Quality Scalability Research Articles

Choice-based macroscopic lane-change prediction model for weaving areas

This paper introduces two macroscopic lane change models, separately for small vehicles (passenger cars and vans) as well as heavy vehicles. The model is structured using a Nested Logit discrete choice framework. The model was estimated and validated using unique trajectory data collected from a busy weaving section in Antwerp, Belgium. The models exhibit an average and maximum rate of inaccurate prediction results, amounting to 11% and 16%, respectively. Additionally, the model underestimates the total number of lane change maneuvers, with a margin of no more than 4.1–7.7%. This study aims to fill the gap in macroscopic lane change models, which mainly depend on theoretical underpinnings, by including empirical data from the analysis of more than 31,000 observed maneuvers. Furthermore, it differentiates among three maneuver intents, a unique alternative-specific discrete choice framework. This differentiation allows for the consideration of drivers’ systematic taste variations. In contrast to other models that utilize empirical datasets and are heavily dependent on expensive video-based data, including the entire traffic flow, this study introduces a novel approach for estimating the position and frequency of maneuvers by using just a small fraction (1–2%) of vehicle trajectories, supplemented by loop detector data, therefore eliminating the need for information from surrounding vehicles. Additionally, the work’s uniqueness is its validation methods extending beyond just numerical evaluations. The practical usability and usefulness of the model are shown in real-world traffic circumstances. Finally, the model is integrated in the context of a macroscopic simulation that represents a highly consistent lane balance based on the Scalable Quality Value statistics.

Towards Sustainable Cloud Computing: Load Balancing with Nature-Inspired Meta-Heuristic Algorithms

Cloud computing is considered suitable for organizations thanks to its flexibility and the provision of digital services via the Internet. The cloud provides nearly limitless computing resources on demand without any upfront costs or long-term contracts, enabling organizations to meet their computing needs more economically. Furthermore, cloud computing provides higher security, scalability, and reliability levels than traditional computing solutions. The efficiency of the platform affects factors such as Quality of Service (QoS), congestion, lifetime, energy consumption, dependability, and scalability. Load balancing refers to managing traffic flow to spread it across several channels. Asymmetric network traffic results in increased traffic processing, more congestion on specific routes, and fewer packets delivered. The paper focuses on analyzing the use of the meta-optimization algorithm based on the principles of natural selection to solve the imbalance of loads in cloud systems. To sum up, it offers a detailed literature review on the essential meta-heuristic algorithms for load balancing in cloud computing. The study also assesses and analyses meta-heuristic algorithm performance in load balancing, as revealed by past studies, experiments, and case studies. Key performance indicators encompass response time, throughput, resource utilization, and scalability, and they are used to assess how these algorithms impact load balance efficiency.

Explainable Machine Learning for LoRaWAN Link Budget Analysis and Modeling

This article explores the convergence of artificial intelligence and its challenges for precise planning of LoRa networks. It examines machine learning algorithms in conjunction with empirically collected data to develop an effective propagation model for LoRaWAN. We propose decoupling feature extraction and regression analysis, which facilitates training data requirements. In our comparative analysis, decision-tree-based gradient boosting achieved the lowest root-mean-squared error of 5.53 dBm. Another advantage of this model is its interpretability, which is exploited to qualitatively observe the governing propagation mechanisms. This approach provides a unique opportunity to practically understand the dependence of signal strength on other variables. The analysis revealed a 1.5 dBm sensitivity improvement as the LoR’s spreading factor changed from 7 to 12. The impact of clutter was revealed to be highly non-linear, with high attenuations as clutter increased until a certain point, after which it became ineffective. The outcome of this work leads to a more accurate estimation and a better understanding of the LoRa’s propagation. Consequently, mitigating the challenges associated with large-scale and dense LoRaWAN deployments, enabling improved link budget analysis, interference management, quality of service, scalability, and energy efficiency of Internet of Things networks.

