Routing Performance Research Articles

IoT based healthcare system using Fractional dung beetle optimization enabled deep learning for breast cancer classification

Breast cancer classification plays a crucial role in healthcare, especially in the diagnosis and monitoring of patients. Traditional methods for classifying breast cancer based on histopathological images often suffer from limited accuracy, which can hinder early detection and treatment. Hence, this paper devises a novel Internet of Things (IoT) based healthcare system using SqueezeNet_Fractional Dung Beetle Optimization (Squeeze_FDBO) for breast cancer detection. Initially, IoT network is simulated, and routing of the histopathological images to the Base Station (BS) is established utilizing FDBO, which is obtained by combining Dung Beetle Optimizer (DBO), and the Fractional Calculus (FC). At BS, breast cancer classification is done, where input is first processed by a bilateral filter. Then, blood cell segmentation is effectuated using LadderNet, and then, feature extraction is performed. Finally, the multigrade classification of breast cancer is executed utilizing SqueezeNet tuned by FDBO. The efficiency of Squeeze_FDBO is validated using various performance measures, and it is found to record an accuracy of 0.919, sensitivity of 0.913, specificity of 0.923, Negative Predictive Value (NPV) of 0.920, and Positive Predictive Value (PPV) of 0.908, and a better routing performance with energy of 0.405J, distance of 6.901m, and delay of 0.650mS.

A new DC bus voltage control strategy for energy routers based on distributed coordination optimization

Abstract The types of power ports of energy routers at the side of the distribution network platform are complex and have different characteristics, which makes the operation conditions of energy routers complex and changeable. Although many pieces of literature have proposed more relevant control strategies to effectively realize the efficient and stable operation of energy routers under the two basic working conditions of grid-connected and isolated islands. However, when considering various complex operating conditions such as start-up, overload/overcharge, sudden failure of distribution network, grid-connected, and inter-island switching, the reliable operation of the existing energy routers is still faced with great challenges. In this paper, a DC bus voltage control strategy based on distributed coordination optimization is proposed for typical operating conditions of energy routers. Simulation results show that this strategy can effectively achieve the DC bus voltage control performance of energy routers under various burst operating conditions and strengthen the robustness of output voltage of energy routers under isolated islands.

Retraction Note: Enhancing low latency and high performance in clustered-based wireless sensor network routing using genetic algorithm for 5G networks

Retraction Note: Enhancing low latency and high performance in clustered-based wireless sensor network routing using genetic algorithm for 5G networks

Link-Quality Aware Routing for Software-Defined Vehicular Networks

The routing protocol for Vehicular Ad Hoc Networks (VANETs) uses hop-count as a routing metric to determine the shortest route from source to destination. The presence of unstable links in the selected route may cause packet failures, resulting in an increase in packet retransmissions, causing network congestion, increasing total end-to-end delay, and reducing throughput. Thus, route selection based on hop count may not always be optimal in terms of route link quality. In Software-Defined Vehicular Networks (SDVN), the global view of link-state information for the vehicular networks is created at the controller; thus, a better route can be computed. Our study proposes an SDVN routing technique that considers link quality while determining the route. Our proposed framework examines the link quality using two metrics: Expected Transmission Count (ETX) and Expected Transmission Time (ETT), and this information is used by the controller to compute the flow rule. The routing performance of the proposed scheme is compared to traditional VANETs and other existing works with the help of network simulations using the NS-3 simulator. The obtained simulation results for our proposed scheme outperform in terms of performance metrics such as packet delivery ratio, average throughput, and end-to-end delay.

Hybrid optimization-based Effective Path Selection and Multipath routing in MANETs

