Network Performance Measures Research Articles

Identification and Counting of Pirapitinga Piaractus brachypomus Fingerlings Fish Using Machine Learning.

Identifying and counting fish are crucial for managing stocking, harvesting, and marketing of farmed fish. Researchers have used convolutional networks for these tasks and explored various approaches to enhance network learning. Batch normalization is one technique that improves network stability and accuracy. This study aimed to evaluate machine learning for identifying and counting pirapitinga Piaractus brachypomus fry with different batch sizes. The researchers used one thousand photographic images of Pirapitinga fingerlings, labeled with bounding boxes. They trained the adapted convolutional network model with batch normalization layers added at the end of each convolution block. They set the training to one hundred and fifty epochs and tested batch sizes of 5, 10, and 20. Furthermore, they measured network performance using precision, recall, and mAP@0.5. Models with smaller batch sizes performed less effectively. The training with a batch size of 20 achieved the best performance, with a precision of 96.74%, recall of 95.48%, mAP@0.5 of 97.08%, and accuracy of 98%. This indicates that larger batch sizes improve accuracy in detecting and counting pirapitinga fry across different fish densities.

Cognisseum: Cognitive radios on Colosseum facing adversaries

Cognitive radio technology brings a lot of interesting features which affect the transmission and reception properties of modern communication devices. Dynamic spectrum sensing, channel hopping and allocation, and software-based control are among the many. The new features allow strategic defense mechanisms while also enabling more capable adversarial attacks. In this work, we study coalitions of secondary users (SUs) against adversaries. In the presence of primary users (PUs), we inspect the behavior of SU pairs in cognitive radio networks, before and after adversarial attacks. We propose algorithms for forming coalitions among SU pairs. We consider two attack strategies for the adversaries: smart or naïve. We study how the channels are allocated if there is an attack and how the payoffs of those SU pairs vary with varying number of channels. We also show the effects of attack from the attackers’ point-of-view and how the attack strategy changes if the adversaries act smart vs. naïve. Using Colosseum, a large-scale wireless channel emulator, we construct a functional cognitive radio network and use its software-defined radio (SDR) hardware as SU and adversarial nodes. Using this setup, we run experiments and record data by running network performance measurement tool iPerf3 for various coalitional setups.

Performance Analysis of Open Source Cloud Computing Architectures

Organizations prioritize cloud computing as the primary solution for ensuring secure and, most importantly, uninterrupted delivery of data. In today's landscape, there are various architectural frameworks offering cloud computing infrastructures to choose from. It is crucial to understand how advantageous the performance offered by a chosen architecture is when making a selection. This study aims to comprehend how effective open-source cloud computing architectures are in specific usage scenarios, provide a comparative perspective, and assist organizations in making informed decisions when selecting their IT infrastructures. In line with the study's objectives, the network, disk, and processor performances of OpenStack, OpenNebula, and Apache CloudStack architectures on the same hardware are examined based on data-driven methods. The Iperf3 tool is used for network performance measurement, the dd (Linux) tool for disk performance measurement, and the Sysbench tool for processor performance measurement. After conducting performance measurements with these tools, it was observed that the OpenNebula architecture outperformed the other architectures in terms of overall performance.

5G-enabled UAVs for energy-efficient opportunistic networking

The article explores the potential of 5G-enabled Unmanned Aerial Vehicles (UAVs) in establishing opportunistic networks to improve network resource management, reduce energy use, and boost operational efficiency. The proposed framework utilizes 5G-enabled drones and edge command and control software to provide energy-efficient network topologies. As a result, UAVs operate edge computing for efficient data collecting and processing. This invention enhances network performance using modern Artificial Intelligence (AI) algorithms to improve UAV networking capabilities while conserving energy.An empirical investigation shows a significant improvement in network performance measures when using 5G technology compared to older 2.4 GHz systems. The communication failure rate decreased by 50 %, from 12 % to 6 %. The round-trip time was lowered by 58.3 %, from 120 Ms to 50 Ms. The payload efficiency improved by 13.3 %, dropping from 15 % to 13 %. The data transmission rate increased significantly from 1 Gbps to 5 Gbps, representing a 400 % boost. The numerical findings highlight the significant impact that 5G technology may have on UAV operations.Testing on a 5G-enabled UAV confirms the effectiveness of our technique in several domains, including precision agriculture, disaster response, and environmental monitoring. The solution seriously improves UAV network performance by reducing energy consumption and using peripheral network command-and-control software. Our results emphasize the versatile networking capacities of 5G-enabled drones, which provide new opportunities for UAV applications.

