Situation-Awareness and Sensor Stream Mining for Sustainable Human Life

Z. Rehman, M. Shahbaz, Muhammad Shaheen, A. Guergachi
{"title":"Situation-Awareness and Sensor Stream Mining for Sustainable Human Life","authors":"Z. Rehman, M. Shahbaz, Muhammad Shaheen, A. Guergachi","doi":"10.1109/SoCPaR.2009.121","DOIUrl":null,"url":null,"abstract":"Criminal activities cause a huge amount of loss both financially and in terms of human lives. Because of these acts, business and social sectors are struggling. This paper illustrates the development of an online sensor stream mining system that is able to analyze the situational behavior of all of the persons in specific areas and in turn propose real-time alert systems to take countermeasures. This system gathers different information from heterogeneous sensors, fuse that information, and generate real-time alerts to minimize the likelihood of disaster. These alerts and alarms assist security personnel in making appropriate decisions in real-time scenarios. The novelty of this approach comprises context-awareness with online diagnoses to take countermeasures in real-time to reduce the loss of lives, and damage to societies and economies. This technique makes the sensor stream mining process more dependable and increases the reliability of the overall system. To fulfill the objectives of this research, we incorporate lightweight online mining algorithms to extract useful but hidden information from the data gathered. Contextual information such as a person’s pattern of movement, current location, personal profile, and area of residence are exploited to detect anomalous behaviors. The major goal of this research is to detect those persons performing malicious activities and in turn minimize society’s exposure to risks and vulnerabilities.","PeriodicalId":284743,"journal":{"name":"2009 International Conference of Soft Computing and Pattern Recognition","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 International Conference of Soft Computing and Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SoCPaR.2009.121","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

Criminal activities cause a huge amount of loss both financially and in terms of human lives. Because of these acts, business and social sectors are struggling. This paper illustrates the development of an online sensor stream mining system that is able to analyze the situational behavior of all of the persons in specific areas and in turn propose real-time alert systems to take countermeasures. This system gathers different information from heterogeneous sensors, fuse that information, and generate real-time alerts to minimize the likelihood of disaster. These alerts and alarms assist security personnel in making appropriate decisions in real-time scenarios. The novelty of this approach comprises context-awareness with online diagnoses to take countermeasures in real-time to reduce the loss of lives, and damage to societies and economies. This technique makes the sensor stream mining process more dependable and increases the reliability of the overall system. To fulfill the objectives of this research, we incorporate lightweight online mining algorithms to extract useful but hidden information from the data gathered. Contextual information such as a person’s pattern of movement, current location, personal profile, and area of residence are exploited to detect anomalous behaviors. The major goal of this research is to detect those persons performing malicious activities and in turn minimize society’s exposure to risks and vulnerabilities.
可持续人类生活的态势感知和传感器流挖掘
犯罪活动造成巨大的经济损失和人命损失。由于这些行为,商业和社会部门正在苦苦挣扎。本文阐述了一种在线传感器流挖掘系统的开发,该系统能够分析特定区域中所有人的情境行为,并提出实时警报系统以采取对策。该系统从不同的传感器收集不同的信息,融合这些信息,并生成实时警报,以最大限度地减少灾难的可能性。这些警报和警报帮助安全人员在实时场景中做出适当的决策。这种方法的新颖之处在于,它具有在线诊断的背景意识,可以实时采取对策,以减少生命损失,减少对社会和经济的损害。该技术使传感器流挖掘过程更加可靠,提高了整个系统的可靠性。为了实现本研究的目标,我们采用了轻量级的在线挖掘算法来从收集的数据中提取有用但隐藏的信息。上下文信息,如一个人的运动模式、当前位置、个人概况和居住区域被用来检测异常行为。这项研究的主要目标是检测那些进行恶意活动的人,从而最大限度地减少社会暴露于风险和脆弱性的风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信