An End-to-End Reliability Framework of the Internet of Things

Abstract

The Internet of Things (IoT) paradigm feeds from many scientific and engineering fields. This involves a diversity and heterogeneity of its underlying systems. When considering End-to-End IoT systems, we can identify the emergence of new classes of problems. The best-known ones are those associated to standardization for better interoperability and compatibility of those systems, and those who gave birth of new paradigms like that of Fog Computing. Predicting the reliability of an End-to-End IoT system is a problem belonging to this category. On one hand, predicting reliability can be mandatory, most times, before the deployment stage. On another hand, it may help engineers at the design and the operational stages to establish effective maintenance policies and may provide the various stakeholders and decision-makers a means to take the relevant actions. We can find in the literature works which consider only fragments of End-to-End IoT systems such as those assessing reliability for Wireless Sensors Networks (WSN) or Cloud subsystems, to cite just a few. Some other works are specific to well-defined industries, like those targeting reliability study of E-health and Smart-Grid infrastructures. Works that aims to assess reliability for an End-to-End IoT system are remarkably rare and particularly restrained in terms of expressiveness, flexibility, and in their implementation time complexity. In this paper, we apply the Reliability Block Diagram (RBD) paradigm to set up a framework for End-to-End IoT system reliability modeling and analysis. Our contribution is four-fold: we propose an IoT network-based layered architecture, we model in depth each layer of the proposed architecture, we suggest a flow chart to deploy the proposed framework, and we perform a numerical investigation of simplified scenarios. We affirm that the proposed framework is expressive, flexible, and scalable. The numerical study reveals mission time intervals which characterize the behavior of an IoT system from the point of view of its reliability.

Parametric Evaluation Techniques for Reliability of Internet of Things (IoT)

“…Improving the reliability of IoT fusion sensor techniques in the setting of mobile phone sensors involves several key considerations [61][62][63]. By implementing sensor calibration, advanced fusion algorithms, redundancy, data validation, energy efficiency optimizations, edge computing, security measures, and continuous testing, it can enhance the reliability of sensor data in mobile phones for IoT applications [64][65][66]. These measures collectively ensure accurate and dependable sensor readings, leading to improved performance and user experience in the IoT ecosystem.…”

Reliability Analysis of Wireless Sensor Network for Smart Farming Applications

“…In [34] a bidirectional data transmission tree model is used to evaluate the reliability of a WSN with a chain structure. The end-to-end reliability of IoT is also investigated in [35] using a reliability block diagram method. In [36] a phased-mission framework is proposed to analyze the communication reliability of WSNs considering both infrastructure communication and application communication.…”

Trends, Technologies, and Key Challenges in Smart and Connected Healthcare

“…[103] [104], [105], [106], [107], [108] [107] to set up a framework for reliability modeling and studying end-to-end IoT systems. iResponse [108] is a fivemodule technology-driven framework for autonomous pandemic management that includes policy implementation, resource preparation and provisioning, data-driven planning and decision-making.…”

“…This architecture has three key parts: (1) the Radio-frequency identification (RFID) enhanced wireless sensor network (WSN), called hybrid sensing network HSN; (2) the IoT smart gateway, and (3) data visualization and management user interfaces. The reliability block diagram (RBD) is recommended in [107] to set up a framework for reliability modeling and studying end-to-end IoT systems. iResponse [108] is a five-module technology-driven framework for autonomous pandemic management that includes policy implementation, resource preparation and provisioning, data-driven planning and decision-making.…”