Making stateless and stateful network performance measurements unbiased

The Benchmarking Working Group (BMWG) of the Internet Engineering Task Force (IETF) has defined a series of Requests for Comments (RFC) to standardize the benchmarking of network interconnect devices (e.g., bridges, routers, different IPv6 transition solutions). The paper points out that there are cases where the performance results are significantly different when a single IP address pair or multiple IP addresses are used. The cause of this phenomenon is rooted in the recent hardware and software advancements: Receive Side Scaling (RSS) makes it possible to distribute packet processing workload over multiple CPU cores. However, this may be implemented in two ways: the first way only includes the IP addresses into the hash function used to distribute the workload among the CPU cores, whereas the second one also includes the port numbers. RFC 4814 proposed an excellent solution for the second case by recommending the usage of pseudorandom port numbers during benchmarking; however, the first case was not handled properly, because no explicit recommendation was given regarding the usage of multiple IP addresses. This paper attempts to bridge this methodological gap; a practical solution is proposed for using pseudorandom IP addresses in various scenarios including the benchmarking of IPv4 and IPv6 routers and Network Address Translation from IPv6 Clients to IPv4 Servers (stateful NAT64) gateways. Its feasibility is shown by disclosing the details of its implementation in siitperf. Then the proposed solution is validated by both stateless and stateful tests. It is shown that the measurement results of the tests following the proposed solution can better characterize the true performance of the network interconnect devices that follow the first type of RSS implementation than the results of the tests using a single IP address pair.

Energy-Efficient and Highly Reliable Geographic Routing Based on Link Detection and Node Collaborative Scheduling in WSN.

Energy efficiency and data reliability are important indicators to measure network performance in wireless sensor networks. In existing research schemes of routing protocols, the impact of node coverage on the network is often ignored, and the possibility that multiple sensor nodes may sense the same spatial point is not taken into account, which results in a waste of network resources, especially in large-scale networks. Apart from that, the blindness of geographic routing in data transmission has been troubling researchers, which means that the nodes are unable to determine the validity of data transmission. In order to solve the above problems, this paper innovatively combines the routing protocol with the coverage control technique and proposes the node collaborative scheduling algorithm, which fully considers the correlation characteristics between sensor nodes to reduce the number of active working nodes and the number of packets generated, to further reduce energy consumption and network delay and improve packet delivery rate. In order to solve the problem of unreliability of geographic routing, a highly reliable link detection and repair scheme is proposed to check the communication link status and repair the invalid link, which can greatly improve the packet delivery rate and throughput of the network, and has good robustness. A large number of experiments demonstrate the effectiveness and superiority of our proposed scheme and algorithm.

Artificial neural network and multiple linear regression modeling for predicting thermal transmittance of plain-woven cotton fabric

The present research compares a machine learning model with a statistical model, with specific emphasis on artificial neural networks and multiple linear regression models. The aim of this study is to forecast the thermal transmittance of a plain-woven cotton fabric using input data such as thread density measured in ends per inch, picks per inch, and fabric thickness. The artificial neural network is built using a network with feed-forward backpropagation, and the MATLAB software’s training function trainlm is used to modify its weight and basic values based on Levenberg–Marquardt optimization techniques. The sigmoid transfer function is used to set the layer output and measure network performance in terms of the root mean squared error, mean absolute error percentage, and coefficient of determination which were determined. For the artificial neural network prediction model, the root mean squared error and mean absolute error percentage were 1.05 and 3.132%, respectively, while the coefficient of determination was 0.9307. In contrast, the multiple linear regression prediction model had root mean squared error and mean absolute error percentage values of 2.98 and 8.97%, respectively, along with a coefficient of determination of 0.4727. The results reveal that the artificial neural network model outperforms the multiple linear regression model, showing superior accuracy and robustness in capturing the intricate interactions between important fabric parameters (ends per inch, picks per inch, and thickness) and thermal transmittance values. This research emphasizes the efficiency of artificial neural network modeling as a superior tool for forecasting thermal transmittance in textile applications rather than employing the time-consuming trial-and-error process for delivering significant insights for material engineering and energy-efficient design.

