Network Design Issues Research Articles

When two chains meet: Optimizing the design of blockchain-enabled supply chain networks

Applications of the blockchain technology are rapidly emerging across sectors. By redefining information flow, blockchain technology promises to instill trust among supply chain members, improve transaction efficiency, thus reshaping the structure and configuration of supply chains. The practical exploratory use of blockchain has shown immense potential in supply chain management. In order to explore the new changes brought by blockchain technology to supply chain network design issues, this study focuses on the supply chain network design problem in the context of blockchain technology. Specifically, we consider a supply chain network comprised of a number of distribution centers (DCs) and retailers. The operation of this physical network is supported by blockchain-based information infrastructure. As the interrelated relationship between these two networks, it is necessary to optimize them simultaneously to improve the profitability and information exchange level. We propose an objective function based on previous studies to maximize the profits of the entire supply chain network, with decision variables including DC locations, retailer assignment, inventory replenishment policy, and blockchain adoption level. At the same time, to characterize the interrelation between the two networks, the demand and certain cost parameters of the model become endogenous depending on the blockchain adoption level, which significantly increases the complexity of the solution algorithm. We reformulate the model and use the polymatroid cutting plane approach to address this issue. We conducted calculations and numerical analysis. The computational results show that the proposed method can solve practically sized problems, and integrating blockchain design reduces the system-wide cost. We discuss managerial insights based on a range of numerical studies and solve a case study using publicly available instance data.

Solving a Two-Level Location Problem with Nonlinear Costs and Limited Capacity: Application of Two-Phase Recursive Algorithm Based on Scatter Search

Abstract This study examines the issue of distribution network design in the supply chain system. There are many production factories and distribution warehouses in this issue. The most efficient strategy for distributing the product from the factory to the warehouse and from the warehouse to the customer is determined by solving this model. This model combines location problems with and without capacity limits to study a particular location problem. In this system, the cost of production and maintenance of the product in the factory and warehouse is a function of its output. This increases capacity without additional costs, and ultimately does not lose customers. This algorithm is a population-based, innovative method that systematically combines answers to obtain the most accurate answer considering quality and diversity. A two-phase recursive algorithm based on a scattered object has been developed to solve this model. Numerical results show the efficiency and effectiveness of this two-phase algorithm for problems of different sizes.

A Novel Energy Efficiency Metric for Next-Generation Green Wireless Communication Network Design

As a core performance metric for green communications, the conventional energy efficiency (EE) definition has successfully resolved many issues in the energy-efficient wireless network design. In the past several generations of wireless communication networks, the traditional EE measure plays an important role to guide many energy-saving techniques for slow varying traffic profiles. However, for the next-generation wireless networks, the traditional EE fails to capture the traffic and capacity variations of wireless networks in temporal or spatial domains, which is shown to be quite popular, especially with ultrascale multiple antennas and space–air–ground integrated network (SAGIN). In this article, we present a novel EE metric named integrated relative EE (IREE), which is able to jointly measure the traffic profiles and the network capacities from the EE perspective. On top of that, the IREE-based green tradeoffs have been investigated and compared with the conventional energy-efficient design. Moreover, we apply the IREE-based green tradeoffs to evaluate several candidate technologies for 6G networks, including reconfigurable intelligent surfaces and SAGIN. Through some analytical and numerical results, we show that the proposed IREE metric is able to capture the wireless traffic and capacity mismatch property, which is significantly different from the conventional EE metric. Since the IREE-oriented design or deployment strategy is able to consider the network capacity improvement and the wireless traffic matching simultaneously, it can be regarded as a useful guidance for future energy-efficient network design.

Traffic Sensors Selection for Complete Link Flow Observability through Simulated Annealing

Sensor selection is a key issue in sensor network design. Due to limited equipment and maintenance costs, and to minimize processing and communication overhead, only a limited number of sensors can reasonably be used, and they must also be reasonably placed. Indeed, the selection of an appropriate number of physical sensors is critical to ensure reliable estimates when monitoring large-scale systems such as traffic flows, water or gas transport infrastructures, or underwater wireless sensor networks. Regarding traffic systems, the full link flow observability problem consists of selecting the minimum number of traffic sensors to be installed from a given larger set (Salari, 2019). In particular, it involves selecting a subset of p, possibly redundant, sensors from a larger set of n ≫ p potential sensors in order to preserve the structural observability property of the entire traffic network. To solve this problem, the classical concept of system observability is exploited as a criterion for sensor selection. In this paper, we refer to the design of a simulated annealing heuristic to approximately solve the problem. The resulting subset of sensors is then used to design a Luenberger observer to properly estimate the system state. Some numerical simulations demonstrate the effectiveness of the proposed approach.

