An emergency event detection approach in real-time for efficient vehicle safety in Smart City

2022 ◽  
Author(s):  
Nidhi Lal ◽  
Shishupal Kumar
Keyword(s):  
Real Time ◽  
Event Detection ◽  
Smart City ◽  
Vehicle Safety ◽  
Emergency Event ◽  
Detection Approach

scholarly journals A Distributed Multi-Tier Emergency Alerting System Exploiting Sensors-Based Event Detection to Support Smart City Applications

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 170 ◽  
Author(s):  
Daniel G. Costa ◽  
Francisco Vasques ◽  
Paulo Portugal ◽  
Ana Aguiar
Keyword(s):  
Real Time ◽  
Event Detection ◽  
Smart City ◽  
Smart Cities ◽  
Sensor Nodes ◽  
Risk Level ◽  
Critical Events ◽  
Emergency Situations ◽  
Alerting System ◽  
Emergency Systems

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

Leveraging cross-media analytics to detect events and mine opinions for emergency management

2017 ◽  
Vol 41 (4) ◽  
pp. 487-506 ◽  
Author(s):  
Wei Xu ◽  
Lingyu Liu ◽  
Wei Shang
Keyword(s):  
Decision Support ◽  
Emergency Management ◽  
Real Time ◽  
Event Detection ◽  
Opinion Mining ◽  
Time Response ◽  
Content Type ◽  
Cross Media ◽  
Emergency Event ◽  
Emergency Events

Purpose Timely detection of emergency events and effective tracking of corresponding public opinions are critical in emergency management. As media are immediate sources of information on emergencies, the purpose of this paper is to propose cross-media analytics to detect and track emergency events and provide decision support for government and emergency management departments. Design/methodology/approach In this paper, a novel emergency event detection and opinion mining method is proposed for emergency management using cross-media analytics. In the proposed approach, an event detection module is constructed to discover emergency events based on cross-media analytics, and after the detected event is confirmed as an emergency event, an opinion mining module is used to analyze public sentiments and then generate public sentiment time series for early warning via a semantic expansion technique. Findings Empirical results indicate that a specific emergency can be detected and that public opinion can be tracked effectively and efficiently using cross-media analytics. In addition, the proposed system can be used for decision support and real-time response for government and emergency management departments. Research limitations/implications This paper takes full advantage of cross-media information and proposes novel emergency event detection and opinion mining methods for emergency management using cross-media analytics. The empirical analysis results illustrate the efficiency of the proposed method. Practical implications The proposed method can be applied for detection of emergency events and tracking of public opinions for emergency decision support and governmental real-time response. Originality/value This research work contributes to the design of a decision support system for emergency event detection and opinion mining. In the proposed approaches, emergency events are detected by leveraging cross-media analytics, and public sentiments are measured using an auto-expansion of the domain dictionary in the field of emergency management to eliminate the misclassification of the general dictionary and to make the quantization more accurate.

An Event Detection Approach Based on Improved CUSUM Algorithm and Kalman Filter

2020 ◽  
Author(s):  
Kaijie Fang ◽  
Yixuan Huang ◽  
Qifeng Huang ◽  
Shihai Yang ◽  
Zhixin Li ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Event Detection ◽  
Detection Approach

UREDT: Unsupervised Learning Based Real-Time Footfall Event Detection Technique in Seismic Signal

2018 ◽  
Vol 2 (1) ◽  
pp. 1-4 ◽  
Author(s):  
Sahil Anchal ◽  
Bodhibrata Mukhopadhyay ◽  
Subrat Kar
Keyword(s):  
Unsupervised Learning ◽  
Real Time ◽  
Event Detection ◽  
Seismic Signal ◽  
Detection Technique
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