GT-TSCH: Game-Theoretic Distributed TSCH Scheduler for Low-Power IoT Networks

TL;DR

This paper introduces GT-TSCH, a distributed game-theoretic scheduler for TSCH networks that adapts to link quality and traffic load, improving throughput and delay in low-power IoT environments.

Contribution

It proposes a novel distributed TSCH scheduling algorithm based on non-cooperative game theory, addressing schedule updates in response to network dynamics.

Findings

GT-TSCH achieves higher throughput than existing methods.

GT-TSCH reduces end-to-end delay in IoT networks.

The game model guarantees a unique Nash equilibrium.

Abstract

Time-Slotted Channel Hopping (TSCH) is a synchronous medium access mode of the IEEE 802.15.4e standard designed for providing low-latency and highly-reliable end-to-end communication. TSCH constructs a communication schedule by combining frequency channel hopping with Time Division Multiple Access (TDMA). In recent years, IETF designed several standards to define general mechanisms for the implementation of TSCH. However, the problem of updating the TSCH schedule according to the changes of the wireless link quality and node's traffic load left unresolved. In this paper, we use non-cooperative game theory to propose GT-TSCH, a distributed TSCH scheduler designed for low-power IoT applications. By considering selfish behavior of nodes in packet forwarding, GT-TSCH updates the TSCH schedule in a distributed approach with low control overhead by monitoring the queue length, the place of the node in the Directed Acyclic Graph (DAG) topology, the quality of the wireless link, and the data packet generation rate. We prove the existence and uniqueness of Nash equilibrium in our game model and we find the optimal number of TSCH Tx timeslots to update the TSCH slotframe. To examine the performance of our contribution, we implement GT-TSCH on Zolertia Firefly IoT motes and the Contiki-NG Operating System (OS). The evaluation results reveal that GT-TSCH improves performance in terms of throughput and end-to-end delay compared to the state-of-the-art method.