RETRACTED: Security management in smart home environment (Retracted Article)

被引:5
作者
Gladence, L. Mary [1 ]
Anu, V. Maria [1 ]
Revathy, S. [1 ]
Jeyanthi, P. [1 ]
机构
[1] Sathyabama Inst Sci & Technol, Chennai, Tamil Nadu, India
关键词
IoT; Edge; Fog; Video surveillance; Real-time security; INTERNET; IOT; THINGS;
D O I
10.1007/s00500-021-06054-z
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A smart home environment comprises various luxurious things which makes us very comfortable to live our lives happily and securely. The only problem that smart homes are facing is "SECURITY." Security for smart homes is the biggest task to achieve. For this purpose, this work is to build a product that provides the security for the smart homes automatically when the crime is going to happen. In general case, if any crime had taken place in any smart home, the general procedure of investigation will take place i.e., the people will complain to the police and the police will visit the place and after that he will observe the surroundings clearly. In order to pull up the clues if any, he will watch the closed circuit television (CCTV) footage, consult the nearby people in-order to draw some facts and then first information report will be filed. In order to get rid of this time-consuming process, automatic crime detection is proposed. Here malware practices are identified, when a person attempts crime activity. This type of automatic process of detection of crime will ensure a complete security for the smart homes. We will track the CCTV camera pictures when the thief is trying to commit a crime and the information along with the pictures will be sent to the fog server and the fog server will analyze whether the person is doing crime or not. In case if a fog server had identified the person as a crime person or a thief then it automatically sends the information to the nearby police station and as well as owners of the house and then provides security for the smart homes.
引用
收藏
页码:1209 / 1209
页数:1
相关论文
共 29 条
[1]   A Review of Smart Homes-Past, Present, and Future [J].
Alam, Muhammad Raisul ;
Reaz, Mamun Bin Ibne ;
Ali, Mohd Alauddin Mohd .
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS, 2012, 42 (06) :1190-1203
[2]  
Chen Xi., 2020, WWW, P1, DOI DOI 10.1109/JSYST.2019.2960088
[3]   CLARIFYING FOG COMPUTING AND NETWORKING: 10 QUESTIONS AND ANSWERS [J].
Chiang, Mung ;
Ha, Sangtae ;
I, Chih-Lin ;
Risso, Fulvio ;
Zhang, Tao .
IEEE COMMUNICATIONS MAGAZINE, 2017, 55 (04) :18-20
[4]   Ensemble machine learning approach for classification of IoT devices in smart home [J].
Cvitic, Ivan ;
Perakovic, Dragan ;
Perisa, Marko ;
Gupta, Brij .
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2021, 12 (11) :3179-3202
[5]   Service Orchestration of Optimizing Continuous Features in Industrial Surveillance Using Big Data Based Fog-Enabled Internet of Things [J].
Din, Sadia ;
Paul, Anand ;
Ahmad, Awais ;
Gupta, B. B. ;
Rho, Seungmin .
IEEE ACCESS, 2018, 6 :21582-21591
[6]  
Gouaillier V., 2009, Intelligent video surveillance: Promises and challenges
[7]   Framework for a Cloud-Based Multimedia Surveillance System [J].
Hossain, M. Anwar .
INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS, 2014,
[8]   The Security of IP-Based Video Surveillance Systems [J].
Kalbo, Naor ;
Mirsky, Yisroel ;
Shabtai, Asaf ;
Elovici, Yuval .
SENSORS, 2020, 20 (17) :1-27
[9]  
Kieran Declan, 2010, Proceedings 7th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS 2010), P97, DOI 10.1109/AVSS.2010.57
[10]   Developing Design Solutions for Smart Homes Through User-Centered Scenarios [J].
Kim, Mi Jeong ;
Cho, Myung Eun ;
Jun, Han Jong .
FRONTIERS IN PSYCHOLOGY, 2020, 11