Pendharkar, Gayatri Arun, authorJayasumana, Anura P., advisorMaciejewski, Anthony A., committee memberMalaiya, Yashwant K., committee member2020-06-222020-06-222020https://hdl.handle.net/10217/208450Driven by increasing potency and decreasing cost/size of the electronic devices capable of sensing, actuating, processing and wirelessly communicating, the Internet of Things (IoT) is expanding into manufacturing plants, complex structures, and harsh environments with the potential to impact the way we live and work. Subnets of simple devices ranging from smart RFIDs to tiny sensors/actuators deployed in massive numbers forming complex 2-D surfaces, manifolds and complex 3-D physical spaces and fabrics will be a key constituent of this infrastructure. Smart Fabrics (SFs) are emerging with embedded IoT devices that have the ability to do things that traditional fabrics cannot, including sensing, storing, communicating, transforming data, and harvesting and conducting energy. These SFs are expected to have a wide range of applications in the near future in health monitoring, space stations, commercial building rooftops and more. With this innovative Smart Fabric technology at hand, there is a need to create algorithms for programming the smart nodes to facilitate communication, monitoring, and data routing within the fabric. Automatically detecting the location, shape, and other such physical characteristics will be essential but without resorting to localization techniques such as Global Positioning System (GPS), the size and cost of which may not be acceptable for many large-scale applications. Measuring the physical distances and obtaining geographical coordinates becomes infeasible for many IoT networks, particularly those deployed in harsh and complex environments. In SFs, the proximity between the nodes makes it impossible to deploy technology like GPS or Received Signal Strength Indicator (RSSI) for distance estimation. This thesis devises a Virtual Coordinate (VC) based method to identify the node positions and infer the shape of SFs with embedded grids of IoT devices. In various applications, we expect the nodes to communicate through randomly shaped fabrics in the presence of oddly-shaped holes. The geometry of node placement, the shape of the fabric, and dimensionality affect the identification, shape determination, and routing algorithms. The objective of this research is to infer the shape of fabric, holes, and other non-operational parts of the fabric with different grid placements. With the ability to construct the topology, efficient data routing can be achieved, damaged regions of fabric could be identified, and in general, the shape could be inferred for SFs with a wide range of sizes. Clothing and health monitoring being two essential segments of living, SFs that combines both would be a success in the textile market. SFs can be synthesized in space stations as compact sensing devices, assist in patient health monitoring, and also bring a spark to the showbiz. Identifying the position of different nodes/devices within SF grids is essential for applications and networking functions. We study and devise strategic methods for localization of SFs with rectangular grid placement of nodes using the VC approach, a viable alternative to geographical coordinates. In our system, VCs are computed using the hop distances to the anchors. For a full grid (no missing nodes), each grid node has predictable unique VCs. However, a SF grid may have holes/voids/obstacles that cause perturbations and distortion in VC pattern and may even result in non-unique VCs. Our shape inference method adaptively selects anchors from already localized nodes to compute VCs with the least amount of perturbation. We evaluate the proposed algorithm to simulate SF grids with varied sizes (i.e. number of nodes) and the number of voids. For each scenario, e.g. a SF grid with length X breadth dimensions - 19X19, 10% missing nodes, and 3 voids, we generate 60 samples of the grid with random possible placements and sizes of voids. Then, the localization algorithm is executed on these grids for all different scenarios. The final results measure the percentages of localized nodes as well as the total number of elected anchors required for the localization. We also investigate SF grids with triangular node placement and localization methods for the same. Additionally, parallelization techniques are implemented using an Message Parsing Interface (MPI) mechanism to run the simulations for rectangular and triangular grid SFs with efficient use of time and resources. To summarize, an algorithm was presented for the detection of voids in smart fabrics with embedded sensor nodes. It identifies the minimum set of node perturbations to be consistent with VCs and adaptively selects anchors to reduce uncertainty.born digitalmasters thesesengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.Text