Matching Items (3)
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- All Subjects: Sensor Networks
- Creators: Zhang, Junshan
Description
The rapid advances in wireless communications and networking have given rise to a number of emerging heterogeneous wireless and mobile networks along with novel networking paradigms, including wireless sensor networks, mobile crowdsourcing, and mobile social networking. While offering promising solutions to a wide range of new applications, their widespread adoption and large-scale deployment are often hindered by people's concerns about the security, user privacy, or both. In this dissertation, we aim to address a number of challenging security and privacy issues in heterogeneous wireless and mobile networks in an attempt to foster their widespread adoption. Our contributions are mainly fivefold. First, we introduce a novel secure and loss-resilient code dissemination scheme for wireless sensor networks deployed in hostile and harsh environments. Second, we devise a novel scheme to enable mobile users to detect any inauthentic or unsound location-based top-k query result returned by an untrusted location-based service providers. Third, we develop a novel verifiable privacy-preserving aggregation scheme for people-centric mobile sensing systems. Fourth, we present a suite of privacy-preserving profile matching protocols for proximity-based mobile social networking, which can support a wide range of matching metrics with different privacy levels. Last, we present a secure combination scheme for crowdsourcing-based cooperative spectrum sensing systems that can enable robust primary user detection even when malicious cognitive radio users constitute the majority.
ContributorsZhang, Rui (Author) / Zhang, Yanchao (Thesis advisor) / Duman, Tolga Mete (Committee member) / Xue, Guoliang (Committee member) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Since the inception of Internet of Things (IoT) framework, the amount of interaction between electronic devices has tremendously increased and the ease of implementing software between such devices has bettered. Such data exchange between devices, whether between Node to Server or Node to Node, has paved way for creating new business models. Wireless Video Sensor Network Platforms are being used to monitor and understand the surroundings better. Both hardware and software supporting such devices have become much smaller and yet stronger to enable these. Specifically, the invention of better software that enable Wireless data transfer have become more simpler and lightweight technologies such as HTML5 for video rendering, Common Gateway Interface(CGI) scripts enabling interactions between client and server and WebRTC from Google for peer to peer interactions. The role of web browsers in enabling these has been vastly increasing.
Although HTTP is the most reliable and consistent data transfer protocol for such interactions, the most important underlying challenge with such platforms is the performance based on power consumption and latency in data transfer.
In the scope of this thesis, two applications using CGI and WebRTC for data transfer over HTTP will be presented and the power consumption by the peripherals in transmitting the data and the possible implications for those will be discussed.
Although HTTP is the most reliable and consistent data transfer protocol for such interactions, the most important underlying challenge with such platforms is the performance based on power consumption and latency in data transfer.
In the scope of this thesis, two applications using CGI and WebRTC for data transfer over HTTP will be presented and the power consumption by the peripherals in transmitting the data and the possible implications for those will be discussed.
ContributorsRentala, Sri Harsha (Author) / Reisslein, Martin (Thesis advisor) / Kitchen, Jennifer (Committee member) / McGarry, Michael (Committee member) / Arizona State University (Publisher)
Created2016
Description
Distributed inference has applications in fields as varied as source localization, evaluation of network quality, and remote monitoring of wildlife habitats. In this dissertation, distributed inference algorithms over multiple-access channels are considered. The performance of these algorithms and the effects of wireless communication channels on the performance are studied. In a first class of problems, distributed inference over fading Gaussian multiple-access channels with amplify-and-forward is considered. Sensors observe a phenomenon and transmit their observations using the amplify-and-forward scheme to a fusion center (FC). Distributed estimation is considered with a single antenna at the FC, where the performance is evaluated using the asymptotic variance of the estimator. The loss in performance due to varying assumptions on the limited amounts of channel information at the sensors is quantified. With multiple antennas at the FC, a distributed detection problem is also considered, where the error exponent is used to evaluate performance. It is shown that for zero-mean channels between the sensors and the FC when there is no channel information at the sensors, arbitrarily large gains in the error exponent can be obtained with sufficient increase in the number of antennas at the FC. In stark contrast, when there is channel information at the sensors, the gain in error exponent due to having multiple antennas at the FC is shown to be no more than a factor of 8/π for Rayleigh fading channels between the sensors and the FC, independent of the number of antennas at the FC, or correlation among noise samples across sensors. In a second class of problems, sensor observations are transmitted to the FC using constant-modulus phase modulation over Gaussian multiple-access-channels. The phase modulation scheme allows for constant transmit power and estimation of moments other than the mean with a single transmission from the sensors. Estimators are developed for the mean, variance and signal-to-noise ratio (SNR) of the sensor observations. The performance of these estimators is studied for different distributions of the observations. It is proved that the estimator of the mean is asymptotically efficient if and only if the distribution of the sensor observations is Gaussian.
ContributorsBanavar, Mahesh Krishna (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Spanias, Andreas (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Duman, Tolga (Committee member) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2010