Calculation Of Response Time Research Articles

Influence of magneto-hydrodynamic and couple stress squeeze film lubrication on conical bearing-a slip velocity model

This paper explores the impact of MHD couple stress and slip velocity over a conical bearing in the presence of magnetic field. The flow is considered as incompressible. A uniform magnetic field is imposed perpendicular to the bearing alongy-axis. The modified Reynolds equation is solved using appropriate boundary conditions in dimensionless form to find the pressure distribution which is then used to obtain the expression for load carrying capacity paving the way for the calculation of response time. Numerical computations of the results show that the MHD couple stress on the conical bearings indicates that enhances the pressure distribution, load carrying capacity and squeeze film time of the conical bearings. Further, decrease in these characteristics was observed for the increased values of slip velocity and cone angle.

Distributed safe formation tracking control of multiquadcopter systems using barrier Lyapunov function.

Coordinating the movements of a robotic fleet using consensus-based techniques is an important problem in achieving the desired goal of a specific task. Although most available techniques developed for consensus-based control ignore the collision of robots in the transient phase, they are either computationally expensive or cannot be applied in environments with dynamic obstacles. Therefore, we propose a new distributed collision-free formation tracking control scheme for multiquadcopter systems by exploiting the properties of the barrier Lyapunov function (BLF). Accordingly, the problem is formulated in a backstepping setting, and a distributed control law that guarantees collision-free formation tracking of the quads is derived. In other words, the problems of both tracking and interagent collision avoidance with a predefined accuracy are formulated using the proposed BLF for position subsystems, and the controllers are designed through augmentation of a quadratic Lyapunov function. Owing to the underactuated nature of the quadcopter system, virtual control inputs are considered for the translational (x and y axes) subsystems that are then used to generate the desired values for the roll and pitch angles for the attitude control subsystem. This provides a hierarchical controller structure for each quadcopter. The attitude controller is designed for each quadcopter locally by taking into account a predetermined error limit by another BLF. Finally, simulation results from the MATLAB-Simulink environment are provided to show the accuracy of the proposed method. A numerical comparison with an optimization-based technique is also provided to prove the superiority of the proposed method in terms of the computational cost, steady-state error, and response time.

A New Method for Analyzing F‐Region Neutral Wind Response to Ion Convection in the Nightside Auroral Oval

AbstractHigh‐latitude neutral winds have a number of drivers, both from solar and magnetospheric origins. Because of this, the neutral wind response to changes in ionospheric convection is not well understood. Previous calculations of response times resulted in a wide range of responses, from tens of minutes to hours. We present a new weighted windowed time‐lagged correlation (weighted WTLC) method for calculating the neutral wind response time. This method provides a time evolution of the neutral wind response time and considers the effects of all thermospheric forces, while previous methods were only capable of one or the other. We use data from SDIs, ASIs, and PFISR to calculate the neutral wind response time using this new method in three case studies. The results are visually validated, and the weighted WTLC method was able to correctly calculate the neutral wind response time. The time evolution of the weighted WTLC time is then compared to previous neutral wind response time calculations in order to investigate the role of ion‐drag on neutral winds. For the substorm event on 2013 Feb 28, we see a shorter response time from the weighted WTLC method, ranging from 0 to 15 min, than the e‐folding time, ranging from 30 to 355 min. The relationship between the two calculation methods and their implications about the ion‐drag force is discussed. Using the time‐dependent feature of the weighted WTLC method, we observe the neutral wind response time decrease over the course of a substorm event, indicating ion‐neutral coupling increased as the substorm progressed.

Anomaly detection in IoT-based healthcare: machine learning for enhanced security

Internet of Things (IoT) integration in healthcare improves patient care while also making healthcare delivery systems more effective and economical. To fully realize the advantages of IoT in healthcare, it is imperative to overcome issues with data security, interoperability, and ethical considerations. IoT sensors periodically measure the health-related data of the patients and share it with a server for further evaluation. At the server, different machine learning algorithms are applied which help in early diagnosis of diseases and issue alerts in case vital signs are out of the normal range. Different cyber attacks can be launched on IoT devices which can result in compromised security and privacy of applications such as health care. In this paper, we utilize the publicly available Canadian Institute for Cybersecurity (CIC) IoT dataset to model machine learning techniques for efficient detection of anomalous network traffic. The dataset consists of 33 types of IoT attacks which are divided into 7 main categories. In the current study, the dataset is pre-processed, and a balanced representation of classes is used in generating a non-biased supervised (Random Forest, Adaptive Boosting, Logistic Regression, Perceptron, Deep Neural Network) machine learning models. These models are analyzed further by eliminating highly correlated features, reducing dimensionality, minimizing overfitting, and speeding up training times. Random Forest was found to perform optimally across binary and multiclass classification of IoT Attacks with an approximate accuracy of 99.55% under both reduced and all feature space. This improvement was complimented by a reduction in computational response time which is essential for real-time attack detection and response.

