations.

Wireless sensor networks have been considered as an effective solution to a wide range of applications due to their prominent characteristics concerning information retrieving and distributed processing. When visual information can be also retrieved by sensor nodes, applications acquire a more comprehensive perception of monitored environments, fostering the creation of wireless visual sensor networks. As such networks are being more often considered for critical monitoring and control applications, usually related to catastrophic situation prevention, security enhancement and crises management, fault tolerance becomes a major expected service for visual sensor networks. A way to address this issue is to evaluate the system dependability through quantitative attributes (e.g., reliability and availability), which require a proper modeling strategy to describe the system behavior. That way, in this paper, we propose a methodology to model and evaluate the dependability of wireless visual sensor networks using Fault Tree Analysis and Markov Chains. The proposed modeling strategy considers hardware, battery, link and coverage failures, besides considering routing protocols on the network communication behavior. The methodology is automated by a framework developed and integrated with the SHARPE (Symbolic Hierarchical Automated Reliability and Performance Evaluator) tool. The achieved results show that this methodology is useful to compare different network implementations and the corresponding dependability, enabling the uncovering of potentially weak points in the network behavior.

Wireless visual sensor networks have been adopted in different contexts to provide visual information in a more flexible and distributed way, supporting the development of different innovative applications. Although visual data may be central for a considerable set of applications in areas such as Smart Cities, Industry 4.0, and Vehicular Networks, the actual visual data quality may be not easily determined since it may be associated with many factors that depend on the characteristics of the considered application scenario. This entails several aspects from the quality of captured images (sharpness, definition, resolution) to the characteristics of the networks such as employed hardware, power consumption, and networking efficiency. In order to better support quality analysis and performance comparisons among different wireless visual sensor networks, which could be valuable in many monitoring scenarios, this article surveys this area with special concern on assessment mechanisms and quality metrics. In this context, a novel classification approach is proposed to better categorize the diverse applicable metrics for quality assessment of visual monitoring procedures. Hence, this article yields a practical guide for analyzing different visual sensor network implementations, allowing fairer evaluations and comparisons among a variety of research works. Critical analysis are also performed regarding the relevance and usage of the proposed categories and identified quality metrics. Finally, promising open issues and research directions are discussed in order to guide new developments in this research field.