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This paper presents an emergency-oriented procedure to recognize trajectory patterns by analyzing GPS data collected from intelligent sensor devices. An overall description, including design architecture and system modules, is presented. The primary issues are devoted to satisfying the requirements of key group identification and surveillance under...
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... It is essential to have a functioning event detection system for disaster management in order to detect any undesirable events. Any incident may be detected within the first few seconds of happening thanks to event detection driven by IoT-based sensor data [42]. The capacity to recognize patterns in textual or geographical data sets, which is essential for disaster management, is provided by pattern recognition mechanisms [43]. ...
A modern-day society demands resilient, reliable, and smart urban infrastructure for effective and in telligent operations and deployment. However, unexpected, high-impact, and low-probability events such as earthquakes, tsunamis, tornadoes, and hurricanes make the design of such robust infrastructure more complex. As a result of such events, a power system infrastructure can be severely affected, leading to unprecedented events, such as blackouts. Nevertheless, the integration of smart grids into the existing framework of smart cities adds to their resilience. Therefore, designing a resilient and reliable power system network is an inevitable requirement of modern smart city infras tructure. With the deployment of the Internet of Things (IoT), smart cities infrastructures have taken a transformational turn towards introducing technologies that do not only provide ease and comfort to the citizens but are also feasible in terms of sustainability and dependability. This paper presents a holistic view of a resilient and sustainable smart city architecture that utilizes IoT, big data analytics, unmanned aerial vehicles, and smart grids through intelligent integration of renew able energy resources. In addition, the impact of disasters on the power system infrastructure is investigated and different types of optimization techniques that can be used to sustain the power flow in the network during disturbances are compared and analyzed. Furthermore, a comparative review analysis of different data-driven machine learning techniques for sustainable smart cities is performed along with the discussion on open research issues and challenges.
... Tao et al. [19] summarise five most commonly used trajectory similarity measures and their improvements, and conducted comparative analysis in different application scenarios. Zhang et al. [20] cluster GPS trajectory data to discover important locations and behaviour patterns of daily human activities. Gary Reyes-Zambrano et al. [21] propose a GPS trajectory clustering algorithm to analyse vehicle flow for decision-making in intelligent transportation systems. ...
In the military and civilian surveillance domain, it is of great significance to mine regular behaviours of targets for situation awareness and command decision support. Most of the existing trajectory clustering algorithms only consider the similarity of spatial position of the trajectory, without sufficient multi‐dimensional information such as time, course and velocity. Some approaches based on information fusion take these multi‐dimensional information into account, but the features with different dimensions fused by weight coefficients are not robust and universal for different scenarios. In this paper, a regular behaviour mining method based on spatiotemporal trajectory multi‐dimensional features and density clustering is proposed. Firstly, multi‐dimensional Hausdorff similarity is defined to measure spatiotemporal trajectory from different feature dimensionalities. Different from methods based on information fusion, the proposed method defines trajectory density in feature similarity of different dimensions and adaptively determines parameters according to feature distribution in different dimensions. Experimental results in simulated and radar measured trajectory data show that the proposed method can be accurate and robust in clustering evaluation indexes such as Purity, Precision, Recall and Rand Index from different scenarios, which has a good application prospect in intelligent surveillance tasks.
... Event detection is very critical in disaster management and needs to be operational to identify any disastrous event that occurs. Event detection backed by IoT sensor data and social media streams can detect any incident within the first few seconds of its occurrence [47]. Pattern recognition mechanism offers the machine learning ability to detect the useful patterns of information from textual or spatial data sets crucial for disaster management [48]. ...
