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Showing papers on "Network planning and design published in 2020"


Proceedings ArticleDOI
Ilija Radosavovic1, Raj Prateek Kosaraju1, Ross Girshick1, Kaiming He1, Piotr Dollár1 
14 Jun 2020
TL;DR: The RegNet design space provides simple and fast networks that work well across a wide range of flop regimes, and outperform the popular EfficientNet models while being up to 5x faster on GPUs.
Abstract: In this work, we present a new network design paradigm. Our goal is to help advance the understanding of network design and discover design principles that generalize across settings. Instead of focusing on designing individual network instances, we design network design spaces that parametrize populations of networks. The overall process is analogous to classic manual design of networks, but elevated to the design space level. Using our methodology we explore the structure aspect of network design and arrive at a low-dimensional design space consisting of simple, regular networks that we call RegNet. The core insight of the RegNet parametrization is surprisingly simple: widths and depths of good networks can be explained by a quantized linear function. We analyze the RegNet design space and arrive at interesting findings that do not match the current practice of network design. The RegNet design space provides simple and fast networks that work well across a wide range of flop regimes. Under comparable training settings and flops, the RegNet models outperform the popular EfficientNet models while being up to 5x faster on GPUs.

1,041 citations


Journal ArticleDOI
TL;DR: In this paper, a low-latency multi-access scheme for edge learning is proposed, where the updates simultaneously transmitted by devices over broadband channels should be analog aggregated "over-the-air" by exploiting the waveform-superposition property of a multiaccess channel.
Abstract: To leverage rich data distributed at the network edge, a new machine-learning paradigm, called edge learning, has emerged where learning algorithms are deployed at the edge for providing intelligent services to mobile users. While computing speeds are advancing rapidly, the communication latency is becoming the bottleneck of fast edge learning. To address this issue, this work is focused on designing a low-latency multi-access scheme for edge learning. To this end, we consider a popular privacy-preserving framework, federated edge learning (FEEL), where a global AI-model at an edge-server is updated by aggregating (averaging) local models trained at edge devices. It is proposed that the updates simultaneously transmitted by devices over broadband channels should be analog aggregated “over-the-air” by exploiting the waveform-superposition property of a multi-access channel. Such broadband analog aggregation (BAA) results in dramatical communication-latency reduction compared with the conventional orthogonal access (i.e., OFDMA). In this work, the effects of BAA on learning performance are quantified targeting a single-cell random network. First, we derive two tradeoffs between communication-and-learning metrics, which are useful for network planning and optimization. The power control (“truncated channel inversion”) required for BAA results in a tradeoff between the update-reliability [as measured by the receive signal-to-noise ratio (SNR)] and the expected update-truncation ratio. Consider the scheduling of cell-interior devices to constrain path loss. This gives rise to the other tradeoff between the receive SNR and fraction of data exploited in learning. Next, the latency-reduction ratio of the proposed BAA with respect to the traditional OFDMA scheme is proved to scale almost linearly with the device population. Experiments based on a neural network and a real dataset are conducted for corroborating the theoretical results.

310 citations


Journal ArticleDOI
TL;DR: In this article, a multiple-rate compressive sensing neural network framework was proposed to compress and quantize the channel state information (CSI) in massive MIMO networks, which not only improves reconstruction accuracy but also decreases storage space at the UE.
Abstract: Massive multiple-input multiple-output (MIMO) is a promising technology to increase link capacity and energy efficiency. However, these benefits are based on available channel state information (CSI) at the base station (BS). Therefore, user equipment (UE) needs to keep on feeding CSI back to the BS, thereby consuming precious bandwidth resource. Large-scale antennas at the BS for massive MIMO seriously increase this overhead. In this paper, we propose a multiple-rate compressive sensing neural network framework to compress and quantize the CSI. This framework not only improves reconstruction accuracy but also decreases storage space at the UE, thus enhancing the system feasibility. Specifically, we establish two network design principles for CSI feedback, propose a new network architecture, CsiNet+, according to these principles, and develop a novel quantization framework and training strategy. Next, we further introduce two different variable-rate approaches, namely, SM-CsiNet+ and PM-CsiNet+, which decrease the parameter number at the UE by 38.0% and 46.7%, respectively. Experimental results show that CsiNet+ outperforms the state-of-the-art network by a margin but only slightly increases the parameter number. We also investigate the compression and reconstruction mechanism behind deep learning-based CSI feedback methods via parameter visualization, which provides a guideline for subsequent research.

