Connected Autonomous Electric Vehicles as Enablers for Low-Carbon Future

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

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 Novel Ensemble Method for Advanced Intrusion Detection in Wireless Sensor Networks

© 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

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

Prohibitive-link Detection and Routing Protocol

Abstract In this paper we investigate the limits of routing according to left-or righthand 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. We note, however, that if planarity is violated then LHR is only guaranteed to eventually return to the point of origin. Our work seeks to understand why. An enumeration and analysis of possible intersections leads us to propose the Prohibitive-link Detection and Routing Protocol (PDRP) that can guarantee delivery over non-planar graphs. As the name implies, the protocol detects and circumvents the 'bad' links that hamper LHR. Our implementation of PDRP in TinyOS reveals the same level of service as face-routing protocols despite preserving most intersecting links in the network

Preventing and Controlling Epidemics Through Blockchain-Assisted AI-Enabled Networks

The COVID-19 pandemic, which spread rapidly in late 2019, has revealed that the use of computing and communication technologies provides significant aid in preventing, controlling, and combating infectious diseases. With the ongoing research in next-generation networking (NGN), the use of secure and reliable communication and networking is of utmost importance when dealing with users\u27 health records and other sensitive information. Through the adaptation of artificial-intelligence-enabled NGN, the shape of healthcare systems can be altered to achieve smart and secure healthcare capable of coping with epidemics that may emerge at any given moment. In this article, we envision a cooperative and distributed healthcare framework that relies on state-of-the-art computing, communication, and intelligence capabilities, namely, federated learning, mobile edge computing, and blockchain, to enable epidemic (or suspicious infectious disease) discovery, remote monitoring, and fast health authority response. The introduced framework can also enable secure medical data exchange at the edge and between different health entities. This technique, coupled with the low latency and high bandwidth functionality of 5G and beyond networks, would enable mass surveillance, monitoring, and analysis to occur at the edge. Challenges, issues, and design guidelines are also discussed in this article with highlights on some trending solutions

Availability constrained routing and wavelength assignment techniques for optical WDM networks

