Real time broadcast scheduling in TSCH for smart healthcare using cuckoo search algorithm

• Proposes a Cuckoo Search Algorithm (CSA)-based broadcast scheduling method for TSCH in Smart Healthcare Sensor Networks (SHSNs). • Demonstrates improved performance over Genetic Algorithm (GA) and Immune Genetic Algorithm (IGA) in terms of throughput, delay, and computational time. • Validated through simulations on networks ranging from 10 to 100 nodes, and a real-time implementation on a 21-node IoT testbed. • Includes statistical analysis with confidence intervals and t -tests to support the significance of results. • Discusses the potential for real-world deployment and scalability, with future work targeting dynamic topology and integrated security features. Smart Healthcare Sensor Networks (SHSNs) have garnered increasing attention due to their potential to support real-time health monitoring. However, SHSNs often suffer from low bandwidth, leading to reduced successful packet transmission. To overcome this limitation and achieve efficient broadcast scheduling without collisions, Time Slotted Channel Hopping (TSCH) is utilized. The broadcast scheduling challenge in SHSNs arises from limited bandwidth and the need for simultaneous data transmissions that avoid interference. This work addresses the problem by enabling concurrent transmissions in the same time slot, provided that they are interference-free, thereby reducing the overall network turnaround time and enhancing time slot utilization within the TSCH frame. The primary goal is to minimize the TSCH cycle duration and maximize node communication efficiency with reduced computational overhead. To this end, the proposed method employs Genetic Algorithm (GA), Immune Genetic Algorithm (IGA), and Cuckoo Search Algorithm (CSA) to optimize TSCH scheduling. Unlike GA and IGA, CSA emphasizes intelligent exploration of the search space by leveraging Lévy flights and adaptive strategies, which helps it exploit known information more effectively. The core philosophy behind using CSA lies in its bio-inspired, adaptive mechanism that balances global search and local refinement, making it highly suitable for dynamic and constrained environments like SHSNs. Simulation results demonstrate that CSA outperforms GA and IGA in terms of throughput, delay, and computational time. Specifically, CSA achieves up to a 12.4 % increase in throughput, a 17.8 % reduction in transmission delay, and a 15.6 % improvement in computational efficiency, validating its superiority and practical applicability in real-world smart healthcare systems.