161 citations


Journal ArticleDOI
TL;DR: Two novel lightweight networks are proposed that can obtain higher recognition precision while preserving less trainable parameters in the models and can be useful when deploying deep convolutional neural networks (CNNs) on mobile embedded devices.
Abstract: Deeper neural networks have achieved great results in the field of computer vision and have been successfully applied to tasks such as traffic sign recognition. However, as traffic sign recognition systems are often deployed in resource-constrained environments, it is critical for the network design to be slim and accurate in these instances. Accordingly, in this paper, we propose two novel lightweight networks that can obtain higher recognition precision while preserving less trainable parameters in the models. Knowledge distillation transfers the knowledge in a trained model, called the teacher network, to a smaller model, called the student network. Moreover, to improve the accuracy of traffic sign recognition, we also implement a new module in our teacher network that combines two streams of feature channels with dense connectivity. To enable easy deployment on mobile devices, our student network is a simple end-to-end architecture containing five convolutional layers and a fully connected layer. Furthermore, by referring to the values of batch normalization (BN) scaling factors towards zero to identify insignificant channels, we prune redundant channels from the student network, yielding a compact model with accuracy comparable to that of more complex models. Our teacher network exhibited an accuracy rate of 93.16% when trained and tested on the CIFAR-10 general dataset. Using the knowledge of our teacher network, we train the student network on the GTSRB and BTSC traffic sign datasets. Thus, our student model uses only 0.8 million parameters while still achieving accuracy of 99.61% and 99.13% respectively on both datasets. All experimental results show that our lightweight networks can be useful when deploying deep convolutional neural networks (CNNs) on mobile embedded devices.

158 citations


Journal ArticleDOI
TL;DR: In this article, the authors propose a novel network model based on Graph Neural Network (GNN) that is able to understand the complex relationship between topology, routing, and input traffic to produce accurate estimates of the per-source/destination per-packet delay distribution and loss.
Abstract: Network modeling is a key enabler to achieve efficient network operation in future self-driving Software-Defined Networks. However, we still lack functional network models able to produce accurate predictions of Key Performance Indicators (KPI) such as delay, jitter or loss at limited cost. In this paper we propose RouteNet, a novel network model based on Graph Neural Network (GNN) that is able to understand the complex relationship between topology, routing, and input traffic to produce accurate estimates of the per-source/destination per-packet delay distribution and loss. RouteNet leverages the ability of GNNs to learn and model graph-structured information and as a result, our model is able to generalize over arbitrary topologies, routing schemes and traffic intensity. In our evaluation, we show that RouteNet is able to predict accurately the delay distribution (mean delay and jitter) and loss even in topologies, routing and traffic unseen in the training (worst case MRE = 15.4%). Also, we present several use cases where we leverage the KPI predictions of our GNN model to achieve efficient routing optimization and network planning.

145 citations


Journal ArticleDOI
TL;DR: Simulation results show that the proposed method can reconstruct end-to-end network traffic with a high degree of accuracy, and in comparison with previous methods, this approach exhibits a significant performance improvement.
Abstract: Estimation of end-to-end network traffic plays an important role in traffic engineering and network planning. The direct measurement of a network's traffic matrix consumes large amounts of network resources and is thus impractical in most cases. How to accurately construct traffic matrix remains a great challenge. This paper studies end-to-end network traffic reconstruction in large-scale networks. Applying compressive sensing theory, we propose a novel reconstruction method for end-to-end traffic flows. First, the direct measurement of partial Origin-Destination (OD) flows is determined by random measurement matrix, providing partial measurements. Then, we use the K-SVD approach to obtain a sparse matrix. Combined with compressive sensing, this partially known OD flow matrix can be used to recover the entire end-to-end network traffic matrix. Simulation results show that the proposed method can reconstruct end-to-end network traffic with a high degree of accuracy. Moreover, in comparison with previous methods, our approach exhibits a significant performance improvement.

