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Showing papers on "Network planning and design published in 2021"


Book
27 Aug 2021
TL;DR: End-to-End QoS Network Design is a detailed handbook for planning and deploying QoS solutions to address current business needs and considers detailed design examples that illustrate where, when, and how to deploy various QoS features to provide validated and tested solutions for voice, video, and critical data over the LAN, WAN, and VPN.
Abstract: Best-practice QoS designs for protecting voice, video, and critical data while mitigating network denial-of-service attacks Understand the service-level requirements of voice, video, and data applications Examine strategic QoS best practices, including Scavenger-class QoS tactics for DoS/worm mitigation Learn about QoS tools and the various interdependencies and caveats of these tools that can impact design considerations Learn how to protect voice, video, and data traffic using various QoS mechanisms Evaluate design recommendations for protecting voice, video, and multiple classes of data while mitigating DoS/worm attacks for the following network infrastructure architectures: campus LAN, private WAN, MPLS VPN, and IPSec VPN Quality of Service (QoS) has already proven itself as the enabling technology for the convergence of voice, video, and data networks. As business needs evolve, so do the demands for QoS. The need to protect critical applications via QoS mechanisms in business networks has escalated over the past few years, primarily due to the increased frequency and sophistication of denial-of-service (DoS) and worm attacks. End-to-End QoS Network Design is a detailed handbook for planning and deploying QoS solutions to address current business needs. This book goes beyond discussing available QoS technologies and considers detailed design examples that illustrate where, when, and how to deploy various QoS features to provide validated and tested solutions for voice, video, and critical data over the LAN, WAN, and VPN. The book starts with a brief background of network infrastructure evolution and the subsequent need for QoS. It then goes on to cover the various QoS features and tools currently available and comments on their evolution and direction. The QoS requirements of voice, interactive and streaming video, and multiple classes of data applications are presented, along with an overview of the nature and effects of various types of DoS and worm attacks. QoS best-practice design principles are introduced to show how QoS mechanisms can be strategically deployed end-to-end to address application requirements while mitigating network attacks. The next section focuses on how these strategic design principles are applied to campus LAN QoS design. Considerations and detailed design recommendations specific to the access, distribution, and core layers of an enterprise campus network are presented. Private WAN QoS design is discussed in the following section, where WAN-specific considerations and detailed QoS designs are presented for leased-lines, Frame Relay, ATM, ATM-to-FR Service Interworking, and ISDN networks. Branch-specific designs include Cisco SAFE recommendations for using Network-Based Application Recognition (NBAR) for known-worm identification and policing. The final section covers Layer 3 VPN QoS design-for both MPLS and IPSec VPNs. As businesses are migrating to VPNs to meet their wide-area networking needs at lower costs, considerations specific to these topologies are required to be reflected in their customer-edge QoS designs. MPLS VPN QoS design is examined from both the enterprise and service provider's perspectives. Additionally, IPSec VPN QoS designs cover site-to-site and teleworker contexts. Whether you are looking for an introduction to QoS principles and practices or a QoS planning and deployment guide, this book provides you with the expert advice you need to design and implement comprehensive QoS solutions.

199 citations


Book
25 Jan 2021
TL;DR: This monograph covers the foundations of User-centric Cell-free Massive MIMO, starting from the motivation and mathematical definition, and describes the state-of-the-art signal processing algorithms for channel estimation, uplink data reception.
Abstract: Modern day cellular mobile networks use Massive MIMO technology to extend range and service multiple devices within a cell. This has brought tremendous improvements in the high peak data rates that can be handled. Nevertheless, one of the characteristics of this technology is large variations in the quality of service dependent on where the end user is located in any given cell. This becomes increasingly problematic when we are creating a society where wireless access is supposed to be ubiquitous. When payments, navigation, entertainment, and control of autonomous vehicles are all relying on wireless connectivity the primary goal for future mobile networks should not be to increase the peak rates, but the rates that can be guaranteed to the vast majority of the locations in the geographical coverage area. The cellular network architecture was not designed for high-rate data services but for low-rate voice services, thus it is time to look beyond the cellular paradigm and make a clean-slate network design that can reach the performance requirements of the future. This monograph considers the cell-free network architecture that is designed to reach the aforementioned goal of uniformly high data rates everywhere. The authors introduce the concept of a cell-free network before laying out the foundations of what is required to design and build such a network. They cover the foundations of channel estimation, signal processing, pilot assignment, dynamic cooperation cluster formation, power optimization, fronthaul signaling, and spectral efficiency evaluation in uplink and downlink under different degrees of cooperation among the access points and arbitrary linear combining and precoding. This monograph provides the reader with all the fundamental information required to design and build the next generation mobile networks without being hindered by the inherent restrictions of modern cellular-based technology.

