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Resilient smart cities, largely driven by developments in the Internet-of-Things (IoT), artificial intelligence (AI), and data science (DS) technologies, have been promoted as a prominent way to mitigate emergencies and improve the perceived quality of life. However, despite the increasing affordability and effectiveness of these technologies, an unequal distribution of smart city services has been noted, usually resulting from spatial and socioeconomic disparities. Therefore, as cities prepare themselves to better face the negative impacts of climate change and increasing urbanization challenges, it is crucial to address inherent urban inequalities when planning and deploying technological solutions to detect and mitigate urban emergencies. This article discusses key aspects that should be considered when promoting resilient smart cities that benefit all urban residents. [ABSTRACT FROM AUTHOR]

In response to the significant impact of urban waterlogging on residents, the economy, and urban infrastructure in recent years, this study introduces an innovative wargame-based evaluation approach for emergency rescue plans. The primary goal of this research is to improve emergency rescue capabilities while minimizing costs and identifying gaps in existing emergency rescue plans. To effectively evaluate these capabilities, we extract specific content related to OODA (Observe, Orient, Decide, Act) dynamics in rescue actions. Furthermore, a comprehensive index system is developed to evaluate emergency rescue capabilities in the context of urban waterlogging scenarios. To address the challenges associated with intelligent optimization and evaluation of such systems, we employ a radial basis function neural network and conduct wargame experiments to obtain data and measure capability indices. The evaluation model is trained using data samples to ensure robust performance. In addition to the proposed model evaluation and analysis framework, we also present an evaluation and analysis method for RBF (Radical Basis Function) neural networks and compare the prediction results with those obtained from GRNN (Generalized Regression Neural Network), PNN (Product-based Neural Network), and BP (Back Propagation) neural network algorithms. This model efficiently processes and fits data by simulating expert experience for evaluation purposes. Such an approach takes advantage of machine learning’s sensitivity to data characteristics, effectively avoiding the influence of human factors while stably reflecting the mapping relationship between indicators and performance outcomes. This research presents a novel solution with significant implications for the development of urban emergency rescue systems that address the challenges posed by urban waterlogging incidents.

Magnitude is one of the fundamental parameters of earthquake and also one of the essential information for earthquake early warning (EEW). Rapidly and accurately determining the high-or low-magnitude event is important for mitigating the damage of earthquake hazard. To address the problem of predicting whether an earthquake event is high magnitude ($M \ge5.5$) or low magnitude ($M < 5.5$), this letter proposes a machine learning magnitude classification framework (MCFrame) using single station data, which consists of feature extraction module and magnitude classifier, and we train the feature extraction module of MCFrame to learn the characteristics of high-magnitude ($M \ge5.5$) and low-magnitude ($M < 5.5$) earthquake events, using strong-motion records collected from the Japanese Kyoshin network (K-NET) seismic network. Then, the extracted features are used as an input of magnitude classifier to classify earthquake magnitudes. Meanwhile, we analyze the impact of three different magnitude classifiers on the performance of MCFrame, which include the deep neural network (DNN), support vector machine (SVM), and random forest (RF). We show that within 5 s after the P-wave arrival, for these three different classifiers, the magnitude classification accuracy of MCFrame is close, and the MCFrame proposed in this work has a better performance than baseline models for magnitude classification. Additionally, for the MCFrame, the accuracy of high-magnitude events ($M \ge5.5$) is more than 90%, and the accuracy of low-magnitude events ($M < 5.5$) is more than 99%. These results indicate that the MCFrame proposed in this work is significant for EEW. [ABSTRACT FROM AUTHOR]

Improving the efficacy of emergency responses with digital means is receiving increasing attention. Currently, several innovative information technologies and systems are being developed to raise the situation awareness of first responders like firefighters. Among them, emergency response information systems (ERIS) appear to provide a particularly promising platform, which helps to gather, analyze, and share relevant information during emergencies. However, the conditions under which firefighters accept or reject such systems remain unclear. Existing theories explain the acceptance of information technologies only on a general level that does not consider the specific usage constraints existing in the firefighter domain. To fill this literature gap, we propose a detailed, domain-specific acceptance model with factors that explain the acceptance of ERIS by firefighters. It combines findings of the user satisfaction and the technology acceptance literature and was developed based on the input of 82 domain experts. An evaluation of the acceptance model in a survey with 212 firefighters from Germany indicates that it is effective in predicting a firefighter's intention to use an ERIS. The identified acceptance factors provide guidance for the design and evaluation of ERIS, enabling the so far mostly theoretical benefits of ERIS to be transferred into practical applications more effectively. [ABSTRACT FROM AUTHOR]

The purpose of this study is to develop an eCall system that can accurately assess the scene of a traffic accident and to analyze the expected effect of the system through integrated demonstrations. The eCall system was designed to collect information requested by emergency response agencies, such as location history immediately before and after an accident, the severity of injuries, and in-vehicle image information. In this study, an eCall in-vehicle system (IVS) was installed in service vehicles, including taxis, fire trucks, and research team members’ cars. Moreover, an eCall center was set up to relay information to emergency response agencies. The results of integrated demonstration tests indicated that the communication success rate between the eCall IVS and the call center was 99.25%, and the mean location information error was 1.2 m. In addition, this study analyzed the amount of time required for an accident victim to arrive at a hospital when the eCall system was implemented, based on 10 years of traffic accident data, and found that he or she would get there approximately 3 min earlier. [ABSTRACT FROM AUTHOR]