Enabling Delay-Guaranteed Congestion Control With One-Bit Feedback in Cellular Networks

Unexpected large packet delays are often observed in cellular networks due to huge network queuing caused by excessive traffic coming into the network. To deal with the large queue problem, many congestion control algorithms try to find out how much traffic the network can accommodate, either by measuring network performance or by directly providing explicit information. However, due to the nature of the control in which queue growth should be observed or the necessity to modify the overall network architecture, existing algorithms are experiencing difficulties in keeping queues within a strict bound. In this paper, we propose a novel congestion control algorithm based on simple feedback, ECLAT which can provide bounded queuing delay using only one-bit signaling already available in traditional network architecture. To do so, a base station or a router running ECLAT 1) calculates how many packets each flow should transmit and 2) analyzes when congestion feedback needs to be forwarded to adjust the flow’s packet transmission to the desired rate. Our extensive experiments in our testbed demonstrate that ECLAT achieves strict queuing delay bounds, even in the dynamic cellular network environment.

Using a collaborative learning health system approach to improve disease activity outcomes in children with juvenile idiopathic arthritis in the Pediatric Rheumatology Care and Outcomes Improvement Network.

The Pediatric Rheumatology Care and Outcomes Improvement Network (PR-COIN) is a North American learning health network focused on improving outcomes of children with juvenile idiopathic arthritis (JIA). JIA is a chronic autoimmune disease that can lead to morbidity related to persistent joint and ocular inflammation. PR-COIN has a shared patient registry that tracks twenty quality measures including ten outcome measures of which six are related to disease activity. The network's global aim, set in 2021, was to increase the percent of patients with oligoarticular or polyarticular JIA that had an inactive or low disease activity state from 76% to 80% by the end of 2023. Twenty-three hospitals participate in PR-COIN, with over 7,200 active patients with JIA. The disease activity outcome measures include active joint count, physician global assessment of disease activity, and measures related to validated composite disease activity scoring systems including inactive or low disease activity by the 10-joint clinical Juvenile Arthritis Disease Activity Score (cJADAS10), inactive or low disease activity by cJADAS10 at 6 months post-diagnosis, mean cJADAS10 score, and the American College of Rheumatology (ACR) provisional criteria for clinical inactive disease. Data is collated to measure network performance, which is displayed on run and control charts. Network-wide interventions have included pre-visit planning, shared decision making, self-management support, population health management, and utilizing a Treat to Target approach to care. Five outcome measures related to disease activity have demonstrated significant improvement over time. The percent of patients with inactive or low disease activity by cJADAS10 surpassed our goal with current network performance at 81%. Clinical inactive disease by ACR provisional criteria improved from 46% to 60%. The mean cJADAS10 score decreased from 4.3 to 2.6, and the mean active joint count declined from 1.5 to 0.7. Mean physician global assessment of disease activity significantly improved from 1 to 0.6. PR-COIN has shown significant improvement in disease activity metrics for patients with JIA. The network will continue to work on both site-specific and collaborative efforts to improve outcomes for children with JIA with attention to health equity, severity adjustment, and data quality.

Gray Correlation Entropy-Based Influential Nodes Identification and Destruction Resistance of Rail-Water Intermodal Coal Transportation Network

Evaluating the importance of nodes in coal transportation networks and identifying influential nodes is a crucial study in the field of network science, vital for ensuring the stable operation of such complex networks. However, most existing studies focus on the performance analysis of single-medium networks, lacking research on combined transportation, which is not applicable to China’s coal transportation model. To address this issue, this paper first establishes a static topological structure of China’s coal-iron-water combined transportation network based on complex network theory, constructing a node importance evaluation index system through four centrality indicators. Subsequently, an enhanced TOPSIS method (GRE-TOPSIS) is proposed based on the Grey Relational Entropy Weight (GRE) to identify key nodes in the complex network from local and positional information dimensions. Compared to previous studies, this research emphasizes composite networks, breaking through the limitations of single-medium network research, and combines gray relational analysis with entropy weighting, enhancing the objectivity of the TOPSIS method. In the simulation section of this paper, we establish the model of China’s coal-iron-water combined transportation network and use the algorithm to comprehensively rank and identify key nodes in 84 nodes, verifying its performance. Network efficiency and three other parameters are used as measures of network performance. Through simulated deliberate and random attacks on the network, the changing trends in network performance are analyzed. The results show that in random attacks, the performance drops to around 50% after damaging nearly 40 ordinary nodes. In contrast, targeting close to 16 key nodes in deliberate attacks achieves a similar effect. Once key nodes are well protected, the network exhibits a certain resistance to damage.

Formula omitted]-Coverage Reliability for Wireless Multihop Network incorporating Boundary Effect

Coverage is one of the crucial performance measures for Wireless Multihop Networks (WMN) and is widely explored by researchers. The coverage performance of the network indicates how effectively the deployed network is able to provide the required coverage for a given period of time. The coverage performance of a finite network is influenced by different factors such as the presence of obstacles in the signal propagation path, the shape and size of the network deployment region, sensor node characteristics, and many others. Therefore, it is necessary to compute the performance of the network before its actual deployment in the desired region. The existing paper proposes an analytical solution for the network coverage considering sensor node failure and boundary effect using three different sensing models i.e., Boolean, Shadow Fading, and Elfes, separately. The Boolean Sensing Model (BSM) is deterministic and ignores the channel randomness, whereas the other two are probabilistic sensing models and are more realistic as they also consider the channel randomness caused by environmental factors. In the existing research, coverage estimation is determined separately for each individual model. However, coverage estimation for all three models and coverage reliability under boundary conditions have not been explored in the literature yet. Coverage reliability is another crucial metric determining how long a network can provide the required coverage. This paper aims to compute the Network Coverage Reliability (NCR) of a WMN deployed in given environments and with sensor node failure conditions. We have proposed an analytical solution to estimate the NCR of a WMN deployed in a finite circular region considering boundary effect using all three models viz., BSM, Shadowing & Multipath Fading (SMF), and Elfes Sensing Model (ESM) assuming sensor node failure condition. The Mean Time to Failure (MTTF) parameter has been determined for all three models and based on that the value of NCR is estimated. The proposed work considers κ-coverage reliability with 1≤κ≤3 where κ is the number of distinct sensor node required to cover the target and render desired NCR for different path fading and non-fading sensing models. The analytical results are validated through simulation runs and found to be consistent. Further, we have analyzed the impact of different parameters, such as the number of sensor node, sensing range, SMF parameters, sensor failure rate, and required reliability on the κ-coverage and κ-coverage reliability, referred to as NCR.

Reputation-Based Opportunistic Routing Protocol Using Q-Learning For Manet Attacked By Malicious Nodes: A Survey

Mobile Ad-hoc Networks (MANETs) have emerged as a viable paradigm for creating dynamic communication between mobile nodes in the absence of a permanent infrastructure. However, since MANETs are inherently decentralized and self-organizing, they are vulnerable to a variety of security concerns, especially when hostile nodes seek to interrupt communication and undermine network operations. As a consequence, developing efficient and robust routing protocols is critical to ensuring dependable and secure data transmission in MANETs. This review study gives an in-depth look into Reputation-Based Opportunistic Routing Protocols (RORPs), which use Q-Learning methods to improve MANET resistance against malicious node assaults. We provide a thorough study of current RORPs, delving into their core design ideas, underlying processes, and comparative performance evaluations. We begin with a comprehensive introduction to the fundamental concepts, issues, and security risks associated with MANETs. Following that, we explore the concepts of reputation-based routing, emphasizing the need of reputation management in discriminating between malicious and genuine nodes. The review then goes into further detail on the incorporation of Q-Learning methods into RORPs, emphasizing on how reinforcement learning mechanisms may adaptively change routing choices depending on the developing network state and the dynamic reputations of participating nodes. This article reviews 38 research papers of opportunistic routing protocol and explores the potential of computer-assisted methods for MANET. We next divide the studied RORPs into categories depending on their approach to reputation computation, learning algorithms, and cooperative enforcement methods. Furthermore, we analyze their strengths and weaknesses in terms of resilience to different sorts of attacks, as well as their influence on network performance measures including packet delivery ratio, end-to-end latency, and throughput.

A Neural Radiance Field-Based Architecture for Intelligent Multilayered View Synthesis

A mobile ad hoc network is made up of a number of wireless portable nodes that spontaneously come together en route for establish a transitory network with no need for any central management. A mobile ad hoc network (MANET) is made up of a sizable and reasonably dense community of mobile nodes that travel across any terrain and rely solely on wireless interfaces for communication, not on any well before centralized management. Furthermore, routing be supposed to offer a method for instantly delivering data across a network between any two nodes. Finding the best packet routing from across infrastructure is the major issue, though. The proposed protocol's major goal is to identify the least-expensive nominal capacity acquisition that assures the transportation of realistic transport that ensures its durability in the event of any node failure. This study suggests the Optimized Route Selection via Red Imported Fire Ants (RIFA) Strategy as a way to improve on-demand source routing systems. Predicting Route Failure and energy Utilization is used to pick the path during the routing phase. Proposed work assess the results of the comparisons based on performance parameters like as energy usage, packet delivery rate (PDR), and end-to-end (E2E) delay. The outcome demonstrates that the proposed strategy is preferable and increases network lifetime while lowering node energy consumption and typical E2E delay under the majority of network performance measures and factors.

Using Data Envelope Analysis for Measurement of Entrepreneurial Network Performance in Manufacturing Firms

Abstract The aim of the paper is the analysis of the possibilities of measurement of entrepreneurial network performance in manufacturing firms, using Data Envelope Analysis (DEA) method – a non-parametric approach, allowing for the assessment of the effectiveness of the entrepreneur’s social networks in the context of generating information effects. The research uses data obtained from 30 randomly selected Polish enterprises to explore the levels of entrepreneurial networks and their information effects (as performance indicator). The research limitations are the following: the research is limited exclusively to Polish randomly selected entities, and the results cannot be generalized. The originality/value of this paper consists in the fact that this study constitutes both the contribution to the development of the discipline of management and practical guidelines for managers-entrepreneurs since it should be pinpointed that the recognition of the efficiency of gathering information from social networks can be the basis for the conscious creation of the effectiveness of acquiring information from social networks, and thus the conscious use of social networks in economic activity. The novelty of the results presented consists in filling the gap while conducting the research taking into account the assessment of entrepreneurial networks performance using DEA method.

Optimisation of Buffer Allocations in Manufacturing Systems: A Study on Intra and Outbound Logistics Systems Using Finite Queueing Networks

Optimal buffer allocations can significantly improve system throughput by managing variability and disruptions in manufacturing or service operations. Organisations can minimise waiting times and bottlenecks by strategically placing buffers along the flow path, leading to a smoother and more efficient production or service delivery process. Determining the optimal size of buffers poses a challenging dilemma, as it involves balancing the cost of buffer allocation, system throughput, and waiting times at each service station. This paper presents a framework that utilises finite queueing networks for performance analysis and optimisation of topologies, specifically focusing on buffer allocations. The proposed framework incorporates a finite closed queuing network to model the intra-logistics material transfer process and a finite open queueing network to model the outbound logistics process within a manufacturing setup. The generalised expansion method (GEM) is employed to calculate network performance measures of the system, considering the blocking phenomenon. Discrete event simulation (DES) models are constructed using simulation software, integrating optimisation configurations to determine optimal buffer allocations to maximise system throughput. The findings of this study have significant implications for decision-making processes and offer opportunities to enhance the efficiency of manufacturing systems. By leveraging the proposed framework, organisations can gain valuable insights into supply chain performance, identify potential bottlenecks, and optimise buffer allocations to achieve improved operational efficiency and overall system throughput.

Load Balancing and Fail Over MikroTik Implementation Using Per Connection Classifier (PCC) on Two Internet Providers Interconnection

Load balancing is tasked with dividing and balancing the traffic load on two or more ISPs in a balanced way so that the output obtained when using this technique is to maximize throughput, minimize response time and avoid overload on one ISP line when one of the ISPs experiences a disconnection. This can be seen from the automatic connection switching to an active ISP gateway, so that network performance continues to run normally. Throughput testing in the morning, afternoon, and evening showed very good value changes after load balancing was applied. Delay and Packet Loss tests also show a change in value from bad to very good after the implementation of the Load balancing system. The results of this study indicate that in measuring network performance for conditions of delay, packet loss, jitter and throughput before and after system implementation using load balancing and failover with the Per Connection Classifier (PCC) method, different results were found

