[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Air quality, water pollution, and radiation pollution are major factors that pose genuine challenges in the environment. Suitable monitoring is necessary so that the world can achieve sustainable growth, by maintaining a healthy society. In recent years, the environment monitoring has turned into a smart environment monitoring (SEM) system, with the advances in the internet of things (IoT) and the development of modern sensors. Under this scenario, the present manuscript aims to accomplish a critical review of noteworthy contributions and research studies on SEM, that involve monitoring of air quality, water quality, radiation pollution, and agriculture systems. The review is divided on the basis of the purposes where SEM methods are applied, and then each purpose is further analyzed in terms of the sensors used, machine learning techniques involved, and classification methods used. The detailed analysis follows the extensive review which has suggested major recommendations and impacts of SEM research on the basis of discussion results and research trends analyzed. The authors have critically studied how the advances in sensor technology, IoT and machine learning methods make environment monitoring a truly smart monitoring system. Finally, the framework of robust methods of machine learning; denoising methods and development of suitable standards for wireless sensor networks (WSNs), has been suggested.

https://www.mdpi.com/1424-8220/20/11/3113/pdf

Advances in Smart Environment Monitoring Systems Using IoT and Sensors

https://hal.archives-ouvertes.fr/hal-01715576/document

A survey on health monitoring systems for health smart homes

The introduction of the Information and Communication Technologies throughout the last decades has created a trend of providing daily objects with smartness, aiming to make human life more comforta...

https://journals.sagepub.com/doi/pdf/10.1177/1550147719853984

Smart cities survey: Technologies, application domains and challenges for the cities of the future:

The Internet of Things has the potential of transforming health systems through the collection and analysis of patient physiological data via wearable devices and sensor networks. Such systems can offer assisted living services in real-time and offer a range of multimedia-based health services. However, service downtime, particularly in the case of emergencies, can lead to adverse outcomes and in the worst case, death. In this paper, we propose an e-health monitoring architecture based on sensors that relies on cloud and fog infrastructures to handle and store patient data. Furthermore, we propose stochastic models to analyze availability and performance of such systems including models to understand how failures across the Cloud-to-Thing continuum impact on e-health system availability and to identify potential bottlenecks. To feed our models with real data, we design and build a prototype and execute performance experiments. Our results identify that the sensors and fog devices are the components that have the most significant impact on the availability of the e-health monitoring system, as a whole, in the scenarios analyzed. Our findings suggest that in order to identify the best architecture to host the e-health monitoring system, there is a trade-off between performance and delays that must be resolved.

/pdf/analyzing-the-availability-and-performance-of-an-e-health-2n8c48p3wt.pdf

Analyzing the availability and performance of an e-health system integrated with edge, fog and cloud infrastructures

https://hal.archives-ouvertes.fr/hal-01646731/document

Adaptive monitoring system for e-health smart homes

For improving e-health services, we propose a context-aware framework to monitor the activities of daily living of dependent persons. We define a strategy for generating long-term realistic scenarios and a framework containing an adaptive monitoring algorithm based on three approaches for optimizing resource usage. The used approaches provide a deep knowledge about the person's context by considering: the person's profile, the activities and the relationships between activities. We evaluate the performances of our framework and show its adaptability and significant reduction in network, energy and processing usage over a traditional monitoring implementation.

Context-Aware Adaptive Framework for e-Health Monitoring

This work focuses on the evaluation of the need and help required by elderly and dependent persons. We study the most widely used evaluation models in the health domain. We propose a new algorithm to assess and compare the performance of these models and the possibility to use them in future eHealth systems and platforms. The objective is to shed some light on the weakness of existing models and to gain a better knowledge about the context of persons in order to provide them with eHealth services that match their context and meet their needs.

/pdf/analysis-of-dependency-evaluation-models-for-ehealth-2dudlvqzq2.pdf

Analysis of dependency evaluation models for eHealth services

https://hal.archives-ouvertes.fr/hal-01281875/document

Haider Mshali

Papers

A survey on health monitoring systems for health smart homes

Adaptive monitoring system for e-health smart homes

Context-Aware Adaptive Framework for e-Health Monitoring

Analysis of dependency evaluation models for eHealth services

Context-Aware Adaptive Framework for e-Health Monitoring