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Data Reduction for IoT Networks: Algorithms and ImplementationAuthor: Anish Shastri Date: 2019-07-29 Report no: IIIT/TH/2019/78 Advisor:Sachin Chaudhari AbstractWith the advent of internet of things (IoT) technology, millions of smart devices will be connected to each other as well as to the internet. These connected devices will generate a huge amount of data and collaboratively dispense meaningful insights about the surrounding physical phenomenon by sensing, processing and actuating other devices to cater to various applications. In IoT networks, the sensor nodes need to continuously measure and transmit sensor data to the sink node or to the cloud directly. The communication cost in this transaction is usually high as compared to other operations of a sensor node. Keeping in mind that these battery-operated devices have a limited lifetime, efforts have been made in the wireless sensor networks (WSN) and IoT literature to improve the lifetime of the nodes. Of the many possible ways to increase the lifetime, reducing the sensor data communication to the sink node or other nodes is one of the most efficient ways. These data-transmission reduction techniques are often referred to as data reduction schemes. This thesis focuses on the popular data reduction schemes based on the classical least mean square (LMS) algorithm and the Shewhart test-based scheme. The algorithms have been implemented on a custom-designed low-cost sensor node using easily accessible hardware. The results of the proposed implementations have been evaluated upon real-time deployment of the nodes in our lab. Various results and measurements such as percentage reduction in data transmission, the current consumption of the node and estimation of the battery lifetime have been quantified based on the observations. Comparison of the LMS and Shewhart test-based data reduction schemes on different datasets encourages us to improve the accuracy of the estimated time series data at the sink node. To achieve this, a piggybacking and interpolation based scheme has been proposed along with the Shewhart test-based scheme to improve the mean square error (MSE) of the estimated data, without increasing the number of data transmissions to the sink. The proposed algorithm has also been implemented on the custom-designed sensor node. The current consumption and battery lifetime have been analyzed. It is seen that there is an inherent trade-off between the decrease in the MSE and the reduction in the battery-lifetime for the number of piggybacks chosen. However, the decrease in the battery lifetime is negligible as compared to the improvement in the accuracy. Later, the Shewhart test-based data reduction scheme is implemented on NodeMCU and integrated with oneM2M standard to show the proof-of-concept for using these algorithms for industrial and real-world IoT applications. Full thesis: pdf Centre for Others |
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