Praise for the Third Edition: "This is one of the best books available. Its excellent organizational structure allows quick reference to specific models and its clear presentation . . . solidifies the understanding of the concepts being presented."IIE Transactions on Operations EngineeringThoroughly revised and expanded to reflect the latest developments in the field, Fundamentals of Queueing Theory, Fourth Edition continues to present the basic statistical principles that are necessary to analyze the probabilistic nature of queues. Rather than presenting a narrow focus on the subject, this update illustrates the wide-reaching, fundamental concepts in queueing theory and its applications to diverse areas such as computer science, engineering, business, and operations research.This update takes a numerical approach to understanding and making probable estimations relating to queues, with a comprehensive outline of simple and more advanced queueing models. Newly featured topics of the Fourth Edition include:Retrial queuesApproximations for queueing networksNumerical inversion of transformsDetermining the appropriate number of servers to balance quality and cost of serviceEach chapter provides a self-contained presentation of key concepts and formulae, allowing readers to work with each section independently, while a summary table at the end of the book outlines the types of queues that have been discussed and their results. In addition, two new appendices have been added, discussing transforms and generating functions as well as the fundamentals of differential and difference equations. New examples are now included along with problems that incorporate QtsPlus software, which is freely available via the book's related Web site.With its accessible style and wealth of real-world examples, Fundamentals of Queueing Theory, Fourth Edition is an ideal book for courses on queueing theory at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners who analyze congestion in the fields of telecommunications, transportation, aviation, and management science.

Fundamentals of Queueing Theory

We survey research on self-driving cars published in the literature focusing on autonomous cars developed since the DARPA challenges, which are equipped with an autonomy system that can be categorized as SAE level 3 or higher. The architecture of the autonomy system of self-driving cars is typically organized into the perception system and the decision-making system. The perception system is generally divided into many subsystems responsible for tasks such as self-driving-car localization, static obstacles mapping, moving obstacles detection and tracking, road mapping, traffic signalization detection and recognition, among others. The decision-making system is commonly partitioned as well into many subsystems responsible for tasks such as route planning, path planning, behavior selection, motion planning, and control. In this survey, we present the typical architecture of the autonomy system of self-driving cars. We also review research on relevant methods for perception and decision making. Furthermore, we present a detailed description of the architecture of the autonomy system of the self-driving car developed at the Universidade Federal do Espirito Santo (UFES), named Intelligent Autonomous Robotics Automobile (IARA). Finally, we list prominent self-driving car research platforms developed by academia and technology companies, and reported in the media.

Self-driving cars: A survey

Fast depleting fossil fuels and the growing awareness for environmental protection have led us to the energy crisis. Hence, efforts are being made by researchers to investigate new ways to extract energy from renewable sources. ‘Microgrids’ with Distributed Generators (DG) are being implemented with renewable energy systems. Optimization methods justify the cost of investment of a microgrid by enabling economic and reliable utilization of the resources. This paper strives to bring to light the concept of Hybrid Renewable Energy Systems (HRES) and state of art application of optimization tools and techniques to microgrids, integrating renewable energies. With an extensive literature survey on HRES, a framework of diverse objectives has been outlined for which optimization approaches were applied to empower the microgrid. A review of modelling and applications of renewable energy generation and storage sources is also presented.

Optimization in microgrids with hybrid energy systems – A review

Modern cars are incorporating an increasing number of driver assist features, among which automatic lane keeping. The latter allows the car to properly position itself within the road lanes, which is also crucial for any subsequent lane departure or trajectory planning decision in fully autonomous cars. Traditional lane detection methods rely on a combination of highly-specialized, hand-crafted features and heuristics, usually followed by post-processing techniques, that are computationally expensive and prone to scalability due to road scene variations. More recent approaches leverage deep learning models, trained for pixel-wise lane segmentation, even when no markings are present in the image due to their big receptive field. Despite their advantages, these methods are limited to detecting a pre-defined, fixed number of lanes, e.g. ego-lanes, and can not cope with lane changes. In this paper, we go beyond the aforementioned limitations and propose to cast the lane detection problem as an instance segmentation problem - in which each lane forms its own instance - that can be trained end-to-end. To parametrize the segmented lane instances before fitting the lane, we further propose to apply a learned perspective transformation, conditioned on the image, in contrast to a fixed ”bird’s-eye view” transformation. By doing so, we ensure a lane fitting which is robust against road plane changes, unlike existing approaches that rely on a fixed, predefined transformation. In summary, we propose a fast lane detection algorithm, running at 50 fps, which can handle a variable number of lanes and cope with lane changes. We verify our method on the tuSimple dataset and achieve competitive results.

Towards End-to-End Lane Detection: an Instance Segmentation Approach

With the rapid proliferation of new technologies and services in the wireless domain, spectrum scarcity has become a major concern. The allocation of the Industrial, Medical and Scientific (ISM) band has enabled the explosion of new technologies (e.g. Wi-Fi) due to its licence-exempt characteristic. The widespread adoption of Wi-Fi technology, combined with the rapid penetration of smart phones running popular user services (e.g. social online networks) has overcrowded substantially the ISM band. On the other hand, according to a number of recent reports, several parts of the static allocated licensed bands are under-utilized. This has brought up the idea of the opportunistic use of these bands through the, so-called, cognitive radios and cognitive radio networks. Cognitive radios have enabled the opportunity to transmit in several licensed bands without causing harmful interference to licensed users. Along with the realization of cognitive radios, new security threats have been raised. Adversaries can exploit several vulnerabilities of this new technology and cause severe performance degradation. Security threats are mainly related to two fundamental characteristics of cognitive radios: cognitive capability, and reconfigurability. Threats related to the cognitive capability include attacks launched by adversaries that mimic primary transmitters, and transmission of false observations related to spectrum sensing. Reconfiguration can be exploited by attackers through the use of malicious code installed in cognitive radios. Furthermore, as cognitive radio networks are wireless in nature, they face all classic threats present in the conventional wireless networks. The scope of this work is to give an overview of the security threats and challenges that cognitive radios and cognitive radio networks face, along with the current state-of-the-art to detect the corresponding attacks. In addition, future challenges are addressed.