137 citations


Journal ArticleDOI
TL;DR: The vision of the society development towards 2030 and the new application scenarios for mobile communication, and then the key performance requirements are derived from the service and application perspective are identified.
Abstract: With the 5th Generation (5G) Mobile network being rolled out gradually in 2019, the research for the next generation mobile network has been started and targeted for 2030. To pave the way for the development of the 6th Generation (6G) mobile network, the vision and requirements should be identified first for the potential key technology identification and comprehensive system design. This article first identifies the vision of the society development towards 2030 and the new application scenarios for mobile communication, and then the key performance requirements are derived from the service and application perspective. Taken into account the convergence of information technology, communication technology and big data technology, a logical mobile network architecture is proposed to resolve the lessons from 5G network design. To compromise among the cost, capability and flexibility of the network, the features of the 6G mobile network are proposed based on the latest progress and applications of the relevant fields, namely, on-demand fulfillment, lite network, soft network, native AI and native security. Ultimately, the intent of this article is to serve as a basis for stimulating more promising research on 6G.

130 citations


Journal ArticleDOI
TL;DR: A multi-objective multi-period sustainable location-allocation supply chain network model that addresses the challenge of different levels of technology for vehicle fleet and its implications for sustainability.
Abstract: In this paper, a multi-objective multi-period sustainable location-allocation supply chain network model will be presented. Different levels of technology for vehicle fleet, which leads to differen...

118 citations


Journal ArticleDOI
TL;DR: A new optimization model for the network design problem of the demand-responsive customized bus (CB) is proposed and a hierarchical decision-making model is proposed to describe the interactive manner between operator and passengers.
Abstract: This paper proposes a new optimization model for the network design problem of the demand-responsive customized bus (CB). The proposed model consists of two phases: inserting passenger requests dynamically in an interactive manner (dynamic phase) and optimizing the service network statically based on the overall demand (static phase). In the dynamic phase, we propose a hierarchical decision-making model to describe the interactive manner between operator and passengers. The CB network design problem is formulated in a mixed-integer program with the objective of maximizing operator’s revenue. The CB passenger’s travel behavior is measured by a discrete choice model given the trip plan provided by the operator. A dynamic insertion method is developed to address the proposed model in the dynamic phase. For the network design problem in the static phase, the service network is re-optimized based on the confirmed passengers with strict time deviation constraints embedded in the static multi-vehicle pickup and delivery problem. An exact solution method is developed based on the branch-and-bound (B&B) algorithm. Numerical examples are conducted to verify the proposed models and solution algorithms.

107 citations


Posted Content
Ilija Radosavovic1, Raj Prateek Kosaraju1, Ross Girshick1, Kaiming He1, Piotr Dollár1 
TL;DR: In this paper, the authors propose a new network design paradigm called RegNet, where instead of focusing on designing individual network instances, they design network design spaces that parametrize populations of networks.
Abstract: In this work, we present a new network design paradigm. Our goal is to help advance the understanding of network design and discover design principles that generalize across settings. Instead of focusing on designing individual network instances, we design network design spaces that parametrize populations of networks. The overall process is analogous to classic manual design of networks, but elevated to the design space level. Using our methodology we explore the structure aspect of network design and arrive at a low-dimensional design space consisting of simple, regular networks that we call RegNet. The core insight of the RegNet parametrization is surprisingly simple: widths and depths of good networks can be explained by a quantized linear function. We analyze the RegNet design space and arrive at interesting findings that do not match the current practice of network design. The RegNet design space provides simple and fast networks that work well across a wide range of flop regimes. Under comparable training settings and flops, the RegNet models outperform the popular EfficientNet models while being up to 5x faster on GPUs.

