Minimum Response Time Research Articles

A Survey of Road Accident Reporting and Driver’s Behavior Awareness Systems: The Case of Tanzania

Road traffic accidents are a leading cause of death in developed and developing countries. It has been shown that road accident reporting systems could reduce their effects by minimizing response time and mapping road accident-prone areas. This paper provides an overview of the systems and applications for road accident reporting and drivers’ behavior awareness. A field survey, conducted in Dar es Salaam region in Tanzania, investigated the current state of road traffic accident reporting. Findings showed that the main means of reporting road accidents were physical reporting and police emergency phone calls. The absence of alternative means for reporting road accidents causes information delay and lack of precise accident location for the emergency first responders. This paper concludes by proposing a mobile application system for road accident reporting and drivers’ over-speed awareness, in order to improve road safety in developing countries.

Location-Aware and Budget-Constrained Service Deployment for Composite Applications in Multi-Cloud Environment

Enterprise application providers are increasingly moving their workloads to the cloud for technical and economic benefits. Multi-cloud environment makes it possible to orchestrate multiple cloud resources. With the increasing number of available cloud resources provided by multiple cloud providers at different locations with different prices, application providers face the challenge to select proper cloud resources to deploy their applications in the form of a workflow of component service units. Existing studies usually consider minimizing execution time or/and deployment cost. From the perspective of application providers, however, they also pay huge attention to application response time, including particularly network latency between deployed services and users. Meanwhile, application deployment is often subject to stringent budgetary control to ensure financial viability. This article studies a new type of composite application deployment problem that jointly considers both the performance optimization and budget control in multi-cloud at the global scale. To find solutions with minimal response time without running into the risk of over-spending, we propose a hybrid GA-based approach, featuring new design of domain-tailored service clustering, repair algorithm, solution representation, population initialization, and genetic operators. Extensive experiments using the real-world dataset demonstrate that our proposed hybrid GA approach outperforms some recently proposed approaches.

Synthesis of MWCNT/PPY nanocomposite using oxidation polymerization method and its employment in sensing such as CO2 and humidity

This paper includes the synthesis of a composite of multiwall carbon nanotube (MWCNT) and conducting polymer polypyrrol (PPY) and its characterizations along with the applications direct liquid injection chemical vapour deposition (DLICVD) and oxidation polymerization method have been used for the synthesis of MWCNT and MWCNT/PPY nanocomposite respectively. MWCNT/PPY's thin film was prepared using the technique of spin coating and characterized by scanning electron microscope (SEM), transmission electron microscope (TEM), particle size analyzer and X-ray diffractometer (XRD). Raman spectroscopy has been used to observe the vibrational and rotational spectra of MWCNT and MWCNT/PPY nanocomposite. The optical bandgap and minimum crystallite size have been calculated and found to be 3.2 eV and 8.1 nm respectively. The synthesized MWCNT/PPY has been used for CO2 and humidity sensing at room temperature (300 K). The sensor response of thin-film towards CO2 was found to be 7.2 at 1000 ppm and minimum response and recovery time at 250 ppm were found to be 30 s and 37 s respectively. The sensitivity of thin-film towards humidity was found to be 41.33 kΩ/%RH. The theoretical calculation was also performed in support of experimental data.

Priority, network and energy‐aware placement of IoT‐based application services in fog‐cloud environments

Fog computing is a decentralised model which can help cloud computing for providing high quality-of-service (QoS) for the Internet of Things (IoT) application services. Service placement problem (SPP) is the mapping of services among fog and cloud resources. It plays a vital role in response time and energy consumption in fog–cloud environments. However, providing an efficient solution to this problem is a challenging task due to difficulties such as different requirements of services, limited computing resources, different delay, and power consumption profile of devices in fog domain. Motivated by this, in this study, we propose an efficient policy, called MinRE, for SPP in fog–cloud systems. To provide both QoS for IoT services and energy efficiency for fog service providers, we classify services into two categories: critical services and normal ones. For critical services, we propose MinRes, which aims to minimise response time, and for normal ones, we propose MinEng, whose goal is reducing the energy consumption of fog environment. Our extensive simulation experiments show that our policy improves the energy consumption up to 18%, the percentage of deadline satisfied services up to 14% and the average response time up to 10% in comparison with the second-best results.

