The implementation of a wireless sensor network has been utilized in many applications of urban and rural areas. One such application is smart-grid Internet of Things, (IoT)-based monitoring systems that work on real-time communication. They require an intelligent scheduling mechanism to scrap delay in dynamic traffic flows without undergoing any efficiency loss. This study presents a data-driven TDMA-based paradigm, called traffic-adaptive slot-assignment MAC (TASA-MAC) that supports IoT-channel dynamics in time-critical large-scale monitoring systems while combating the packet delay. The proposed work showcases low run-time scheduler-complexity for low-cost end nodes independent of the network’s scalability owing to TASA-MAC’s optimal fitness-function parameters under intermittent IoT-traffic scenarios, i.e., energy cost and residual energy ratio. The proposed scheduler minimizes the delay of exigency packets while also prompts the system to deliver them before their deadline expiry. The analytical and simulation model of TASA-MAC demonstrates its superiority in terms of moderated delay, packet throughput, and energy demand as compared to the existing models.

无线传感器网络的实现已在城市和农村地区的许多应用中得到利用。一种这样的应用是基于智能网格的物联网（IoT）的监视系统，该系统可进行实时通信。他们需要一种智能的调度机制，以消除动态业务流中的延迟，而不会造成任何效率损失。这项研究提出了一种基于数据驱动的基于TDMA的范例，称为流量自适应时隙分配MAC（TASA-MAC），它在时间紧迫的大型监控系统中支持IoT通道动态，同时消除了数据包延迟。由于在间歇性IoT流量场景下TASA-MAC的最佳适应度函数参数的影响，拟议的工作展示了低成本终端节点的低运行时间调度程序复杂性，而与网络的可扩展性无关，即 能源成本和剩余能源比率。拟议的调度程序将紧急数据包的延迟降到最低，同时还提示系统在其截止日期到期之前将其发送出去。与现有模型相比，TASA-MAC的分析和仿真模型证明了其在适度延迟，数据包吞吐量和能量需求方面的优势。