Interconnection Of Physical Devices Research Articles

Lightweight Privacy Preserving Scheme for IoT based Smart Home

Background: The Internet of Things (IoT) is the interconnection of physical devices, controllers, sensors and actuators that monitor and share data to another end. In a smart home network, users can remotely access and control home appliances/devices via wireless channels. Due to the increasing demand for smart IoT devices, secure communication also becomes the biggest challenge. Hence, a lightweight authentication scheme is required to secure these devices and maintain user privacy. The protocol proposed is secure against different kinds of attacks and as well as is efficient. Methods: The proposed protocol offers mutual authentication using shared session key establishment. The shared session key is established between the smart device and the home gateway, ensuring that the communication between the smart devices, home gateway, and the user is secure and no third party can access the information shared. Results: Informal and formal analysis of the proposed scheme is done using the AVISPA tool. Finally, the results of the proposed scheme also compare with existing security schemes in terms of computation and communication performance cost. The results show that the proposed scheme is more efficient and robust against different types of attacks than the existing protocols. Conclusion: In the upcoming years, there will be a dedicated network system built inside the home so that the user can have access to the home from anywhere. The proposed scheme offers secure communication between the user, the smart home, and different smart devices. The proposed protocol makes sure that security and privacy are maintained since the smart devices lack computation power which makes them vulnerable to different attacks.

Enabling Decentralized and Auditable Access Control for IoT through Blockchain and Smart Contracts

The increase in the interconnection of physical devices and the emergence of the 5 G paradigm foster the generation and distribution of massive amounts of data. The complexity associated with the management of these data requires a suitable access control approach that empowers citizens to control how their data are shared, so potential privacy issues can be mitigated. While well-known access control models are widely used in web and cloud scenarios, the IoT ecosystem needs to address the requirements of lightness, decentralization, and scalability to control the access to data generated by a huge number of heterogeneous devices. This work proposes CapBlock, a design that integrates a capability-based access control model and blockchain technology for a fully distributed evaluation of authorization policies and generation of access credentials using smart contracts. CapBlock is intended to manage the access to information in federated IoT environments where data need to be managed through access control policies defined by different data providers. The feasibility of CapBlock has been successfully evaluated in the scope of the EU research project IoTCrawler, which aims at building a secure search engine for IoT data in large-scale scenarios.

Energy Efficient Cluster Based Clinical Decision Support System in IoT Environment

Internet of Things (IoT) has become a major technological development which offers smart infrastructure for the cloud-edge services by the interconnection of physical devices and virtual things among mobile applications and embedded devices. The e-healthcare application solely depends on the IoT and cloud computing environment, has provided several characteristics and applications. Prior research works reported that the energy consumption for transmission process is significantly higher compared to sensing and processing, which led to quick exhaustion of energy. In this view, this paper introduces a new energy efficient cluster enabled clinical decision support system (EEC-CDSS) for embedded IoT environment. The presented EEC-CDSS model aims to effectively transmit the medical data from IoT devices and perform accurate diagnostic process. The EEC-CDSS model incorporates particle swarm optimization with levy distribution (PSO-L) based clustering technique, which clusters the set of IoT devices and reduces the amount of data transmission. In addition, the IoT devices forward the data to the cloud where the actual classification procedure is performed. For classification process, variational autoencoder (VAE) is used to determine the existence of disease or not. In order to investigate the proficient results analysis of the EEC-CDSS model, a wide range of simulations was carried out on heart disease and diabetes dataset. The obtained simulation values pointed out the supremacy of the EEC-CDSS model interms of energy efficiency and classification accuracy.

An ontology-based approach to integrate TV and IoT middlewares

Internet of Things (IoT) is the interconnection of physical devices, known of smart objects, that share data and collaborate among them to support users. There are IoT usage scenarios in several domains. In-home domain, the use of smart objects may support monitoring and automation of the environment. In a home, the TV is one of the most used devices. It may also collaborate with the smart objects to enhance the interactive experience of viewers on watching. To support such a meeting, we propose an (i) conceptual model, IoTTV-Ont, that allows modeling entire homes, with smart objects, people’ profile, and viewers’ interaction events with TV applications; (ii) a software architecture that integrates and interoperates digital TV and IoT middleware. This architecture allows the TV applications: to be aware of the physical environment context, to change aspects of the physical environment, to identify the viewers, and to receive multimodal interactions performed by viewers. We demonstrate our work through use cases. For this, we implement a prototype of our software architecture and modeled home using IoTTV-Ont. Then we developed usage scenarios that enhance TV viewers’ experience by supporting features such as content adaptation, multi-sensorial immersive experience, and multimodal user interaction.

Condition Monitoring of Active Magnetic Bearings on the Internet of Things

A magnetic bearing is an industrial device that supports a rotating shaft with a magnetic field. Magnetic bearings have advantages such as high efficiency, low maintenance, and no lubrication. Active magnetic bearings (AMBs) use electromagnets with actively controlled coil currents based on rotor position monitored by sensors integral to the AMB. AMBs are apt to the Internet of Things (IoT) due to their inherent sensors and actuators. The IoT is the interconnection of physical devices that enables them to send and receive data over the Internet. IoT technology has recently rapidly increased and is being applied to industrial devices. This study developed a method for the condition monitoring of AMB systems online using off-the-shelf IoT technology. Because off-the-shelf IoT solutions were utilized, the developed method is cost-effective and can be implemented on existing AMB systems. In this study, a MBC500 AMB test rig was outfitted with a Raspberry Pi single board computer. The Raspberry Pi monitors the AMB’s position sensors and current sensors via an analog-to-digital converter. Several loading cases were imposed on the experimental test rig and diagnosed remotely using virtual network computing. It was found that remote AMB condition monitoring is feasible for less than USD 100.