Opto-VLSI based WDM multifunction device

The tremendous expansion of telecommunication services in the past decade, in part due to the growth of the Internet, has made the development of high-bandwidth optical net-works a focus of research interest. The implementation of Dense-Wavelength Division Multiplexing (DWDM) optical fiber transmission systems has the potential to meet this demand. However, crucial components of DWDM networks – add/drop multiplexers, filters, gain equalizers as well as interconnects between optical channels – are currently not implemented as dynamically reconfigurable devices. Electronic cross-connects, the traditional solution to the reconfigurable optical networks, are increasingly not feasible due to the rapidly increasing bandwidth of the optical channels. Thus, optically transparent, dynamically reconfigurable DWDM components are important for alleviating the bottleneck in telecommunication systems of the future. In this study, we develop a promising class of Opto-VLSI based devices, including a dynamic multi-function WDM processor, combining the functions of optical filter, channel equalizer and add-drop multiplexer, as well as a reconfigurable optical power splitter. We review the technological options for all optical WDM components and compare their advantages and disadvantages. We develop a model for designing Opto-VLSI based WDM devices, and demonstrate experimentally the Opto-VLSI multi-function WDM device. Finally, we discuss the feasibility of Opto-VLSI WDM components in meeting the stringent requirements of the optical communications industry

Proceedings of the Second International Mobile Satellite Conference (IMSC 1990)

Presented here are the proceedings of the Second International Mobile Satellite Conference (IMSC), held June 17-20, 1990 in Ottawa, Canada. Topics covered include future mobile satellite communications concepts, aeronautical applications, modulation and coding, propagation and experimental systems, mobile terminal equipment, network architecture and control, regulatory and policy considerations, vehicle antennas, and speech compression

Anomaly detection for IoT networks using machine learning

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe Internet of Things (IoT) is considered one of the trending technologies today. IoT affects various industries, including logistics tracking, healthcare, automotive and smart cities. A rising number of cyber-attacks and breaches are rapidly targeting networks equipped with IoT devices. This thesis aims to improve security in IoT networks by enhancing anomaly detection using machine learning. This thesis identified the challenges and gaps related to securing the Internet of Things networks. The challenges are network size, the number of devices, the human factor, and the complexity of IoT networks. The gaps identified include the lack of research on signature-based intrusion detection systems used for anomaly detection, in addition to the lack of modelling input parameters required for anomaly detection in IoT networks. Furthermore, there is a lack of comparison of the performance of machine learning algorithms on standard and real IoT datasets. This thesis creates a dataset to test the anomaly binary classification performance of the Neural Networks, Gaussian Naive Bayes, Support Vector Machine, and Decision Trees machine learning algorithms and compares their results with the KDDCUP99 dataset. The results show that Support Vector Machine and Gaussian Naive Bayes perform lower than the other models on the created IoT dataset. This thesis reduces the number of features required by machine learning algorithms for anomaly detection in the IoT networks to five features only, which resulted in reduced execution time by an average of 58%. This thesis tests CNNwGFC, which is an enhanced Convolutional Neural Network model, in detecting and classifying anomalies in IoT networks. This model achieves an increase of 15.34% in the accuracy for IoT anomaly classification in the UNSW-NB15 compared to the classic Convolutional Neural Network. The CNNwGFC multi-classification accuracy (96.24%) is higher by 7.16 than the highest from the literature

On the economic and technological forces shaping mobile transceiver architecture

Thesis (S.M.)--Massachusetts Institute of Technology, System Design and Management Program, 2009.Includes bibliographical references (p. 166-168).The thesis examines the evolution of mobile transceiver architecture using the management framework pioneered by Carliss Baldwin and Kim Clark. The thesis begins with an introduction and an overview of the wireless communication value network. The author subsequently distills the salient aspects of the Baldwin and Clark management framework predicated on bottleneck analysis, modularity, and return on invested capital. The prominence of bottleneck analysis motivates a technical chapter that summarizes the bottlenecks relevant to all wireless communication systems, namely data rate, error rate, and battery life. A brief chapter discussing the dominant wireless communication network architecture, TDMA and CDMA, corroborates the bottleneck analysis and effectively assigns the error rate and battery life bottlenecks to the handset ODM and supplier layers of the value network. With a clear vision of the competitive bottlenecks, the evolution of transceiver architecture is presented in the context of the aforementioned management framework. Through this analysis, design power is shown to have passed from handset ODMs to integrated circuit suppliers. A noteworthy byproduct of the analysis is the genesis of the bottleneck tree whereby new layers of bottlenecks are emergent upon a firm's selection of a particular design architecture that targets the strategic bottleneck layer. Finally, the thesis is concluded with a summary of the ground covered and the author's opinions of how the architecture may yet evolve and the future nature of the competitive landscape.by Christopher A. Aden.S.M