99 citations


Journal ArticleDOI
TL;DR: A Fuzzy Robust Optimization (FRO) is applied to cope with uncertainty in this research developing a multi-objective mathematical model to configure a Sustainable Closed-Loop Supply Chain network for a water tank considering sustainability measures.

Journal ArticleDOI
TL;DR: This paper discusses solution algorithms for MMF problems related to telecommunications network design, which cannot be tackled by the standard optimization model (mathematical programme), however, one can formulate a sequential lexicographic optimization procedure.
Abstract: Telecommunications networks are facing increasing demand for Internet services. Therefore, the problem of telecommunications network design with the objective to maximize service data flows and provide fair treatment of all services is very up-to-date. In this application, the so-called maxmin fair (MMF) solution concept is widely used to formulate the resource allocation scheme. It assumes that the worst service performance is maximized and the solution is additionally regularized with the lexicographic maximization of the second worst performance, the third one, etc. In this paper we discuss solution algorithms for MMF problems related to telecommunications network design. Due to lexicographic maximization of ordered quantities, the MMF solution concept cannot be tackled by the standard optimization model (mathematical programme). However, one can formulate a sequential lexicographic optimization procedure. The basic procedure is applicable only for convex models, thus it allows to deal with basic design problems but fails if practical discrete restrictions commonly arriving in telecommunications network design are to be taken into account. Then, however, alternative sequential approaches allowing to solve non-convex MMF problems can be used. Keywords—network design, resource allocation, fairness, lexicographic optimization, lexicographic max-min.

Journal ArticleDOI
TL;DR: An in-depth literature overview of existing models and solution methods for liner shipping network design is presented, and the four main families of solution methods are discussed: integrated mixed integer programming models; two-stage algorithms designing services in the first step and flowing containers in the second step; two -stage algorithms first flowing containers and then designing services; and finally algorithms for selecting a subset of proposed candidate services.

Journal ArticleDOI
TL;DR: RO based mathematical modeling to address risks and its applicability for SCND for close loop supply chain is proposed, demonstrated and applied in practical cases and shows that the topology obtained from integrated treatment of risk and uncertainty called as RORU model, outperform other supply chain networks on various network performance indicators.
Abstract: Closed loop supply chain network design (CL-SCND) is a critical economic and environmental activity. The closing of the loop to handle return, uncertainty in business environment, various supply chain risks, impact network design processes and performance of the firm in the long term. Thus, it is important to design robust and reliable supply chain structures and obtain network configurations which can always outperform the other configurations under the worst cases of risks and uncertainty. A generic closed-loop supply chain network based on mixed integer programming formulation is proposed with direct shipping to the customer from manufacturing plants as well as shipping through distribution centers under supply risks, transportation risk and uncertain demand using a robust optimization (RO) approach. A large number of numerical tests are carried out to test the performance of the model by considering a total of four levels of uncertainty for four different network structures types. The results of the tests confirm that the risk and uncertainty based integrated supply chain network models are more efficient (cost effective) than the other set of network configurations which treats the supply chain risks and uncertainty post-ante. To demonstrate the applicability of the proposed model, the case of an Indian e-commerce firm which wants to redesign its supply chain structure is presented. The results of case study show that the topology obtained from integrated treatment of risk and uncertainty called as RORU model, outperform other supply chain networks on various network performance indicators such as supply chain costs, the number of facilities open or close and the amount of products flowing through supply chain echelon. Thus, RO based mathematical modeling to address risks and its applicability for SCND for close loop supply chain is proposed, demonstrated and applied in practical cases.

