Abstract
The Synchronized Single-hop Multiple Gateway (SHMG) is a framework recently proposed to support mobility into 6TiSCH, the standard network architecture defined for Industrial Internet of Things (IIoT) deployments. SHMG supports industrial applications with stringent requirements by adopting the Shared-Downstream Dedicated-Upstream (SD-DU) scheduling policy, which allocates to Mobile Nodes (MNs) a set of dedicated transmission opportunities for uplink data. Such allocation is performed on all the Border Routers (BRs) of the network without considering the location of MNs. Transmission opportunities are reserved also in BRs far from the current location of the MN, resulting in a waste of resources that limits the maximum number of nodes supported by the network. To overcome this problem, we propose a Location-Aware Scheduling Algorithm (LASA) that takes into account the position of MNs to build and maintain an efficient communication schedule. Specifically, LASA tries to prevent conflicts arising due to node mobility, in a preventive manner, so as to minimize packet dropping. We evaluate LASA via simulation experiments. Our results show that LASA allows to increase the number of MNs by more than four times, with respect to SD-DU, yet guaranteeing a Packet Delivery Ratio higher than 98%.
Recommended Citation
M. Pettorali et al., "LASA: Location-Aware Scheduling Algorithm In Industrial IoT Networks With Mobile Nodes," Proceedings - 2023 IEEE 24th International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2023, pp. 185 - 194, Institute of Electrical and Electronics Engineers, Jan 2023.
The definitive version is available at https://doi.org/10.1109/WoWMoM57956.2023.00033
Department(s)
Computer Science
Keywords and Phrases
6TiSCH; IIoT; Location aware; Mobility; Scheduling Function
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2023 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2023
Comments
National Science Foundation, Grant I53C22000690001