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A Method to Detect Data Stream Changes in the Wireless Sensor Network using the Gossiping Protocol


  • Master of Science in Software Engineering, Department of Computer Engineering, College of Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran, Islamic Republic of
  • Department of Computer Engineering, College of Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran, Islamic Republic of


Background: Due to the increasing volume of data, data models and identifying events that may lead to a lot of damage over time, identifying data changes has become an important issue. Methods: In this paper, first, sensor networks, their features and applications in various fields have been discussed. Then, data change algorithms and their properties are discussed. Next, algorithms concerning identifying changes in sensor networks are analyzed, and ultimately, an efficient way to detect environmental changes using the gossiping protocol is dealt with. Results: The purpose of this study is to optimize the propagation time of environmental changes among sensors, network loading concerning data volume transmitted between the sensors, performance efficiency of sensors and accuracy of detecting changes in the environment. Simulation results show that the proposed method (optimization of data stream changes identification in a wireless sensor network using the gossiping protocol) is better in comparison with other methods. Conclusion: The superiorities include reducing propagation time of environmental changes to sensors, data volume transmitted among sensors, the effect of low efficiency of a specific sensor on the performance of other sensors and increasing the accuracy of event identification.


Change Detection, Data Stream, Gossiping Protocol, Merger Decisions, Sensor Network.

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  • Zhu Y, Shasha D. StatStream: statistical monitoring of thousands of data streams in real time. VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases. 2002; 54:358–69.
  • Dasu T, Krishnan S, Enkatasubramanian S, Yi K. An information-theoretic approach to detecting changes in multi-dimensional data streams. Proceedings Symposium on the Interface of Statistics, Computing Science and Applications. 2006; 67:1–24.
  • Aggarwal C. A framework for diagnosing changes in evolving data streams. SIGMOD '03 Proceedings of the 2003 ACM SIGMOD International Conference on Management of data. 2003; 21:575–86.
  • Azari L, Ghaffari A. Proposing a novel method based on network- coding for optimizing error recovery in wireless sensor networks. Indian Journal of Science and Technology. 2015; 8:34–59. DOI: 10.17485/ijst/2015/v8i9/54915.
  • Bifet A. Adaptive learning and mining for data streams and frequent patterns. ACM SIGKDD Explorations Newsletter. 2009; 11:55–6.
  • Cabanes G, Bennani Y. Change detection in data streams through unsupervised learning. The 2012 International Joint Conference on Neural Networks (IJCNN). 2012; 23:1–6.
  • Gillick B, Gaber M, Krishnaswamy S, Zaslavsky A. Visualisation of cluster dynamics and change detection in ubiquitous data stream mining. Proceedings the third International Workshop on Knowledge Discovery in Data Streams; 2006.
  • Yang J, Simon T, Mueller C, Klan D, Sattler K. Comparing and refining gossip protocols for wireless P2P system in disaster scenarios. 19th Euromicro International Conference on Distributed and Network-Based Processing (PDP). 2011; 21:595–99.
  • Stankovic S, Ilic N, Stankovic M, Johansson K. Distributed change detection based on a consensus algorithm. IEEE Transactions on Signal Processing. 2010; 59:5686–97.
  • Hayashibara N, Cherif A, Katayama T. Failure detectors for large-scale distributed systems. 21st IEEE Symposium on Reliable Distributed Systems. 2002; 12:404–9.
  • Botan I, Fischer M, Kossmann D, Tatbul N. Transactional stream processing. Proceedings of the 15th International Conference on Extending Database Technology. 2010; 21:204–15.
  • Cohen E, Kaplan H. How to estimate change from samples? arXiv preprint arXiv; 2012.
  • Cormode G. The continuous distributed monitoring model. SIGMOD Record. 2013; 42:1–21.
  • Branch J, Szymanski B. In-network outlier detection in wireless sensor networks. Knowledge and Information Systems. 2013; 34:23–54.
  • Busch C, Tirthapura S. A deterministic algorithm for summarizing asynchronous streams over a sliding window. 2007; 13:465–76.


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