116 research outputs found

    Connected Autonomous Electric Vehicles as Enablers for Low-Carbon Future

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    Transportation is the main cause of various harmful gases being released into the atmosphere. Due to dependency on fossil fuels, conventional internal-combustion engine vehicles cause major impacts on air pollution and climate change. Achieving greenhouse gas (GHG) reduction targets requires electrification of transportation at the larger scale. Zero-emission vehicles are developing rapidly with consequences for energy use and GHG emissions, and their penetration is rising throughout the world. Such vehicles are widely considered as a promising solution for GHG reduction and a key to low-carbon mobility future. Recent trend in transportation system is a rapid shift toward connected autonomous vehicles. Connected autonomous electric vehicle (CAEV) will play a vital role in emerging revolution in sustainable low-carbon mobility. They can result in major reductions in GHG emissions and be at the forefront of rapid transformation in transportation. CAEVs have great potential to operate with higher vehicle efficiency, if they are charged using renewable energy sources that will significantly reduce emissions and dependency on fossil fuels. This book chapter is intended not only to provide understanding of potential environmental implications of CAEV technologies by reviewing the existing studies and research works but also to discuss environmental impacts including GHG emissions and improvement of vehicle efficiency

    Enabling Trustworthiness in Sustainable Energy Infrastructure Through Blockchain and AI-Assisted Solutions

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    Network trustworthiness is a critical component of network security, as it builds on positive inter-actions, guarantees, transparency, and accountability. And with the growth of smart city services and applications, trustworthiness is becoming more important. Most current network trustworthiness solutions are insufficient, particularly for critical infrastructures where end devices are vulnerable and easily hacked. In terms of the energy sector, blockchain technology transforms all currencies into digital modes, thereby allowing one person to manage and exchange energy with others. This has drawn the attention of experts in many fields as a safe, low-cost platform to track billions of transactions in a distributed energy economy. Security and trust issues are still relatively new in the current centralized energy management scheme. With blockchain technology, a decentralized energy infrastructure enables parties to establish micro- grid trading energy transactions and apply artificial intelligence (AI). Using AI in energy systems enables machines to learn various parameters, such as predicted required amounts, excess amounts, and trusted partners. In this article, we envision a cooperative and distributed framework based on cutting-edge computing, communication, and intelligence capabilities such as AI and blockchain in the energy sector to enable secure energy trading, remote monitoring, and trustworthiness. The proposed framework can also enable secure energy trading at the edge devices and among multiple devices. There are also discussions on difficulties, issues, and design principles, as well as spotlights on some of the more popular solutions

    A Comparative Study of AI-Based Intrusion Detection Techniques in Critical Infrastructures

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    Volunteer computing uses Internet-connected devices (laptops, PCs, smart devices, etc.), in which their owners volunteer them as storage and computing power resources, has become an essential mechanism for resource management in numerous applications. The growth of the volume and variety of data traffic on the Internet leads to concerns on the robustness of cyberphysical systems especially for critical infrastructures. Therefore, the implementation of an efficient Intrusion Detection System for gathering such sensory data has gained vital importance. In this article, we present a comparative study of Artificial Intelligence (AI)-driven intrusion detection systems for wirelessly connected sensors that track crucial applications. Specifically, we present an in-depth analysis of the use of machine learning, deep learning and reinforcement learning solutions to recognise intrusive behavior in the collected traffic. We evaluate the proposed mechanisms by using KDD\u2799 as real attack dataset in our simulations. Results present the performance metrics for three different IDSs, namely the Adaptively Supervised and Clustered Hybrid IDS (ASCH-IDS), Restricted Boltzmann Machine-based Clustered IDS (RBC-IDS), and Q-learning based IDS (Q-IDS), to detect malicious behaviors. We also present the performance of different reinforcement learning techniques such as State-Action-Reward-State-Action Learning (SARSA) and the Temporal Difference learning (TD). Through simulations, we show that Q-IDS performs with detection rate while SARSA-IDS and TD-IDS perform at the order of

