FL-RAEO: Fuzzy Logic Guided Random Access Efficiency Optimization for Massive Access Control in Heterogeneous IoT

Enabling Internet-of-Things (IoT) in fifth generation (5G) networks is challenging due to the low access efficiency in the presence of massive random access (RA) requests. To tackle this, we investigate multi-criterion RA ranking and random access efficiency (RAE) maximization for massive IoT networks to deal with devices' heterogeneity and limited RA resources. A fuzzy logic-guided suitability ranking (FL-SR) scheme is proposed, where multiple criteria are considered such as RA delay, movement speed, and battery capacity to ensure that various service and application requirements are met. With normalized suitability and deployability from the FL-SR scheme, the backoff window size gets more flexible than previous work. A fuzzy logic guided random access efficiency optimization (FL-RAEO) algorithm is proposed to maximize the RAE. Thanks to the derived closed-form expressions for the optimal RAE, the FL-RAEO algorithm achieves optimal performance in average access delay, access throughput. and successful access rate.