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A masters thesis focussing on achieving improvements in transmission reliability and energy efficiency for a battery-powered wireless sensor node on the edge of an industrial heterogeneous wireless network that consists predominantly of mains-powered nodes. A router-switching technique is proposed to allow the sensor node to make gains in transmission reliability and energy efficiency by taking advantage of the scenario where multiple wireless routers are in range and switching between them, instead of only being able to transmit to one router. The research involves simulation of a number of network scenarios where the router-switching technique is enabled and disabled, to measure the advantage gained for the sensor in terms of its functional lifetime. The simulation is based on an abstract model that focusses on the edge of the mains-powered area of the network, where the battery-powered sensor is located. The simulation results show that for many cases, router-switching provides a higher level of transmission reliability and lower levels of energy consumption than the scenario where router-switching is disabled, as well as improvements in data loss rates.

This thesis looks at the problem of optimally placing binary directional proximity markers to assist a robot as it navigates waypoints through an environment. A simple planar fiducial marker is developed to serve as the binary directional proximity marker. A scoring function is proposed for marker placement as well as a method for random generation of hallway maps. Several common metaheuristic algorithms are run to find optimal marker placements with respect to the scoring function for a number of randomly generated hallway maps. From these results, placements are then evaluated by physical experimentation on an iRobot Create equipped with relatively inexpensive webcams. / Graduate

Emerging body-wearable sensor devices for continuous health monitoring are severely energy constrained and yet required to offer high communication reliability under fluctuating channel conditions. This thesis aims at investigating the opportunities and challenges in the use of dynamic radio transmit power control for prolonging the lifetime of such devices. We first present extensive empirical evidence that the wireless link quality can change rapidly in body area networks, and a fixed transmit power results in either wasted energy (when the link is good) or low reliability (when the link is bad). We then propose a class of schemes feasible for practical implementation that adapt transmit power in real-time based on feedback information from the receiver. We show conservative, balanced, and aggressive adaptations of our scheme that progressively achieve higher energy savings of 14%-30% in exchange for higher potential packet losses (up to 10%). We also provide guidelines on how the parameters can be tuned to achieve the desired trade-off between energy savings and reliability within the chosen operating environment. Finally, we implement and profile our scheme on a MicaZ mote based platform, demonstrating that energy savings are achievable even with imperfect feedback information, and report preliminary results on the ultra-low-power integrated healthcare monitoring platform from our collaborating partner Toumaz Technology. In conclusion, our work shows adaptive radio transmit power control as a low-cost way of extending the battery-life of severely energy constrained body wearable devices, and opens the door to further optimizations customized for specific deployment scenarios.

In this thesis we present LaSLAT, a sensor network algorithm thatsimultaneously localizes sensors, calibrates sensing hardware, andtracks unconstrained moving targets using only range measurementsbetween the sensors and the target. LaSLAT is based on a Bayesian filter, which updates a probabilitydistribution over the quantities of interest as measurementsarrive. The algorithm is distributable, and requires only a constantamount of space with respect to the number of measurementsincorporated. LaSLAT is easy to adapt to new types of hardware and newphysical environments due to its use of intuitive probabilitydistributions: one adaptation demonstrated in this thesis uses amixture measurement model to detect and compensate for bad acousticrange measurements due to echoes.We also present results from a centralized Java implementation ofLaSLAT on both two- and three-dimensional sensor networks in whichranges are obtained using the Cricket ranging system. LaSLAT is ableto localize sensors to within several centimeters of their groundtruth positions while recovering a range measurement bias for eachsensor and the complete trajectory of the mobile.

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.