Microsense

With the advancement of portable devices and sensors, there has been a need to build a universal framework, which can serve as a nodal point to aggregate data from different kinds of devices and sensors. We propose a unified framework that will provide a robust set of guidelines for sensors with varied degree of complexities connected to common set of System-on-Chip (SoC). These will help to monitor, control and visualize real time data coming from different type of sensors connected to these SoCs. We have defined a set of APIs, which will help the sensors to register with the server. These APIs will be the standard to which the sensors will comply while streaming data when connected to the client platforms.

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Fossel: Efficient Latency Reduction in Approximating Streaming Sensor Data

Sustainability ◽

10.3390/su122310175 ◽

2020 ◽

Vol 12 (23) ◽

pp. 10175

Author(s):

Fatima Abdullah ◽

Limei Peng ◽

Byungchul Tak

Keyword(s):

Real Time ◽

Data Analytics ◽

Streaming Data ◽

Sensor Data ◽

Simple Type ◽

Data Sampling ◽

Time Data ◽

Latency Reduction ◽

Network Delays ◽

Real Time Data

The volume of streaming sensor data from various environmental sensors continues to increase rapidly due to wider deployments of IoT devices at much greater scales than ever before. This, in turn, causes massive increase in the fog, cloud network traffic which leads to heavily delayed network operations. In streaming data analytics, the ability to obtain real time data insight is crucial for computational sustainability for many IoT enabled applications such as environmental monitors, pollution and climate surveillance, traffic control or even E-commerce applications. However, such network delays prevent us from achieving high quality real-time data analytics of environmental information. In order to address this challenge, we propose the Fog Sampling Node Selector (Fossel) technique that can significantly reduce the IoT network and processing delays by algorithmically selecting an optimal subset of fog nodes to perform the sensor data sampling. In addition, our technique performs a simple type of query executions within the fog nodes in order to further reduce the network delays by processing the data near the data producing devices. Our extensive evaluations show that Fossel technique outperforms the state-of-the-art in terms of latency reduction as well as in bandwidth consumption, network usage and energy consumption.

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Virtual Channel aware Scheduling for Real Time Data-Flows on Network on-Chip

Scalable Computing Practice and Experience ◽

10.12694/scpe.v20i3.1423 ◽

2019 ◽

Vol 20 (3) ◽

pp. 495-510

Author(s):

Amine Meghabber ◽

Lakhdar Loukil ◽

Richard Olejnik ◽

Abou El Hassan Benyamina ◽

Abdelkader Aroui

Keyword(s):

Real Time ◽

Network On Chip ◽

Time Constraints ◽

Time Data ◽

Virtual Channels ◽

Software Designers ◽

Data Flows ◽

Real Time Data ◽

Real Time Applications ◽

On Chip

The increasing complexity of real-time applications presents a challenge to researchers and software designers. The tasks of these applications usually exchange large volume of data-flows and often need to satisfy real-time constraints. Although the Network on-Chip (NoC) paradigm offers an underlying communication infrastructure that gives more hardware resources, it is unable to safe tasks and data-flows deadlines. In recent works, preemptive wormhole switching with fixed priority has been introduced to meet real-time constraints of real-time applications. However, it suffers some bottleneck such as hardware requirement where none of these works takes account of the number of implemented virtual channels on the router. To alleviate this problem, we propose a novel scheduler for soft real-time data-flows application that takes into account the lack on resource in routers in term of Virtual channels. Experimental results obtained on a benchmark of synthetic and soft real applications have shown the efficiency of our approach in term of real-time constraints satisfaction for data-flow traffics and hardware requirements.

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

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.c6272.029320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 3840-3844 ◽

Cited By ~ 1

Keyword(s):

Deep Learning ◽

Real Time ◽

Data Streams ◽

Learning Algorithm ◽

Streaming Data ◽

Experimental Result ◽

Time Data ◽

Data Set ◽

Public Data ◽

Real Time 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.

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Developing a Real-Time Data Analytics Framework for Twitter Streaming Data

2017 IEEE International Congress on Big Data (BigData Congress) ◽

10.1109/bigdatacongress.2017.49 ◽

2017 ◽

Cited By ~ 4

Author(s):

Babak Yadranjiaghdam ◽

Seyedfaraz Yasrobi ◽

Nasseh Tabrizi

Keyword(s):

Real Time ◽

Data Analytics ◽

Streaming Data ◽

Time Data ◽

Real Time Data

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A robust join operator to process streaming data in real-time data warehousing

Eighth International Conference on Digital Information Management (ICDIM 2013) ◽

10.1109/icdim.2013.6693964 ◽

2013 ◽

Cited By ~ 2

Author(s):

M. Asif Naeem

Keyword(s):

Real Time ◽

Data Warehousing ◽

Streaming Data ◽

Time Data ◽

Real Time Data

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Optimizing Semi-Stream CACHEJOIN for Near-Real- Time Data Warehousing

Journal of Database Management ◽

10.4018/jdm.2020010102 ◽

2020 ◽

Vol 31 (1) ◽

pp. 20-37 ◽

Cited By ~ 1

Author(s):

M. Asif Naeem ◽

Erum Mehmood ◽

M. G. Abbas Malik ◽

Noreen Jamil

Keyword(s):

Real Time ◽

Data Warehousing ◽

Streaming Data ◽

Service Rate ◽

Stream Data ◽

Time Data ◽

Join Algorithm ◽

Real Time Data ◽

Two Phases ◽

Critical Process

Streaming data join is a critical process in the field of near-real-time data warehousing. For this purpose, an adaptive semi-stream join algorithm called CACHEJOIN (Cache Join) focusing non-uniform stream data is provided in the literature. However, this algorithm cannot exploit the memory and CPU resources optimally and consequently it leaves its service rate suboptimal due to sequential execution of both of its phases, called stream-probing (SP) phase and disk-probing (DP) phase. By integrating the advantages of CACHEJOIN, this article presents two modifications for it. The first is called P-CACHEJOIN (Parallel Cache Join) that enables the parallel processing of two phases in CACHEJOIN. This increases number of joined stream records and therefore improves throughput considerably. The second is called OP-CACHEJOIN (Optimized Parallel Cache Join) that implements a parallel loading of stored data into memory while the DP phase is executing. This research presents the performance analysis of both of the approaches defined within the paper existing CACHEJOIN empirically using synthetic skewed dataset.

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Design and Implementation of Real Time Data Acquisition System in All Programmable System on Chip

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.j9652.0881019 ◽

2019 ◽

Vol 8 (10) ◽

pp. 3680-3684

Keyword(s):

Integrated Circuits ◽

Data Acquisition ◽

Real Time ◽

Semiconductor Industry ◽

Data Acquisition System ◽

Functional System ◽

System On Chip ◽

Acquisition System ◽

Time Data ◽

On Chip

In the recent era, the world has faced drastic improvement in automation industries and there is an need of speedy multi-functional system with higher performance device. The demand for faster, smaller and energy efficient computing systems for miniaturized devices is tremendously increased day by day. Over the past decades, there is a drastic change in semiconductor industry due to its advantages and higher performance of integrated circuits even there is more complexity in design and more expensive. The semiconductor industry started to embrace new design and reuse policies are mutually discussed as system-on-chip (SoC) design. This technology allows to develop a system with higher performance only with required blocks for different applications. In this paper, it is proposed to implement SoC based data acquisition system to measure the atmospheric parameters with different sensors in real time.

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