Lightweight IoT platform for rapid application development and deployment

Sensors connected to the cloud services equipped with data analytics has created a plethora of new type of applications from personal to industrial levels. In other words, the smart devices, the network, and the data come together to form Internet-of-Things (IoT). In this context, IoT provides an opportunity to increase efficiency in how things are done. IoT-based system normally follows a pattern of data collection, data analytics, automation, and system improvement recommendations. However, most application would have its own unique requirements in terms of smart devices, communication technologies as well as its application provisioning service. Although various services are commercially available that provide services such as Backend-as-a-service (BaaS) and Software-as-a-service (SaaS) hosted on the cloud, this, in turn, raises the issues of security and privacy. Individuals and organizations alike would like to protect their sensitive information for various reasons. Therefore, in this project, a lightweight and secure IoT platform is proposed. The platform consists of Raspberry Pi as an IoT device with a pre-configured image that contains hotspot module, user login, PHP, Apache server, MySQL database, Node.js, and Domain Name Server (DNS). The platform also contains a middleware that provides Application Programming Interfaces (API) for both the sensor layer and the application layer. Moreover, the platform has a Graphical User Interface (GUI) designed using Angular to provide management tools and to enable data display sent by the IoT device for the end-user. The middleware is designed using JavaScript programming language in Node.js development framework to provide a lightweight and scalable features which is proven to save up to 45% of memory. The middleware is connected to NoSQL database that allows the platform to be distributed and thus, enhance security and privacy. The performance analysis of the system shows the developed platform has a Hypertext Transfer Protocol (HTTP) operation which is around 600 Bytes, with the system processor not exceeding 6% of usage. It also demonstrates a reduction by 53% and 41% of byte size and time consumed, respectively, for GET operation over a Local Area Network in UTM campus

Lightweight IoT middleware for rapid application development

Sensors connected to the cloud services equipped with data analytics has created a plethora of new type of applications ranging from personal to an industrial level forming to what is known today as Internet of Things (IoT). IoT-based system follows a pattern of data collection, data analytics, automation, and system improvement recommendations. However, most applications would have its own unique requirements in terms of the type of the smart devices, communication technologies as well as its application provisioning service. In order to enable an IoT-based system, various services are commercially available that provide services such as backend-as-a-service (BaaS) and software-as-a-service (SaaS) hosted in the cloud. This, in turn, raises the issues of security and privacy. However there is no plug-and-play IoT middleware framework that could be deployed out of the box for on-premise server. This paper aims at providing a lightweight IoT middleware that can be used to enable IoT applications owned by the individuals or organizations that effectively securing the data on-premise or in remote server. Specifically, the middleware with a standardized application programming interface (API) that could adapt to the application requirements through high level abstraction and interacts with the application service provider is proposed. Each API endpoint would be secured using Access Control List (ACL) and easily integratable with any other modules to ensure the scalability of the system as well as easing system deployment. In addition, this middleware could be deployed in a distributed manner and coordinate among themselves to fulfil the application requirements. A middleware is presented in this paper with GET and POST requests that are lightweight in size with a footprint of less than 1 KB and a round trip time of less than 1 second to facilitate rapid application development by individuals or organizations for securing IoT resources

SEGURANÇA EM INTERNET DAS COISAS: UM SURVEY DE SOLUÇÕES LIGHTWEIGHT

A conexão intermitente de dispositivos, máquinas e sensores em cenários com inteligência computacionais conectados à internet tem se tornado cada vez mais presente. A essa integração, dá-se o nome de Internet das Coisas (Internet of Things – IoT). Esse novo paradigma traz desafios de segurança, principalmente pela heterogeneidade e a quantidade de dispositivos com baixo poder computacional presentes nesse cenário. Propostas de segurança tradicionais não são viáveis nestes cenários e novas soluções são então necessárias. Surgem então as soluções lightweight. Entende-se por lightweight todas as técnicas, arquiteturas e esquemas de segurança consideradas “leves” em termos de consumo de recursos e adaptáveis a diferentes dispositivos. Neste trabalho é analisado o atual cenário de segurança lightweight em redes IoT, por meio de uma revisão e classificação da Literatura. São apresentadas propostas de algoritmos de criptografia baseadas em credenciais, uso da nuvem para autenticação, redução de latência, de consumo de energia e de perda de pacotes, entre outras vantagens. É pretendido assim, contribuir com o avanço das pesquisas em segurança em Internet das Coisas, apresentando as tecnologias de segurança “leves” em IoT, os desafios, os desenvolvimentos recentes, as questões em aberto e também os pontos futuros de pesquisa

Securing the Internet of Things: A Study on Machine Learning-Based Solutions for IoT Security and Privacy Challenges

The Internet of Things (IoT) is a rapidly growing technology that connects and integrates billions of smart devices, generating vast volumes of data and impacting various aspects of daily life and industrial systems. However, the inherent characteristics of IoT devices, including limited battery life, universal connectivity, resource-constrained design, and mobility, make them highly vulnerable to cybersecurity attacks, which are increasing at an alarming rate. As a result, IoT security and privacy have gained significant research attention, with a particular focus on developing anomaly detection systems. In recent years, machine learning (ML) has made remarkable progress, evolving from a lab novelty to a powerful tool in critical applications. ML has been proposed as a promising solution for addressing IoT security and privacy challenges. In this article, we conducted a study of the existing security and privacy challenges in the IoT environment. Subsequently, we present the latest ML-based models and solutions to address these challenges, summarizing them in a table that highlights the key parameters of each proposed model. Additionally, we thoroughly studied available datasets related to IoT technology. Through this article, readers will gain a detailed understanding of IoT architecture, security attacks, and countermeasures using ML techniques, utilizing available datasets. We also discuss future research directions for ML-based IoT security and privacy. Our aim is to provide valuable insights into the current state of research in this field and contribute to the advancement of IoT security and privacy