A Comprehensive Study on Energy Efficient-Cluster Based Routing Protocols in the Internet of Things: Hierarchical Routing Protocol

Wireless sensor networks (WSNs) have several uses in a variety of industries; they are crucial components in many cutting-edge applications. WSNs are considered one of the newest applications to emerge is in the field of the Internet of Things (IoT), which enables the interconnection of various items or machines over the Internet, including the Internet of Things. This is why increase in the lifetime of the networks requires a strategy (protocol) that reduces the power consumption of the transmission or reception of data by the sensor nodes. A lot of research has been conducted, recently, to extend the lifetime of network sensors. The Hierarchical Cluster-based protocols and the Hierarchical Chain-based approaches have been created as solutions to this issue to reduce network traffic heading down the sink and so increase the lifetime of the network. In this survey, we look into the benefits and drawbacks of clustering when IoT is combined with cutting-edge technologies for computing and communication like 5G, fog/edge computing, and blockchain. Additionally, this survey offers helpful insights into the field of IoT clustering studies, enables a deeper comprehension of its design issues for IoT networks, and sheds the light on its potential future applications in cutting-edge IoT-integrated technologies.

Analyzing the wastewater treatment facility location/network design problem via system dynamics: Antalya, Turkey case

Wastewater treatment facility location selection and network design issues have become attractive topics in the field of wastewater management due to increasing human population, resource scarcity, environmental concerns, and rise of necessity for sustainable solutions for future policy designs. Especially in areas where the demand for wastewater treatment increases dramatically over the years because of reasons such as high migration levels, rapid industrialization, and tourism activities, the problem turns out to be more critical and dynamic. The existing studies try to deal with the issue through mathematical modeling approaches based on optimization perspectives, which require significant computational effort. In this study, an alternative approach based on system dynamics (SD) method is proposed to examine the complex dynamic and nonlinear structure of wastewater treatment facility location selection and network design problems. The proposed SD simulation model is designed for a densely populated industrial and tourism spot, the city of Antalya, located on the Mediterranean coast of Turkey. The model is capable of determining where and when to build a new wastewater treatment facility as well as generating the generic wastewater network structure to be built for the five districts situated in the city center based on cost issues for 2015–2040 period. In addition, the impacts of demand level changes for wastewater treatment due to population variations are analyzed via several scenarios to help decision makers to develop sustainable and cost-efficient management policies. Although SD is a frequently utilized approach in the water/wastewater management arena, to the best of our knowledge, this study is the first attempt to examine the complex and dynamic nature of wastewater treatment facility location selection and network design problems through SD approach.

Optimizing the Issue of Blood Supply Chain Network Design with a Reliability Approach

Supply chain network design of a product is one of the primary and strategic measures in supply chain management that plays a vital role in supply chain performance. Since blood is a special commodity and has no substitute, supply chain management and optimization have a special place among researchers. In this research, for the first time, a two-objective nonlinear mixed integer model is presented to help make strategic and operational decisions in the blood supply chain. The first objective function is related to cost minimization and the second objective function is related to supply chain reliability maximization, which is considered as a series-parallel system. To check the validity of the model, a numerical example is solved using GAMS software, then using MOPSO meta-heuristic algorithm, the model is solved in larger dimensions and while comparing the performance criteria of multi-objective algorithms, the results are reviewed.

