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Special Issue "Emerging Sensor Communication Network based AI/ML Driven Intelligent IoT"

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

Deadline for manuscript submissions: 31 October 2022.

Special Issue Editors

Prof. Bhisham Sharma
E-Mail Website
Guest Editor
Department of Computer Science Engineering, Chitkara University, Himachal Pradesh, Baddi, India
Interests: wireless communication; wireless sensor networks; wireless mesh networks; next generation networking; network security; internet of things; UAV; medical image processing and edge/fog computing
Dr. Deepika Koundal
E-Mail Website
Guest Editor
Department of Virtualization, School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India
Interests: Image Processing; Artificial Intelligence; Machine Learning; Cyber Physical System; Engineering education
Dr. Rabie A. Ramadan
E-Mail Website
Guest Editor
Computer Engineering Department, Cairo University, Cairo, Egypt
Interests: Sensor Networks; computational Intelligence; mobile computing; Internet of Things; Big Data
Prof. Dr. Juan M. Corchado
E-Mail Website
Guest Editor
BISITE Research Group, Edificio Multiusos I+D+i, University of Salamanca, 37007 Salamanca, Spain
Interests: artificial Intelligence; machine learning; edge computing; distributed computing; Blockchain; consensus model; smart cities; smart grid
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, the field of the Internet of Things (IoT) is one of the fastest growing areas in terms of Artificial Intelligence (AI) and Machine Learning (ML) techniques. In the future, the IoT market will expand to 24 billion devices globally around 2030. With these new developments in AI and ML, approaches with the support of the IoT have empowered various real-life applications such as industry, e-healthcare, smart cities, smart utilities, smart transportation, and smart homes. AI and ML have both been the most striking topics as these technologies progressively determine their path from everything to everywhere, that is, from pioneering healthcare systems and novel quantum computing to “elegant” peculiar assistants and consumer electronics. Therefore, there is a need to design scalable and resource-efficient sensor-based systems that can perform well in different types of wireless systems with the increase of IoT devices in heterogeneous applications. ML techniques are advantageous in communication systems whereas, deep learning techniques are widely utilized in Big Data analysis for prediction and performance improvement. The utilization of AI and ML is increasingly intertwined with IoT. AI, ML and deep learning are now being utilized for making IoT services and devices smarter and more secure .

In the modern context, one IT megatrend has been identified as Hyperautomation, which is based on AI, ML, and robotic process automation. The COVID-19 pandemic has given birth to this concept in which anything within the organization can be automated, a concept known as digital/intelligent business process automation. These automated processes can adapt to any fluctuating situations and respond to unanticipated circumstances. Some other trends include the provision of security and connectivity to different types of IoT devices. Therefore, there is a need to develop automated, efficient, and scalable strategies that can identify, classify, apply and monitor policies for ensuring appropriate functionality without affecting other services on the network. Moreover, for unlocking the potential of AI in businesses, AI processing and its data have been placed from the cloud, to the edge, to the fog of the network. Further, there is a requirement for a network that can provide dynamic performance, low latency communications, and end-to-end bandwidth. Intent-based networking is a new type of network that leverages the new capabilities for meeting business goals. In this, network uses NLP for communication from any business line and then performs translation into the set of policies to help in taking automatic decisions. Therefore, many application drawbacks, such as unplanned downtime, can be avoided by predicting equipment failure by using data analytics for scheduling the systematic continuation processes. Otherwise, Predictive Maintenance can aid in mitigating the destructive economical cost of unexpected interruption.

Operative efficiency can be increased by predicting working environments and identifying the factors that need to be adapted on the fly for the maintenance of ideal outcomes that can improve operational efficiency. It helps in services with the help of NLP for speaking with machinery, fleet management, AI-enabled robots, and drones. With the integration of AI and IoT, risk management can be enhanced by predicting various types of risks in advance to mechanize a quick reply. AI has been a standard accompaniment to IoT operations, helping to improve operations and offering an economical edge in business performance.

Developments in the IoT are playing a significant role in our daily lives. In IoT, a huge number of devices such as actuators and sensors are deployed and connected for the collection of different types of data such as healthcare, transportation, public safety, energy, manufacturing, and smart city infrastructure espousing systems. At the same time, ML/DL has shown substantial success in the transformation of complex and massive datasets into precise comprehension as output, which can significantly facilitate intelligence, analysis, automation, and decision-making. ML has provided a means of performing giant modeling and intelligence with the integration of developments in big-data analytics, big-networking technologies, and big-data computing, to achieve enormous accomplishments in diverse areas. Despite these achievements, the leveraging of machine learning in IoT faces significant challenges to achieving an AI-enabled Internet of controllable and dependable things, and we must take into account the outstanding necessities for latency, connectivity, accessibility, scalability, resiliency, and security. The unified fusion of ML into IoT, consequently, produces prospects for necessitating interdisciplinary endeavors and novel research in order to provide a solution to various challenges.

This Special Issue focuses on the results of the research presented in the above-mentioned domains. Contributions are invited in the field of AI/ML models for IoT devices and deployed networks. Moreover, research on big-data analytics and approaches and decision-making are also invited, along with new practices and concepts with AI/ML automated systems. Authors are invited from both academia and industry to work on the application of AI/ML techniques to computer systems for the submission of their original articles with designing, optimizing, and implementation of protocols, models, and optimization methods.

Topics: This Special Issue includes the following topics of interest:

  • Machine learning for theoretical foundation and models for IoT;
  • Machine learning for IoT system deployment and operation;
  • Machine learning for IoT assisted industrial automation;
  • Machine learning-enabled real-time IoT data analytics;
  • Machine learning-enabled sensing and decision-making for IoT;
  • Machine learning-enabled cloud/edge computing systems for IoT;
  • Evaluation platforms and hardware-in-the-loop testbeds for machine learning-enabled IoT;
  • Machine learning-assisted intrusion and malware detection for IoT;
  • Machine learning for access congestion management in edge computing IoT networks;
  • AI/DL-based IoT-cloud convergent algorithms/applications for healthcare;
  • ML-driven long-term risk of pandemics prediction;
  • AI/DL-empowered data fusion for healthcare;
  • Sensor based human-centric AI for IoT systems;
  • Explainable AI (XAI) and predictive data analytics for healthcare;
  • DL-techniques for handling post COVID-19 crisis;
  • IoT-cloud healthcare big data storage, processing, analysis using ML/DL techniques;
  • Adversarial attacks, threats, and defenses for DL-enabled healthcare;
  • Protocols and algorithms for intelligent IoT systems;
  • 5G/6G technology-enabled AIoT.

Prof. Bhisham Sharma
Dr. Deepika Koundal
Dr. Rabie A. Ramadan
Prof. Dr. Juan M. Corchado
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 papers will be 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 2200 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

This special issue is now open for submission.
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