A Global Brain fuelled by Local intelligence: Optimizing Mobile Services and Networks with AI

Artificial intelligence (AI) is among the most influential technologies to improve daily lives and to promote further economic activities. Recently, a distributed intelligence, referred to as a global brain, has been developed to optimize mobile services and their respective delivery networks. Inspired by interconnected neuron clusters in the human nervous system, it is an architecture interconnecting various AI entities. This paper models the global brain architecture and communication among its components based on multi-agent system technology and graph theory. We target two possible scenarios for communication and propose an optimized communication algorithm. Extensive experimental evaluations using the Java Agent Development Framework (JADE), reveal the performance of the global brain based on optimized communication in terms of network complexity, network load, and the number of exchanged messages. We adapt activity recognition as a real-world problem and show the efficiency of the proposed architecture and communication mechanism based on system accuracy and energy consumption as compared to centralized learning, using a real testbed comprised of NVIDIA Jetson Nanos. Finally, we discuss emerging technologies to foster future global brain machinelearning tasks, such as voice recognition, image processing, natural language processing, and big data processing.