Mobile Adhoc Networks (MANETs) have emerged as a popular technology because of their wide application and independent mobility. Nevertheless, communication through MANETs can be effective because of their inherent nature of high mobility, which in turn results in a higher rate of data loss and delay. Therefore, for effective communication to be achieved, the MANETs’ routing performance must be enhanced. Some extant routing protocols like AOMDV can yield moderate Routing Overhead (RO) during communication. The review of previous studies shows that there is a need for improvement in the following areas: Computational E2E delay: the proposed Genetic Algorithm- Ant Colony Optimization (GA-ACO) was unable to reduce the computational E2E delay that occurs during communication. RO: The schemes that were proposed failed to minimize the RO in the network. Energy consumption: the reduction energy consumed by the network was reduced, however, high throughput and PDR could not be achieved by the method. On the contrary, the EHO-based AOMDV routing protocol achieved low E2E delay and RO but was unable to decrease the energy consumed by the network. Throughput and PDR: The protocol proposed failed to achieve high Quality of Services (QoS). Consequently, this paper aims to provide a solution to this communication problem by proposing a Hybrid Optimization-based Effective Path Selection and Multipath routing (HOEPSM) approach for MANETs. The proposed approach, which is HOEPSM is made up of two parts: Elephant Herding Optimization (EHO)-based AOMDV routing protocol, which makes use of EHO for the identification of the shortest path from the source to the destination. The second part which is the Hybrid optimization using Genetic Ant Optimization (GAO) algorithm, involves finding the best solution through the use of a GAO-based hybrid optimization algorithm. The results of the study revealed that the use of the HOEPSM causes a significant decrease in routing RO while enhancing the network’s Quality of Forwarding (QoF) of the network. Also, the simulation of the proposed approach was carried out in NS2, and afterward, compared with earlier proposed methods like DRARP and MDRARP. Based on the simulation results, the HOEPSM outperformed the other methods, with high Energy Efficiency (EE) (91%), Packet Delivery Ratio (PDR) (95%), RO (155), and End to End (E2E delay) (102ms). These results can be attributed to the HOEPSM’s choice of the best and steady route from the source nodes and destination nodes.

How to Help Older Adults Navigate Through VR? Effects in Outdoor–Indoor Transition Environments

In more complex outdoor–indoor transition scenarios, people were more prone to getting lost due to difficulties in effectively integrating proprioceptive cues and external navigation cues. Previous research had mostly focused on spatial perception driven by head or limb movements, with less discussion on locomotion methods that involved significant visual discrepancies. The challenge in designing navigation systems lies in balancing the enhancement of wayfinding performance with the potential negative impact on spatial cognition. We believe that navigation legibility might be a key optimization approach. Additionally, the differences in performance between different age groups were also worth investigating. Therefore, this study aimed to explore how locomotion methods and navigation legibility affected wayfinding performance, navigation interaction, navigation regression, and spatial cognition in younger and older adults during outdoor–indoor transition scenarios. Twenty-four younger (aged 19–27) and 24 older adults (aged 60–83) underwent VR wayfinding tasks. Participants initially followed virtual phone navigation from outdoor starting points to indoor endpoints, then drew cognitive route maps. Five main findings emerged: Continuous teleportation positively affected navigation performance and global representation of routes. Improving navigation legibility boosted navigation interaction, navigation regression, and global representation of routes. Older adults engaged more in navigation interaction but less in navigation regression, exhibiting poorer performance and route representation. Location-specific differences were observed in locomotion, legibility, and age effects. Furthermore, most older adults and over half of younger adults displayed a deflection effect based on outdoor–indoor (OI) space in route representation. These findings indicated that continuous teleportation and enhanced navigation legibility could improve user experience and spatial cognition, highlighting the importance of considering user age and the characteristics of outdoor–indoor transition scenarios when optimizing navigation methods.

Evaluating air route performance with context-dependent data envelopment analysis: A case study in Taiwan

The aviation industry has a key role in international trade activities. Most studies measure aviation performance based on airlines or airports. However, the frontline operation is delivered via air routes and more operating characteristics can be revealed with route-based performance evaluation. Further, the traditional data envelopment analysis (DEA) approach cannot rank performance in a particular context which represents an efficiency frontier built by decision making units (DMUs). Therefore, this study uses a context-dependent DEA approach to measure and rank the performance of air routes with a case study of 112 international routes from two airlines in Taiwan. Model results identify eight performance levels with most air routes at efficiency performance level 2 and level 3 and most route destinations in East Asia (e.g., Japan). The results of this study can inform operators about a clear relationship between performance and potential markets as well as improvement strategies.