Database Native Model Selection: Harnessing Deep Neural Networks in Database Systems

The growing demand for advanced analytics beyond statistical aggregation calls for database systems that support effective model selection of deep neural networks (DNNs). However, existing model selection strategies are based on either training-based algorithms that deliver high-performing models at the expense of high computational cost, or training-free algorithms that enhance computational efficiency with reduced effectiveness. These strategies often disregard computational cost and response time Service-Level Objectives (SLOs), which are of concern to average or budget-conscious machine learning users. In addition, they lack a well-designed integration of the model selection algorithms with DBMSs, which hinders efficient in-database model selection. This paper presents TRAILS, a resource-efficient and SLO-aware in-database model selection system. To leverage the strengths of both training-free and training-based model selection, we first characterize nine state-of-the-art training-free model evaluation metrics and propose a more effective one named JacFlow, and then, restructure the conventional model selection procedure into two phases: filtering and refinement. A novel coordinator is also introduced to strike a balance between the high efficiency of train-free algorithms and the high effectiveness of training-based algorithms, ensuring high-performing model selection while adhering to target SLOs. Moreover, we incorporate the proposed algorithm into PostgreSQL to develop TRAILS, thereby both enhancing resource efficiency and reducing model selection latency. This integration establishes a foundation for declarative model definition and selection within DBMSs. Empirical results demonstrate that our TRAILS reduces model selection time and computational expenses considerably by up to 24.38x and 29.32x respectively compared to existing model selection systems.

Cost-based resource allocation method for efficient allocation of resources in a heterogeneous cloud environment

Cloud computing is appealing due to features like adaptability, portability, utility service and on-demand service. Cloud resource providers are a source of computing, and each provider delivers different types of resources. In an active cloud environment, timely resource allocation is more important. In order to increase the effectiveness and user-friendliness of resource allocation in the heterogeneous cloud, the paper suggests an efficient cost-based resource allocation (ECRA) method and framework. In the heterogeneous cloud, there is no centralized resource allocation manager (CRAM) to get all requested resources from a single counter. The proposed methodology for allocating resources divides them according to their cost. The paper’s framework for allocating resources consists of various parts. The Unified Heterogeneous Resource Allocation Manager (UHRAM) part of the framework collects and manages resources from several cloud resource providers. The resource identifier is one of the components in the framework, which is coupled to UHRAM to determine the cost of the resources. The low-cost resources are scheduled and to be in a ready state for allocation. The proposed ECRA is simulated and compared based on parameters like total computation time, response time and resource allocation percentage with existing resource allocation methods. The results prove that the proposed ECRA is efficient in allocating the resources in minimum response time and it allocates maximum resources for lower cost.

Mengevaluasi Efisiensi Pengontrol Input-Output dalam Arsitektur Komputer Modern

Input and output devices are a system that exists on a computer. A computer is a system consisting of various specific components. These components will later be connected to each other and work together, to carry out processes so that they can produce information. In general, the function of a computer is to receive, process and produce data output, various forms of information in the form of text, audio, images or video. The method used in this research is an adapted literature study, and the research flow is described as follows. Figure 1 shows the data collection method used in this research. The purpose of this data collection is to obtain data that will be analyzed and processed so that any existing problems can be found and it is hoped that this research will find solutions to these problems. To collect data, study the literature. This research uses hardware and software to prove the input devices and output devices that are usually used in organizing computers. The characteristics of the I/O system are influenced by the technology of other components, for example the processor. The performance of the I/O system is more complex than the processor. Therefore, increasing CPU time without directly increasing I/O time does not necessarily improve system I/O performance. Computer system performance can be improved by improving hardware and software component technology. In this research, the focus is on I/O device performance, specifically I/O time. This research examines the relationship between CPU time, I/O time, and elapsed time. The results show that increasing CPU time results in increasing I/O time. I/O system performance is very important for computer users. Response time, also known as total elapsed time, is an important performance measure, especially for single-user and personal computers. In commercial computing, high throughput and short response times are required. Behavior, partners, and data rate are three characteristics used to evaluate I/O systems. The performance of a computer system is determined by various factors, including different users, performance measures, and aspects of the system. Performance improvements can be achieved by improving hardware factors that support overall system performance. Performance measurement can be done through response time (execution time) or throughput. Response time aims to minimize the time between starting and completing a task, while higher throughput can be achieved by serving multiple requests simultaneously. Based on research, it can be concluded that the quality of input, process and output in Islamic educational institutions is influenced by various factors. Student input is critical in determining the quality of education, and the institution has implemented a selective process to ensure the admission of motivated and dedicated students. The educational process is characterized by the application of discipline, adherence to schedules, and the instillation of religious values. The institution's output is reflected in the academic achievements of its students, as well as their motivation and participation in various competitions and activities. This research uses a qualitative approach with a case study method, collecting data through documentation and observation. Data analysis was carried out using triangulation and interpreted qualitatively. This study highlights the importance of a holistic approach to education, integrating religious values, community support, and parental involvement to improve the quality of education.