Dalgaboyu bölmeli çoğullama (WDM) tekniği ile optik ağlar tarafından sunulan yüksek bandgenişliği, optik hatlarda veya ağ bileşenlerinde oluşabilecek hatalar karşısında da yoğun miktarda veri kaybı riskini beraberinde getirmektedir. Bu durumun önüne geçmek için, bağlantılar belirli bir sürdürülebilirlik politikası ile korunarak kurulmaktadır. Ağda oluşabilecek hata durumlarınnda da bağlantının kullanılabilir ve sunulan hizmetin kesintisiz olması kullanıcılar tarafından beklenmektedir. Bu nedenle, bağlantı istekleri kurulurken, yol ve dalgaboyu atamasında, ilgili sürdürülebilirlik politikası altında kullanılabilirlik kısıtının göz önünde bulundurulması gerekmektedir. Bu çalışmada, paylaşımlı yol koruma politikası altında kurulan bağlantıların kullanılabilirlik isteklerini göz önünde bulundurarak yol ve dalgaboylarını atayan iki farklı teknik önerilmektedir. Bu tekniklerden ilki, G-DAP (Global Differentiated Availability-Aware Provisioning) sezgisel olarak yedek dalgaboyu kanalları üzerinde, her bir kullanılabilirlik sınıfı için global bir paylaşım derecesi kestirir. Diğer teknik LBL-DAP (Link-By-Link Differentiated Availability-Aware Provisioning) ise bir optimizasyon modeli kullanarak, her bir kullanılabilirlik sınıfı için yedek kanallar üzerindeki paylaşım derecesini, her bir optik hat için ayrıca hesaplar. Bağlantı isteklerinin %98, %99, %99.9, %99.99% ve %99.999 kullanılabilik düzeyinin birinden geldiği ortamda yapılan testlerde, önerilen teknikler yaygın olan CAFES (Compute-A-Feasible Solution) algoritmasıyla NSFNET ve EON topolojilerinde karşılaştırılmıştır. Bağlantıların sınıflar arasında düzgün ve heterojen dağıldığı ortamlarda toplanan sonuçlar, önerilen tekniklerin daha yüksek bağlantı kabul oranı ve kullanılabilirlik sağladığını göstermektedir. Ayrıca, yedek kaynak kullanım oranını düşürmesi nedeniyle LBL-DAP’ın en iyi başarımı sağladığı görülmüştür. Anahtar Kelimeler: Optik ağlar, dalgaboyu bölmeli çoğullama, kullanılabilirlik, yol atama.As a result of the increase in the bandwidth demand of the next generation Internet applications, Optical Wavelength division Multiplexing (WDM) networks seem to be the most appropriate technology that can be deployed in the backbone. Optical WDM networks introduce the advantage of offering bandwidth partitioned into a number of gigabits per second wavelength channels. However, the advantage introduced by the huge bandwidth offer also introduces a disadvantage when the network experiences a failure. Service interrupts on any component along the lightpath may lead to significant amount of data loss since the fiber capacity is huge. Factors like multiple errors, long fault recovery duration, and component failure characteristics introduce availability constraint for the network elements, and also for the connections. Therefore, connections are required to be provisioned by taking availability constraint into consideration. In short, availability stands for the probability of a network component, a channel or a link being in the operational state at any time t. Significant amount of the previous work is concerned with availability aware routing and wavelength assignment (RWA) under shared backup protection. The first and the most common availability aware routing scheme is compute-a-feasible-solution (CAFES). In this scheme, a number of candidate working paths are selected. For each working path, a corresponding backup path is selected by forcing the backup channels to be shared. The working and backup path pair that leads to the highest availability or another lowest cost metric is selected, and assigned to the incoming connection request. In this work, we present two dynamic connection provisioning schemes for differentiated availability-constrained RWA. Both of the schemes are derived from the conventional reliable provisioning scheme CAFES. In the dynamic environment, connections arrive with the availability requirements of 98% (class-1), 99% (class-2), 99.9% (class-3), 99.99% (class-4), and 99.999% (class-5). First scheme is called Global Differentiated Availability-aware Provisioning (G-DAP). This scheme monitors the average availability per connection for each class and resource-overbuild throughout the network. In order to enhance the performance of the connection provisioning, G-DAP also takes the advantage of the trade-off between resource overbuild and connection unavailability where resource overbuild is the ratio of the number of backup channels to the number of working channels in the network, and unavailability is one's complement of the availability. Based on the change in these two parameters it attempts to specify a feasible global sharing degree for all wavelength channels per availability class. The trade-off function is defined as the product of these two parameters. Hence, if the tradeoff is monitored to be decreasing for the related availability class, the last action (increment or decrement) taken on the sharing degree of that class is repeated; otherwise, it is reversed. The second scheme is called Link-by-Link Differentiated Availability-aware Provisioning (LBL-DAP). LBL-DAP estimates a separate feasible sharing degree per class for the channels of each link. It periodically runs an integer linear programming (ILP) function to obtain the feasible sharing degrees on each link. When searching for a backup RWA configuration, both schemes modify the link costs based on the feasible sharing degree obtained for the availability class of the incoming connection and current load for the connection?s class on the link respectively. Since we aim to improve the performance in terms of resource overbuild, connection availability, and blocking probability, we use the conventional reliable provisioning scheme, CAFES as a base in our simulations. Moreover, since connections arrive with differentiated availability requirements, we also modify CAFES to enable a connection to be provisioned unprotected if a selected working path can meet its availability requirement. Thus, resource consumption overhead of this scheme is modified for its favor. Performance of G-DAP and LBL-DAP are compared to that of CAFES by simulation under NSFNET and EON topologies. Simulation results are collected under two different conditions where the connection requests are distributed uniformly and heterogeneously among the availability classes. It is shown that the proposed schemes lead to enhanced blocking ratio and connection availability. Moreover, by taking the advantage of optimization and considering the feasible sharing degrees for the links separately, LBL-DAP also introduces significant decrease in resource overbuild. Keywords: Optical networks, wavelength division multiplexing, availability, routing

Prior Knowledge based Advanced Persistent Threats Detection for IoT in a Realistic Benchmark

The number of Internet of Things (IoT) devices being deployed into networks is growing at a phenomenal level, which makes IoT networks more vulnerable in the wireless medium. Advanced Persistent Threat (APT) is malicious to most of the network facilities and the available attack data for training the machine learning-based Intrusion Detection System (IDS) is limited when compared to the normal traffic. Therefore, it is quite challenging to enhance the detection performance in order to mitigate the influence of APT. Therefore, Prior Knowledge Input (PKI) models are proposed and tested using the SCVIC-APT- 2021 dataset. To obtain prior knowledge, the proposed PKI model pre-classifies the original dataset with unsupervised clustering method. Then, the obtained prior knowledge is incorporated into the supervised model to decrease training complexity and assist the supervised model in determining the optimal mapping between the raw data and true labels. The experimental findings indicate that the PKI model outperforms the supervised baseline, with the best macro average F1-score of 81.37%, which is 10.47% higher than the baseline.Comment: IEEE Global Communications Conference (Globecom), 2022, 6 pages, g figures, 6 table