Navigating uncertainty: A dynamic Bayesian network-based risk assessment framework for maritime trade routes

Maritime safety is crucial for international seaborne trade and the global economy. Acknowledging the inevitable and multifaceted risks present in maritime navigation, we introduce a novel approach to evaluate the time-dependent risk performance of various routes, with the objective to mitigate vulnerabilities and fortify resilience. To achieve this goal, we have developed an effective framework using a Dynamic Bayesian network (DBN) model to capture the interactive relationships among the influential factors in probabilistic terms. This framework employs a combination of the synthetic minority over-sampling technique and edited nearest neighbor approach to address the issue of imbalanced maritime accident reports. Furthermore, the gradient descent algorithm is utilized to calculate the conditional probabilities among the influential factors, while the Markov chain model is applied to develop the DBN model. The framework enables dynamical consideration of the risk performance of the maritime trade routes and identifies the key influential factors during different time intervals. Our research scientifically identifies the devastating impact of terrorism on shipping routes, followed by other risk factors such as piracy, military conflicts, and various induced risks, ranked in descending order of severity. The case study on the Indian Ocean shipping route, which applies the proposed methodology, reveals an initial phase of fluctuating increases, succeeded by a prevailing downward trend over the span of 2009–2019. The methodology and results provide valuable support for maritime stakeholders in making informed decisions.

Analysis of Operational Performance of Leading Provincial City Transport Routes (AKDP) in Gorontalo Province

Consumer interest in using intra-provincial city transportation (AKDP) is decreasing. The cause is due to the increase in people's purchasing power so that to meet their transportation needs, people tend to choose to buy private vehicles rather than use public transportation. On the other hand, the operational performance of public transit, both from technical aspects, management and operational policies, is different from applicable quality standards. This research aims to identify superior AKDP routes in Gorontalo Province based on their characteristics and operational performance and formulate strategies to improve the performance of these superior routes through survey, observation and documentation approaches. The data analysis method uses qualitative and quantitative descriptive analysis by identifying the operational characteristics of services, then assessing their suitability based on public transport service standards according to A World Bank Study 1986 standards, Decree of the Director General of Transportation Land Number SK.687/AJ.206/ DRJD/2002 and Minister of Transportation Regulation R. I. No. 10 of 2012. The research results show that the operational performance of AKDP transport services in Gorontalo Province is quite good; this is indicated by the fulfilment of minimum performance quality standards in terms of travel speed (37.85 km/hour) and travel time (1.68 minutes/km). Meanwhile, those that do not comply with performance quality standards, namely, load factor 62%; headway 44.57 minutes; waiting time 22.29 minutes and frequency only 2 (two) vehicles/hour.

A constructive delay-aware model for opportunistic routing protocol in MANET

Mobile ad-hoc Networks (MANET) with mobile sinks are measured as a practical substitute to the complex deployment of conventional wireless sensing environments. This research concentrates on the high-throughput opportunistic routing with lower delay during the transmission service by the mobile nodes with an arbitrary number of mobile sinks and common network topology. Here a novel opportunistic routing (OR) model known as Delay-Aware Estimated Transmission Rate (DA-ETR) is proposed to evaluate the delay caused due to the intermittent mobile nodes and packet re-transmission process. With the execution of DA-ETR, the work demonstrates the functionality of DA-ETR seamlessly over the ETR. It specifies that the available ETR-based routing protocols for dynamic MANET are extended easily to MANET with minimum modification using the anticipated DA-ETR model. Also, the anticipated DA-ETR model with efficiency measure provides superior throughput and enhances the routing stability of OR. The novelty of the work relies on enhancing the routing performance. Thus, the proposed DA-ETR model seems lightweight and achieves easier deployment with no constant mobility prediction. The DA-ETR model for OR outperforms various existing approaches and shows better performance by achieving scalability, reliability, reduced routing and communication overhead and transmission delay. The proposed model shows a queue length of 6.5 packets, 6.3 packets/node/sec in transmission and reception rate, the delay is 50 ms, storage overhead is 80 packets, communication overhead is 20 packets and 2.5 ms packet loss. The simulation is done in the MATLAB 2020a environment.

