An AI-based Framework to Optimize Mobile Services

In current digital networks, novel verticals require diversity and extreme efficiency from network infrastructures. Recent studies have shown the key role of Artificial intelligence (AI) to improve resource management and to reduce operational costs of mobile services and networks. Furthermore, the rapid development of connected Internet of Things devices has created a variety of services. Since the complexity of mobile services has increased, efficient designs for these services are required to satisfy increasing power and processing requirements. Mobile services, however, face numerous challenges, including a shortage of data, inefficient data processing, and a lack of multi-service collaboration mechanisms. These setbacks have delayed the development of commercial applications that rely on mobile networks. Therefore, this thesis presents a framework, based on the "global brain" concept, to orchestrate various mobile services. The framework enhances mobile services for two scenarios: 1) low data rate (e.g., ambient intelligence) and 2) high data rate (e.g., virtual reality (VR) streaming). The distinction between low and high data rates is based upon bandwidth requirement. For low data rate scenarios, this thesis investigates the usefulness of emotion-based data for static (e.g., customer satisfaction) and dynamic (e.g., location-based recommendations) environments. We begin by presenting a practical multimodal emotion recognition system based on audio and video information, which improves emotion recognition accuracy. The results indicate that the integration of emotions is vital for customer satisfaction assessment and recommendation systems. For high data rate scenarios, we address VR streaming due to its high computational cost and latency. Notably, improved viewport (VP) and gaze prediction schemes are critical for enabling smoother VR streaming, as these features are the foundation of the user's experience while wearing the headset. Thus, the global brain proposes novel VP and gaze prediction schemes which produce high prediction accuracy and reduced resource consumption. Finally, this thesis proposes a novel knowledge sharing mechanism that enables mobile services to improve their models by learning from others and incorporating global knowledge, which significantly increases inference accuracy. To prevent sensitive information leakage during knowledge sharing among service providers, we propose a lightweight multi-key homomorphic encryption scheme that allows mobile services to protect their knowledge (i.e., deep neural network weights).