Journal ArticleDOI
TL;DR: The applications of linear optimization theory including Karush–Kuhn–Tucker conditions, the big M method, and a linear expression of power loss to transform the nonlinear planning problem into a mixed-integer quadratically constrained programming (MIQCP) formulation, which is solved by commercial solvers.
Abstract: The interdependency of transportation and electric power networks is becoming tighter due to the proliferation of electric vehicles (EVs), which introduces additional difficulties in the planning of the two networks. This paper presents the enhanced solution for the coordinated planning of multiple facilities in the two networks, including electric power lines, transportation roads, energy storage systems and fast charging stations. In order to calculate the optimal solution for the proposed coordinated planning problem, we introduce the applications of linear optimization theory including Karush–Kuhn–Tucker conditions, the big M method, and a linear expression of power loss to transform the nonlinear planning problem into a mixed-integer quadratically constrained programming (MIQCP) formulation, which is solved by commercial solvers. The proposed MIQCP formulation is decomposed into two corresponding subproblems by Lagrangian relaxation to represent transportation and electric power networks. The case studies validate the proposed planning model and demonstrate that the proposed solution can enhance the coordinated network planning with the proliferation of EVs.

Journal ArticleDOI
TL;DR: Two types of deep neural networks are presented as fast alternatives for simulating seismic waves in horizontally layered and faulted 2D acoustic media and it is shown that seismic inversion can be carried out by retraining the network with its inputs and outputs reversed, offering a fast alternative to existing inversion techniques.
Abstract: . The simulation of seismic waves is a core task in many geophysical applications. Numerical methods such as Finite Difference (FD) modelling and Spectral Element Methods (SEM) are the most popular techniques for simulating seismic waves in complex media, but for many tasks their computational cost is prohibitively expensive. In this work we present two types of deep neural networks as fast alternatives for simulating seismic waves in horizontally layered and faulted 2D acoustic media. In contrast to the classical methods both networks are able to simulate the seismic response at multiple locations within the media in a single inference step, without needing to iteratively model the seismic wavefield through time, resulting in an order of magnitude reduction in simulation time. This speed improvement could pave the way to real-time seismic simulation and benefit seismic inversion algorithms based on forward modelling, such as full waveform inversion. Our first network is able to simulate seismic waves in horizontally layered media. We use a WaveNet network architecture and show this is more accurate than a standard convolutional network design. Furthermore we show that seismic inversion can be carried out by retraining the network with its inputs and outputs reversed, offering a fast alternative to existing inversion techniques. Our second network is significantly more general than the first; it is able to simulate seismic waves in faulted media with arbitrary layers, fault properties and an arbitrary location of the seismic source on the surface of the media. It uses a convolutional autoencoder network design and is conditioned on the input source location. We investigate the sensitivity of different network designs and training hyperparameters on its simulation accuracy. We compare and contrast this network to the first network. To train both networks we introduce a time-dependent gain in the loss function which improves convergence. We discuss the relative merits of our approach with FD modelling and how our approach could be generalised to simulate more complex Earth models.

Journal ArticleDOI
Yang Lei1, Dan Wang1, Hongjie Jia1, Jingcheng Chen, Jingru Li, Yi Song, Jiaxi Li1 
TL;DR: A multi-objective stochastic planning model based on chance constraints of the energy network is developed to minimize the investment cost and the energy pipeline risk and the Pareto fronts of the optimized expansion planning schemes are demonstrated.

Journal ArticleDOI
TL;DR: The numerical results reveal that using the min–max robust model enhances the pharmaceutical relief network’s effectiveness and efficiency considerably.
Abstract: This paper addresses an integrated relief network design problem for pharmaceutical items. The proposed bi-objective model accounts for perishability of pharmaceutical items, mobility of relief facilities, and benefits from a cooperative coverage mechanism in designing the network. A min–max robust model is developed to tackle the demand uncertainty. Several numerical experiments are conducted to explore the performance of the robust model. Also, by conducting a real case study, useful managerial insights are derived through performing several sensitivity analyses. The numerical results reveal that using the min–max robust model enhances the pharmaceutical relief network’s effectiveness and efficiency considerably.

