Data Transmission Time Research Articles

Fuzzy Theory-Based Data Placement for Scientific Workflows in Hybrid Cloud Environments

In hybrid cloud environments, reasonable data placement strategies are critical to the efficient execution of scientific workflows. Due to various loads, bandwidth fluctuations, and network congestions between different data centers as well as the dynamics of hybrid cloud environments, the data transmission time is uncertain. Thus, it poses huge challenges to the efficient data placement for scientific workflows. However, most of the traditional solutions for data placement focus on deterministic cloud environments, which lead to the excessive data transmission time of scientific workflows. To address this problem, we propose an adaptive discrete particle swarm optimization algorithm based on the fuzzy theory and genetic algorithm operators (DPSO-FGA) to minimize the fuzzy data transmission time of scientific workflows. The DPSO-FGA can rationally place the scientific workflow data while meeting the requirements of data privacy and the capacity limitations of data centers. Simulation results show that the DPSO-FGA can effectively reduce the fuzzy data transmission time of scientific workflows in hybrid cloud environments.

Secure Data Management in Cloudlet Assisted IoT Enabled e-Health Framework in Smart City

Internet of Things (IoT) is being adopted widely impacting every sphere around us. IoT enabled health-care is making the way ahead as it enables remote monitoring of health status seamlessly. The health-care industry historically has generated a large volume of data. With the induction of IoT, this volume has gone up drastically. Therefore, the health data is required to be stored properly to make sense of the data. In this paper, we propose cloudlet assisted IoT enabled e-Health framework. This e-Health framework aims to ease real-time data access using cloudlets. In this framework, we propose and implement a health-care data management scheme to store the enormous health data and process the queries for retrieval of the health data by end-users. We have used NoSQL based model to store health data. Performance of the proposed model has been studied in terms of data transmission time, energy consumption, query response time and data packet loss. Finally, by comparing the evaluation results of our proposed model with performances of the traditional cloud storage-based e-Health system, we justified that our proposed model is performing far better than existing cloud-based e-Health solutions.

DewMusic: crowdsourcing-based internet of music things in dew computing paradigm

Internet of Things is a promising paradigm that integrates a plethora of heterogeneous computational devices, incorporating the crowd, frameworks, additional system elements, and infrastructure. Information sensing, modeling, retrieval, and distribution perform an emerging role in the Internet of Things network. Dew computing is a challenging research issue, which needs to demonstrate its impact on the sensor data in the domain of parallel and distributed computing. We have presented a dew-cloud computing-based music crowdsourcing framework in this paper, to address the dew computing effectiveness in the context of the Internet of Things. The crowdsourcing paradigms are efficient to collect and analyze billions of information efficiently with a diminutive cost. In this promising paradigm, participated sound sensing devices sense acoustic information from the environment; transmit the sensor data to fog computing devices through dew repository, and eventually, cloud data center stores the processed data for providing aggregated musical information and relevant services to the end-users. This paper presents a Dew-Cloud based music crowdsourcing framework in the ambiance of the Internet of Things. We have illustrated a semantic mathematical background for the proposed crowdsourcing-based Internet of Music Things architecture in the dew-cloud computing framework. We have also discussed the system performance metrics, in terms of information transmission time, service latency, and energy dissipation in this endeavor. We have additionally illustrated a comparative analysis between the proposed paradigm and the conventional cloud computing schema in terms of data transmission time and overall system energy dissipation. The goal of this paper is to conceptualize, how the end-users can be benefitted from data analytics through data sensing, computing, and distributed scenario using a dew-cloud computational framework in the Internet of Things environment.

Design and Performance Evaluation of Multi-Gb/s Silicon Photonics Transmitters for High Energy Physics

Optical links are rapidly becoming pervasive in the readout chains of particle physics detector systems. Silicon photonics (SiPh) stands as an attractive candidate to sustain the radiation levels foreseen in the next-generation experiments, while guaranteeing, at the same time, multi-Gb/s and energy-efficient data transmission. Integrated electronic drivers are needed to enable SiPh modulators’ deployment in compact on-detector front-end modules. A current-mode logic-based driver harnessing a pseudo-differential output stage is proposed in this work to drive different types of SiPh devices by means of the same circuit topology. The proposed driver, realized in a 65 nm bulk technology and already tested to behave properly up to an 8 MGy total ionizing dose, is hybridly integrated in this work with a lumped-element Mach–Zehnder modulator (MZM) and a ring modulator (RM), both fabricated in a 130 nm silicon-on-insulator (SOI) process. Bit-error-rate (BER) performances confirm the applicability of the selected architecture to either differential and single-ended loads. A 5 Gb/s data rate, in line with the current high energy physics requirements, is achieved in the RM case, while a packaging-related performance degradation is captured in the MZM-based system, confirming the importance of interconnection modeling.

