On Rendezvous in Mobile Sensing Networks

A rendezvous is a temporal and spatial vicinity of two sensors. In this chapter, we investigate rendezvous in the context of mobile sensing systems. We use an air quality dataset obtained with the OpenSense monitoring network to explore rendezvous properties for carbon monoxide, ozone, temperature, and humidity processes. Temporal and spatial locality of a physical process impacts the number of rendezvous between sensors, their duration, and their frequency. We introduce a rendezvous connection graph and explore the trade-off between locality of a process and the amount of time needed for the graph to be connected. Rendezvous graph connectivity has many potential use cases, such as sensor fault detection. We successfully apply the proposed concepts to track down faulty sensors and to improve sensor calibration in our deployment.

[1]  Yoon-Hwa Choi,et al.  Neighbor-Based Malicious Node Detection in Wireless Sensor Networks , 2012 .

[2]  Yu-Chee Tseng,et al.  Exploiting spatial correlation at the link layer for event-driven sensor networks , 2012, Int. J. Sens. Networks.

[3]  Karl Aberer,et al.  A middleware for fast and flexible sensor network deployment , 2006, VLDB.

[4]  Volkan Cevher,et al.  Sensor array calibration via tracking with the extended Kalman filter , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[5]  Weihua Zhuang,et al.  DCS: Distributed Asynchronous Clock Synchronization in Delay Tolerant Networks , 2012, IEEE Transactions on Parallel and Distributed Systems.

[6]  Md. Rafiqul Islam,et al.  Anomaly Detection in Wireless Sensor Network , 1969, J. Networks.

[7]  J. Gulliver,et al.  A review of land-use regression models to assess spatial variation of outdoor air pollution , 2008 .

[8]  Tarek F. Abdelzaher,et al.  Towards optimal sleep scheduling in sensor networks for rare-event detection , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[9]  Miao Xie,et al.  Anomaly Detection in Wireless Sensor Networks , 2013 .

[10]  Lothar Thiele,et al.  OpenSense: open community driven sensing of environment , 2010, IWGS '10.

[11]  Emiliano Miluzzo,et al.  CaliBree: A Self-calibration System for Mobile Sensor Networks , 2008, DCOSS.

[12]  Mingyan Liu,et al.  Network coverage using low duty-cycled sensors: random & coordinated sleep algorithms , 2004, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.

[13]  Lothar Thiele,et al.  PermaDAQ: A scientific instrument for precision sensing and data recovery in environmental extremes , 2009, 2009 International Conference on Information Processing in Sensor Networks.

[14]  Tian He,et al.  Exploring In-Situ Sensing Irregularity in Wireless Sensor Networks , 2007, IEEE Transactions on Parallel and Distributed Systems.

[15]  Amy L. Murphy,et al.  Monitoring heritage buildings with wireless sensor networks: The Torre Aquila deployment , 2009, 2009 International Conference on Information Processing in Sensor Networks.

[16]  Do Young Eun,et al.  Characterizing link connectivity for opportunistic mobile networking: Does mobility suffice? , 2013, 2013 Proceedings IEEE INFOCOM.

[17]  S.Sumithra,et al.  Rendezvous Planning in Wireless Sensor Networks with Mobile Elements , 2015 .

[18]  Zoran Obradovic,et al.  Examination of the influence of data aggregation and sampling density on spatial estimation. , 2000 .

[19]  B. R. Badrinath,et al.  Cleaning and querying noisy sensors , 2003, WSNA '03.

[20]  Prasant Mohapatra,et al.  Power conservation and quality of surveillance in target tracking sensor networks , 2004, MobiCom '04.

[21]  Lothar Thiele,et al.  Model-Driven Accuracy Bounds for Noisy Sensor Readings , 2013, 2013 IEEE International Conference on Distributed Computing in Sensor Systems.

[22]  Sunggu Lee,et al.  Optimal Stop Points for Data Gathering in Sensor Networks with Mobile Sinks , 2012 .

[23]  Ke Shi Semi-Probabilistic Routing in Intermittently Connected Mobile Ad Hoc Networks , 2007, 2007 Second International Conference on Communications and Networking in China.

[24]  József Balogh,et al.  On k-coverage in a mostly sleeping sensor network , 2004, MobiCom '04.

[25]  J. Navarro-Pedreño Numerical Methods for Least Squares Problems , 1996 .

[26]  Miodrag Potkonjak,et al.  Sleeping Coordination for Comprehensive Sensing Using Isotonic Regression and Domatic Partitions , 2006, Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications.

[27]  Deborah Estrin,et al.  The design and implementation of a self-calibrating distributed acoustic sensing platform , 2006, SenSys '06.

[28]  Lothar Thiele,et al.  On-the-Fly Calibration of Low-Cost Gas Sensors , 2012, EWSN.

[29]  Giuseppe Lo Re,et al.  Detecting faulty wireless sensor nodes through Stochastic classification , 2011, 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).

[30]  Stefan Schmid,et al.  Algorithmic models for sensor networks , 2006, Proceedings 20th IEEE International Parallel & Distributed Processing Symposium.

[31]  Boi Faltings,et al.  Sensing the Air We Breathe - The OpenSense Zurich Dataset , 2021, AAAI.

[32]  Ashish Goel,et al.  Set k-cover algorithms for energy efficient monitoring in wireless sensor networks , 2003, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.

[33]  Guoliang Xing,et al.  Exploiting sensing diversity for confident sensing in wireless sensor networks , 2011, 2011 Proceedings IEEE INFOCOM.

[34]  Kai Wang,et al.  Rendezvous Data Collection Using a Mobile Element in Heterogeneous Sensor Networks , 2012, Int. J. Distributed Sens. Networks.