Efficient Real-Time Decision Making Using Streaming Data Analytics in IoT Environment

2018 ◽  
pp. 165-173 ◽  
Author(s):  
S. Valliappan ◽  
P. Bagavathi Sivakumar ◽  
V. Ananthanarayanan
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Data Analytics ◽  
Streaming Data

scholarly journals Fossel: Efficient Latency Reduction in Approximating Streaming Sensor Data

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.

scholarly journals Data-driven decision support under concept drift in streamed big data

2019 ◽  
Vol 6 (1) ◽  
pp. 157-163 ◽  
Author(s):  
Jie Lu ◽  
Anjin Liu ◽  
Yiliao Song ◽  
Guangquan Zhang
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Real Time ◽  
Concept Drift ◽  
High Volume ◽  
Streaming Data ◽  
Data Driven ◽  
Research Directions ◽  
Decision Outcomes ◽  
Past Data

Abstract Data-driven decision-making ($$\mathrm {D^3}$$D3M) is often confronted by the problem of uncertainty or unknown dynamics in streaming data. To provide real-time accurate decision solutions, the systems have to promptly address changes in data distribution in streaming data—a phenomenon known as concept drift. Past data patterns may not be relevant to new data when a data stream experiences significant drift, thus to continue using models based on past data will lead to poor prediction and poor decision outcomes. This position paper discusses the basic framework and prevailing techniques in streaming type big data and concept drift for $$\mathrm {D^3}$$D3M. The study first establishes a technical framework for real-time $$\mathrm {D^3}$$D3M under concept drift and details the characteristics of high-volume streaming data. The main methodologies and approaches for detecting concept drift and supporting $$\mathrm {D^3}$$D3M are highlighted and presented. Lastly, further research directions, related methods and procedures for using streaming data to support decision-making in concept drift environments are identified. We hope the observations in this paper could support researchers and professionals to better understand the fundamentals and research directions of $$\mathrm {D^3}$$D3M in streamed big data environments.

Expounding the Edge/Fog Computing Infrastructures for Data Science

2018 ◽  
pp. 1-32 ◽  
Author(s):  
Pethuru Raj ◽  
Pushpa J.
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Analytical Methods ◽  
Data Analytics ◽  
Data Science ◽  
Storage Systems ◽  
Fog Computing ◽  
Next Generation ◽  
Distributed Source ◽  
Real Time Applications

Data is the new fuel for any system to deliver smart and sophisticated services. Data is being touted as the strategic asset for any organization to plan ahead and provide next-generation capabilities with all the clarity and confidence. Whether data is internally sourced or aggregated from different and distributed source, it is essential for all kinds of data to be continuously and consciously collected, transmitted, cleansed, and hosted on storage systems. There are several types of analytical methods and machines to do deeper and decisive analytics on those curated and consolidated data to extract actionable insights in real-time. Precise and concise analytics guarantee perfect decision-making and action. We need competent and highly integrated analytics platform for speeding up, simplifying and streamlining data analytics, which is becoming a hard nut to crack due to the multi-structured and massive quantities of data. On the infrastructure front, we need highly optimized compute, storage and network infrastructure for achieving data analytics with ease. Another noteworthy point is that there are batch, real-time, and interactive processing of data. Most of the personal and professional applications need real-time insights in order to produce real-time applications. That is, real-time capture, processing, and decision-making are being insisted and hence the edge or fog computing concept has become very popular. This chapter is exclusively designed in order to tell all on how to accomplish real-time analytics on fog devices data.

scholarly journals A Real-Time Patient Monitoring Framework for Fall Detection

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Dharmitha Ajerla ◽  
Sazia Mahfuz ◽  
Farhana Zulkernine
Keyword(s):  
Real Time ◽  
Data Analytics ◽  
Short Term Memory ◽  
Detection System ◽  
Fall Detection ◽  
Edge Computing ◽  
Streaming Data ◽  
Real Time Analysis ◽  
Long Short Term Memory ◽  
Lstm Network

Fall detection is a major problem in the healthcare department. Elderly people are more prone to fall than others. There are more than 50% of injury-related hospitalizations in people aged over 65. Commercial fall detection devices are expensive and charge a monthly fee for their services. A more affordable and adaptable system is necessary for retirement homes and clinics to build a smart city powered by IoT and artificial intelligence. An effective fall detection system would detect a fall and send an alarm to the appropriate authorities. We propose a framework that uses edge computing where instead of sending data to the cloud, wearable devices send data to a nearby edge device like a laptop or mobile device for real-time analysis. We use cheap wearable sensor devices from MbientLab, an open source streaming engine called Apache Flink for streaming data analytics, and a long short-term memory (LSTM) network model for fall classification. The model is trained using a published dataset called “MobiAct.” Using the trained model, we analyse optimal sampling rates, sensor placement, and multistream data correction. Our edge computing framework can perform real-time streaming data analytics to detect falls with an accuracy of 95.8%.

HCache: A Hash-based Hybrid Caching Model for Real-Time Streaming Data Analytics

2018 ◽  
pp. 1-1
Author(s):  
Feng Zhao ◽  
Shao Feng Li ◽  
Bing B. Zhou ◽  
Hai Jin ◽  
Laurence T. Yang
Keyword(s):  
Real Time ◽  
Data Analytics ◽  
Streaming Data

Fog-Cloud Collaboration for Real-Time Streaming Applications

2019 ◽  
pp. 128-147
Author(s):  
Biji Nair ◽  
S. Mary Saira Bhanu
Keyword(s):  
Real Time ◽  
Data Analytics ◽  
High Speed ◽  
Fog Computing ◽  
Data Representation ◽  
Streaming Data ◽  
Cloud Data ◽  
Streaming Applications ◽  
Stream Management

Real-time streaming applications (RTSAs) generate huge volumes of temporally ordered, infinite, continuous, high speed data streams demanding both real-time and long-term data analytics. Fog computing is a reliable solution for processing and analyzing real-time streaming data as it offers low latency, location-aware, geographically distributed service at fog node and provides long-term services at the cloud data center (DC). This chapter addresses the challenge of coordinating the fog nodes and cloud for efficient processing of real-time streaming data in motion and at rest. The fog-cloud collaboration framework proposed in this chapter employs data stream management system (DSMS) schema at the fog node for real-time stream data processing and response generation. The data representation in micro-clusters at fog node and macro-clusters at DC facilitates accurate data analytics. The coordination between fog node and DC is through local ontology and global ontology respectively.

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