Abstract
Ensuring continued sustainable communication characteristics are still questionable fact to be obtained by existing data fusion techniques. We have reviewed existing studies to find more scope towards reliability. This paper has presented a novel model called as RaESS or Reliable-and-Efficient Statistical Spreading Data Fusion Mechanism which mainly aims to achieve higher number of unique transmission and lower utilization of resources. We introduced Degree of Information that compliments to increase reliable transmission while minimizing packet drops. Compared to existing technique, proposed technique shows reduced energy consumption and enhanced communication performance (data delivery ratio, delay, algorithm processing time).
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References
Rehmani, M.H., Pathan, A.-S.K.: Emerging Communication Technologies Based on Wireless Sensor Networks: Current Research and Future Applications. CRC Press-Computers, Boca Raton (2016)
Behmann, F., Wu, K.: Collaborative Internet of Things (C-IoT): for Future Smart Connected Life and Business. Wiley, Chichester (2015)
Kamila, N.K.: Handbook of Research on Wireless Sensor Network Trends, Technologies, and Applications. IGI Global (2016)
Fahmy, H.M.A.: Wireless Sensor Networks: Concepts, Applications, Experimentation and Analysis. Springer, Singapore (2016)
Mahmoud, M.S., Xia, Y.: Networked Filtering and Fusion in Wireless Sensor Networks. CRC Press, New York (2014)
Castanedo, F.: A review of data fusion techniques. Sci. World J. (2013). Hindawi Publishing Corporation
Sidek, O., Quadri, S.A.: A review of data fusion models and systems. Int. J. Image Data Fusion 3(1), 3–21 (2012). Taylor & Francis
Braca, P., Goldhahn, R., Ferri, G., LePage, K.D.: Distributed information fusion in multistatic sensor networks for underwater surveillance. IEEE Sens. J. 16(11), 4003–4014 (2016)
Jayasri, B.S., Raghavendra Rao, G.: Need For Energy Efficient Data Fusion in Wireless Sensor Networks. Int. J. Eng. Res. Technol. (IJERT) 3(1), January 2014
Jayasri, B.S., Rao, G.R.: Reviewing the research paradigm of techniques used in data fusion in WSN. In: IEEE International Conference in Computing and Communications Technologies (ICCCT), pp. 83–88, 26–27 February 2015
Liu, L., Luo, G., Qin, K., Zhang, X.: An algorithm based on logistic regression with data fusion in wireless sensor networks. EURASIP J. Wirel. Commun. Networking (2017). Springer
Chen, Y.L., et al.: Inexpensive multimodal sensor fusion system for autonomous data acquisition of road surface conditions. IEEE Sens. J. 16(21), 7731–7743 (2016)
Farias, R.C., Cohen, J.E., Comon, P.: Exploring multimodal data fusion through joint decompositions with flexible couplings. IEEE Trans. Sig. Process. 64(18), 4830–4844 (2016)
Habib, C., Makhoul, A., Darazi, R., Salim, C.: Self-adaptive data collection and fusion for health monitoring based on body sensor networks. IEEE Trans. Ind. Inf. 12(6), 2342–2352 (2016)
Baccarelli, E., et al.: Green multimedia wireless sensor networks: distributed intelligent data fusion, in-network processing, and optimized resource management. IEEE Wirel. Commun. 21(4), 20–26 (2014)
Yassine, A., Nasser, Y., Awad, M., Uguen, B.: Hybrid positioning data fusion in heterogeneous networks with critical hearability. EURASIP J. Wirel. Commun. Networking (2015)
Zhang, Z.J., Lai, C.F., Chao, H.C.: A green data transmission mechanism for wireless multimedia sensor networks using information fusion. IEEE Wirel. Commun. 21(4), 14–19 (2014)
Larios, D.F., Barbancho, J., RodrÃguez, G., Sevillano, J.L., Molina, F.J., León, C.: Energy efficient wireless sensor network communications based on computational intelligent data fusion for environmental monitoring. IET Commun. 6(14), 2189–2197 (2012)
Neves, P.A.C.S., Rodrigues, J.J.P.C., Lin, K.: Data fusion on wireless sensor and actuator networks powered by the zensens system. IET Commun. 5(12), 1661–1668 (2011)
Tan, R., Xing, G., Liu, B., Wang, J., Jia, X.: Exploiting data fusion to improve the coverage of wireless sensor networks. IEEE/ACM Trans. Networking 20(2), 450–462 (2012)
Nemati, S., Malhotra, A., Clifford, G.: Data fusion for improved respiration rate estimation. EURASIP J. Adv. Sig. Process. 2010, 926305 (2010)
Yue, Y., Fan, H., Li, J., Qin, Q.: Large-scale mobile wireless sensor network data fusion algorithm. In: 2016 IEEE International Conference on Big Data Analysis (ICBDA), Hangzhou, pp. 1–5 (2016)
Jayasri, B.S., Raghavendra Rao, G.: EEDF: energy efficient data fusion supportive of virtual multipath propagation in WSN. Int. J. Appl. Eng. Research (IJAER) 10(86) (2015)
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Jayasri, B.S., Raghavendra Rao, G. (2017). RaESS: Reliable-and-Efficient Statistical Spreading Data Fusion Mechanism in Wireless Sensor Network. In: Silhavy, R., Silhavy, P., Prokopova, Z., Senkerik, R., Kominkova Oplatkova, Z. (eds) Software Engineering Trends and Techniques in Intelligent Systems. CSOC 2017. Advances in Intelligent Systems and Computing, vol 575. Springer, Cham. https://doi.org/10.1007/978-3-319-57141-6_33
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DOI: https://doi.org/10.1007/978-3-319-57141-6_33
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