Development and evaluation of low cost 2-d lidar based traffic data collection methods

Traffic data collection is one of the essential components of a transportation planning exercise. Granular traffic data such as volume count, vehicle classification, speed measurement, and occupancy, allows managing transportation systems more effectively. For effective traffic operation and management, authorities require deploying many sensors across the network. Moreover, the ascending efforts to achieve smart transportation aspects put immense pressure on planning authorities to deploy more sensors to cover an extensive network. This research focuses on the development and evaluation of inexpensive data collection methodology by using two-dimensional (2-D) Light Detection and Ranging (LiDAR) technology. LiDAR is adopted since it is economical and easily accessible technology. Moreover, its 360-degree visibility and accurate distance information make it more reliable. To collect traffic count data, the proposed method integrates a Continuous Wavelet Transform (CWT), and Support Vector Machine (SVM) into a single framework. Proof-of-Concept (POC) test is conducted in three different places in Newark, New Jersey to examine the performance of the proposed method. The POC test results demonstrate that the proposed method achieves acceptable performances, resulting in 83% ~ 94% accuracy. It is discovered that the proposed method\u27s accuracy is affected by the color of the exterior surface of a vehicle since some colored surfaces do not produce enough reflective rays. It is noticed that the blue and black colors are less reflective, while white-colored surfaces produce high reflective rays. A methodology is proposed that comprises K-means clustering, inverse sensor model, and Kalman filter to obtain trajectories of the vehicles at the intersections. The primary purpose of vehicle detection and tracking is to obtain the turning movement counts at an intersection. A K-means clustering is an unsupervised machine learning technique that clusters the data into different groups by analyzing the smallest mean of a data point from the centroid. The ultimate objective of applying K-mean clustering is to identify the difference between pedestrians and vehicles. An inverse sensor model is a state model of occupancy grid mapping that localizes the detected vehicles on the grid map. A constant velocity model based Kalman filter is defined to track the trajectory of the vehicles. The data are collected from two intersections located in Newark, New Jersey, to study the accuracy of the proposed method. The results show that the proposed method has an average accuracy of 83.75%. Furthermore, the obtained R-squared value for localization of the vehicles on the grid map is ranging between 0.87 to 0.89. Furthermore, a primary cost comparison is made to study the cost efficiency of the developed methodology. The cost comparison shows that the proposed methodology based on 2-D LiDAR technology can achieve acceptable accuracy at a low price and be considered a smart city concept to conduct extensive scale data collection