Underwater Wireless Sensors Increase Routing Performance using Impact Efficient localization-based Routing protocols

Underwater Wireless Sensors Increase Routing Performance using Impact Efficient localization-based Routing protocols

Analisa Kinerja Protokol Routing Destination Sequenced Distance Vector Di Jaringan VANET

Vehicular network is a network where vehicles communicate via an ad-hoc network system that runs based on certain ad-hoc routing protocols. A vehicle ad-hoc network consists of two communication parts known as vehicle-to-vehicle communication and vehicle-to-infrastructure communication. Even though the roads have become very congested with many vehicles, controlling vehicle movement has become an urgent need. Automatic traffic control, critical moment notifications, post-accident notifications, correct route selection become very important and these can be implemented using vehicle ad-hoc network systems. For good communication between vehicles, vehicle ad-hoc networks use several protocols. One of the problems faced by VANET is routing. Routing in VANETs can perform challenging tasks due to high mobility, network topology interference, and path selection processes. DSDV is one of the routing used in VANET and simulations were carried out with NS2 to obtain DSDV routing performance in the VANET network based on quality of service with scenarios of 25 and 33 nodes. DSDV routing was chosen because it has the best performance in its class. From the simulation results, the end to end delay for 25 nodes is 22.22 and 33 nodes is 24.07, the packet drop obtained for 25 nodes is 0.85, and 33 nodes is 0.80. Furthermore, the throughput value obtained in the simulation for 25 nodes is 274.19 and 33 nodes 351.12, then the packet delivery ratio for 25 nodes is 2990, and 33 nodes is 4888

Vehicle Route Planning for Relief Item Distribution under Flood Uncertainty

Flooding, a pervasive and severe natural disaster, significantly damages environments and infrastructure and endangers human lives. In affected regions, disruptions to transportation networks often lead to critical shortages of essential supplies, such as food and water. The swift and adaptable delivery of relief goods via vehicle is vital to sustain life and facilitate community recovery. This paper introduces a novel model, the Vehicle Routing Problem for Relief Item Distribution under Flood Uncertainty (VRP-RIDFU), which focuses on optimizing the speed of route generation and minimizing waiting times for aid delivery in flood conditions. The Genetic Algorithm (GA) is employed because it effectively handles the uncertainties typical of NP-Hard problems. This model features a dual-population strategy: random and enhanced populations, with the latter specifically designed to manage uncertainties through anticipated route performance evaluations, incorporating factors like waiting times and flood risks. The Population Sizing Module (PSM) is implemented to dynamically adjust the population size based on the dispersion of affected nodes, using standard deviation assessments. Introducing the Complete Subtour Order Crossover (CSOX) method improves solution quality and accelerates convergence. The model’s efficacy is validated through simulated flood scenarios that emulate various degrees of uncertainty in road conditions, affirming its practicality for real-life rescue operations. Focusing on prioritizing waiting times over travel times in routing decisions has proven effective. The model has been tested using standard CVRP problems with 20 distinct sets, each with varying node numbers and patterns, demonstrating superior performance and efficiency in generating vehicle routing plans compared to the shortest routes, which serve as the benchmark for optimal solutions. The results highlight the model’s capability to deliver high-quality solutions more rapidly across all tested scenarios.

An In Vitro Hybrid Biocatalytic System Enabled by a Combination of Surface-Displayed, Purified, and Cell-Free Expressed Enzymes.

Enzymatic cascades have become a green and sustainable approach for the synthesis of valuable chemicals and pharmaceuticals. Using sequential enzymes to construct a multienzyme complex is an effective way to enhance the overall performance of biosynthetic routes. Here we report the design of an efficient in vitro hybrid biocatalytic system by assembling three enzymes that can convert styrene to (S)-1-phenyl-1,2-ethanediol. Specifically, we prepared the three enzymes in different ways, which were cell surface-displayed, purified, and cell-free expressed. To assemble them, we fused two orthogonal peptide-protein pairs (i.e., SpyTag/SpyCatcher and SnoopTag/SnoopCatcher) to the three enzymes, allowing their spatial organization by covalent assembly. By doing this, we constructed a multienzyme complex, which could enhance the production of (S)-1-phenyl-1,2-ethanediol by 3 times compared to the free-floating enzyme system without assembly. After optimization of the reaction system, the final product yield reached 234.6 μM with a substrate conversion rate of 46.9% (based on 0.5 mM styrene). Taken together, our strategy integrates the merits of advanced biochemical engineering techniques, including cellular surface display, spatial enzyme organization, and cell-free expression, which offers a new solution for chemical biosynthesis by enzymatic cascade biotransformation. We, therefore, anticipate that our approach will hold great potential for designing and constructing highly efficient systems to synthesize chemicals of agricultural, industrial, and pharmaceutical significance.

