Analysis of Signalling Time of Community Model

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Boudhayan Bhattacharya 1,* Banani Saha 2

1. Sabita Devi Education Trust – Brainware Group of Institutions, 398 Ramakrishnapur Road, Barasat, Kolkata -700124,West Bengal, India

2. University of Calcutta, JD-2, JD Block, Sector III, Bidhannagar, Kolkata - 700098, West Bengal, India

* Corresponding author.


Received: 29 Apr. 2015 / Revised: 13 Jun. 2015 / Accepted: 6 Jul. 2015 / Published: 8 Aug. 2015

Index Terms

Master Fusion Filter, Reference Sensor, Local Filter, Community Model, Data Transmission Time, Normalized Signalling Time


Data fusion is generally defined as the application of methods that combines data from multiple sources and collect information in order to get conclusions. This paper analyzes the signalling time of different data fusion filter models available in the literature with the new community model. The signalling time is calculated based on the data transmission time and processing delay. These parameters reduce the signalling burden on master fusion filter and improves throughput. A comparison of signalling time of the existing data fusion models along with the new community model has also been presented in this paper. The results show that our community model incurs improvement with respect to the existing models in terms of signalling time. 

Cite This Paper

Boudhayan Bhattacharya, Banani Saha,"Analysis of Signalling Time of Community Model", International Journal of Mathematical Sciences and Computing(IJMSC), Vol.1, No.2, pp.8-21, 2015.DOI: 10.5815/ijmsc.2015.02.02


[1] Bhattacharya, B., Saha, B. Community Model - A New Data Fusion Filter Paradigm, American Journal of Advanced Computing, Vol. II Issue I, January 2015, 25-31.

[2] Bhattacharya, B., Saha B. Community Model Architecture – A New Data Fusion Paradigm for Implementation, International Journal of Innovative Research in Computer and Commmucation Engineering, vol. 2, issue 6, (June 2014), 4774–4783.

[3] Bar-Shalom, Y., Fortmann T.E. Tracking and Data Association, Academic Press, San Diego, California, (1988).

[4] Bloch I., Maître H. Fusion de données en traitement d'images: modèles d'information et décisions, Traitement du Signal, vol. 11, no. 6, (1994), 435-446.

[5] Blackman, S.S. Multiple Targets Tracking with Radar Applications. Artech House Inc. (1986)

[6] Hall, D. L., Llinas, J. An Introduction to Multisensor Data Fusion, Proceedings of the IEEE Vol. 85, No. 1, (Jan. 1997), 6 -23

[7] (Last accessed on May 29, 2015)

[8] Luo, R. C., Kay, M. G. Multisensor Integration and Fusion in Intelligent System, IEEE Trans. on System, Man and Cybernetics, Vol. 19, No. 5, (Sept/Oct 1989), 901-931.

[9] Kokar, M., Kim, K. Review of Multisensor Data Fusion: Architecture and Techniques, Proceedings of The International Symposium on Intelligent Control, Chicago, Illinois, USA, (Aug. 1993), 261-266.

[10] Esteban, J., Starr, A., Willetts, R., Hannah, P., Bryanston-Cross P. A Review of Data Fusion Models and Paradigms: Towards Engineering Guidelines, Journal Neural Computing and Applications Volume 14 Issue 4, (December 2005) 273-281.

[11] Elmenreich, W. A Review on System Architectures for Sensor Fusion Applications, Lecture Notes in Computer Science, Springer, Vol. 4761, (2007) 547-559.

[12] Luo, R. C., Chang C. C., Lai, C. C. Multisensor Fusion and Integration: Theories, Applications, and its Perspectives, IEEE Sensors Journal, Vol. 11 No. 12, (December 2011), 3122-3138.

[13] Bedworth, M. D., O'Brien, J. C. The Omnibus Model: A New Model of Data Fusion?, Aerospace and Electronic Systems Magazine, IEEE, Vol. 15 Issue 4, (April 2000), 30-36.

[14] Nakamura, E. F., Loureiro A. A. F., Frery, A. C. Information Fusion for Wireless Sensor Networks: Methods, Models, and Classifications, ACM Computing Surveys, Vol. 39 No. 3, (August 2007), 9/1-9/55.

[15] Zhang, X., Castellanos, J. G., Campbell A. T. P-MIP: Paging Extension for Mobile IP Columbia University, (2002), 127-141.

[16] Wang, M., Georgiades, M., Tafazolli R. Signalling Cost Evaluation of Mobility Management Schemes for Different Core Network Architectural Arrangements in 3GPP LTE/SAE Vehicular Technology Conference 2008, (May 2008), 2253-2258.

[17] Karatsinides, S. E. Enhancing Filter Robustness in Cascaded GPS-INS Integrations, IEEE Transactions on Aerospace and Electronic Systems, Vol. 30, No. 4, (Oct., 1994), 1001-1008.

[18] Bell, W.B., Gorre, R.G., Cockrell, L.D. Cascading Filtered DTS Data into a Loosely Coupled GPS/INS System, Proceedings of IEEE PLANS'98, (1998), 586-593.

[19] Carlson, N.A. Federated Filter for Fault-Tolerant Integrated Systems, Proceedings of 1988 IEEE PLANS, (1988), 110-119.

[20] Felter, S.C. An overview of decentralized Kalman filter techniques, Proceedings of IEEE Southern Tier Technical Conference, (1990), 79-87.

[21] Liggins, M. E., Chong, C-Y., Kadar, I., Alford, M. G., Vannicola, V., Thomopoulos, S. Distributed Fusion Architectures and Algorithms for Target Tracking, Proceedings of the IEEE, Vol. 85, No. 1, (Jan. 1997), 95-107.

[22] Evans, F.A., Wilcox, J. C. Experimental Strapdown Redundant Sensor Inertial Navigation System, Journal of Spacecraft and Rockets, Vol. 7, No. 9, (Sept., 1970), 1070-1074.

[23] Potter, J.E., Deckert, J.C.: Minimax Failure Detection and Identification in Redundant Gyro and Accelerometer System, Journal of Spacecraft, Vol. 10, No. 4, (April, 1973), 236-243.

[24] Spitzer, C.R.: The Avionics Handbook, CRC Press LLC, (2001).

[25] Signalling Cost Analysis of Community Model, FICTA 2014, Bhubaneswar, India, Springer, Advances in Intelligent Systems and Computing, Vol 328 (2014), 49-56.