Outlier analysis-pattern mining (OAPM) algorithm in wireless sensor networks

In this work, a novel clustering and aggregating algorithm is proposed to reduce energy consumption in wireless sensor networks (WSN) by focusing on regulating the inter-cluster traffic and data aggregation at the cluster heads. Previous works have aimed at reducing the intra-cluster traffic by choosing the node at the mean position inside as cluster head. But intra-cluster distance is small compared with inter-cluster distance. Hence reducing inter-cluster traffic will have a greater impact in power optimization. In our approach, inter-cluster traffic is reduced by clustering where cluster heads are chosen by outlier analysis. To reduce energy consumption further, we have implemented data aggregation using pattern mining at the cluster heads. Aggregation techniques proposed so far are based on probability and they arrive at implementing a static timer for data aggregation. Because of varying network traffic, a static timer results in constant delay. Implementing a dynamic timer is a possible solution for improvising the performance of data aggregation. Outlier analysis together with aggregation using pattern mining helps to reduce energy consumption.

[1]  Anthony Ephremides,et al.  The Architectural Organization of a Mobile Radio Network via a Distributed Algorithm , 1981, IEEE Trans. Commun..

[2]  Krishna M. Sivalingam,et al.  On Performance of Node Placement Approaches for Hierarchical Heterogeneous Sensor Networks , 2009, Mob. Networks Appl..

[3]  Azzedine Boukerche,et al.  An Inter-cluster Communication based Energy Aware and Fault Tolerant Protocol for Wireless Sensor Networks , 2008, Mob. Networks Appl..

[4]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[5]  Ravi Prakash,et al.  Max-min d-cluster formation in wireless ad hoc networks , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[6]  Azzedine Boukerche,et al.  An Energy-Aware and Fault Tolerant Inter-Cluster Communication Based Protocol for Wireless Sensor Networks , 2007, IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference.

[7]  Min Xiang,et al.  Energy-Efficient Intra-Cluster Data Gathering of Wireless Sensor Networks , 2010, J. Networks.

[8]  Edward J. Coyle,et al.  An energy efficient hierarchical clustering algorithm for wireless sensor networks , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[9]  Imrich Chlamtac,et al.  An Energy-Efficient Method for Nodes Assignment in Cluster-Based Ad Hoc Networks , 2004, Wirel. Networks.

[10]  Alhussein A. Abouzeid,et al.  Optimal Stochastic Policies for Distributed Data Aggregation in Wireless Sensor Networks , 2009, IEEE/ACM Transactions on Networking.

[11]  Marwan Krunz,et al.  Coverage-time optimization for clustered wireless sensor networks: a power-balancing approach , 2010, TNET.

[12]  Carlo Fischione,et al.  System Level Design for Clustered Wireless Sensor Networks , 2007, IEEE Transactions on Industrial Informatics.

[13]  Ian F. Akyildiz,et al.  Sensor Networks , 2002, Encyclopedia of GIS.

[14]  Mohamed K. Watfa,et al.  A Sensor Network Data Aggregation Technique , 2009 .

[15]  Longjiang Guo,et al.  Mining Recent Approximate Frequent Items in Wireless Sensor Networks , 2009, 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery.

[16]  Ravi Prakash,et al.  Load-balancing clusters in wireless ad hoc networks , 2000, Proceedings 3rd IEEE Symposium on Application-Specific Systems and Software Engineering Technology.

[17]  Edward J. Coyle,et al.  Minimizing communication costs in hierarchically clustered networks of wireless sensors , 2003, 2003 IEEE Wireless Communications and Networking, 2003. WCNC 2003..