Minimizing response time to accidents in big cities: a two ranked level model for allocating fire stations

In crowded cities, like Tehran, when a major accident occurs, such as a fire, the response from more than one fire station is usually needed at the scene. The present study focuses on demand allocation to fire stations at two ranked levels to determine the priorities of fire stations to service relevant demands. To solve this problem, this paper uses the Vector Assignment Ordered Median Problem (VAOMP), a new location–allocation model that can allocate demands to facilities at several ranked levels, based on the particular objective function. Thus, this paper uses the meta-heuristic methods of Tabu and genetic algorithms to minimize the arrival time from fire stations to demands, at two levels, at up to 5 min in the GIS environment of the 21st and 22nd districts of Tehran. The optimum parameters for each algorithm were obtained through sensitivity analysis. The results of applying the model with two algorithms in these districts with 10 existing fire stations and 336,600 inhabitants showed that the current stations are insufficient for two levels of service and that 52,840 people at level 1 and 81,320 people at level 2 have no access to services. As such, the results of two algorithms for relocation–reallocation analysis at two levels with different weightings for 13 potential and existing fire stations showed that at least 3 new stations need to be created. Furthermore, the genetic algorithm produced qualitatively superior results, in optimal values, the accuracy of allocation and timeframe, compared with the Tabu algorithm.

Machine Learning Assisted Information Management Scheme in Service Concentrated IoT

Internet of Things (IoT) has gained significant importance due to its flexibility in integrating communication technologies and smart devices for the ease of service provisioning. IoT services rely on a heterogeneous cloud network for serving user demands ubiquitously. The service data management is a complex task in this heterogeneous environment due to random access and service compositions. In this article, a machine learning aided information management scheme is proposed for handling data to ensure uninterrupted user request service. The neural learning process gains control over service attributes and data response to abruptly assign resources to the incoming requests in the data plane. The learning process operates in the data plane, where requests and responses for service are instantaneous. This facilitates the smoothing of the learning process to decide upon the possible resources and more precise service delivery without duplication. The proposed data management scheme ensures less replication and minimum service response time irrespective of the request and device density.

MAESP: Mobility aware edge service placement in mobile edge networks

Mobile edge networks can provide ultra-low latency by deploying services at the edge of networks. However, it is impracticable to place services on all the edge servers due to limited deployment cost requirement. It is desirable to make the optimal edge application placement decisions in a minimum response time and deployment cost, which involve two possibly conflicting objectives. An important issue here is that the number of edge application request from mobile users, which is the key factor determining the realization of two objectives, can vary considerably and the number of edge application request usually unknown before deploying edge application in a particular edge site. It has been found recently that human mobility (location and time features) have a strong influence on what kinds of application that mobile users choose to use. Based on the above observation, we investigate the mobility aware edge service placement problem aiming at optimizing service latency and deployment cost. The problem is formulated as a multi-objective optimization problem and can be solved by multi-objective context multi-armed bandit with a dominant objective. The features of user mobility (time, location) are considered as the context information guiding the edge application placement decisions. We develop mobility-aware edge service placement (MAESP) method and analyse performance measures of MAESP using the 2-dimensional (2D) regret. We show that the 2D regret of MAESP are sublinear in the number of rounds. Based on a real-world dataset, we carry out extensive simulations to evaluate the performance of MAESP. The results show that MAESP outperforms the benchmark algorithms.

Broadband Light Amplitude Tuning Characteristics of SnSe2 Coated Microfiber

Broadband light amplitude tuning characteristics are demonstrated in a 2-D tin diselenide (SnSe2) nanosheets coated microfiber (MF) structure (SnSe2CMF). Due to the strong absorption characteristics of SnSe2 in broadband wavelengths, the transmitted optical power (TOP) of SnSe2CMF can be tuned by external pump lights with different wavelengths (405, 532, and 660 nm) over a broadband spectrum (1520–1620 nm). Five wavelengths are extracted in order to investigate the tuning performance. The TOP of SnSe2CMF increases with the pump light power and a maximum tuning efficiency of ∼0.3074 dB/mW is achieved. In terms of tuning speed, the minimum response time is ∼3.7/3.9 ms. The experimental results have qualitatively good agreement with the simulations predicted by FEM models. The demonstrated structure has the advantages of broad tuning bandwidth and high tuning efficiency. Therefore, it might be a promising candidate for building high performance broadband fiber-optic sensors and modulators.