The rapid progress in informatisation and electrification in transportation has gradually transferred public transport junctions such as metro stations into the nexus of pedestrian flows, information flows, computation flows and energy flows. These smart environments that are efficient in handling large volume passenger flows in routine circumstances can become even more vulnerable during emergency situations and amplify the losses in lives and property owing to power outage triggered service degradation and destructive crowed behaviours. On the bright side, the increasingly abundant resources contained in smart environments have enlarged the optimisation space of an evacuation process, yet little research has concentrated on the joint optimal resource allocation between transportation infrastructures and pedestrians. Hence, in the paper, we propose a queueing network based resource allocation model to comprehensively optimise various types of resources during emergency evacuations. Experiments are conducted in a simulated metro station environment with realistic settings. The simulation results show that the proposed model can considerably improve the evacuation efficiency as well as the robustness of the emergency response system during emergency situations. [ABSTRACT FROM AUTHOR]

The advancements in Internet of Things (IoT) and embedded systems, 5G networks and next-generation wireless systems, and embedded and distributed artificial intelligence are empowering Industry 4.0. In this new industrial era, smart autonomous and connected systems will improve efficiency, reduce cost and pollution, and increase productivity and safety in industrial 4.0 based applications. Nevertheless, the new generation technologies mentioned above have not been explored to improve safety in multiplant and industrial zones. In this article, we shed light on the design of unmanned aerial vehicles (UAVs)-assisted systems for emergence response in multiplant and industrial zones. We discuss the important contributions of UAV-mounted cloudlet systems to support SAR missions by providing computation, communication, and storage resources to first responders, surveillance of the affected area, real-time monitoring of SAR members and victims, and caching nodes to improve content delivery among SAR teams. Besides, we design an envisioned UAV-cloudlet reference system for emergency response in industrial areas. Moreover, we highlight the current challenges that are being addressed in the literature aimed at making possible the efficient use of the designed UAV-mounted cloudlet systems for emergency response. Finally, we point out some future research directions that require further investigation. [ABSTRACT FROM AUTHOR]

Intelligent traffic signal control strategies for emergency vehicles (EV) is a research hotspot in intelligent transportation systems. Its research results will promote the shortening of the travel time of EVs, which is vital to saving lives and reducing property losses. This paper proposes a novel real-time traffic signal control strategy. The first step is called on-demand signal timing for reducing road saturation, which makes it possible that ordinary vehicles(OV) give way to EVs and make EVs travel faster, according to three indicators: the emergency response level(ERL), the congestion level of the road section(CLRS), and the time urgency level(TUL). The second step is called novel signal preemption, which combines non-intrusive preemption and intrusive preemption. This step provides green indicates and makes EVs pass through intersections quickly without stopping. The final step is called the recovery cycle strategy, which restores the road network to the normal situation as soon as possible by using linear programming(LP) to find the shortest green time in each phase after an EV passes the intersection. This paper conducts simulation experiments on an urban traffic simulator SUMO. The experimental results show that our novel strategy can shorten the travel time and reduce the traffic impact on the road network caused by prioritizing the EVs significantly. Compared with the fixed-time control method (FTCM), Min’s flexible signal preemption method(FSPM), and Qin’s intrusive signal preemption method (ISPM), our method can optimize travel time by up to 62.85%, 50.83%, and 11.62%, respectively.

Search and Rescue operations in emergency response to natural or human catastrophes have the main objective of locating and rescuing potential victims as fast as possible, thus quick response and accurate actions are mandatory. While standard communications may be affected, a Wireless Sensor Network can be deployed to support the rescue team. This kind of network allows data acquisition close to events and enables persistence over time, among other advantages. However, enhancements must be made to improve the adaptation to this kind of scenario. This work presents two Hybrid Wireless Sensor Networks, based on ZigBee and LoRa, developed to address some of the challenges that Search and Rescue operations pose to the use of Wireless Sensor Networks, and tested in realistic scenarios in cooperation with first responders. Likewise, several software developments that increase the performance of the networks are described. Finally, the conclusions presented, and the lessons learnt are supported by a high amount of data, gathered in realistic exercises in cooperation with civilian and military first responders.

CardioCare helps people with health setbacks like strokes by providing real-time heart monitoring through a wearable device and mobile app. An algorithm analyses data to determine emergencies. The wearable device sounds an alarm and sends notifications for ambulance dispatch or ideal travel routes to medical facilities. It also keeps a QR-coded medical history for proactive care and faster medical response. [ABSTRACT FROM AUTHOR]

This article addresses the admission control of a loss queueing network, or shortly loss network, of $N$ parallel multiserver stations with no waiting rooms. $M$ classes of customers arrive at a Poisson rate and require an exponential service time with common mean. A customer can be served by any free server of a station in the set of stations determined by the class of the customer. Admission of a customer to a station brings different rewards that depend on customer class and the preference order of that station. Our objective is to find the optimal admission control policy that maximizes the average reward. We adopt a research strategy that evolves from simpler networks to more complicated ones. First, we consider a one-station $M$ -class loss network whose results form a fundamental basis for the analysis of more complicated networks. Second, we consider a two-station $M$ -class loss network for which we establish the existence of an optimal threshold admission policy. Finally, we consider a general $N$ -station $M$ -class loss network and propose an iterative approximation policy (IAP) and a mixed-integer linear programming model that is proven to compute an upper bound. We demonstrate the near-optimal performance of the proposed IAP and the tightness of the upper bound on several numerical instances motivated by real-life networks, such as healthcare emergency service networks and emergency call centers. Note to Practitioners—This article is mainly motivated by real-time scheduling of emergency service networks, such as emergency healthcare and telecommunication networks. In such networks, emergent demands need to be served promptly and are lost otherwise. They can be served by multiple hospitals/operator groups but have their own preferences. The decision maker has to decide dynamically whether to keep the capacity of a hospital/operator for its own customers to meet customer preference or serve the incoming demand to improve the resource utilization. We set the problem as the admission control of a multiclass multistation loss network, study the properties of the optimal policy, and propose an iterative approximation policy that is proven to be near-optimal for large-size loss networks. The proposed policy is found to improve by up to 16% and 52% the no-control policy admitting the customer to the most preferred available station and the no-overflow policy with admission to only the most preferred station, respectively. Managerial insights and extensions are also discussed. [ABSTRACT FROM AUTHOR]

