IoT Architectural Framework: Connection and Integration Framework for IoT Systems

Uviase, Onoriode; Kotonya, Gerald

doi:10.4204/eptcs.264.1

Cited by 57 publications

(22 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar to the above work, but differs by adopting microservice is the approach proposed in [67]. The architectural style contains various patterns, including Client-server, Peer-to-Peer, and cloud computing pattern, etc, the framework proposes the adoption of micro-service.…”

Section: Service Oriented Computing DImentioning

confidence: 99%

Distributed Intelligence in the Internet of Things: Challenges and Opportunities

et al. 2021

View full text Add to dashboard Cite

Widespread adoption of smart IoT devices is accelerating research for new techniques to make IoT applications secure, scalable, energy-efficient, and capable of working in mission-critical use cases, which require an ability to function offline. In this context, the novel combination of distributed ledger technology (DLT) and distributed intelligence (DI) is seen as a practical route towards the decentralisation of IoT architectures. This paper surveys DI techniques in IoT and commences by briefly explaining the need for DI, by proposing a comprehensive taxonomy of DI in IoT. This taxonomy is then used to review existing techniques and to investigate current challenges that require careful attention and consideration. Based on the taxonomy, IoT DI techniques can be classified into five categories based on the factors that support distributed functionality and data acquisition: cloud-computing, mist-computing, distributed-ledger-technology, service-oriented-computing and hybrid. Existing techniques are compared and categorized mainly based on related challenges, and the level of intelligence supported. We evaluate more than thirty current research efforts in this area. We define many significant functionalities that should be supported by DI frameworks and solutions. Our work assists system architects and developers to select the correct low-level communication techniques in an integrated IoT-to-DLT-to-cloud system architecture. The benefits and shortcomings of different DI approaches are presented, which will inspire future work into automatic hybridization and adaptation of DI mechanisms. Finally, open research issues for distributed intelligence in IoT are discussed.

show abstract

Section: Service Oriented Computing DImentioning

confidence: 99%

Distributed Intelligence in the Internet of Things: Challenges and Opportunities

et al. 2021

View full text Add to dashboard Cite

show abstract

“…It is used to monitor and control objects in various applications, including smart building, smart home, and also smart grid [8]. The IoT architecture comprises three layers including the perception and control layer, network and connectivity layer, and application and service layer [9]. The perception layer consists of sensors responsible for collecting environmental data [10].…”

Section: 2mentioning

confidence: 99%

Flashover Prevention System using IoT and Machine Learning for Transmission and Distribution Lines

Saraubon

Wiriyanuruknakon

Tangthirasunun

2021

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

Flashover on transmission and distribution line insulators occurs when the insulator’s resistance drops to a critical level and causes frequent power outages. Thin layers of dust, salt, and airborne particles, gradually deposited on the surface of insulators, as well as humidity, form an electrolyte which causes flashover. In this paper, a flashover prevention system using IoT technology and machine learning is proposed in order to reduce loss and increase power reliability. The system includes an IoT module, a service and clients. The IoT module prototype was installed at a distribution line pole located in Pracha-utit, Bangkok, Thailand and had collected data for thirty-four months. The data were pre-processed and split for the training process and evaluation. In this study, we built and compared four models including linear regression, polynomial regression, Auto-regressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) models. The results revealed that the LSTM model outperformed (<em>R</em><sup>2</sup>=.931, RMSE= 530.74) the others.

show abstract

“…IoT acts as a gigantic network of people, or things sorted out and are allotted to those accumulated assets corresponding to how they have been used and know about the encompassing situation. Currently, all the things are ordinarily detected along with its comparing registering framework; anyway, it is capable of interoperating inside the current Internet foundation 53,54 . For straightforwardness, IoT contains a specific condition, which can associate an assortment of things, where total things can be in connection with other associated gadgets.…”

Section: Background Of Bc and The Iotmentioning

confidence: 99%

Security challenges of blockchain in Internet of things: Systematic literature review

Cao

Chen

2020

Trans Emerging Tel Tech

View full text Add to dashboard Cite

Since security is the most important issue in the Internet of things (IoT) and blockchain (BC), this article aims to identify, analyze, and organize the literature about security in the IoT and BC context up to August 2020 and proposes an agenda for future research. The 39 related articles have been ranked into three main categories, smart city, data management and information, and architecture and framework. The findings showed that the distinctive technical aspects of BC might impressively find a solution for privacy and security problems encountering the IoT development, supply distributed storage, transparency, trust, and other support for IoT. It is conducted to form a valid, impressive, and secure distributed IoT network and supply a beneficial guarantee for the security of IoT users. Since the devices of IoT are simply reachable, a BC‐depended solution can be executed, which may certify the confidentiality and safety of the info preserved in the devices. In addition, BC technology facilitates the secure sharing of IoT datasets and secure IoT systems. However, blockchains require more computational strength and high bandwidth and are not perfectly proper for many resource‐restricted IoT devices.

show abstract

IoT Architectural Framework: Connection and Integration Framework for IoT Systems

Cited by 57 publications

References 14 publications

Distributed Intelligence in the Internet of Things: Challenges and Opportunities

Distributed Intelligence in the Internet of Things: Challenges and Opportunities

Flashover Prevention System using IoT and Machine Learning for Transmission and Distribution Lines

Security challenges of blockchain in Internet of things: Systematic literature review

Contact Info

Product

Resources

About