Hybrid Scalable Scheduling Reservation Protocol for Industrial IoT Networks

Abstract

In dense Wireless Sensor Networks (WSNs), communication fails due to multipath fading and external interferences (EI). In Industry 4.0 environments, low-power wireless standards such as ZigBee, Bluetooth Low Energy (BLE), WirelessHART, and ISA100.11a face challenges in terms of reliability. Recently, Time-Slotted Channel Hopping (TSCH) protocol has been adopted as a default mode in IEEE 802.15.4, providing resilience against EIs and multipath fading through time synchronization and channel hopping. Although WirelessHART also adopts TSCH, its centralized and non-IP-based architecture introduces significant control overhead, which limits scalability and seamless Internet integration. BLE, relying on distinct MAC and physical-layer mechanisms, remains susceptible to packet loss under harsh industrial conditions. Conversely, traditional IP-enabled solutions that do not employ TSCH often depend on primitive channel-access protocols, leading to internal collisions and reduced reliability in dense deployments. 6TiSCH (RFC 9030) standard extends TSCH by providing IPv6 connectivity through a lightweight 6LoWPAN stack and the 6top sublayer, enabling integration with the IPv6 protocol suite and improved architectural flexibility. Despite these advantages, network scalability remains an open challenge, as existing 6TiSCH Scheduling Functions (SFs) operating over the 6top sublayer impose trade-offs among reliability, latency, and energy consumption. In this paper, we present a Hybrid Scalable Scheduling Reservation Protocol (SSR), addressing important challenges with scalability, reliability, latency, and energy efficiency. The proposal, with its cake-slicing technique, network depth mapping, efficient traffic adaption, and congestion mitigation, shows potential for significant improvements over existing solutions like Minimal Scheduling Function (MSF) (RFC 9033). The experimental results show significant improvements over fixed hysteresis-based scheduling policies of MSF and demonstrate scalable performance using hundreds of IoT nodes within a subnet.