Reliable Data Collection from Mobile Users for Real-Time Clinical Monitoring

Real-time patient monitoring is critical to early detection of clinical patient deterioration in general hospital wards. A key challenge in such applications is to reliably deliver sensor data from mobile patients. We present an empirical analysis on the reliability of data collection from wireless pulse oximeters attached to users. We observe that most packet loss occur from mobile users to their first-hop relays. Based on this insight we developed the Dynamic Relay Association Protocol (DRAP), a simple and effective mechanism for dynamically discovering the right relays for wireless sensors attached to mobile users. DRAP enables highly reliable data collection from mobile users without requiring any change to complex routing protocols. We have implemented DRAP on the TinyOS platform and a prototype clinical monitoring system. Empirical evaluation showed DRAP delivered at least 96% of pulse oximetry data from multiple users, while maintaining a radio duty cycle below 2.8% and reducing the RAM footprint by 65% when compared to CTP. Our results demonstrates the feasibility and efficacy of wireless sensor network technology for real-time clinical monitoring

Hydrologic data relay by satellite from remote areas

The author has identified the following significant results. Experimental use of LANDSAT data collection system and the GOES system has demonstrated the feasibility of using this technology to relay hydrologic data from remote areas on a near real time basis. The system has proved to be accurate, reliable, and cost effective

The surrounding habitat of marine algae in Malta

Chapter 10The study of algae has been conducted throughout the ages recreating multiple times scientific research with the latest technology and development that renders the results more efficient and reliable. In the Maltese scenario certain advances have not been backed up with the local situation and therefore they lack the real counterpart issue of what in reality we can observe. The Maltese Islands have undergone several infrastructural changes, which in some cases altered the natural setting. The arena of algae in relation to the anthropogenic disturbance being imposed on them has not been scrutinised in depth. Th is study aims to focus on such field in order to have real life analyses of the environment that surrounds us. In order to homogenise an array of features, biotic and abiotic factors have been emphasised on. Chemical tests are part of the lab analysis that consume most of the time since the water samples are very critical to the study. Th e time span required for the data collection and tests to be carried out is one of the issues for such data to create a determinative pattern.peer-reviewe

Continuous and automated data collection in migraine research - Extending the data collection capabilities of the Empatica E4

Migraine is a recurrent headache disorder that afflicts significant portions of the global population. There is no current cure and migraines are mainly managed through symptomatic medical treatments and manual biofeedback routines. Automated data collection and prediction of migraine attacks through machine learning could be viable approaches for helping migraineurs and for reducing the impact of migraines, both on a societal and an individual level. However, machine learning approaches require access to large amounts of high-quality real-time data for facilitating prompt and reliable prediction under everyday conditions and within useful timeframes. The Empatica E4 is an unobtrusive wearable sensor device that can satisfy these data collection needs, although not without flaws and shortcomings. Several studies have reported issues with E4 data collection, most regarding participant involvement and the logistical aspects of the collection process. On top of this, the native systems provided by Empatica for storing, retrieving, and utilizing collected data do not properly facilitate real-time data analysis or machine learning approaches. This project creates a flexible data collection solution based on the E4 for facilitating real-time prediction of migraine attacks. It incorporates features and elements for increasing user involvement and for maximizing the data collection potential of the E4. Additionally, the solution is integrated with the mSpider data storage platform, facilitating reliable and flexible data storage and retrieval options. The prototype system was tested on three potential end-users under everyday conditions over the course of 20 days. After the data collection period, each user attended a semi-structured interview. Testing and interview results show that the data collection capabilities of the prototype system are on-par with other similar systems, it offers stable data collection under everyday conditions, and it can store data in the mSpider system. However, the added features for increasing participant involvement had little discernible effect on the data collection process or the amount of collected data. This was probably caused by the low intensity of the added features or the short duration of the testing period. Additionally, the testing process found that the high technical proficiency requirements and the necessary daily maintenance of the E4 makes it unsuited for continuous migraine treatment purposes, although it is a good tool for migraine research. Future prototype iterations should increase the intensity of the participant involvement features and greatly increase the length of testing periods

High reliability Android application for multidevice multimodal mobile data acquisition and annotation

We have completed the collection of one of the richest accurately annotated mobile dataset of modes of transportation and locomotion. To do this, we developed a highly reliable Android application called DataLogger capable of recording multisensor data from multiple synchronized smartphones simultaneously. The application allows real-time data annotation. We explain how we designed the app to achieve high reliability and ease of use. We also present an evaluation of the application in a big-data collection (750 hours, 950 GB of data, 17 different sensor modalities), analysing the data loss (less than 0.4‰) and battery consumption (≈6% on average per hour). The application is available as open source

A New Real-Time Ocean Observing Station on Ship Shoal on Louisiana Shelf

One of the major challenges that we are facing in the northern Gulf of Mexico coastal area is the need of a better and reliable offshore met-ocean real time data collection system that supports the mission of Bureau of Ocean Energy Management (BOEM) and other federal and local agencies for coastal management, protection, and restoration, especially along the Louisiana coast. This area has a suite of environmental problems that require the acquisition of real time data for immediate assessment or model-based assessment and predictions that rely on this kind of data. One such system providing this kind of data is managed by the Wave-Current-Surge Information System at LSU

A New Real-Time Ocean Observing Station on Ship Shoal on Louisiana Shelf

One of the major challenges that we are facing in the northern Gulf of Mexico coastal area is the need of a better and reliable offshore met-ocean real time data collection system that supports the mission of Bureau of Ocean Energy Management (BOEM) and other federal and local agencies for coastal management, protection, and restoration, especially along the Louisiana coast. This area has a suite of environmental problems that require the acquisition of real time data for immediate assessment or model-based assessment and predictions that rely on this kind of data. One such system providing this kind of data is managed by the Wave-Current-Surge Information System at LSU

Emergency Caching: Coded Caching-based Reliable Map Transmission in Emergency Networks

Many rescue missions demand effective perception and real-time decision making, which highly rely on effective data collection and processing. In this study, we propose a three-layer architecture of emergency caching networks focusing on data collection and reliable transmission, by leveraging efficient perception and edge caching technologies. Based on this architecture, we propose a disaster map collection framework that integrates coded caching technologies. Our framework strategically caches coded fragments of maps across unmanned aerial vehicles (UAVs), fostering collaborative uploading for augmented transmission reliability. Additionally, we establish a comprehensive probability model to assess the effective recovery area of disaster maps. Towards the goal of utility maximization, we propose a deep reinforcement learning (DRL) based algorithm that jointly makes decisions about cooperative UAVs selection, bandwidth allocation and coded caching parameter adjustment, accommodating the real-time map updates in a dynamic disaster situation. Our proposed scheme is more effective than the non-coding caching scheme, as validated by simulation