Advances in mobile information systems (MIS) promise to add immense value to our lives with smart technologies and services. In general, connectivity and digitization are the two most significant aspects of MIS applications, especially when dealing with user-centered innovations, digital transformation, data-driven optimization, and automation. Enhancing the interconnection between physical devices and embedded sensing and communication networks helps to add smartness to MIS systems, including sensors, actuators, and automation. Ambient intelligence is the future of MIS implementation, services, and applications which require technology to be an integral part of human interaction. Properties such as usability, technical feasibility, positive socio-economic impacts, and trustworthiness make its application possible for various MIS applications.

However, several major challenges are present in ambient intelligence for massive communication in mobile information systems. One big challenge is the limited performance of ambient intelligence, and so intelligent algorithms must be developed to reveal how many communications can be supported by ambient intelligence in MIS. Another challenge is assessing how ambient intelligence will impact conventional communication schemes, such as multiple-input and multiple-output (MIMO), orthogonal frequency-division multiplexing (OFDM), caching, physical-layer security, and edge computing. It is therefore essential to develop deep learning-based algorithms in this field. Another challenge is the complicated resource management of ambient intelligence for MIS, where deep reinforcement learning-based schemes should be developed to fully exploit the system communication and computing resources.

This Special Issue aims to obtain better value from ambient intelligence technology and transform real-time MIS applications and services. We invite researchers and practitioners to share their contributions on cutting-edge research, current trends, and best practices of implementing ambient intelligence for MIS applications and operations.

Potential topics include but are not limited to the following:

• Trends in ambient intelligence for smart environments
• Ambient intelligence for context awareness in smart environments
• Intention recognition and behavior modeling with ambient intelligence
• Cognitive and emotional awareness in MIS applications with ambient intelligence techniques
• Innovative sensing devices with ambient intelligence
• Application of artificial intelligence in various MIS scenarios, such as Industrial Internet of Things and smart cities
• Seamless communication and interactions with ambient intelligence techniques
• Deep learning, federated learning, and transfer learning
• Mobile and wearable ambient intelligence for MIS applications
• Ambient intelligence for the automation of MIS services and applications
• Ambient intelligence ins edge computing