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

In this chapter we deal with the following nonlinear fractional programming problem: 

$$P:\mathop{{\max }}\limits_{{x \in s}} q(x) = (f(x) + \alpha )/((x) + \beta )$$

where f, g: R n → R, α, β ∈ R, S ⊆ R n . To simplify things, and without restricting the generality of the problem, it is usually assumed that, g(x) + β + 0 for all x ∈ S,S is non-empty and that the objective function has a finite optimal value.

Nonlinear Fractional Programming

Railway track fastener defect detection based on image processing and deep learning techniques: A comparative study

Wireless video sensor networks (WVSNs) have been envisioned for a wide range of important applications, including battlefield intelligence, security monitoring, emergency response, and environmental tracking. Compared to traditional communication system, the WVSN operates under a set of unique resource constraints, including limitations with respect to energy supply, on-board computational capability, and transmission bandwidth. The objective of this paper is to study the resource utilization behavior of a wireless video sensor and analyze its performance under the resource constraints. More specifically, we develop an analytic power-rate-distortion (P-R-D) model to characterize the inherent relationship between the power consumption of a video encoder and its rate-distortion performance. Based on the P-R-D analysis and a simplified model for wireless transmission power, we study the optimum power allocation between video encoding and wireless transmission and introduce a measure called achievable minimum distortion to quantify the distortion under a total power constraint. We consider two scenarios in wireless video sensing, small-delay wireless video monitoring and large-delay wireless video surveillance, and analyze the performance limit of the wireless video sensor in each scenario. The analysis and results obtained in this paper provide an important guideline for practical wireless video sensor design.

Resource Allocation and Performance Analysis of Wireless Video Sensors

Visible light communication (VLC) has been proposed as a promising and efficient solution to indoor ubiquitous broadband connectivity. In this paper, non-orthogonal multiple access, which has been recently introduced as an effective scheme for fifth generation (5G) wireless networks, is considered in the context of VLC systems under different channel uncertainty models. To this end, we first derive a novel closed-form expression for the bit-error-rate (BER) under perfect channel state information (CSI). Capitalizing on this, we then quantify the effect of noisy and outdated CSI by deriving a simple and accurate approximation for the former and a tight upper bound for the latter. The offered results are corroborated by respective results from extensive Monte Carlo simulations and assist in developing useful insights on the effect of imperfect CSI knowledge on the overall system performance. Furthermore, it was shown that while noisy CSI leads to slight degradation in the BER performance, outdated CSI can cause considerable performance degradation, if the order of the users’ channel gains change due to the involved mobility.

On the Performance of Visible Light Communication Systems With Non-Orthogonal Multiple Access

Extending the sensor life time is one of the most important issues in widespread use of Wireless Sensor Networks (WSNs). The Energy Harvesting (EH) sensors have been proposed to overcome the mentioned problem in recent years. These sensors can harvest their required energy from environment in different methods, resulting in longer life time. We consider a TDMA based Wireless Energy Harvesting Sensor Network (WEHSN) in which the time slot consists of two time intervals; the first one is utilized to absorb energy whereas the second one is used to transmit the sensors’ data. We investigate the energy efficient resource allocation in WEHSN with constraints on time scheduling parameters and transmission power consumption, where an EH sensor is allowed to transmit its data if the amount of its harvested energy is more than the consumption power. We derive the closed form expression for the optimization problem, corresponding to the energy efficiency and convert it to a parametric form, using Dinkelbach method. Then, we solve the new problem using Karush-Kuhn-Tucker (KKT) conditions. The numerical results shows the effectiveness of the proposed method.

Energy Efficient Resource Allocation in Wireless Energy Harvesting Sensor Networks

IEEE 802.15.7 is a physical and MAC layer standard for visible-light communication. This paper thoroughly analyzes the carrier sensing multiple access with collision avoidance (CSMA/CA) mechanism of the MAC protocol of this standard. The nodal behavior of the CSMA/CA mechanism is modeled using a Markov chain. Performance parameters are derived for saturated traffic in a star topology network. The derived performance metrics of the network include throughput, transmission probability, collision probability, packet discard probability, delay, and power consumption. Furthermore in order to improve the obtained analytic results, a semi-analytic approach is developed. To verify the obtained results, CSMA/CA is completely simulated in a MATLAB environment, and network parameters are derived and compared with analytic models. Simulation results match the analytic ones very closely, which proves the validity of the proposed model.

Comprehensive performance analysis of IEEE 802.15.7 CSMA/CA mechanism for saturated traffic

In this letter, we consider an RF-powered green cognitive radio network (RF-GCRN), where a central node, called a power beacon (PB), harvests green energy from ambient sources and wirelessly delivers random harvested energy to cognitive users. Random in-band energy transmission by PB is the only energy source of cognitive users. Performance of this network, with a single pair of secondary users, is analyzed under two spectrum access schemes, i.e., random access and spectrum sensing-based access schemes. Results show feasibility of the RF-GCRN model, if the energy transmission rate is below a certain threshold. This threshold is determined according to maximum tolerable delay of primary user and parameters of spectrum access scheme. Finding a closed form expression for this threshold results in a quite complicated formula, which cannot be helpful in determining feasibility region, due to excessive complexity. Instead, we numerically calculated feasibility regions of both access schemes to facilitate parameter selection process.

RF-Powered Green Cognitive Radio Networks: Architecture and Performance Analysis

In this paper, we propose a cognitive radio sensor network with green power beacon (PB) to solve spectrum and energy scarcity problems in the resource-limited wireless sensor networks. In the proposed model, a PB node harvests energy from ambient sources and transmits the harvested energy to cognitive sensor node (CSN) through in-band energy transmission. The CSN uses the received energy to transfer its data to the sink. Resource-constrained condition of sensor network motivates us to deploy random spectrum access scheme to control the interference between primary and secondary networks, and also self-interference inside the secondary network (i.e., between the CSN and the PB). This spectrum access scheme benefits from low hardware complexity and energy consumption. Using tools from queueing theory, we analyze the service rate of the CSN and primary network for both limited-battery as well as infinite-battery CSN. Then, by optimizing the spectrum access parameters of the PB and the CSN, service rate of the CSN is maximized, while the quality of service (QoS) constraint of primary network is satisfied. For each energy harvesting rate and QoS constraint, it is determined whether energy or spectrum or both are limiting the performance of the CSN. This result can be used to adopt proper solutions for improving the performance of the network.

Cognitive Radio Sensor Network With Green Power Beacon

In this paper we propose a new wavelet-based approach for solving the edge detection problem. A primary knowledge about the magnitude and the direction of the edges extracted by the ordinary wavelet-based edge detector is used to classify the image pixels as edge points, candidate edge points, or not edge points by double thresholding using extended Otsu's thresholding method explained here. Then based on the computed direction of the candidate edge points, the directional wavelet-based edge detector is applied on these pixels and the new magnitude of the wavelet transform coefficients is computed and then the pixel classified as edge points or not edge points. Using this method we can enhance the quality and performance of the wavelet-based edge detection in the presence of noise.

Javad Musevi Niya

Papers

Energy Efficient Resource Allocation in Wireless Energy Harvesting Sensor Networks

Comprehensive performance analysis of IEEE 802.15.7 CSMA/CA mechanism for saturated traffic

RF-Powered Green Cognitive Radio Networks: Architecture and Performance Analysis

Cognitive Radio Sensor Network With Green Power Beacon

Edge detection using directional wavelet transform