Real-Time Data Reduction Scheme for Efficient WSN Systems

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dc.contributor.advisorCavallaro, Joseph R.en_US
dc.creatorMohamed, Nadya A.en_US
dc.date.accessioned2023-01-04T16:40:17Zen_US
dc.date.available2023-01-04T16:40:17Zen_US
dc.date.created2022-12en_US
dc.date.issued2022-11-30en_US
dc.date.submittedDecember 2022en_US
dc.date.updated2023-01-04T16:40:17Zen_US
dc.description.abstractWith the rapid development of various wireless sensors and Internet-of-things devices, a massive amount of time-varying data is generated and continuously streamed to the cloud. Given the transmission latency and cost, real-time or near real-time data analytics using the conventional cloud-centric approach are challenging. Hierarchical computing models have been introduced to reduce the amount of data uploaded to the cloud and enable timely data analysis at different network endpoints. Edge computing enables analytics to be physically close to the data source at sensing and aggregation nodes. The limited computational resources on the sensor nodes and aggregation nodes motivate the development of lightweight, energy-efficient algorithms. In this thesis work, we develop a two-tier data reduction framework that exploits the spatial and temporal characteristics of the monitored environment using Deep Neural Network (DNN) architectures. Dual prediction (DP), outlier detection, and data compression (DC) techniques are adopted to reduce the number of transmissions while ensuring the quality of the collected sensor data. The dual prediction exploits the temporal correlation at the sensing nodes to minimize communication between sensor nodes and the cluster aggregation node. At the same time, outlier detection uses the spatiotemporal characteristic among multiple sensor nodes to enable real-time event detection. We further integrate data compression to reduce redundant data streams between the aggregation node and the cloud. Moreover, to accelerate the computations of the proposed DNN-based framework while considering the limited computational resources and power consumption, we introduce a novel unified CORDIC-based computing kernel. Through experiments in an FPGA-based testbed, we show that the proposed scheme could achieve up to a 70% reduction in streamed traffic. In addition, the sensor data could be recovered at the receiving network endpoint with minimal accuracy loss.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationMohamed, Nadya A.. "Real-Time Data Reduction Scheme for Efficient WSN Systems." (2022) Diss., Rice University. <a href="https://hdl.handle.net/1911/114205">https://hdl.handle.net/1911/114205</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/114205en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectWireless sensor networken_US
dc.subjectdata reductionen_US
dc.subjectdata compressionen_US
dc.subjectdata predictionen_US
dc.subjectoutlier detectionen_US
dc.subjectrecurrent neural networksen_US
dc.subjectlong short-term memoryen_US
dc.subjectserial-parallel computationen_US
dc.subjectsystolicen_US
dc.subjectfixed-point arithmeticen_US
dc.subjectCORDICen_US
dc.subjectFPGAen_US
dc.subjectacceleratoren_US
dc.titleReal-Time Data Reduction Scheme for Efficient WSN Systemsen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentElectrical and Computer Engineeringen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophyen_US
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