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Target Tracking Based on base Station Node Using Prediction Method and Cluster Structure in Wireless Sensor Networks


Affiliations
1 Department of Computer Engineering, Istanbul Sabahttin Zaim University, Turkey
2 Department of Computer Engineering, Education Technology and Information in Zanjan Province, Iran, Islamic Republic of
     

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One of the most important and major challenging issues of wireless sensor networks is the tracking of mobile targets. The network continuously reports the spatial information of moving objects during specified periods to the base station. In this paper, by introducing new a protocol with two versions, of which, one of them is based on dynamic clustering with a focus on the base station, and the other is based on a predictive system for increasing the tracking accuracy of the objects movement and decreasing the energy consumption as well. In this paper, the task of clustering involves in determining the cluster heads, the number of cluster members, the selection of cluster members, and managing the activation of the nodes that is done by the base station. On the other hand, given that the base station is outside the field of wireless sensor networks and is connected to an unlimited power source. The second version of the proposed protocol is based on a predictive algorithm that it was inspired from the first proposed version in the role of the base station node by a prediction method. In this paper, three heuristic models are introduced to select the speed and direction in prediction models. They are instant, average and exponential-average models. These models can track the relevant targets more accurately and reduce the number of missing targets. The simulations are done in different scenarios in a custom developed tool. The results of simulation show a good performance of them in the network lifetime and target tracking applications.

Keywords

Wireless Sensor Networks, Dynamic Clustering, Mobile Target Tracking, Network Lifetime, Target Prediction.
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  • F. Kiani, A. Rad, M.K. Sis, A. Kut and A. Alpkocak, “EEAR: An Energy Effective-Accuracy Routing Algorithm for Wireless Sensor Networks”, Life Science Journal, Vol. 10, No. 2, pp. 39-45, 2013.

  • X.Q. Yu, Z.L. Zhang and W.T. Han, “Evaluation of Communication in Wireless Underground Sensor Networks”, IOP Conference Series: Earth and Environmental Science, Vol. 69, pp. 1-8, 2017.

  • M. Adam, M. Anisi, I. Ali, “Object Tracking Sensor Networks in Smart Cities: Taxonomy, Architecture, Applications, Research Challenges and Future Directions”, Future Generation Computer Systems, pp. 1-15, 2017.

  • H. Jawad et al., “Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review”, Sensors, Vol. 17, No. 8, pp. 1-45, 2017.

  • F. Kiani, “AR-RBFS: Aware-Routing Protocol Based on Recursive Best-First Search Algorithm for Wireless Sensor Networks”, Journal of Sensors, Vol. 2016, pp. 1-10, 2016.

  • T.A. Alhmiedat and S.H. Yang, “A Survey: Localization and Tracking Mobile Targets through Wireless Sensors Network”, Proceedings of 8th Annual Network Symposium, pp. 2-9, 2007.

  • M. Akter, “Energy-Efficient Tracking and Localization of Objects in Wireless Sensor Networks”, IEEE Access, Vol. 6, pp. 17165-17177, 2018.

  • W. Zhang and G. Cao, “DCTC: Dynamic Convoy Tree-Based Collaboration for Target Tracking in Sensor Networks”, IEEE Transactions on Wireless Communications, Vol. 3, No. 5, pp. 1689-1701, 2004.

  • H.T. Kung and D. Vlah, “Efficient Location Tracking using Sensor Networks”, Proceedings of IEEE Wireless Communications and Networking Conference, pp. 1-8, 2003.

  • F. Kiyani, H. Chalangari and S. Yari, “DCSE: A Dynamic Clustering for Saving Energy in Wireless Sensor Network”, Proceedings of IEEE International Conference on Communication Software and Networks, pp. 13-17, 2010.

  • H. Ahmadi, F. Viani and R. Bouallegue, “An Accurate Prediction Method for Moving Target Localization and Tracking in Wireless Sensor Networks”, Ad Hoc Networks, Vol. 70, pp. 14-22, 2018.

  • F. Kiani, “Designing New Routing Algorithms Optimized for Wireless Sensor Network”, Academic Publishing, 2014.

  • A. Ali, Y. Ming, S. Chakraborty and S. Irem, “A Comprehensive Survey on Real-Time Applications of WSN”, Future Internet, Vol. 9, No. 77, pp. 1-22, 2017.

  • R. Shorey, A. Ananda, M. Chan and W. Ooi, “Mobile, Wireless and Sensor Networks”, Wiley-Blackwell, 2006.

