454 159
Full Length Article
Fusion: Practice and Applications
Volume 1 , Issue 2, PP: 66-78 , 2020 | Cite this article as | XML | Html |PDF


The Interplay Between Missing Data and Out-of-Order Measurements using Data Fusion in Wireless Sensor Networks

Authors Names :   Piyush K. Shukla   1 *     Ozen Ozer   2  

1  Affiliation :  Department of Computer Science Engineering, University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya (UIT-RGPV), Bhopal, Madhya Pradesh 462033, India

    Email :  piyush@rgpv.ac.in

2  Affiliation :  Department of Mathematics, Kirklareli University, Kirklareli, 39100, Turkey

    Email :  ooozenozer@gmail.com

Doi   :   https://doi.org/10.54216/FPA.010202

Received: January 22, 2020 Accepted: April 02, 2020

Abstract :

Multi-data transmission is the most important processing of target detection with a reduction in delay in the transmission of the data. This may occur in certain technological circumstances, and it happens significantly often in wireless sensor networks—processing such data to keep track of and make predictions about targets of interest might result in errors due to the inherent nature of the data. The Kalman filter and other algorithms with equivalent functionality are most useful for their principal application, estimating the states of dynamic systems. This difficulty of modeling and filtering such delayed states and missing data is dealt with synergistically throughout this proposed work. This is done to ensure that the best possible results are obtained. Filtering methods similar to the optimal Kalman filter are most utilized in fusing measurement data at different levels. This relatively creative technique includes filtering delayed states while also using observations that have been randomly excluded, then putting those screened delayed states and words to use in a process that involves fusing data. One of these applications is the fusion of images. To successful the task of performance evaluation for the integrated plan, the use of numerical simulations is essential. The state delay, as well as the data that is absent at random, are both included in four distinct alternative algorithms. These algorithms are then investigated, and the results are given in this paper. Referring to the gain fusion, the H-infinity a posteriori filter, the H-infinity risk sensitive filter, and the H-infinity risk sensitive filter. To accommodate a scenario that involves MATLAB and the integration of sensor data, global filtering approaches are being updated and evaluated with the use of numerical simulations that are being carried out. In addition, we provide a nonlinear observer based on the gain of the continuous time data fusion filter. Using the Lyapunov energy function, we can conclude on asymptotic convergence in the system.  These observers are presented after the previous step. Therefore, the filtering algorithms and the observers described in the current proposed work make a definite step towards improvement for controlling state delays and randomly missing data synergistically for wireless sensor networks.

Keywords :

Delay; fusion; sensor data; Kalman gain; H-infinity gain; Lyapunov energy.

References :

[1] Aid, J.P., Humax and Knightly, K.W. “Impact of denial-of-service attacks on Adhoc networks”

IEEE/ACM Trans. Net., Vol.16, No.4, pp.791-802, 2008.

[2] Abbas, S., Meranti, M. and Llewellyn-Jones, D. “Signal Strength Based Sybil Attack Detection

in Wireless Ad Hoc Networks” in Proc. IEEE. Developments of e-system Engineering., pp. 192-

195, 2009.

[3] Akyildiz, I.F., Weilian, Su., Sankarasubramaniam, Y. and Cayirci, E. “A survey on sensor

networks” IEEE Trans. Comm. Vol.40, No.8, pp.102 -144, 2002.

[4] Amitabh Mishra, Ketan Nadkarni, and Animesh Patcha, "Intrusion detection in wireless Adhoc

networks" IEEE Trans. Wireless Comm., Vol.11, No.1, pp.48 - 60, 2004.

[5] Arafat, J. and Dweik, A.L. “Exact performance analysis of synchronous FH-MFSK wireless

networks” IEEE Trans. Comm., Vol.58, No.7, pp.3771-3776, 2009.

[6] Binwei Deng, Wen Li, Guangming Huang, Shouyin Liu, and Qin Zhang, "High-accuracy and

low-cost localization scheme for wireless sensor networks", J. Electronics, Vol.99, pp.455-476,


[7] Bhalaji, N. and Shanmugam, A. “Reliable routing against selective packet drop attack in DSR

based MANET”, J. Software, Vol.4, No.6, pp.536-543 2009.