Community Model - A New Data Fusion Filter Paradigm

Data fusion is an important area in different fields of applications as it provides a detailed analysis of the data store which helps in analytics and decision support system. Among the numerous Data Fusion models available in literature, the six pioneer pieces of work in terms of Data Fusion Models are – Waterfall Model, Multisensor Integration Fusion Model, Behavioral Knowledge-based Data Fusion Model, Omnibus Model, Dasarathy Model and JDL Model. In this paper, we have given a comparative study of the above models based on basic features like signalling overhead, power consumption, fault tolerance, survivability and robustness facilities subsequently. Here, a new improved model, named Community Model has been proposed for better throughput in terms of Data Transmission Time (DTT). This model can be applied to any number of filter levels irrespective of application areas.

A non-data-aided SNR estimator based on maximum likelihood method for communication between orbiters

Signal-to-noise ratio (SNR) is an important metric for performance assessment in numerous scenerios. In order to ensure the reliability and effectiveness of the system performance, plenty of situations require the information of SNR estimate. At the same time, Mars exploration has been a hot topic in recent years, which leads to the research attention of scholars extending to deep space. In this paper, a new SNR estimator related to deep space scene is proposed. On the one hand, the time of essential data transmission is limited in Mars exploration system. On the other hand, the relative position and condition between orbiters vary quickly all the time, which makes it difficult to obtain the accurate and significant information for Mars exploration. Therefore, it is obvious that the information of SNR can promote the system to adjust the signal transmission rate automatically. Subsequently, the estimation of SNR becomes a fundamental research in automatic digital communications. In this paper, an SNR estimation method based on non-data-aided (NDA) with maximum likelihood (ML) estimation is proposed to enhance the accuracy and reliability of Mars exploration process. Additionally, the Cramer-Rao lower bound (CRLB) related to the designed ML algorithm is derived. Finally, the Monte Carlo simulation results demonstrate that the proposed ML estimator algorithm obtains a superior performance when compared to the existing SNR estimators.

Two-level fusion big data compression and reconstruction framework combining second-generation wavelet and lossless compression

In view of the characteristics of big data, fuzziness, and real time of data acquisition and transmission in the fuzzy information system faced by aircraft health management, to reduce the load of airborne data processing and transmission system under the condition of limited airborne computing resources and strong time constraints, the data collected by the airborne system are first compressed, and the amount of data are reduced before transmission and reconstructed after transmission. In view of the situation that the compression ratio of primary data compression is too small and the compression time is too long for large-scale fuzzy systems to meet the transmission requirements of the system, this paper combines the advantages of lossy compression method which consumes less time and lossless compression method which has higher compression ratio, and innovatively proposes a two-level data compression and reconstruction framework combining lossy compression and lossless compression. The optimization analysis is carried out. Taking a real aero-engine health sample as an example, the validity, scientificity, and robustness of the proposed framework are verified by comparing with data compression and reconstruction algorithm based on redundant sparse representation and compressed sensing.

Rapid prediction of earthquake ground shaking intensity using raw waveform data and a convolutional neural network

SUMMARY This study describes a deep convolutional neural network (CNN) based technique to predict intensity measurements (IMs) of earthquake ground shaking. The input data to the CNN model consists of multistation, 3C acceleration waveforms recorded during the 2016 Central Italy earthquake sequence for M ≥ 3.0 events. Using a 10 s window starting at the earthquake origin time, we find that the CNN is capable of accurately predicting IMs at stations far from the epicentre which have not yet recorded the maximum ground shaking. The CNN IM predictions do not require previous knowledge of the earthquake source (location and magnitude). Comparison between the CNN model predictions and those obtained with the Bindi et al. GMPE (which requires location and magnitude) shows that the CNN model features similar error variance but smaller bias. Although the technique is not strictly designed for earthquake early warning, we find that it can provide useful estimates of ground motions within 15–20 s after earthquake origin time depending on various setup elements (e.g. times for data transmission, computation, latencies). The technique has been tested on raw data without any initial data pre-selection in order to closely replicate real-time data streaming. When noise examples were included with the earthquake data the CNN was found to be stable, accurately predicting the ground shaking intensity corresponding to the noise amplitude.

