Wireless Sensor Network Lifetime Analysis And Energy Efficient Techniques
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Wireless microsensor networks is one of the most important technologies for the 21st century. In distributed sensor networks energy-aware techniques are used to reduce energy consumption. Various sensor network applications have taken energy efficiency into consideration. This thesis report focuses on a new approach based on fuzzy logic systems to analyze the lifetime of a wireless sensor network. It demonstrates that a type-2 fuzzy membership function (MF), i.e., a Gaussian MF with uncertain standard deviation (std) is most appropriate to model a single node lifetime in wireless sensor networks. This research studies two basic sensor placement schemes: square-grid and hex-grid. Two fuzzy logic systems (FLSs): a singleton type-1 FLS and an interval type-2 FLS are designed to perform lifetime estimation of the sensor network. Simulation results show that the FLS offers a feasible method to analyze and estimate the sensor network lifetime and the interval type-2 FLS in which the antecedent membership functions are modeled as Gaussian with uncertain std outperforms the singleton type-1 FLS. In the later chapters, two energy efficient techniques in wireless sensor networks are presented: fuzzy optimization for distributed sensor deployment and spectrum efficient coding scheme for correlated non-binary sources in wireless sensor networks. For the sensor deployment topic, it is shown that given a finite number of sensors, optimizing the sensor deployment will provide sufficient sensor coverage and ameliorate the quality of communications. We apply fuzzy logic systems to optimize the sensor placement after an initial random deployment. We use the outage probability due to cochannel interference to evaluate the communication quality. Fenton-Wilkinson method is applied to approximate the sum of log-normal random variables. Our algorithm is compared against the existing distributed self-spreading algorithm. Simulation results show that our approach achieves faster and stabler deployment and maximizes the sensor coverage with minimum energy consumption. Outage probability, as a measure of communication quality gets effectively decreased in our algorithm but it was not taken into consideration in the distributed self-spreading algorithm. In the case of correlated binary sources, distributed source coding has been literally studied in information theory. However, data sources from real sensor networks are normally non-binary. We proposed a spectrum efficient coding scheme for correlated non-binary sources in sensor networks. Our approach constructs the codeword cosets for the interested source, taking advantage of statistical characters of the distinct observations from sensor nodes. The coset leaders are then transmitted via the channel and decoding is performed with the available side information. Simulations are carried out over independent and identically distributed (i.i.d) Gaussian sources and data collected from Xbow wireless sensor network test bed. Simulation results show that the proposed scheme performs at 0.5 - 1.5 dB from the Wyner-Ziv distortion bound. The wireless sensor technology can be applied to many real world applications. In this dissertation report, we present two applications when the sensor technology is used in the multi-target data fusion and underwater target positioning. For the multi-target data fusion, we consider the decision fusion of Rayleigh fluctuating targets in multi-radar sensor networks. Decision fusion and data fusion in Wireless Sensor Networks (WSNs) has been widely studied in order to save energy. Radar system as a special sensor network, when implemented for battlefield surveillance, faces bandwidth constraint in real-time applications instead of energy restriction. A reliable detection of multiple targets in clutter is perhaps the most important objective in such an echo-location system. In this work, we study the decision fusion rules of multiple fluctuating targets in multi-radar (MT-MR) sensor networks. The MT-MR decision fusion problem is modeled as a multi-input multi-output (MIMO) system. We assume that each radar makes binary decision for each target from the observation, i.e. if the target is present or not. We derive our MIMO fusion rules based on the target fluctuation model and compare against the optimal likelihood ratio method (LR), maximum ratio combiner (MRC) and equal gain combiner (EGC). Simulation results show that the MIMO fusion rules approach the optimal-LR and utperforms MRC and EGC at high signal to clutter ratio (SCR). In Chapter 6, we present a silent positioning scheme termed as UPS for underwater acoustic sensor networks. UPS relies on the time-difference of arrivals measured locally at a sensor to detect range differences from the sensor to four anchor nodes. These range differences are averaged over multiple beacon intervals before they are combined to estimate the 3D sensor location through trilateration. UPS requires no time-synchronization and provides location privacy at underwater vehicles/sensors whose locations need to be determined. To study the performance of UPS, we model the underwater acoustic channel as a modified Ultra Wide Band (UWB) S-V model: the arrival of each path cluster and paths within each cluster follow double Poisson distributions, and the multipath channel gain follows a Rician distribution. Based on this channel model, we perform both theoretical analysis and simulation study on the position error of UPS under acoustic fading channels. The obtained results indicate that UPS is an effective scheme for underwater vehicle/sensor self-positioning.