In the last few years, the Internet of Things (IoT) has become one of the most popular buzzwords due to its uses in solving real-world problems such as smart homes, the healthcare sector, smart cities, smart grids, smart education, smart transport, smart communication, etc. The design of IoT applications requires a huge amount of structured and unstructured data since it has many challenges like data production, data capturing, and data organization. To overcome these challenges, big data analytics is one of the most important technologies that have to be adapted.

Big data helps in the increased adaptability of IoT. However, it comes with its own set of issues, such as dealing with large amounts of data and the process of storing and analyzing vast amounts of data across multiple data stores. Machine learning (ML) is a one-of-a-kind solution for boosting IoT and big data platforms. ML is the study of computer algorithms that can learn and improve on their own through experience and data. Supervisory learning methods (support vector machines (SVMs), decision trees (DTs), Naive Bayes) are used to improve data processing in large data sets and provide essential information. In addition, optimization techniques such as nature-inspired algorithms, physics-based algorithms, etc., can be used to obtain fruitful results. By combining all of these, data may be handled more efficiently, and correct data can be communicated between IoT users. There are several concerns about the risks in the growth of IoT and mobile computing, particularly in wireless networks. The majority of the technical security problems are the same as those that apply to traditional servers, workstations, and IoT devices. These hazards are significant because they affect businesses' technical, organizational, and legal aspects.

In this Special Issue, we hope to promote the discussion of innovative solutions for the widespread concerns of various issues in wireless communication and mobile computing data with machine learning, IoT, and optimization techniques. We welcome original research and review articles focused on the fusion of machine learning, big data analytics, and optimization techniques for the IoT.

Potential topics include but are not limited to the following:

• Wireless networks and applications
• IoT for biometric technologies and systems
• Detection and prevention of IoT-based security attacks
• Secure wireless channel and traffic models
• Trust management IoT architectures
• Data and knowledge management
• Healthcare services and health informatics
• Performance evaluation and modeling
• Software engineering for big data analytics
• Cross-domain trust management in smart networks
• Nature-inspired algorithms for IoT and big data
• ML, deep learning, and IoT-based computer vision for wireless communications
• Smart cities, homes/apartments, and education
• Data science and predictive technologies for patient monitoring and weather monitoring