Machine Learning Techniques on IoT Applications

Dear Colleagues,

Emerging technologies and major advances in computing systems, software, hardware, and communication technologies have boosted the connection speed of the internet and the physical world via the internet of things (IoT). These enhancements have made communication between varied devices easier than ever before. The IoT is a combination of complex, heterogeneous, and dynamic embedded technologies, including wireless and wired communications, actuator and sensor devices, and the physical objects (things) connected to the internet. IoT systems must be capable of accessing or sensing the raw data from varied resources over networks and extracting meaningful information (knowledge). Considering its diverse nature, the management of such IoT systems is difficult and there is a need for improvement in terms of diversity, efficiency, effectiveness, and security. Recently, numerous studies have made advances in applying machine learning (ML) to improve IoT applications and render IoT-enabled services, such as internet traffic classification, network management, traffic engineering, security, and quality of service optimization. The IoT can benefit by leveraging support from ML, as ML can play a vital role in data intelligence and thereby help to explore the real world. ML is considered to be the most suitable computational paradigm that provides embedded intelligence to IoT systems and helps to infer useful information from device- or human-generated data. Furthermore, ML techniques have been useful in tasks such as regression, classification, and density estimation in a wide range of applications, including computer vision, speech recognition, malware detection, bioinformatics, and authentication. Considering its ability to provide feasible solutions to mine the hidden features and information from IoT data, ML enables users to utilize deep analytics and develop secured, intelligent, and efficient IoT applications. Even though ML-based IoT applications are experiencing explosive growth, there exist numerous unidentified and unfilled gaps between current solutions and the orchestrating demands of its development lifecycle. Accordingly, this Special Issue seeks to showcase research papers, short communications, and review articles that focus on novel methodological developments in ML-based advances for IoT applications.

Dr. Vinay Chamola
Dr. Bharat Bhushan
Guest Editors

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• machine learning
• internet of things
• artificial intelligence
• deep learning
• smart city
• industry 4.0
• security
• prediction model
• malware detection
• smart data