Optimizing flight trajectory of UAV for efficient data collection in wireless sensor networks

Unmanned Aerial Vehicles (UAVs) are expected to be important components in the upcoming wireless communication field, which are increasingly used as data collectors to gather sensory data from Wireless Sensor Networks (WSNs) due to their high mobility, flexible deployment. Since the storage capacity and lifetime of sensors are increasing with the development of science and technology, sensors can store more and more sensing data about the monitoring area. However, due to the energy limitation of UAVs and a large amount of data carried by sensors, we can not collect all data from WSN within the limited time. Therefore, in this paper, we investigate two problems: (1) without the energy limitation of UAV, how to optimize the trajectory of UAV to minimize the sum of traveling time and data transmission time of UAV while guaranteeing the amount of data collected from each sensor reaches to a certain proportion of the original data, which is called the Minimizing Transportation and Communication Latency (MTCL) problem; (2) given the limited budget of UAV, how to find the optimal trajectory of UAV to maximize the minimum ratio of the collected data to the stored data among all sensors, which is called the Maximizing Data Collection Proportion (MDCP) problem. We first prove that both the problems are NP-Complete. Then we study a special case of the MTCL problem, which is called the MTCL-disjoint problem, in which any pair of data collection areas are disjoint, and we propose an approximation algorithm to solve the MTCL-disjoint problem. Based on the MTCL-disjoint problem, we propose an approximation algorithm for the general MTCL problem. Afterward, an approximation algorithm for the MDCP problem is proposed on the basis of the algorithm for the MTCL problem. Finally, we present numerical results in different scenarios to assess the effectiveness of the proposed algorithms.

An Effective Mapping Scheme for Visible Light Communication With Smartphone Camera

In this paper, an effective mapping scheme is proposed and experimentally demonstrated for visible light communication (VLC) based on light emitting diode (LED) and smartphone camera. At the transmitter, parking information is mapped to a number of bits using the proposed mapping scheme. Based on the parking information, the parking position can be achieved quickly, and the extra processing time is not required for data reception. In addition, the BER performance of the proposed mapping scheme under different distances and speeds are investigated. Experimental results show that optimum receiving distance range is from 0.5 to 1.0 m when exposure time is 1/12800s and international standard organization (ISO) is 160. Moreover, the bit error rate (BER) performance is not affected by low velocity range of 0.5-0.9 m/s under same conditions. And the transmission time of data takes only 1/15s using the proposed scheme, which is much less than that of traditional picture. As the picture resolution is 360×180, its transmission of data is 225 times more than that of using the proposed scheme.

AKUISISI DATA MENGGUNAKAN LABVIEW DENGAN ARDUINO SEBAGAI PERANGKAT KERAS BERBIAYA RENDAH

Industrial and academic laboratories cannot be separated from the use of DAQ, most of industrial automation generally requires massive amounts of DAQ and high speed. DAQ LabView software provides various facilities so that projects can be faster. Honestly, the price of hardware for DAQ is relatively expensive, luckily LabView give the opportunity to support ability many hardware including Arduino Board. Using the LINX library from Makerhub are free, so that the DAQ Practicum as industry standard can be done by affordable prices. The results without Linx, the average travel time for serial deliveries is 5.98 us/byte at 2Mbps. With Linx, "loop rate (Hz)" feature is valid as a 2-time (trigger-echo) measurement, and the data transmission time is below 20.41 ms, the digital DAQ of 2-way by (trigger-echo) is 20.41, also flip-flop transmission has an average time 13.7 ms/loop, also analog DAQ from LM35 have average time 7.87 ms/loop.

Enhanced constrained application protocol for secured medical data transmission model for internet of things

AbstractThe medical data produced from various sensors of sensitive information so there is a need to secure them through security solutions. At present, many security solutions are available in the market to secure the medical data. Transport layer security (TLS) is designed to transfer the client from the client to the server in a more secure manner. The major advantage of using TLS is it will not lose any data while transferring the message from client to server. Datagram transport layer security (DTLS) is designed specifically to use in constrained networks. The DTLS protocol consists of major components like base protocol, record layer, and handshake protocol. The main disadvantage of DTLS is the attacker can send multiple Hello messages to the server. This can cause an attack against the server and this attack encourages a new connection between the client and server and also it increases required bandwidth and resources for every message. The main objective of the proposed work is to use the constrained application protocol (CoAP) with DTLS in place of user datagram protocol (UDP) for the secure transmission of data. To evaluate the efficiency of the system, the packet loss ratio, data transmission time, and handshake time are calculated.

