Aaron St. Leger’s research while affiliated with United States Military Academy and other places

... Most of the app API, and most importantly, the fuel level monitoring part, is written in the Rust programming language. Rust is a compiled language that allows you to write programs that are as fast as C or C++ programs [29], [30] while being more modern and, most importantly, type-safe [31]. Therefore, Rust is perfect for dealing with the real-time constraints related to processing the data from the CAM and for integrating the information from various external sources, ensuring the system remains reliable under heavy loads. ...

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P.I.E.N.O. - Petrol-filling Itinerary Estimation aNd Optimization

... designed a communication infrastructure that deploys several wireless technologies to test their efficiency in monitoring and controlling WAMS operations. These infrastructures include wireless LAN, Wi-Fi, Wi-Max, GPRS, and GSM infrastructure [55]. Different communication infrastructures are selected to address the specific requirements of a given energy grid. ...

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Smart Grid Fault Mitigation and Cybersecurity with Wide-Area Measurement Systems: A Review

... Unlike fault diagnosis and detection, which identify the objective for the existence of a diagnosed burden or specific fault that has previously happened, [308] the designated "anomaly" specifies that the structure is different from standard operational circumstances, so further info is unavailable. Anomalies can be initiated by numerous causes, such as disturbances of several nature, incorrect operations, and equipment disasters, and can have substantial concerns if left hidden [309]. Hence, anomaly finding shows a desperate function in arguing the efficiency, reliability, and safety of several industrial methods [310][311]. ...

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Integration of digital twin, blockchain, artificial intelligence in an IoT Metaverse environment for mitigation and assessment of cascading failure events in smart grids: Recent advancement and future research challenges

... AI-based anomaly detection techniques are being developed to identify unusual behavior or deviations from normal operations in smart grids. These methods leverage machine learning algorithms to establish baseline behavior and detect abnormal activities that may signify a cyberattack [72,73]. AI is being applied to enhance the intrusion detection systems in smart grids. ...

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Cyberattacks in Smart Grids: Challenges and Solving the Multi-Criteria Decision-Making for Cybersecurity Options, Including Ones That Incorporate Artificial Intelligence, Using an Analytical Hierarchy Process

... Edge intelligence can also help ensure the reliability and resilience of the power grid. One such example has been proposed and described by Matthews et al. [64]. In their works, the authors proposed an edge-based system for anomaly detection in synchrophasor data, allowing the system to detect anomalies quicker and be more resilient to network outages. ...

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Combining Machine Learning and Edge Computing: Opportunities, Challenges, Platforms, Frameworks, and Use Cases

... The three CIA parameters are all impacted by MITM attacks, which can be conducted in various layers, particularly the transport and MAC layers. Comprehensive packet analysis software should be a part of cyber security systems, and strong authentication procedures can thwart MITM breaches [100]. ...

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Security of Smart Grid: Cybersecurity Issues, Potential Cyberattacks, Major Incidents, and Future Directions

... Thus, breach or loss of data could affect the power security and stability of the power grid [157]. Ref. [3] suggests resolving the issue of cybersecurity with blockchain technology where all the parameters can be retained in blockchains and accessed through blockchain protocols [158]. Another option could be the design of advanced and secured protocols with authentication and authorization as methods for access and control [73]. ...

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Sensor Technologies for Transmission and Distribution Systems: A Review of the Latest Developments

... In [30] authors are completely agnostic to the data being transmitted between PMUs and PDC. Instead of checking the validity of the data they use k-means clustering to separate the network traffic not typical for PMU to PDC communication. ...

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Using Deep Learning for Detecting Mirroring Attacks on Smart Grid PMU Networks

... Hashgraphs is a DLT type that transactions are recorded and stored in a form of a directed acyclic graph, but it employs a different type of a consensus mechanisms that uses virtual voting with an aim of gaining network consensus. This means that the nodes in the network then do not need to validate each of the transactions in the network [17]. Holochain DLT is decentralized than blockchain. ...

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Underlying Consensus Algorithms, Architectures and Data Structures in Distributed Ledger Technologies Applications

... The so-called smart grid is the natural evolution of the traditional electrical grid, where an increased number of intelligent devices and measuring instruments are requiring pervasive communication infrastructures [1] [2], to implement advanced control capabilities, even over legacy equipment [3]. These new functionalities are enabled by smart sensors and actuators, i.e., objects which offer computing power and network communication interfaces. ...

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An Experimental Characterization of Time of Arrival Accuracy for Time Synchronization of Medium Voltage Smart Grid Solutions