Disasters (natural or man-made) can be lethal to human life, the environment, and infrastructure. The recent advancements in the Internet of Things (IoT) and the evolution in Big Data Analytics (BDA) technologies have provided an open opportunity to develop highly needed disaster resilient smart city environments. In this paper, we propose and discuss the novel reference architecture and philosophy of a Disaster Resilient Smart City (DRSC) through the integration of IoT and BDA technologies. The proposed architecture offers a generic solution for disaster management activities in smart city incentives. A combination of the Hadoop Ecosystem and Spark are reviewed to develop an efficient DRSC environment that supports both real-time and offline analysis. The implementation model of the environment consists of data harvesting, data aggregation, data pre-processing, and big data analytics and service platform. A variety of datasets (i.e., smart buildings, city pollution, traffic simulator and twitter) are utilized for the validation and evaluation of the system to detect and generate alerts for a fire in a building, pollution level in the city, emergency evacuation path and the collection of information about natural disasters (i.e., earthquakes and tsunamis). The evaluation of the system efficiency is measured in terms of processing time and throughput that demonstrates the performance superiority of the proposed architecture. Moreover, the key challenges faced are identified and briefly discussed.
... Mohamed [37] introduced a method to more accurately track targets rather than trajectory analysis. Fu [38] and Zhang [39] used the method of measuring the similarity of trajectory distance or trajectory structure to compare and analyze trajectories. The former uses feature learning based on dynamic time planning and a space-time collaboration algorithm to analyze the orbit, focusing on detecting abnormal trajectories; the latter uses a clustering algorithm to divide the trajectories, but focuses on predicting the potential location of the crowd. ...
The study of crowd movement has recently become a popular research topic due to the increasing frequency of public safety issues. Compared with human evacuation experiments and drills, which may have personal safety risks and require a large number of volunteers, simple and convenient computer simulation has become the mainstream research method. Computer simulation first needs to characterize small groups in the crowd to model the motion state of crowds for more accurate crowd modeling. In this paper, a top-bottom hierarchical clustering algorithm based on off-line crowd trajectories is proposed to provide small group information for crowd motion simulation. First, unmanned aerial vehicle (UAV) and tracking technology are used to capture the pedestrian flow and extract the pedestrian trajectory. Second, a top-bottom hierarchical clustering strategy is proposed to divide the crowd into groups, which solves the problem of the difficulty of ascertaining small groups. This method solves the problem of automatically determining cluster centers by using the improved density peak clustering algorithm combined with a greedy algorithm. One factor of distinguishing small groups is improved by replacing the angle of the direction of motion with the distance difference, thus reducing the computational complexity. Specifically, the mean distance between trajectories based on the Euclidean distance is used for the top-level coarse-grained clustering; then, the improved Hausdorff mean distance is determined in the bottom-level fine-grained clustering. Third, the proposed algorithm is validated by classifying groups of pedestrians in real videos. The experiments show that the proposed method is applicable and effective.
... Other related use cases of GIS information have also emerged for surveillance [9] and location-based service (LBS) applications [36]. In many of these applications, trajectory data is exploited for knowledge acquisition tasks [17], the integration of movement pa erns to uncover "patterns of life" over a region [43], to expand situational awareness in crises [40], and to support the value added by a LBS application [13]. ...
This paper presents a framework for intrinsic point of interest discovery from trajectory databases. Intrinsic points of interest are regions of a geospatial area innately defined by the spatial and temporal aspects of trajectory data, and can be of varying size, shape, and resolution. Any trajectory database exhibits such points of interest, and hence are intrinsic, as compared to most other point of interest definitions which are said to be extrinsic, as they require trajectory metadata, external knowledge about the region the trajectories are observed, or other application-specific information. Spatial and temporal aspects are qualities of any trajectory database, making the framework applicable to data from any domain and of any resolution. The framework is developed under recent developments on the consistency of nonparametric hierarchical density estimators and enables the possibility of formal statistical inference and evaluation over such intrinsic points of interest. Comparisons of the POIs uncovered by the framework in synthetic truth data to thousands of parameter settings for common POI discovery methods show a marked improvement in fidelity without the need to tune any parameters by hand.
