Analyzing Frameworks for IoT Data Storage, Representation and Analysis: A Statistical Perspective

Abstract

Data representation, storage and processing in Internet of Things (IoT) devices must be done such that the network lifetime and the quality of service (QoS) for the network is minimally affected. In order solve this constraint researchers use sleep scheduling, aggregation, effective data representation, pre-processing, and other techniques. Each of these techniques has its own nuances and advantages. Techniques which involve sleep scheduling improve the network lifetime, but reduce other QoS parameters like speed of operation, while techniques involving pre-processing of data guarantees high throughput but increases energy consumption in the network. A statistical survey of these techniques is mentioned in this text. Moreover, this text also recommends a set of approaches that can be applied in order to improve the performance of IoT networks via better data representation, enhanced data storage structures and effective data analysis techniques. Use of Machine learning and blockchain technologies for improving QoS and security performance of the network are also evaluated. Using this text researchers in the IoT domain can evaluate techniques needed for effective IoT network design and improve the QoS parameters of the network.