System Reliability Assessment of a Fast Retransmit Through ${k}$ Separate Minimal Paths Under the Latency

A fast retransmit, which reduces the sender’s waiting time before retransmitting a lost segment, is applied to guarantee data integrity with no data loss in transmission. Based on fast retransmit, many application protocols have been enhanced and evolved to ensure quality of service and reduce data transmission time. One of application protocols is the multipath transmission control protocol which is catholically applied in modern computer networks. Communication lines used in this kind of network have different states, namely, failure, partial failure, and maintenance. Therefore, a computer network with a fast retransmit can be classified as stochastic and is called a stochastic-flow computer network with a fast retransmit (SCNFR). This paper assesses the system reliability of SCNFR for the successful transmission of demand d through ${k}$ ( ${k} \,\, >1$ ) separate minimal paths ( ${k}$ SMiP) under the latency. An algorithm is proposed to find all minimal capacity vectors (MCVs) that satisfy the demand and latency. Then, the system reliability is computed based on all MCVs. We adopt two practical cases of the pan-European research and education network and the Taiwan academic network to explore the effectiveness of the algorithm. The results show that the system reliability can be classified as decision reference while the manager decides the better ${k}$ SMiP with higher system reliability.

ER-KFPSO: Energetic Routing Technique Based Kernel Fuzzy Latency Particle Swarm Optimization Algorithm in Wireless Sensor Network

The applications of Wireless Sensor Networks in the recent times are wide in various places in sensor forms. Subsequently, the routing has to be done in a more energetic manner in line with the mobility and not compromising the QoS. Various techniques were proposed for the energetic routing in order to make a significant progress in the stability of the energy as it is a vital parameter of a WSN in times of data transmission for communication. These techniques are used for the life time enhancement of the sensor networks. Load balancing methods along with energetic routing are made used at the time of clustering. This article proposes an Energetic Routing which is based on the Kernel Fuzzy Latency PSO (ER-KFPSO) for supporting the energy consumption in Wireless Sensor Networks for improvisation in the life time of the network. The proposed method gives assistance in shaping the clusters by making use of the Energy Fitness value along with assignment of CHs. The proposed technique achieves better results on experiments when compared with the other existing methods that uses Particle Swarm optimization based nodes and life time prediction method through linkage (PNLP).

Event-based networked predictive control for networked control systems subject to two-channel delays

This paper is concerned with a new combination of the event-triggered scheme and the networked predictive control technique for the networked control systems (NCSs) subject to time delays in both sensor-to-controller and controller-to-actuator channels. Firstly, the output-based Luenberger observer is designed for the considered NCSs. Secondly, in order to stabilize the NCSs, the model-based networked predictive control technique is proposed to compensate for the network-induced two-channel delays. Next, two different analysis frameworks are presented, and sufficient conditions for the asymptotic stability of the resulting closed-loop systems are obtained, respectively. Particularly, the proposed event-triggered scheme based on the measured outputs and the state predictions have considerably reduced the times of data transmission over the bandwidth-limited communication networks. Finally, an example of the buck DC-DC converter system is provided to demonstrate the effectiveness of the developed method.

Application of Greenhouse Gas Monitoring System Using General Packet Radio Service on GSM Network

Emissions of carbon dioxide (CO2) is the greatest influence on the air quality for the largest contribution to global warming is about 50%. These gases absorb and reflect radiation emitted waves of the earth, so that the heat will be stored on the surface of the earth. The absorption capacity on mangrove plants is one of the efforts to reduce CO2 emissions. In this work, a gas monitoring system was developed to monitor greenhouse gas utilizing GSM network using GPRS service. Based on experiment result, this system can monitor the absorption of CO2 emissions and the formation of O2 gas in mangrove plants as one of the efforts to reduce the impact of global warming. The information can be transmitted using SMS service on GSM network in less packet size and data transmission time, and also can be accessed through web-based application. As conclusion, the use of small and portable gas-sensing air pollution monitoring technologies can obtain more spatially and temporally representative air pollution data.