GUIDELINES FOR THE DESIGN OF ENHANCED, COST EFFECTIVE NETWORKS IN A MANUFACTURING ENVIRONMENT

Investigations into the transmission of real-time interactive speech over local area networks (LAN) in an industriai/commerciai environment to eventually obviate the need for a private automatic branch exchange and ultimately prepare the way for a single interactive integrated information system (PS) that provides work stations, which are networked via a LAN, with a fully interactive speech and graphics facility commensurate with the future requirements in computer integrated manufacturing (CIM). The reasons for conducting this programme of research were that existing LANs do not offer a real time interactive speech facility. Any verbal communication between workstation users on the LAN has to be carried out over a telephone network (PABX). This necessitates the provision of a second completely separate network with its associated costs. Initial investigations indicate that there is sufGcient capacity on existing LANs to support both data and real-time speech provided certain data packet delay criteria can be met. Earlier research work (in the late 1980s) has been conducted at Bell Labs and MIT. [Ref 25, 27 & 28], University of Strathclyde [Ref 24] and at BTRL [Ref 22 and 37]. In all of these cases the real time implementation issues were not fijlly addressed. In this thesis the research work reported provides the main criteria for the implementation of real-time interactive speech on both existing and newly installed networks. With such enhanced communication facilities, designers and engineers on the shop floor can be projected into their suppliers, providing a much greater integration between manufacturer and supplier which will be beneficial as Concurrent and Simultaneous Engineering Methodologies are further developed. As a result, various LANs have been evaluated as to their suitability for the transmission of real time interactive speech. As LANs, in general, can be separated into those with either deterministic or stochastic access mechanisms, investigations were carried out into the ability of both the: (i) Token Passing Bus LANs supporting the Manufacturing and Automation Protocol (MAP)—Deterministic and (u) Carrier Sense Multiple Access/Collision Detection (CSMA/CD) LANs supporting the Technical Office Protocol (TOP)— Stochastic to support real time interactive speech, as both are used extensively in commerce and manufacturing. The thesis that real time interactive speech can be transmitted over LANs employed in a computer integrated manufacturing environment has to be moderated following the tests carried out in this work, as follows:- The Token Passing LAN presents no serious problems under normal traffic conditions, however, the CSMA/CD LAN can only be used in relatively light traffic conditions i.e. below 30% of its designed maximum capacity, providing special arrangements are made to minimise the access, transmission and processing delays of speech packets. Given that a certain amount of delay is inevitable in packet switched systems (LANs), investigations have been carried out into techniques for reducing the subjective efifect of speech packet loss on real-time interactive systems due to the unacceptable delays caused by the conditions mentioned above

ATOM : a distributed system for video retrieval via ATM networks

The convergence of high speed networks, powerful personal computer processors and improved storage technology has led to the development of video-on-demand services to the desktop that provide interactive controls and deliver Client-selected video information on a Client-specified schedule. This dissertation presents the design of a video-on-demand system for Asynchronous Transfer Mode (ATM) networks, incorporating an optimised topology for the nodes in the system and an architecture for Quality of Service (QoS). The system is called ATOM which stands for Asynchronous Transfer Mode Objects. Real-time video playback over a network consumes large bandwidth and requires strict bounds on delay and error in order to satisfy the visual and auditory needs of the user. Streamed video is a fundamentally different type of traffic to conventional IP (Internet Protocol) data since files are viewed in real-time, not downloaded and then viewed. This streaming data must arrive at the Client decoder when needed or it loses its interactive value. Characteristics of multimedia data are investigated including the use of compression to reduce the excessive bit rates and storage requirements of digital video. The suitability of MPEG-1 for video-on-demand is presented. Having considered the bandwidth, delay and error requirements of real-time video, the next step in designing the system is to evaluate current models of video-on-demand. The distributed nature of four such models is considered, focusing on how Clients discover Servers and locate videos. This evaluation eliminates a centralized approach in which Servers have no logical or physical connection to any other Servers in the network and also introduces the concept of a selection strategy to find alternative Servers when Servers are fully loaded. During this investigation, it becomes clear that another entity (called a Broker) could provide a central repository for Server information. Clients have logical access to all videos on every Server simply by connecting to a Broker. The ATOM Model for distributed video-on-demand is then presented by way of a diagram of the topology showing the interconnection of Servers, Brokers and Clients; a description of each node in the system; a list of the connectivity rules; a description of the protocol; a description of the Server selection strategy and the protocol if a Broker fails. A sample network is provided with an example of video selection and design issues are raised and solved including how nodes discover each other, a justification for using a mesh topology for the Broker connections, how Connection Admission Control (CAC) is achieved, how customer billing is achieved and how information security is maintained. A calculation of the number of Servers and Brokers required to service a particular number of Clients is presented. The advantages of ATOM are described. The underlying distributed connectivity is abstracted away from the Client. Redundant Server/Broker connections are eliminated and the total number of connections in the system are minimized by the rule stating that Clients and Servers may only connect to one Broker at a time. This reduces the total number of Switched Virtual Circuits (SVCs) which are a performance hindrance in ATM. ATOM can be easily scaled by adding more Servers which increases the total system capacity in terms of storage and bandwidth. In order to transport video satisfactorily, a guaranteed end-to-end Quality of Service architecture must be in place. The design methodology for such an architecture is investigated starting with a review of current QoS architectures in the literature which highlights important definitions including a flow, a service contract and flow management. A flow is a single media source which traverses resource modules between Server and Client. The concept of a flow is important because it enables the identification of the areas requiring consideration when designing a QoS architecture. It is shown that ATOM adheres to the principles motivating the design of a QoS architecture, namely the Integration, Separation and Transparency principles. The issue of mapping human requirements to network QoS parameters is investigated and the action of a QoS framework is introduced, including several possible causes of QoS degradation. The design of the ATOM Quality of Service Architecture (AQOSA) is then presented. AQOSA consists of 11 modules which interact to provide end-to-end QoS guarantees for each stream. Several important results arise from the design. It is shown that intelligent choice of stored videos in respect of peak bandwidth can improve overall system capacity. The concept of disk striping over a disk array is introduced and a Data Placement Strategy is designed which eliminates disk hot spots (i.e. Overuse of some disks whilst others lie idle.) A novel parameter (the B-P Ratio) is presented which can be used by the Server to predict future bursts from each video stream. The use of Traffic Shaping to decrease the load on the network from each stream is presented. Having investigated four algorithms for rewind and fast-forward in the literature, a rewind and fast-forward algorithm is presented. The method produces a significant decrease in bandwidth, and the resultant stream is very constant, reducing the chance that the stream will add to network congestion. The C++ classes of the Server, Broker and Client are described emphasizing the interaction between classes. The use of ATOM in the Virtual Private Network and the multimedia teaching laboratory is considered. Conclusions and recommendations for future work are presented. It is concluded that digital video applications require high bandwidth, low error, low delay networks; a video-on-demand system to support large Client volumes must be distributed, not centralized; control and operation (transport) must be separated; the number of ATM Switched Virtual Circuits (SVCs) must be minimized; the increased connections caused by the Broker mesh is justified by the distributed information gain; a Quality of Service solution must address end-to-end issues. It is recommended that a web front-end for Brokers be developed; the system be tested in a wide area A TM network; the Broker protocol be tested by forcing failure of a Broker and that a proprietary file format for disk striping be implemented

