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The ever-increasingly urban populations and their material demands havebrought unprecedented burdens to cities. Smart cities leverage emergingtechnologies like the Internet of Things (IoT), Cognitive Radio Wireless SensorNetwork (CR-WSN) to provide better QoE and QoS for all citizens. However,resource scarcity is an important challenge in CR-WSN. Generally, this problemis handled by auction theory or game theory. To make CR-WSN nodes intelligentand more autonomous in resource allocation, we propose a multi-agentreinforcement learning (MARL) algorithm to learn the optimal resourceallocation strategy in the oligopoly market model. Firstly, we model amulti-agent scenario, in which the primary users (PUs) is the sellers and thesecondary users (SUs) is the buyers. Then, we propose the Q-probabilisticmultiagent learning (QPML) and apply it to allocate resources in the market. Inthe multi-agent interactive learning process, the PUs and SUs learn strategiesto maximize their benefits and improve spectrum utilization. Experimentalresults show the efficiency of our QPML approach, which can also convergequickly.

Market-Based Model in CR-WSN: A Q-Probabilistic Multi-agent Learning Approach