Efficient Data Suppression for Wireless Sensor Networks

Due to critical resource restrictions, wireless sensor networks (WSNs) often face a trade-off between the cost of data transmission and the accuracy of event detection. By exploring the potential spatial and temporal correlations among sensory data, a WSN may intelligently select only a subset of nodes, whose data can still keep the major properties of those collected by the whole network, to transmit. Two important issues are examined in this study. First, which of those sensors should be selected? Second, how can the lifetime of the selected sensors be maximized? We propose a Singular Value Decomposition (SVD) based Sensory Data Suppression (SSS) Mechanism, which removes unnecessary data transmissions and prolong the lifetime of sensor networks. We also balance transmission duties among sensor nodes by leveraging the load balancing algorithms with both one-attribute and multi-attribute scenarios.

[1]  Dimitrios Gunopulos,et al.  Correlating synchronous and asynchronous data streams , 2003, KDD '03.

[2]  Qiang Yang,et al.  An Incremental Subspace Learning Algorithm to Categorize Large Scale Text Data , 2005, APWeb.

[3]  Yunhao Liu,et al.  Sea Depth Measurement with Restricted Floating Sensors , 2007, 28th IEEE International Real-Time Systems Symposium (RTSS 2007).

[4]  Qilian Liang,et al.  Redundancy reduction in wireless sensor networks using SVD-QR , 2005, MILCOM 2005 - 2005 IEEE Military Communications Conference.

[5]  Yunhao Liu,et al.  Quality of Trilateration: Confidence-Based Iterative Localization , 2008, IEEE Transactions on Parallel and Distributed Systems.

[6]  Yunhao Liu,et al.  Rendered Path: Range-Free Localization in Anisotropic Sensor Networks With Holes , 2007, IEEE/ACM Transactions on Networking.

[7]  Harish Viswanathan,et al.  Dynamic load balancing through coordinated scheduling in packet data systems , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[8]  Nick Roussopoulos,et al.  Compressing historical information in sensor networks , 2004, SIGMOD '04.

[9]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[10]  Yunhao Liu,et al.  Non-Threshold based Event Detection for 3D Environment Monitoring in Sensor Networks , 2007, 27th International Conference on Distributed Computing Systems (ICDCS '07).

[11]  Paramvir Bahl,et al.  Hot-spot congestion relief and service guarantees in public-area wireless networks , 2002, CCRV.

[12]  Nathan Srebro,et al.  Learning with matrix factorizations , 2004 .

[13]  Jimeng Sun,et al.  Streaming Pattern Discovery in Multiple Time-Series , 2005, VLDB.

[14]  Tzu-Chieh Tsai,et al.  IEEE 802.11 Hot Spot Load Balance and QoS-Maintained Seamless Roaming , 2003 .

[15]  Jing Liang,et al.  SVD-QR-T FCM Approach for Virtual MIMO Channel Selection in Wireless Sensor Networks , 2007, International Conference on Wireless Algorithms, Systems and Applications (WASA 2007).

[16]  Wei Hong,et al.  Approximate Data Collection in Sensor Networks using Probabilistic Models , 2006, 22nd International Conference on Data Engineering (ICDE'06).

[17]  Jun Yang,et al.  Constraint chaining: on energy-efficient continuous monitoring in sensor networks , 2006, SIGMOD Conference.

[18]  Paramvir Bahl,et al.  Hot-spot congestion relief in public-area wireless networks , 2002, Proceedings Fourth IEEE Workshop on Mobile Computing Systems and Applications.

[19]  Michael D. Logothetis,et al.  A study on dynamic load balance for IEEE 802.11b wireless LAN , 2002 .