Multi-protocol Aware Federated Matching for Architecture Design in Heterogeneous IoT
Haitham H. Esmat, X. Xia, Beatriz Lorenzo, Linke Guo · GLOBECOM 2022 - 2022 IEEE Global Communications Conference · 2022
Enabling timely data collection in heterogeneous IoT networks under different protocols and spectrum bands (e.g., WiFi, Bluetooth, Zigbee, LoR$a$) is crucial to implementing large-scale IoT systems. This paper presents a federated matching framework for heterogeneous IoT networks in which an intermediate layer of multi-protocol mobile gateways (M-MGs) is deployed by different service providers (SPs) to collect and relay data from IoT objects and perform computing tasks. The aim is to develop collaborative strategies between M-MGs and SPs to minimize the average weighted sum of the age-of-information and energy consumption. A novel collaborative framework based on a 2-level multi-protocol multi-agent actor-critic (MP-MAAC) is presented, where M-MGs and SPs can learn the interactive strategies through their own observations. The M-MGs strategies include the selection of IoT objects for data collection, execution, and offloading t o S Ps' a ccess points while SPs decide on the spectrum allocation. Moreover, we incorporate federated matching (Fed-Match) into the multi-agent collaborative framework to improve the convergence of the learning process. The numerical results show that our Fed-Match algorithm reduces the Aol by factor 4, collects twice more packets than existing approaches and establishes design principles for the stability of the training process.