131 citations


Proceedings ArticleDOI
20 Jun 2021
TL;DR: In this paper, a model-based denoising method is proposed to improve the interpretability of deep networks by introducing a non-linear filtering operator, a reliability matrix, and a high-dimensional feature transformation function.
Abstract: Recent studies have shown that deep networks can achieve promising results for image denoising. However, how to simultaneously incorporate the valuable achievements of traditional methods into the network design and improve network interpretability is still an open problem. To solve this problem, we propose a novel model-based denoising method to inform the design of our denoising network. First, by introducing a non-linear filtering operator, a reliability matrix, and a high-dimensional feature transformation function into the traditional consistency prior, we propose a novel adaptive consistency prior (ACP). Second, by incorporating the ACP term into the maximum a posteriori framework, a model-based denoising method is proposed. This method is further used to inform the network design, leading to a novel end-to-end trainable and interpretable deep denoising network, called DeamNet. Note that the unfolding process leads to a promising module called dual element-wise attention mechanism (DEAM) module. To the best of our knowledge, both our ACP constraint and DEAM module have not been reported in the previous literature. Extensive experiments verify the superiority of DeamNet on both synthetic and real noisy image datasets.

89 citations


Journal ArticleDOI
TL;DR: Examination of the resilient sustainable reverse logistics network process for end-of-life vehicles (ELVs) in Iran shows that cost savings for organizations are achieved through optimal planning of the centers' capacity to save cost, increase services, and ensure effective government response to cost-effective and instrumental market competition.
Abstract: With new global regulations on supply chains (SCs), sustainable regulation mechanisms have become subject to controversy. The intention is to create and expand green and sustainable supply chains (SSC) to meet environmental and economic standards and to boost one’s position in competitive markets. This study examines the resilient sustainable reverse logistics network (RLN) process for end-of-life vehicles (ELVs) in Iran. We pursue both actual and uncertain situations that possess big data characteristics (3 V’s) in information between facilities of the proposed reverse logistics (RL), and we consider recycling technology due to its societal impacts. Due to unpredictable environmental and social factors, the various proposed network facilities may not utilize their full capacity, so we also consider situations in which the network facility capacity is disrupted. Our primary objective is to minimize the total cost of the resilient sustainable RLN. For most parameters, finding the best solution through traditional methods is time-consuming and costly. Hence, to enhance decision-making power, the value of model parameters in each scenario is considered. A Cross-Entropy (CE) algorithm with basic scenario concepts is used in robust model optimization. The results demonstrate that changing the scenario situation significantly impacts optimal environmental and social costs. In particular, when the situation is “pessimistic,” environmental impact costs are at their highest levels. Hence, scenario-based modeling of the network is a good approach to implement under uncertainty conditions. On the other hand, results show that cost savings for organizations are achieved through optimal planning of the centers' capacity to save cost, increase services, and ensure effective government response to cost-effective and instrumental market competition.

67 citations


Journal ArticleDOI
TL;DR: In this article, a multi-objective modeling of the network design problem is proposed to design accurate Convolutional Neural Networks (CNNs) with a small structure, which makes use of a graph-based representation of the solutions.
Abstract: With the development of deep learning, the design of an appropriate network structure becomes fundamental. In recent years, the successful practice of Neural Architecture Search (NAS) has indicated that an automated design of the network structure can efficiently replace the design performed by human experts. Most NAS algorithms make the assumption that the overall structure of the network is linear and focus solely on accuracy to assess the performance of candidate networks. This paper introduces a novel NAS algorithm based on a multi-objective modeling of the network design problem to design accurate Convolutional Neural Networks (CNNs) with a small structure. The proposed algorithm makes use of a graph-based representation of the solutions which enables a high flexibility in the automatic design. Furthermore, the proposed algorithm includes novel ad-hoc crossover and mutation operators. We also propose a mechanism to accelerate the evaluation of the candidate solutions. Experimental results demonstrate that the proposed NAS approach can design accurate neural networks with limited size.

