Complex Patterns of Failure: Fault Tolerance via Complex Event Processing for IoT Systems

Abstract

Fault-tolerance (FT) support is a key challenge for ensuring dependable Internet of Things (IoT) systems. Many existing FT-support mechanisms for IoT are static, tightly coupled, and inflexible, and so they struggle to provide effective support for dynamic IoT environments. This paper proposes Complex Patterns of Failure (CPoF), an approach to providing FT support for IoT systems using Complex Event Processing (CEP) that promotes modularity and reusability in FT-support design. System defects are defined using our Vulnerabilities, Faults, and Failures (VFF) framework, and error-detection strategies are defined as nondeterministic finite automata (NFA) implemented via CEP systems. We evaluated CPoF on an automated agriculture system and demonstrated its effectiveness against three types of errordetection checks: reasonableness, timing, and reversal. Using CPoF, we identified unreasonable environmental conditions and performance degradation via sensor data analysis.

Rule based complex event processing for IoT applications: Review, classification and challenges

“…Using complex event processing, Power and Kotonya (2019a) introduced the complex event power failure (CoPF) for fault tolerance in IoT systems. Faults are characterized as non deterministic finite automata (NFA) implemented through CEP, and they are defined using various vulnerabilities, failure frameworks, and error detection techniques.…”

Uma Revisão Sistemática da Literatura Sobre Tolerância a Falhas em Internet das Coisas

“…Os microsserviços recebem informações dos dispositivos e suas ações são baseadas na análise dos dados recebidos. Os mesmos autores também empregam o CEP aliado a ciência do contexto para propor um arcabouço que descreve genericamente as falhas e seus efeitos no sistema [44].…”

BoboCEP: Distributed Complex Event Processing for Resilient Fault-Tolerance Support in IoT

“…A. FT Scenario 1) Long-Term Event Processing: For our FT scenario, we considered the trend check NFA from our previous work [6], [7]. This NFA had two states: the first consumed an initial water level data event e 1 ; the second consumed a water level event e 2 , such that e 2 was at least 3 days after e 1 and satisfied:…”

“…In our previous work [6], [7], we explored how CEP systems could be used as an effective means of providing data-centric error detection, assessment, and recovery. Errors were defined as NFAs that are completed when applicable steam data fulfilled the states of the automaton; a completion represented the detection of an error.…”