Cost-aware monitoring of network-wide aggregates in wireless sensor networks

Abstract Motivated by applications of wireless sensor networks, there have been growing interests in monitoring large scale distributed systems. In these applications, we usually wish to monitor global system conditions defined as a function of network measurements. In this paper, we study optimal strategies for reactive monitoring to be employed for monitoring network-wide aggregates of sensor nodes’ measurements. Our primary concern in adopting such a monitoring mechanism is to reduce the communication cost which is the dominant factor of energy drain in wireless sensor networks. To adapt the structure of monitoring mechanism to the statistics of nodes’ measurements, we devise a simple yet efficient algorithm that is appropriate for a class of distribution functions. Towards this, we consider a sigmoid approximation of the CDF of the underlying event and cast the underlying design problem as a convex optimization problem. This allows us to propose an algorithm to set monitoring parameters in accordance to the statistics of the events measured by spatially scattered sensor nodes. Through simulation experiments, we illustrate that the proposed algorithm, referred to as SATA, can significantly reduce the communication overhead of the monitoring mechanism in sensor networks. Our results show that compared to heuristic methods, the cost of monitoring mechanism can be reduced significantly.

[1]  Jennifer Widom,et al.  Adaptive precision setting for cached approximate values , 2001, SIGMOD '01.

[2]  Dimitri P. Bertsekas,et al.  Nonlinear Programming , 1997 .

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

[4]  Pushpraj Shukla,et al.  Efficient Constraint Monitoring Using Adaptive Thresholds , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[5]  Ambuj K. Singh,et al.  Optimization Techniques for Reactive Network Monitoring , 2009, IEEE Transactions on Knowledge and Data Engineering.

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

[7]  Mohamed A. Sharaf,et al.  Balancing energy efficiency and quality of aggregate data in sensor networks , 2004, The VLDB Journal.

[8]  T. W. Lambert,et al.  Modern estimation of the parameters of the Weibull wind speed distribution for wind energy analysis , 2000 .

[9]  Baltasar Beferull-Lozano,et al.  On network correlated data gathering , 2004, IEEE INFOCOM 2004.

[10]  Danny Raz,et al.  Efficient reactive monitoring , 2002, IEEE J. Sel. Areas Commun..

[11]  Emiliano Miluzzo,et al.  People-centric urban sensing , 2006, WICON '06.

[12]  Assaf Schuster,et al.  A geometric approach to monitoring threshold functions over distributed data streams , 2006, Ubiquitous Knowledge Discovery.

[13]  Dan Olteanu,et al.  Forward node-selecting queries over trees , 2007, TODS.

[14]  Rolf Stadler,et al.  Robust monitoring of network-wide aggregates through gossiping , 2009, IEEE Trans. Netw. Serv. Manag..

[15]  Jennifer Widom,et al.  Adaptive filters for continuous queries over distributed data streams , 2003, SIGMOD '03.

[16]  John N. Tsitsiklis,et al.  Parallel and distributed computation , 1989 .

[17]  Rolf Stadler,et al.  Decentralized detection of global threshold crossings using aggregation trees , 2008, Comput. Networks.

[18]  Ahmad Khonsari,et al.  Cost-Aware Reactive Monitoring in Resource-Constrained Wireless Sensor Networks , 2009, 2009 IEEE Wireless Communications and Networking Conference.

[19]  Thomas G. Dietterich What is machine learning? , 2020, Archives of Disease in Childhood.

[20]  Y. Rahmat-Samii,et al.  Particle swarm optimization in electromagnetics , 2004, IEEE Transactions on Antennas and Propagation.

[21]  Rolf Stadler,et al.  H-GAP: estimating histograms of local variables with accuracy objectives for distributed real-time monitoring , 2010, IEEE Transactions on Network and Service Management.

[22]  Jeffrey Considine,et al.  Approximate aggregation techniques for sensor databases , 2004, Proceedings. 20th International Conference on Data Engineering.

[23]  Deborah Estrin,et al.  Embedding the Internet: introduction , 2000, Commun. ACM.

[24]  Rajeev Rastogi,et al.  Efficient Detection of Distributed Constraint Violations , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

[25]  Ahmad Khonsari,et al.  Distributed Threshold Selection for Aggregate Threshold Monitoring in Sensor Networks , 2009, 2009 6th IEEE Consumer Communications and Networking Conference.

[26]  Rolf Stadler,et al.  Gossiping for threshold detection , 2009, 2009 IFIP/IEEE International Symposium on Integrated Network Management.

[27]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[28]  Christopher Olston,et al.  Distributed top-k monitoring , 2003, SIGMOD '03.

[29]  Ahmad Khonsari,et al.  Flooding-Assisted Threshold Assignment for Aggregate Monitoring in Sensor Networks , 2009, ICDCN.

[30]  Graham Cormode,et al.  Communication-efficient distributed monitoring of thresholded counts , 2006, SIGMOD Conference.