For the emergency response, every second counts. Intersections are prone to congestion, which greatly hinders the fast response of emergency vehicles. Although emergency vehicles possess the privilege to run a red light, it can be unsafe, and a congested intersection will prevent the exercise of this privilege. When an emergency vehicle arrives, the greedy preemption scheme offers a green signal promptly until it leaves the intersection. This guarantees a fast emergency response in most cases. However, this scheme will lead to an adverse impact on vehicles of conflicting directions and may not work when there are other emergency vehicles traveling from conflicting directions simultaneously. Employing deep reinforcement learning techniques, recent studies have shown promising results for traffic signal control. In this work, we deliver an early attempt to control the traffic signal for emergency vehicles through deep reinforcement learning, which ensures an expeditious emergency response in various scenarios and alleviates the negative influence on the traffic efficiency of conflicting directions. We conduct realistic simulations using traffic data in a real-world network with multiple intersections on different testing parameters. The results verify the feasibility and effectiveness of our model and indicate that our method notably outperforms the other five baseline methods in terms of various performance metrics. [ABSTRACT FROM AUTHOR]

In many scenarios, information must be disseminated over intermittently-connected environments when the network infrastructure becomes unavailable, e.g., during disasters where first responders need to send updates about critical tasks. If such updates pertain to a shared data set, dissemination consistency is important. This can be achieved through causal ordering and consensus. Popular consensus algorithms, e.g., Paxos, are most suited for connected environments. While some work has been done on designing consensus algorithms for intermittently-connected environments, such as the One-Third Rule (OTR) algorithm, there is still need to improve their efficiency and timely completion. We propose CoNICE, a framework to ensure consistent dissemination of updates among users in intermittently-connected, infrastructure-less environments. It achieves efficiency by exploiting hierarchical namespaces for faster convergence, and lower communication overhead. CoNICE provides three levels of consistency to users, namely replication, causality and agreement. It uses epidemic propagation to provide adequate replication ratios, and optimizes and extends Vector Clocks to provide causality. To ensure agreement, CoNICE extends OTR to also support long-term network fragmentation and decision invalidation scenarios; we define local and global consensus pertaining to within and across fragments respectively. We integrate CoNICE’s consistency preservation with a naming schema that follows a topic hierarchy-based dissemination framework, to improve functionality and performance. Using the Heard-Of model formalism, we prove CoNICE’s consensus to be correct. Our technique extends previously established proof methods for consensus in asynchronous environments. Performing city-scale simulation, we demonstrate CoNICE’s scalability in achieving consistency in convergence time, utilization of network resources, and reduced energy consumption. [ABSTRACT FROM AUTHOR]

Effective communication among first responders during and in the aftermath of a disaster can dramatically affect outcomes - saving lives by providing appropriate, timely assistance. In this article, we discuss the design of a resilient architecture that enables effective first responder communications in the challenging situations caused by a disaster. Our ReDiCom (Resilient Disaster Communications) architecture builds resilience into its framework across all the layers. The information layer allows communication by roles and identities instead of addresses to support communication among dynamically formed first responder teams. The network layer provides robust and resilient communication even when facilities are error- and disruption-prone. Our coded computation further improves resilience and enables efficient distributed data processing potentially with first responder and civilian mobile devices. [ABSTRACT FROM AUTHOR]

Recently, frequent emergencies seriously impact people’s life and production. The timely emergency material can save more people. Hence, emergency material demand prediction is a core of emergency rescue. However, emergency is a continuous, uncertain evolution process. The emergency of different periods has various situations. The time-varying and uncertain characteristics of emergency have made this representation and prediction work much more complex. In order to address this concern, this article proposes a time-varying intuitionistic fuzzy set (TVIFS) to precisely depict the time-varying scenario and the uncertainty of emergencies. For utilizing the TVIFS of different time frames, we deeply explore the integral aggregation operator of TVIFS, which can take full account of decision hesitation and possible information. Due to the different time importance of different time frames, we investigate the integral of TVIFS with constant period weight in advance. Considering the time-varying period weight, the TVIFS integral with time-varying period weight is investigated. For the sake of calculation feasibility, inspired by the relationship between continuity and dispersion, we translate the TVIFS integral with variable period weight into the TVIFS integral with constant period weight. Furthermore, we develop time-varying intuitionistic fuzzy case-based reasoning (CBR) model to forecast the emergency materials demand by designing hybrid similarity between cases. Finally, we elaborate the application of time-varying intuitionistic fuzzy CBR model by using an example of forest fire and verify the validity of our proposed method. [ABSTRACT FROM AUTHOR]

We present a novel framework for modeling traffic congestion events over road networks. Using multi-modal data by combining count data from traffic sensors with police reports that report traffic incidents, we aim to capture two types of triggering effect for congestion events. Current traffic congestion at one location may cause future congestion over the road network, and traffic incidents may cause spread traffic congestion. To model the non-homogeneous temporal dependence of the event on the past, we use a novel attention-based mechanism based on neural networks embedding for point processes. To incorporate the directional spatial dependence induced by the road network, we adapt the “tail-up” model from the context of spatial statistics to the traffic network setting. We demonstrate our approach’s superior performance compared to the state-of-the-art methods for both synthetic and real data. [ABSTRACT FROM AUTHOR]