60 citations


Proceedings ArticleDOI
09 Aug 2021
TL;DR: NeuroPlan as mentioned in this paper proposes a deep reinforcement learning (RL) approach to solve the network planning problem, which involves multi-step decision making and cost minimization, which can be naturally cast as a deep RL problem.
Abstract: Network planning is critical to the performance, reliability and cost of web services. This problem is typically formulated as an Integer Linear Programming (ILP) problem. Today's practice relies on hand-tuned heuristics from human experts to address the scalability challenge of ILP solvers. In this paper, we propose NeuroPlan, a deep reinforcement learning (RL) approach to solve the network planning problem. This problem involves multi-step decision making and cost minimization, which can be naturally cast as a deep RL problem. We develop two important domain-specific techniques. First, we use a graph neural network (GNN) and a novel domain-specific node-link transformation for state encoding, in order to handle the dynamic nature of the evolving network topology during planning decision making. Second, we leverage a two-stage hybrid approach that first uses deep RL to prune the search space and then uses an ILP solver to find the optimal solution. This approach resembles today's practice, but avoids human experts with an RL agent in the first stage. Evaluation on real topologies and setups from large production networks demonstrates that NeuroPlan scales to large topologies beyond the capability of ILP solvers, and reduces the cost by up to 17% compared to hand-tuned heuristics.

54 citations


Journal ArticleDOI
TL;DR: An analytical framework based on a queueing system that evaluates communication performances of UASNs, wherein each underwater sensor distributed within a 3D space under the sea surface performs fountain code (FC)-based automatic repeat request (ARQ) transmissions under the slotted-Aloha medium access control protocol is presented.
Abstract: Despite the potential benefits of Internet of Underwater Things, a number of issues hinder its realization, including the need for communication reliability and cost-effectiveness. This article aims to optimize network design to implement cost-effective underwater acoustic sensor networks (UASNs) with 3D topology while supporting diverse communication quality of service (QoS) requirements. First, we present an analytical framework based on a queueing system that evaluates communication performances of UASNs, wherein each underwater sensor distributed within a 3D space under the sea surface performs fountain code (FC)-based automatic repeat request (ARQ) transmissions under the slotted-Aloha medium access control protocol. Under the proposed framework, we evaluate communication performances given in terms of successful FC-based ARQ transmission probability and the average queueing delay of an underwater sensor. When evaluating the performances, we formulate the service time of each underwater sensor as a function of network parameters, i.e., the density of data sink and amount of redundancy for FC-based ARQ transmission, before solving a function for accurate service time, such that each sensor can be represented by an M/G/1 queue. Further, our analysis can formulate an optimization problem that aims at minimizing total cost incurred to install and operate 3D UASNs, without compromising two communication QoS requirements. To solve this problem, we propose a recursive algorithm to approach an optimal solution in reasonable time. Numerical evaluations demonstrate the validity of the proposed algorithm.

51 citations


Journal ArticleDOI
TL;DR: A two-stage multi-objective possibilistic integer linear programming sustainable supply chain network design model, minimizing the economic, environmental goals and maximizing the social sustainability goals is proposed, which provides a large combination of the trade-off between the cost, emission and social sustainability.
Abstract: This paper proposes a two-stage multi-objective possibilistic integer linear programming sustainable supply chain network design model, minimizing the economic, environmental goals and maximizing the social sustainability goals. The proposed model determines the openings of facilities and the amount of flow of goods across the supply chain. It introduces supplier green image factors in the design of the supply chain network. The model has considered epistemic uncertainty to model the unknown capacity, cost, and demand. The proposed study has been carried out in two stages. In the first stage, BWM (Best-Worst method) and TOPSIS are applied to evaluate the green image weights of suppliers. Further, these green weights are being used in the second phase for the supply chain network design. The study has adopted combined possibilistic programming and Epsilon (e) constraint method, which was reported infrequently in the literature. Epsilon (e) constraint method generates distinct Pareto-optimal solutions, which provided a large combination of the trade-off between the cost, emission, and social sustainability. The results facilitate decision-makers to take the decision in an uncertain environment.

