Raspberry Pi Based Smart Surveillance Enhanced with Wi-Fi Tecnology


  • C. Murugan
  • H. Balachandar
  • M. Beston James
  • T. Suriya Vekatesh




Webcam, PIR Sensor, Smoke sensor, Raspberry PI, Wi Fi, HDR, USB, Internet of Things (IOT), wireless LAN, Bluetooth, Transistor-transistor logic (TTL), Linux.


Nowadays, mobile devices are integrated with our everyday life. The security and remote surveillance system is increasingly prominent features on the mobile phone. The modern surveillance is integrated with many automation technologies.  In this modern world crime has become ultramodern tools. In this current time a lot of incident occurs like robbery, stealing unwanted entrance happens, threatening abruptly robbery. So the does matters in this daily life. People always remain busy in their daily to daily work also wants to ensure their safety of their beloved things. To prevent such incidents, we are proposing a smart surveillance system enhanced with WI-FI technology. This work presents the monitoring and controlling of surveillance robot for safety enhanced with Wi-Fi technology. This system consists of webcam, PIR sensor smoke sensor and Raspberry PI. In this system, we are using PIR sensor to detect the motions or to trace out the intruders and smoke sensor to detect fire accidents. In above of any human movement or fire accident occurs, the system will activate the Web camera. The webcam will capture live data in the surroundings and transmitting the live video to the social network through WI-FI. Simultaneously, the alert message is send to the respective people. The system also consists of buzzer to alert the nearby people and sprays the chloroform liquid on the intruders.




[1] J. Pan, R. Jain, S. Paul, T. Vu, A. Saifullah and M. Sha, "An Internet of Things Framework for Smart Energy in Buildings: Designs, Prototype, and Experiments," IEEE Internet of Things Journal, vol. 2, no. 6, pp. 527-537, 2015.

[2] R. Piyare, “Internet of Things: Ubiquitous Home Control and Monitoring System using Android based Smart Phone,†International Journal of Internet of Things, vol. 2 no. 1, pp. 5-11, 2013.

[3] J. Wan, M.J. O’Grady, G.M.P. O’Hare, "Dynamic Sensor Event Segmentation for Real-Time Activity Recognition in a Smart Home Context," Personal and Ubiquitous Computing, vol. 19, no. 2, pp. 287-301, 2015.

[4] K. Afifah, S. Fuada, R.V.W. Putra, T. Adiono, M.Y. Fathany, “Design of Low Power Mobile Application for Smart Home,†Proc. of Int. Symposium on Electronics and Smart Devices (ISESD), pp. 127-131, November 2016.

[5] Videla and J.J.W. Williams, RabbitMQ in Action Distributed Messaging for Everyone, New York: Manning Publication Co., 2012.

[6] Z. R. Lai, D. Q. Dai, C. X. Ren, and K. K. Huang, “Discriminative and compact coding for robust face recognition,†IEEE Transactions on Cybernetics, vol. 45, pp. 1900–1912, 2015.

[7] Dr. AntoBennet, M, Sankar Babu G, Natarajan S, “Reverse Room Techniques for Irreversible Data Hidingâ€, Journal of Chemical and Pharmaceutical Sciences 08(03): 469-475, September 2015.

[8] Dr. AntoBennet, M , Sankaranarayanan S, Sankar Babu G, “ Performance & Analysis of Effective Iris Recognition System Using Independent Component Analysisâ€, Journal of Chemical and Pharmaceutical Sciences 08(03): 571-576, August 2015.

[9] Dr. AntoBennet, M, Suresh R, Mohamed Sulaiman S, “Performance &analysis of automated removal of head movement artifacts in EEG using brain computer interfaceâ€, Journal of Chemical and Pharmaceutical Research 07(08): 291-299, August 2015.

[10] .Dr. AntoBennet, M “A Novel Effective Refined Histogram For Supervised Texure Classificationâ€, International Journal of Computer & Modern Technology , Issue 01 ,Volume02 ,pp 67-73, June 2015.

[11] Dr. AntoBennet, M, Srinath R,Raisha Banu A,“Development of Deblocking Architectures for block artifact reduction in videosâ€, International Journal of Applied Engineering Research,Volume 10, Number 09 (2015) pp. 6985-6991, April 2015.

[12] AntoBennet, M & JacobRaglend, “Performance Analysis Of Filtering Schedule Using Deblocking Filter For The Reduction Of Block Artifacts From MPEQ Compressed Document Imagesâ€, Journal of Computer Science, vol. 8, no. 9, pp. 1447-1454, 2012.

[13] AntoBennet, M & JacobRaglend, “Performance Analysis of Block Artifact Reduction Scheme Using Pseudo Random Noise Mask Filteringâ€, European Journal of Scientific Research, vol. 66 no.1, pp.120-129, 2011.

View Full Article: