Small-scale Networks Research Articles

Metropolitan Area Network Model Design Using Regional Railways Information for Beyond 5G Research

To accelerate research on Beyond 5G (B5G) technologies in Japan, we propose an algorithm that designs mesh-type metropolitan area network (MAN) models based on a priori Japanese regional railway information, because ground-truth communication network information is unavailable. Instead, we use the information of regional railways, which is expected to express the necessary geometric structure of our metropolitan cities while remaining strongly correlated with their population densities and demographic variations. We provide an additional compression algorithm for use in reducing a small-scale network model from the original MAN model designed using the proposed algorithm. Two Tokyo MAN models are created, and we provide day and night variants for each while highlighting the number of passengers alighting/boarding at each station and the respective population densities. The validity of the proposed algorithm is verified through comparisons with the Japan Photonic Network model and another model designed using the communication network information, which is not ground-truth. Comparison results show that our proposed algorithm is effective for designing MAN models and that our result provides a valid Tokyo MAN model.

Optimal pruned tree-cut mapping-based fast shielding for large-scale networks

Random failure is a common threat to a network, where the failure of a few edges can disconnect a large-scale sparse network. To enhance the robustness of network, the shielding of important edges is a practical strategy, where the cut is a useful entity to help locate important edges in existing shielding methods. However, as there is no available way to quickly locate target cuts, the existing shielding algorithm is not efficient enough and can only be applied to small-scale backbone networks. Fortunately, by using the optimal pruned tree-cut mapping, we found an efficient and high-precision cut edge enumeration method, which can help quickly locate target cuts and their edges in a large-scale network, leading to a cost-effective shielding plan. Theoretical analysis indicates that more than 99% of candidate cuts can be found with a limited number of preprocessing passes, and experimental results in typical networks show that in small-scale networks, with little extra cost (< 6%), the serial implementation of the algorithm in an off-shelf computing node can be 6 orders of magnitude faster than the optimal method, while in large-scale sparse networks with a million nodes, it can also help defend at least 99.9% of random failures with only tens of seconds of preprocessing overhead.

Multiscale pore-fracture hybrid pore network modeling for drainage in tight carbonate

Tight carbonate possesses both fractures and multiscale pores. Flow through tight carbonate rocks involves several length scales and the complicated flow physics. Capturing and modeling the entire void space into a single pore network model for drainage is challenging. Here, we present a novel method for modeling of a multiscale pore-fracture hybrid pore network, which enables various void spaces at multiple length scales and the flow characteristics in them to be included. Pores and fractures in a low-resolution CT image are extracted by medial axis-maximal ball algorithm and medial surface algorithm, respectively, to generate a pore-fracture hybrid pore network. Then, a statistics-based small-scale stochastic pore network is established in the CT image solid domains to represent the micropores unresolved by the image. The two pore networks are organically integrated into a multiscale hybrid pore network by cross-scale connections. The statistics of small-scale pore network are determined by experimental permeability. We study the effects of the small-scale pore network density on connectivity, single- and two-phase flow properties of the model to obtain the robust parameter. The influence of fractures on two-phase flow properties is analyzed. Results show the significance of incorporating fractures and micropores in the model. The model is validated via comparison between simulation result and experimental data. This method can accurately, efficiently, conveniently construct multiscale pore-fracture hybrid pore network of tight carbonate rock for evaluating the drainage characteristics.

Ensemble Transfer Learning Midcourse Guidance Algorithm for Velocity Maximization

This paper proposes an ensemble transfer learning guidance algorithm for angular-constrained midcourse guidance to maximize the terminal velocity. The algorithm developed improves the generalization capability of the trained deep neural network to adapt to a new environment. First several deep neural guidance networks are trained for some specific working environments via supervised learning. A small-scale ensemble transfer learning network is then leveraged to fuse the knowledge of different pretrained deep neural network. This requires much less labeled data to transfer existing knowledge to a new working environment and hence greatly improves the learning efficiency, compared to the supervised learning philosophy. Extensive numerical simulations are performed to demonstrate the effectiveness of the proposed algorithm.

A Two-Stage Swarm Optimizer With Local Search for Water Distribution Network Optimization.