51 citations


Journal ArticleDOI
TL;DR: A state–space–time network-based bi-objective mixed integer programming model is constructed to optimize the vehicle routes in order to meet customer demands for essential materials with the lowest cost and highest emergency response speed under limited transportation resources.
Abstract: The occurrence of natural disasters or accidents causes the obstruction or interruption of road traffic connectivity and affects the transportation of essential materials, especially for cross-regional delivery under emergency situations. Affected by COVID-19, government administrators establish cross-regional quarantine roadblocks to reduce the risk of virus transmission caused by cross-regional transportation. In this study, we propose an emergency logistics network design problem with resource sharing under collaborative alliances. We construct a state–space–time network-based bi-objective mixed integer programming model to optimize the vehicle routes in order to meet customer demands for essential materials with the lowest cost and highest emergency response speed under limited transportation resources. A two-stage hybrid heuristic algorithm is then proposed to find good-quality solutions for the problem. Clustering results are obtained using a 3D k-means clustering algorithm with the consideration of time and space indices. The optimization of the initial population generated by the improved Clarke and Wright saving method and improved nondominated sorting genetic algorithm-II with elite retention strategy provides stable and excellent performance for the searching of Pareto frontier. The cost difference of the entire emergency logistics network before and after collaboration, i.e., the profit, is fairly allocated to the participants (i.e., logistics service providers) through the Shapley value method. A real-world case in Chongqing City, China is used to validate the effectiveness of the proposed model and algorithm. This study contributes to smart transportation and logistics system in emergency planning and has particular implications for the optimal response of existing logistics system to the current COVID-19 pandemic.

48 citations


Journal ArticleDOI
TL;DR: In this article, a review article scrutinizes the issues of interferences observed and studied in different structures and techniques of the 5G and beyond network, focusing on the various interference effect in HetNet, RN, D2D, and IoT.
Abstract: In the modern technological world, wireless communication has taken a massive leap from the conventional communication system to a new radio communication network. The novel concept of Fifth Generation (5G) cellular networks brings a combination of a diversified set of devices and machines with great improvement in a unique way compared to previous technologies. To broaden the user’s experience, 5G technology provides the opportunity to meet the people’s potential necessities for efficient communication. Specifically, researchers have designed a network of small cells with unfamiliar technologies that have never been introduced before. The new network design is an amalgamation of various schemes such as Heterogeneous Network (HetNet), Device-to-Device (D2D) communication, Internet of Things (IoT), Relay Node (RN), Beamforming, Massive Multiple Input Multiple Output (M-MIMO), millimeter-wave (mm-wave), and so on. Also, enhancement in predecessor’s techniques is required so that new radio is compatible with a traditional network. However, the disparate technological models’ design and concurrent practice have created an unacceptable intervention in each other’s signals. These vulnerable interferences have significantly degraded the overall network performance. This review article scrutinizes the issues of interferences observed and studied in different structures and techniques of the 5G and beyond network. The study focuses on the various interference effect in HetNet, RN, D2D, and IoT. Furthermore, as an in-depth literature review, we discuss various types of interferences related to each method by studying the state-of-the-art relevant research in the literature. To provide new insight into interference issue management for the next-generation network, we address and explore various relevant topics in each section that help make the system more robust. Overall, this review article’s goal is to guide all the stakeholders, including students, operators, engineers, and researchers, aiming to explore this promising research theme, comprehend interferences and their types, and related techniques to mitigate them. We also state methodologies proposed by the $3^{\mathrm {rd}}$ Generation Partnership Project (3GPP) and present the promising and feasible research directions toward this challenging topic for the realization of 5G and beyond network.

44 citations


Journal ArticleDOI
TL;DR: An online mobility-aware offloading and resource allocation (OMORA) algorithm is proposed based on the Lyapunov optimization and semidefinite programming (SDP), which optimizes the offloading scheme without the need to have prior knowledge of the user mobility, EH model, and channel condition.
Abstract: Mobile edge computing (MEC)-enabled Internet of Things (IoT) networks have been deemed a promising paradigm to support massive energy-constrained and computation-limited IoT devices. Energy harvesting (EH) further enhances the operating capabilities of IoT devices that normally only possess very limited energy support. Nevertheless, many studies show that IoT devices using EH can experience uncertainty and unpredictability, which can complicate EH-based IoT network design. Furthermore, with many new services in 5G and the forthcoming 6G eras such as autonomous driving and vehicular communications, mobility consideration in IoT networks becomes more and more important. In this paper, we study the computing offloading and resource allocation problems in an IoT network that supports both mobility and energy harvesting. The long-term average sum service cost of all the mobile IoT devices (MIDs) is minimized by optimizing the harvested energy, task-partition factors, the CPU frequencies, the transmit power, and the association vector of MIDs. An online mobility-aware offloading and resource allocation (OMORA) algorithm is proposed based on Lyapunov optimization and Semi-Definite Programming (SDP). This online algorithm optimizes the offloading scheme without the need to have prior knowledge of user mobility, the EH model, and the channel condition. Theoretical analysis shows that the proposed OMORA algorithm can achieve asymptotic optimality. Simulation results demonstrate that the proposed algorithm can effectively balance the system service cost and energy queue length, and outperform other offloading benchmark algorithms on the system service cost and packet losses.

