Indian Journal of Science and Technology

Abstract

Objectives: Randomly moving target search in an unknown location is a complex problem which does not have a linear solution. This paper proposes Energy aware - Bio-inspired Hybrid technique for target finding Area surveillance application. Methods/Statistical Analysis: In recent years, many researchers tried to find a generic solution which requires minimum energy for target searching problem. For the proposed work four algorithms are proposed which are MSN’s Movement Prediction Algorithm (MMPA), Leader Selection Algorithm (LSA), and Leader’s Movement Predication Algorithm (LMPA), and Follower’s Algorithm. Particle Swarm Optimization (PSO) technique is used to move the Mobile Sensor Nodes (MSN) in searching area. Findings: There is a need to study the hybrid or heterogeneous Sensor network (combination of the Static sensor network and Mobile sensor network) as it is less studied in research literature. The major hurdle is communication between heterogeneous sensor nodes. Due to the mobility of MSN, topology changes frequently and it affects the communication in the network. In area surveillance domain, finding the randomly moving target using sensor node is a complex and non-linear problem. There is no straight forward solution. Nature based techniques may be more effective for such problem. Bio-inspired Hybrid WSN technique is to be studied for its suitability for target finding and target tracking application in area surveillance domain. To perform the work, two scenarios are considered and calculated their energy to prove that ifmore number of SSNs is planted, the target can be found in less time and ultimately overall energy of network reduces. Through the simulation, it is shown that E-BHAS reduces the energy when more number of SSNs is involved in the network. Application/ Improvements: Borderline Surveillance, Disaster Management, under water Surveillance, Traffic Monitoring etc. 

Keywords: Area Surveillance, Energy, Hybrid Wireless Sensor Network, Particle Swarm Optimization