Shape-driven lightweight CNN for finger-vein biometrics

Finger-vein recognition has been widely explored for biometrics security over the past two or three decade. Its size and characteristics against presentation attacks make it suitable for various commercial, governmental and forensics applications. As with other biometric traits, finger-vein recognition approaches mainly depend on well-engineering feature descriptors and recently, more deep learning (DL) models have been designed to achieve high-performance biometrics. However, the existing DL approaches usually employ the neural networks with increasing layers and parameters, which inevitably leads to an increase in memory consumption and algorithm complexity. Besides, all the vein recognition networks use square filters, which are influenced by network designed for object detection and classification. In this paper, two network design issues for finger-vein recognition are explored: shallow network using rectangular filters and lightweight semi-pretrained network. The exhaustive experiments on three benchmark databases HKPU, FV-USM and SDUMLA demonstrate that rectangular filters outperform square filters for DL-based finger-vein recognition, and the lightweight semi-pretrained network also outperforms non-pretrained network.

Performance trade-offs in reconfigurable networks for HPC

Designing efficient interconnects to support high-bandwidth and low-latency communication is critical toward realizing high performance computing (HPC) and data center (DC) systems in the exascale era. At extreme computing scales, providing the requisite bandwidth through overprovisioning becomes impractical. These challenges have motivated studies exploring reconfigurable network architectures that can adapt to traffic patterns at runtime using optical circuit switching. Despite the plethora of proposed architectures, surprisingly little is known about the relative performances and trade-offs among different reconfigurable network designs. We aim to bridge this gap by tackling two key issues in reconfigurable network design. First, we study how cost, power consumption, network performance, and scalability vary based on optical circuit switch (OCS) placement in the physical topology. Specifically, we consider two classes of reconfigurable architectures: one that places OCSs between top-of-rack (ToR) switches—ToR-reconfigurable networks (TRNs)—and one that places OCSs between pods of racks—pod-reconfigurable networks (PRNs). Second, we tackle the effects of reconfiguration frequency on network performance. Our results, based on network simulations driven by real HPC and DC workloads, show that while TRNs are optimized for low fan-out communication patterns, they are less suited for carrying high fan-out workloads. PRNs exhibit better overall trade-off, capable of performing comparably to a fully non-blocking fat tree for low fan-out workloads, and significantly outperform TRNs for high fan-out communication patterns.

Surveying Position Based Routing Protocols for Wireless Sensor and Ad-hoc Networks

A focus of the scientific community is to design network oriented position-based routing protocols and this has resulted in a very high number of algorithms, different in approach and performance and each suited only to particular applications. However, though numerous, very few position-based algorithms have actually been adopted for commercial purposes. This article is a survey of almost 50 position-based routing protocols and it comes as an aid in the implementation of this type of routing in various applications which may need to consider the advantages and pitfalls of position-based routing. An emphasis is made on geographic routing, whose notion is clarified as a more restrictive and more efficient type of position-based routing. The protocols are therefore divided into geographic and non-geographic routing protocols and each is characterized according to a number of network design issues and presented in a comparative manner from multiple points of view. The main requirements of current general applications are also studied and, depending on these, the survey proposes a number of protocols for use in particular application areas. This aims to help both researchers and potential users assess and choose the protocol best suited to their interest.

Network Design Issues in Wireless Body Area Network

This paper presents a survey on various aspects of Wireless Body Area Network (WBAN) and its utility in healthcare system. Along with applications of WBAN in healthcare different areas of research with other prospective opportunities and prominent challenges are also discussed. Development in the field of wireless communication, particularly in the field of Personal Area Network and mobile networks, has lead to development of mobile healthcare system. As WBAN essentially consist of body sensors to sense various physical properties related to health. Apart from applications of WBAN, communication in WBAN and other important aspects like security, scalability are discussed. Some of the other important issues are physical and MAC layer design and network protocols being analysed.