An emergency response process outlines the workflow of different activities that need to be performed in response to an emergency. Effective emergency response requires communication and coordination with the operational systems belonging to different collaborating organizations. Therefore, it is necessary to establish information sharing and system-level interoperability among the diverse operational systems. Unlike typical e-government processes that are well structured and have a well-defined outcome, emergency response processes are knowledge-centric and their workflow structure and execution may evolve as the incident unfolds. It is impractical to define static plans and response process workflows for every possible situation. Instead, a dynamic response should be adaptable to the changing situation. We present an integrated approach that facilitates the dynamic composition of an executable response process. The proposed approach employs ontology-based reasoning to determine the default actions and resource requirements for the given incident and to identify relevant response organizations based on their jurisdictional and mutual aid agreement rules. The Web service APIs of the identified response organizations are then used to generate an executable response process that evolves dynamically. The proposed approach is implemented and experimentally validated using an example scenario derived from the FEMA Hazardous Materials Tabletop Exercises Manual. [ABSTRACT FROM AUTHOR]

OPS-SAT is the first publicly accessible Hardware/Software Innovation Lab in low Earth orbit. It was launched by the European Space Agency on December 18, 2019, and is open to European academia and industry, allowing new concepts to be tested in space with a range of interesting payloads. This article discusses the implementation and results of an in-orbit demonstration (IOD) using the software-defined radio payload of OPS-SAT together with onboard RF processing techniques. We demonstrate the design of a software configurable search and rescue receiver using GNU Radio, running on Linux in a 3U CubeSat. The system runs on the Satellite Experimental Processing Platform (SEPP) Cyclone V ARM system-on-chip and is able to autonomously detect and decode transmissions from terrestrial 406-MHz beacons from the global COSPAS-SARSAT search and rescue system. Decoded beacon information is logged onboard together with metadata and is downloaded to mission control at ESA/ESOC. The successful IOD paves the way for in-orbit RF experimentation and bridges the gap between satellite operations and the Internet of Things era. [ABSTRACT FROM AUTHOR]

We present the design and development of a data visualization service (RAMPVIS) in response to the urgent need to support epidemiological modeling workflows during the COVID-19 pandemic. Facing a set of demanding requirements and several practical challenges, our small team of volunteers had to rely on existing knowledge and components of services computing, while thinking on our feet in configuring services composition and adopting suitable approaches to services engineering. Through developing the RAMPVIS service, we have gained useful experience of ensuring conformation to services computing standards, enabling rapid development and early deployment, and facilitating effective and efficient maintenance and operation with limited resources. This experience can be valuable to the ongoing effort for combating the COVID-19 pandemic, and provides a blueprint for visualization service development when future needs for visual analytics arise during emergency response. [ABSTRACT FROM AUTHOR]

The use of big data has led to many technologies that were previously thought to be impossible. We are now able to analyze the spread of a disaster automatically through the use of social networking analysis, which is effectively served by internet or cloud services. One problem with using such algorithms in these cases is that internet services and connections to the cloud can often be damaged. In order to combat this issue, mobile base stations can be deployed, allowing for an emergency internet network to be used until the landlines can be repaired. These emergency networks have limitations in speed and cost, but seem to be the most promising technology for the future. Forwarding all of the data through the network results in the lowest cost but yields a large amount of data overflow, forcing the system to cache data, thus increasing the delay. Fully processing data in the edge resources results in a higher cost. A genetic algorithm was used to find the ideal balance between processing and sending the data, which allowed for the most data to be transmitted without causing data overflow. Results show that the proposed algorithm closely matched the results of the genetic algorithm, while being executable with minimal clock cycles.

This paper proposes a reliable hypercube queuing problem where servers (e.g., vehicles) can be dispatched either individually or jointly as cooperative units, and these servers are subjected to probabilistic disruptions. The probability of a server disruption depends on the amount of resources (e.g., firefighter staffing number) allocated to this server. If any server in a cooperation unit is disrupted, a less preferred (e.g., based on capabilities and source station) but functioning and available unit will be dispatched instead. This paper derives formulas for steady-state system performance metrics for this new hypercube queuing system. The queuing model is embedded into a mixed-integer non-linear program to optimize the allocation of resources so as to minimize the total costs under server disruptions. An empirical case study is used to compare the system performance measures under a range of implementation scenarios, and sensitivity analyses are conducted to reveal insights. [ABSTRACT FROM AUTHOR]

In the context of sixth-generation (6G) networks, emergency management systems based on wireless communications have recently gained increasing interest. Hereby, fundamentals and open problems of post-disaster communications are discussed, especially focusing on their topological aspects. The motivation behind this choice is that whenever a natural or man-made disaster occurs, there is a high chance that the terrestrial communication infrastructure is compromised, and therefore alternative networks need to be deployed efficiently in order to enable the majority of the civilians and first responders to communicate. In this article, we first provide a brief review of existing aerial ad hoc networks for post-disaster communications. Next, we shed light on some new aspects of this problem, which are related to the topology of the network supporting the impacted area. Finally, with the aid of selected simulation results, we show how the cellular infrastructure requirements for a disaster-struck region significantly depend on its location and extension. [ABSTRACT FROM AUTHOR]

Multi-access edge computing (MEC) systems where the base station (BS) is equipped with multiple antennas are basic application systems in the future. Moreover, unmanned aerial vehicles (UAVs) can be used as relays for responding to emergency service situations in the systems above. Thus, in this work, we investigate a UAV-relaying-assisted MEC system with multi-antenna BS. The optimization for offloading and scheduling strategies should be considered for the energy consumption minimization. First, a circular deployment scheme is adopted to overcome the difficulty of UAVs’ location optimizations. Then, the original problem is established, which aims to minimize the weighted sum of transmission and hovering energy consumptions by jointly optimizing offloading and scheduling strategies. Moreover, the non-convex original problem is transformed into four sub-problems and their optimal solutions are obtained by proposed algorithms. Finally, an iterative algorithm is proposed to obtain a sub-optimal solution of the original problem. Simulation results show that the proposed algorithm outperforms the three benchmark algorithms in terms of the weighted sum and approaches the performance of the relaxed lower bound algorithm. Furthermore, simulation results demonstrate that appropriate number of UAVs and BS's antennas can achieve better energy-saving performance. [ABSTRACT FROM AUTHOR]