Optimized Multi-objective Clustering using Fuzzy Based Genetic Algorithm for Lifetime Maximization of WSN

Background: Wireless Sensor Networks (WSNs) have gained significant attention due to their diverse applications, including border area security, earthquake detection, and fire detection. WSNs utilize compact sensors to detect environmental events and transmit data to a Base Station (BS) for analysis. Energy consumption during data transmission is a critical issue, which has led to the exploration of additional energy-saving techniques, such as clustering. Objective: The primary objective is to propose an algorithm that selects optimal Cluster Heads (CHs) through a fuzzy-based genetic approach. This algorithm aims to address energy consumption concerns, enhance load balancing, and improve routing efficiency within WSNs. Methods: The proposed algorithm employs a fuzzy-based genetic approach to optimize the selection of CHs for data transmission. Four key parameters are considered: the average remaining energy of CHs, the average distance between CHs and the BS, the average distance between member nodes and CHs, and the standard deviation of the distance between member nodes and CHs. Results: The algorithm's effectiveness is demonstrated through simulation results. When compared to popular models like LEACH, MOEES, and FEEC, it demonstrates an 8-20% improvement in the lifetime of WSNs. The proposed approach achieves enhanced efficiency, lifetime extension, and improved performance in CH selection, load balancing, and routing. Conclusion: In conclusion, this study introduces a novel algorithm that utilizes fuzzy-based genetic techniques to optimize CH selection in WSNs. By considering four key parameters and addressing energy consumption challenges, the proposed algorithm offers significant improvements in efficiency, lifespan, and overall network performance, as validated through simulation results.

Improving Routing Performance in Mobile Ad Hoc Networks Using Artificial Neural Networks for Mobility Prediction using deep learning

In this study, a research was done to examine the effectiveness of deep learning models, such as the Artificial Neural Networks, Convolutional Neural Networks, Gated Recurrent Units and Generative Adversarial Networks, to boost routing’s efficiency and performance in the Mobile Ad Hoc Networks through mobility prediction. In total, 10 experiments were conducted, and ANNs demonstrated an average prediction accuracy of 0.86, MAE of 0.10, RMSE of 0.16, and correlation coefficient of 0.93. CNNs showed the most impressive performance, featuring the following indicators: the average prediction accuracy of 0.87, MAE of 0.09, RMSE of 0.15, and the correlation coefficient of 0.94. GRUs, in their turn, displayed quite decent performance, with an average prediction accuracy of 0.87, MAE of 0.10, RMSE of 0.16, and the correlation coefficient of 0.94; in the meanwhile, GANs can be also seen as offering a decent performance, with the average prediction accuracy of 0.87, MAE of 0.10, RMSE of 0.16, and the correlation coefficient of 0.94. In the end, the identified findings imply that deep learning models can be used to enhance the routing efficiency in MANETs, since they can predict the node movement with the high level of accuracy. The identified changes are possible to lead to the creation of effective wireless communication networks.

Enhancing Traffic Routing Inside a Network through IoT Technology & Network Clustering by Selecting Smart Leader Nodes

. IoT networking uses real items as stationary or mobile nodes. Mobile nodes complicate networking. Internet of Things (IoT) networks have a lot of control overhead messages because devices are mobile. These signals are generated by the constant flow of control data as such device identity, geographical positioning, node mobility, device configuration, and others. Network clustering is a popular overhead communication management method. Many cluster-based routing methods have been developed to address system restrictions. Node clustering based on the Internet of Things (IoT) protocol, may be used to cluster all network nodes according to predefined criteria. Each cluster will have a Smart Designated Node. SDN cluster management is efficient. Many intelligent nodes remain in the network. The network design spreads these signals. This paper presents an intelligent and responsive routing approach for clustered nodes in IoT networks. An existing method builds a new sub-area clustered topology. The Nodes Clustering Based on the Internet of Things (NCIoT) method improves message transmission between any two nodes. This will facilitate the secure and reliable interchange of healthcare data between professionals and patients. NCIoT is a system that organizes nodes in the Internet of Things (IoT) by grouping them together based on their proximity. It also picks SDN routes for these nodes. This approach involves selecting one option from a range of choices and preparing for likely outcomes problem addressing limitations on activities is a primary focus during the review process. Predictive inquiry employs the process of analyzing data to forecast and anticipate future events. This document provides an explanation of compact units. The Predictive Inquiry Small Packets (PISP) improved its backup system and partnered with SDN to establish a routing information table for each intelligent node, resulting in higher routing performance. Both principal and secondary roads are available for use. The simulation findings indicate that NCIoT algorithms outperform CBR protocols. Enhancements lead to a substantial 78% boost in network performance. In addition, the end-to-end latency dropped by 12.5%. The PISP methodology produces 5.9% more inquiry packets compared to alternative approaches. The algorithms are constructed and evaluated against academic ones.