  • M. Navia, J. Campelo and A. Bonastre, “GTSO: Global Trace Synchronization and Ordering Mechanism for Wireless Sensor Network Monitoring Platforms”, Sensors, Vol. 18, No. 28, pp. 1-22, 2018.

  • H. Karl and A. Willig, “Protocols and Architectures for Wireless Sensor Networks”, Wiley-Blackwell, 2005.

  • O. Demigha, N. Badache, M. Aissani and A. Mellouk, “Fault-Tolerant Prediction-based Scheme for Target Tracking Application”, Proceedings of IEEE Global Telecommunications Conference, pp. 1-6, 2009.

  • J. Tapeiro, H. Medeiros and R.H. Bishop, “Predicting Multiple Target Tracking Performance for Applications on Video Sequences”, Machine Vision and Applications, Vol. 28, No. 5, pp. 539-550, 2017.

  • N. Liang, G. Wu and W. Kang, “Real-Time Long-Term Tracking with Prediction-Detection-Correction”, IEEE Transactions on Multimedia, pp. 1-14, 2018.

  • D. Malan, T. F. Jones, M. Welsh and S. Moulton, “An Ad Hoc Sensor Network Infrastructure for Emergency Medical Care”, Proceedings of International Workshop on Wearable and Implantable Body Sensor Networks, pp. 1-5, 2004.

  • D. Wajgi and N. Thakur, “Load Balancing Algorithms in Wireless Sensor Network: A Survey”, International Journal of Computer Networks and Wireless Communications, Vol. 2, No. 1, pp. 2250-3501, 2012.

  • H. Tahmasebirad, “Target Tracking based on Dynamic Clustering with Energy Saving in Wireless Sensor Networks”, Master Thesis, Department of Computer Science, Islamic Azad University, 2012.

  • A. Ghazi and A. Ahiod, “Impact of Random Waypoint Mobility Model on Ant-based Routing Protocol for Wireless Sensor Networks”, Proceedings of International Conference on Big Data and Advanced Wireless Technologies, pp. 1-7, 2016.

  • M. Guo, E. Olule, G. Wang and S. Guo, “Designing Energy Efficient Target Tracking Protocol with Quality Monitoring in Wireless Sensor Networks”, Journal Supercomputer, Vol. 51, No. 2, pp. 131-148, 2009.

  • W. Heinzelman, A. Chandrakasan and H. Balakrishnan, “An Application-Specific Protocol Architecture for Wireless Microsensor Networks”, IEEE Transactions on Wireless Communications, Vol. 1, No. 4, pp. 660-670, 2002.

  • X. Ning and C.G. Cassandras, “Dynamic Sleep Time Control in Wireless Sensor Networks”, ACM Transactions on Sensor Networks, Vol. 6, No. 3, pp. 1-37, 2010.


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  • Target Tracking Based on base Station Node Using Prediction Method and Cluster Structure in Wireless Sensor Networks

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Authors

Farzad Kiani
Department of Computer Engineering, Istanbul Sabahttin Zaim University, Turkey
Hamidreza Tahmasebirad
Department of Computer Engineering, Education Technology and Information in Zanjan Province, Iran, Islamic Republic of

Abstract


One of the most important and major challenging issues of wireless sensor networks is the tracking of mobile targets. The network continuously reports the spatial information of moving objects during specified periods to the base station. In this paper, by introducing new a protocol with two versions, of which, one of them is based on dynamic clustering with a focus on the base station, and the other is based on a predictive system for increasing the tracking accuracy of the objects movement and decreasing the energy consumption as well. In this paper, the task of clustering involves in determining the cluster heads, the number of cluster members, the selection of cluster members, and managing the activation of the nodes that is done by the base station. On the other hand, given that the base station is outside the field of wireless sensor networks and is connected to an unlimited power source. The second version of the proposed protocol is based on a predictive algorithm that it was inspired from the first proposed version in the role of the base station node by a prediction method. In this paper, three heuristic models are introduced to select the speed and direction in prediction models. They are instant, average and exponential-average models. These models can track the relevant targets more accurately and reduce the number of missing targets. The simulations are done in different scenarios in a custom developed tool. The results of simulation show a good performance of them in the network lifetime and target tracking applications.

Keywords


Wireless Sensor Networks, Dynamic Clustering, Mobile Target Tracking, Network Lifetime, Target Prediction.

References