LAUSR

Edge computing-based framework is capable of improving users’ quality of experience in cognitive Internet of Things (IoT) networks. To explore the advantages of this edge computing-based framework, possible offloading and processing delay resulting from computation bottlenecks, and the offloading latency caused due to inter-cell interference must be properly considered. This paper thus considered a multi-class channel access mechanism for cognitive edge computing-based IoT networks where IoT users were categorized based on their quality of experience requirements. Essential IoT devices are permitted to offload to the edge server at any time following the hybrid channel access model, while delay-tolerant IoT devices are only permitted to offload to the server when the channel is idle following the overlay channel access model. Analyses were obtained for transmission rate and offloading delay to demonstrate the performance of the proposed mechanism, while important metrics such as total offloading latency and total offloading cost were investigated. The total offloading costs were formulated through the mixed strategy Nash equilibrium method. The proposed mechanism achieves lower offloading latencies and costs for both type 1 and type 2 CUs when compared with existing methods. The obtained results showed that multi-class channel access mechanisms can reduce packet offloading delay in cognitive edge computing-based IoT networks.