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Real-Time AI over IoT Data
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Real-Time AI over IoT Data

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (31 October 2019) | Viewed by 7116

Special Issue Editors

Prof. Dr. Arkady Zaslavsky

E-Mail Website
Guest Editor
School of Information Technology, Deakin University, Burwood, VIC 3125, Australia
Interests: IoT; context-awareness; smart cities; waste management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Prem Prakash Jayaraman

E-Mail Website
Guest Editor
Faculty of Science, Engineering & Technology, Swinburne University of Technology, 1 Alfred Street, Hawthorn, VIC 3122, Australia
Interests: internet of things; distributed computing; mobile and cloud computing
Special Issues, Collections and Topics in MDPI journals
Dr. Sylvain Kubler

E-Mail Website
Guest Editor
Université de Lorraine, CRAN (Centre de Recherche en Automatique de Nancy), UMR 7039 Campus Sciences, BP 70239–54506, Vandoeuvre Cedex, France
Interests: internet of things; cyber physical systems; semantic web; multi criteria decision making (MCDM); context-awareness
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues

The Internet of Things (IoT) is a new internet evolution that involves connecting the billions of devices we refer to as smart things. The IoT offers enormous potential in delivering timely and accurate information that with relevant contextual information and intelligent processing can support business decisions. Another emerging area that complements the IoT and underpins its ability in delivering support for business decisions is artificial intelligence. Artificial intelligence (AI) is a branch of computer science that develops machines and software capable of reasoning, discovering meaning, learning and generalising past experience. AI coupled with real-time IoT data streams enables various forms of analytics ranging from predictive to prescriptive, a key element in supporting real-time business decisions. IoT data coupled with real-time AI underpins the ability to develop AI-based IoT ecosystem-wide awareness of everything that is happening, happened, or might happen within the system realm, whether that is processes, events, activities, locations, or preferences.

In order to realise the enormous potential offered by combining IoT and AI, AI-driven IoT solutions need to be developed to support the real-time needs of the IoT while providing support for context-aware predictive to prescriptive analytics and actuation. This MDPI Sensors Special Issue is currently open for submission and aims to bring together researchers and application developers working at the intersection of the IoT and AI to develop the next generation of AI techniques, algorithms and solutions to support real-time analytics and actuation over IoT data. We also welcome quality review articles that analyse the gap between the state-of-the-art and the state-of-the-practice in the above areas. Potential topics include, but are not limited to:

  • Tools, services, technologies, algorithms and methods for real-time AI over IoT data
  • Novel computing architectures (e.g. fog/edge/cloud) for supporting AI-driven analytics
  • Techniques and frameworks for AI-driven IoT actuation
  • Open issues, challenges, and future perspective at the intersection of AI and IoT
  • Machine learning and simulation models for different IoT application domains
  • Case studies of applications of AI over IoT data
  • AI-enabled context- and situation-awareness in IoT
  • Cognitive Internet of Things
  • AI-driven privacy preservation in IoT
  • AI-driven IoT advanced sensing
  • Human, IoT and AI
  • AI-driven trust and secure IoT systems/solutions

Application topics of interest that demonstrate real-time AI over IoT include but are not limited to

  • Industry 4.0 use cases
  • Digital agriculture use cases
  • Smart cities use cases
  • Digital supply chain
  • Defence
  • Banking and finance

Prof. Dr. Arkady Zaslavsky
Dr. Prem Prakash Jayaraman
Dr. Sylvain Kubler
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (2 papers)

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Research

34 pages, 6669 KiB  
Article
Efficient Execution of Complex Context Queries to Enable Near Real-Time Smart IoT Applications
by Alireza Hassani, Alexey Medvedev, Arkady Zaslavsky, Pari Delir Haghighi, Prem Prakash Jayaraman and Sea Ling
Sensors 2019, 19(24), 5457; https://doi.org/10.3390/s19245457 - 11 Dec 2019
Cited by 13 | Viewed by 3106
Abstract
As the Internet of Things (IoT) is evolving at a fast pace, the need for contextual intelligence has become more crucial for delivering IoT intelligence, efficiency, effectiveness, performance, and sustainability. Contextual intelligence enables interactions between IoT devices such as sensors/actuators, smartphones and connected [...] Read more.
As the Internet of Things (IoT) is evolving at a fast pace, the need for contextual intelligence has become more crucial for delivering IoT intelligence, efficiency, effectiveness, performance, and sustainability. Contextual intelligence enables interactions between IoT devices such as sensors/actuators, smartphones and connected vehicles, to name but a few. Context management platforms (CMP) are emerging as a promising solution to deliver contextual intelligence for IoT. However, the development of a generic solution that allows IoT devices and services to publish, consume, monitor, and share context is still in its infancy. In this paper, we propose, validate and explain the details of a novel mechanism called Context Query Engine (CQE), which is an integral part of a pioneering CMP called Context-as-a-Service (CoaaS). CQE is responsible for efficient execution of context queries in near real-time. We present the architecture of CQE and illuminate its workflows. We also conduct extensive experimental performance and scalability evaluation of the proposed CQE. Results of experimental evaluation convincingly demonstrate that CoaaS outperforms its competitors in executing complex context queries. Moreover, the advanced functionality of the embedded query language makes CoaaS a decent candidate for real-life deployments. Full article
(This article belongs to the Special Issue Real-Time AI over IoT Data)
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19 pages, 7119 KiB  
Article
Design and Implementation of a Trust Information Management Platform for Social Internet of Things Environments
by Tai-Won Um, Eunhee Lee, Gyu Myoung Lee and Yongik Yoon
Sensors 2019, 19(21), 4707; https://doi.org/10.3390/s19214707 - 29 Oct 2019
Cited by 10 | Viewed by 3288
Abstract
As the vast amount of data in social Internet of Things (IoT) environments considering interactions between IoT and people is accumulated and processed through cloud and big data technologies, the services that utilize them are applied in various fields. The trust between IoT [...] Read more.
As the vast amount of data in social Internet of Things (IoT) environments considering interactions between IoT and people is accumulated and processed through cloud and big data technologies, the services that utilize them are applied in various fields. The trust between IoT devices and their data is recognized as the core of IoT ecosystem creation and growth. Connection with suspicious IoT devices may pose a risk to services and system operation. Therefore, it is essential to analyze and manage trust information for devices, services, and people, as well as to provide the trust information to the other devices or users that need it. This paper presents a trust information management framework which contains a generic IoT reference model with trust capabilities to achieve the goal of converged trust information management. Additionally, a trust information management platform (TIMP) consisting of trust agents, trust information brokers, and trust information management systems has been proposed, which aims to provide trustworthy and safe interactions among people, virtual objects, and physical things. Implementing and deploying a TIMP enables a trustworthy ecosystem to be built while activating social IoT businesses by reducing transaction costs, as well as by eliminating the uncertainties in the use of social IoT services and data transactions. Full article
(This article belongs to the Special Issue Real-Time AI over IoT Data)
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