Journal ArticleDOI
TL;DR: This article modeled the network traffic prediction problem as a Markov decision process, and then, predicted network traffic by Monte Carlo by solving the real-time requirement of the proposed reinforcement learning-based mechanism.
Abstract: Intelligent Internet of Things (IIoT) is comprised of various wireless and wired networks for industrial applications, which makes it complex and heterogeneous.The openness of IIoT has led to the intractable problems of network security and management. Many network security and management functions rely on network traffic prediction techniques, such as anomaly detection and predictive network planning. Predicting IIoT network traffic is significantly difficult because its frequently updated topology and diversified services lead to irregular network traffic fluctuations. Motivated by these observations, we proposed a reinforcement learning-based mechanism in this article. We modeled the network traffic prediction problem as a Markov decision process, and then, predicted network traffic by Monte Carlo $Q$ -learning. Furthermore, we addressed the real-time requirement of the proposed mechanism and we proposed a residual-based dictionary learning algorithm to improve the complexity of Monte Carlo $Q$ -learning. Finally, the effectiveness of our mechanism was evaluated using the real network traffic.

Journal ArticleDOI
TL;DR: This research proposes a choice modeling approach embedded in a two-stage stochastic programming model to determine the optimal layout and types of EV supply equipment for a community while considering randomness in demand and drivers’ behaviors.
Abstract: Electric vehicles (EVs) provide a cleaner alternative that not only reduces greenhouse gas emissions but also improves air quality and reduces noise pollution. The consumer market for electrical vehicles is growing very rapidly. Designing a network with adequate capacity and types of public charging stations is a challenge that needs to be addressed to support the current trend in the EV market. In this research, we propose a choice modeling approach embedded in a two-stage stochastic programming model to determine the optimal layout and types of EV supply equipment for a community while considering randomness in demand and drivers’ behaviors. Some of the key random data parameters considered in this study are: EV’s dwell time at parking location, battery’s state of charge, distance from home, willingness to walk, drivers’ arrival patterns, and traffic on weekdays and weekends. The two-stage model uses the sample average approximation method, which asymptotically converges to an optimal solution. To address the computational challenges for large-scale instances, we propose an outer approximation decomposition algorithm. We conduct extensive computational experiments to quantify the efficacy of the proposed approach. In addition, we present the results and a sensitivity analysis for a case study based on publicly available data sources.

Journal ArticleDOI
TL;DR: In this paper, an electricity network planning and transformation roadmap, which has two milestones, is put forward, in which a mathematical model is proposed based on the average cost of carbon emission reduction to realize the cooperation of coal-fired power plants retirement and renewable energy investment.
Abstract: The acceleration of distributed energy resources and carbon pricing policies have compelled utilities to act and to prioritize carbon-constrained infrastructure augmentation in their capital programs. To implement various carbon emission reduction policies, power system transmission planning has become more challenging. The existing energy system will face massive retirement of coal-fired power plants (CFPPs), large scale integration of renewable energy and network expansion. In this paper, an electricity network planning and transformation roadmap, which has two milestones, is put forward. In the first stage, a mathematical model is proposed based on the average cost of carbon emission reduction to realize the cooperation of CFPPs retirement and renewable energy investment. It can help the network carry out the transition from a fossil-fuel dominated system to a low-carbon oriented system. Because of the promising prospect of power-to-gas (P2G) technology, in the second milestone, a method based on carbon emission flow (CEF) is employed to help the power-to-gas stations (P2GSes) to select the construction site and capacity. The gas network constraints are modeled to guarantee that P2GSes can work smoothly without energy flow congestion in both electricity and gas networks. According to the simulation results in case studies, our method can reach the emission reduction target more economically and effectively, and the P2GSes can produce and absorb clean energy.

