Publication Type : Book Chapter
Publisher : Computational Intelligence in Wireless Sensor Networks.
Source : Book chapter, Springer's Studies in Computational Intelligence Series,Computational Intelligence in Wireless Sensor Networks: Recent Advances and Future Challenges (CIWSN 2015)
Url : https://www.researchgate.net/publication/328628250_Efficient_Anomaly_Detection_System_for_Video_Surveillance_Application_in_WVSN_with_Particle_Swarm_Optimization
Campus : Chennai
School : School of Engineering
Department : Computer Science
Year : 2015
Abstract : Wireless sensor networks consist of several tiny low cost sensor nodes that are deployed for many applications such as military, civil, industrial, healthcare, home automation, etc. Recent technological developments have enabled the use of wireless visual sensor networks (WVSNs) for sensitive applications such as video surveillance and monitoring applications. Limited memory, energy and bandwidth are the major constraints in WVSN that can be simplified by the use of compressed sensing (CS), which asserts that sparse signals can be reconstructed from very few measurements. CS a computational intelligence solution is about acquiring and recovering the signal in the most efficient manner possible using incoherent projection basis. In the case of video surveillance applications, the entire video may not be useful hence, with the help of efficient algorithms the presence of the anomalies can be detected and transmitted to help user at the monitoring site to take necessary action. In this chapter, particle swarm optimization (PSO) based efficient anomaly detection system (EADS) is proposed which will detect the presence of anomalies and transmit the required measurements via TelosB nodes to the network operator. This system adopts the concept of CS to obtain the compressive measurements so that the object detection algorithm can be applied to the measurements rather than samples. PSO is employed for optimizing compressive measurements while a mean based measurement differencing approach is used for detecting the object. This proposed efficient system has the intelligence of detecting targets with fewer measurements and transmit the required compressive measurements for reconstruction with less energy, thereby increasing the network lifetime. PSO is used to optimize the transmission distance with minimum number of hops towards destination, to achieve reduced energy consumption. However, the lifetime of the network is still bounded by batteries, the sole source of energy in WVSNs. Alternative energy utilization can be effectively included to recharge the batteries on-board and extend the lifetime of the network. Solar energy harvesting forms an effective resource due to its ambient presence. Hence, solar energy harvester is incorporated in the proposed EADS to extend its lifetime.
Cite this Research Publication : Dr. S. Aasha Nandhini, Hemalatha Rajendran, Radha Sankararajan, "Efficient Anomaly Detection System for Video Surveillance Application in WVSN with Particle Swarm Optimization” Book chapter, Springer's Studies in Computational Intelligence Series,Computational Intelligence in Wireless Sensor Networks: Recent Advances and Future Challenges (CIWSN 2015), vol 676