The aim of this article is to present an architecture to support reconfigurable multimedia services for a practical emergency environment use case of rescue operations. Specifically, radio communication and video surveillance services are provided by means of a small device carried by a mobile vehicle. This solution is validated by implementing a digital mobile radio (DMR) standard radio hotspot and a video streaming server in an unmanned aerial vehicle (UAV) that conveys a device with scarce resources in order to minimize power consumption. To achieve a fast and flexible deployment of the envisioned services, a virtualization-based approach is proposed. Namely, a Kubernetes orchestrator is used to manage the life-cycle of services deployed on a small resource device, endowing the architecture with scalability and management flexibility. This article describes the executed performance tests that measured key parameters such as deployment time and recovery time after disconnection. Highly promising results were obtained, showing that the proposed architecture can be deployed in less than 4 minutes and can recover from network disconnections in less than 10 seconds. Thus, the performance, reliability and flexibility of the overall solution are demonstrated.

Ultra-miniaturized and highly power-efficient ELF (0.3-3 kHz) transmitters are desirable for underground and undersea wireless communications. This paper proposes the use of rotating permanently polarized dipoles (magnets or electrets) for such applications. A power-efficient data modulation scheme based on continuous-frequency FSK (CF-FSK) is also proposed for such electro-mechanical transmitters. Theoretical analysis and simulations show that 7-ary CF-FSK is optimal in the sense that it minimizes average mechanical torque on the mechatronic antenna for a given bit rate. Preliminary experimental results from a prototype ELF magnetic-field based transmitter based on a high-strength rare-earth (NdFeB) magnet and small brushless DC (BLDC) motor are presented. The results highlight local environment challenges such as power line interference that affect the choice of data encoding and modulation schemes. Reliable non-line-of-sight (NLOS) communication through concrete barriers at 100 Hz and ~0.25 bit/sec is demonstrated at distances up to ~5 m. The proposed mechatronic antenna and modulation scheme can be scaled up in both frequency and transmit power for more challenging applications in surface-to-undersea, surface-to-cave, and other conductive environments that require ELF channels.

Many fire situations have represented the loss of lives and material costs due to the lack of early fire detection through smoke or gas sensing, which can become complex and critical. Meanwhile, engineers worldwide develop and test multiple systems for smoke and gas detection, commonly based on sensor networks, digital image processing, or computer vision. Furthermore, the detection system must work thoroughly with alarms and warnings that aware of a risk situation for prompt evacuation of the population in the surroundings based on a reliable data network topology with adequate device deployments that will let us know the moment a fire outbreak. This paper presents a low-cost Internet of Things (IoT) prototype for fire detection in outdoor environments based on sensors and Low Power Wide Area Network (LPWAN), focused on the accuracy in the temperature and gas measurement at the moment a fire starts. For its achievement, we integrated wireless components, development boards, and electronic devices, following the management of information updates through a database schema for the alarm settings based on the data gathered from the sensors.

North Bay Hydro Services Inc., an affiliate of north bay hydro distribution Limited, set out to deploy Canada?s first utility-scale microgrid, completing the project in 2019 The North Bay, Ontario, company wanted to build a more resilient grid to support the community in case of an emergency. In conjunction with the community, the microgrid?s development helped form a community hub that benefits the city of North Bay during both normal and emergency events. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) can be utilized to provide communication services for the areas where the communication is unavailable due to the high mobility, agility and low cost. In this letter, we investigate the energy efficient UAVs deployment and the movement for emergency response in the multi-UAVs enabled wireless communication system. The positions of the UAVs, the users association and the transmit power of the ground users are jointly optimized to maximize the energy efficiency of all ground users. And when one UAV leaves because of the breakdown or the drained battery, how the other UAVs move with the minimum energy cost is also studied. An alternating algorithm based on successive convex approximation (SCA) technique is proposed to solve the formulated optimization problems. Numerical results show the proposed multi-UAVs deployment and movement outperform the benchmarks. [ABSTRACT FROM AUTHOR]

An important role of postearthquake emergency management is to minimize the restoration time, which is the sum of the travel time and the response time. The travel time is the time needed to reach the affected area from the dispatch location, whereas the response time is the time required to bring the situation under control after reaching the affected area. A number of built environment variables, e.g., building collapse probability, and natural variables, e.g., flooding probability, are known to affect the restoration time. Data from St. Louis, MO, USA, are used in conjunction with a discrete-event-based simulation model to identify the statistically significant variables via an analysis of variance. The experimental results show that in order to reduce the loss of life, the volume of resources and the building collapse and flooding probabilities are significant factors that should be accounted for in the emergency-response planning for an earthquake. [ABSTRACT FROM AUTHOR]

Actions taken immediately following a life-threatening personal health incident are critical for the survival of the sufferer. The timely arrival of specialist ambulance crew in particular often makes the difference between life and death. As a consequence, it is critical that emergency ambulance services achieve short response times. This objective sets a considerable challenge to ambulance services worldwide, especially in metropolitan areas, where the density of incident occurrence and traffic congestion are high. Using London as a case study, in this paper, we consider the advantages and limitations of data-driven methods for ambulance routing and navigation. Our long-term aim is to enable considerable improvements to their operational efficiency through the automated generation of more effective response strategies and tactics. A key ingredient of our approach is to use a large historical dataset of incidents and ambulance location traces to model route selection and arrival times. Working on the London road network graph modified to reflect the differences between emergency and civilian vehicle traffic, we develop a methodology for the precise estimation of expected ambulance speed at the individual road segment level. We demonstrate how a model that exploits this information achieves best predictive performance by implicitly capturing route-specific persistent patterns in changing traffic conditions. We then present a predictive method that achieves a high route similarity score while minimising journey duration error. This is achieved through the combination of a technique that correctly predicts routes selected by the current LAS navigation system and our best performing speed estimation model. This hybrid approach outperforms alternative mobility models. To the best of our knowledge, this paper represents the first attempt to apply a data-driven methodology for route selection and the estimation of arrival times of ambulances travelling with blue lights and sirens on.

