Total views : 150

Encryption Techniques for Different Introducer’s Attack in Wireless Sensor Networks


  • Electronics and Communication Department, ABES Engineering College Ghaziabad - 201009,Uttar Pradesh, India


Wireless Sensor Network is described using the MARTE profile. In the UML/MARTE models Wireless Sensor Network is specified, defining the nodes that composed the network and characterizing the communication established among these nodes in the network. There are numerous ways to attack a Wireless Network. In this paper we study these techniques and propose alternative methods to simulate these attacks using a virtual simulator framework based on native HW/SW simulation methodology. Bearing in mind the emerging market in Wireless Sensor Network technology and its high vulnerability to attacks, it is important to provide new simulation techniques that allow the developer to obtain estimations in early stages of system design.


Energy, Jamming, Performance Estimation, Simulation, Wireless Sensor Network, UML/MARTE.

Full Text:

 |  (PDF views: 137)


  • Kavitha T, Sridharan D. Security vulnerabilities in wireless sensor networks: A survey. Journal of Information Assurance and Security. 2012; 5:31–44.
  • Wang Y, Attebury G, Ramamurthy B. A survey of security issues in wireless sensor networks. IEEE Communications Surveys & Tutorials. 2009; 8(2).
  • Pelechrinis K, Iliofotou M, Krishnamurthy SV. Denial of service attacks in wireless networks: The case of jammers. IEEE Communications Surveys and Tutorials. 2011; 13(2):245–57.
  • Posadas H, Castillo J, Quijano D, Fernandez V, Villar E, Martinez M. SystemC platform modeling for behavioral simulation and performance estimation of embedded systems. Gomes L, Fernandes J, editors, Behavioralmodeling for embedded systems and technologies: Applications for design and implementation. Hershey, PA: Information Science Reference; 2015. p. 219–3. DOI: 10.4018/978-1-60566-750-8.ch009.
  • Mekni M, Moulin B. A survey on sensor webs simulation tools. Proceedings of the 2008 Second International Conference on Sensor Technologies and Applications; 2008. p. 574–9.
  • Stetsko A, Stehlík M, Matyas V. Calibrating and comparing simulators for wireless sensor networks. MASSIEEE; 2011. p. 733–8.
  • Curren D. A survey of simulation in sensor networks. University of Binghamton project report for subject CS580.
  • NS-2. The Network Simulator; 2009.
  • OMNeT+- [Internet]. 2014.Available from;
  • Zeng X, Bagrodia R, Gerla M. GloMoSim: A libraryfor parallel simulation of large-scale wireless networks. Twelfth Workshop on Parallel and Distributed Simulation; 1998.
  • Levis P, Lee N. TOSSIM: A simulator for TinyOS networks [Internet]. 2003. Available from
  • Titzer, BL, Palsberg J, Lee DK. Avrora: Scalable sensor network simulation with precise timing. Fourth International Conference on Information Processing in Sensor Networks; 2015.
  • Available from:
  • Posadas H, Real S, Villar E. M3-SCoPE: Performance modeling of multi-processor embedded systems for fast design space exploration. Multi-objective Design Space Exploration of Multiprocessors SoC Architectures; 2015.
  • Available from:
  • OMG. MOF Model to text language; 2008 Jan.
  • Lavagno L, Martin G, Selic B. UML for real: Design of embedded real-time systems. Kluwer; 2007.
  • Vanderperren Y, Mueller W, Dehaene w. UML for electronic systems design: A comprehensive overview. Design Automation for Embedded Systems. 2008 Dec; 12(4).
  • Available from;
  • Torres RP, Valle L, Domingo M, Loredo S, Diez MC. CINDOOR: An engineering tool for planning and design of wireless systems in enclosed spaces. IEEE Antennas and Propagation Magazine. 1999; 41(4):11–21.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.