Browsing by Subject "Wireless sensor networks"

Now showing 1 - 3 of 3
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    Estimation with wireless sensor networks:censoring and quantization perspectives.
    (2011-07) Msechu, Eric James
    In the last decade there has been an increase in application areas for wireless sensor networks (WSNs), which can be attributed to the advances in the enabling sensor technology. These advances include integrated circuit miniaturization and mass-production of highly-reliable hardware for sensing, processing, and data storage at a lower cost. In many emerging applications, massive amounts of data are acquired by a large number of low-cost sensing devices. The design of signal processing algorithms for these WSNs, unlike in wireless networks designed for communications, face a different set of challenges due to resource constraints sensor nodes must adhere to. These include: (i) limited on-board memory for storage; (ii) limited energy source, typically based on irreplaceable battery cells; (iii) radios with limited transmission range; and (iv) stringent data rates either due to a need to save energy or due to limited radio-frequency bandwidth allocated to sensor networks. This work addresses distributed data-reduction at sensor nodes using a combination of measurement-censoring and measurement quantization. The WSN is envisioned for decentralized estimation of either a vector of unknown parameters in a maximum likelihood framework, or, for decentralized estimation of a random signal using Bayesian optimality criteria. Early research effort in data-reduction methods involved using a centralized computation platform directing selection of the most informative data and focusing computational and communication resources toward the selected data only. Robustness against failure of the central computation unit, as well as the need for iterative data-selection and data-gathering in some applications (e.g., real-time navigation systems), motivates a rethinking of the centralized data-selection approach. Recently, research focus has been on collaborative signal processing in sensor neighborhoods for the data-reduction step. It is in this spirit that investigation of methods for sensor node-based data reduction is pursued, where each sensor decides whether and what to communicate. The scope of this dissertation encompasses distributed algorithms for the measurementreduction step and algorithms for estimation which are amenable to either in-network or fusion center-based implementation using a WSN. Clearly-defined optimality criteria are used as the foundation for development of algorithms for data reduction and estimation. Performance analysis is provided and corroborated using simulated and real-world test cases illustrating the potential of the novel methods.
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    Privacy-preserving location-based services.
    (2010-05) Chow, Chi Yin
    Location-based services (LBS for short) providers require users' current locations to answer their location-based queries, e.g., range and nearest-neighbor queries. Revealing personal location information to potentially untrusted service providers could create privacy risks for users. To this end, our objective is to design a privacy-preserving framework for LBS where users can obtain LBS and preserve their location privacy. In this thesis, we propose privacy-preserving LBS frameworks for different environments: (1) client-server environments in Euclidean space (the Casper system), (2) client-server environments in road networks, (3) mobile peer-to-peer environments, and (4) location monitoring services in wireless sensor networks (the TinyCasper system). In general, these frameworks have two main modules, namely, location anonymization and privacy- aware query processing. The location anonymization module blurs an user's exact location into a cloaked area (or a cloaked road segment set in road network environments) that satisfies the user's privacy requirements. The proposed frameworks support the two most popular privacy requirements, k-anonymity, i.e., a user is indistinguishable among k users, and minimum area Amin (or minimum total length of a cloaked road segment set), i.e., the size of a cloaked area is at least Amin. The user is able to specify his/her privacy requirements in a privacy profile and change the privacy profile at any time. The privacy-aware query processing module is embedded inside a database server to provide LBS based on cloaked areas (or cloaked road segment sets). To prove the concept of our privacy-preserving LBS frameworks, we implement system prototypes for Casper and TinyCasper. For each proposed privacy-preserving LBS framework, we conduct extensive experiments to evaluate the performance of its location anonymization and privacy-aware query processing modules. All experiment results show that the proposed frameworks are scalable and efficient with respect to large numbers of users, large numbers of queries, and various privacy requirements, and they provide high quality services in terms of the accuracy of query answers and the query response time.
