The rapid progress of the Internet of Things (IoT) has continued to offer humanity numerous benefits, including many security and safety-critical applications. However, unlocking the full potential of IoT applications, especially in high-consequence domains, requires the assurance that IoT devices will not constitute risk hazards to the users or the environment. To design safe, secure, and reliable IoT systems, numerous frameworks have been proposed to analyse the safety and security, among other properties. This paper reviews some of the prominent classical and model-based system engineering (MBSE) approaches for IoT systems’ safety and security analysis. The review established that most analysis frameworks are based on classical manual approaches, which independently evaluate the two properties. The manual frameworks tend to inherit the natural limitations of informal system modelling, such as human error, a cumbersome processes, time consumption, and a lack of support for reusability. Model-based approaches have been incorporated into the safety and security analysis process to simplify the analysis process and improve the system design’s efficiency and manageability. Conversely, the existing MBSE safety and security analysis approaches in the IoT environment are still in their infancy. The limited number of proposed MBSE approaches have only considered limited and simple scenarios, which are yet to adequately evaluate the complex interactions between the two properties in the IoT domain. The findings of this survey are that the existing methods have not adequately addressed the analysis of safety/security interdependencies, detailed cyber security quantification analysis, and the unified treatment of safety and security properties. The existing classical and MBSE frameworks’ limitations obviously create gaps for a meaningful assessment of IoT dependability. To address some of the gaps, we proposed a possible research direction for developing a novel MBSE approach for the IoT domain’s safety and security coanalysis framework.

The agricultural industry has the potential to undergo a revolutionary transformation with the use of Internet of Things (IoT) technology. Crop monitoring can be improved, waste reduced, and efficiency increased. However, there are risks associated with system failures that can lead to significant losses and food insecurity. Therefore, a proactive approach is necessary to ensure the effective safety assessment of new IoT systems before deployment. It is crucial to identify potential causes of failure and their severity from the conceptual design phase of the IoT system within smart agricultural ecosystems. This will help prevent such risks and ensure the safety of the system. This study examines the failure behaviour of IoT-based Smart Irrigation Systems (SIS) to identify potential causes of failure. This study proposes a comprehensive Model-Based Safety Analysis (MBSA) framework to model the failure behaviour of SIS and generate analysable safety artefacts of the system using System Modelling Language (SysML). The MBSA approach provides meticulousness to the analysis, supports model reuse, and makes the development of a Fault Tree Analysis (FTA) model easier, thereby reducing the inherent limitations of informal system analysis. The FTA model identifies component failures and their propagation, providing a detailed understanding of how individual component failures can lead to the overall failure of the SIS. This study offers valuable insights into the interconnectedness of various component failures by evaluating the SIS failure behaviour through the FTA model. This study generates multiple minimal cut sets, which provide actionable insights into designing dependable IoT-based SIS. This analysis identifies potential weak points in the design and provides a foundation for safety risk mitigation strategies. This study emphasises the significance of a systematic and model-driven approach to improving the dependability of IoT systems in agriculture, ensuring sustainable and safe implementation.

The widespread adoption of Internet of Things (IoT) systems emphasises the need for dependable performance. However, challenges emerge due to the inadequacy of informal failure analysis models in evaluating the complex and distributed nature of IoT designs. These challenges can lead to inconsistencies, complicated system modifications, and extended development timelines. This paper presents a Model-Based Systems Engineering (MBSE) approach to create an integrated quantitative analysis framework for assessing the reliability of IoT systems. This approach simplifies the modelling and analysis of failure behaviours in IoT design, linking failure models to system design using standard engineering frameworks. Based on descriptions of IoT systems, the integrated approach establishes a traceability link between the system description and the analysable artefacts generated, such as stochastic Fault Tree Analysis (FTA) and Bayesian network (BN) models, which are essential for effective failure analysis of IoT systems. The approach demonstrates how to quantify system-level failures, identify and prioritise the weakest links in the design, and show how targeted modifications can improve the overall reliability of IoT systems. An illustrative case study on IoT is included to highlight the effectiveness and practical applicability of the proposed framework.