Keywords and Phrases
Data compression; IoT; Routing; Target tracking; Wireless sensor network
“This research proposes several innovative approaches to collect data efficiently from large scale WSNs. First, a Z-compression algorithm has been proposed which exploits the temporal locality of the multi-dimensional sensing data and adapts the Z-order encoding algorithm to map multi-dimensional data to a one-dimensional data stream. The extended version of Z-compression adapts itself to working in low power WSNs running under low power listening (LPL) mode, and comprehensively analyzes its performance compressing both real-world and synthetic datasets. Second, it proposed an efficient geospatial based data collection scheme for IoTs that reduces redundant rebroadcast of up to 95% by only collecting the data of interest. As most of the low-cost wireless sensors won’t be equipped with a GPS module, the virtual coordinates are used to estimate the locations. The proposed work utilizes the anchor-based virtual coordinate system and DV-Hop (Distance vector of hops to anchors) to estimate the relative location of nodes to anchors. Also, it uses circle and hyperbola constraints to encode the position of interest (POI) and any user-defined trajectory into a data request message which allows only the sensors in the POI and routing trajectory to collect and route. It also provides location anonymity by avoiding using and transmitting GPS location information. This has been extended also for heterogeneous WSNs and refined the encoding algorithm by replacing the circle constraints with the ellipse constraints. Last, it proposes a framework that predicts the trajectory of the moving object using a Sequence-to-Sequence learning (Seq2Seq) model and only wakes-up the sensors that fall within the predicted trajectory of the moving object with a specially designed control packet. It reduces the computation time of encoding geospatial trajectory by more than 90% and preserves the location anonymity for the local edge servers”--Abstract, page iv.
Madria, Sanjay Kumar
Das, Sajal K.
Luo, Tony Tie
Zawodniok, Maciej Jan, 1975-
Ph. D. in Computer Science
Missouri University of Science and Technology
Journal article titles appearing in thesis/dissertation
- Efficient Z-order encoding based multi-modal data compression in WSNS
- Multi-modal Z-compression for high speed data streaming and low power sensor networks
- Efficient geospatial data collection in IoT networks for mobile edge computing
- Efficient data collection in IoT networks using trajectory encoded with geometric shapes
- An efficient moving object tracking framework for WSNS using sequence-to-sequence learning model
- A WSN testbed for Z-order encoding based multi-modal sensor data compression
- A testbed for data routing in low-power WSNS using DV-Hop based trajectory encoding algorithm
xx, 274 pages
© 2021 Xiaofei Cao, All rights reserved.
Dissertation - Open Access
Electronic OCLC #
Cao, Xiaofei, "Data and resource management in wireless networks via data compression, GPS-free dissemination, and learning" (2021). Doctoral Dissertations. 3070.