Problems of implementation of integrated satellite-ground NTN-network

The article is devoted to the discussion of challenges that arise during the rapid technological progress in satellite communication systems. The paper proposes an integrated satellite-ground system for systems of the 5G generation and above, discusses its architecture, methods and problems of its implementation, and scenarios for the use of the described system. Application areas requiring the depicted integrated architecture are described, and how this flexible multi-technology system can be used to provide global coverage and reliable access, provide transport communication via a satellite link, and data processing and content caching at the mobile edge. A multi-level network architecture with satellite and terrestrial elements will allow to create a telecommunications network with minimal delay, high deployment speed, guaranteed high quality of service and dynamic scalability, which can be controlled depending on the degree of system load and user needs in real time. Among the necessary technological solutions required for the successful implementation of the proposed system in the implementation of 5G are the following: millimeter-band communication, multi-access edge computing, network management and sharing, and spectrum sharing solutions.

Low Latency Device to Device Communications for Sustainable Development

The 5G device to device (D2D) communication is being given impetus as technology for achieving sustainable development goals (SDG) in various fields like health, smart city, education, infrastructure and clean energy. The low latency device to device communications are key enablers for achieving the goals and objectives promulgated as SDG. Network virtualisation, software defined networks, beamforming, artificial intelligence and cognitive methods are facilitating roll out of applications enabling achievement of SDG. The interference mitigation, efficient resource allocation, energy efficiency, mobility management, Quality of Service, scalability, standardisation and interoperability are few challenges being addressed to facilitate implementation of technology and its applications. In this paper existing work on D2D communications, its applications and research challenges has been reviewed. The impetus to D2D and mission critical communications by 3GPP frameworks and releases has been summarized. The SDG applications and research challenges have been analysed to highlight its implications on roll out of technology to achieve the SDG objectives and goals. These SDG applications and technology have the prospective to transform varied fields of health, education, infrastructure and industry for achieving goals.

Healthcare data quality assessment for improving the quality of the Korea Biobank Network.

Numerous studies make extensive use of healthcare data, including human materials and clinical information, and acknowledge its significance. However, limitations in data collection methods can impact the quality of healthcare data obtained from multiple institutions. In order to secure high-quality data related to human materials, research focused on data quality is necessary. This study validated the quality of data collected in 2020 from 16 institutions constituting the Korea Biobank Network using 104 validation rules. The validation rules were developed based on the DQ4HEALTH model and were divided into four dimensions: completeness, validity, accuracy, and uniqueness. Korea Biobank Network collects and manages human materials and clinical information from multiple biobanks, and is in the process of developing a common data model for data integration. The results of the data quality verification revealed an error rate of 0.74%. Furthermore, an analysis of the data from each institution was performed to examine the relationship between the institution's characteristics and error count. The results from a chi-square test indicated that there was an independent correlation between each institution and its error count. To confirm this correlation between error counts and the characteristics of each institution, a correlation analysis was conducted. The results, shown in a graph, revealed the relationship between factors that had high correlation coefficients and the error count. The findings suggest that the data quality was impacted by biases in the evaluation system, including the institution's IT environment, infrastructure, and the number of collected samples. These results highlight the need to consider the scalability of research quality when evaluating clinical epidemiological information linked to human materials in future validation studies of data quality.

Abstract 19161: EchoMap Automatically Maps Echocardiogram Report Text to Ontology

