Artificial Intelligence of Things Enabled Smart Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (15 May 2022) | Viewed by 7575

Special Issue Editors


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Guest Editor
School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore
Interests: network security and wireless networking

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Guest Editor
School of Informatics, University of Leicester, Leicester LE1 7RH, UK
Interests: data analytics; AI; cloud computing; service computing; IoT; blockchain
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Guest Editor
School of Automation, Wuhan University of Technology, Wuhan 430070, China
Interests: data intensive distributed systems; Internet of Things and smart grid

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Guest Editor
College of Informatics, Huazhong Agricultural University, Wuhan, China
Interests: wireless networking; mobile computing

Special Issue Information

Dear Colleagues,

The advent of Artificial Intelligence (AI), Fifth Generation (5G) communication and Internet of Things (IoT) is expected not only to make it possible to collect and disseminate information for various crowd-sensing services in densely populated environments, but also to put forward higher request on these services with the rapid evolution of Artificial Intelligence of Things (AIoT) to provide diverse services and applications in smart cities and societies, which integrate information and communication technologies, smart sensing, analysis and processing solutions to reduce costs and resource consumption, enhance performance, and connect and engage more effectively and actively with its citizens. This vast and semi-structured collection of city and citizen-related data provides many opportunities for the development of smart applications. In addition to IoT, cyber-physical systems, software-defined sensor networks, sensing as a service, and intelligence-based big data processing on sensing data are leading to the transformation of conventional services, which also needs effective machine-learning or data-mining techniques, novel data-acquisition and processing methodologies to support all kinds of smart applications, such as smart energy, smart driving, smart homes, smart living, smart governance, and smart health. Thus, AIoT-related techniques can accelerate the content deliveries and improve the quality of services and smart applications, an issue which is attracting more and more attention from academia and industry because of its advantages in throughput, delay, network scalability and intelligence. Meanwhile, AIoT also brings us new challenges, such as costs, communications, data processing and management, security and privacy issues.

The objective of this Special Issue is to present a collection of high-quality research papers that report the latest research advances addressing the related challenges and perspectives in the area of AIoT-enabled smart applications. Specific topics include but are not restricted to the following:

  • Physical layer challenges in AIoT
  • Cross-layer solutions in AIoT
  • Device-to-device networks for AIoT
  • Novel sensory data-acquisition techniques in AIoT
  • Security, privacy, and trust in AIoT
  • Computing and sensing infrastructures
  • Data management in AIoT
  • Sensing as a service in AIoT
  • Cyber-physical systems for AIoT
  • Self-learning for pattern discovery, prediction, auto-configuration in AIoT
  • Smart users experience in AIoT
  • AIoT-driven smart governance, smart economy, and smart environments
  • AIoT-driven smart energy, smart driving, smart homes, smart living,
  • AIoT-enabled COVID-19 analysis and smart health
  • Deployment, test bed, experimental experiences, and innovative applications

Prof. Dr. Maode Ma
Prof. Dr. Lu Liu
Dr. Xiaozhu Liu
Prof. Dr. Rongbo Zhu
Guest Editors

Manuscript Submission Information

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Keywords

  • Internet of Things
  • Artificial Intelligence
  • Big Data Analytics
  • Smart Applications

Published Papers (2 papers)

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Research

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23 pages, 8771 KiB  
Article
A Contrastive Evaluation Method for Discretion in Administrative Penalty
by Hui Wang, Haoyu Xu, Yiyang Zhou and Xueqing Li
Electronics 2022, 11(9), 1388; https://doi.org/10.3390/electronics11091388 - 26 Apr 2022
Viewed by 1877
Abstract
Discretion, namely discretionary power, indicates that administrative agencies could make modifiable decisions under personal judgment when facing situations defined in the law. It plays an essential part in an administrative practice that existing laws and regulations could hardly cover all cases. However, this [...] Read more.
Discretion, namely discretionary power, indicates that administrative agencies could make modifiable decisions under personal judgment when facing situations defined in the law. It plays an essential part in an administrative practice that existing laws and regulations could hardly cover all cases. However, this may also cause the abuse of enforcement power. The rapid development of the Internet of Things (IoT) and databases has provided a powerful tool to measure discretionary power, such as judging if a given administrative punishment is appropriate, and recommending similar cases for a new law-violation record. In this paper, we develop a multi-task framework to extract contrastive patterns from historical records and recommend unprocessed penalties. There is massive ambiguity in collected records, where the limited samples of specific penalties and a large number of whole records make it hard to distinguish factors in individual administrative enforcement actions. We propose an automatic data-labeling method based on data pattern discovery, clustering, and statistical analysis to replace manual labeling under potential personal prejudice. We estimate the distribution of collected penalty records to distinguish deviated and reasonable ones, then produce contrastive samples, which are fed into different network branches. We build a complete IoT platform and collect three-year administrative penalty records nationwide as an empirical evaluation. Experiments show that our proposed methods can learn reasonable discretion by measuring the objectiveness in samples and combining it with a joint training strategy. The final results of penalty amount forecasting and penalty reasonableness judging tasks reach ready-to-use performance. Full article
(This article belongs to the Special Issue Artificial Intelligence of Things Enabled Smart Applications)
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Review

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24 pages, 2218 KiB  
Review
An Overview of Machine Learning-Based Energy-Efficient Routing Algorithms in Wireless Sensor Networks
by Qianao Ding, Rongbo Zhu, Hao Liu and Maode Ma
Electronics 2021, 10(13), 1539; https://doi.org/10.3390/electronics10131539 - 25 Jun 2021
Cited by 38 | Viewed by 4904
Abstract
Machine learning (ML) technology has shown its unique advantages in many fields and has excellent performance in many applications, such as image recognition, speech recognition, recommendation systems, and natural language processing. Recently, the applicability of ML in wireless sensor networks (WSNs) has attracted [...] Read more.
Machine learning (ML) technology has shown its unique advantages in many fields and has excellent performance in many applications, such as image recognition, speech recognition, recommendation systems, and natural language processing. Recently, the applicability of ML in wireless sensor networks (WSNs) has attracted much attention. As resources are limited in WSNs, identifying how to improve resource utilization and achieve power-efficient load balancing is becoming a critical issue in WSNs. Traditional green routing algorithms aim to achieve this by reducing energy consumption and prolonging network lifetime through optimized routing schemes in WSNs. However, there are usually problems such as poor flexibility, a single consideration factor, and a reliance on accurate mathematical models. ML techniques can quickly adapt to environmental changes and integrate multiple factors for routing decisions, which provides new ideas for intelligent energy-efficient routing algorithms in WSNs. In this paper, we survey and propose a theoretical hypothetic model formulation of ML as an effective method for creating a power-efficient green routing model that can overcome the limitations of traditional green routing methods. In addition, the study also provides an overview of past, present, and future progress in green routing schemes in WSNs. The contents of this paper will appeal to a wide range of audiences interested in ML-based WSNs. Full article
(This article belongs to the Special Issue Artificial Intelligence of Things Enabled Smart Applications)
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