The Emergence of a Sustainable and Reliable Supply Chain Paradigm in Supply Chain Network Design

Shorter product life due to technology development and changing customer needs requires optimal supply chain management and structuring. Achieving this optimization brings productivity to the organization. A sustainable and reliable supply chain (S&RSC) as an emerging paradigm, with a comprehensive view, leads the supply chain network design (SCND) issue to the desired situation. This study, as part of an emerging theme, presents a systematic review and classification of 42 articles at the intersection of sustainability, reliability, and supply chain network design. In this regard, in a historical course, the four concepts of green supply chain, socially responsible supply chain, sustainable supply chain, and reliable supply chain in the form of four structures of history and definition, deriver and importance, substrate and practice, evaluation, and implementation are discussed. The result of the review shows that the articles that study the integration of sustainability and reliability in SCND are vibrant and rapidly growing in various industries. Developing countries and manufacturing companies with the largest number of articles have been the research fields of the studies. Most of the studies have considered the structure of the supply chain as a forward flow. The key contribution of this review is the development of a comprehensive framework of S&RSC from the conceptual point of view, elements, assessment tools, and its implementation context. The present review can serve as a guide for various stakeholders. In particular, practitioners and policymakers can effectively develop their own strategies and policies to advance the sustainable‐reliable program.

A Local 3D Voronoi-Based Optimization Method for Sensor Network Deployment in Complex Indoor Environments.

Optimal sensor network deployment in built environments for tracking, surveillance, and monitoring of dynamic phenomena is one of the most challenging issues in sensor network design and applications (e.g., people movement). Most of the current methods for sensor network deployment and optimization are empirical and they often result in important coverage gaps in the monitored areas. To overcome these limitations, several optimization methods have been proposed in the recent years. However, most of these methods oversimplify the environment and do not consider the complexity of 3D architectural nature of the built environments specially for indoor applications (e.g., indoor navigation, evacuation, etc.). In this paper, we propose a novel local optimization algorithm based on a 3D Voronoi diagram, which allows a clear definition of the proximity relations between sensors in 3D indoor environments. This proposed structure is integrated with an IndoorGML model to efficiently manage indoor environment components and their relations as well as the sensors in the network. To evaluate the proposed method, we compared our results with the Genetic Algorithm (GA) and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithms. The results show that the proposed method achieved 98.86% coverage which is comparable to GA and CMA-ES algorithms, while also being about six times more efficient.

A Review on Wireless Sensor Networks and its applications

Around the globe, wireless sensor nodes are utilized in a variety of real-time applications. Significant research in the field of wireless sensor networks (WSNs) has been done in the last decade due to an increase in demand in various applications all over the world. WSNs are a new field of research that is being investigated to provide solutions to existing issues such as energy consumption, sensor location, routing algorithms, robustness, efficiency, and many more. Various scholars have grappled with a variety of network design and computational complexity issues during the last decade. Because of its uses in a number of areas, including as healthcare, espionage, environmental monitoring, industrial monitoring, forest fire detection, and natural disaster prevention, WSNs are gaining popularity throughout the world. This article discusses the use of WSN infrastructure and its applicability in many sectors to solve current problems.

A takeover time-driven adaptive evolutionary algorithm for mobile user tracking in pre-5G cellular networks

Cellular networks are one of today’s most popular means of communication. This fact has made the mobile phone industry subject to a huge scientific and economic competition, where the quality of service is key. Such a quality is measured on the basis of reliability, speed and accuracy when delivering a service to a user no matter his location or behaviour are. This fact has placed the users’ tracking process among the most difficult and determining issues in cellular network design. In this paper, we present an adaptive bi-phased evolutionary algorithm based on the takeover time to solve this problem. The proposal is thoroughly assessed by tackling twenty-five real-world instances of different sizes. Twenty-eight of the state-of-the-art techniques devised to address the users’ mobility problem have been taken as the comparison basis, and several statistical tests have been also conducted. Experiments have demonstrated that our solver outperforms most of the top-ranked algorithms.

Reducing Power Consumption in Hexagonal Wireless Sensor Networks Using Efficient Routing Protocols

Power consumption and network lifetime are vital issues in wireless sensor network (WSN) design. This motivated us to find innovative mechanisms that help in reducing energy consumption and prolonging the lifetime of such networks. In this paper, we propose a hexagonal model for WSNs to reduce power consumption when sending data from sensor nodes to cluster heads or the sink. Four models are proposed for cluster head positioning and the results were compared with well-known models such as Power Efficient Gathering In Sensor Information Systems (PEGASIS) and Low-Energy Adaptive Clustering Hierarchy (LEACH). The results showed that the proposed models reduced WSN power consumption and network lifetime.

