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Selective data gathering in community sensor networks

Citation

Faulkner, Matthew Nicholas (2014) Selective data gathering in community sensor networks. Master's thesis, California Institute of Technology. http://resolver.caltech.edu/CaltechTHESIS:04102014-131741107

Abstract

Smartphones and other powerful sensor-equipped consumer devices make it possible to sense the physical world at an unprecedented scale. Nearly 2 million Android and iOS devices are activated every day, each carrying numerous sensors and a high-speed internet connection. Whereas traditional sensor networks have typically deployed a fixed number of devices to sense a particular phenomena, community networks can grow as additional participants choose to install apps and join the network. In principle, this allows networks of thousands or millions of sensors to be created quickly and at low cost. However, making reliable inferences about the world using so many community sensors involves several challenges, including scalability, data quality, mobility, and user privacy.

This thesis focuses on how learning at both the sensor- and network-level can provide scalable techniques for data collection and event detection. First, this thesis considers the abstract problem of distributed algorithms for data collection, and proposes a distributed, online approach to selecting which set of sensors should be queried. In addition to providing theoretical guarantees for submodular objective functions, the approach is also compatible with local rules or heuristics for detecting and transmitting potentially valuable observations. Next, the thesis presents a decentralized algorithm for spatial event detection, and describes its use detecting strong earthquakes within the Caltech Community Seismic Network. Despite the fact that strong earthquakes are rare and complex events, and that community sensors can be very noisy, our decentralized anomaly detection approach obtains theoretical guarantees for event detection performance while simultaneously limiting the rate of false alarms.

Item Type:Thesis (Master's thesis)
Subject Keywords:Sensor Networks, Community Sensing, Sensor Selection, Submodular Optimization
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Computer Science
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Heaton, Thomas H. (advisor)
  • Krause, R. Andreas (co-advisor)
  • Chandy, K. Mani (co-advisor)
Thesis Committee:
  • None, None
Defense Date:1 May 2014
Non-Caltech Author Email:mnfaulk (AT) gmail.com
Record Number:CaltechTHESIS:04102014-131741107
Persistent URL:http://resolver.caltech.edu/CaltechTHESIS:04102014-131741107
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:8185
Collection:CaltechTHESIS
Deposited By: Matthew Faulkner
Deposited On:28 Apr 2014 21:56
Last Modified:13 Apr 2015 19:56

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