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Flooding in urban areas is expected to become even more common due to climatic changes, putting pressure on cities to implement effective response measures. Practical mechanisms for assessing flood risk have become highly desired, but existing solutions have been devoted to evaluating only specific cities and consider only limited risk perspectives, constraining their general applicability. This article presents an innovative approach for assessing the flood risk of delimited urban areas by exploiting geospatial information from publicly available databases, providing a method that is applicable to any city in the world and requiring minimum configurations. A set of mathematical equations is defined for numerically assessing risk levels based on elevation, slope, and proximity to rivers, while the existence of emergency-related urban infrastructure is considered as a risk reduction factor. Then, computed risk levels are used to classify areas, allowing easy visualisation of flood risk for a city. This smart city approach not only serves as a valuable tool for assessing the expected flood risk based on different parameters but also facilitates the implementation of cutting-edge strategies to effectively mitigate critical situations, ultimately enhancing urban resilience to flood-related disaster.

The urbanisation of the last century has created a significant demand for urban areas to deliver public services that are efficient, sustainable, and adaptable. This demand is further amplified by the anticipated impacts of climate change and the complexities introduced by rapidly expanding urban populations. In this scenario, smart city initiatives have emerged as a pivotal strategy to address these challenges, leveraging innovative technologies and paradigms such as Internet of Things, Artificial Intelligence, and Data Science to improve the quality of urban life in a more sustainable way. Recently, research efforts in these domains have focused on creating systems that can adapt and respond to changing conditions, enabling greater adaptivity and responsiveness to the needs of urban landscapes. Therefore, this article provides a comprehensive survey of adaptive urban services within a smart city context. Through analyses of recent literature, we investigate the technological foundations that enable the adaptivity of urban services and explore their applications in various urban scenarios, including energy, mobility, emergency management, public safety, and waste management. This article also highlights the benefits of adopting adaptive urban services, such as improved traffic management, resilient emergency management, and enhanced citizen engagement, bringing an important contribution to this broad research area. Finally, we provide a discussion on current technology enablers and challenges and present an overview for future research directions on adaptive urban systems at the forefront of transforming cities into more intelligent ecosystems.

Urban areas are undergoing significant changes with the rise of smart cities, with technology transforming how cities develop through enhanced connectivity and data-driven services. However, these advancements also bring new challenges, especially in dealing with urban emergencies that can disrupt city life and infrastructure. The emergency management systems have become crucial elements for enabling cities to better handle urban emergencies, although ensuring the reliability and detectability of such system remains critical. This article introduces a new method to perform reliability and detectability assessments. By using Fault Tree Markov chain models, this article evaluates their performance under extreme conditions, providing valuable insights for designing and operating urban emergency systems. These analyses fill a gap in the existing research, offering a comprehensive understanding of emergency management systems functionality in complex urban settings.

Emergency response plays a critical role in mitigating the impact of disasters and ensuring public safety. Understanding a city's capability for emergency response is vital for effective disaster management and urban planning. This paper describes a comprehensive geospatial dataset that assesses the emergency response capability of cities in Portugal based on their urban infrastructure, accounting for the number of hospitals, police stations, fire department units, and metro/railway stations. These infrastructures are essential for attending to victims, mitigating emergency situations, and performing rescue operations. Besides that, the GeoJSON definitions of all Portuguese cities are also provided in the dataset, which were used to compute the number of the target facilities based on data from OpenStreetMap. The potential applications of this dataset are numerous, ranging from urban planning and resource allocation to disaster response strategy development. Moreover, it indicates where public investments are most required, especially when combined with others continuously updated public datasets with incidents in urban areas.

CityZones is an online tool to support when computing risk zones in a city, which is performed by mathematically evaluating the emergency response capability of an urban area using real data. The tool allows users to define polygonal Areas of Interest (AoI) on a map, which are categorised into low, medium, and high-risk levels. As urban emergencies in general will typically demand the assistance of hospitals (to attend victims), firefighters (for rescue operations and damages reduction), policemen (to control and manage critical situations), and even metro stations (for evacuation), the risk zones within an AoI will be computed according to the distances to such existing infrastructure, which is extracted from the OpenStreetMap database. The coordinates of an AoI are defined at the client-side and submitted to a server as a regular or irregular polygon, which is then stored in a GeoJSON file along with the computed classifications for further processing according to a multi-tier pipeline. Adopting a configurable square-based mathematical model to compute distances and relative risk levels, the risk zones are computed on-demand and exported to CSV files to be used by any application, with an additional representation for web-based visualisation. Moreover, as a practical exploitation of the computed maps, the CityZones tool also suggests ideal positions for sensors-based Emergency Detection Units (EDUs) within an AoI according to the chosen positioning algorithm, assuming that the computed risk zones indicate how severe an undetected emergency would be. While the CityZones tool is not intended to provide a mitigation approach at all, since it does not have knowledge about current emergency situations, it stands as a valuable pre-mitigation tool to be used in the planning phase of emergency management in smart cities, bringing an important contribution for practical applications in this area.