Response of a building structure due to sloshing effect in an overhead water tank

Sloshing is a violent resonant fluid motion in a water tank or any other tank containing liquid. When a fluid moves and interacts with its container, the dynamic pressures of such interaction may cause large deformation in the container wall as well as the supporting structure, which results in failure of the tank. From the available literatures on water tank and sloshing effects on the tank, it was found that, an oscillation of the liquid, may result in negative effects such as deformations and ruptures of the tank walls due to impact. From the literatures, it has been observed that when the earthquake occurs, the tank moves dynamically creating a sloshing effect on the tank’s surface and creating impact loads over the structural parts of the tank, especially on the walls of the tank, generating more stresses and ultimately resulting in critical failure. But at the same time very less work was carried out on the response of the structure due to the sloshing effect in an overhead water tank for pure water inside condition for various a/H ratios (a-height of water inside tank and H-Height of the tank).In the literatures impact loading is not considered, It could be considered along with sloshing effect on structure. According to the various literatures that have been studied in this paper, the study for the creation of structural control devices to manage sloshing has received significant attention recently, particularly focused on reducing the effects of earthquake and wind actions, and it was found that Tuned Liquid Dampers are helpful in this regard. Few of the researchers analyzed various earthquakes, namely, El Centro earthquake, Bhuj earthquake, Washington earthquake, and values of deflections, bending moments, shear forces, base shear values, displacement values, etc., were studied and compared. Even few authors investigated soil-structure interaction and how the sloshing affects the behavior of tank concluding that in stiff soil, effect of sloshing on responses is more. This study is aimed at the gaps that have been made in literature and suggests carry out the analysis for checking the response of a building structure finding due to the sloshing effect in an overhead water tank. This work proposes that, the tank design must be carried out by considering and analyzing the dynamic response of a structure during sloshing, comparing the response of the structure against impact loads, temperature stresses, and other critical loads during sloshing of the water tank by using computational fluid dynamics and response spectrum method or time history method. Also, experimental analysis (shake table analysis) should be carried out to validate the simulation results with the theoretical calculations in terms of displacement, base shear, and the time period.

A smart oyster mushroom cultivation using automatic fuzzy logic controller

Oyster mushroom cultivation is an eco-friendly, profitable agribusiness because of its nutritious and medicinal value but it is labor-intensive, and it grows in a sensitive environment. It must use an autonomous environment control system to optimize and offer the necessary growing conditions. To optimize the temperature and humidity and consequently maximize mushroom production, a smart mushroom watering, monitoring, and controlling system using fuzzy logic controllers (FLC) have been developed. This study suggests installing an Internet of Things (IoT) node that uses DHT22 sensors, an ESP32 board, and relays to control actuators (sprinkler, cooling fan, humidifier, and heater) in a smart oyster mushroom house. The controller on sensing current temperature and humidity values using the fuzzy logic technique switches on/off the actuators automatically. The data collected at each IoT node is collected and visualized on the ThingSpeak platform. The computed response time for actuators using FLC is compared with that calculated using a simple ON-OFF method. The findings demonstrate that response time calculation using fuzzy logic is significantly accurate and faster than a simple ON-OFF approach. Additionally, the graphs created in live on ThingSpeak, demonstrate that the FLC algorithm is more robust and effective than the simple ON-OFF algorithm.

Design of Active Fault-Tolerant Control System for Air-Fuel Ratio control of Internal Combustion engine using nonlinear regression-based observer model.

