Design of an intelligent wireless Scatternet Sensor network with location estimation

Wireless communication devices are widely subjected to intruder attacks and in the existing security system, the algorithm consists of a ranging phase that uses the received signal strength to estimate a network radio map and a localization phase which uses a genetic algorithm to obtain the most likely position of the intruder node within the map using the genetic algorithm which is not time consuming. This paper proposes an intrusion detection system with the proposed LERSS algorithm that effectively calculates the location of the intruder when experimented using tree topology. Additionally, the algorithm has better accuracy, and is capable of locating the intruder both within the same system as well as from outside the system. The random population generation and line of sight problems are overcome by this algorithm. To calculate the distance that enables to locate the position of the intruder accurately thereby providing an intelligent security system.

[1]  Ivan Stojmenovic,et al.  Partial Delaunay triangulation and degree limited localized Bluetooth scatternet formation , 2004, IEEE Transactions on Parallel and Distributed Systems.

[2]  Stefano Basagni,et al.  A performance comparison of scatternet formation protocols for networks of Bluetooth devices , 2003, Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)..

[3]  James M. Keller,et al.  Proceedings of the 12th IEEE International Conference on Fuzzy Systems : Sunday 25 May-Wednesday 28 May, 2003, St. Louis, Missouri, USA : exploring new frontiers : FUZZ-IEEE 2003 , 2003 .

[4]  Mohammed Bouhorma,et al.  Performance comparison of ad-hoc routing protocols AODV and DSR , 2009, 2009 International Conference on Multimedia Computing and Systems.

[5]  Ling Liu,et al.  Improving Wireless Positioning with Look-ahead Map-Matching , 2007, 2007 Fourth Annual International Conference on Mobile and Ubiquitous Systems: Networking & Services (MobiQuitous).

[6]  R. Marimuthu,et al.  A Multipath Reliable Routing for detection and isolation of malicious nodes in MANET , 2008, 2008 International Conference on Computing, Communication and Networking.

[7]  Jon Crowcroft,et al.  A survey and comparison of peer-to-peer overlay network schemes , 2005, IEEE Communications Surveys & Tutorials.

[8]  A. Karygiannis,et al.  Detecting critical nodes for MANET intrusion detection systems , 2006, Second International Workshop on Security, Privacy and Trust in Pervasive and Ubiquitous Computing (SecPerU'06).

[9]  Pascal Felber,et al.  Efficient search in unstructured peer-to-peer networks , 2004, SPAA '04.

[10]  Raheem A. Beyah,et al.  A Passive Approach to Rogue Access Point Detection , 2007, IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference.

[11]  Shinsuke Hara,et al.  Experimental Performance Comparison of RSSI- and TDOA-Based Location Estimation Methods , 2008, VTC Spring 2008 - IEEE Vehicular Technology Conference.

[12]  Kai-Ten Feng,et al.  GALE: An Enhanced Geometry-Assisted Location Estimation Algorithm for NLOS Environments , 2008, IEEE Transactions on Mobile Computing.

[13]  Anand Sivasubramaniam,et al.  Semantic small world: an overlay network for peer-to-peer search , 2004, Proceedings of the 12th IEEE International Conference on Network Protocols, 2004. ICNP 2004..

[14]  Ivan Stojmenovic,et al.  Bluetooth scatternet formation for single-hop ad hoc networks based on virtual positions , 2004, Proceedings. ISCC 2004. Ninth International Symposium on Computers And Communications (IEEE Cat. No.04TH8769).

[15]  C.J. Debono,et al.  Location estimation of an intruder in wireless ad hoc networks , 2008, MELECON 2008 - The 14th IEEE Mediterranean Electrotechnical Conference.