abstract: Distributed wireless sensor networks (WSNs) have attracted researchers recently due to their advantages such as low power consumption, scalability and robustness to link failures. In sensor networks with no fusion center, consensus is a process where all the sensors in the network achieve global agreement using only local transmissions. In this dissertation, several consensus and consensus-based algorithms in WSNs are studied. Firstly, a distributed consensus algorithm for estimating the maximum and minimum value of the initial measurements in a sensor network in the presence of communication noise is proposed. In the proposed algorithm, a soft-max approximation together with a non-linear average consensus algorithm is used. A design parameter controls the trade-off between the soft-max error and convergence speed. An analysis of this trade-off gives guidelines towards how to choose the design parameter for the max estimate. It is also shown that if some prior knowledge of the initial measurements is available, the consensus process can be accelerated. Secondly, a distributed system size estimation algorithm is proposed. The proposed algorithm is based on distributed average consensus and L2 norm estimation. Different sources of error are explicitly discussed, and the distribution of the final estimate is derived. The CRBs for system size estimator with average and max consensus strategies are also considered, and different consensus based system size estimation approaches are compared. Then, a consensus-based network center and radius estimation algorithm is described. The center localization problem is formulated as a convex optimization problem with a summation form by using soft-max approximation with exponential functions. Distributed optimization methods such as stochastic gradient descent and diffusion adaptation are used to estimate the center. Then, max consensus is used to compute the radius of the network area. Finally, two average consensus based distributed estimation algorithms are introduced: distributed degree distribution estimation algorithm and algorithm for tracking the dynamics of the desired parameter. Simulation results for all proposed algorithms are provided. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2017

Smart home automation is the dwelling bridge of smart grid technology, as it integrates the modern home appliances power consumption information over communication networks in the smart grid system. Among all the appliances, Heating, Ventilation and Cooling (HVAC) systems is one of the most primary concerns. Since a great amount of power consumption is contributed by these HVAC systems. Traditionally, HVAC systems run at a fixed schedule without automatic monitoring and control systems, which causes load variation, fluctuations in the electricity demand and inefficient utility operation. In this thesis, we propose a Finite State Machine (FSM) system to model the air condition working status to acquire the relationship between temperature changing and cooling/heating duration. Finally, we introduce the Zigbee communciation protocol into the model, the performance analysis of the impact of end-to-end delay over HVAC systems is presented.

Solar and other renewable power sources are becoming an integral part of the electrical grid in the United States. In the Southwest US, solar and wind power plants already serve over 20% of the electrical load during the daytime on sunny days in the Spring. While solar power produces fewer emissions and has a lower carbon footprint than burning fossil fuels, solar power is only generated during the daytime and it is variable due to clouds blocking the sun. Electric utilities that are required to maintain a reliable electricity supply benefit from anticipating the schedule of power output from solar power plants. Forecasting the irradiance reaching the ground, the primary input to a solar power forecast, can help utilities understand and respond to the variability. This dissertation will explore techniques to forecast irradiance that make use of data from a network of sensors deployed throughout Tucson, AZ. The design and deployment of inexpensive sensors used in the network will be described. We will present a forecasting technique that uses data from the sensor network and outperforms a reference persistence forecast for one minute to two hours in the future. We will analyze the errors of this technique in depth and suggest ways to interpret these errors. Then, we will describe a data assimilation technique, optimal interpolation, that combines estimates of irradiance derived from satellite images with data from the sensor network to improve the satellite estimates. These improved satellite estimates form the base of future work that will explore generating forecasts while continuously assimilating new data.

The thesis deals with sensor networks and their localization. First section describes sensor networks in general and explains problems of localization and routing. The second part deals with localization using anchors. The principal of the Dv-hop and DV-Distance are there described in detail. These algorithms are used for simulations in Matlab in the main part of this thesis. According to the simulations the most sufficient number of sensors for good localization is estimated.

