Probabilistic Time Slot Leasing in TDMA-Based IoT Networks for Enhanced Channel Utilization

TL;DR

This paper presents a distributed TDMA scheduling protocol for IoT networks that adaptively reallocates time slots using a probabilistic mechanism, significantly improving channel utilization, throughput, and reliability in dynamic, resource-constrained environments.

Contribution

It introduces a novel adaptive, probabilistic TDMA scheduling protocol that dynamically reallocates time slots to optimize channel utilization in IoT networks.

Findings

Enhanced throughput, latency, and reliability demonstrated in simulations.

Significant improvement in channel utilization and network efficiency.

Robust and scalable scheduling suitable for next-generation IoT environments.

Abstract

In large-scale resource-constrained wireless networks, such as those prevalent in the Internet of Things (IoT), efficient communication scheduling remains a critical challenge. Among the various approaches, Time Division Multiple Access (TDMA) protocols have been widely adopted for their structured and collision-free communication capabilities. Nevertheless, despite extensive research in this area, current solutions often exhibit suboptimal performance, particularly in dynamic environments where node activity levels fluctuate over time. This paper introduces a novel fully distributed TDMA-based scheduling protocol that intelligently maximizes the utilization of communication resources. The proposed approach adaptively reallocates underutilized time slots, originally assigned to temporarily inactive nodes, to those experiencing higher communication demands. This dynamic reallocation not only improves channel utilization but also reduces idle periods, thereby enhancing overall network efficiency. To further enhance performance, we incorporate a lightweight probabilistic mechanism that governs the temporal leasing of unused slots. This mechanism balances the trade-off between slot availability and transmission reliability, minimizing packet loss while preserving fairness and stability within the network. Simulations across a range of network scenarios demonstrate that our protocol significantly improves throughput, latency, and reliability in resource-constrained environments. These results highlight the protocol's potential as a robust and scalable solution for adaptive and energy-efficient scheduling in next-generation IoT networks.