LiMoSense: live monitoring in dynamic sensor networks

We present LiMoSense, a fault-tolerant live monitoring algorithm for dynamic sensor networks. This is the first asynchronous robust average aggregation algorithm that performs live monitoring, i.e., it constantly obtains a timely and accurate picture of dynamically changing data. LiMoSense uses gossip to dynamically track and aggregate a large collection of ever-changing sensor reads. It overcomes message loss, node failures and recoveries, and dynamic network topology changes. The algorithm uses a novel technique to bound variable size. We present the algorithm and formally prove its correctness. We use simulations to illustrate its ability to quickly react to changes of both the network topology and the sensor reads, and to provide accurate information.

[1]  Johannes Gehrke,et al.  Gossip-based computation of aggregate information , 2003, 44th Annual IEEE Symposium on Foundations of Computer Science, 2003. Proceedings..

[2]  Paulo Sérgio Almeida,et al.  Fault-Tolerant Aggregation by Flow Updating , 2009, DAIS.

[3]  Miguel A. Mosteiro,et al.  Fault-tolerant aggregation: Flow-Updating meets Mass-Distribution , 2011, Distributed Computing.

[4]  Dongyan Xu,et al.  Robust computation of aggregates in wireless sensor networks: distributed randomized algorithms and analysis , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[5]  G. Asada,et al.  Wireless integrated network sensors: Low power systems on a chip , 1998, Proceedings of the 24th European Solid-State Circuits Conference.

[6]  Wei Hong,et al.  Proceedings of the 5th Symposium on Operating Systems Design and Implementation Tag: a Tiny Aggregation Service for Ad-hoc Sensor Networks , 2022 .

[7]  Srinivasan Seshan,et al.  Synopsis diffusion for robust aggregation in sensor networks , 2004, SenSys '04.

[8]  Kristofer S. J. Pister,et al.  Smart Dust: Communicating with a Cubic-Millimeter Computer , 2001, Computer.

[9]  Anil K. Bera,et al.  Efficient tests for normality, homoscedasticity and serial independence of regression residuals , 1980 .

[10]  Márk Jelasity,et al.  Gossip-based aggregation in large dynamic networks , 2005, TOCS.

[11]  Márk Jelasity,et al.  Epidemic-style proactive aggregation in large overlay networks , 2004, 24th International Conference on Distributed Computing Systems, 2004. Proceedings..

[12]  Idit Keidar,et al.  LiMoSense: live monitoring in dynamic sensor networks , 2011, Distributed Computing.

[13]  Rolf Stadler,et al.  Robust monitoring of network-wide aggregates through gossiping , 2007, IEEE Transactions on Network and Service Management.

[14]  Yin Zhang,et al.  Usenix Association 8th Usenix Symposium on Operating Systems Design and Implementation 87 Network Imprecision: a New Consistency Metric for Scalable Monitoring , 2022 .

[15]  Idit Keidar,et al.  Efficient Dynamic Aggregation , 2006, DISC.

[16]  Philippe Flajolet,et al.  Probabilistic Counting Algorithms for Data Base Applications , 1985, J. Comput. Syst. Sci..

[17]  Stephen P. Boyd,et al.  Gossip algorithms: design, analysis and applications , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[18]  Sandro Zampieri,et al.  Randomized consensus algorithms over large scale networks , 2007 .

[19]  Stephen P. Boyd,et al.  Randomized gossip algorithms , 2006, IEEE Transactions on Information Theory.

[20]  Devavrat Shah,et al.  Computing separable functions via gossip , 2005, PODC '06.

[21]  Gabor Karsai,et al.  Smart Dust: communicating with a cubic-millimeter computer , 2001 .

[22]  BabaogluOzalp,et al.  Gossip-based aggregation in large dynamic networks , 2005 .

[23]  Andrew S. Tanenbaum,et al.  Computer Networks , 1981 .