    Federated Reinforcement Learning-Supported IDS for IoT-steered Healthcare Systems

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    Wireless Networks lack clear boundaries which leads to security concerns and vulnerabilities to numerous kinds of intrusions. With the growth of cyber intruders, the risks on crucial applications monitored by networked systems have also grown. Effective and vigorous Intrusion Detection Systems (IDSs) for protecting shared information continues to be an essential task to keep private data safe especially in the healthcare sphere. Constructing an IDS that detects and returns information efficiently and with the highest accuracy is a challenging task. Machine Learning (ML) techniques have been effectively adopted in IDSs to detect network intruders. Reinforcement learning is considered as one of the main developments in ML. IDS mainly performs a higher accuracy rate, detection rate as well as a higher performance of a classification (ROC curve). According to these and to tackle the security issues, a Federated Reinforcement Learning-based Intrusion Detection System (FRL-IDS) in the Internet of Things (IoT) networks for healthcare infrastructures has been proposed. The proposed model has been evaluated and compared to a similar model (i.e. SVM system). The proposed model shows superiority over the SVM-steered IDS with accuracy and detection rates of β‰ˆ 0.985 and β‰ˆ 96.5%, respectively. This proposed infrastructure will not only aid in intrusion detection of large health care systems but also other wireless decentralized networks found across multiple real-world applications

    On the Feasibility of Split Learning, Transfer Learning and Federated Learning for Preserving Security in ITS Systems

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    Due to the absence of distinct boundaries, wireless networks are vulnerable to a variety of intrusions. As the number of intruders has increased, the risks on critical infrastructures monitored by networked systems have also increased. Protecting shared information using effective and robust Intrusion Detection Systems (IDSs) remains a critical issue, especially with the growing implementation of vehicular networks. Building an IDS that detects threats efficiently with maximum accuracy and detection is a challenging undertaking. Machine Learning (ML) mechanisms have been successfully adopted in IDSs to detect a variety of network intruders. Split learning is considered one of the main developments in creating efficient ML approaches. In utilizing the Split Learning approach, an IDS is successful in performing at higher accuracy, and detection rate as well as a higher classification performance (Precision, Recall). In this work, a Split Learning-based IDS (SplitLearn) for Intelligent Transportation System (ITS) infrastructures has been proposed to address the potential security concerns. The proposed model has been evaluated and compared against other models (i.e., Federated Learning (FedLearn) and Transfer Learning (TransLearn)-based solutions). With the highest accuracy and detection rates, the proposed model (SplitLearn) outperforms FedLearn and TransLearn by 2 to 5 % respectively. We also see a decrease in power consumption when utilizing SplitLearn versus FedLearn

    Realizing Health 4.0 in Beyond 5G Networks

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    The advancements of Edge and Internet of Things (IoT) devices in terms of their processing, storage and communication capabilities, in addition to the advancements in wireless communication and networking technologies, have led to the rise in Intelligent Edge-enabled IoT architectures. Federated Learning (FL) is one example in which intelligence is adapted to the edge to offload some of the processing load from centralized entities and maintain secure localized model training. With Health 4.0, it is anticipated that distributed and edge-supported Artificial Intelligence (AI) will enable faster and more accurate early-stage disease discovery that relies significantly on intelligent remote and on-site IoT devices. Given that healthcare systems are highly scrutinized by both governments and patients to maintain high levels of data privacy and security, FL coupled with the support of blockchain will provide an optimal solution to reinforce today\u27s healthcare frameworks. In this paper, we propose a FL-enabled framework for healthcare systems that is supported by edge-computing, blockchain and intelligent IoT devices. The solution considers a pneumonia detection use-case as a proof-of-concept and is applicable to an extended set of health-related use-cases. Different pre-trained models are compared against the proposed FL-supported model, namely, CNN, GG16, VGG19, InceptionV3, ResNet, DenseNet, and Xception. Results show high model accuracy attainment and significant improvements in terms of data privacy

