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

Doctor of Philosophy / Department of Biological & Agricultural Engineering / Naiqian Zhang / A three-tier wireless sensor network (WSN) was developed and deployed to remotely monitor suspended sediment concentration and stream velocity in real-time. Two years of field experiments have demonstrated the achievement of such capabilities. But several weak points emerged and required essential performance improvement and additional research on the radio propagation mechanism within the original three-tier WSN. In the original three-tier WSN, long time delay, potential data loss, and limited network throughput all restricted the network transmission performance. Upon the above issues, the transmission delay was reduced through shortening the raw data storage buffer and the data packet length; the data loss rate was decreased by adopting a mechanism using semaphores and adding feedback after data transmission; the network throughput was enlarged through the event- and time-driven scheduling method. In order to find a long-range wireless transmission method as an alternative to the commercial cellular service used in the original WSN, a central station using meteor burst communication (MBC) technology was developed and deployed. During an 8-month field test, it was capable of performing long distance communication with a low data loss rate and transmission error rate. But due to unstable availability of the meteor trails, the MBC network throughput was constrained. To reduce in-situ maintenance, over-the-air programming was implemented. Thus, programs running in the central station and the gateway station can be updated remotely. To investigate the radio propagation in densely vegetative areas, a 2.4 GHz radio propagation path loss model was derived to predict the short-range path loss from the path loss in the open area and the path loss due to dense vegetation. In addition, field experiments demonstrated that ambient air temperature, relative humidity, and heavy rainfall could also affect wireless signal strength.

ITC/USA 2010 Conference Proceedings / The Forty-Sixth Annual International Telemetering Conference and Technical Exhibition / October 25-28, 2010 / Town and Country Resort & Convention Center, San Diego, California / The requirements for a seismic telemetry-network are even more stringent than the well known problems of sensor networks. Existing medium access control (MAC) protocols suggest reducing energy consuming network activity by reducing costly transmissions and idle listening. Furthermore, it is required to set up communication patterns in different priority levels as well as ensuring fast handling of critical events. A protocol is proposed that operates with two parallel sets of time schedules in a time-division-multiple-access (TDMA) sense of periodic activity for listening and for transmitting. Synchronization packets sent from a central base station ensure optimal response times.

ITC/USA 2007 Conference Proceedings / The Forty-Third Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2007 / Riviera Hotel & Convention Center, Las Vegas, Nevada / This paper investigates the sum-rate capacity of a cognitive multiple access (MAC) sensor network. The multiple access network consists of K sensors communicating to a common base station. Outside of the network exists another user of the radio spectrum. Each sensor of the MAC network is aware (i.e. cognitive) of this user, denoted the primary user, and transmits in a manner to avoid any interference to this user. No interference transmission is achieved using the dirty-paper coding technique. The sum-rate capacity is the theoretical maximum of the sum of the simultaneously achievable rates of each sensor within the network. Using a recently derived iterative algorithm, we quantify the sum-rate capacity of this network and investigate its behavior as a function of the number of sensors, cognitive signal-to-noise ratio (CSNR) and primary SNR (PSNR) in a Rayleigh fading environment. We also derive bounds and scaling results for the ergodic sum-rate capacity.

Seismic tomography is a technique for illuminating the physical dynamics of the Earth by seismic waves generated by earthquakes or explosions. In both industry and academia, the seismic exploration does not yet have the capability of imaging seismic tomography in real-time and with high resolution. There are two reasons. First, at present raw seismic data are typically recorded on sensor nodes locally then are manually collected to central observatories for post processing, and this process may take months to complete. Second, high resolution tomography requires a large and dense sensor network, the real-time data retrieval from a network of large-amount wireless seismic nodes to a central server is virtually impossible due to the sheer data amount and resource limitations. This limits our ability to understand earthquake zone or volcano dynamics. To obtain the seismic tomography in real-time and high resolution, a new design of sensor network system for raw seismic data processing and distributed tomography computation is demanded. Based on these requirements, three research aspects are addressed in this work. First, a distributed multi-resolution evolving tomography computation algorithm is proposed to compute tomography in the network, while avoiding costly data collections and centralized computations. Second, InsightTomo, an end-to-end sensor network emulation platform, is designed to emulate the entire process from data recording to tomography image result delivery. Third, a sensor network testbed is presented to verify the related methods and design in real world. The design of the platform consists of hardware, sensing and data processing components.