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Fire Insurance And Aging Infrastructure: Addressing Electrical Risks
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By Changjiang Xiao Changjiang Xiao Scilit Preprints.org Google Scholar 1, 2, Nengcheng Chen Nengcheng Chen Scilit Preprints.org Google Scholar 1, 2, * , Jianya Gong Jianya Gong Scilit Preprints.org Google Scholar 1, 2, 2, 3, Wang Scilit Preprints.org Google Scholar 1, 2, Chuanbo Hu Chuanbo Hu Scilit Preprints.org Google Scholar 1 and Zeqiang Chen Zeqiang Chen Scilit Preprints.org Google Scholar 1, 2
State Key Laboratory for Information Engineering in Sensing, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
Pipeline Hack Points To Growing Cybersecurity Risk For Energy System
Received: 24 May 2017 / Revised: 17 July 2017 / Accepted: 3 August 2017 / Published: 15 August 2017
The smart city has become a popular topic of research. How to focus on multiple distributed information sources to effectively deal with public emergencies and support individual decision-making is a very important issue in building smart cities. In this paper, an event-driven dispatching service (EDFS) method using cyber-physical infrastructures for emergency response in smart cities is proposed. The method consists of an attentional service model at the top level, an information representation of the model and an attentional service process to make the service model work in emergency response. The alert service method follows an event-driven mechanism that allows the alert service process to be triggered by public emergencies sensed by wireless sensor networks (WSN) and mobile crowd sensing, and it meets the needs of various community institutions. in terms of emergency response. and information resources, thereby providing comprehensive and personalized support for decision-making. Furthermore, an EDFS prototype system has been designed and implemented based on the proposed method. An experiment using a real-world scenario – the August 2014 gas leak in Taiyuan, China – is presented to demonstrate the feasibility of the proposed method to assist various community institutions in coping and responding effectively to public emergencies. .
The smart city, one of the most popular topics and the most advanced problems, has attracted wide attention and renewed the concept of the traditional city [1, 2, 3, 4, 5]. Compared to a living organism, the intelligence of a smart city resides in its effective combination of digital telecommunications networks (medicine), embedded intelligence (brain), sensors and tags (sense organs), and software (cognition). and cognitive ability) [6]. Furthermore, from a system perspective, a smart city can be considered as a dynamic and complex system that evolves in space and time following directions that are difficult to predict [7], mainly including a physical part and a cyber part. . also called cyber-physical systems (CPS) [8, 9, 10, 11, 12]. Figure 1 presents a conceptual diagram of an operational smart city. As shown in this figure, the sensing infrastructures are related to the physical component of the CPS and different sensing devices such as different heterogeneous sensors (local/remote and fixed/mobile sensors, such as radio-frequency identification [RFID], Moderate Resolution Imaging Spectroradiometer [MODIS], and Global Positioning System [GPS] sensors in smart phones) [13, 14]. Communication infrastructures (eg, 3G/4G/ZigBee/Wi-Fi/WiMAX/WSN) and cloud computing infrastructures (eg, Amazon EC2, Microsoft Azure, Xen Cloud Platform, Hadoop, HBase, Hive, Impala, Storm, Pig, and SPARK ) belongs to the cyber component of CPS.
The operational smart city is driven by various information sources, both data sources and internal. Data sources include archived data (e.g., geospatial baseline data, archived historical sensor data, remotely sensed images, unmanned aerial vehicle images, RFID data, and video surveillance data) and real-time data streams (e.g., monitoring data for city water, electricity, fuel, and gas supply) generated by sensor networks. Service resources include geographic data services built on consistent standards [15], e.g., Web Feature Service (WFS) [16] for geographic features, Web Mapping Service (WMS) [17] for geo-registered map images. , Web Coverage. Services for raster data, Sensor Observation Services (SOS) [18] for real-time sensor observations, Web Processing Service (WPS) [19] for gathering analysis and decision models for smart cities, Sensor Event Service ( SIX). ) [20] for filtering and subscribing to sensor observations (events), and Web Information Service (WNS) [21] for message reporting. Meanwhile, various public emergencies, such as security incidents, transportation accidents, and accidents involving public facilities and equipment, occur frequently and can cause heavy losses and economic damages [22]. These rich sources of information can be used to help city-wise decision makers cope with common emergency situations and make decisions. However, these resources are increasing and geographically distributed across different network nodes. Therefore, the effective discovery of proprietary information resources and their focus on effective use and personal decision-making support for public emergencies is an urgent problem that must be solved for the smart city paradigm [2]. In order to solve this problem, some studies have suggested a focus service.
Pdf) Hurricane Harvey Infrastructure Resilience Investigation Report
Service Focusing, in essence, focuses multiple sources of information on a specific task and provides personalized service to the various roles involved. In this regard, some preliminary work has been done, primarily on its conceptual aspects. Yang et al. Attention service was considered as one of the main solutions to provide personalized, accurate service of information sources of intention [23]. Huang et al. saw information focus as a new means of information service and presented a concept and a mathematical model for information focus based on semantic relationship [24]. Zhu et al. proposed a hierarchical semantic constraint model to focus on remote sensing information services, in which constraints establish links between user semantics, data service and processing services, and semantic reasoning underlying service discovery, selection, and composition. does [25].
Aggregation of data and services on demand is one method to focus on the many sources of available information and thus perform the service of attention. Yu et al. developed a semantically-enhanced geographic catalog service to meet the demands of geographic information discovery and analysis (spatial data, services/service chains) in a cyber infrastructure [ 26 ]. Yang et al. proposed a RESTFul-based workflow communication method to integrate heterogeneous workflows, for example, one workflow to access sensor information and one to process it, into a unified unit [ 27 ]. In addition, Chen et al. used SensorML to build a geoprocessing e-Science workflow model to integrate sensors, observations and processes (physical and non-physical) within a sensor network environment [28]. Moreover, to operate in a cloud environment, an agent-based approach has been proposed to support the implementation of services that require a lot of computational resources and expensive hardware in one or more clouds [29]. These works have primarily investigated the technical possibilities and various possibilities of the design, implementation and implementation of the service. Moreover, these studies have focused on remote sensors for large-scale applications, such as vegetation difference indices (NDVI) [ 28 ] [28] , rather than on-site or mobile smart sensor applications, which are more relevant. for large applications. for the rapid operation of cities due to their low cost, relatively easy implementation, high accuracy, durability and immediacy.
The event management mechanism is essential for a public emergency response that has a minimum tolerance for reaction time to incidents in smart cities. Yu et al. A BPEL-based geoprocessing web service proposal that can be automatically executed by triggering an event, such as the acquisition of a new observation at a new time, to implement message-level coordination of sensors and earth science models in Sensor. web environment [30]. Fan et al. It considers both Observation & Measurement (O&M) information and state changes of tasks as events and designed an active on-demand service method for geographic data acquisition based on event-driven architecture [31]. These studies have characterized micro and abstract events particularly well. However, they are somewhat inappropriate for macro urban public emergency scenarios. In this way, the development of a new focused service method that is driven by macro urban events for speed
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