Journal ArticleDOI
TL;DR: A novel multi-stage solution methodology is developed to solve the sustainable biomass supply chain network design problem and is capable of making strategic decisions (optimal biogas facility locations with capacities) along with the tactical decisions (transportation network flows).

Journal ArticleDOI
TL;DR: Monitor information from an operating network combined with supervised machine learning (ML) techniques is used to understand the network conditions and propose two supervised ML regression models, implemented with Support Vector Machine Regression (SVMR), to estimate the individual penalties of the two effects and then a combined model.
Abstract: For reliable and efficient network planning and operation, accurate estimation of Quality of Transmission (QoT) before establishing or reconfiguring the connection is necessary. In optical networks, a design margin is generally included in a QoT estimation tool (Qtool) to account for modeling and parameter inaccuracies, ensuring the acceptable performance. In this article, we use monitoring information from an operating network combined with supervised machine learning (ML) techniques to understand the network conditions. In particular, we model the penalties generated due to i) Erbium Doped Fiber Amplifier (EDFA) gain ripple effect, and ii) filter spectral shape uncertainties at Reconfigurable Optical Add and Drop Multiplexer (ROADM) nodes. Enhancing the Qtool with the proposed ML regression models yields estimates for new or reconfigured connections that account for these two effects, resulting in more accurate QoT estimation and a reduced design margin. We initially propose two supervised ML regression models, implemented with Support Vector Machine Regression (SVMR), to estimate the individual penalties of the two effects and then a combined model. On Deutsche Telekom (DT) network topology with 12 nodes and 40 bidirectional links, we achieve a design margin reduction of ∼1 dB for new connection requests.

Journal ArticleDOI
TL;DR: This paper presents a fundamentally different approach for path loss distribution prediction directly from 2D satellite images based on deep convolutional neural networks, and results show that the path losses can be accurately predicted for different communication frequencies and transmitter heights.
Abstract: Path loss prediction is essential for network planning in any wireless communication system. For cellular networks, it is usually achieved through extensive received signal power measurements in the target area. When the 3D model of an area is available, ray tracing simulations can be utilized; however, an important drawback of such an approach is the high computational complexity of the simulations. In this paper, we present a fundamentally different approach for path loss distribution prediction directly from 2D satellite images based on deep convolutional neural networks. While training process is time consuming and completed offline, inference can be done in real time. Another advantage of the proposed approach is that 3D model of the area is not needed during inference since the network simply uses an image captured by an aerial vehicle or satellite as its input. Simulation results show that the path loss distribution can be accurately predicted for different communication frequencies and transmitter heights.

Journal ArticleDOI
TL;DR: Results show that there are different distributions for the different cost components in response to the different network sizes and that when the network is large enough, changing its size results in a very little change in the cost components, hence better controlling the cost variability is achieved.
Abstract: Using an expert system, this study is a pioneer in the formulation of an original Mixed Integer Linear Programming to integrate Vendor Managed Inventory strategy into the general multi-project multi-resource multi-supplier Construction Supply Chain (CSC) network design and facility location problems in a minimum cost. The framework is capable of dynamically scheduling resources in terms of timing and delivery as well as selecting appropriate suppliers and suitable candidate locations restricted to only authorized facilities in a capacitated network. Results show that there are different distributions for the different cost components in response to the different network sizes. Since changes in ratios of transportation cost and inventory holding cost to the total cost are mirroring each other, the total transportation and inventory costs in proportion to the total network's cost does not depend on the problem's size. In other point of view, when the network is large enough, changing its size results in a very little change in the cost components, hence better controlling the cost variability is achieved. Besides, increasing the number of projects may increase the total cost of the CSC with increasing rates, and the disparity between the number of projects and the number of suppliers increases the cost of the network, nonlinearly. Further, if the duration of the projects is given fixed, the greater the number of time periods for providing resources, the lower the transportation costs. Finally, the higher replenishment frequency results in the lower inventory cost and brings benefits for both sides of the CSC.