When natural disasters such as earthquakes happen, network architectures are highly vulnerable. Once the network connections cannot be guaranteed, even rescue operations are difficult to carry out. in this article, to help the people in affected areas retrieve network services, we come up with a solution based on fog computing to quickly deploy emergent distributed service. in case the original network infrastructures are damaged to varying degrees, we make use of the available equipment including routers and mobile devices as fog nodes to realize emergency networking and communication. in the simulation evaluation, we compare our method with existed one. The results show that our method can reduce time costs on fast deployment and improve work efficiency. [ABSTRACT FROM AUTHOR]

A group of geographically dispersed and logically collaborated emergency organizations are involved when emergency occurs. Two challenging issues are a reasonable emergency resource allocation mechanism and an efficient resource conflict checking and resolution control mechanism to ensure the conflict-free execution of global cross-organization emergency response processes. To address them, this paper proposes an approach to support emergency resource management including both intraorganization private resource management and cross-organization public resource management. The former has been fully discussed in our previous work using E-net. To cope with the latter, this paper presents a novel type of Petri net that is extended with both time and resource factors to model cross-organization emergency response processes. Then, according to the resource requirement analysis, their worst, delayed, and best cases are obtained. Next, emergency resource conflict checking and four conflict resolution strategies are proposed to resolve the detected resource conflicts for the delayed execution case. This paper shows how to use different resolution strategies to construct the conflict-free model by designing corresponding resolution controllers. Finally, their performance is evaluated by a fire emergency response example. [ABSTRACT FROM AUTHOR]

The papers in this special section focus on the topic of crisis management - from events such as nuclear accidents to the COVID-19 pandemic. Discusses the impact such events have on society, the economy, the environment, and politics. [ABSTRACT FROM AUTHOR]

Due to the urgent and unpredictable nature of disaster relief, emergency management systems (EMS) faces an enormous challenge of real-time data analysis without the complete information from emergency communication networks (ECNs). In this letter, we propose an incomplete information based two-tier game model (IITG) to realize collaborative computing at the edge of ECNs, which incentivizes idle computing devices (ICDs) to share computation resources through maximizing utilities of EMS and ICDs. Furthermore, we develop a near-optimal IITG algorithm (N-IITG) to seek the unique Bayesian Nash equilibrium. Simulation results reveal that N-IITG outperforms the existing incomplete information based methods in terms of computation latency and participants utilities. [ABSTRACT FROM AUTHOR]

Emergency call centers (ECCs) are upstream of the prehospital emergency medical system and the life of many people depends on their effectiveness and responsiveness. This notwithstanding, the way their operations are organized and managed differs from one place to another. Also, depending on the number of incoming calls and available resources, they can operate differently. In the face of these heterogeneous situations, some ECCs do not always meet the expected performance levels: people still wait for too long before their call is answered. Moreover, they may have difficulties in managing an important upsurge of calls, especially in periods of crisis. Therefore, to support ECCs’ organizational improvement steps, this article aims to develop a tool-based framework that would enable to make clear and objective diagnoses, especially as regards responsiveness. Our proposal allows considering both nominal (normal days) and exceptional (crisis days) demands. It is based on data science, process mining, and discrete event simulation tools. By experimenting it on a French real case, the results show that such a tool-based framework can be very valuable for improving the performance of ECC organizational setups in both normal and disrupted situations. [ABSTRACT FROM AUTHOR]

Water distribution systems have a safety-critical role and are vulnerable to a range of intentional threats. Committing to emergency preparedness, critical and missing metrics are the residual water storage in tanks and the estimated operative autonomy (in hours). This article presents a high-fidelity simulator developed to calculate the mentioned metrics and to evaluate the impact of adverse events, such as faults and/or cyber-attacks on a real water distribution system. This simulator utilizes a refined model of the network for accurate results; a commercial simulator to trustworthily solve the hydraulic equations; and an ad-hoc module to follow the evolution in time of the event. A large real water distribution network has been modeled thanks to real data provided by an operator and used as a case study. A comprehensive impact analysis of the single failure has been carried out. Results show that the simulator has been able to elicit unforeseen use cases and correctly quantify the required metrics. This article has been implemented in the framework of the European project ATENA. [ABSTRACT FROM AUTHOR]

When an emergency occurs, one of the important challenges is how to form an effective and timely response. An emergency disposal plan is usually organized as a series of emergency response processes manipulated by one emergency command center and several subordinate emergency organizations. Moreover, these subordinate organizations are usually geographically dispersed and need to collaborate with each other. In this case, designing and verifying such cross-organizational collaborative emergency response processes are complicated and time-consuming. To address this problem, we propose a hierarchical modeling and correctness verification approach. A general framework for hierarchical modeling and correctness verification of such processes is first introduced. Then, a top-level model and two kinds of bottom-level models (complex and simple bottom-level ones) are proposed to model such processes and collaboration patterns from different abstraction levels. Next, Petri net refinement operation is adopted to refine the top-level model by using its corresponding bottom-level models to obtain the refined model. Finally, the correctness of the refined model is verified based on reachability graph. A typical running case of cross-organization collaborative fire emergency response processes is given to validate our proposed method. [ABSTRACT FROM AUTHOR]