The need to achieve extended service life by battery powered Wireless Sensor Networks (WSNs) requires new concepts and technqiues beyond the state-of-the-art low-power designs based on fixed hardware platforms or energy-efficient protocols. This thesis investigates reconfiguration techniques that enable sensor hardware to adapt its energy consumption to external dynamics, by means of Dynamic Voltage Scaling (DVS), Dynamic Modulation Scaling (DMS), and other related concepts. For sensor node-level reconfiguration, an integration of DVS and DMS techniques was proposed to minimize the total energy consumption. A dynamic time allocation algorithm was developed, demonstrating an average of 55% energy reduction. For network-level reconfiguration, a node activation technique was presented to reduce the cost of recharging energy-depleted sensor nodes. Network operation combined with node activation was modeled as a stochastic decision process, where the activation decisions directly affected the energy efficiency of the network. An experimental test bed based on the Imote2 sensor node platform was realized, which demonstrated energy reduction of up to 50%. Such energy saving can be effectively translated into prolonged service life of the sensor network.

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Wireless sensor network technologies have gained significant popularity in home automation due to their scalability, system mobility, wireless connectivity, inexpensive and easy commissioning. Thread, a new wireless protocol aiming for home automation, is proposed by Google Nest and standardized by Thread Group. This thesis presents a thorough experimental evaluation of Thread wireless protocol with the hardware platform from NXP. The test plan, implementation, and analysis of the experiments is discussed in details, including signal coverage, unicast and multicast latency, reliability, and availability. Furthermore, a system level model considering the delay in different layers for the latency of Thread mesh network is presented, and validated by the experimental results. Finally, a friendly tool was developed for installers to estimate the latency of Thread mesh network.

Master of Science / Department of Computing and Information Sciences / Gurdip Singh / TinyOS is an open-source component based operating system designed for highly memory constrained wireless embedded sensor network. TinyOS includes interfaces and components for communication management, routing and data acquisition tools to be refined further for custom applications. This project aims at developing a system which detects overlapping paths for data collection in different applications in the network and utilizing that information for efficient data acquisition. This prevents a reconfiguring the entire network of wireless sensor nodes (called motes) for each new application request. The application for initial or first data acquisition request tries to build the tree architecture on motes in the network where each node in the tree knows its immediate parent and children. The application builds the tree routed at the base station for the initial request and each intermediate node sends data to its parent when the data request is made. Each base station can request Light, Temperature and Passive Infrared sensory data from all or a subset of motes present in the system. When a new base station comes and connects to the network through a mote/node in the tree, the system reconfigures only those parts of the tree built in the initial phase which do not overlap with the tree required for the new base station as the root, all the other overlapping parts of the tree are left unchanged. We present experimental result to illustrate the efficiency of the approach.

abstract: Fully distributed wireless sensor networks (WSNs) without fusion center have advantages such as scalability in network size and energy efficiency in communications. Each sensor shares its data only with neighbors and then achieves global consensus quantities by in-network processing. This dissertation considers robust distributed parameter estimation methods, seeking global consensus on parameters of adaptive learning algorithms and statistical quantities. Diffusion adaptation strategy with nonlinear transmission is proposed. The nonlinearity was motivated by the necessity for bounded transmit power, as sensors need to iteratively communicate each other energy-efficiently. Despite the nonlinearity, it is shown that the algorithm performs close to the linear case with the added advantage of power savings. This dissertation also discusses convergence properties of the algorithm in the mean and the mean-square sense. Often, average is used to measure central tendency of sensed data over a network. When there are outliers in the data, however, average can be highly biased. Alternative choices of robust metrics against outliers are median, mode, and trimmed mean. Quantiles generalize the median, and they also can be used for trimmed mean. Consensus-based distributed quantile estimation algorithm is proposed and applied for finding trimmed-mean, median, maximum or minimum values, and identification of outliers through simulation. It is shown that the estimated quantities are asymptotically unbiased and converges toward the sample quantile in the mean-square sense. Step-size sequences with proper decay rates are also discussed for convergence analysis. Another measure of central tendency is a mode which represents the most probable value and also be robust to outliers and other contaminations in data. The proposed distributed mode estimation algorithm achieves a global mode by recursively shifting conditional mean of the measurement data until it converges to stationary points of estimated density function. It is also possible to estimate the mode by utilizing grid vector as well as kernel density estimator. The densities are estimated at each grid point, while the points are updated until they converge to a global mode. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2017