Towards a Decentralized Algorithm for Mapping Network and Computational Resources for Distributed Data-Flow Computations

This research is done for optimizing telemedicine framework by using fogging or fog computing for smart healthcare systems. Fog computing is used to solve the issues that arise on telemedicine framework of smart healthcare system like Infrastructural, Implementation, Acceptance, Data Management, Security, Bottleneck system organization, and Network latency Issues. we mainly used Distributed Data Flow (DDF) method using fog computing in order to fully solve the listed issues.

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Low-Power Distributed Data Flow Anomaly-Monitoring Technology for Industrial Internet of Things

Sensors ◽

10.3390/s19122804 ◽

2019 ◽

Vol 19 (12) ◽

pp. 2804 ◽

Cited By ~ 3

Author(s):

Han ◽

Tian ◽

Shi ◽

Huang ◽

Li

Keyword(s):

Internet Of Things ◽

Low Power ◽

Monitoring System ◽

Data Flow ◽

Real Data ◽

Communication Overhead ◽

Distributed Data ◽

Flow Monitoring ◽

Industrial Use ◽

The Relationship

. In recent years, the industrial use of the internet of things (IoT) has been constantly growing and is now widespread. Wireless sensor networks (WSNs) are a fundamental technology that has enabled such prevalent adoption of IoT in industry. WSNs can connect IoT sensors and monitor the working conditions of such sensors and of the overall environment, as well as detect unexpected system events in a timely and accurate manner. Monitoring large amounts of unstructured data generated by IoT devices and collected by the big-data analytics systems is a challenging task. Furthermore, detecting anomalies within the vast amount of data collected in real time by a centralized monitoring system is an even bigger challenge. In the context of the industrial use of the IoT, solutions for monitoring anomalies in distributed data flow need to be explored. In this paper, a low-power distributed data flow anomaly-monitoring model (LP-DDAM) is proposed to mitigate the communication overhead problem. As the data flow monitoring system is only interested in anomalies, which are rare, and the relationship among objects in terms of the size of their attribute values remains stable within any specific period of time, LP-DDAM integrates multiple objects as a complete set for processing, makes full use of the relationship among the objects, selects only one “representative” object for continuous monitoring, establishes certain constraints to ensure correctness, and reduces communication overheads by maintaining the overheads of constraints in exchange for a reduction in the number of monitored objects. Experiments on real data sets show that LP-DDAM can reduce communication overheads by approximately 70% when compared to an equivalent method that continuously monitors all objects under the same conditions.

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A Framework for Building Distributed Data Flow Chains in Clusters

Lecture Notes in Computer Science - Applied Parallel Computing ◽

10.1007/3-540-48051-x_45 ◽

2002 ◽

pp. 454-464 ◽

Cited By ~ 2

Author(s):

Timm M. Steinbeck ◽

Volker Lindenstruth ◽

Dieter Röhrich ◽

Anders Strand Vestbo ◽

Arne Wiebalck

Keyword(s):

Data Flow ◽

Distributed Data

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Continuous Join Query Processing over Structured Overlay Networks

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.467-469.859 ◽

2011 ◽

Vol 467-469 ◽

pp. 859-861

Author(s):

Yang Liu ◽

Ke Jun Zhao ◽

Yi Hong Qiu ◽

Qi Liu

Keyword(s):

Query Processing ◽

Network Traffic ◽

Overlay Networks ◽

Data Flow ◽

P2p Network ◽

Distributed Data ◽

Structured Overlay ◽

Structured Overlay Networks ◽

Global Query ◽

P2p Overlay

P2P network is more suitable for distributed data flow processing. This paper concentrates on how to answer continuous join query in structured p2p overlay networks. In the algorithm proposed, the data that cannot contribute the queries’ results will not be distributed in the network based on global query indices. Experiment shows that the algorithm ensures the availability of join query and network traffic is reduced.

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