ARIMA-modeling based prediction mechanism in object tracking sensor networks

Target tracking is a typical and important application of wireless sensor networks which has gained much attention during recent years. Existing target tracking algorithms focus mainly on energy efficiency, maintenance of tracking accuracy and reducing the number of nodes involved in the tracking process with help of prediction mechanisms. Using prediction mechanisms with high accuracy and low computational complexity has an important role in reducing energy consumption and maintaining tracking accuracy. In this paper, we propose a novel algorithm called Auto-Regressive integrated Moving Average-based distributed predictive tracking (ARIMA-DPT) which presents a prediction model with high accuracy for prediction of target next location using ARIMA time series. Experimental results obtained by NS2 simulator improves the efficiency of proposed ARIMA-DPT protocol in terms of prediction accuracy and reducing energy consumption of network.

[1]  R. K. Agrawal,et al.  An Introductory Study on Time Series Modeling and Forecasting , 2013, ArXiv.

[2]  Ian F. Akyildiz,et al.  Wireless sensor networks: a survey , 2002, Comput. Networks.

[3]  Honglong Chen,et al.  A Novel Mobility Management Scheme for Target Tracking in Cluster-Based Sensor Networks , 2010, DCOSS.

[4]  Mahsa Ghasembaglou,et al.  HaarWavelet Based Distributed Predictive Target Tracking Algorithm for Wireless Sensor Networks , 2013 .

[5]  Rekha Jain,et al.  Wireless Sensor Network -A Survey , 2013 .

[6]  T. Andrew Yang,et al.  OCO: Optimized Communication & Organization for Target Tracking in Wireless Sensor Networks , 2006, IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (SUTC'06).

[7]  Honglong Chen,et al.  A Hybrid Cluster-Based Target Tracking Protocol for Wireless Sensor Networks , 2013, Int. J. Distributed Sens. Networks.

[8]  Hsiao-Hwa Chen,et al.  Trust, Security, and Privacy in Next-Generation Wireless Sensor Networks , 2013, Int. J. Distributed Sens. Networks.

[9]  Xinping Guan,et al.  Prediction-based protocol for mobile target tracking in wireless sensor networks , 2011 .

[10]  Elizabeth Chang,et al.  Wireless Sensor Networks: A Survey , 2009, 2009 International Conference on Advanced Information Networking and Applications Workshops.

[11]  Wang-Rong Chang,et al.  CODA: A Continuous Object Detection and Tracking Algorithm for Wireless Ad Hoc Sensor Networks , 2008, 2008 5th IEEE Consumer Communications and Networking Conference.

[12]  K. Ramya,et al.  A Survey on Target Tracking Techniques in Wireless Sensor Networks , 2012 .

[13]  Yong Wang,et al.  Energy-Efficient Node Selection for Target Tracking in Wireless Sensor Networks , 2013, Int. J. Distributed Sens. Networks.

[14]  Sania Bhatti,et al.  Survey of Target Tracking Protocols Using Wireless Sensor Network , 2009, 2009 Fifth International Conference on Wireless and Mobile Communications.

[15]  Myong-Soon Park,et al.  Dynamic Clustering for Object Tracking in Wireless Sensor Networks , 2006, UCS.

[16]  Biswanath Mukherjee,et al.  Wireless sensor network survey , 2008, Comput. Networks.

[17]  Bo Jiang,et al.  Energy Efficient Target Tracking in Wireless Sensor Networks: Sleep Scheduling, Particle Filtering, and Constrained Flooding , 2010 .

[18]  Biplab Sikdar,et al.  A protocol for tracking mobile targets using sensor networks , 2003, Proceedings of the First IEEE International Workshop on Sensor Network Protocols and Applications, 2003..