Identification of mobile vehicle through multilayer intercommunication

In the recent times of data transmission, applying the Internet of Things (IoT) device, networking to the various IoT modules is still a great challenge with the deployment stage. Very few localities in the world have the deployed IoT module integrated in its communication, but still maximum of the location are in testbed. As a practical application, the IoT modules can be used to collect the information about either the drunken person who is driving the vehicle or the heavily loaded vehicles. The vehicle that we are about to search should be integrated with the IoT module. In this paper, a novel approach for object tracking specifically the drunken driving person and the heavily loaded vehicle is proposed to ensure the follow of rules and prevent any mis happening. The task is to identify the exact vehicle which does not follow the rules which can be tracked and eliminated. For that we are proposing a simple methodology on identifying the vehicle by the tracking the locality of the vehicle as a cluster of nodes where the cluster provides the key inference on how the vehicles are interrelated in the learning environment.

Improving Performance of Opportunistic Routing Protocol using Fuzzy Logic for Vehicular Ad-hoc Networks in Highways

Vehicular ad hoc networks are an emerging technology with an extensive capability in various applications including vehicles safety, traffic management and intelligent transportation systems. Considering the high mobility of vehicles and their inhomogeneous distributions, designing an efficient routing protocol seems necessary. Given the fact that a road is crowded at some sections and is not crowded at the others, the routing protocol should be able to dynamically make decisions. On the other hand, VANET networks environment is vulnerable at the time of data transmission. Broadcast routing, similar to opportunistic routing, could offer better efficiency compared to other protocols. In this paper, a fuzzy logic opportunistic routing (FLOR) protocol is presented in which the packet rebroadcasting decision-making process is carried out through the fuzzy logic system along with three input parameters of packet advancement, local density, and the number of duplicated delivered packets. The rebroadcasting procedures use the value of these parameters as inputs to the fuzzy logic system to resolve the issue of multicasting, considering the crowded and sparse zones. NS-2 simulator is used for evaluating the performance of the proposed FLOR protocol in terms of packet delivery ratio, the end-to-end delay, and the network throughput compared with the existing protocols such as: FLOODING, P-PERSISTENCE and FUZZBR. The performance comparison also emphasizes on effective utilization of the resources. Simulations on highway environment show that the proposed protocol has a better QoS efficiency compared to the above published methods in the literature

A Link Distance Division Based Time Division Multiple Access Protocol for Directional Aeronautical Relay Networks

In directional aeronautical relay networks, the airplane relay explores the advantages of the directional antenna, in terms of long transmission distance, low transmission power, small interference range and so on, to help the ground nodes transmitting data. However, data transmission delay extension problem occurs when the distance of the transmission link extends, where the wireless signal transmission time approximates the data transmission time such that the wireless signal transmission time cannot be omitted. To address the data transmission delay extension problem, a link distance division based time division multiple access protocol, LDD-TDMA, is proposed in this paper. Different from the traditional TDMA protocol, where the time slots are equal and are determined by the longest transmission link, the length of the time slots are different and are determined by different transmission links. Furthermore, the concept of communication coverage ring is proposed where the nodes located in the same ring communicate with the relay utilizing the same time slot length. The relationships between the number of rings, the ring radius and the transmission range of the LDD-TDMA are modelled and derived as a closed formula, where the ring radius are optimized such that the gain is maximized. Simulation results show that LDD-TDMA outperforms TDMA by 13.37% when the transmission range is 200 km and the ring number is 4.

Shared data-aware dynamic resource provisioning and task scheduling for data intensive applications on hybrid clouds using Aneka

In the recent years, data-intensive applications have been growing at an increasing rate and there is a critical need to solve the high-performance and scalability issues. Hybrid Cloud Computing paradigm provides a promising solution to harness local infrastructure and remote resources and provide high Quality of Service (QoS) for time sensitive and data-intensive applications. Generally, hybrid cloud deployments have a heterogeneous pool of resources and it becomes a challenging task to efficiently utilize resources to provide optimum results. In modern data hungry applications, it is crucial to optimize bandwidth consumption, latency and networking overheads. Moreover, most of them have large extent of file sharing capability. The existing algorithms do not explicitly consider file sharing scenarios that leads large data transmission times and has severe effects on latency. In this direction, this paper focuses on building upon existing dynamic resource provisioning and task scheduling algorithms to provide better QoS in hybrid cloud environments for data intensive applications in a shared file task environment. The efficiency of proposed algorithms is demonstrated by deploying them on Microsoft Azure using Aneka, a platform for developing scalable applications on the Cloud. Experiments using real-world applications and datasets show that proposed algorithms are able to allocate tasks and extend to public cloud resources more efficiently, reducing deadline violations and improving response times to give response time reduction of upto 40.12% for a sample local alignment search application on genome sequences.