[8] Brown, J. and Xiaojiang, Du, “Detection of selective forward attacks in heterogeneous sensor

networks” in Proc. IEEE Comm., pp.1583-1587, 2008. 9.

[9] Bojkovic, Z. and Bakmaz, Z. “A Survey on sensor network deployment” WSEAS Trans.

Comm., Vol.7, No.12, pp.28-466, 2008.

[10] Ngai, C.H., Jiangchuann, L. and Michael, R. “On the Intruder detection for sinkhole attack in

wireless sensor networks” in Proc. IEEE ICC, 2006.

[11] Nait-Abdesselam, F. "Detecting and Avoiding wormhole attack in wireless Adhoc networks"

IEEE Trans. Comm., Vol.46, No.4, pp.127-133, 2008.

[12] Newsome, J., Shi, E. and Song, D. “The Sybil attack in sensor network: analysis & defenses” in

Proc. IPSN, ACN Press, USA, pp.259-268, 2004.

[13] Papdimitriou, A., Fessant, F.L., Viana, A.C. and Sengul, C. “Cryptographic protocols to fight

sinkhole attacks on tree-based routing in wireless sensor networks” in Proc. NPSEC, 2009.

[14] Perrig, A., Szewczyk, R., Wen, V., Culler, D. and Tygar, J.D. “SPINS: security protocols for

sensor networks” ACM Trans. Wireless Net. Vol.8, No.5, pp.521-523, 2002.

[15] Pursley, Michael, B. Introduction to digital communications. Upper Saddle River, Pearson

Prentice Hall, 2005.

[16] Qinghua, Z., Pan, W., Douglas, S. and Ning, P. "Defending against Sybil attacks in sensor

networks" in Proc. Distributed Computing Systems, pp.185-191, 2005.

[17] Qiu Hui-Min, "Principle of Sybil attack and the defense", J. Net. and Computer Security, Vol 10,

pp.63-65, 2005.

[18] Rajasegarar, S., Leckie, C. and Panaiswami, M. “Anomaly detection in wireless sensor

networks” IEEE Trans. Wireless Comm., Vol.15, No.4, pp.34-40, 2008.

[19] Ramakrishnan, M. and Shanmugavel, S. “New Approaches to Routing techniques of MANET

node for optimal network performance”, J. Computer Science and Net. Security, Vol.8, No.11,

pp. 369-376, 2008.

[20] Ryu H.G. “Performance of DS/SFHSSMA system with overlapping BFSK in the presence of

both MTJ and MAI” IEEE Trans. Veh. Tech., Vol.52, pp.267-273, 2003.

[21] Sabbah, E. and Kang, K.D. “Security in wireless sensor networks”, in Guide to wireless sensor

network Springer-Verlag, London, pp.491-512, 2009.

[22] Satyajayant Misra and Sowmya Myneni, "On identifying power control performing Sybil nodes

in wireless sensor networks using RSSI" in Proc. IEEE Globecom, pp.1-5, 2010.

[23] Shaohe, L., Xiaodong, W.F., Xin, Z. and Xingming, Z. "Detecting the Sybil attack

cooperatively in wireless sensor networks" in Proc. Computational Intelligence and Security,

pp.442-446, 2008.

[24] Shila, D.M., Cheng, Y.U. and Anjali, T. “Mitigating selective forward attacks with a channelaware

approach in WMNS” IEEE Trans. Wireless Comm., Vol.9, No.5, pp.1661-1675, 2010.

[25] Shi, E., and Perrig, A. "Designing secure sensor networks", IEEE Wireless Comm., Vol.11,

No.6, pp.38-48, 2006.

Cite this Article as :
Piyush K. Shukla , Ozen Ozer, The Interplay Between Missing Data and Out-of-Order Measurements using Data Fusion in Wireless Sensor Networks, Fusion: Practice and Applications, Vol. 1 , No. 2 , (2020) : 66-78 (Doi   :  https://doi.org/10.54216/FPA.010202)