Internal Combustion (IC) engines are prevalent in the process sector, and maintaining sufficient Air-Fuel Ratio (AFR) regulation in their fuel system is crucial for enhanced engine performance, fuel economy, and environmental safety. Faults in the AFR system's sensors cause the engine to shut down, hence, fault tolerance is essential. In order to avoid engine shutdown, this paper offers a novel Active Fault-Tolerant Control System (AFTCS) for air-fuel ratio control of an Internal Combustion (IC) engine in a process plant. In the Fault Detection and Isolation (FDI) unit, the proposed AFTCS uses a nonlinear regression-based observer model for analytical redundancy. The suggested system was simulated in the MATLAB / Simulink environment. The proposed system was tested at two different speeds (300 r/min and 600 r/min) and the results show that the system's response is within the acceptable bound without compromising the stability. The findings also demonstrate the higher fault tolerance capability for sensor defects of the AFR control system, particularly for the MAP sensor (at 300 r/min) in terms of reduced oscillatory response in comparison to the current literature. Compared to the linear regression-based and Genetic Algorithm (GA) based model, the nonlinear regression-based model results in a more accurate estimation of the faulty sensors. The proposed model is also efficient in terms of computation power and response time.

Towards a cooperative hierarchical healthcare architecture using the HMAN offloading scenarios and SRT calculation algorithm

The Internet of medical things (IoMT) is one of the most promising fields that is expected to rapidly expand in the near future. Consequently, vast amounts of data will be generated, necessitating faster and more intensive processing capabilities. Several healthcare architectures based on Edge/Fog technologies have been created to lower healthcare expenses and provide better and more reliable services. Scalability, availability, capacity, latency, and privacy are some of the most pressing issues to consider when designing such architectures due to the critical and sensitive nature of healthcare data. To contribute to the reliability and robustness of electronic health services, this work proposes Healthcare Metropolitan Area Network (HMAN), a cooperative hierarchical Edge/Fog computing-based architecture for the urban healthcare systems. The presented architecture suggests HMAN offloading scenarios and system response time calculation (HOSSC) algorithm which is specially designed to provide an abundance of offloading and processing scenarios within the network. The architecture also connects patients to the healthcare system by utilising the existing infrastructure in cities (e.g. medical centres and hospitals). Simulation results revealed that the designed architecture produced a ubiquitous and scalable healthcare system with promising and competitive performance, such as the computing capacity and service availability, by adopting multiple cooperative hierarchical offloading scenarios across the framework units. Moreover, the HMAN system was evaluated for latency and found to be very robust, with a short response time ranging between 6.043 and 31.45 ms in responding to 1 to 300 patients simultaneously sending. In addition to these appealing features, the proposed architecture ensures patients' privacy because the data are locally stored and processed in the most anticipated scenarios. The proposed architecture is a viable solution to providing healthcare services to a large number of patients.

HydroPredicT_Extreme: A probabilistic method for the prediction of extremal high-flow hydrological events

Disastrous losses related to high-flow events have increased dramatically over the past decades largely due to an increase in flood-prone regions settlements and shift in hydrological trends largely due to Climate Change. To mitigate the societal impact of hydrological and hydraulic extremes, knowledge of the processes leading to these extreme events is vital. Hydrological modelling is one of the main tools in this quest for knowledge but comes with uncertainties. For that it is necessary to deeply study the impact of hydrological models’ structure on the magnitude and timing of extreme rainfall-runoff events. This paper is mainly aimed to show the development of a method called “HydroPredicT_Extreme” based on Bayesian Causal Modelling (BCM), a technique within Artificial Intelligence (AI). This method may enhance predictive capacity of extreme rainfall-runoff events. “HydroPredicT_Extreme” follows an iterative methodology that comprise 2 main stages. First one comprises a mixed graphical/analytical method from Hydrograph. This stage is conditioned by two initial constraints which are, a) pluviometry station is representative of hydrograph downstream flow behaviour; b) there must be independence of events. This first stage comprises sub-phases such as: 1.1. Calculation of Response Time (RT) through a mixed graphical/analytical approach, 1.2 Subtraction of RT from the flow series to remove the Rainfall-Flow delay; 1.3 Calculation base flow rate; 1.4 Subtraction base-flow from flow series to work on absolute inputs. Second man stage is called Bayesian Causal Modelling Translation (BCMT) that comprises the 2.1 Learning, 2.2 Training, 2.3 Simulation through BCM modelling, 2.4 Sensitivity Analysis-Validation. This whole methodology will become a digital application and software that could be extrapolated to several similar case studies. This may be coupled with posterior devices for the prevention of catastrophic flood consequences in the form of MultiHazard-Early Warning System (MH-EWS) or others.