Journal ArticleDOI
Yutong Zhang1, Boya Di1, Pengfei Wang1, Jinlong Lin1, Lingyang Song1 
TL;DR: A heterogeneous multi-layer mobile edge computing (HetMEC), where different devices, ranging from edge devices (EDs), i.e., the mobile devices that generate raw data of computing tasks in the radio access networks, to the cloud center (CC), are inherently involved different layers of the network and collaborate for data processing.
Abstract: Driven by an increasing number of mobile applications, mobile edge computing (MEC) has been considered as a promising candidate to support the huge amount of data processing services. However, the conventional MEC suffers from the insufficient utilization of computing and transmission resources through the entire network, resulting in inevitably long processing and transmission latency especially to computation-intensive applications in the 6 G era. In this article, we propose a heterogeneous multi-layer mobile edge computing (HetMEC), where different devices, ranging from edge devices (EDs), i.e., the mobile devices that generate raw data of computing tasks in the radio access networks, to the cloud center (CC), are inherently involved different layers of the network and collaborate for data processing. To support a low-latency service, a reinforcement learning-based framework is constructed to adapt to the unstable wireless environments as well as the dynamically varying data generation speed of each ED. Under this framework, key research issues and solutions including task offloading, cognitive radio based spectrum access, pricing scheme design, and network congestion control are presented. Some further research directions and opening issues are also discussed in the perception of network planning and optimization, network control, and application-specific issues.

Journal ArticleDOI
TL;DR: An approximation algorithm, termed the weighted-maximum-coverage-based algorithm (WMCBA), is proposed for the subproblem of the TAR-CC problem and the Steiner-tree-based algorithms (STBA) is proposed, demonstrating that the STBA provides better performance than the other methods.
Abstract: In mobile wireless sensor networks (MWSNs), because the movement of sensors consumes much more power than that in sensing and communication, the problem of scheduling mobile sensors to cover all targets and maintain network connectivity such that the total movement distance of mobile sensors is minimized has received a great deal of attention. However, network design in fact indicates that there are situations (limited budget, sensor failure, or obstacle in a sensing field) in which the number of active mobile sensors is insufficient to cover all targets or form a connected network. Therefore, targets must be weighted by their importance. The more important a target, the higher the weight of the target. A more general problem for target coverage and network connectivity, termed the Maximum Weighted Target Coverage and Sensor Connectivity with Limited Mobile Sensors (TAR-CC) problem, is studied. In this paper, an approximation algorithm, termed the weighted-maximum-coverage-based algorithm (WMCBA), is proposed for the subproblem of the TAR-CC problem. Based on the WMCBA, the Steiner-tree-based algorithm (STBA) is proposed for the TAR-CC problem. Simulation results demonstrate that the STBA provides better performance than the other methods.

Journal ArticleDOI
TL;DR: Radio frequency measurements and evaluation of KPIs taken at 1876.6MHz with a bandwidth of 10MHz for an operational 4G LTE network in Nigeria are presented and interdependence amongst the KPIs are presented to ease understanding of the interrelationships among the tested KPIs.

Journal ArticleDOI
TL;DR: Experimental results have shown that the developed algorithm is capable of obtaining high quality solutions within a short computation time, in addition to performing well in other measures such as solution diversity.
Abstract: The optimal design of a supply chain network is a challenging problem, especially for large networks where there are multiple objectives. Such problems are usually formulated as mixed integer programs. Solving this type of network design problem takes a long time using exact algorithms and for large-scale problems it is not even possible. This has given rise to the use of meta-heuristic techniques. In this paper, an effective tabu search algorithm for solving multi-product, multi-objective, multi-stage supply chain design problems is proposed. The desirable characteristics of the algorithm are developed, coded and tested. The results of the developed algorithm are compared with the results obtained by an improved augmented e-constraint algorithm embedded in the General Algebraic Modeling System (GAMS) software for small-scale, medium-scale, and large-scale instances of multi-objective supply chain problems. Experimental results have shown that the developed algorithm is capable of obtaining high quality solutions within a short computation time, in addition to performing well in other measures such as solution diversity.

