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Energy Efficient Data Aggregation Using Packet Driven Timing Algorithm In Wireless Sensor Network

G. Kalaiselvi*, M. Jenora**

*-** PG Scholar, Department of CSE, Govt. College of Engineering, Tirunelveli, India.

Periodicity:January - March'2015
DOI : https://doi.org/10.26634/jwcn.3.4.3174

Abstract

Energy in Wireless Sensor Networks (WSNs) is quite limited. Since sensor nodes are usually much dense, data sampled by sensor nodes have much redundancy, data aggregation becomes an effective method to eliminate redundancy, minimize the number of transmission, and then to save energy. Many applications can be deployed in WSNs and various sensors are embedded in nodes, the packets generated by heterogeneous sensors or different applications have different attributes. The packets from different applications cannot be aggregated. Otherwise, most data aggregation schemes employ static routing protocols, which cannot dynamically or intentionally forward packets according to network state or packet types. The spatial isolation caused by static routing protocol is unfavourable to data aggregation. To make data aggregation more efficient, in this paper, the authors introduce the concept of packet attribute, and then propose an Attribute-aware Data Aggregation (ADA) scheme consisting of a packet-driven timing algorithm and a special dynamic routing protocol. Inspired by the concept of potential in physics and pheromone in ant colony, a potential-based dynamic routing is elaborated to support an ADA strategy.

Keywords

Attribute Aware Data Aggregation, Dynamic Routing Protocol, Packet Driven Timing Control Algorithm, Wireless Sensor Network.

How to Cite this Article?

Kalaiselvi, G., and Jenora, M. (2015). Energy Efficient Data Aggregation Using Packet Driven Timing Algorithm In Wireless Sensor Network. i-manager's Journal on Wireless Communication Networks, 3(4), 8-13. https://doi.org/10.26634/jwcn.3.4.3174

References

[1]. R.Szewczyk, A.Mainwaring, J.Polaster, and D. Culler, (2004). “An Analysis of a Large Scale Habitat Monitoring Application, ”Proc. ACM Int'l Conf. Embedded Networked Sensor Systems(SenSys). [2]. Q. Fang, F. Zhao, and L. Guibas, (2003). “Lightweight Sensing and Communication Protocols for Target Enumeration and Aggregation, ”Proc. Fourth Int'l ACM Symp. Mobile Ad Hoc Networking and Computing, [3]. D.L. Hall and J. Llinas, (1997). “An Introduction to Multisensor Data Fusion, ” Proc. IEEE, Vol. 85, No. 1, pp. 6-23. [4]. T. Clouqueur, K.K. Saluja, and P. Ramanathan, (2004). “Fault Tolerance in Collaborative Sensor Networks for Target Detection,” IEEE Trans. Computer, Vol. 53, No. 2, pp. 320- 333. [5]. X. Sheng and Y.H. Hu, (2003). “Energy Based Acoustic Source Localization”, Proc. IEEE Int'l Conf. Information Processing in Sensor Networks, pp. 285-300. [6]. M. Duarte and Y.H. Hu, (2004). “Vehicle Classification in istributed Sensor Networks,” J. Parallel and Distributed Computing, Vol. 64, No. 7, pp. 826-838. [7]. J. Heidemann, F. Silva, C. Intanagonwiwat, R. Govindan, D.Estrin, and D. Ganesan, (2001). “Building Efficient Wireless Sensor Networks with Low-Level Naming,” Proc. ACM Symp. Operating Systems Principles, pp. 146- 159, [8]. W.R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, (2002). “Application-Specific Protocol Architecture for Wireless Networks, ”IEEE Trans. Wireless Comm., Vol. 1, No. 4, pp. 660-670. [9]. S. Lindsey, C. Raghavendra, and K.M. Sivalingam, (2013). “Data Gathering Algorithms in Sensor Networks Using Energy Metrics, ”IEEE Trans. Parallel and Distributed Systems, Vol. 13, No. 9, pp. 924-935, 890. [10]. O. Younis and S. Fahmy, (2004). “HEED: A Hybrid, Energy-Efficient, Distributed Clustering Approach for Ad Hoc Sensor networks, ”IEEE Trans. Mobile Computing, Vol. 3, No. 4, pp. 366-379. [11]. M. Ding, X. Cheng, and G. Xue, (2003). “Aggregation Tree n Construction in Sensor Networks,” Proc. IEEE 58th Vehicular Technology Conf.,pp. 2168-2172. [12]. H.O. Tan and I. Korpeoglu, (2003). “Power Efficient Data Gathering and Aggregation in Wireless Sensor Networks,” SIGMOD Record, Vol. 32, No. 4, pp. 66-71. [13]. R. Cristescu, B. Beferull-Lozano, and M. Vetterli, (2004). “On Network Correlated Data Gathering,” Proc. IEEE INFOCOM, pp. 2571-2582. [14]. J. Li, A. Deshpande, and S. Khuller, (2010). “On Computing Compression Trees for Data Collection in Wireless Sensor Networks,” Proc. IEEE INFOCOM. [15]. W. Zhang and G. Cao, (2004). “DCTC: Dynamic Convoy Tree-based Collaboration for Target Tracking in Sensor Networks,” Ad Hoc and Sensor Wireless Networks, Vol. 3, pp. 1689-1701. [16]. Y.-K. Kim, R. Bista, and J.-W. Chang, (2009). “A Designated Path Scheme for Energy-Efficient Data Aggregation in Wireless Sensor Networks,” Proc. IEEE Int'l Symp. Parallel and Distributed Processing with Applications, pp. 408-415. [17]. K. Fan, S. Liu, and P. Sinha, (2006). “On the Potential of Structure-Free Data Aggregation in Sensor Networks,” Proc. IEEE INFOCOM, pp. 1-12. [18]. G. Theraulaz and E. Bonabeau, (1999). “A Brief History of Stigmergy, ”Artificial Life, Vol. 5, No. 2, pp. 97-116. [19]. C. Liu and G. Cao, (2010) “Distributed Monitoring and Aggregation in Wireless Sensor Networks,” Proc. IEEE INFOCOM. [20]. W. Pu, D. Rui, and I. Akyildiz,( 2010). “Collaborative Data Compression Using Clustered Source Coding for Wireless Multimedia Sensor Networks,” Proc. IEEE INFOCOM, pp. 1-9. [21]. S.J. Park and R. Sivakumar, (2008) “Energy Efficient Correlated Data Aggregation for Wireless Sensor Networks,” Int'l J. Distributed Sensor Networks, Vol. 4, No. 1, pp. 13-17.

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