MOSAIC: Multi-objective Optimization Strategy for AI-aided Internet-of-Things Communications

Future Internet-of-Things (IoT) communication trends toward heterogeneous services and diverse quality-of-service requirements pose fundamental challenges for network management strategies. In particular, multi-objective optimization is necessary in resolving the competition among different nodes sharing limited wireless network resources. A unified coordination mechanism is essential such that individual nodes conduct the opportunistic maximization of heterogeneous local objectives for efficient distributed resource allocation. To such a problem, this paper proposes an artificial intelligence (AI) based framework which is termed as multi-objective optimization strategy for AI-aided Internet-of-Things communications (MOSAIC). This framework enables to tackle numerous MOO tasks in IoT network management with simple reconfiguration of learning rules. In this strategy, a component unit associated with an individual network node includes a pair of DNNs to learn optimal local functions responsible for calculation and distributed coordination, respectively. The resultant AI module swarm called DNN tiles realizes the node cooperation that collectively seeks distributed MOO calculation rules. The advantage of MOSAIC is characterized by Pareto tradeoffs among conflicting performance metrics in diverse wireless networking configurations subject to severe interference and distinct criteria for multiple targets.