Multinode Data Offloading for Urban Wireless Sensor Networks Based on Fog Computing: A Multiarmed Bandit Approach

Abstract

Urban wireless sensor networks (UWSNs) are an important application scenario for the Internet of Things (IoT). With the emergence of many computationally intensive applications based on urban environments, sensors in wireless sensor networks are unable to meet demands such as latency due to their limited resources. Fog computing architectures have the potential to liberate data transmission from resource-constrained sensor nodes through data offloading. Therefore, data collection and scalable collaboration based on fog architectures are seen as a challenge. For the multinode data offloading problem, a multinode data offloading strategy based on stable matching and MAB (multi-armed bandit) model is proposed to maximize the offloading success rate while guaranteeing the latency requirements of the source task nodes. Firstly, the multinode data offloading problem is modelled. Secondly, the case of multinode selection conflict is considered, and selection conflict and information exchange are reduced by the MAB model and the fallback timer. Then, an adjustment strategy is proposed based on the idea of delayed reception in the stable matching of game theory. Finally, the multinode data offloading problem is solved by successive iterations. The proposed algorithm not only accomplishes coordinated cooperation between nodes to achieve high-quality data offloading and avoid collisions between nodes but also reduces the amount of information exchanged between nodes. The effectiveness of the algorithm is demonstrated by theoretical analysis and simulation experiments.