Evolutionary computation (EC) algorithms have been successfully applied to the small-scale water distribution network (WDN) optimization problem. However, due to the city expansion, the network scale grows at a fast speed so that the efficacy of many current EC algorithms degrades rapidly. To solve the large-scale WDN optimization problem effectively, a two-stage swarm optimizer with local search (TSOL) is proposed in this article. To address the issues caused by the large-scale and multimodal characteristics of the problem, the proposed algorithm divides the optimization process into an exploration stage and an exploitation stage. It first finds a promising region of the search space in the exploration stage. Then, it searches thoroughly in the promising region to obtain the final solution in the exploitation stage. To search effectively the huge search space, we propose an improved level-based learning optimizer and use it in both the exploration and exploitation stages. Two new local search algorithms are proposed to further improve the quality of the solution. Experiments on both synthetic benchmark networks and a real-world network show that the proposed algorithm has outperformed the state-of-the-art metaheuristic algorithms.

Efficient Strategies for Signal Aggregation in Low-Power Wireless Sensor Networks With Discrete Transmission Ranges

Sensors with different transmission ranges are preferred for future-ready low-power wireless sensor networks because of their functional advantages. However, different transmission ranges introduce connectivity constraints that affect the signal aggregation process. In this letter, we address the sensor signal aggregation problem in such networks and propose solutions. We introduce a mathematical framework that captures the connectivity restrictions and propose an integer linear program for small-scale networks and an approximation technique for large-scale networks. The performance of the proposed method is evaluated under compression techniques based on compressed sensing.

Effects of parity, frustration, and stochastic fluctuations on integrated conceptual information for networks with two small-sized loops

This paper presents an evaluation of the system-level integrated conceptual information of a major complex for a small-scale network containing two loops in accordance with the integrated information theory 3.0 framework. We focus on the following parameters characterizing the system model: (1) number of nodes in the loop, (2) frustration of the loop, and (3) temperature controlling the stochastic fluctuation of the state transition. Effects of these parameters on the integrated conceptual information and conditions for major complexes formed by a single loop, rather than the entire network, are investigated. Our first finding is that parity of the number of nodes forming a loop has a strong effect on the integrated conceptual information. For loops with an even number of nodes, the number of concepts tends to decrease, and the integrated conceptual information becomes smaller. Our second finding is that a major complex is more likely to be formed by a small number of nodes under small stochastic fluctuations. On the other hand, the entire network can easily become a major complex under larger stochastic fluctuations, and this tendency can be reinforced by frustration. It is also shown that, although counterintuitive, the integrated conceptual information can be maximized in the presence of stochastic fluctuations. These results suggest that even when several small subnetworks are connected by only a few connections, such as a bridge, the entire network may become a major complex by introducing some stochastic fluctuations and by frustrating loops with an even number of nodes.

A Novel Stochastic Optimizer Solving Optimal Reactive Power Dispatch Problem Considering Renewable Energy Resources

Optimal Reactive Power Dispatch (ORPD is thought of as a noncontinuous, nonlinear global optimization problem. Within the system’s constraints, the ORPD manages to accomplish the reactive power flow. Due to its more intricate linkage of variables, the reactive power issue is more challenging to resolve than the optimum power flow issue. With the existence of renewable energy resources (RERs), solving the ORPD problem to attain the most stable and secure system condition has become a more challenging task. The goal of this article is to solve the objective function of ORPD combined with RERs using a metaheuristic novel optimizer named the African Vultures Optimization Algorithm abbreviated by (AVOA), where the formulation of the ORPD issue including minimization of three single objective functions as follows, voltage deviation, system operating cost, and real power loss, is introduced and also transmission power loss minimization is embraced with the simultaneous incorporation of the optimal renewable energy resources (RERs). Where the ORPD problem complexity grows exponentially with a mixture of continuous and discrete control variables, two distinct continuous and discrete types of optimization variables are considered, and the proposed single objective functions that meet different operating constraints are then transformed into a coefficient multi-objective ORPD problem and elucidated using the weighted sum approach. To validate the suggested algorithm’s effectiveness in addressing the ORPD issue, it is evaluated on three standard IEEE networks: the IEEE-30 bus small-scale network, the IEEE-57 bus medium-scale network, and the IEEE-118 bus large-scale network using different scenarios and the outcomes are compared to these other popular optimization techniques. The findings show that the suggested AVOA algorithm provides an efficient and sturdy high-quality solution for tackling ORPD situations and vastly enhances the overall system performance of power at all scales.

Remodelling of the fibre-aggregate structure of collagen gels by cancer-associated fibroblasts: A time-resolved grey-tone image analysis based on stochastic modelling.