Background: Big data has the potential to revolutionize echocardiography by enabling novel research and rigorous, scalable quality improvement. Text reports are a key part of such analyses. Currently, echocardiogram reports include both structured and free text and vary across institutions, hampering attempts to mine text for useful insights. Natural language processing (NLP) can help and includes both non-deep learning and deep-learning (e.g., large language model, or LLM) based techniques. Challenges to date in using echo text with LLMs include small size, domain-specific language, and high need for accuracy and clinical meaning in model results. Hypotheses: We tested whether we could map echocardiography text to a structured ontology using NLP. Methods: We developed a three-tier ontology for the echocardiographic anatomic structures, functional elements, and descriptive characteristics in an adult transthoracic echocardiogram using 919 sentences from UCSF’s structured echocardiogram report text. We tested LLM fine-tuning as well as non-LLM techniques to map echocardiography sentences to this ontology. Two-hundred twenty-eight UCSF sentences served as an internal test set. Additional test datasets included free text from UCSF reports; structured text sentences from two other hospitals; and sentences from reports representing 17 additional hospitals. Results: Despite all adhering to clinical guidelines for reporting, there were notable differences by institution in what structural and functional information was included in structured reporting. A non-LLM hierarchical model performed best in mapping sentences to the ontology, with internal test accuracy of 96% for the first level of the ontology, 91% for the second level, and 77% for the third level. Echomap retained good performance across diverse datasets and displayed the ability to extrapolate to ontological terms not initially included in training. Conclusions: We show that non-LLM NLP methods can achieve good performance and may be especially useful for small, specialized text datasets where clinical meaning is important. These results highlight the utility of a high-resolution, standardized cardiac ontology to harmonize reports across institutions.

Empowering small businesses with the force of big data analytics and AI: A technological integration for enhanced business management

Small and medium-sized businesses (SMEs) in developing economies still face a number of obstacles that prevent them from adopting digital technologies. In contrast, SMEs have achieved greater success in emerging markets. Due to its potential benefits for numerous businesses, machine learning (ML) has become a hot topic in recent years. Particularly a few major organizations, for example, Amazon, Google and Microsoft have shown a few effective cases on coordinating simulated intelligence capacity in their own organizations. This research suggests a fresh method for bettering private companies by combining large-scale data analysis with artificial intelligence and enhanced safety measures. Here, cloud edge administration with task planning using a dynamic joined real channel Kubernetes obstruction task scheduler improves business for the executives. Then, the organization's security is bolstered by a form of differential encryption on the blockchain that takes into account the need for security. We also propose assigning new jobs to the load node with the lightest workload. Experiments show that our strategy shortens job completion times and distributes work evenly across edge nodes. The experimental investigation is conducted in terms of latency, quality of service, energy efficiency, data integrity, and scalability. The proposed technique attained latency of 0.8354, QoS of 0.9395, energy efficiency of 0.9879, data integrity of 0.1189, scalability of 0.8400.

A review on evolving domains of Internet of Things: Architecture, applications, and technical challenges

SummaryThe Internet of Things (IoT) is a system that includes smart items with different sensors, advanced technologies, analytics, cloud servers, and other wireless devices that integrate and work together to create an intelligent environment that benefits end users. With its wide spectrum of applications, IoT is revolutionizing both the current and future generations of the Internet. IoT systems can be employed for broad‐ranging real applications, such as agriculture, the environment, cities, healthcare, and the industrial sector. In this paper, we briefly discuss the three‐tier architectural view of IoT, its different communication technologies, and the smart sensors. Moreover, we study various application areas of IoT such as the environmental domain, healthcare, agriculture, smart cities, and industrial, commercial, and general aspects. A critical analysis is shown for the existing schemes and techniques related to this work. Further, this paper addresses the basic context, tools and evaluation approaches, future scope, and the advantages and disadvantages of the aforestated IoT applications. A comprehensive analysis is provided for each domain along with its fundamental parameters like the quality of service (QoS), network longevity, scalability, energy efficiency, accuracy, and cost. Finally, this study highlights the technical challenges and open research problems existing in different IoT applications.

