Social Impact Theory-Based Node Placement Strategy for Wireless Sensor Networks

1. Yick, J., B. Mukherjee, and D. Ghosal, Wireless sensor network survey. Computer Networks, 2008. 52(12): p. 2292–2330.

   Google Scholar 

2. Rawat, P., et al., Wireless sensor networks: a survey on recent developments and potential synergies. The Journal of Supercomputing, 2014. 68(1): p. 1–48.

   Google Scholar 

3. Ishizuka, M. and M. Aida. Performance study of node placement in sensor networks. in Distributed Computing Systems Workshops, 2004. Proceedings. 24th International Conference on. 2004.

   Google Scholar 

4. Izadi, D., J. Abawajy, and S. Ghanavati, An Alternative Node Deployment Scheme for WSNs. Sensors Journal, IEEE, 2015. 15(2): p. 667–675.

   Google Scholar 

5. Bhondekar, A.P., et al., A multiobjective Fuzzy Inference System based deployment strategy for a distributed mobile sensor network. Sensors & Transducers, 2010. 114(3): p. 66.

   Google Scholar 

6. Viani, F., et al. Pervasive remote sensing through WSNs. in Antennas and Propagation (EUCAP), 2012 6th European Conference on. 2012.

   Google Scholar 

7. Kim, K.T., et al. An energy efficient and optimal randomized clustering for wireless sensor networks. in Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), 2015 16th IEEE/ACIS International Conference on. 2015.

   Google Scholar 

8. Shafali, et al. An algorithm to minimize energy consumption using nature-inspired technique in wireless sensor network. in Circuit, Power and Computing Technologies (ICCPCT), 2015 International Conference on. 2015.

   Google Scholar 

9. Srbinovski, B., et al. Energy aware adaptive sampling algorithm for energy harvesting wireless sensor networks. in Sensors Applications Symposium (SAS), 2015 IEEE. 2015.

   Google Scholar 

10. Slijepcevic, S. and M. Potkonjak. Power efficient organization of wireless sensor networks. in Communications, 2001. ICC 2001. IEEE International Conference on. 2001.

    Google Scholar 

11. Krishnamachari, B. and F. Ordonez. Analysis of energy-efficient, fair routing in wireless sensor networks through non-linear optimization. in Vehicular Technology Conference, 2003. VTC 2003-Fall. 2003 IEEE 58th. 2003.

    Google Scholar 

12. Zhou, C. and B. Krishnamachari. Localized topology generation mechanisms for wireless sensor networks. in Global Telecommunications Conference, 2003. GLOBECOM ‘03. IEEE. 2003.

    Google Scholar 

13. Chen, S.Y. and Y.F. Li, Automatic sensor placement for model-based robot vision. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 2004. 34(1): p. 393–408.

    Google Scholar 

14. Alageswaran, R., et al. Design and implementation of dynamic sink node placement using Particle Swarm Optimization for life time maximization of WSN applications. in Advances in Engineering, Science and Management (ICAESM), 2012 International Conference on. 2012.

    Google Scholar 

15. Deif, D.S. and Y. Gadallah, Classification of Wireless Sensor Networks Deployment Techniques. Communications Surveys & Tutorials, IEEE, 2014. 16(2): p. 834–855.

    Google Scholar 

16. Dondi, D., et al., Modeling and Optimization of a Solar Energy Harvester System for Self-Powered Wireless Sensor Networks. Industrial Electronics, IEEE Transactions on, 2008. 55(7): p. 2759–2766.

    Google Scholar 

17. Elshaikh, M., et al. Energy consumption optimization with Ichi Taguchi method for Wireless Sensor Networks. in Electronic Design (ICED), 2014 2nd International Conference on. 2014.

    Google Scholar 

18. Hortos, W.S. Bio-inspired, cross-layer protocol design for intrusion detection and identification in wireless sensor networks. in Local Computer Networks Workshops (LCN Workshops), 2012 IEEE 37th Conference on. 2012.

    Google Scholar 

19. Kolega, E., V. Vescoukis, and D. Voutos. Assessment of network simulators for real world WSNs in forest environments. in Networking, Sensing and Control (ICNSC), 2011 IEEE International Conference on. 2011.

