Publicação
Network Monitoring for Industrial IoT
| Resumo: | The increasing complexity of industrial applications demands robust, reliable, and deterministic communication solutions, particularly in Low-Power and Lossy Networks (LLNs), such as those found in the Industrial Internet of Things (IIoT). Time-Slotted Channel Hopping (TSCH), defined in the IEEE 802.15.4e amendment, is designed to meet these requirements, ensuring energy efficiency while maintaining strict performance metrics, such as low latency and high reliability. However, existing TSCH scheduling strategies often lack the flexibility required to adapt to the dynamic and unpredictable environments typically encountered in industrial settings.This research makes several key contributions to the field. First, it presents a comprehensive survey of assessment metrics for evaluating TSCH scheduling in industrial environments, identifying critical gaps in existing performance evaluation methods. Second, it pushes the TSCH scheduling mechanisms to their operational limits, analysing their impact on application by assessing key performance metrics under high-stress conditions. Third, it introduces the RippleTrickle scheduling algorithm, which integrates cross-layer information with the Trickle timer from RPL (Routing Protocol for Low-Power and Lossy Networks). This algorithm dynamically adapts to changing network conditions, optimising communication paths, and enhancing network reliability. RippleTrickle Ranked (RT-Ranked) is also proposed, which incorporates the network topology into the scheduling process, allowing more efficient resource management and improving overall network performance.The proposed solutions were rigorously evaluated at both the application and Media Access Network (MAC) layers, revealing significant improvements in network stability, latency, energy consumption, and overall efficiency. This thesis advances the understanding of TSCH scheduling limitations and offers an innovative adaptive scheduling framework tailored for mission-critical industrial applications that significantly improves network resilience and performance. |
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| Autores principais: | Júnior, Ivanilson França Vieira |
| Assunto: | IIoT IoT Network Monitoring Resilience Assessment Resilience Metrics Avaliação de resiliência IIoT IoT Monitorização de rede Métricas de resiliência |
| Ano: | 2025 |
| País: | Portugal |
| Tipo de documento: | tese de doutoramento |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade de Coimbra |
| Idioma: | inglês |
| Origem: | Estudo Geral - Universidade de Coimbra |
| Resumo: | The increasing complexity of industrial applications demands robust, reliable, and deterministic communication solutions, particularly in Low-Power and Lossy Networks (LLNs), such as those found in the Industrial Internet of Things (IIoT). Time-Slotted Channel Hopping (TSCH), defined in the IEEE 802.15.4e amendment, is designed to meet these requirements, ensuring energy efficiency while maintaining strict performance metrics, such as low latency and high reliability. However, existing TSCH scheduling strategies often lack the flexibility required to adapt to the dynamic and unpredictable environments typically encountered in industrial settings.This research makes several key contributions to the field. First, it presents a comprehensive survey of assessment metrics for evaluating TSCH scheduling in industrial environments, identifying critical gaps in existing performance evaluation methods. Second, it pushes the TSCH scheduling mechanisms to their operational limits, analysing their impact on application by assessing key performance metrics under high-stress conditions. Third, it introduces the RippleTrickle scheduling algorithm, which integrates cross-layer information with the Trickle timer from RPL (Routing Protocol for Low-Power and Lossy Networks). This algorithm dynamically adapts to changing network conditions, optimising communication paths, and enhancing network reliability. RippleTrickle Ranked (RT-Ranked) is also proposed, which incorporates the network topology into the scheduling process, allowing more efficient resource management and improving overall network performance.The proposed solutions were rigorously evaluated at both the application and Media Access Network (MAC) layers, revealing significant improvements in network stability, latency, energy consumption, and overall efficiency. This thesis advances the understanding of TSCH scheduling limitations and offers an innovative adaptive scheduling framework tailored for mission-critical industrial applications that significantly improves network resilience and performance. |
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