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The Internet of Things (IoT) is paving the way to becoming necessary in numerous aspects of people’s lives. IoT is becoming integrated in many domains, such as medical, industrial, and personal. Recent years have witnessed the creation of many IoT technologies that differ not only in their applications and use cases but also in standards. The absen...
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... Despite being adapted into multiple functional areas, such as healthcare, agriculture, waste treatment and industrial automation, the IoT still face issues related to identifying faulty data being collected and shared [8,9]. Also, due to its heterogenous nature, the security and privacy of the architecture is also not standardized [10]. From the four-layer architecture it is understandable that the data collected from the environment has a greater impact over the entire IoT architecture, as any corruption in the SED-data can result in overall system failure [11]. ...
... Using fault-detecting algorithm to identify the occurrence has been widely reported, but identifying the responsible element(s) is more Despite being adapted into multiple functional areas, such as healthcare, agriculture, waste treatment and industrial automation, the IoT still face issues related to identifying faulty data being collected and shared [8,9]. Also, due to its heterogenous nature, the security and privacy of the architecture is also not standardized [10]. From the fourlayer architecture it is understandable that the data collected from the environment has a greater impact over the entire IoT architecture, as any corruption in the SED-data can result in overall system failure [11]. ...
The Internet of Things (IoT) is seen as the most viable solution for real-time monitoring applications. But the faults occurring at the perception layer are prone to misleading the data driven system and consume higher bandwidth and power. Thus, the goal of this effort is to provide an edge deployable sensor-fault detection and identification algorithm to reduce the detection, identification, and repair time, save network bandwidth and decrease the computational stress over the Cloud. Towards this, an integrated algorithm is formulated to detect fault at source and to identify the root cause element(s), based on Random Forest (RF) and Fault Tree Analysis (FTA). The RF classifier is employed to detect the fault, while the FTA is utilized to identify the source. A Methane (CH4) sensing application is used as a case-study to test the proposed system in practice. We used data from a healthy CH4 sensing node, which was injected with different forms of faults, such as sensor module faults, processor module faults and communication module faults, to assess the proposed model’s performance. The proposed integrated algorithm provides better algorithm-complexity, execution time and accuracy when compared to FTA or standalone classifiers such as RF, Support Vector Machine (SVM) or K-nearest Neighbor (KNN). Metrics such as Accuracy, True Positive Rate (TPR), Matthews Correlation Coefficient (MCC), False Negative Rate (FNR), Precision and F1-score are used to rank the proposed methodology. From the field experiment, RF produced 97.27% accuracy and outperformed both SVM and KNN. Also, the suggested integrated methodology’s experimental findings demonstrated a 27.73% reduced execution time with correct fault-source and less computational resource, compared to traditional FTA-detection methodology.
Traffic lights play vital roles in urban traffic management systems, providing clear directional guidance for vehicles and pedestrians while ensuring traffic safety. However, the vast quantity of traffic lights widely distributed in the transportation system aggravates energy consumption. Here, a self‐powered traffic light system is proposed through wind energy harvesting based on a high‐performance fur‐brush dish triboelectric nanogenerator (FD‐TENG). The FD‐TENG harvests wind energy to power the traffic light system continuously without needing an external power supply. Natural rabbit furs are applied to dish structures, due to their outstanding characteristics of shallow wear, high performance, and resistance to humidity. Also, the grid pattern of the dish structure significantly impacts the TENG outputs. Additionally, the internal electric field and the influences of mechanical and structural parameters on the outputs are analyzed by finite element simulations. After optimization, the FD‐TENG can achieve a peak power density of 3.275 W m ⁻³ . The portable and miniature features of FD‐TENG make it suitable for other natural environment systems such as forests, oceans, and mountains, besides the traffic light systems. This study presents a viable strategy for self‐powered traffic lights, establishing a basis for efficient environmental energy harvesting toward big data and Internet of Things applications.
This paper focuses on analyzing the connectivity of Xiaomi IoT devices to the cloud to propose solutions to identified problems in the case of DNS server failure. The connectivity analysis of the IoT devices was performed in a test environment that included different scenarios such as adding IoT devices to the application, the functionality of the IoT device, the automation of the IoT device and the voice assistant for the IoT devices. The testing scenarios were performed with a functional network and a simulation of a DNS server outage. Based on the identified problems in the communication between IoT devices and the cloud, solutions were proposed to ensure reliable communication between IoT devices and the cloud without depending on the DNS server. In this paper, solutions are proposed that can help improve the stability and reliability of the IoT when using DNS services.
The internet of things (IoT) refers to the network of connected devices embedded in everyday objects that enable digital transformation. The rapid proliferation of IoT devices has led to significant advancements in technology and data exchange capabilities. However, the security of user data and IoT systems has become a paramount concern. This chapter focuses on the security challenges and approaches in IoT. Various attacks, such as denial of service, password guessing, replay, and insider attacks, pose significant threats to IoT security. It investigates the state-of-the-art technologies, future challenges and open issues currently facing IoT security. The findings from this chapter serve as a foundation for future work in improving IoT security and protecting user data effectively.