Journal ArticleDOI
TL;DR: This work proposes the flow-based forwarding traffic prediction algorithm to forecast to SDN traffic, which enables more accurate measurements of flow traffic via a direct and low-overhead way compared with traditional traffic measurements.
Abstract: Traffic prediction is significantly important for performance analysis and network planning in Software Defined Networking (SDN). However, to effectively predict network traffic in current networks is very difficult and nearly prohibitive. As a new cutting-edge network technology, SDN decouples the control and data planes of network switch devices to enable the flexibility of network measurements and managements. The SDN architecture of the flow-based forwarding idea brings forth a promising of network traffic capture and prediction. We propose a lightweight traffic prediction algorithm for SDN applications. Firstly, different from traditional network traffic measurements, our method uses the flow-based forwarding idea in SDN to extract traffic statistic from data plane. The statistical variable describes network flow information forwarded in SDN and enables more accurate measurements of flow traffic via a direct and low-overhead way compared with traditional traffic measurements. Secondly, based on the temporal nature of traffic, the time-correlation theory is utilized to model flow traffic, where the time-series analysis theory and regressive modeling approach are used to characterize network traffic in SDN. A fully new method is proposed to perform traffic prediction. Thirdly, we propose the flow-based forwarding traffic prediction algorithm to forecast to SDN traffic. The detailed algorithm process is discussed and analyze. Finally, sufficient experiments are presented and designed to validate the proposed method. Simulation results show that our method is feasible and effective.

Journal ArticleDOI
TL;DR: In the paper, existing load modelling techniques and NILM methodologies are critically examined to inform and guide research activity, equipment development and regulator thinking, as well as network operators.

Journal ArticleDOI
TL;DR: In this article, an integrated multi-objective mixed-integer linear programming (MOMILP) model is proposed to design sustainable closed-loop supply chain networks with cross-docking, location-inventory-routing, time window, supplier selection, order allocation, transportation modes with simultaneous pickup, and delivery under uncertainty.

Journal ArticleDOI
TL;DR: A robust bi-level model of the single-product multi-period network design problem is proposed for a competitive green supply chain considering pricing and inventory decisions under uncertainty and disruption risks and is capable of dealing with such uncertainties by implementing resilience strategies including, inventory decisions and having a contract with reliable suppliers.
Abstract: A robust bi-level model of the single-product multi-period network design problem is proposed for a competitive green supply chain considering pricing and inventory decisions under uncertainty and ...

Journal ArticleDOI
TL;DR: A deep neural network based wireless channel model is exploited to model the wireless propagation, which was very difficult to deterministically predict at a fast speed in previous research due to the high computation demanding.
Abstract: With the increasing demand for rail transit, wireless communication technologies are playing a growing significant role in train control systems, which enables the railway systems to provide a higher capacity and more efficient services. However, due to the nature of radio frequency propagation, the quality of the train-to-ground wireless connections is highly dependent on a well-planned deployment of the wayside access points. To improve both the accuracy and the efficiency in railway network planning, in this paper, a deep learning technology is exploited to model the wireless propagation, which was very difficult to deterministically predict at a fast speed in our previous research due to the high computation demanding. In this proposed wireless propagation model, Kalman filter is utilized to update the neural network parameters online, which makes this model can meet the variation of the environment. The numeric evaluation result shows that the deep neural network based wireless channel model can precisely predict the outage probability with a very low computational cost.

Journal ArticleDOI
TL;DR: A novel multi-objective mixed integer nonlinear programming model for a closed-loop green supply chain network design problem, which aims to minimize the total costs, total CO 2 emissions, and robustness costs in both forward and reverse directions, simultaneously.
Abstract: In recent years, due to governmental legislation, environmental groups’ pressures, customer green expectation, etc., closed-loop green supply chains have gained paramount consideration. Accordingly, this study develops a novel multi-objective mixed integer nonlinear programming model for a closed-loop green supply chain network design problem. The proposed model aims to minimize the total costs, total CO2 emissions, and robustness costs in both forward and reverse directions, simultaneously. To cope with flexible constraints and epistemic uncertainty in the model’s parameters, a robust flexible-possibilistic programming approach is tailored. The model is solved using an efficient interactive solution approach, in which, the presented model is analyzed under various carbon emission mechanisms to assess the influence of these mechanisms on the achieved solution. An illustrative example in the copier industry is also provided to validate the applicability of the presented optimization model. Numerical results indicate the superiority of the carbon cap-and-trade policy in most of the cases.