Enhancing model quality and scalability for mining business processes with invisible tasks in non-free choice

At present, business processes are growing rapidly, resulting in various types of activity relationships and big event logs. Discovering invisible tasks and invisible tasks in non-free choice is challenging. α$ mines invisible prime tasks in non-free choice based on pairs of events, so it consumes considerable processing time. In addition, the invisible tasks formation by α $ is limited to skip, switch, and redo conditions. This study proposes a graph-based algorithm named Graph Advanced Invisible Task in Non-free choice (GAITN) to form invisible tasks in non-free choice for stacked branching relationships condition and handle large event logs. GAITN partitions the event log and creates rules for merging the partitions to scale up the volume of discoverable events. Then, GAITN utilises rules of previous graph-based process mining algorithm to visualises branching relationships (XOR, OR, AND) and creates rules of mining invisible tasks in non-free choice based on obtained branching relationships. This study compared the performance of GAITN with that of Graph Invisible Task (GIT), α $, and Fodina and found that GAITN produces process models with better fitness, precision, generalisation, and simplicity measure based on higher number of events. GAITN significantly improves the quality of process model and scalability of process mining algorithm.

On Scalability of FDD-Based Cell-Free Massive MIMO Framework.

Cell-free massive multiple-input multiple-output (MIMO) systems have the potential of providing joint services, including joint initial access, efficient clustering of access points (APs), and pilot allocation to user equipment (UEs) over large coverage areas with reduced interference. In cell-free massive MIMO, a large coverage area corresponds to the provision and maintenance of the scalable quality of service requirements for an infinitely large number of UEs. The research in cell-free massive MIMO is mostly focused on time division duplex mode due to the availability of channel reciprocity which aids in avoiding feedback overhead. However, the frequency division duplex (FDD) protocol still dominates the current wireless standards, and the provision of angle reciprocity aids in reducing this overhead. The challenge of providing a scalable cell-free massive MIMO system in an FDD setting is also prevalent, since computational complexity regarding signal processing tasks, such as channel estimation, precoding/combining, and power allocation, becomes prohibitively high with an increase in the number of UEs. In this work, we consider an FDD-based scalable cell-free network with angular reciprocity and a dynamic cooperation clustering approach. We have proposed scalability for our FDD cell-free and performed a comparative analysis with reference to channel estimation, power allocation, and precoding/combining techniques. We present expressions for scalable spectral efficiency, angle-based precoding/combining schemes and provide a comparison of overhead between conventional and scalable angle-based estimation as well as combining schemes. Simulations confirm that the proposed scalable cell-free network based on an FDD scheme outperforms the conventional matched filtering scheme based on scalable precoding/combining schemes. The angle-based LP-MMSE in the FDD cell-free network provides 14.3% improvement in spectral efficiency and 11.11% improvement in energy efficiency compared to the scalable MF scheme.

Finding Patient Zero and Tracking Narrative Changes in the Context of Online Disinformation Using Semantic Similarity Analysis

Disinformation in the form of news articles, also called fake news, is used by multiple actors for nefarious purposes, such as gaining political advantages. A key component for fake news detection is the ability to find similar articles in a large documents corpus, for tracking narrative changes and identifying the root source (patient zero) of a particular piece of information. This paper presents new techniques based on textual and semantic similarity that were adapted for achieving this goal on large datasets of news articles. The aim is to determine which of the implemented text similarity techniques is more suitable for this task. For text similarity, a Locality-Sensitive Hashing is applied on n-grams extracted from text to produce representations that are further indexed to facilitate the quick discovery of similar articles. The semantic textual similarity technique is based on sentence embeddings from pre-trained language models, such as BERT, and Named Entity Recognition. The proposed techniques are evaluated on a collection of Romanian articles to determine their performance in terms of quality of results and scalability. The presented techniques produce competitive results. The experimental results show that the proposed semantic textual similarity technique is better at identifying similar text documents, while the Locality-Sensitive Hashing text similarity technique outperforms it in terms of execution time and scalability. Even if they were evaluated only on Romanian texts and some of them are based on pre-trained models for the Romanian language, the methods that are the basis of these techniques allow their extension to other languages, with few to no changes, provided that there are pre-trained models for other languages as well. As for a cross-lingual setup, more changes are needed along with tests to demonstrate this capability. Based on the obtained results, one may conclude that the presented techniques are suitable to be integrated into a decentralized anti-disinformation platform for fact-checking and trust assessment.