Tactile identification of embossed lines and square areas in diverse dot heights by blind individuals

Most of the braille embossers incorporate a mode for the creation of braille embossed graphics (BEG). Some of them can produce dots in different elevations, but also various dot densities. Dot elevation and dot density constitute the main variables that dominate the identification and conceptualization by blind users of tactile elements, such as lines and areas, in BEG. Nevertheless, there are not enough published research data in this domain. We present the results of a study with 30 congenitally blind participants to examine their detection performance in tactile identification of context-free embossed lines and square areas in eight dot elevations H1 to H8 (from 0.03 to 0.47 mm) and two dot densities (10 and 20 dpi). The detection accuracy and response time of the matching task were examined for each type of test stimuli. The results of correct and misclassified stimuli are presented in confusion matrixes, along with the overall mean response time for each task. The participants classify with better accuracy the stimuli with the three lower dot elevations (between 0.03 mm and 0.15 mm) for both types of stimuli and in both dot densities. The minimum overall mean response time of the identification task was achieved for the larger dot height H8 in the case of raised-dot square areas of 10 dpi. The analysis of the results indicates that better classification accuracy is achieved with the combination of four dot heights H1, H2, H4, and H8. Blind users can distinguish textures produced by dots with very low heights in line with the recent literature suggestions. The current study constitutes a first step toward recommendations for the designers of BEG in applying textures with variable dot height and dot density for educational purposes or for producing tactile maps to benefit users who are blind.

Balancing Coverage and Response Time in Area Target Scheduling for Satellite Networks

We study the area target scheduling problem in satellite networks, aiming at maximizing coverage ratio whilst minimizing response time. To balance these two conflicting objectives, we consider their weighted sum and formulate an integer programming problem. By leveraging this problem's underlying structure, we equivalently decompose it into two nested subproblems. We then propose an approximation algorithm that is shown to have 1-e -1 performance bound. Finally, our simulation results show that the maximum performance loss is within 8% over a wide range of weights between the coverage ratio and the response time.

An energy efficient service composition mechanism using a hybrid meta-heuristic algorithm in a mobile cloud environment

By increasing mobile devices in technology and human life, using a runtime and mobile services has gotten more complex along with the composition of a large number of atomic services. Different services are provided by mobile cloud components to represent the non-functional properties as Quality of Service (QoS), which is applied by a set of standards. On the other hand, the growth of the energy-source heterogeneity in mobile clouds is an emerging challenge according to the energy saving problem in mobile nodes. In order to mobile cloud service composition as an NP-Hard problem, an efficient selection method should be taken by problem using optimal energy-aware methods that can extend the deployment and interoperability of mobile cloud components. Also, an energy-aware service composition mechanism is required to preserve high energy saving scenarios for mobile cloud components. In this paper, an energy-aware mechanism is applied to optimize mobile cloud service composition using a hybrid Shuffled Frog Leaping Algorithm and Genetic Algorithm (SFGA). Experimental results capture that the proposed mechanism improves the feasibility of the service composition with minimum energy consumption, response time, and cost for mobile cloud components against some current algorithms.

A Multi-Optimization Technique for Improvement of Hadoop Performance with a Dynamic Job Execution Method Based on Artificial Neural Network

The improvement of Hadoop performance has received considerable attention from researchers in cloud computing fields. Most studies have focused on improving the performance of a Hadoop cluster. Notably, various parameters are required to configure Hadoop and must be adjusted to improve performance. This paper proposes a mechanism to improve Hadoop, schedule jobs, and allocate and utilize resources. Specifically, we present an improved ant colony optimization method to schedule jobs according to the job size and the time expected for execution. Priority is given to the job with the minimum data size and minimum response time. The resource usage and running jobs by data node are predicted using an artificial neural network, and job activity and resource usage are monitored using the resource manager. Moreover, we enhance the Hadoop Name node performance by adding an aggregator node to the default HDFS framework architecture. The changes involve four entities: the name node, secondary name node, aggregator nodes, and data nodes, where the aggregator node is responsible for assigning the jobs among the data node, and the Name node keeps tracking only the aggregator nodes. We test the overall scheme among Amazon EC2 and S3, and show the results of throughput and CPU response time for different data sizes. Finally, we show that the proposed approach shows significant improvement compare to native Hadoop and other approaches.