Solid tumors consist of tumor cells associated with stromal and immune cells, secreted factors and extracellular matrix (ECM), which together constitute the tumor microenvironment. Among stromal cells, activated fibroblasts, known as cancer-associated fibroblasts (CAFs) are of particular interest. CAFs secrete a plethora of ECM components including collagen and modulate the architecture of the ECM, thereby influencing cancer cell migration. The characterization of the collagen fibre network and its space and time-dependent microstructural modifications is key to investigating the interactions between cells and the ECM. Developing image analysis tools for that purpose is still a challenge because the structural complexity of the collagen network calls for specific statistical descriptors. Moreover, the low signal-to-noise ratio of imaging techniques available for time-resolved studies rules out standard methods based on image segmentation. In this work, we develop a novel approach based on the stochastic modelling of the gel structure and on grey-tone image analysis. The method is then used to study the remodelling of a collagen matrix by migrating breast cancer-derived CAFs in a three-dimensional spheroid model of cellular invasion imaged by time-lapse confocal microscopy. The structure of the collagen at the scale of a few microns consists in regions with high fibre density separated by depleted regions, which can be thought of as aggregates and pores. The approach developped captures this two-scale structure with a clipped Gaussian field model to describe the aggregates-and-pores large-scale structure, and a homogeneous Boolean model to describe the small-scale fibre network within the aggregates. The model parameters are identified by fitting the grey-tone histograms and correlation functions of the images. The method applies to unprocessed grey-tone images, and it can therefore be used with low magnification, noisy time-lapse reflectance images. When applied to the CAF spheroid time-resolved images, the method reveals different matrix densification mechanisms for the matrix in direct contact or far from the cells. We developed a novel and multidisciplinary image analysis approach to investigate the remodelling of fibrillar collagen in a 3D spheroid model of cellular invasion. The specificity of the method is that it applies to the unprocessed grey-tone images, and it can therefore be used with noisy time-lapse reflectance images of non-fluorescent collagen. When applied to the CAF spheroid time-resolved images, the method reveals different matrix densification mechanisms for the matrix in direct contact or far from the cells.

Physical and Geometric Effects on the Classical Geodetic Observations in Small-Scale Control Networks

In classical two-dimensional (2D) geodetic networks, reducing slope distances to horizontal ones is an important task for engineers. These horizontal distances along with horizontal directions are used in 2D geodetic adjustment. The common practice for this reduction is the use of vertical angles to reduce distances using trigonometric rules. However, one faces systematic effects when using vertical angles. These effects are mainly due to refraction, deflection of the vertical (DOV), and the geometric effect of the reference surface (sphere or ellipsoid). To mitigate refraction and DOV effects, one can choose to observe the vertical angles reciprocally if the baseline points’ elevation difference is small. This paper quantifies these effects and proposes a proper solution to eliminate the effects in small-scale geodetic networks (where the longest distances are less than 5 km). The goal is to calculate slope distances into horizontal ones appropriately. For this purpose, we used the SWEN17_RH2000 quasigeoid model (in Sweden) to study the impact of the DOV applying different baseline lengths, azimuths, and vertical angles. Finally, we propose an approach to study the impact of the geometric effect on vertical angles. We illustrate that the DOV and the geometric effects on vertical angles measured reciprocally are significant if the height difference of the start point and endpoint in the baseline is large. Geometric correction should be considered for the measured vertical angles to calculate horizontal distances correctly if the network points are not on the same elevation, even if the vertical angles are measured reciprocally.

Development characteristics of multi-scale fracture network systems in metamorphic buried hills

Natural fractures are regarded as important reservoir spaces and effective seepage channels at metamorphic buried hills. Continuous networks associated with multi-scale fractures with good connectivity is critical for high-quality reservoirs as well as high and stable production in the tight metamorphic rocks. The multi-scale fractures in Bozhong 19–6 metamorphic buried hills were well characterized through integrating image logs, cores, thin-sections, and scanning electron microscope, etc. After that, power-law distribution of multi-scale fractures was established to understand contribution of fractures to reservoir quality and figure out structure models of fracture networks as well as their impact on production. Results show that parameters of fracture systems vary regularly with fracture scales. Fracture development degree, e.g., cumulative areal density, increases as a power law function with decreasing fracture size from macro to micro (e.g., aperture and/or length), where storage space associated with micro fractures is also increased. Reversely, fracture connectivity and permeability follow a significant decreasing trend. Five structure models of fracture network were established based on combination pattern of multi-scale fractures: multi-scale fracture network with high-density and multi-sets, large-scale fracture network with medium-density and multi-sets, small-scale fracture network with high-density and multi-sets, large-scale fracture network with low-density and multi-sets, and small-scale fracture network with low-density and single-set. The former two fracture networks can be widely developed into high-quality reservoirs, contributing greatly to high and stable yields. Fracturing is required for the third and the fourth fracture networks to obtain stable production, while it is difficult for the fifth fracture network to obtain industrial oil and gas flow.

