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Machine Learning for IoT Applications:Sensor Data Analytics and Data Reduction TechniquesAuthor: Adarsh Pal Singh Date: 2020-06-30 Report no: IIIT/TH/2020/64 Advisor:Sachin Chaudhari AbstractInternet of things (IoT) devices are steadily becoming mainstream in our lives and simplifying themanner in which we perform everyday tasks. With billions of smart devices, embedded with all kinds oftransducers, communicating with each other and us humans via the internet, data is being generated atan alarming rate. Consequently, data analytics has become an integral part of the IoT ecosystem. SeveralIoT applications, including those that come under the umbrella of smart home, intelligent transportationsystem, smart healthcare and smart grid, rely on machine learning (ML) to gain inference from sensordata and aid in decision making. Apart from user applications, ML is also used in the lower layers,including the network layer and the medium access control (MAC) layer, to improve the efficiency ofIoT networks.The focus of this thesis is on the application of ML in making IoT systems smarter and more effi-cient. More specifically, a new ML-based paradigm for occupancy estimation is introduced along withan ML-based data transmission reduction scheme that is validated for occupancy applications. Havingknowledge of the occupancy status of rooms can help in automating lighting systems, cooling/heatingsystems as well as a myriad of other appliances throughout the building. The proposed occupancyestimation paradigm employs ML algorithms on a deployment of multiple non-intrusive sensor nodesin a room. Two new datasets were created in the process which are thoroughly investigated by ap-plying various preprocessing, feature engineering and ML techniques. Multiple performance metricslike accuracy, F1 score and confusion matrix are reported for a variety of homogeneous and heteroge-neous feature combinations. A novel ML-based data transmission reduction scheme is also proposed forapplication-specific IoT networks that takes the application into account when deciding the initiation oftransmission. Wireless data transmission is infamous for being the most energy consuming activity ofa sensor node. Therefore, intelligently reducing data transmissions can prolong the lifetime of battery-powered sensor nodes without comprising much on the data quality. To enable the proposed scheme forconstrained sensor nodes, the complexities of various ML algorithms are discussed, both theoreticallyand practically, from the perspective of constrained microcontrollers. One of the occupancy estimationdatasets along with a standard dataset for occupancy detection are used for validating the proposed datatransmission reduction scheme. Experimental results demonstrate a humongous reduction in the num-ber of data transmissions while upholding similar performance metrics. The proposed scheme is alsoshown to significantly outperform the Shewhart change detection algorithm for occupancy applications Full thesis: pdf Centre for Others |
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