Distributed Channel Ranking Scheduling Function for Dense Industrial 6TiSCH Networks

Abstract

The Industrial Internet of Things (IIoT) is considered a key enabler for Industry 4.0. Modern wireless industrial protocols such as the IEEE 802.15.4e Time-Slotted Channel Hopping (TSCH) deliver high reliability to fulfill the requirements in IIoT by following strict schedules computed in a Scheduling Function (SF) to avoid collisions and to provide determinism. The standard does not define how such schedules are built. The SF plays an essential role in 6TiSCH networks since it dictates when and where the nodes are communicating according to the application requirements, thus directly influencing the reliability of the network. Moreover, typical industrial environments consist of heavy machinery and complementary wireless communication systems that can create interference. Hence, we propose a distributed SF, namely the Channel Ranking Scheduling Function (CRSF), for IIoT networks supporting IPv6 over the IEEE 802.15.4e TSCH mode. CRSF computes the number of cells required for each node using a buffer-based bandwidth allocation mechanism with a Kalman filtering technique to avoid sudden allocation/deallocation of cells. CRSF also ranks channel quality using Exponential Weighted Moving Averages (EWMAs) based on the Received Signal Strength Indicator (RSSI), Background Noise (BN) level measurements, and the Packet Delivery Rate (PDR) metrics to select the best available channel to communicate. We compare the performance of CRSF with Orchestra and the Minimal Scheduling Function (MSF), in scenarios resembling industrial environmental characteristics. Performance is evaluated in terms of PDR, end-to-end latency, Radio Duty Cycle (RDC), and the elapsed time of first packet arrival. Results show that CRSF achieves high PDR and low RDC across all scenarios with periodic and burst traffic patterns at the cost of increased end-to-end latency. Moreover, CRSF delivers the first packet earlier than Orchestra and MSF in all scenarios. We conclude that CRSF is a viable option for IIoT networks with a large number of nodes and interference. The main contributions of our paper are threefold: (i) a bandwidth allocation mechanism that uses Kalman filtering techniques to effectively calculate the number of cells required for a given time, (ii) a channel ranking mechanism that combines metrics such as the PDR, RSSI, and BN to select channels with the best performance, and (iii) a new Key Performance Indicator (KPI) that measures the elapsed time from network formation until the first packet reception at the root.

Low-Latency and Q-Learning-Based Distributed Scheduling Function for Dynamic 6TiSCH Networks

“…Many studies have been done on TSCH networks to develop a good scheduling function that meets various application requirements. In [20], the channel ranking scheduling function (CRSF) was introduced as a distributed scheduling function. This function determines the required number of cells by utilizing bandwidth estimation with Kalman filtering.…”

Thorough Performance Evaluation & Analysis of the 6TiSCH Minimal Scheduling Function (MSF)

“…Recently, Amezcua Valdovinos et al [31] proposed the Channel Ranking Scheduling Function (CRSF), another 6TiSCH scheduling function that estimates the number of required cells as the current queue depth minus the number of allocated cells and then pass it through a Kalman filter. In addition to this, a channel selection mechanism is introduced that relies on a composite metric including RSSI, PDR and background noise, all measured passively.…”

Energy-Constrained Design of Joint NOMA-Diversity Schemes with Imperfect Interference Cancellation

“…These latter studies have proved the reciprocal benefits among NOMA and PR, but their joint applicability in a real network requires a further deepening of some practical aspects. The first one concerns the reception criterion, which usually relies on the simplified erasure model and not on the more realistic capture one [ 19 , 20 , 21 ]. The second aspect involves the assumption of perfect IC, which does not occur in an actual receiver [ 22 , 23 , 24 , 25 , 26 ], and leads to two main consequences, related to the need of increasing the energy separation to allow the decoding of packets using different levels.…”