Deep Learning -Powered Computational Intelligence for Cyber-Attacks Detection and Mitigation in 5G-Enabled Electric Vehicle Charging Station

An electric vehicle charging station (EVCS) infrastructure is the backbone of transportation electrification. However, the EVCS has various cyber-attack vulnerabilities in software, hardware, supply chain, and incumbent legacy technologies such as network, communication, and control. Therefore, proactively monitoring, detecting, and defending against these attacks is very important. The state-of-the-art approaches are not agile and intelligent enough to detect, mitigate, and defend against various cyber-physical attacks in the EVCS system. To overcome these limitations, this dissertation primarily designs, develops, implements, and tests the data-driven deep learning-powered computational intelligence to detect and mitigate cyber-physical attacks at the network and physical layers of 5G-enabled EVCS infrastructure. Also, the 5G slicing application to ensure the security and service level agreement (SLA) in the EVCS ecosystem has been studied. Various cyber-attacks such as distributed denial of services (DDoS), False data injection (FDI), advanced persistent threats (APT), and ransomware attacks on the network in a standalone 5G-enabled EVCS environment have been considered. Mathematical models for the mentioned cyber-attacks have been developed. The impact of cyber-attacks on the EVCS operation has been analyzed. Various deep learning-powered intrusion detection systems have been proposed to detect attacks using local electrical and network fingerprints. Furthermore, a novel detection framework has been designed and developed to deal with ransomware threats in high-speed, high-dimensional, multimodal data and assets from eccentric stakeholders of the connected automated vehicle (CAV) ecosystem. To mitigate the adverse effects of cyber-attacks on EVCS controllers, novel data-driven digital clones based on Twin Delayed Deep Deterministic Policy Gradient (TD3) Deep Reinforcement Learning (DRL) has been developed. Also, various Bruteforce, Controller clones-based methods have been devised and tested to aid the defense and mitigation of the impact of the attacks of the EVCS operation. The performance of the proposed mitigation method has been compared with that of a benchmark Deep Deterministic Policy Gradient (DDPG)-based digital clones approach. Simulation results obtained from the Python, Matlab/Simulink, and NetSim software demonstrate that the cyber-attacks are disruptive and detrimental to the operation of EVCS. The proposed detection and mitigation methods are effective and perform better than the conventional and benchmark techniques for the 5G-enabled EVCS