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.

Wireless sensor networks comprising nodes equipped with cameras have become common in many scenarios, providing valuable visual data for some relevant services such as localization, tracking, patterns identification and emergencies detection. In this context, algorithms and optimization approaches have been designed to perform different types of quality assessment or performance enhancement tasks, addressing challenging issues such as networking, compression, availability, reliability, security, energy efficiency and virtually any subject related to the operational challenges of those networks. However, the dynamics of coverage failures have not been properly modelled in visual sensor networks, resulting in unrealistic perceptions when optimizing or assessing quality in most visual sensing scenarios. Particularly, the Field of View of visual sensors will be affected by occlusion caused by obstacles in the monitored field, which may turn such sensors inadequate for the expected monitoring services of the considered network. Therefore, this article proposes a mathematical model to assess occlusion caused by mobile obstacles such as vehicles on a road or forklifts in an industrial plant, aiming at the selection of the visual sensor nodes that will not have their coverage significantly restricted by those obstacles. Doing so, the proposed model can be exploited by any optimization or quality assessment approach in wireless visual sensor networks, providing a preprocessing method when selecting visual nodes.

Wireless visual sensor networks are commonly employed on several applications contexts such as smart cities, intelligent transportation systems and industrial management, aiming at the use of visual data from cameras to provide enhanced information and to expand the networks utilities. In these scenarios, some applications may require high-definition images when performing more specialized tasks, for example in face and text recognition, adding an important monitoring requirement when using camera-based sensors. In fact, it is important to ensure that the network is able to gather visual data with the associated required quality to each task, and such perceived quality may be processed as a function of the Field of View (FoV) of the visual sensors. In order to address this issue, new quality metrics are proposed for wireless visual sensor networks that are deployed to perform area coverage, exploiting for that different perceptions of the FoV. Those metrics are proposed along with redeployment optimization methods for visual sensor nodes aiming at the improvement of the perceived monitoring quality, which are based on greedy and evolutionary-based approaches. The proposed metrics and algorithms are expected to be more realistic than previous solutions, allowing flexible processing of variables as cameras' positions, orientations and viewing angles, providing then high flexibility on the definition of parameters and significantly contributing to the development of sensor networks based on visual sensors.

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.

In critical industrial monitoring and control applications, dependability evaluation will be usually required. For wireless sensor networks deployed in industrial plants, dependability evaluation can provide valuable information, enabling proper preventive or contingency measures to assure their correct and safe operation. However, when employing sensor nodes equipped with cameras, visual coverage failures may have a deep impact on the perceived quality of industrial applications, besides the already expected impacts of hardware and connectivity failures. This article proposes a comprehensive mathematical model for dependability evaluation centered on the concept of Quality of Monitoring (QoM), processing availability, reliability and effective coverage parameters in a combined way. Practical evaluation issues are discussed and simulation results are presented to demonstrate how the proposed model can be applied in wireless industrial sensor networks when assessing and enhancing their dependability.

The adoption of emergency alerting systems can bring countless benefits when managing urban areas, industrial plants, farms, roads and virtually any area that is subject to the occurrence of critical events, supporting in rescue operations and reducing their negative impacts. For such systems, a promising approach is to exploit scalar sensors to detect events of interest, allowing for the distributed monitoring of different variables. However, the use of cameras as visual sensors can enhance the detection of critical events, which can be employed along with scalar sensors for a more comprehensive perception of the environment. Although the particularities of visual sensing may be challenging in some scenarios, the combination of scalar and visual sensors for the early detection of emergency situations can be valuable for many scenarios, such as smart cities and industry 4.0, bringing promising results. Therefore, in this article, we extend a sensors-based emergency detection and alerting system to also exploit visual monitoring when identifying critical events. Implementation and experimental details are provided to reinforce the use of cameras as a relevant sensor unit, bringing promising results for emergencies management.

The development of wireless sensor networks for control and monitoring functions has created a vibrant investigation scenario, where many critical topics, such as communication efficiency and energy consumption, have been investigated in the past few years. However, when sensors are endowed with low-power cameras for visual monitoring, a new scope of challenges is raised, demanding new research efforts. In this context, the resource-constrained nature of sensor nodes has demanded the use of prioritization approaches as a practical mechanism to lower the transmission burden of visual data over wireless sensor networks. Many works in recent years have considered local-level prioritization parameters to enhance the overall performance of those networks, but global-level policies can potentially achieve better results in terms of visual monitoring efficiency. In this paper, we make a broad review of some recent works on priority-based optimizations in wireless visual sensor networks. Moreover, we envisage some research trends when exploiting prioritization, potentially fostering the development of promising optimizations for wireless sensor networks composed of visual sensors.