Mathematical model of distributed registry architecture based on cloud and fog technologies

The formalized statement of the problem of the distribution of computing and information resources for storing and processing data, as well as the provision of services at the levels of the cloud and fog systems within the framework of optimizing the multi-level topology of the computing network of the distributed registry system from cloud and fog computing paradigm, is presented. The possibility of using a multi-level representation of the topological structure to determine the optimal network architecture of a distributed registry system based on cloud and fog computing using the genetic algorithm for a fractal model of information traffic is shown, in particular, the results of an experiment to minimize response time from the number of generations for Poisson and fractal models are presented traffic.

Energy-Efficient Decision Making for Mobile Cloud Offloading

Mobile cloud offloading migrates heavy computation from mobile devices to remote cloud resources or nearby cloudlets. It is a promising method to alleviate the struggle between resource-constrained mobile devices and resource-hungry mobile applications. Caused by frequently changing location mobile users often see dynamically changing network conditions which have a great impact on the perceived application performance. Therefore, making high-quality offloading decisions at run time is difficult in mobile environments. To balance the energy-delay tradeoff based on different offloading-decision criteria (e.g., minimum response time or energy consumption), an energy-efficient offloading-decision algorithm based on Lyapunov optimization is proposed. The algorithm determines when to run the application locally, when to forward it directly for remote execution to a cloud infrastructure and when to delegate it via a nearby cloudlet to the cloud. The algorithm is able to minimize the average energy consumption on the mobile device while ensuring that the average response time satisfies a given time constraint. Moreover, compared to local and remote execution, the Lyapunov-based algorithm can significantly reduce the energy consumption while only sacrificing a small portion of response time. Furthermore, it optimizes energy better and has less computational complexity than the Lagrange Relaxation based Aggregated Cost (LARAC-based) algorithm.

A Conceptual Framework Towards Implementing a Cloud-Based Dynamic Load Balancer Using a Weighted Round-Robin Algorithm

Cloud load balancing has become one of the most vital aspects of Cloud computing that has captured the attention of IT organizations and business firms in recent years. Among the issues related to this particular aspect, one such issue which needs to be addressed is the issue of effectively serving the clients' requests among multiple servers using an appropriate load balancer. Previous survey papers discussed various issues of cloud load balancing and accordingly devised various methods and techniques to address those issues with the objectives of reduction of processing time and response time along with optimization of costs. In this article, we have discussed an effective load balancing technique using the weighted Round-Robin algorithm which can process the client requests among multiple servers with minimal response time. Considering all these aspects, a cloud-based dynamic load balancer is being used to solve the problem of load balancing in the cloud infrastructure.

Enhancing response time thresholds with response behaviors for detecting disengaged examinees

The item responses of examinees who rapid-guess, who do not spend enough time reading and engaging with an item, will not reflect their true ability on that item. Rapid-disengagement refers to rapidly selecting a response to multiple-choice items (i.e., rapid-guess), omitting items, or providing short-unrelated answers to open-ended items in an unreasonably short time. Such rapid responses need to be identified to enhance the validity of the arguments from the test data. Detection of rapid-guessing behaviors is typically based on identifying a threshold to represent the minimum response time required for the student to have thoughtfully considered a given item. This study investigates whether using response behaviors can improve the detection of rapid-disengagement by investigating two approaches: (a) using response behaviors to decide on the size of the threshold, and (b) using response behaviors as a condition for detecting disengaged examinees in addition to response times, referred as enhanced methods. Process data and item responses from the PISA 2012 computer-based mathematics assessment were used to examine both approaches under threshold values varying from very small (5 s) to very large (60 s). Results suggested that response behaviors can provide meaningful input on establishing the size of the threshold and that while enhanced methods performed better than using only response times in recognizing rapid-disengagement in some cases, no clear pattern was observed as to when such improvement occurs. This study makes a unique contribution by inspecting the response behaviors of disengaged examinees and providing guidelines on using response behaviors to decide on the size of the threshold. This study suggests response behavior categories that can be applicable to many item and response types, which make them suitable for use in digitally-based large-scale assessments.