A New Centralized Detection-Based Process for Evaluating Anomalies and Analyzing the First Causes Using Machine Learning and Web Semantic

In the last decades, many works have been done to enhance data performances in the computer field. Data performance consists to describe all improvements which can be added to data traffic. More precisely, we are talking about techniques allowing improving the evaluation of big data using machine learning. Data evaluation is composed of several variables such as security, quality of service, data synchronization, scalability, and data structuring. In this work, we complete our proceedings done to supervise the continuity of technological evolution in terms of big data and safety. In other words, we aim to add brick to our previous processes to take into consideration the enhancement of the analysis of the causes generating frauds and intrusions preventing data traffic. To achieve this end, we increase current machine learning techniques with prior knowledge based on data thresholds set by experts in the first place. We also aim to integrate knowledge facilitating the interpretation of the causes causing all kinds of anomalies in the second place. Finally, our process will be endowed with the requirements to improve the rate of detection of anomalies and reduce human involvement operation.

Item distribution, scalability and internal consistency of the QUALIDEM quality of life assessment for patients with dementia in acute hospital settings

BackgroundQuality of life (QoL) of people with dementia (PwD) is an important indicator of quality of care. Studying the impact of acute hospital settings on PwD’s QoL requires assessment instruments that consider environmental factors. Until now, dementia-specific QoL instruments have not yet demonstrated their feasibility in acute hospitals because their use takes up too much time or their validity depends on observation periods that usually exceed the average length of hospital stays. Therefore, validated instruments to study QoL-outcomes of patients with dementia in hospitals are needed.MethodsData stem from a study that analyzed the impact of a special care concept on the QoL of patients with dementia in acute hospitals. Total sample size consisted of N = 526 patients. Study nurses were trained in using an assessment questionnaire and conducted the data collection from June 2016 to July 2017. QoL was assessed with the QUALIDEM. This instrument consists of nine subscales that can be applied to people with mild to severe dementia (N = 344), while six of the nine subscales are applicable for people with very severe dementia (N = 182). Scalability and internal consistency were tested with Mokken scale analysis.ResultsFor people with mild to severe dementia, seven out of nine subscales were scalable (0.31 ≤ H ≤ 0.75). Five of these seven subscales were also internally consistent (ρ ≥ 0.69), while two had insufficient reliability scores (ρ = 0.53 and 0.52). The remaining two (positive self-image, feeling at home) subscales had rather low scalability (H = 0.17/0.16) and reliability scores (ρ = 0.35/0.36). For people with very severe dementia, all six subscales were scalable (0.34 ≤ H ≤ 0.71). Five out of six showed acceptable internal consistency (ρ = 0.65–0.91). Only the item social relations had insufficient reliability (ρ = 0.55).ConclusionsIn comparison with a previous evaluation of the QUALIDEM in a long-term care setting, the application in a hospital setting leads to very similar, acceptable results for people with mild to severe dementia. For people with very severe dementia, the QUALIDEM seems to fit even better in a hospital context. Results suggest either a revision of unsatisfactory items or a general reduction to six items for the QUALIDEM, for all PwD. In general, the QUALIDEM can be recommended as instrument to assess the QoL for PwD in the context of hospital research. Additionally, an investigation of the inter-rater reliability is necessary because the qualification of the nurses and the length of stay of the patients in the hospital differ from the previous investigations of the inter-rater reliability of QUALIDEM in the nursing home.