    A Federated Learning and Blockchain-enabled Sustainable Energy-Trade at the Edge: A Framework for Industry 4.0

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    Through the digitization of essential functional processes, Industry 4.0 aims to build knowledgeable, networked, and stable value chains. Network trustworthiness is a critical component of network security that is built on positive interactions, guarantees, transparency, and accountability. Blockchain technology has drawn the attention of researchers in various fields of data science as a safe and low-cost platform to track a large number of eventual transactions. Such a technique is adaptable to the renewable energy trade sector, which suffers from security and trustworthy issues. Having a decentralized energy infrastructure, that is supported by blockchain and AI, enables smart and secure micro-grid energy trading. The new age of industrial production will be highly versatile in terms of production volume and customization. As such a robust collaboration solution between consumers, businesses, and suppliers must be both secure and sustainable. In this article, we introduce a cooperative and distributed framework that relies on computing, communication, and intelligence capabilities of edge and end-devices to enable secure energy trading, remote monitoring, and network trustworthiness. The blockchain and Federated Learning-enabled solution provides secure energy trading between different critical entities. Such a technique, coupled with 5G and beyond networks, would enable mass surveillance, monitoring and analysis to occur at the edge. Performance evaluations are conducted to test the effectiveness of the proposed solution in terms of reliability and responsiveness in a vehicular network energy-trade scenario

    A Novel Ensemble Method for Advanced Intrusion Detection in Wireless Sensor Networks

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    Β© 2020 IEEE. With the increase of cyber attack risks on critical infrastructures monitored by networked systems, robust Intrusion Detection Systems (IDSs) for protecting the information have become vital. Designing an IDS that performs with maximum accuracy with minimum false alarms is a challenging task. Ensemble method considered as one of the main developments in machine learning in the past decade, it finds an accurate classifier by combining many classifiers. In this paper, an ensemble classification procedure is proposed using Random Forest (RF), DensityBased Spatial Clustering of Applications with Noise (DBSCAN) and Restricted Boltzmann Machine (RBM) as base classifiers. RF, DBSCAN, and RBM techniques have been used for classification purposes. The ensemble model is introduced for achieving better results. Bayesian Combination Classification (BCC) has been adopted as a combination technique. Independent BCC (IBCC) and Dependent BCC (DBCC) have been tested for performance comparison. The model shows a promising result for all classes of attacks. DBCC performs over IBCC in terms of accuracy and detection rates. Through simulations under a wireless sensor network scenario, we have verified that DBCC-based IDS works with \approx 100\% detection and \approx 1.0 accuracy rate in the existence of intrusive behavior in the tested Wireless Sensor Network (WSN)

    An Analysis of Planarity in Face-Routing

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    In this report we investigate the limits of routing according to left- or right-hand rule (LHR). Using LHR, a node upon receipt of a message will forward to the neighbour that sits next in counter-clockwise order in the network graph. When used to recover from greedy routing failures, LHR guarantees success if implemented over planar graphs. This is often referred to as face or geographic routing. In the current body of knowledge it is known that if planarity is violated then LHR is guaranteed only to eventually return to the point of origin. Our work seeks to understand why a non-planar environment stops LHR from making delivery guarantees. Our investigation begins with an analysis to enumerate all node con gurations that cause intersections. A trace over each con guration reveals that LHR is able to recover from all but a single case, the `umbrella' con guration so named for its appearance. We use this information to propose the Prohibitive Link Detection Protocol (PDLP) that can guarantee delivery over non-planar graphs using standard face-routing techniques. As the name implies, the protocol detects and circumvents the `bad' links that hamper LHR. The goal of this work is to maintain routing guarantees while disturbing the network graph as little as possible. In doing so, a new starting point emerges from which to build rich distributed protocols in the spirit of protocols such as CLDP and GDSTR
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