A Survey on Security Threats and Detection Techniques in Cognitive Radio Networks

The wireless mesh network is emerging as a promising technology in providing economical and scalable broadband Internet accesses to communities. The backbone of the wireless mesh network consists of mesh routers, which connect each other in an ad hoc manner via wireless links. The presence of backbone mesh routers and utilization of multiple channels and interfaces allow the wireless mesh network to have better capacity than that of the infrastructure-free ad hoc network formed by mesh clients directly. A special type of the mesh routers, referred to as gateway nodes, is capable of Internet connection, and other mesh routers and associated terminal clients have to access the Internet through the gateway nodes. In this paper, we present the analytically traceable stochastic models to characterize the average delay and throughput performance in wireless mesh networks. We model the forwarding mesh routers as an open queuing network. The analytical model takes into account the mesh router density, the random packet arrival process, the degree of locality of traffic and the collision avoidance mechanism of the IEEE802.11 DCF random access MAC. Our simulation results suggest that the analytical results are quite accurate, which can provide valuable insights in system performance and an effective guideline for the scalable design and optimization in wireless mesh networks.

Performance Analysis of IEEE802.11 Wireless Mesh Networks

Accurate lateral localization of an ego-vehicle is one of the core technologies for autonomous driving. Conventional approaches have utilized GPS data, pre-built map information, and lane detection results to estimate the lateral location of an ego-vehicle. However, these approaches demonstrate several performance limitations due to inaccurate data from GPS, high costs for building and maintaining maps, and insufficient visual cues for handling various tasks in diverse driving environments. In this paper, we propose an accurate ego-lane recognition framework that utilizes multiple evidence from visual processing upon the theory of the Bayesian Network to overcome these limitation. We show that more accurate and reliable lateral localization results can be achieved by combining several visual cues, which increases confidence and reliability of the results. We also show that our approach can be applicable to various driving environments without maps because the framework analyzes multiple context information of driving environments simultaneously. We verify the robustness of our algorithm in various driving scenarios such as highways and wide/narrow urban roadways.

Accurate ego-lane recognition utilizing multiple road characteristics in a Bayesian network framework

A new multipath multistage interconnection network called the composite banyan network is proposed. The network incorporates both the banyan and the reverse banyan networks and is constructed by superimposing the two. The basic building blocks in the composite banyan network are 3/spl times/3 switching elements with log/sub 2/N stages. A major advantage of the composite banyan network over existing networks with 3/spl times/3 SEs is an efficient and fast control algorithm that sets up a path between any source and destination pair. Instead of complex numerical calculations, the network can easily generate a primary routing tag and alternate tags through simple binary operations. Also, the network has a lot of favorable features, including regularity, symmetry, and easy rerouting capability under faults and conflicts. It is shown that at least two totally disjoint paths exist between any source and destination pair, which increase the degree of fault-tolerance. A deterministic permutation routing algorithm is also developed for the 8/spl times/8 composite banyan network, Using a simple tabular method, it is shown that the algorithm always finds a set of conflict-free tags. >

The composite banyan network

The wormhole attack, which is accomplished by selectively relaying packets between two adversaries, can ruin routing and communication of the network without compromising any legitimate nodes. There have been a few countermeasures against the wormhole attack in generic ad hoc networks, but they are not appropriate for sensor networks since they require special devices (e.g. directional antenna) or put much overhead on each node. In this paper, we propose a new countermeasure against the wormhole attack for sensor networks, named WODEM. In WODEM, a few detector nodes equipped with location-aware devices and longer-lasting batteries detect wormholes, and normal sensor nodes are only required to forward control packets from the detector nodes. Therefore, WODEM is efficient in cost and energy. From the simulation results, we show that 10 detector nodes can detect a wormhole within the accuracy of 90% in a densely deployed sensor network.

WODEM: wormhole attack defense mechanism in wireless sensor networks

The dynamics of multi-agent in nature have been largely studied for a long time to investigate how the aggregation of agents can move smoothly in complex environments without collision. The main insights can be summarized such that the aggregated dynamics of animals and particles can be explained by an individual's simple rules. In a similar vein, we conjecture that such simple rules for vehicle maneuvering can accommodate the fluid flow of traffic and reduce car accidents in highway and urban areas. In this paper, we first show the Reynolds' three rules are applicable to autonomous driving on a single lane. Moreover, we provide additional requirements and algorithms for multiple lanes. Based on these results, we show that the proposed nature-inspired driving maneuver can increase traffic flow by 1) mitigating shockwave at bottlenecks and 2) extending the perception range for better path planning, which requires the support of the vehicle autonomy and wireless communication, respectively. Finally, we prove the feasibility of our work with experiments using multiple UAVs.

Seung-Woo Seo

Papers

Performance Analysis of IEEE802.11 Wireless Mesh Networks

Accurate ego-lane recognition utilizing multiple road characteristics in a Bayesian network framework

The composite banyan network

WODEM: wormhole attack defense mechanism in wireless sensor networks

Multiple vehicle driving control for traffic flow efficiency