Edge Computing Terminal Equipment Planning Method for Real-time Online Monitoring Service of Power Grid

AbstractMultiple governmental agencies and private organisations have made commitments for the colonisation of Mars. Such colonisation requires complex systems and infrastructure that could be very costly to repair or replace in cases of cyber-attacks. This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real-time intelligence in edge computing. The paper presents a new mathematical approach for integrating concepts for cognition engine design, edge computing and Artificial Intelligence and Machine Learning to automate anomaly detection. This engine instigates a step change by applying Artificial Intelligence and Machine Learning embedded at the edge of IoT networks, to deliver safe and functional real-time intelligence for predictive cyber risk analytics. This will enhance capacities for risk analytics and assists in the creation of a comprehensive and systematic understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when Artificial Intelligence and Machine Learning technologies are migrated to the periphery of the internet and into local IoT networks.

Download Full-text

Energy Consumption Optimal Design of Power Grid Inspection Trajectory for UAV Mobile Edge Computing Node

2021 6th Asia Conference on Power and Electrical Engineering (ACPEE) ◽

10.1109/acpee51499.2021.9436834 ◽

2021 ◽

Author(s):

Yu Zhang ◽

Junfeng Li ◽

Litong Zhang ◽

Na Zhao ◽

Wei Tang ◽

...

Keyword(s):

Energy Consumption ◽

Optimal Design ◽

Power Grid ◽

Edge Computing ◽

Mobile Edge Computing ◽

Computing Node

Download Full-text

Power Hardware-in-the-Loop: Response of Power Components in Real-Time Grid Simulation Environment

Energies ◽

10.3390/en14030593 ◽

2021 ◽

Vol 14 (3) ◽

pp. 593

Author(s):

Moiz Muhammad ◽

Holger Behrends ◽

Stefan Geißendörfer ◽

Karsten von Maydell ◽

Carsten Agert

Keyword(s):

Real Time ◽

Power Distribution ◽

Control Strategies ◽

Power Grid ◽

Energy System ◽

Hardware In The Loop ◽

Distribution Grid ◽

Compensation Strategy ◽

Software Environment ◽

The Real

With increasing changes in the contemporary energy system, it becomes essential to test the autonomous control strategies for distributed energy resources in a controlled environment to investigate power grid stability. Power hardware-in-the-loop (PHIL) concept is an efficient approach for such evaluations in which a virtually simulated power grid is interfaced to a real hardware device. This strongly coupled software-hardware system introduces obstacles that need attention for smooth operation of the laboratory setup to validate robust control algorithms for decentralized grids. This paper presents a novel methodology and its implementation to develop a test-bench for a real-time PHIL simulation of a typical power distribution grid to study the dynamic behavior of the real power components in connection with the simulated grid. The application of hybrid simulation in a single software environment is realized to model the power grid which obviates the need to simulate the complete grid with a lower discretized sample-time. As an outcome, an environment is established interconnecting the virtual model to the real-world devices. The inaccuracies linked to the power components are examined at length and consequently a suitable compensation strategy is devised to improve the performance of the hardware under test (HUT). Finally, the compensation strategy is also validated through a simulation scenario.

Download Full-text

Integration of Minimum Energy Point Tracking and Soft Real-Time Scheduling for Edge Computing

2021 22nd International Symposium on Quality Electronic Design (ISQED) ◽

10.1109/isqed51717.2021.9424343 ◽

2021 ◽

Author(s):

Takumi Komori ◽

Yutaka Masuda ◽

Jun Shiomi ◽

Tohru Ishihara

Keyword(s):

Real Time ◽

Minimum Energy ◽

Edge Computing ◽

Real Time Scheduling ◽

Energy Point ◽

Point Tracking ◽

Time Scheduling ◽

Minimum Energy Point

Download Full-text

An edge based hybrid intrusion detection framework for mobile edge computing

Complex & Intelligent Systems ◽

10.1007/s40747-021-00498-4 ◽

2021 ◽

Author(s):

Ashish Singh ◽

Kakali Chatterjee ◽

Suresh Chandra Satapathy

Keyword(s):

Intrusion Detection ◽

Real Time ◽

Network Architecture ◽

Denial Of Service ◽

Edge Computing ◽

Mobile Edge Computing ◽

Hybrid Detection ◽

Game Theoretical Approach ◽

Improved Performance ◽

Edge Based

AbstractThe Mobile Edge Computing (MEC) model attracts more users to its services due to its characteristics and rapid delivery approach. This network architecture capability enables users to access the information from the edge of the network. But, the security of this edge network architecture is a big challenge. All the MEC services are available in a shared manner and accessed by users via the Internet. Attacks like the user to root, remote login, Denial of Service (DoS), snooping, port scanning, etc., can be possible in this computing environment due to Internet-based remote service. Intrusion detection is an approach to protect the network by detecting attacks. Existing detection models can detect only the known attacks and the efficiency for monitoring the real-time network traffic is low. The existing intrusion detection solutions cannot identify new unknown attacks. Hence, there is a need of an Edge-based Hybrid Intrusion Detection Framework (EHIDF) that not only detects known attacks but also capable of detecting unknown attacks in real time with low False Alarm Rate (FAR). This paper aims to propose an EHIDF which is mainly considered the Machine Learning (ML) approach for detecting intrusive traffics in the MEC environment. The proposed framework consists of three intrusion detection modules with three different classifiers. The Signature Detection Module (SDM) uses a C4.5 classifier, Anomaly Detection Module (ADM) uses Naive-based classifier, and Hybrid Detection Module (HDM) uses the Meta-AdaboostM1 algorithm. The developed EHIDF can solve the present detection problems by detecting new unknown attacks with low FAR. The implementation results illustrate that EHIDF accuracy is 90.25% and FAR is 1.1%. These results are compared with previous works and found improved performance. The accuracy is improved up to 10.78% and FAR is reduced up to 93%. A game-theoretical approach is also discussed to analyze the security strength of the proposed framework.

Download Full-text

Toward a Comfortable Driving Experience for a Self-Driving Shuttle Bus

Electronics ◽

10.3390/electronics8090943 ◽

2019 ◽

Vol 8 (9) ◽

pp. 943 ◽

Cited By ~ 11

Author(s):

Il Bae ◽

Jaeyoung Moon ◽

Jeongseok Seo

Keyword(s):

Real Time ◽

High Performance ◽

Autonomous Driving ◽

Planning Method ◽

Dynamics Simulation ◽

Driving Experience ◽

Optimal Velocity ◽

Planning Approach ◽

Velocity Planning ◽

Time Optimal

The convergence of mechanical, electrical, and advanced ICT technologies, driven by artificial intelligence and 5G vehicle-to-everything (5G-V2X) connectivity, will help to develop high-performance autonomous driving vehicles and services that are usable and convenient for self-driving passengers. Despite widespread research on self-driving, user acceptance remains an essential part of successful market penetration; this forms the motivation behind studies on human factors associated with autonomous shuttle services. We address this by providing a comfortable driving experience while not compromising safety. We focus on the accelerations and jerks of vehicles to reduce the risk of motion sickness and to improve the driving experience for passengers. Furthermore, this study proposes a time-optimal velocity planning method for guaranteeing comfort criteria when an explicit reference path is given. The overall controller and planning method were verified using real-time, software-in-the-loop (SIL) environments for a real-time vehicle dynamics simulation; the performance was then compared with a typical planning approach. The proposed optimized planning shows a relatively better performance and enables a comfortable passenger experience in a self-driving shuttle bus according to the recommended criteria.

Download Full-text