Journal ArticleDOI
TL;DR: Results show that incorporating passenger-route assignment optimization and the transfer operation produces a more cost-effective CB operational network with less operational costs and higher service quality.
Abstract: Customized bus (CB) is an increasingly popular mode of transportation in many cities around the world. However, studies on CB network design have mostly overlooked three options that may further improve system performance: passenger-route assignment, passenger transfer, and modular vehicles. To bridge this gap, this paper proposes to design a transfer-based CB network with a modular fleet while simultaneously optimizing the passenger-route assignment. To solve the optimal network structure with this new design paradigm, we formulate the network design problem into a nonlinear mixed integer optimization model. A linearization approach and a particle swarm optimization (PSO) algorithm are proposed to solve the exact and near-optimal solution(s) to the model, respectively. Numerical experiments are conducted on the Sioux Falls network and a large-scale network in Chengdu, China. Results show that the customized PSO algorithm efficiently provides high quality near-optimal solutions compared with CPLEX, the genetic algorithm, and the simulated annealing algorithm. Results also show that incorporating passenger-route assignment optimization and the transfer operation produces a more cost-effective CB operational network with less operational costs and higher service quality. The benefit increases as the passenger demand grows.

Journal ArticleDOI
TL;DR: In this paper, a distributionally robust fuzzy GCLSC network design model was developed with the perspective of a trade-off between uncertainty and risk originating from the size and complexity of network, and the distribution of uncertain parameter may be ambiguous.

Journal ArticleDOI
TL;DR: Numerical simulation confirms the capability of the proposed LRA framework in obtaining a resilient distribution network during natural disasters.

Journal ArticleDOI
TL;DR: A stochastic integrated multi-objective mixed integer nonlinear programming model is developed in this paper, in which sustainability outcomes as well as efficiency of facility resource utilization are considered in the design of a sustainable supply chain network.

Journal ArticleDOI
TL;DR: This work proposes a scheduled service network design formulation for the tactical planning of such extended systems, and develops an efficient Benders decomposition algorithm, which includes a tailored partial decomposition technique for deterministic mixed-integer linear-programming formulations.

Journal ArticleDOI
TL;DR: In this paper, the authors proposed a novel heuristic to reconstruct application-layer messages in the common case of encrypted traffic, and compared the suitability of the provided modeling approaches for different tasks: characterization of network traffic (at different granularities, such as application, application category, and application version), and prediction of traffic at both packet and message level.
Abstract: Modeling network traffic is an endeavor actively carried on since early digital communications, supporting a number of practical applications, that range from network planning and provisioning to security Accordingly, many theoretical and empirical approaches have been proposed in this long-standing research, most notably, Machine Learning (ML) ones Indeed, recent interest from network equipment vendors is sparking around the evaluation of solid information-theoretical modeling approaches complementary to ML ones, especially applied to new network traffic profiles stemming from the massive diffusion of mobile apps To cater to these needs, we analyze mobile-app traffic available in the public dataset MIRAGE-2019 adopting two related modeling approaches based on the well-known methodological toolset of Markov models (namely, Markov Chains and Hidden Markov Models ) We propose a novel heuristic to reconstruct application-layer messages in the common case of encrypted traffic We discuss and experimentally evaluate the suitability of the provided modeling approaches for different tasks: characterization of network traffic (at different granularities, such as application, application category, and application version), and prediction of network traffic at both packet and message level We also compare the results with several ML approaches, showing performance comparable to a state-of-the-art ML predictor (Random Forest Regressor) Also, with this work we provide a viable and theoretically sound traffic-analysis toolset to help improving ML evaluation (and possibly its design), and a sensible and interpretable baseline

Journal ArticleDOI
TL;DR: In this article, the authors proposed an emergency communication system (ECS) using the optimal cluster head (CH) technique to improve the energy transfer efficiency for sustainable network connectivity, and the proposed approach has proven to reduce the computational complexity.
Abstract: The energy consumption and coverage range of unmanned aerial vehicles (UAVs) are major challenges in UAV-based postdisaster communications. To address these challenges, energy harvesting is employed to power communication devices and prolong the lifetime of the wireless communication network during a disaster. In addition, clustering techniques and device-to-device (D2D) communication are needed to increase the overall network coverage and provide sustainable connectivity during the disaster and postdisaster phases. We have proposed a novel emergency communication system (ECS) using the optimal cluster head (CH) technique to improve the energy transfer efficiency for sustainable network connectivity. We have developed a UAV deployment model assisted by the clustering technique and D2D links that is capable of harvesting energy to increase the network lifetime. This new approach is expected to enhance the reliability of the network in disaster situations. The proposed methods have been evaluated by measuring the energy efficiency performance and the network outage probability. The simulation results demonstrate improved performance with the deployment of optimal CHs, while the outage probability has been effectively reduced. Moreover, the proposed approach has been proven to reduce the computational complexity. In conclusion, UAV deployment with the optimal CH algorithm is a suitable network design to recover from natural disasters and potentially save many lives.