Improved Response Time and Energy Management for Mobile Cloud Computing Using Computational Offloading

The mobile devices capabilities are found to be greater than before by utilizing the cloud services. There are various of service rendered by the cloud paradigm and the mobile devices usually allows the execution of the resource-intensive applications on the resource- constrained mobile device to be offloaded to the cloudlets that are resource rich thus enhancing the its processing capabilities. But accessing the cloud services within the minimum response time and energy consumption still remains as a serious research problem. So the proposed method put forth in the paper scopes in developing a frame work to choose the optimal cloud service provider. The frame work proposed is categorized into two stages where the initial stage engages the classifier to segregate the mobile device according to the fuzzy K-nearest neighbor and cultivates an improved computational offloading employing the Hidden Markov Model and ACO- ant colony optimization. The algorithm proffered is implemented in the MATLAB version 9.1 and the performance is evinced on the basis of the response time, energy consumption and the processing cost. The results obtained through the proposed method proves to provide an 89% better response time, 95 % better energy consumption and 50% enhanced processing cost compared to the few existing computational offloading methods put forth for the mobile cloud computing.

Plant disease identification using Deep Learning: A review

The paper reviews various classification techniques exclusively used for plant disease identification. Early stage plant disease identification is extremely important as that can adversely affect both quality and quantity of crops in agriculture. For identification of plant diseases, different approaches like image processing, machine learning, artificial neural networks, and deep learning are in use. This review focusses on an in-depth analysis on recently emerging deep learning-based methods starting from machine learning techniques. The paper highlights the crop diseases they focus on, the models employed, sources of data used and overall performance according to the performance metrics employed by each paper for plant disease identification. Review findings indicate that Deep Learning provides the highest accuracy, outperforming existing commonly used disease identification techniques and the main factors that affect the performance of deep learning-based tools. This paper is an attempt to document all such approaches for increasing performance accuracy and minimizing response time in the identification of plant diseases. The authors also present the attempts for disease diagnosis in Indian conditions using real dataset.

Scalable and Reliable Deep Learning Model to handle real time Streaming Data

We have real-time data everywhere and every day. Most of the data comes from IoT sensors, data from GPS positions, web transactions and social media updates. Real time data is typically generated in a continuous fashion. Such real-time data are called Data streams. Data streams are transient and there is very little time to process each item in the stream. It is a great challenge to do analytics on rapidly flowing high velocity data. Another issue is the percentage of incoming data that is considered for analytics. Higher the percentage greater would be the accuracy. Considering these two issues, the proposed work is intended to find a better solution by gaining insight on real-time streaming data with minimum response time and greater accuracy. This paper combines the two technology giants TensorFlow and Apache Kafka. is used to handle the real-time streaming data since TensorFlow supports analytics support with deep learning algorithms. The Training and Testing is done on Uber connected vehicle public data set RideAustin. The experimental result of RideAustin shows the predicted failure under each type of vehicle parameter. The comparative analysis showed 16% improvement over the traditional Machine Learning algorithm.

Cloud computing using load balancing and service broker policy for IT service: a taxonomy and survey

Cloud computing is the big boom technology in IT industry infrastructure. Many people are moving to cloud computing because of dynamic allocation of resources and reduction in cost. Cloud computing delivers infrastructure, software, and platforms as a service to all consumers. But still, it has numerous issues related to performance unpredictability, resource sharing, security, storage capacity, availability of resources on each requirement, data confidentiality and many more. Load balancing and service brokering are the two main key areas, which ensures reliability, scalability, minimize response time, maximize throughput and cost in the cloud environment. These are the main things we have to focus to improve the performance of the computation. This survey paper presents a comparative and comprehensive study of various load balancing algorithm used in the load balancer and brokering policy used for each service and their scheduling types. The objectives of this survey is to (1) Determine, illustrate, compare and analyze newer methods developed for load balancing and service brokering (the most notable problem) by systematically reviewing papers from the year 2015 to 2018; (2) Classify and analyze techniques based on the key parameters in cloud computing techniques; (3) Ultimately set an updated, thorough and rigorous discussion on load balancing and service broker techniques so as to motivate and direct with valuable references for future research development and direction.