With the exponential growth of science, Internet of Things (IoT) innovation, and expanding significance in renewable energy, Smart Grid has become an advanced innovative thought universally as a solution for the power demand increase around the world. The smart grid is the most practical trend of effective transmission of present-day power assets. The paper aims to survey the present literature concerning predictive maintenance and different types of faults that could be detected within the smart grid. Four databases (Scopus, ScienceDirect, IEEE Xplore, and Web of Science) were searched between 2012 and 2020. Sixty-five (n = 65) were chosen based on specified exclusion and inclusion criteria. Fifty-seven percent (n = 37/65) of the studies analyzed the issues from predictive maintenance perspectives, while about 18% (n = 12/65) focused on factors-related review studies on the smart grid and about 15% (n = 10/65) focused on factors related to the experimental study. The remaining 9% (n = 6/65) concentrated on fields related to the challenges and benefits of the study. The significance of predictive maintenance has been developing over time in connection with Industry 4.0 revolution. The paper’s fundamental commitment is the outline and overview of faults in the smart grid such as fault location and detection. Therefore, advanced methods of applying Artificial Intelligence (AI) techniques can enhance and improve the reliability and resilience of smart grid systems. For future direction, we aim to supply a deep understanding of Smart meters to detect or monitor faults in the smart grid as it is the primary IoT sensor in an AMI.
Internet of Things (IoT) turns into another time of the Internet, which contains connected smart objects over the Internet. IoT has numerous applications, for example, smart city, smart home, smart grid and healthcare. In common, the IoT system comprises of heterogeneous devices that deliver then trade endless sums of safety-critical information, also as privacysensitive information. Nevertheless, connected devices can give your business a genuine lift, yet anything that is connected to the Internet can be vulnerable to cyberattacks. Most present IoT arrangements rely upon centralized architecture by associating with cloud servers through the Internet. The public cloud is described as computing services publicized by third-party suppliers over the Internet, making them accessible to anybody who needs to use or buy them. This solution gives magnificent flexible calculation and information the executives capacities, as IoT systems are developing increasingly mind-boggling; nonetheless, despite everything, it faces different of security issues. One of the weaknesses is that your information moving in IoT devices by means of public cloud could be in danger, despite the fact that the hacker was not explicitly focusing on you and with the public cloud you have insignificant authority over how rapidly you can grow the cloud. In this case, a secured protocol in IoT is vital to ensure optimum security to the information being traded between connected devices. To overcome the limitation, in this paper, we conduct a comprehensive review on existing security protocols and propose a development methodology of a blockchain-based lightweight security model that provides end to end security. By utilizing lightweight, an authenticated client can get to the information of IoT sensors remotely. The presentation investigation shows that lightweight offers better security, less overheads, and low communication.
Recent advances in Power Grid (PG) technology pose an important problem of measuring the effectiveness of power grid configurations. Current assessment models are not adequate to mitigate the setup issues due to the absence of a highfidelity evaluation framework that can consider diverse scenarios based on the market interest. Consequently, we develop a highly flexible Ontology-based Evaluation System that can accommodate and assess different scenarios. The use of ontology as middleware is the best approach to produce an efficient, semantically aware, and operationally accurate system environment for managing flexibility in evaluation. The evaluation is made by predicting the failure intensity and subsequently generate a maintenance report of a particular configuration. The selection of the best configuration is made by comparing the maintenance report of different configurations. The developed evaluation system consists of three main components which are Configuration Generator Tool (GCT), Failure Prediction Model (FDM), and Hybrid Simulation Platform (HSP). The GCT is a knowledge-based system that provides a powerful tool for engineers to generate alternative configurations. The GCT data were collected from literature, validated by experts, and modeled using Web Ontology Language (OWL). While the HSP was developed using several modelings and ontology-based tools such as blender 3D modeling, unity 3d, asp.net, my sql, and apache Jena fuseki. Finally, the FDM was developed based on the impact and relationship of odd events to power grid components and the impact of a failed component to other components, the prediction is modeled using two methods Poisson Model and Likelihood Estimation Method.
The Internet of things (IoT) is on the rise, and it is increasingly becoming a ubiquitous computing service that requires large data storage and processing. Due to the limited capabilities of most IoT devices, they always rely on cloud services for storage and computing, posing security and privacy risks. Due to its significant aspects, such as data security and privacy, Blockchain (BC) has drawn significant interest in alleviating these concerns. Despite its potential benefits, Blockchain’s high complexity and limited scalability make it computationally expensive and incompatible with IoT devices requiring lightweight solutions. Although several models have been proposed for this purpose, the issue is still far from being resolved. These models have significant constraints, such as resource optimization during operation. By presenting a scalable lightweight Blockchain integrated model (LightBlock), the primary objective of this work is to enable the usage of BC in the IoT environment. LightBlock is implemented by optimizing the components of a lightweight, scalable Blockchain. Lastly, the study’s findings should provide IoT users with a high level of security and privacy by ensuring applicability and offering end-to-end security.
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