Wireless sensor data processing for on-site emergency response

This thesis is concerned with the problem of processing data from Wireless Sensor Networks (WSNs) to meet the requirements of emergency responders (e.g. Fire and Rescue Services). A WSN typically consists of spatially distributed sensor nodes to cooperatively monitor the physical or environmental conditions. Sensor data about the physical or environmental conditions can then be used as part of the input to predict, detect, and monitor emergencies. Although WSNs have demonstrated their great potential in facilitating Emergency Response, sensor data cannot be interpreted directly due to its large volume, noise, and redundancy. In addition, emergency responders are not interested in raw data, they are interested in the meaning it conveys. This thesis presents research on processing and combining data from multiple types of sensors, and combining sensor data with other relevant data, for the purpose of obtaining data of greater quality and information of greater relevance to emergency responders. The current theory and practice in Emergency Response and the existing technology aids were reviewed to identify the requirements from both application and technology perspectives (Chapter 2). The detailed process of information extraction from sensor data and sensor data fusion techniques were reviewed to identify what constitutes suitable sensor data fusion techniques and challenges presented in sensor data processing (Chapter 3). A study of Incident Commanders’ requirements utilised a goal-driven task analysis method to identify gaps in current means of obtaining relevant information during response to fire emergencies and a list of opportunities for WSN technology to fill those gaps (Chapter 4). A high-level Emergency Information Management System Architecture was proposed, including the main components that are needed, the interaction between components, and system function specification at different incident stages (Chapter 5). A set of state-awareness rules was proposed, and integrated with Kalman Filter to improve the performance of filtering. The proposed data pre-processing approach achieved both improved outlier removal and quick detection of real events (Chapter 6). A data storage mechanism was proposed to support timely response to queries regardless of the increase in volume of data (Chapter 7). What can be considered as “meaning” (e.g. events) for emergency responders were identified and a generic emergency event detection model was proposed to identify patterns presenting in sensor data and associate patterns with events (Chapter 8). In conclusion, the added benefits that the technical work can provide to the current Emergency Response is discussed and specific contributions and future work are highlighted (Chapter 9)

Approaches to Disaster Management

Approaches to Disaster Management regards critical disaster management issues. Ten original research reports by international scholars centered on disaster management are organized into three general areas of hazards and disaster management. The first section includes discussions of perspectives on vulnerability and on evolving approaches to mitigation. The second section highlights approaches to improve data use and information management in several distinct applications intended to promote prediction and communication of hazard. The third section regards the management of crises and post-event recovery in the private sector, in the design of urban space and among the victims of disaster. This volume contributes both conceptual and practical commentary to the disaster management literature

Wireless sensor data processing for on-site emergency response

This thesis is concerned with the problem of processing data from Wireless Sensor Networks (WSNs) to meet the requirements of emergency responders (e.g. Fire and Rescue Services). A WSN typically consists of spatially distributed sensor nodes to cooperatively monitor the physical or environmental conditions. Sensor data about the physical or environmental conditions can then be used as part of the input to predict, detect, and monitor emergencies. Although WSNs have demonstrated their great potential in facilitating Emergency Response, sensor data cannot be interpreted directly due to its large volume, noise, and redundancy. In addition, emergency responders are not interested in raw data, they are interested in the meaning it conveys. This thesis presents research on processing and combining data from multiple types of sensors, and combining sensor data with other relevant data, for the purpose of obtaining data of greater quality and information of greater relevance to emergency responders. The current theory and practice in Emergency Response and the existing technology aids were reviewed to identify the requirements from both application and technology perspectives (Chapter 2). The detailed process of information extraction from sensor data and sensor data fusion techniques were reviewed to identify what constitutes suitable sensor data fusion techniques and challenges presented in sensor data processing (Chapter 3). A study of Incident Commanders' requirements utilised a goal-driven task analysis method to identify gaps in current means of obtaining relevant information during response to fire emergencies and a list of opportunities for WSN technology to fill those gaps (Chapter 4). A high-level Emergency Information Management System Architecture was proposed, including the main components that are needed, the interaction between components, and system function specification at different incident stages (Chapter 5). A set of state-awareness rules was proposed, and integrated with Kalman Filter to improve the performance of filtering. The proposed data pre-processing approach achieved both improved outlier removal and quick detection of real events (Chapter 6). A data storage mechanism was proposed to support timely response to queries regardless of the increase in volume of data (Chapter 7). What can be considered as “meaning” (e.g. events) for emergency responders were identified and a generic emergency event detection model was proposed to identify patterns presenting in sensor data and associate patterns with events (Chapter 8). In conclusion, the added benefits that the technical work can provide to the current Emergency Response is discussed and specific contributions and future work are highlighted (Chapter 9).EThOS - Electronic Theses Online ServiceGBUnited Kingdo

J Emerg Manag

Objective:Emergency management and operations (EMO) personnel require up-to-date information to make informed decisions during natural and man-made disasters. However, information gaps present challenges for accessing human health risk assessment and risk management strategies for dermal exposure. This article describes the development of a decision support system, the Dermal Exposure Risk Management and Logic (DERMaL) eToolkit.Design:The DERMaL eToolkit provides information on key resources used in emergency incidents. Resources were classified according to response phase, resource categories, and information category and evaluated on reliability, accessibility, and preference by subject matter experts in emergency management fields. These rankings were used to generate a value of information score, unique for each resource, which aids in developing reference lists for users during each incident phase.Results:This tool will identify and prioritize information resources on dermal risks, and can readily find the most relevant information to suit EMO needs.Conclusion:The DERMaL eToolkit can be used as an aid in finding information resources targeted to scenario-driven needs by providing well-vetted and prioritized resources related to dermal hazards, exposure, and risk assessments for EMO.CC999999/Intramural CDC HHS/United States2018-10-10T00:00:00Z30044489PMC6179145vault:3097

Interoperability Performance Among Campus Law Enforcement Agencies

The September 11, 2001 terrorist attacks exposed considerable breakdowns in communications interoperability and information sharing among first responders. Multijurisdictional responses to the active-shooter incidents at the University of Texas in 2010; Sandy Hook Elementary of Newtown, Connecticut in 2012, and the Reynolds High School shooting of Multnomah County, Oregon in 2014 were replete with interoperability failures as well. Recent multijurisdictional response events continue to illuminate difficulties with first-responder interoperability and minimal research exists to promote understanding of the interoperability challenges of university police departments. The purpose of this study was to explore the barriers that impede communications of campus based law enforcement agencies during multiagency or multijurisdictional response. General systems theory and the unified theory of acceptance and use of technology model provided the conceptual framework for this qualitative case study. Face-to-face interviews were conducted with 10 leaders of university public safety agencies in California. Data were collected, inductively coded, and thematically analyzed. Key findings indicate that participants perceived barriers of funding, policy, inclusiveness, and training that affect communications interoperability performance. The positive social change implications from this study include recommendations of policy change for improved interoperability during multiagency or multijurisdictional response which can contribute to increased first-responder safety, more efficient multijurisdictional response, and improved safety of students and society at large