Cosine deep convolutional neural network for Parkinson’s disease detection and severity level classification using hand drawing spiral image in IoT platform

In recent years, the Internet of Things (IoT) is an emerging technology that plays a vital role in the early detection and diagnosis of Parkinson’s disease-affected patients using various healthcare monitoring systems at any time. The utilization of IoT in real-time by monitoring Parkinson’s disease-affected patients in-home care to enhance the quality of life. Thus in this research, a hybrid deep learning classifier Cosine Deep Convolutional Neural network (CosineDCNN) is developed for early detection and classification of Parkinson’s disease. Initially, routing is performed using the designed Sine Cosine Geese Migration Optimization (SCGA) to determine the optimal routing path to transmit the hand-drawing spiral images to the base station. At the base station, the hand-drawing spiral images are pre-processed, and augmented, and the features are extracted from them for Parkinson’s disease detection and severity classification using CosineDCNN. The results obtained from the experiment proved that the CosineDCNN achieved superior performance with 89.98 % accuracy, 87.41 % Positive Predictive Value (PPV), 89.77 % True Negative Rate (TNR), 87.82 % Negative Predictive Value (NPV), and 89.84 % True Positive Rate (TPR) as compared with other techniques. Moreover, the routing performance of the SCGA model is also computed, which recorded a minimum of 0.283mS delay and a maximum of 0.507 J energy.

Detection and prevention of sinkhole attacks in MANETS based routing protocol using hybrid AdaBoost-Random forest algorithm

WSN (Wireless Sensor Network) is considered as one of the promising technologies which are utilized in various fields for various applications. Along with it, MANET (Mobile Ad Hoc Network) has attracted a distinct attention, as they serve communication means across innumerable domain. In recent years, due to the development of both wired and wireless technologies, attacks are becoming more frequent and these attacks can be in various form such as worm hole, black hole, grey-hole, sinkhole and others. These attacks can cause loss of security, increase the number of drop packets, decrease packet delivery ratio which result in poor routing performance. In order to overcome these issues, the proposed study employs PBCS (Particle Bee Colony Swarm) algorithm for finding the shortest path between the nodes, which helps in reducing the routing cost and makes the model more efficient and effective. In addition to this, Hybrid AdaBoost-Random forest algorithm is utilized in proposed model. This model helps in reducing the training time which makes the model more reliable and efficient than existing models. It can be implemented in huge dataset and in Hybrid AdaBoost-Random Forest algorithm, estimator of random forest is used, which result in high accuracy and low loss. AODV (Ad-Hoc on Demand Vector) protocol helps to identify the prevention of attack as this routing protocol perform better for longer and extensive duration of traffic than other protocols. The performance of the proposed model is evaluated using different metrics which includes accuracy, recall, precision, sensitivity and specificity. Besides, energy consumption, throughput, network lifetime, delay of the proposed model is also evaluated. The proposed model is then compared with various existing models in order to determine the efficiency and effectiveness of the proposed framework.

Non-Iterative Cluster Routing: Analysis and Implementation Strategies

In conventional routing, a capsule network employs routing algorithms for bidirectional information flow between layers through iterative processes. In contrast, the cluster routingtechnique utilizes a non-iterative process and can outperform state-of-the-art models with fewer parameters, while preserving the part–whole relationship and demonstrating robust generalization to novel viewpoints. This paper aims to further analyze and clarify this concept, providing insights that allow users to implement the cluster routing technique efficiently. Additionally, we expand the technique and propose variations based on the routing principle of achieving consensus among votes in distinct clusters. In some cases, these variations have the potential to enhance and boost the cluster routing performance while utilizing similar memory and computing resources.