Actualmente, los sistemas de comunicación juegan un papel fundamental en la difusión de información sobre catástrofes naturales. Este artículo presenta una revisión sistemática de literatura (SLR) sobre el uso de sistemas de comunicación como base para aplicar diferentes escenarios de respuesta a emergencias de catástrofes naturales. Además, tiene como objetivo analizar de forma exhaustiva la literatura existente sobre sistemas de comunicación utilizados en ambientes de catástrofes naturales. La primera parte se centra en las fuentes de información; luego se describen las investigaciones que utilizaron técnicas en los sistemas de comunicación. Finalmente, los resultados obtenidos pueden ser utilizados para tomar decisiones en la asignación óptima y manejo de recursos de acuerdo al evento de catástrofe.
Trajectory data mining is widely used in military and civil applications, such as early warning and surveillance system, intelligent traffic system and so on. Through trajectory similarity measurement and clustering, target behavior patterns can be found from massive spatiotemporal trajectory data. In order to mine frequent behaviors of targets from complex historical trajectory data, a behavior pattern mining algorithm based on spatiotemporal trajectory multidimensional information fusion is proposed in this paper. Firstly, spatial-temporal Hausdorff distance is proposed to measure multidimensional information differences of spatiotemporal trajectories, which can distinguish the behaviors with similar location but different course and velocity. On this basis, by combining the idea of k-nearest neighbor and density peak clustering, a new trajectory clustering algorithm is proposed to mine behavior patterns from trajectory data with uneven density distribution. Finally, we implement the proposed algorithm in simulated and radar measured trajectory data respectively. The experimental results show that the proposed algorithm can mine target behavior patterns from different complex application scenarios more quickly and accurately compared to the existing methods, which has a good application prospect in intelligent monitoring tasks.
Received 17 March 2022; revised 18 April 2022; accepted 15 July 2022
In the real world, a human tourist can quickly arrive at a target along the route planned by his/her instinctive cognition of prepared maps. While on the mobile robot, it is required to know every way-points of the static or dynamic planning paths to reach a goal point. The previous navigation modes are mostly from sensor location, speech interaction, grid maps and so on. They rely on sensing performance, the speech instruction of site personnel or complex algorithms for the path searching and tracking. For improving the performance of interaction and tele-operated path tracking, in this paper, a novel interactive navigation based on the recognition of hand-drawn paths is proposed for mobile robots. Pen, paper, and camera are employed as the interface between users and the mobile robot. Initially, the image of a hand drawn path in the paper map is captured by a camera and segmented from the background by the color detection model. It is hypothesized that the captured frame of hand-drawn paths is slant to the original orthogonal map. Therefore, this method should firstly project the smoothed skeleton of slant path into the coordinate space of orthogonal map to guide the robot to perform path tracking. Then the hand-drawn path is rectified and remapped into the registered reference image of the orthogonal map through feature matching and perspective transformation. The combination of SIFT and RANSAC algorithms are employed for improving the accuracy of the projection of slant paths in the orthogonal map. Sequentially, a limited number of way-points are picked out from the corrected path in an orthogonal 2D map space usable for 2D navigation. The oriented sequential motion vectors are estimated by linking all successive way-points and employed for the path tracking along the predefined route step by step. Eventually, random navigation paths are designed to validate the robustness, effectiveness and accuracy of the constructed interactive navigation system.
In the age of emerging applications, such as IoT, big data, and data mining, our life becomes more convenient through customized services that utilize a huge amount of personal data generated and collected by various IoT devices. To fully exploit the data value as well as enhance the data utilization, more and more data are being traded in online data markets. While enjoying the benefit from data trading, data sellers are also suffering from severe risk of privacy leakage. In this paper, our objective is to maximize data seller’s received utility via balancing the trade-off between data trading benefit and data privacy cost. To achieve this, contract theory is utilized to design optimal contract trading mechanisms for both complete and incomplete information markets. From our thorough theoretical analysis, comprehensive simulations and real-data experiments, the effectiveness of our proposed optimal contract mechanisms can be validated, i.e., the maximum utility can be obtained at the seller side, the individual rationality and incentive compatibility can be guaranteed at the buyer side, and the advantages of our mechanism over the single contract mechanisms can be confirmed.