Meighen Ice Cap: changes in geometry, mass, and climatic response since 1959

Remote sensing (satellite and airborne) and in situ monitoring measurements indicate that the Meighen Ice Cap has thinned by 10 ± 3 m between 1960 and 2016, resulting in areal shrinkage by 32 km2 (38%) and total mass loss of 0.71 ± 0.2 gigatonnes. Retreat of the ice margin along the North basin by up to 2.5 km accounted for 50% of the total area loss over the 56-year period of this study. A strong inverse relationship between atmospheric summer temperature anomalies, and in situ mass balance measurements (r = −0.74) indicates that accelerated rates of mass loss from the Meighen Ice Cap are largely driven by regional scale summer warming. Increases in summer temperature anomalies by 1.9 °C from 1960–2004 to 2005–2016 coincided with a five-fold increase in surface melt across the Meighen Ice Cap. Since the 1990s, the Equilibrium Line Altitude has been positioned above the summit of the Meighen Ice Cap, resulting in loss of the accumulation zone that persisted for the first three decades of this study. Since the early 2000s, the ice cap summit has thinned by more than 4 m, which is unprecedented in the period of record. Response time calculations based on 2006–2016 average mass balance data indicate that the Meighen Ice Cap, which has been in existence for ∼3500 years, will completely disappear in ∼175 years.

Energy-efficient SSD-LMS algorithm for state-space estimation in distributed networks

In a distributed environment, estimation takes place at multiple nodes. The computational complexity at each node can therefore significantly increase the computational cost of the overall network. In this research, we propose a novel energy-efficient state-space diffusion least mean square (SSD-LMS) algorithm for distributed networks. The proposed algorithm minimizes the computational complexity at each node and providing a significant advantage in terms of computational cost and response time of the overall network. Furthermore, the proposed algorithm required less communications for data sharing in the distributed network. Both, the convergence in the mean and the mean square analysis of the proposed algorithm is performed. Furthermore, the transient and the steady-state behavior is also analyzed. As a result of the convergence analysis, recursion is developed to evaluate the transient excess mean square error (EMSE) of the proposed algorithm. The proposed algorithm is evaluated by comparing the theoretical findings with the simulation results. Furthermore, the proposed SSD-LMS and the standard diffusion Kalman filter (D-KF) are compared for two different examples: (1) Channel Estimation, (2) Real-time tracking and (3) estimation of 3-degree-of-freedom piezo-actuator-driven stages. First two examples are a case-studies of small-scale systems while the third one relates to a large-scale system. The results show that the proposed SSD-LMS algorithm provides approximately identical results to the D-KF at a significantly low computational cost.

A Dynamic Context-Aware Workflow Management Scheme for Cyber-Physical Systems Based on Multi-Agent System Architecture

Although Cyber-Physical Systems (CPS) provides a paradigm to accommodate frequent changes in manufacturing sector, modeling and managing operations of CPS are challenging issues due to the complex interactions between entities in the system. Development of an effective context-aware workflow management system to guide the entities in the system is a critical factor to attain the potential benefits of CPS. In this paper, we will address the issue on the design of context-aware workflow management systems for CPS in IoT-enabled manufacturing environment. A CPS consists two parts, the Physical World and the Cyber World. To achieve the goal to design a context-aware information system for CPS, the Cyber World models of the entities in the system are constructed based on discrete timed Petri nets (DTPN) and a multi-agent system architecture in which each entity in the system is modeled as an agent to capture the interactions of entities in CPS. To develop context-aware workflow management systems for CPS, a Configuration/Scheduling Feasibility Problem and a Context Generation Problem in CPS are formulated. A condition for configuration/scheduling feasibility based on transformation of the Cyber World Models is established to develop an algorithm to generate contextual information to guide the operation of CPS. The proposed method is illustrated by examples. A series of experiments have been conducted to demonstrate the practicality of the proposed method in terms of computation time and response time. The results indicate that the computation time and total response time increase polynomially with respect to problem size parameters and show that the proposed method is effective in solving real problems.

