Clustering in Asynchronous Distributed Transmission for IoT Applications

Asynchronous distributed transmission (ADT) supports scalable, energy-efficient Internet of Things (IoT) communication but suffers from time, frequency, and phase offsets at the receiver. Bit error rate (BER) performance in ADT depends on the frequency offsets between the transmitting nodes, making it a suitable parameter for system design. Motivated by this, we propose a clustering framework that groups IoT devices into a fixed number of transmitters in ADT with high intra-cluster frequency diversity to enhance BER performance. We model the clustering as a constrained combinatorial optimization problem, which is NP-hard, and solve it by proposing a Frequency-Aware Size-Constrained (FASC) clustering algorithm. FASC achieves near-optimal performance with reduced computational cost and consistently outperforms state-of-the-art schemes in terms of BER and convergence, making it well-suited for real-world ADT-based IoT systems.