Deterministic Scheduling With Optimization of Average Transmission Delays in Industrial Wireless Sensor Networks

As a key characteristic for industrial wireless sensor networks, deterministic scheduling aims to ensure that real-time data flows arrive at destination devices under deadline constraints by allocating necessary communication resources, such as time slots and channels. Current research on deterministic scheduling mainly focuses on how to obtain a feasible scheduling solution. However, optimizing average transmission delays under deterministic flow deadlines is rarely considered when multiple scheduling solutions exist. To address this issue, in this paper we propose two scheduling algorithms: branch and bound based on link conflict classification, and least conflict degree first. The prior algorithm obtains optimal schedulable ratio by constructing a search tree and adopting necessary conditions of scheduling. The latter algorithm dynamically adjusts the scheduling order of flows to distribute channels in a heuristic manner, and achieves approximate optimal schedulable ratio in a short time with low complexity. Simulation results show that both of the proposed algorithms effectively reduce the average transmission delays of real-time data flows while guaranteeing that all flows are delivered before their deadlines.