Provably Secure and Lightweight Identity-Based Authenticated Data Sharing Protocol for Cyber-Physical Cloud Environment

Secure and efficient file storage and sharing via authenticated physical devices remain challenging to achieve in a cyber-physical cloud environment, particularly due to the diversity of devices used to access the services and data. Thus in this paper, we present a lightweight identity-based authenticated data sharing protocol to provide secure data sharing among geographically dispersed physical devices and clients. The proposed protocol is demonstrated to resist chosen-ciphertext attack (CCA) under the hardness assumption of decisional-Strong Diffie-Hellman (SDH) problem. We also evaluate the performance of the proposed protocol with existing data sharing protocols in terms of computational overhead, communication overhead, and response time.

Fog Nodes Deployment Based on Space–Time Characteristics in Smart Factory

The emergence of fog computing has improved the performance and efficiency of intelligent manufacturing. In a fog computing platform, fog nodes can provide the distributed and proximity resources for the tasks related to intelligent manufacturing. Due to the heterogeneous characteristics of the fog nodes and the space–time characteristics of intelligent manufacturing, the effective deployment of the fog nodes is a precondition for the fog computing implementation and it is also a key to provide high-performance services with the fog computing platform. However, traditional deployment strategies cannot satisfy the performance requirements of intelligent manufacturing systems. In this article, the problem of the fog nodes deployment is studied and a fog nodes deployment strategy based on the space–time characteristics (TSBP) is proposed. A fog nodes deployment system model is set up and the objective function of the fog nodes deployment is built up, in which the optimization goal is to minimize the computing response time and realize the load balancing of the fog nodes. In addition, the discrete differential evolution algorithm is applied to search the optimal fog nodes deployment solution. Finally, the effectiveness of the proposed TSBP strategy is verified using a candy packaging intelligent production line prototype platform.

UAVs for traffic monitoring: A sequential game-based computation offloading/sharing approach

Recently, UAVs or Unnamed Aerial Vehicles have been proposed as flexible aerial support to assist ground vehicles for different applications such as rescue and traffic surveillance missions. UAVs can collect different data information about the road/traffic state usually as aerial photography and videos. The processing of this kind of data consists usually on pattern recognition and video processing which are complex tasks that necessitate powerful computing and energy resources. Unfortunately, the moderate UAV's computational and energy capabilities restrict local data processing. Fortunately, UAVs can leverage the computation resources of the surrounding edge network entities to enhance their computational capabilities. In this paper, we aim to achieve efficient data processing for the data collected by UAVs in the context of UAVs-aided vehicular networks for traffic monitoring missions. For this purpose, we propose a new system model where UAVs can offload and/or share intensive computation tasks with other nearby network nodes. Then, we use the computation response time, the energy consumed for the computation, the cost of cellular communication and the computation cost as the main system metrics to make any computation offloading/sharing decisions that optimize the system performance. We then modele the offloading/sharing decision-making problem as a sequential game, where we provide complete proof of the existence of the Nash equilibrium and propose an algorithm to reach such an equilibrium. The simulation results showed that the proposed game-based model outperforms other approaches by delivering better performance in terms of overall system utility with a data processing efficiency that varies between 43% and 97% depending on the computation approach, and provides a more efficient computation time and energy average.

An analysis on the optimum location of fire department based on ambulance dispatch situation—A case study in Utsunomiya City

AbstractThe purpose of this study is to analyze the optimum location of fire departments. The idea of this research is to minimize the response time by confirming emergency services dispatched and absence situations. The method of analysis is the calculation of average response times using the optimization algorithm containing a dispatch simulation. We calculated the results by using actual past dispatch data. The results show that the median model shortened traveling distances by about 367m and the simulation model shortened distances by about 438m as compared with the current location.

An Indoor Positioning and Navigation System Using Named Data Networking

Named data networking (NDN) is a new and promising Internet architecture, which aims to replace the current transmission control protocol/Internet protocol (TCP/IP)-based Internet. The NDN internet architecture has introduced several benefits in numerous applications. An NDN-based indoor positioning and navigation system can further optimize existing localization technologies with reduced the server load and faster response time. In this study, an NDN-based approach for the existing Wi-Fi fingerprinting-based indoor positioning and navigation was investigated. Among the many features of NDN, the network-level caching can reduce the computational load and response time of the localization and navigation server. The theoretical analysis of the runtime complexity shows that the NDN's network-level caching performance is better than those of the conventional algorithms. In this paper, naming methods for different services and a server-side algorithm for handling the NDN requests are proposed. The real-world implantation and testing results show a better overall performance than that of TCP/IP. This network-level optimization for indoor positioning and navigation opens new opportunities because it can be combined with other application-level optimization techniques for more efficient indoor positioning and navigation in the future.