The IEEE 802.15.4/ZigBee set of standards is one of the most used wireless sensor network technologies. This set of standards supports cluster-tree networks, which are suitable topologies for wide-scale deployments. The design of wide-scale wireless sensor networks is a challenging task because it is difficult to test, analyse and validate new designs in real scenarios. Thus, simulation becomes a convenient and feasible method for its assessment before deployment. Within this context, we provide a set of simulation models for IEEE 802.15.4/ZigBee-based networks, which are able to deal with wide-scale cluster-tree wireless sensor networks and to address their major challenges. The provided simulation models implement important mechanisms for the assessment of wide-scale cluster-tree networks and associated data communication mechanisms, enabling an easier design and test of wide-scale wireless sensor network implementations.

The development of efficient sensing technologies and the maturation of the Internet of Things (IoT) paradigm and related protocols have considerably fostered the expansion of sensor-based monitoring applications. A great number of those applications has been developed to monitor a set of information for better perception of the environment, with some of them being dedicated to identifying emergency situations. Current IoT-based emergency systems have limitations when considering the broader scope of smart cities, exploiting one or just a few monitoring variables or even allocating high computational burden to regular sensor nodes. In this context, we propose a distributed multi-tier emergency alerting system built around a number of sensor-based event detection units, providing real-time georeferenced information about the occurrence of critical events, while taking as input a configurable number of different scalar sensors and GPS data. The proposed system could then be used to detect and to deliver emergency alarms, which are computed based on the detected events, the previously known risk level of the affected areas and temporal information. Doing so, modularized and flexible perceptions of critical events are provided, according to the particularities of each considered smart city scenario. Besides implementing the proposed system in open-source electronic platforms, we also created a real-time visualization application to dynamically display emergency alarms on a map, demonstrating a feasible and useful application of the system as a supporting service. Therefore, this innovative approach and its corresponding physical implementation can bring valuable results for smart cities, potentially supporting the development of adaptive IoT-based emergency-aware applications.

When camera-enabled sensors are deployed for visual monitoring, a new set of innovative applications is allowed, enriching the use of wireless sensor network technologies. In these networks, energy-efficiency is a highly desired optimization issue, mainly because transmission of images and video streams over resource-constrained sensor networks is more stringent than transmission of conventional scalar data. Due to the nature of visual monitoring, that follows a directional sensing model, camera-enabled sensors may have different relevancies for the application, according to the desired monitoring tasks and the current sensors’ poses and fields of view. Exploiting this concept, each data packet may be associated with a priority level related to the packet’s origins, which may be in turn mapped to an energy threshold level. In such way, we propose an energy-efficient relaying mechanism where data packets are only forwarded to the next hop if the associated energy threshold level is below the current energy level of the relaying node. Thus, packets from low-relevant source nodes will be silently dropped when the current energy level of intermediate nodes run below the pre-defined thresholds. Doing so, energy is saved potentially prolonging the network lifetime. Besides the sensing relevancies of source nodes, the relevance of DWT subbands for reconstruction of original images is also considered. This allows the creation of a second level of packet prioritization, assuring a minimal level of image quality even for the least relevant source nodes. We performed simulations for the proposed relaying mechanism, assessing the expected performance over a traditional relaying paradigm.

Wireless Sensor Networks (WSN) currently represent the best candidate to be adopted as the communication solution for the last mile connection in process control and monitoring applications in industrial environments. Most of these applications have stringent dependability (reliability and availability) requirements, as a system failure may result in economic losses, put people in danger or lead to environmental damages. Among the different type of faults that can lead to a system failure, permanent faults on network devices have a major impact. They can hamper communications over long periods of time and consequently disturb, or even disable, control algorithms. The lack of a structured approach enabling the evaluation of permanent faults, prevents system designers to optimize decisions that minimize these occurrences. In this work we propose a methodology based on an automatic generation of a fault tree to evaluate the reliability and availability of Wireless Sensor Networks, when permanent faults occur on network devices. The proposal supports any topology, different levels of redundancy, network reconfigurations, criticality of devices and arbitrary failure conditions. The proposed methodology is particularly suitable for the design and validation of Wireless Sensor Networks when trying to optimize its reliability and availability requirements.

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.