Underwater Adaptive Video Transmissions Using MIMO-Based Software-Defined Acoustic Modems

Achieving reliable acoustic wireless video transmissionsin the extreme and uncertain underwater environment is a challenge due to the limited bandwidth and the error-prone nature of the channel. Aiming at optimizing the received video quality and the user’s experience, an adaptive solution for underwater video transmissions is proposed that is specifically designed for Multi-Input Multi-Output (MIMO)-based Software-Defined Acoustic Modems (SDAMs). To keep the video distortion under an acceptable threshold and to keep the Physical-Layer Throughput (PLT) high, cross-layer techniques utilizing diversity-spatial multiplexing and Unequal Error Protection (UEP) are presented along with the scalable video compression at the application layer. Specifically, the scalability of the utilized SDAM with high processing capabilities is exploited in the proposed structure along with the temporal, spatial, and quality scalability of the Scalable Video Coding (SVC) H.264/MPEG-4 AVC compression standard. The transmitter broadcasts one video stream and realizes multicasting at different users. Experimental results at the Sonny Werblin Recreation Center, Rutgers University-NJ, are presented. Several scenarios for unknown channels at the transmitter are experimentally considered when the hydrophones are placed in different locations in the pool to achieve the required SVC-based video Quality of Service (QoS) and Quality of Experience (QoE) given the channel state information and the robustness of different SVC scalability. The video quality level is determined by the best communication link while the transmission scheme is decided based on the worst communication link, which guarantees that each user is able to receive the video with appropriate quality.

High-Speed-Microscopy for Scalable Quality Control in Automated Production of Stem Cell Spheroids for Tissue Engineering

The EU Horizon 2020 project »JointPromise« implies the conception and implementation of an end-to-end automated production platform for three-dimensional joint implants, paving the way for tissue-engineered implants able to regenerate deep osteochondral defects. Spheroid-based implants provide a novel approach in tissue engineering by aggregating progenitor cells into potent microtissues. After the differentiation of cartilaginous microtissues, functional joint implants are assembled via 3D bioprinting to match the complex structural organization of native cartilage tissue. As the automation approach of the project aims to overcome bottlenecks in manual production such as product variability, lack of scalability and high personnel costs, a high-throughput quality control system is crucial for the production of reliable Advanced Therapy Medicinal Products (ATMPs).By establishing not only a technical solution for the full digitization of the cell culture plates but also an intelligent image processing algorithm for the detection of the cell spheroids, relevant process parameters like size distribution and growth curves can be detected. Critical thresholds in spheroid growth are evaluated to minimize risks of carcinogenic tissue formation in vivo as well as to define harvest criteria to prevent inhomogeneous bioprinting results. In order to calculate the required throughput and elaborate optimization potentials of the automated spheroid production, voids in the cultivation vessel or disrupted aggregates due to media changes or transportation are detected. Ultimately, the highspeed-microscopy complies with the requirements of a high-throughput automated cell production platform to meet the rising demand for alternative therapeutic approaches in regenerative medicine.

3D printed anthropomorphic left ventricular myocardial phantom for nuclear medicine imaging applications

BackgroundAnthropomorphic torso phantoms, including a cardiac insert, are frequently used to investigate the imaging performance of SPECT and PET systems. These phantom solutions are generally featuring a simple anatomical representation of the heart. 3D printing technology paves the way to create cardiac phantoms with more complex volume definition. This study aimed to describe how a fillable left ventricular myocardium (LVm) phantom can be manufactured using geometry extracted from a patient image.MethodsThe LVm of a healthy subject was segmented from 18F-FDG attenuation corrected PET image set. Two types of phantoms were created and 3D printed using polyethylene terephthalate glycol (PETG) material: one representing the original healthy LVm, and the other mimicking myocardium with a perfusion defect. The accuracy of the LVm phantom production was investigated by high-resolution CT scanning of 3 identical replicas. 99mTc SPECT acquisitions using local cardiac protocol were performed, without additional scattering media (“in air” measurements) for both phantom types. Furthermore, the healthy LVm phantom was inserted in the commercially available DataSpectrum Anthropomorphic Torso Phantom (“in torso” measurement) and measured with hot background and hot liver insert.ResultsPhantoms were easy to fill without any air-bubbles or leakage, were found to be reproducible and fully compatible with the torso phantom. Seventeen segments polar map analysis of the "in air” measurements revealed that a significant deficit in the distribution appeared where it was expected. 59% of polar map segments had less than 5% deviation for the "in torso” and "in air” measurement comparison. Excluding the deficit area, neither comparison had more than a 12.4% deviation. All the three polar maps showed similar apex and apical region values for all configurations.ConclusionsFillable anthropomorphic 3D printed phantom of LVm can be produced with high precision and reproducibility. The 3D printed LVm phantoms were found to be suitable for SPECT image quality tests during different imaging scenarios. The flexibility of the 3D printing process presented in this study provides scalable and anthropomorphic image quality phantoms in nuclear cardiology imaging.