Emergency communication infrastructures are of critical importance in disaster rescue scenarios, responsible for providing reliable connection services among victims, rescuers, and public safety command centers. Moreover, many rescue missions demand effective perception and real-time decision making, which highly rely on effective data collection and processing, and the availability of low-latency computation platforms. In this article, we propose an edge intelligence-based MCWEN to address these challenges, by leveraging edge-based technologies including edge caching, edge computing, and edge learning. In particular, MCWEN showcases a three-layer architecture, consisting of end devices, edge servers, and remote clouds. Intelligent mission cognition involves the understanding of key features of various tasks, as well as the environment and available rescue resources, and it plays an essential role in the MCWEN. We highlight edge-assisted approaches to support the key functionalities of MCWEN, including data collection, information extraction, and decision making. Typical application examples are provided to illustrate the practical significance of the proposed MCWEN framework. [ABSTRACT FROM AUTHOR]

It is a challenging problem to make the team of unmanned aerial vehicles perform well to gather up-to-date situational awareness in dynamic environments. To solve the challenge, in this paper, a novel algorithm is presented on the multiagent information gathering problem. First, the physical environment is modeled as an undirected graph, where information obeying the multistate Markov chain is distributed in each vertex. Each agent is allocated in a designated area, where the objective of the team is to gather as much valuable information as possible. Second, this problem is formulated as the factored multiagent partially observable Markov decision process. Furthermore, a scalable centralized online planning algorithm is proposed by iteratively computing patrolling routes for each agent in a greedy fashion. These patrols are bounded optimal in specific conditions. In addition, we evaluate the algorithm on multiagent information gathering scenarios empirically by benchmarking it against some state-of-the-art online planning solvers, i.e., partially observable Monte Carlo planning and factor-value partially observable Monte Carlo planning. Experimental results show that the algorithm typically performs at least 9.04% better than these solvers in the four-agent patrolling problem with coupling degree being three, and can scale up to 100 agents with complex coupling relationships. [ABSTRACT FROM AUTHOR]

The objective of this paper is to create and test an integrative framework for performance analysis of disaster management system-of-systems (DM-SoS). Effective disaster management systems and processes are critical for saving lives and protecting properties. However, the lack of integration between heterogeneous systems and entities involved in disaster management impacts coordination and operational efficiency. Greater integration and coordination among multiple heterogeneous entities is essential in improving performance and achieving network-centric disaster management. To address this gap, this paper, first examines the dimensions of analysis and a framework for a system-of-systems assessment of disaster management. In the proposed framework, the main entities include stakeholders, operations, resources, information, and infrastructure. Second, a metanetwork framework was employed to capture the network interactions among various types of entities. Then, measures of network efficiency (e.g., operational and coordination efficiency) were used to identify critical entities and evaluate the effectiveness of system integration strategies. The application of the proposed framework was shown in a case study of disaster management processes in the 2017 Hurricane Harvey. The findings highlight the importance of the SoS approach for evaluating the performance of disaster management processes under different disaster-induced perturbations (e.g., resources limitation, infrastructure disruptions, and lack of information). The findings also identify ways to improve system integration and coordination among various heterogeneous entities interacting in networks. [ABSTRACT FROM AUTHOR]

In the last decade, many efforts have been devoted to indoor localization and positioning. In this paper, a hybrid indoor localization system has been developed within the European project REFIRE for emergency situations. The REFIRE solution estimates the user’s pose according to a prediction-correction scheme. The user is equipped with a waist-mounted inertial measurement unit and a radio frequency identification (RFID) reader. In the correction phase, the estimation is updated by means of geo-referenced information fetched from passive RFID tags predeployed into the environment. Accurate position correction is obtained through a deep analysis of the RFID system radiation patterns. To this end, extensive experimental trials have been performed to assess the RFID system performance, both in static and dynamic operating conditions. Experimental validation in realistic environments shows the effectiveness of the proposed indoor localization system, even during long-last missions and/or using a limited number of tags. [ABSTRACT FROM AUTHOR]

Disaster situations require a prompt emergency response. The failure of the legacy (wired) emergency response systems to efficiently cope in such time-critical environments has led to a great interest in wireless-based emergency response systems. In this regard, a number of wireless technologies and systems - each with its peculiar characteristics and pros and cons - have been proposed for use in various emergency response situations. This paper presents, to the best of our knowledge, the first comprehensive survey of the research on this practically important topic. We motivate the use of wireless technology for emergency response and present a comparative analysis of the available wireless technologies. After outlining the scope and requirements of emergency response systems, we provide an overview of the architectures and features of wireless-based emergency response systems and then compare different wireless technologies for different emergency settings. We provide not only details about individual emergency response systems and technologies but also expend efforts to show the forest for the trees to emergency response practitioners - in particular, we provide general high-level guidelines that can help in deciding the right technology for a particular situation, and discuss potential pitfalls from the large-scale emergency-network deployment experience of one of the authors. The emergency response operation is split into five functionalities: we present general guidelines that would help public safety agencies in choosing suitable wireless technologies for each one of them. Finally, we also highlight opportunities and identify the potential areas for further investigation.

With the number of nonhuman devices expected to significantly overtake human users of long-term evolution networks, it is no surprise that First Responders in mission critical (MC) operations will need to interact with an increasing number of unmanned devices, “bots”or drones. In this paper, we propose the MC “bot”concept as an entity capable of gathering environmental/situational information and triggering certain automated actions without the need of human intervention. We prove that in certain circumstances these bots can help quickly resolve emergency situations and complement traditional centralized coordination from dispatch control rooms. We explain how such “bots”relate to and expand the 3GPP MC Communications architecture framework, considering different architectural approaches and complexity levels. Importantly, because First Responders must remain focused, hands free, and context aware most of the time, we cover specifically the case where man-machine interaction is based on voice communication without having to use hands or look at a screen. It is hence of particular interest to convert “bot”interactions into audio information exchanged over push-to-talk communication services, be it through the cellular network or leveraging the 3GPP device-to-device capability. This paper is complemented with theoretical use cases as well as description and multimedia material of a prototype implementation of a concept emulator.