    Google Scholar 

20. Menon, K.A.U., D. Maria, and H. Thirugnanam. Power optimization strategies for wireless sensor networks in coal mines. in Wireless and Optical Communications Networks (WOCN), 2012 Ninth International Conference on. 2012.

    Google Scholar 

21. Mohamaddoust, R., et al. Designing the Lighting Control System Based on WSN with Optimization of Decision Making Algorithm. in Computational Intelligence and Communication Networks (CICN), 2010 International Conference on. 2010.

    Google Scholar 

22. Iram, R., et al. Computational intelligence based optimization in wireless sensor network. in Information and Communication Technologies (ICICT), 2011 International Conference on. 2011.

    Google Scholar 

23. Lejiang, G., et al. WSN Cluster Head Selection Algorithm Based on Neural Network. in Machine Vision and Human-Machine Interface (MVHI), 2010 International Conference on. 2010.

    Google Scholar 

24. Payal, A., C.S. Rai, and B.V.R. Reddy. Artificial Neural Networks for developing localization framework in Wireless Sensor Networks. in Data Mining and Intelligent Computing (ICDMIC), 2014 International Conference on. 2014.

    Google Scholar 

25. Serpen, G., et al. WSN-ANN: Parallel and distributed neurocomputing with wireless sensor networks. in Neural Networks (IJCNN), The 2013 International Joint Conference on. 2013.

    Google Scholar 

26. Singh, P. and S. Agrawal. TDOA Based Node Localization in WSN Using Neural Networks. in Communication Systems and Network Technologies (CSNT), 2013 International Conference on. 2013.

    Google Scholar 

27. Subha, C.P., S. Malarkan, and K. Vaithinathan. A survey on energy efficient neural network based clustering models in wireless sensor networks. in Emerging Trends in VLSI, Embedded System, Nano Electronics and Telecommunication System (ICEVENT), 2013 International Conference on. 2013.

    Google Scholar 

28. Wen-Tsai, S., et al. Enhance the efficient of WSN data fusion by neural networks training process. in Computer Communication Control and Automation (3CA), 2010 International Symposium on. 2010.

    Google Scholar 

29. Macaš, M., L. Lhotská, and V. Křemen, Social Impact based Approach to Feature Subset Selection, in Nature Inspired Cooperative Strategies for Optimization (NICSO 2007), N. Krasnogor, et al., Editors. 2008, Springer Berlin Heidelberg. p. 239–248.

    Google Scholar 

30. Bhondekar, A.P., et al., A novel approach using Dynamic Social Impact Theory for optimization of impedance-Tongue (iTongue). Chemometrics and Intelligent Laboratory Systems, 2011. 109(1): p. 65–76.

    Google Scholar 

31. Kaur, R., et al., Enhancing electronic nose performance: A novel feature selection approach using dynamic social impact theory and moving window time slicing for classification of Kangra orthodox black tea (Camellia sinensis (L.) O. Kuntze). Sensors and Actuators B: Chemical, 2012. 166–167: p. 309–319.

    Google Scholar 

32. Macaš, M., et al., Binary social impact theory based optimization and its applications in pattern recognition. Neurocomputing, 2014. 132: p. 85–96.

    Google Scholar 

33. Kaur, R., et al., Human opinion dynamics: An inspiration to solve complex optimization problems. Sci. Rep., 2013. 3.

    Google Scholar 

34. Latané, B., The Psychology of Social Impact. American Psychologist, 1981. XXXVI (4): p. 343–356.

    Google Scholar 

35. Nowak, A., J. Szamrej, and B. Latané, From private attitude to public opinion: a dynamic theory of social impact. Psychological Review, 1990. 97(3): p. 362–376.

    Google Scholar 

36. Bhondekar, A.P., et al. Genetic Algorithm Based Node Placement Methodology For Wireless Sensor Networks. in International MultiConference of Engineers and Computer Scientists. 2009. Hong Kong: IAENG.

    Google Scholar 

37. Ferentinos, K.P. and T.A. Tsiligiridis, Adaptive design optimization of wireless sensor networks using genetic algorithms. Computer Networks, 2007. 51(4): p. 1031–1051.

    Google Scholar 

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