Book ChapterDOI
01 Jan 2020
TL;DR: The results indicate that the proposed hybrid algorithm provides Pareto solutions with acceptable quality and diversity and is implemented on several test problems in different sizes.
Abstract: In this research, designing a sustainable, multi-level, multi-products, and multi-period closed-loop supply chain network for perishable products is addressed. For this purpose, an integrated mathematical model is proposed. The main objectives are to minimize the production, distribution, and customer satisfaction related costs, minimize total CO2 emissions, and maximize social responsibility. The contributions of this research include considering lead time for production and delivering perishable products in the supply chain network design problem, and proposing a novel hybrid algorithm based on whale optimization algorithm (WOA) and genetic algorithm (GA). To solve the problem and optimize the mathematical model, the proposed hybrid algorithm is implemented on several test problems in different sizes. The obtained results are compared with augmented epsilon constraint in order to evaluate the performance of the proposed algorithm. The results indicate that the proposed algorithm provides Pareto solutions with acceptable quality and diversity.

Journal ArticleDOI
TL;DR: A novel SDN-based architecture for 5G networks that will be able to enhance quality-of-experience (QoE) monitoring and management by integrating SDN and eTOM is illustrated.
Abstract: The 5th Generation (5G) network design is still under exploration and will require an innovative approach to network monitoring and management with a relentless focus on customer needs. Among the recent technologies, software-defined networking (SDN) and network functions virtualization (NFV), which are already offering network programmability and automation, have been proposed to improve resource management in 5G systems. Furthermore, the enhanced telecom operations map (eTOM) can be used to manage both End-to-End (E2E) service and end-customer experience. In this paper, we illustrate a novel SDN-based architecture for 5G networks that will be able to enhance quality-of-experience (QoE) monitoring and management by integrating SDN and eTOM. This architecture is deployed and evaluated by simulations of a service-level agreement (SLA) verification scenario. As a result, the implemented platform is capable of identifying SLA violation and can successfully enforce suitable mechanisms to dynamically adjust network parameters, thereby satisfying the SLA constraints.

Posted Content
TL;DR: An effective routing scheme to enable automatic responses for multiple requests of entanglement generation between source-terminal stations on a quantum lattice network with finite edge capacities is proposed and three algorithms are proposed and compared for the scheduling of capacity allocation on the edges of quantum network.
Abstract: Quantum network is a promising platform for many ground-breaking applications that lie beyond the capability of its classical counterparts. Efficient entanglement generation on quantum networks with relatively limited resources such as quantum memories is essential to fully realize the network's capabilities, the solution to which calls for delicate network design and is currently at the primitive stage. In this study we propose an effective routing scheme to enable automatic responses for multiple requests of entanglement generation between source-terminal stations on a quantum lattice network with finite edge capacities. Multiple connection paths are exploited for each connection request while entanglement fidelity is ensured for each path by performing entanglement purification. The routing scheme is highly modularized with a flexible nature, embedding quantum operations within the algorithmic workflow, whose performance is evaluated from multiple perspectives. In particular, three algorithms are proposed and compared for the scheduling of capacity allocation on the edges of quantum network. Embodying the ideas of proportional share and progressive filling that have been well-studied in classical routing problems, we design a new scheduling algorithm, the propagatory update method, which in certain aspects overrides the two algorithms based on classical heuristics in scheduling performances. The general solution scheme paves the road for effective design of efficient routing and flow control protocols on applicational quantum networks.

Journal ArticleDOI
TL;DR: A multi-disaster-scenario based distributionally robust planning model (MDS-DRM) is proposed to hedge against two types of natural disaster-related uncertainties: random offensive resources of various natural disasters and random probability distribution of line outages that are incurred by a certain natural disaster.