Management and Security of IoT systems using Microservices

Devices that assist the user with some task or help them to make an informed decision are called smart devices. A network of such devices connected to internet are collectively called as Internet of Things (IoT). The applications of IoT are expanding exponentially and are becoming a part of our day to day lives. The rise of IoT led to new security and management issues. In this project, we propose a solution for some major problems faced by the IoT devices, including the problem of complexity due to heterogeneous platforms and the lack of IoT device monitoring for security and fault tolerance. We aim to solve the above issues in a microservice architecture. We build a data pipeline for IoT devices to send data through a messaging platform Kafka and monitor the devices using the collected data by making real time dashboards and a machine learning model to give better insights of the data. For proof of concept, we test the proposed solution on a heterogeneous cluster, including Raspberry Pi’s and IoT devices from different vendors. We validate our design by presenting some simple experimental results

A survey on subjecting electronic product code and non-ID objects to IP identification

Over the last decade, both research on the Internet of Things (IoT) and real-world IoT applications have grown exponentially. The IoT provides us with smarter cities, intelligent homes, and generally more comfortable lives. However, the introduction of these devices has led to several new challenges that must be addressed. One of the critical challenges facing interacting with IoT devices is to address billions of devices (things) around the world, including computers, tablets, smartphones, wearable devices, sensors, and embedded computers, and so on. This article provides a survey on subjecting Electronic Product Code and non-ID objects to IP identification for IoT devices, including their advantages and disadvantages thereof. Different metrics are here proposed and used for evaluating these methods. In particular, the main methods are evaluated in terms of their: (i) computational overhead, (ii) scalability, (iii) adaptability, (iv) implementation cost, and (v) whether applicable to already ID-based objects and presented in tabular format. Finally, the article proves that this field of research will still be ongoing, but any new technique must favorably offer the mentioned five evaluative parameters.Comment: 112 references, 8 figures, 6 tables, Journal of Engineering Reports, Wiley, 2020 (Open Access