A Scalable Byzantine Fault Tolerance Algorithm Based on a Tree Topology Network

The consortium chain is the main form of application of blockchain technology in the actual industry, and its consensus mechanism mostly adopts the practical Byzantine fault tolerance (PBFT) algorithm. The traditional PBFT algorithm is only suitable for small-scale local area networks, but in large-scale wide-area network environments, its scalability bottleneck has a serious impact on the performance of the system. Therefore, in this paper, a scalable Byzantine fault tolerance algorithm based on a tree topology network is proposed (STBFT), which can take different steps to reach consensus according to the abnormal situation of the system. First, the STBFT algorithm divides the consensus nodes into different layers and groups based on the tree topology network structure, which transforms from global consensus to local consensus and drastically reduces communication consumption. Then, the division method of the group is based on a verifiable random function (VRF), with the purpose of preventing targeted attacks and colluding Byzantine nodes from affecting the normal consensus of the system. Finally, a feedback mechanism is proposed for the first time to reduce the influence of Byzantine failure on hierarchical network systems. The simulation results show that the proposed algorithm reduces the communication complexity and improves the fault tolerance of the system, and the scalability of the tree topology network structure can be better applied in large-scale scenarios such as IoT and health care.

Leakage Diagnosis Framework for Water Distribution Networks using ABC

In this work, we present a novel approach for leakage localization and Identification in water distribution networks using Approximate Bayesian Computation(ABC). A reduced pressure graph theory-based model is derived for the water networks. A leakage is considered an unknown parameter of the model which is to be estimated. Consumer demands are considered stochastic in nature and owing to that the model output, which is pressure, is also stochastic. The first moment of the model and the pressure measurements are compared to estimate probable leakages. Further, the probability distribution for the estimated leakages is computed using ABC. Results from an experimental test on a small-scale water network are also presented to demonstrate the approach.

Small-scale fisheries and agricultural trade networks are socially embedded: emerging hypotheses about responses to environmental changes

Small-scale fisheries and agricultural trade networks are socially embedded: emerging hypotheses about responses to environmental changes

The Evolutionary Game of Cooperative Air Pollution Management under Complex Networks

In this paper, based on complex networks and evolutionary game theory, we use Text Mining and Analytics, MATLAB Simulation, and other technical means to study the decision-making process of each subject in the collaborative air pollution management network, taking the “limited rational” local government as the decision-making subject of the evolutionary game. The study finds that the cooperative network of small-scale air pollution management is a very important element in the evolutionary process. The small-scale air pollution collaborative governance network has the effect of significantly improving the evolution speed of local government collaborative governance decisions in the network. It can better mobilize local governments to participate in collaborative air pollution governance and realize the cooperative emergence with the ratio interval of income heterogeneity in [0.6, 1], preference heterogeneity in [1.2, 1.4], and allocation heterogeneity in [0.6, 1]. The results of the study are consistent with the actual situation, which verifies the validity and operability of the model. Finally, the article also proposes countermeasures to improve the dilemma of air pollution synergy caused by regional heterogeneity.

LightMAN: A Lightweight Microchained Fabric for Assurance- and Resilience-Oriented Urban Air Mobility Networks

Rapid advancements in the fifth generation (5G) communication technology and mobile edge computing (MEC) paradigm have led to the proliferation of unmanned aerial vehicles (UAV) in urban air mobility (UAM) networks, which provide intelligent services for diversified smart city scenarios. Meanwhile, the widely deployed Internet of drones (IoD) in smart cities has also brought up new concerns regarding performance, security, and privacy. The centralized framework adopted by conventional UAM networks is not adequate to handle high mobility and dynamicity. Moreover, it is necessary to ensure device authentication, data integrity, and privacy preservation in UAM networks. Thanks to its characteristics of decentralization, traceability, and unalterability, blockchain is recognized as a promising technology to enhance security and privacy for UAM networks. In this paper, we introduce LightMAN, a lightweight microchained fabric for data assurance and resilience-oriented UAM networks. LightMAN is tailored for small-scale permissioned UAV networks, in which a microchain acts as a lightweight distributed ledger for security guarantees. Thus, participants are enabled to authenticate drones and verify the genuineness of data that are sent to/from drones without relying on a third-party agency. In addition, a hybrid on-chain and off-chain storage strategy is adopted that not only improves performance (e.g., latency and throughput) but also ensures privacy preservation for sensitive information in UAM networks. A proof-of-concept prototype is implemented and tested on a micro-air–vehicle link (MAVLink) simulator. The experimental evaluation validates the feasibility and effectiveness of the proposed LightMAN solution.

