Efficient Large-scale Medical Data (eHealth Big Data) Analytics in Internet of Things

Plageras, Andreas P.; Stergiou, Christos; Kokkonis, George; Psannis, Kostas E.; Ishibashi, Yutaka; Kim, Byung-Gyu; Gupta, Brij B.

doi:10.1109/cbi.2017.3

Cited by 39 publications

(18 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, they do not handle network attacks like those in RPL. In [ 39 ], authors propose an IoT architecture where collected data from different sensors are processed and analyzed in the cloud. This collection of medical data uses real-time big data analysis.…”

Section: Related Workmentioning

confidence: 99%

Efficient Anomaly Detection for Smart Hospital IoT Systems

Said

Yahyaoui

Abdellatif

2021

Sensors

View full text Add to dashboard Cite

In critical Internet of Things (IoT) application domains, such as the Defense Industry and Healthcare, false alerts have many negative effects, such as fear, disruption of emergency services, and waste of resources. Therefore, an alert must only be sent if triggered by a correct event. Nevertheless, IoT networks are exposed to intrusions, which affects event detection accuracy. In this paper, an Anomaly Detection System (ADS) is proposed in a smart hospital IoT system for detecting events of interest about patients’ health and environment and, at the same time, for network intrusions. Providing a single system for network infrastructure supervision and e-health monitoring has been shown to optimize resources and enforce the system reliability. Consequently, decisions regarding patients’ care and their environments’ adaptation are more accurate. The low latency is ensured, thanks to a deployment on the edge to allow for a processing close to data sources. The proposed ADS is implemented and evaluated while using Contiki Cooja simulator and the e-health event detection is based on a realistic data-set analysis. The results show a high detection accuracy for both e-health related events and IoT network intrusions.

show abstract

Section: Related Workmentioning

confidence: 99%

Efficient Anomaly Detection for Smart Hospital IoT Systems

Said

Yahyaoui

Abdellatif

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…In [63], provide the convergence of IoT, CC, and BGD for e-health application. According to this study, BGD is collected from ultraviolet sensors attached to the human body.…”

Section: Related Workmentioning

confidence: 99%

Role of Emerging IoT Big Data and Cloud Computing for Real Time Application

Humayun¹

2020

IJACSA

View full text Add to dashboard Cite

Although the Internet of things (IoT), cloud computing (CC), and Big Data (BGD) are three different approaches that have evolved independently of each other over time; however, with time, they are becoming increasingly interconnected. The convergence of IoT, CC, and BGD provides new opportunities in various real-time applications, including telecommunication, healthcare, business, education, science, and engineering. Together, these approaches are facing various challenges during data gathering, processing, and management. The focus of this research paper is to pinpoint the emerging trends in IoT, CC, and BGD. The convergence of these approaches and their impact on various real-time applications, benefits, and challenges associated with all these approaches, current industry trends, and future research directions with especial focus on the healthcare domain. The paper also provides a conceptual framework that integrates IoT, CC, and BGD and provides an IoT centric cloud infrastructure using BGD. Finally, this paper summarizes by providing directions for researchers and practitioners about how to leverage the benefits of combining these approaches.

show abstract

“…In [17], the authors present the contributions of both mobile cloud computing (MCC) and the IoT to the technology of big data. Plageras et al [18] performed an analytic study of IoT technology, cloud computing, and large-scale data to resolve various issues facing the health sector. All these studies reveal the capability of cloud computing to satisfy many IoT requirements (e.g., monitoring, sensor stream processing, and visualization tasks).…”

Section: Related Workmentioning

confidence: 99%

“…For CoAP, we used the txThings 18 library, which is a Pythonimplemented CoAP library for the twisted framework. The CoAP image server at the source node transmitted images according to the GET requests made by the client.…”

Section: E: Coapmentioning

confidence: 99%

Choice of Application Layer Protocols for Next Generation Video Surveillance Using Internet of Video Things

Sultana

Wahid

2019

IEEE Access

View full text Add to dashboard Cite

Video surveillance has become ubiquitous due to the increasing security requirements in every sphere of life. The next generation video surveillance system (VSS) possesses great challenges in various applications, such as intelligent urban surveillance systems and smart cities. In these applications, we need to deal with the fast-growing number of surveillance nodes which introduce several constraints, e.g., high latency, high bandwidth, high energy consumption, and CPU and memory usage. To address these issues, the Internet of Video Things (IoVT), which is considered to be a part of the Internet of Things (IoT), can be a solution. The IoVT is composed of visual sensors (i.e., cameras) connected to the Internet. Unlike conventional systems, the VSS under an IoVT framework provides multiple layers (i.e., edge, fog, and cloud) of communication and decision making by capturing and analyzing rich contextual and behavioral information. Since an appropriate application layer protocol (ALP) can help in alleviating the challenges of future VSSs, the selection of ALPs is important for IoVT-based systems. Therefore, this paper presents a generic architecture of an IoVT-based VSS and a comparative analysis of several ALPs, such as MQTT, AMQP, HTTP, XMPP, CoAP, and DDS, with real-time experimentation. This analysis will assist the users to choose the appropriate ALPs in various surveillance applications and determine their suitability at different nodes of the IoVT framework.INDEX TERMS Application layer protocols, the Internet of Video Things (IoVT), video analytics, video surveillance.

show abstract

Efficient Large-scale Medical Data (eHealth Big Data) Analytics in Internet of Things

Cited by 39 publications

References 13 publications

Efficient Anomaly Detection for Smart Hospital IoT Systems

Efficient Anomaly Detection for Smart Hospital IoT Systems

Role of Emerging IoT Big Data and Cloud Computing for Real Time Application

Choice of Application Layer Protocols for Next Generation Video Surveillance Using Internet of Video Things

Contact Info

Product

Resources

About