Investigation on jamming detection in WSN using optimal decision rule

A jamming attack is a special case of a Denial of Service (DoS) attack that completely blocks the data transmission in Wireless Sensor Networks (WSNs). When sensor nodes are distributed in the field, numerous attacks, such as collision, black hole, selective forwarding, jamming, etc., caused by the presence of malicious nodes have the potential to cause network damage. Jamming is a highly risky attack that completely blocks data transmission within the wireless network. The existing technique for detecting jamming attacks are based on predetermined hopping-sequence, cryptographic, or random frequency hopping techniques. However, these mechanisms are more complex and frequently have energy constraints and high overhead. A novel jamming detection method based on a statistical approach that provides high network performance measures is proposed. It is a technique that uses energy-based clustering with a Received Signal Strength Indicator (RSSI). The selection of thresholds used for the detection of jamming is analyzed. The proposed approach employs three detection performance metrics for investigating the jamming attack, namely, Packet to Delivery Ratio (PDR), ENERGY, and RSSI. The jamming node is identified using the Optimal Decision Rule (ODR), which is determined by the hypothesis rule. If the hypothesis is not satisfied, then jamming exists; otherwise, there is no jamming. The novel technique is implemented using a Network Simulator, and various performance metrics such as PDR, Energy consumption, Network throughput, Routing overhead, network, and node lifetime are evaluated to conclude that the statistical approach outperforms the timestamp and IEWMA approaches.

ANALISIS QUALITY OF SERVICE PADA SISTEM MONITORING KULKAS DUA PINTU MENGGUNAKAN KAMERA USBWEBCAM BERBASIS ANDROID

The high level of public consumption in the field of technology encourages innovations made in the refrigerator so that it can be monitored remotely. This study aims to monitor the inside of the refrigerator by streaming and with color video quality and measuring network performance from the motion application on the Raspberry pi to the client. This system uses one camera usbwebcam and one lamp with an input voltage of 5 volts DC connected to the Raspberry Pi 3B. The software used to configure the camera is motion while controlling the lights uses the php language stored in the Raspberry Pi 3B. The application on Android has a display of both camera data, buttons light control, and graphical information from the MQ135, MQ136, and MQ137 sensors. Network quality based on 4 QoS parameters, namely throughput, delay, jitter, and packet loss in 3 conditions can be categorized as good based on the TIPHON standard. Thevalue is throughput 119kbps to 200kbps, thevalue is delay 25.56ms to 39.26ms, thevalue is jitter 50.36ms to 77.46ms and the packet loss value is 0% to 11.1%. Thevalues are throughput, delay, and jitter affected by the light captured by the camera, while packet loss is not affected by light and temperature in the refrigerator.

A Comparative Analysis of Ookla Speedtest and Measurement Labs Network Diagnostic Test (NDT7)

Consumers, regulators, and ISPs all use client-based "speed tests" to measure network performance, both in single-user settings and in aggregate. Two prevalent speed tests, Ookla's Speedtest and Measurement Lab's Network Diagnostic Test (NDT), are often used for similar purposes, despite having significant differences in both the test design and implementation, and in the infrastructure used to perform measurements. In this paper, we present the first-ever comparative evaluation of Ookla and NDT7 (the latest version of NDT), both in controlled and wide-area settings. Our goal is to characterize when and to what extent these two speed tests yield different results, as well as the factors that contribute to the differences. To study the effects of the test design, we conduct a series of controlled, in-lab experiments under a comprehensive set of network conditions and usage modes (e.g., TCP congestion control, native vs. browser client). Our results show that Ookla and NDT7 report similar speeds under most in-lab conditions, with the exception of networks that experience high latency, where Ookla consistently reports higher throughput. To characterize the behavior of these tools in wide-area deployment, we collect more than 80,000 pairs of Ookla and NDT7 measurements across nine months and 126 households, with a range of ISPs and speed tiers. This first-of-its-kind paired-test analysis reveals many previously unknown systemic issues, including high variability in NDT7 test results and systematically under-performing servers in the Ookla network.

A Fairness-Based Approach to Network Restoration

A number of recent works dealing with network resilience have offered approaches to planning for the restoration of disrupted network components, primarily driven by measures of network performance (e.g., proportion of met demand). With this short communication, we propose an alternative perspective on restoration driven by how inequalities in the restoration process impact the entire network. Namely, we examine restoration strategies that restore the Kolm–Pollak equally distributed equivalent (EDE) measure. We illustrate this initial exploration with two networks of differing sizes, and we propose several areas of future research in incorporating the EDE measure in more comprehensive network restoration studies.