Special Issue "Machine Learning in IoT Networking and Communications"
Deadline for manuscript submissions: 31 March 2022.
The fast and wide spread of Internet of Things (IoT) applications offers new opportunities in multiple domains but also presents new challenges. A skyrocketing number of IoT devices (sensors, actuators, etc.) is deployed to collect critical data and to control environments such as manufacturing, healthcare, urban/built areas, and public safety. At the same time, machine learning (ML) has shown significant success in transforming heterogeneous and complex datasets into coherent output and actionable insights. Thus, the marriage of ML and IoT has a pivotal role in enabling smart environments with precision in decision-making and adaptive automation. However, leveraging ML and IoT still faces significant challenges obstructing the full realisation of foreseen opportunities. Direct challenges relate to scalability, security, accessibility, resilience and latency, all of which have resulted in a growing corpus of research addressing one or more of these issues. Nevertheless, the overarching challenge concerns the exportability of advancements in this area across multiple applications. For instance, an acoustic scene classification method that successfully detects violence in a small town would completely fail in busy cities, and an autonomous pod trained to deliver groceries in a controlled environment would not succeed elsewhere. Thus, the biggest challenge in pushing forward the seamless integration of ML and IoT systems is the exportability of technologies which creates opportunities for novel research and interdisciplinary efforts.
The papers in this Special Issue will focus on state-of-the-art research and challenges in leveraging ML and IoT. In this Special Issue, we shall solicit papers that cover numerous topics of interest that include but are not limited to:
- ML and IoT for system deployment and operation;
- ML and IoT for assisted automation;
- ML-enabled real-time IoT data analytics;
- ML- and IoT-enabled digital twin;
- Cloud/edge computing systems for IoT employing ML;
- ML-enabled spatial-temporal IoT data fusion for intelligent decision making;
- Data-centric simulations for IoT systems;
- ML for IoT application orchestration;
- ML for managing security in IoT data processing;
- ML for IoT attack detection and prevention;
- Testbed and empirical studies.
Dr. Mona Jaber
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. Journal of Sensor and Actuator Networks is an international peer-reviewed open access quarterly 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 1600 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.
- machine learning
- artificial intelligence
- Internet of Things
- digital twin
- exportable AI
- IoT security
- IoT data fusion
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Trends in Intelligent Communication Systems; AI/ML, Standards & Research: A Roadmap from the Radio Access Network to the Edge and Core Networks
Authors: Mehrdad Dianati, et al.
Author Affiliations: WMG, University of Warwick, Coventry, CV4 7AL, UK
Abstract: While machine learning techniques have been applied successfully in many disciplines like computer vision and natural language processing, their application in communication networks is still in its infancy. On the one hand, this is because of the lack of established and widely-accepted training datasets. On the other hand, this is due to the ability to capture and define mathematically accurate models for the optimization of communication networks, which are the outcomes of extensive research activities in the past 50 years. Unfortunately, the increasing complexity of communication systems, following the advent of heterogeneous networks and services, challenges the use of the so far developed models. At the same time, the paradigm of network virtualization and software-defined networking enables the collection and analysis of data giving rise to the emerging field of knowledge-defined networking. Ongoing standardization activities are key in the adoption of machine learning in communication networks. For instance, 3GPP has introduced the Network Data Analytics Function for the control and management of network slices using machine learning and data analytics in current and future networks. In this paper, we review model-aided machine learning at the PHY and MAC layers for transceiver design, radio resource management, and aspects of intelligent control of the propagation channel. We proceed with applications of machine learning at the edge and core networks with emphasis on algorithms that can learn to optimize routing decisions in unseen network topologies. Finally, we conclude with a summary of recent and currently-funded projects on intelligent communications and networking.
Title: Pothole detection and maintenance system based on 3D reconstruction
Authors: Edwin Salcedo, Ammar Yasir Naich, Jesús Requena-Carrión and Mona Jaber
Author Affiliations: Queen Mary University of London, UK
Abstract: Maintenance of critical infrastructure is a costly necessity where developing countries often struggle to deliver timely repairs. The transport system acts as the arteries of any economy in development, and the formation of potholes on the roads can lead to injuries and loss of lives. Although many countries have enabled pothole reporting platforms, duplicate records remain a challenge as well as the differentiation between a deep pothole and a shallow one. To this end, automatic pothole detection has been proposed in which a variety of data sources is used to identify and localize potholes. However, the majority of research fails to classify the potholes in terms of size and depth, albeit this information would be key in prioritizing repair work and improving the safety of the roads. In this work, we conduct a brief survey of the recent research efforts in this area. Then, we introduce a depth and size detection algorithm based on stereo vision, and we contrast its efficiency against two recent techniques. We also present this work using an example of a georeferenced pothole reporting, evaluation, and maintenance system which will implement the developed algorithm and let us draw conclusions about the advantages of the proposed approach. Keywords: Pothole detection; machine learning; stereo vision; computer vision road surface modelling; smart maintenance
Title: Machine Learning enabled food contamination detection using RFID and Internet of things system
Authors: Abubakar Sharif, Shuja Ansari, Kia Dashtipour, Hasan Tahir Abbas, Qammer Hussain Abbasi, Muhammad Ali Imran
Author Affiliations: James Watt School of Engineering
Abstract: This paper presents an approach for contamination sensing of food items and drinks such as soft drinks and alcohol. We employ an RFID wireless sticker and machine learning approach for contaminations sensing. The RFID tag antenna was mounted on pure product and RSSI and phase of backscattered signal is measured using Tagformance Pro devices. Moreover, the performance is further characterize using an android phone connected RFID reader unit. We used machine learning xbost algorithm for further training of model and accuracy of sensing is about 90%. Therefore, this research study paves a way for ubiquitous contamination sensing using RFID and machine learning technologies that can inform their users about the health and safety of their food.
Title: Challenges of Malware Detection in the IoT and a Review of Artificial Immune System Approaches
Authors: Hadeel Alrubayyi, Gokop Goteng, Mona Jaber, James Kelly
Author Affiliations: School of Electronic Engineering and Computer Science: Queen Mary University of London
Abstract: The fast growth of the Internet of Things (IoT) and diverse applications increase the risk of cyberattacks, one of which is malware attacks. Due to the IoT devices' different capabilities and the dynamic and ever-evolving environment, applying complex security measures is challenging, and applying only basic security standards is risky. Artificial Immune Systems (AIS) are intrusion detecting algorithms inspired by the human body's adaptive immune system techniques. Most of these algorithms imitate the B-cell and T-cell defensive mechanisms. They are lightweight, adaptive, and able to detect malware attacks without prior knowledge. In this work, we review the recent advances in employing AIS for improved detection of malware in IoT networks. We present a critical analysis that highlights the limitations of the state-of-the-art in AIS research and offer insights into promising new research directions.