Enhancing Security and Privacy of Machine Learning based Outdoor Air Pollution Prediction System using IOTA

Abstract

Internet of Things (IoT) is a combination of devices, which are network-enabled and work for one shared cause. IoT has transformed several fields and continue to do so, for instance, agriculture, smart housing, development and planning, security systems, and communication networks. The security of IoT networks and devices remains compromised and has not been particularly worked upon. It poses many threats and challenges like jamming networks, insecure physical interface, sleep-deprivation attack and high-level attacks like Sybil attacks, authentication and communication, buffer reservation attacks. To deal with the issue of security, Blockchain Technology have been using advanced security algorithms for hashing and different resources like miners to compute the proof of work. It implements centralized consensus to verify and add more block to the chain acting as an authentication scheme. Over the course of last decade, the decentralized applications and their performance have been under observation while work on them is continuously in progress. Distributed Ledger Technology (DLT) has emerged as an advanced system to record and confirm transactions and authenticate the network to add another node to it. DLT have been used to verify, secure, improve the data that is produced and is sent over any network in its domain. As IoT continues to make progress, it continues to encounter verification challenges. To counter one these issues, a DLT called IOTA Tangle has exclusively been designed for IoT – machine to machine communication. This project aims to work on enhancing security and privacy concerns of machine learning based air pollution prediction system using IOTA Tangle as its resource. IOTA light node and full node will be developed to upload the sensor data securely on Tangle. In this work, performance of the network will be evaluated in terms of end-to-end node delay, power consumption and accuracy of air quality index (AQI).