An Adaptive Schedule for TSCH Networks in the Industry 4.0

Abstract

TSCH (Time Slotted Channel Hopping) networks are excellent candidates to support applications of industry 4.0 with latency, energy and reliability requirements. However, such applications have to face topology, traffic or even application changes. How can TSCH networks adapt to such changes? We propose a solution ranging from network construction to data gathering. We show how the schedule can adapt to changing topology, traffic and application conditions. We compute the theoretical bounds with regard to key performance indicators and compare them with the values obtained by NS3 simulation.

Comparative Analysis of Time-Slotted Channel Hopping Schedule Optimization Using Priority-Based Customized Differential Evolution Algorithm in Heterogeneous IoT Networks

“…A debt-based scheduler is presented in [ 15 ]. In this approach, a debt value is calculated for each TSCH device that has a message to transmit, which is equal to the multiplication of the remaining number of data messages that the node has to transmit and the depth of the device in the network.…”

“…Similarly, Ines Khoufi et al [ 16 ] proposed a multislotframe to determine the lower bound number of slots required to perform data gathering and to support sensor flows with data delivery constraints. These two approaches [ 15 , 16 ] only allocate one cell per node to handle all traffic. Therefore, the performance of the algorithm degrades under high traffic loads.…”

A Survey of 802.15.4 TSCH Schedulers for a Standardized Industrial Internet of Things

“…Centralized schedulers may therefore be more suitable when topology and links are stable and less numerous. Coloring & matching TASA-RTX [119] Coloring & matching, Inverse Greedy Farias et al [125] Queue-based MODESA [126] Greedy MODESA Wu et al [124] Margin slots Yang et al [127] SSA, FSC, free node Dawn [128] Not specified Chen et al [129] LSS & LPS Ojo et al [130] Hungarian EES & V-H. [118] Greedy, VAM ADP [116] Approximate Dynamic Programming Khoufi et al [131] Debt-based PRCOS [117] Coloring & pruning, Cross-layer MILS [132] Constrained Satisfaction Problem Minet et al [133] Debt-based CONCISE [120] Cross-layer Devaja et al [134] Message-passing max-product belief prop. SPRF [135] Coloring & matching, blossom & heuristic Khorov et al [136] Retry & shared cell optimization Brun-Laguna et al [137] Load-based MASTER [121] Flow-based TX & Reverse Longest Path First Portaluri et al [138] Shell-game-based Heterogeneous traffic is typically addressed since most centralized schedulers take the offered traffic or similar information from each individual node as input when building the schedule.…”

Toward an Utra-Low Latency Based Q-learning Assisted 6TiSCH Network in Smart Industries

“…In recent years, Industry 4.0 brings revolution in the industries it also introduces the requirement of seamless internet connectivity, communication ability, and computational abilities. Some research efforts have been made for fog based Industry 4.0 [15,16] and 6TiSCH based Industry 4.0 [17,18]. The fog assisted industries work well in delay sensitive industrial applications while 6TiSCH provides connectivity among objects.…”