High-Accuracy Insulator Defect Detection for Overhead Transmission Lines Based on Improved YOLOv5

As a key component in overhead cables, insulators play an important role. However, in the process of insulator inspection, due to background interference, small fault area, limitations of manual detection, and other factors, detection is difficult, has low accuracy, and is prone to missed detection and false detection. To detect insulator defects more accurately, the insulator defect detection algorithm based on You Only Look Once version 5 (YOLOv5) is proposed. A backbone network was built with lightweight modules to reduce network computing overhead. The small-scale network detection layer was increased to improve the network for small target detection accuracy. A receptive field module was designed to replace the original spatial pyramid pooling (SPP) module so that the network can obtain feature information and improve network performance. Finally, experiments were carried out on the insulator image dataset. The experimental results show that the average accuracy of the algorithm is 97.4%, which is 7% higher than that of the original YOLOv5 network, and the detection speed is increased by 10 fps, which improves the accuracy and speed of insulator detection.

Design and Implementation of 3-D Measurement Method for Container Handling Target

In the process of automatic container terminal loading and unloading, the three-dimensional attitude of the container affects the security of loading and unloading operations, so the three-dimensional attitude positioning of the container is very important. In this paper, a visual non-contact measurement method is used to realize the real-time orientation of the three-dimensional attitude of the container. First, the container corner is coarsely positioned by a small-scale deep learning network. Secondly, the precise position of the container keyhole is obtained by the secondary positioning of the container corner through the traditional image processing algorithm, and the container posture is measured in three dimensions by combining the physical motion model of the container during loading and unloading. After testing, unlike previous measurement methods, the measurement accuracy of this method met the requirements of automatic loading and unloading of container terminals, and the measurement time met the requirements of real-time measurement.

A Novel Linear Precoder Design for Reliable UL/DL Detection in TDD Cellular Networks

The ever-growing demands on wireless connectivity, especially with the emergence of various data-intensive low-latency applications, require novel multiplexing solutions capable of reliably supporting high rates at low latency. Non-orthogonal time division duplex (TDD) coupled with multiuser detection can meet these emerging needs, provided that accurate channel state information is available. This paper proposes a new pilot-free TDD frame structure that allows designing highly effective multiuser decoders and precoders for uplink and downlink multicell systems, in an unsupervised manner. The key idea is that each user repeats and permutes its uplink data using a pre-assigned permutation code. Invoking canonical correlation analysis (CCA) at the serving BS on the two deinterleaved uplink blocks yields high quality CCA-based beamformers capable of both recovering the uplink and precoding the downlink user signals in a way that effectively mitigates interference. The paper includes a pilotless synchronization framework that leverages CCA to recover the timing and frequency offsets in an asynchronous multiuser MIMO setup, without using pilots. Simulations are used to study the performance of the proposed approach on a large-scale network with multiple users and cells, while laboratory experiments with a small-scale network of software radios are used to demonstrate that the approach works well in practice under common hardware imperfections.

The Design, Development and Assessment of a Novel De-centralised IoT-Based Remote Monitoring of a Small-Scale Anaerobic Digester Network

The implementation of community-level de-centralised anaerobic digestion (AD) systems offers a sustainable solution for organic waste management and energy provision, but there is currently a need for low-cost methods of system control and management at this scale. The problem becomes accentuated with increasing deployment, as greater complexities are created by the need to both monitor and control a wider network of smaller, community-scale plants. This paper describes research to design, deploy and test such a system by creating a network of two independent biogas generation AD reactor sites situated in the United Kingdom and Thailand. Internet of things (IoT) aspects such as inexpensive and widely available open-source control electronics was used in combination with small commercial gas analysers to log, transfer and share data with a central station, achieving real-time monitoring of performance for both networked digesters. The design proved beneficial for collaboration and knowledge exchange purposes along with providing off-site observations on reactor status. This information was used for early intervention to maintain biogas yields and enable the dynamic assessment of process economics. The concept showed potential for upscaling to larger multi-site networks of AD reactors that could maintain process optimisation without the need for skilled staff on site.