The operation of Wireless Sensor Networks (WSNs) is subject to multiple constraints, among which one of the most critical is available energy. Sensor nodes are typically powered by electrochemical batteries. The stored energy in battery devices is easily influenced by the operating temperature and the discharge current values. Therefore, it becomes difficult to estimate their voltage/charge behavior over time, which are relevant variables for the implementation of energy-aware policies. Nowadays, there are hardware and/or software approaches that can provide information about the battery operating conditions. However, this type of hardware-based approach increases the battery production cost, which may impair its use for sensor node implementations. The objective of this work is to propose a software-based approach to estimate both the state of charge and the voltage of batteries inWSN nodes based on the use of a temperature-dependent analytical battery model. The achieved results demonstrate the feasibility of using embedded analytical battery models to estimate the lifetime of batteries, without affecting the tasks performed by the WSN nodes.

Energy consumption is a major issue in Wireless Sensor Networks (WSNs), as nodes are powered by chemical batteries with an upper bounded lifetime. Estimating the lifetime of batteries is a difficult task, as it depends on several factors, such as operating temperatures and discharge rates. Analytical battery models can be used for estimating both the battery lifetime and the voltage behavior over time. Still, available models usually do not consider the impact of operating temperatures on the battery behavior. The target of this work is to extend the widely-used Kinetic Battery Model (KiBaM) to include the effect of temperature on the battery behavior. The proposed Temperature-Dependent KiBaM (T-KiBaM) is able to handle operating temperatures, providing better estimates for the battery lifetime and voltage behavior. The performed experimental validation shows that T-KiBaM achieves an average accuracy error smaller than 0.33%, when estimating the lifetime of Ni-MH batteries for different temperature conditions. In addition, T-KiBaM significantly improves the original KiBaM voltage model. The proposed model can be easily adapted to handle other battery technologies, enabling the consideration of different WSN deployments.

Camera networks have been considered for a large set of visual monitoring applications. For some of them, cameras may be continuously monitoring scenes or groups of targets, but some events may trigger a critical level of visual monitoring, as in public security, industrial automation, and response to natural disasters. In such way, some critical events as a bomb explosion, a volcanic eruption, forest wildfire, or a car accident must be captured with high relevance, potentially helping when identifying responsibilities and during rescue operations. New relevance levels, which can be reflected in higher quality of transmitted images or video streams or even higher priority during transmission over the network must be quickly assigned to cameras that can view the critical events. In this work we propose a methodology to dynamically assign relevancies to cameras that view the area of critical events, employing scalar sensors and a decentralized decision mechanism. The resulting multimodal camera network can considerably enhance the critical surveillance in different monitoring applications. We propose some optimizations that exploit the monitoring relevance in such scenarios.

The use ofWireless Sensor Network (WSN) technologies is an attractive option to support wide-scale monitoring applications, such as the ones that can be found in precision agriculture, environmental monitoring and industrial automation. The IEEE 802.15.4/ZigBee cluster-tree topology is a suitable topology to build wide-scale WSNs. Despite some of its known advantages, including timing synchronisation and duty-cycle operation, cluster-tree networks may suffer from severe network congestion problems due to the convergecast pattern of its communication traffic. Therefore, the careful adjustment of transmission opportunities (superframe durations) allocated to the cluster-heads is an important research issue. This paper proposes a set of proportional Superframe Duration Allocation (SDA) schemes, based on well-defined protocol and timing models, and on the message load imposed by child nodes (Load-SDA scheme), or by number of descendant nodes (Nodes-SDA scheme) of each cluster-head. The underlying reasoning is to adequately allocate transmission opportunities (superframe durations) and parametrize buffer sizes, in order to improve the network throughput and avoid typical problems, such as: network congestion, high end-to-end communication delays and discarded messages due to buffer overflows. Simulation assessments show how proposed allocation schemes may clearly improve the operation of wide-scale cluster-tree networks.

The timing behavior of the EDCA mechanism defined in the IEEE 802.11e standard is analyzed. More specifically, the target of this paper is to evaluate the limitations of the highest priority level of the EDCA mechanism (voice category) when supporting real-time (RT) communication. By RT communication, we mean small-sized packets generated in periodic intervals that must be delivered before the end of the message stream period. Otherwise, the message is considered to be delayed and a deadline loss occurs. We have assessed the EDCA mechanism considering an open communication environment, where both RT and non-RT stations share the same frequency band. Furthermore, a realistic error-prone model channel was used to measure the impact of interferences against an error-free channel. We show that, for most part of the evaluated scenarios, when using the default parameters of the EDCA mechanism both the number of packet losses and the average packet delays forecast an unacceptable number of deadline losses. However, if adequate Contention Windows (CW) parameters are configured in the set of RT stations, it becomes possible to adequately handle RT traffic. As a conclusion of this paper, we present some potential future directions toward improved QoS in wireless networks.