Hybrid Kriging and multilayer perceptron neural network technique for coverage prediction in cellular networks

ABSTRACT Coverage prediction is a crucial issue in wireless network planning and design. Typically, coverage prediction techniques start by model identification given a set of measurements at specified locations. Then, this model is used to predict the signal strength at other locations using some machine-learning like approach. However, when the drive tests (or test-beds) become costly because of some environmental constraints, the performance of the machine learning-based model is questionable due to the insufficiency of the training dataset. In this context, we suggest exploiting the geostatistical interpolation technique named Ordinary Kriging to enrich the training data set. For this purpose, a set of received signal strengths from wireless transmitters has been collected at known locations through cellular network technology, which are then used to generate an enriched dataset according to the Ordinary Kriging interpolation technique. The results show that the hybrid Ordinary Kriging and machine learning model significantly enhances path loss accuracy and offers a new setting for data reproducibility. A database is constructed from received signal strength measurements which is not enough to produce accurate results when it is used to train a feed forward neural network for the task of coverage prediction. A geospatial interpolated technique named Kriging is used in order to produce a big amount of data. The neural network is trained by both of the original and the interpolated datasets. The coverage prediction results more reproducible and accurate than the results without the interpolation stage.

Designing recycling networks for construction and demolition waste based on reserve logistics research field

Construction and demolition waste (CDW) has a significant effect on the environment. A well-designed recycling network is a prerequisite of CDW recycling, which is favourable for improving the recycling rate. However, the lack of quantitative tools to measure the characteristics of the construction industry has a serious effect on the effective design of CDW recycling network. Extensive studies on reverse logistics network design over the past decades have provided a solid foundation for recycling network design. Thus, in this study, reverse logistics research is explored to determine possible solutions to the issue of CDW recycling network design. This study aims to provide a model for CDW treatment plants location planning and capacity expansion planning based on the reverse logistics research in the construction industry. In our model, the conflicts between the objectives of different stakeholders and the dynamic decision-making environment in the construction industry were considered. Then, a multi-period multi-objective mixed-integer linear programming model was developed to minimize the CDW disposal costs of contractors and to maximize the profits of recycling firms and recycling rate of CDW. Chongqing, China was selected as the case to validate the proposed model and derive managerial insights. Results show our model provides an efficient tool for CDW recycling network design.

Large-Scale Wireless-Powered Networks With Backscatter Communications—A Comprehensive Survey

Massive and ubiquitous deployment of devices in networks of fifth generation (5G) and beyond wireless has necessitated the development of ultra-low-power wireless communication paradigms. Recently, wireless-powered networks with backscatter communications (WPN-BCs) has been emerged as a most prominent technology for enabling large-scale self-sustainable wireless networks with the capabilities of RF energy harvesting (EH) and of extreme low power consumption. Therefore, we provide a comprehensive literature review on the fundamentals, challenges and the on-going research efforts in the domain of WPN-BCs. Our emphasis is on large-scale networks. In particular, we discuss signal processing aspects, network design issues and efficient communication techniques. Moreover, we review emerging technologies for WPN-BCs to bring about the best use of resources. Some applications of this innovative technology are also highlighted. Finally, we address some open research problems and future research directions.

Limited-stop bus service: A strategy to reduce the unused capacity of a transit network

Abstract Flexible adaptation to the heterogeneous distribution of travel demand is a challenging issue in the transit network design and planning. Limited-stop service is a strategy used to deal with this problem. In this paper, a bi-level mathematical model is proposed in which the upper-level model minimizes the total fleet unused capacity of limited-stop services, while the objective function of the lower-level model is to minimize total expected travel time for all passengers. Since this model is operator-oriented, some constraints are embedded in it in order to prevent excessive fall of the passengers' service level. Actually, the upper level is a mixed integer linear programming model; but because of NP-hardness and the lower level model, a heuristic algorithm is developed in order to solve the proposed bi-level model. It's worth mentioning that this heuristic approach is based on iterated local search, simulated annealing, and tabu search metaheuristics. Finally, a vast variety of numerical experiments are carried out that are based on some virtual origin-destination demand matrices generated according to different characteristics. Computational results show that by replacing the local service with the set of limited-stop services, the fleet unused capacity can be reduced by up to 48%.