Browsing by Subject "Sensor Networks"
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Item A congestion control scheme for wireless sensor networks(Texas A&M University, 2005-08-29) Xiong, YunliIn wireless sensor networks (WSN), nodes have very limited power due to hardware constraints. Packet losses and retransmissions resulting from congestion cost precious energy and shorten the lifetime of sensor nodes. This problem motivates the need for congestion control mechanisms in WSN. In this thesis, an observation of multiple non-empty queues in sensor networks is first reported. Other aspects affected by congestion like queue length, delay and packet loss are also studied. The simulation results show that the number of occupied queues along a path can be used to detect congestion. Based on the above result, a congestion control scheme for the transport layer is proposed in this thesis. It is composed of three parts: (i) congestion detection by tracking the number of non-empty queues; (ii) On-demand midway non-binary explicit congestion notification (CN) feedback; and (iii) Adaptive rate control based on additive increase and multiplicative decrease (AIMD). This scheme has been implemented in ns2. Extensive simulations have been conducted to evaluate it. Results show that it works well in mitigating and avoiding congestion and achieves good performance in terms of energy dissipation, latency and transmission effciency.Item Communication Algorithms for Wireless Ad Hoc Networks(2012-10-19) Viqar, SairaIn this dissertation we present deterministic algorithms for reliable and efficient communication in ad hoc networks. In the first part of this dissertation we give a specification for a reliable neighbor discovery layer for mobile ad hoc networks. We present two different algorithms that implement this layer with varying progress guarantees. In the second part of this dissertation we give an algorithm which allows nodes in a mobile wireless ad hoc network to communicate reliably and at the same time maintain local neighborhood information. In the last part of this dissertation we look at the distributed trigger counting problem in the wireless ad hoc network setting. We present a deterministic algorithm for this problem which is communication efficient in terms of the the maximum number of messages received by any processor in the system.Item Defenses against Covert-Communications in Multimedia and Sensor Networks(2012-11-29) Jainsky, Julien Sebastien 1981-Steganography and covert-communications represent a great and real threat today more than ever due to the evolution of modern communications. This doctoral work proposes defenses against such covert-communication techniques in two threatening but underdeveloped domains. Indeed, this work focuses on the novel problem of visual sensor network steganalysis but also proposes one of the first solutions against video steganography. The first part of the dissertation looks at covert-communications in videos. The contribution of this study resides in the combination of image processing using motion vector interpolation and non-traditional detection theory to obtain better results in identifying the presence of embedded messages in videos compared to what existing still-image steganalytic solutions would offer. The proposed algorithm called MoViSteg utilizes the specifics of video, as a whole and not as a series of images, to decide on the occurrence of steganography. Contrary to other solutions, MoViSteg is a video-specific algorithm, and not a repetitive still-image steganalysis, and allows for detection of embedding in partially corrupted sequences. This dissertation also lays the foundation for the novel study of visual sensor network steganalysis. We develop three different steganalytic solutions to the problem of covert-communications in visual sensor networks. Because of the inadequacy of the existing steganalytic solutions present in the current research literature, we introduce the novel concept of preventative steganalysis, which aims at discouraging potential steganographic attacks. We propose a set of solutions with active and passive warden scenarii using the material made available by the network. To quantify the efficiency of the preventative steganalysis, a new measure for evaluating the risk of steganography is proposed: the embedding potential which relies on the uncertainty of the image?s pixel values prone to corruption.Item Distributed Estimation in Sensor Networks with Modeling Uncertainty(2013-05-07) Zhou, QingA major issue in distributed wireless sensor networks (WSNs) is the design of efficient distributed algorithms for network-wide dissemination of information acquired by individual sensors, where each sensor, by itself, is unable to access enough data for reliable decision making. Without a centralized fusion center, network-wide reliable inferencing can be accomplished by recovering meaningful global statistics at each sensor through iterative inter-sensor message passing. In this dissertation, we first consider the problem of distributed estimation of an unknown deterministic scalar parameter (the target signal) in a WSN, where each sensor receives a single snapshot of the field. An iterative distributed least-squares (DLS) algorithm is investigated with and without the consideration of node failures. In particular, without sensor node failures it is shown that every instantiation of the DLS algorithm converges, i.e., consensus is reached among the sensors, with the limiting agreement value being the centralized least-squares estimate. With node failures during the iterative exchange process, the convergence of the DLS algorithm is still guaranteed; however, an error exists be- tween the limiting agreement value and the centralized least-squares estimate. In order to reduce this error, a modified DLS scheme, the M-DLS, is provided. The M-DLS algorithm involves an additional weight compensation step, in which a sensor performs a one-time weight compensation procedure whenever it detects the failure of a neighbor. Through analytical arguments and simulations, it is shown that the M-DLS algorithm leads to a smaller error than the DLS algorithm, where the magnitude of the improvement dependents on the network topology. We then investigate the case when the observation or sensing mode is only partially known at the corresponding nodes, perhaps, due to their limited sensing capabilities or other unpredictable physical factors. Specifically, it is assumed that the observation validity at a node switches stochastically between two modes, with mode I corresponding to the desired signal plus noise observation mode (a valid observation), and mode II corresponding to pure noise with no signal information (an invalid observation). With no prior information on the local sensing modes (valid or invalid), we introduce a learning-based distributed estimation procedure, the mixed detection-estimation (MDE) algorithm, based on closed-loop interactions between the iterative distributed mode learning and the target estimation. The online learning (or sensing mode detection) step re-assesses the validity of the local observations at each iteration, thus refining the ongoing estimation update process. The convergence of the MDE algorithm is established analytically, and the asymptotic performance analysis studies shows that, in the high signal-to-noise ratio (SNR) regime, the MDE estimation error converges to that of an ideal (centralized) estimator with perfect information about the node sensing modes. This is in contrast with the estimation performance of a naive average consensus based distributed estimator (with no mode learning), whose estimation error blows up with an increasing SNR.