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    Ranging and positioning in wireless sensor networks.
    (2008-08) Srirangarajan, Seshan
    Ranging and positioning in wireless sensor networks refers to the ability to determine the positions of all nodes in a sensor network using the known positions of a few nodes called reference nodes and pairwise distance or range estimates between neighboring nodes. This is also known as the sensor network localization problem. In this thesis we first present two time-of arrival based localization algorithms for indoor quasi-static environments based on statistical modeling of the ultra-wideband multipath channel. A model of the multipath channel in the form of the signal return and noise characterization is derived, and utilized to distinguish signal components from noise. The first localization algorithm uses multiple (ranging) signal receptions at each reference node, to differentiate between line-of-sight and non-line-of-sight components, and to accurately estimate the position of the line-of-sight component in the received multipath signal. The second localization algorithm employs a time-of-arrival based algorithm to obtain pseudo range estimates which are then used in a spatial domain quasi-maximum likelihood method for location estimation. Furthermore, the associated range estimation error does not increase with increase in the transmitter-receiver range. We next present a distributed solution of the sensor network localization problem based on second-order cone programming relaxation. This algorithm is independent of the ranging technique being used and is computationally more efficient than most contemporary approaches, and scalable to networks with thousands of nodes. We show that the nodes can estimate their positions based on local information. Unlike previous approaches, we also consider the effect of inaccurate reference node positions. In the presence of reference node position errors, the localization is performed in three steps. First, the unlocalized nodes estimate their positions using information from their neighbors. In the second step, the reference nodes refine their positions using relative distance information exchanged with their neighbors and finally, the previously unlocalized nodes refine their position estimates. We demonstrate the convergence of the algorithm numerically. The simulation results, shown for both uniform and irregular network topologies, illustrate the robustness of the algorithm to reference node position and distance estimation errors. We also present the prototype implementation of a directional beacon based positioning algorithm using radio frequency signals. This algorithm allows each unlocalized node to compute its position with respect to a set of reference nodes which are equipped with rotating directional antenna. The directional beacon based algorithm eliminates the need for strict synchronization between the reference nodes and the unlocalized node. In contrast to time-of-arrival based positioning algorithms that rely on signal propagation time and bandwidth, the directional beacon based algorithm depends on the width of the antenna beampattern and the rotational speed of the directional antenna. We will show that these parameters can be optimized in a low cost solution while providing good position estimates. The system implementation is based on the GNU Radio software platform and the Universal Software Radio Peripheral as the hardware component. To deal with obstructed line-of-sight scenarios, we do not rely purely on the received signal strength and instead formulate a least squares problem to estimate the line-of-sight component in a multipath environment. These signal processing techniques yield a more accurate estimation of the bearing of the unlocalized node with respect to each of the reference nodes. We demonstrate the ability to obtain unlocalized node position estimates with sub-meter accuracy by transmitting a narrowband signal of 1 KHz bandwidth in the 2.4 GHz band. Finally, event detection scenarios in sensor networks are considered. The goal in these network deployments is to detect certain critical or emergency conditions with minimum possible delay. We propose a heuristic based sensor selection and a sequential detection procedure that significantly improves the detection speed, measured in terms of the number of measurements needed for detection. In the proposed model, the fusion center selects one sensor at a time for measurement while maximizing a greedy heuristic. Instead of collecting a fixed number of measurements, the fusion center collects one measurement at each time step, until by some sequential decision rule the collection stops and a decision is made. The sequential detection procedure significantly outperforms a non-sequential (or fixed sample size) detector in that it always needs fewer measurements on average to achieve the same detection performance. In addition, we derive a simplified heuristic under the Gaussian probabilistic model. It is seen that the simplified heuristic performs as good as or slightly better than the greedy heuristic. The greedy heuristic based sensor selection provides a general framework for probabilistic models where a simplified heuristic is difficult to obtain. (Abstract shortened by UMI.)