Secure data aggregation in WSN using iterative filtering algorithm

In WSN Aggregation is considered as the most susceptible to node compromising attacks, as wireless sensor networks are not more secured so they are more vulnerable to this type of attacks. For WSN it is essential to check the trustworthiness of the data & reputation of sensor nodes. To handle this issue Iterative Filtering acts as a best option. In Iterative filtering algorithm (IF) the data is aggregated concurrently from multiple sources & IF also render trust assessment of these sources. The trust assessment is in the form of similar weight factors assigned to data supplied by each source. Considering the security as major issue in WSN, In this paper we proposed an advancement for IF method by providing approximation which will make them collusion robust and is converging fast. Advancement in the Iterative Filtering algorithm will enhance the performance of the system with good potential for implementation in WSN, IF algorithm is stretched with novel method for collusion detection & revocation based on an initial approximation of the aggregate values as well as distribution of differences of each sensor readings. The proposed system performance is checked through extensive simulation in C#. The simulation demonstrates the improved results. The RMS value in the graphs for series 2(0.1, 0.18, 0.22, 0.22, 0.22, 0.3, 0.36, 0.38) with standard deviation ranging from 0.5 to 4 shows that the proposed system performance is better as compare to existing system.

[1]  Tao Zhou,et al.  A robust ranking algorithm to spamming , 2010, ArXiv.

[2]  Sencun Zhu,et al.  SDAP: a secure hop-by-Hop data aggregation protocol for sensor networks , 2006, MobiHoc '06.

[3]  Sajal K. Das,et al.  A Trust-Based Framework for Fault-Tolerant Data Aggregation in Wireless Multimedia Sensor Networks , 2012, IEEE Transactions on Dependable and Secure Computing.

[4]  Hasan Çam,et al.  Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks , 2010, IEEE/ACM Transactions on Networking.

[5]  Belle L. Tseng,et al.  User reputation in a comment rating environment , 2011, KDD.

[6]  Elisa Bertino,et al.  A Game-Theoretic Approach for High-Assurance of Data Trustworthiness in Sensor Networks , 2012, 2012 IEEE 28th International Conference on Data Engineering.

[7]  D. Bhattacharya Secure Data Aggregation in Wireless Sensor Networks , 2014 .

[8]  Sajal K. Das,et al.  ZoneTrust: Fast Zone-Based Node Compromise Detection and Revocation in Wireless Sensor Networks Using Sequential Hypothesis Testing , 2012, IEEE Transactions on Dependable and Secure Computing.

[9]  Erman Ayday,et al.  An iterative algorithm for trust and reputation management , 2009, 2009 IEEE International Symposium on Information Theory.

[10]  Aziz Nasridinov,et al.  Skyline-Based Aggregator Node Selection in Wireless Sensor Networks , 2013, Int. J. Distributed Sens. Networks.

[11]  Yi-Cheng Zhang,et al.  Information filtering via Iterative Refinement , 2006, ArXiv.

[12]  Deepak Ganesan,et al.  PRESTO: feedback-driven data management in sensor networks , 2009, TNET.

[13]  Hong Cheng,et al.  Robust Reputation-Based Ranking on Bipartite Rating Networks , 2012, SDM.

[14]  Cristina Nita-Rotaru,et al.  A survey of attack and defense techniques for reputation systems , 2009, CSUR.

[15]  Ping Li,et al.  Trust mechanisms in wireless sensor networks: Attack analysis and countermeasures , 2012, J. Netw. Comput. Appl..

[16]  Mani B. Srivastava,et al.  Reputation-based framework for high integrity sensor networks , 2008, TOSN.

[17]  Paul Van Dooren,et al.  Iterative Filtering in Reputation Systems , 2010, SIAM J. Matrix Anal. Appl..

[18]  Elisa Bertino,et al.  Provenance-based trustworthiness assessment in sensor networks , 2010, DMSN '10.

[19]  Dawn Xiaodong Song,et al.  Secure hierarchical in-network aggregation in sensor networks , 2006, CCS '06.

[20]  Giulio Cimini,et al.  Measuring quality, reputation and trust in online communities , 2012, ISMIS.