Abstract 16: Addressing Gaps In Cholesterol Management In Primary Cardiovascular Prevention In A Resident Run Internal Medicine Clinic: A Mixed Method Approach

Background: Provider and system level barriers can delay timely initiation of statin therapy in primary atherosclerotic cardiovascular disease (ASCVD) prevention. A simple, pragmatic and scalable quality improvement (QI) approach is needed to address these gaps. Methods: We describe methods and preliminary results of a QI initiative in a resident run internal medicine clinic in Cayuga Medical Center (total 19 residents, 5 attendings, ~200 patients seen/month with 10% no shows). First, we assessed rates of lipids tested and statin use in 40-75 years old patients without ASCVD. Second, we performed a focus group discussion (FGD) among residents, attendings and clinic leadership to understand barriers for statin use. Third, we outlined key actionable items informed by FGD. Finally, we will assess the impact of the intervention. Findings: We report baseline data in 325 consecutive patients (Table). Mean age was 55.2 years (50.2% women), 50.6% did not have lipids checked and only 17.5% were on statin therapy in the preceding 3 years. Three major themes from FGD were: lack of automated ASCVD score reporting in electronic medical record (EMR), insufficient clinic time and follow up visits. Subsequently, we adopted automated ASCVD risk reporting in EMR and telemedicine follow up to address identified gaps and associated downstream impact, which is currently ongoing. Conclusion: Using mixed-method approach, we describe an implementation strategy to address gaps in cholesterol management in primary prevention patients in a resident run clinic. If effective, this strategy can be a model for other clinics.

A novel algorithm for software defined networks model to enhance the quality of services and scalability in wireless network

Software defined networks (SDN) have replaced the traditional network architecture by separating the control from forwarding planes. SDN technology utilizes computer resources to provide worldwide effective service than the aggregation of single internet resources usage. Breakdown while resource allocation is a major concern in cloud computing due to the diverse and highly complex architecture of resources. These resources breakdowns cause delays in job completion and have a negative influence on attaining quality of service (QoS). In order to promote error-free task scheduling, this study represents a promising fault-tolerance scheduling technique. For optimum QoS, the suggested restricted Boltzmann machine (RBM) approach takes into account the most important characteristics like current consumption of the resources and rate of failure. The proposed approach's efficiency is verified using the MATLAB toolbox by employing widely used measures such as resource consumption, average processing time, throughput and rate of success.

Energy efficient and effective node deployment for wireless sensor network

SummaryThe development of tiny sensor nodes (SNs) has incarnated numerous applications in the field of wireless sensor networks (WSN). These devices became much popular in multidisciplinary research area such as the internet of things (IoT). However, the use of these devices has been restricted due to some constraints like SN energy, data aggregation, quality of service (QoS), reliability, SN deployment, scalability, energy consumption, and many more. Among all these constraints, we focus on SN energy, SN deployment, and energy consumption of the network. Because if the consumption of SN energy is less, network lifetime automatically increases. So, we propose active–passive node topology on deployed SNs which enhance the network lifetime. With the help of the proposed mechanism, the lifetime and stability have increased by 60% and 3%, respectively, as compared with directed diffusion protocol in Experiment 1. In another experiment, the lifetime has increased by 67%, 64%, and 62% as compared with LEACH, NEAP, and DREEP, respectively, in homogeneous environment. The proposed scheme also performed significant improvement in heterogeneous environment. The simulation performance shows that the proposed protocol increases the network lifetime and stability of the network.