Journal ArticleDOI
TL;DR: In this article, the authors present a LoRa network design framework that utilizes a developed open-source emulator to provide a reliable network coverage estimation, which leverages real interference measurements captured using software defined radio that records the spectrotemporal behavior of the existing traffic in the shared band.
Abstract: Many wireless Internet-of-Things applications require extended battery life ranging from a few months to a few years. Such applications have motivated the recent developments in low power wide area networks, including the rise of Long Range (LoRa) technology. LoRa has a simple modulation scheme designed for extended converge, low battery consumption, and resistance to high interference levels. Thus LoRa is primarily targeted for shared spectrum applications where interference levels are typically higher than controlled spectrum applications where a single operator usually has a dominant control on the quality of service. As a result, it is of paramount importance to carefully design IoT networks while taking into account the impending impacts of interference and propagation environments. This paper presents a novel LoRa network design framework that utilizes a developed open-source emulator to provide a reliable network coverage estimation. The framework is tested in one of the largest open-access IoT network designs in Australia, which enabled the deployment of 294 sensors and 48 gateways. Both the framework and the emulator are implemented using MATLAB scripting, enabling integration with built-in and external radio planning tools. The framework leverages real interference measurements captured using software defined radio that records the spectrotemporal behavior of the existing traffic in the shared band.

Journal ArticleDOI
TL;DR: A new mathematical model to strategically deploy WCSs in the network in such a way that no EV runs out of energy before reaching its destination is suggested, devised a combined combinatorial-classical Benders Decomposition approach and enhanced its efficiency via employing surrogate constraints and an upper bound heuristic.
Abstract: We address a network design problem arising in the deployment of wireless charging stations (WCSs) within an urban transportation network. It is widely acknowledged that, despite the availability of EV conventional charging facilities, the relatively short driving range of EVs (due to low energy density of the batteries) and the long battery charging times (collectively leading to a phenomenon known as “range anxiety”) remain the major factors that hamper EV adoption. Thus, in this research, we study a cost-effective WCS deployment network design that facilitates EV adoption by alleviating these two major anti-adoption factors. We consider the problem from the perspective of a city as the decision maker whose aim is to satisfy the charging demands of all EVs in its urban traffic network at the minimum cost including installation and charging costs. For this purpose, we suggest a new mathematical model to strategically deploy WCSs in the network in such a way that no EV runs out of energy before reaching its destination. To solve the proposed model, we devise a combined combinatorial-classical Benders Decomposition approach and further enhance its efficiency via employing surrogate constraints and an upper bound heuristic. We present computational results illustrating the algorithmic efficiency of our approach as well as an analysis of the effect of varying system and new technology related parameters (i.e., product design) on the resulting network design based on a case study with urban network data from Chicago, IL.

Journal ArticleDOI
TL;DR: Simulation results show that considering the first-order reflections in planning the mmWave network helps reduce the number of PMRs required to cover the NLoS area, which in turn reduces the deployment cost.
Abstract: The use of millimeter-wave (mmWave) bands in 5G networks introduces a new set of challenges to network planning. Vulnerability to blockages and high path loss at mmWave frequencies require careful planning of the network to achieve a desired service quality. In this paper, we propose a novel 3D geometry-based framework for deploying mmWave base stations (gNBs) in urban environments by considering first-order reflection effects. We also provide a solution for the optimum deployment of passive metallic reflectors (PMRs) to extend radio coverage to non-line-of-sight (NLoS) areas. In particular, we perform visibility analysis to find the direct and indirect visibility regions, and using these, we derive a geometry-and-blockage-aided path loss model . We then formulate the network planning problem as two independent optimization problems, placement of gNB(s) and PMRs, to maximize the coverage area, minimize the deployment cost, and maintain a desired quality-of-service level. We test the efficacy of our proposed approach using a generic map and compare our simulation results with the ray tracing solution. Our simulation results show that considering the first-order reflections in planning the mmWave network helps reduce the number of PMRs required to cover the NLoS area. Moreover, the gNB placement aided with PMRs require fewer gNBs to cover the same area, which in turn reduces the deployment cost.