Stream Processing of Healthcare Sensor Data: Studying User Traces to Identify Challenges from a Big Data Perspective

Cortés, Rudyar; Bonnaire, Xavier; Marin, Olivier; Sens, Pierre

doi:10.1016/j.procs.2015.05.093

Cited by 134 publications

(45 citation statements)

References 8 publications

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“…A recent analysis [18] of Endomondo application, a popular sport activity tracking application has revealed number of remarkable observations. The study shows that a single workout generates 170 GPS tuples, and the total number of GPS tuples can reach 6.3 million in a month time.…”

Section: Motivation Scenariomentioning

confidence: 99%

“…Authentication solutions based on Public Key Infrastructure [16] may prove beneficial for this problem. Trusted execution environment (TEE) techniques [17,18] are potential solutions to this authentication problem in fog computing as well. Measurement-based methods may also be used to detect rogue devices and hence reduce authentication cost [18,19].…”

Section: Security and Reliabilitymentioning

confidence: 99%

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Fog Computing: principles, architectures, and applications

et al. 2016

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Abstract-The Internet of Everything (IoE) solutions gradually bring every object online, and processing data in centralized cloud does not scale to requirements of such environment. This is because, there are applications such as health monitoring and emergency response that require low latency and delay caused by transferring data to the cloud and then back to the application can seriously impact the performance. To this end, Fog computing has emerged, where cloud computing is extended to the edge of the network to decrease the latency and network congestion. Fog computing is a paradigm for managing a highly distributed and possibly virtualized environment that provides compute and network services between sensors and cloud data centers. This chapter provides background and motivations on emergence of Fog computing and defines its key characteristics. In addition, a reference architecture for Fog computing is presented and recent related development and applications are discussed.

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Section: Motivation Scenariomentioning

confidence: 99%

Section: Security and Reliabilitymentioning

confidence: 99%

Fog Computing: principles, architectures, and applications

et al. 2016

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“…With the growth of IoT-based systems, a rapid increase in the number of connected devices has led to a massive volume of data that needs to be processed [16,17]. Cloud computing has, thus far, provided scalable and on-demand storage and processing resources to fulfill the requirement of IoT.…”

Section: Fog Computing and Its Benefitsmentioning

confidence: 99%

Optimizing Energy Efficiency of Wearable Sensors Using Fog‐assisted Control

et al. 2020

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Recent advances in the Internet of Things (IoT) technologies have enabled the use of wearables for remote patient monitoring. Wearable sensors capture the patient's vital signs, and provide alerts or diagnosis based on the collected data. Unfortunately, wearables typically have limited energy and computational capacity, making their use challenging for healthcare applications where monitoring must continue uninterrupted long time, without the need to charge or change the battery. Fog computing can alleviate this problem by offloading computationally intensive tasks from the sensor layer to higher layers, thereby not only meeting the sensors' limited computational capacity but also enabling the use of local closed-loop energy optimization algorithms to increase the battery life. Furthermore, the patient's contextual information -including health and activity status -can be exploited to guide energy optimization algorithms more effectively. By incorporating the patient's contextual information, a desired quality of experience can be achieved by creating a dynamic balance between energy-efficiency and measurement accuracy. We present a run-time distributed control-based solution to find the most energy-efficient system state for a given context while keeping the accuracy of decision making process over a certain threshold. Our optimization algorithm resides in the Fog layer to avoid imposing computational overheads to the sensor layer. Our solution can be extended to reduce the probability of errors in the data collection process to ensure the accuracy of the results. The implementation of our fog-assisted control solution on a remote monitoring system shows a significant improvement in energy-efficiency with a bounded loss in accuracy.

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“…The challenge of deriving insights from big data streams is recognized as a key challenge. Various authors proposed various algorithms and techniques for mining big data streams . The proposed algorithms illustrated various techniques to collect, integrate, analyze, and visualize big data streams in real time.…”

Section: Preliminariesmentioning

confidence: 99%

“…Various authors proposed various algorithms and techniques for mining big data streams. 13,14,[33][34][35][36][37][38] The proposed algorithms illustrated various techniques to collect, integrate, analyze, and visualize big data streams in real time. Nevertheless, less attention has been paid to resource scheduling for big data streams.…”

Section: Related Workmentioning

confidence: 99%

Dynamic resource allocation for big data streams based on data characteristics (5Vs)

Kaur

Sood

2017

Int J Network Mgmt

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Summary Various Internet‐based applications such as social media, business transactions, mobile applications, cyber‐physical systems, and Internet of Things have led to the generation of big data streams in every field. The growing need to extract knowledge from big data streams has pioneered the challenge of selecting appropriate cloud resources. The current techniques allocate resources based on data characteristics. But because of the stochastic nature of data generation, the characteristics of data in big data streams are unknown. This poses difficulty in selecting and allocating appropriate resources to big data stream. Working towards this direction, this paper proposes a system that predicts the data characteristics in terms of volume, velocity, variety, variability, and veracity. The predicted values are expressed in a quadruple called Characteristics of Big data (CoBa). Thereafter, the proposed system uses self‐organizing maps to dynamically create clusters of cloud resources. One of these clusters is allocated to the big data stream based on its CoBa. The proposed system is dynamic in the sense that it changes the cloud cluster allocated to big data stream if its CoBa changes. Experimental results show that the proposed system has a performance edge over other streaming data processing tools such as Storm, Flume, and S4.

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Stream Processing of Healthcare Sensor Data: Studying User Traces to Identify Challenges from a Big Data Perspective

Cited by 134 publications

References 8 publications

Fog Computing: principles, architectures, and applications

Fog Computing: principles, architectures, and applications

Optimizing Energy Efficiency of Wearable Sensors Using Fog‐assisted Control

Dynamic resource allocation for big data streams based on data characteristics (5Vs)

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