Emergency medical service (EMS) occurs in a high-pressure and error-prone environment, where paramedics must provide prompt decisions in care while recording information with limited time, incomplete data, restricted resources, and competing priorities. The EMS requires cooperative workflows between patients or caregivers, paramedics and medical centers in the community. In a conventional EMS, they have difficulties in obtaining causes of emergencies and personal medical histories, which are important for a rapid and proper response. We analyzed the requirement of a smart EMS (SEMS) system and derived the key components in connected care environments leveraging information and communication technology. A survey of paramedics (n=113) revealed that a SEMS system using IoT technology should integrate personal lifelogs, electronic medical records, and patient monitoring in ambulances into pre-hospital care recording systems. It also addressed context-awareness in the EMS accelerates first responder's activities, while supporting personalized care not only at the scene of the emergency but also during the entire hospital stay. Based on requirement analysis, we designed and implemented SEMS using health information standards to provide interoperability between devices and systems. As an application of SEMS, an example service is introduced: lifelog-connected EMS for stroke patients with a real-time location service for managing timeline of treatment.

To find the relative positional relationship accurately and efficiently without pre-installed infrastructure in emergent scenarios like firefighting, we proposed a rotating-anchor based indoor geolocation system using time of arrival (TOA) technique in this paper. In this system, the positioning problem is regarded as a template-matching problem to estimate direction of the target. Based on preliminary results, further measurements that the tester held the locator and turned around continuously without any stop. The TOA ranging error for continuous rotation measurement was analyzed and proved to follow non-Guassian with impulsive characteristic in this paper. According to such error characteristics, a direction estimation algorithm of maximum correntropy criterion-based matching algorithm was proposed in this paper. The performance of algorithm was validated by comparing with the least mean square and least mean p-norm with empirical measurements.

Ad-hoc networks have long been studied as an ideal technology to provide communications in emergency operations when network infrastructures are not available. Nevertheless, this area has not yet delivered enough mature technologies or working prototypes. We suspect that, among other issues, this is due to a lack of understanding of this application domain that forces researchers to make too many assumptions. One of those concerns is the mobility of network nodes. This paper describes, analyzes, and simulates the mobility traces of a fire department during 30 wildfires. The analysis shows interesting insights into the communication range and the type of network in these scenarios. For instance, multi-hop routes are unlikely, so the network behaves like a delay-tolerant network. In addition, the simulation results present a clear image of the network performance under different circumstances that can be used to design applications. We found that the network capacity is low due to the sparse network connectivity. Moreover, the buffer size has a much bigger impact on data delivery than the delay-tolerant routing protocol selected. We think that these are valuable insights and that the traces constitute an important asset for the research community.

Data collection and analysis in the field is critical for operations in domains such as environmental science and public safety. However, field workers currently face data- and platform-oriented issues in efficient data collection and analysis in the field, such as limited connectivity, screen space, and attentional resources. In this paper, we explore how visual analytics tools might transform field practices by more deeply integrating data into these operations. We use a design probe coupling mobile, cloud, and immersive analytics components to guide interviews with ten experts from five domains to explore how visual analytics could support data collection and analysis needs in the field. The results identify shortcomings of current approaches and target scenarios and design considerations for future field analysis systems. We embody these findings in FieldView, an extensible, open-source prototype designed to support critical use cases for situated field analysis. Our findings suggest the potential for integrating mobile and immersive technologies to enhance data's utility for various field operations and new directions for visual analytics tools to transform fieldwork. [ABSTRACT FROM AUTHOR]

During rescue missions, transferring injured people from accident scenes to rescue sites is considered crucial and time-sensitive. In particular, a one-second delay could put more lives in danger. Though ambulances are commonly equipped with standard siren devices, such siren signals are not recognized by traffic light controllers. Therefore, rescue missions could be delayed at intersections due to an urgency-unaware traffic light control system. In the worst case, pile-up accidents could also happen when the ambulances lawfully ignores the traffic lights. This paper proposes A2T to enhance the efficiency of rescue missions, by establishing a communication mechanism among ambulances and infrastructures (e.g., traffic light controllers). Thailand, which had the highest road traffic death rate in the world in 2015, is selected as a use case in this paper. A2T promotes information sharing between ambulances and traffic light controllers along the rescue path in advance. Such information, including speeds, locations, and emergency routes, allows the traffic light controllers to launch a prioritized green traffic light accordingly. This allows the ambulances to go through any road intersection efficiently and safely. Our comprehensive performance evaluation shows that A2T achieves 100 percent waiting time reduction for the ambulances, with only 2.48 percent increase in delay of other vehicles at the intersections. [ABSTRACT FROM AUTHOR]

The emerging 5G communication network is gaining tremendous attention from mobile network operators, regulators, and academia due to the provisions of network densification, ultra-low latency and improved spectral and energy efficiencies. However, post-disaster EMS, which nowadays predominantly depends on the wireless communication infrastructure, is significantly lagging behind in terms of innovation, standards, and investments. Since the 5G vision is the revolution of the telecommunication industry, provisions of efficiently handling EMS is expected to be distributed, autonomous, and resilient to the network vulnerabilities due to both manmade and natural disasters. In this article, the 4G LTE approaches for typical post-disaster communication and their shortcomings will be discussed. We elaborate three typical post-disaster network scenarios when the network is congested, partly functional or completely isolated. The possible solution framework, for instance, Device-to-Device communication, drone-assisted communication, mobile ad hoc networks and Internet-of-Things, for post-disaster scenarios will be discussed. Given that spectrum allocation is critical for EMS, we assess the possible schemes for radio resource allocation specific for EMS in addition to the social responsibility of users in such critical situations. [ABSTRACT FROM AUTHOR]