Reliability-Centric Analysis of Offloaded Computation in Cooperative Wearable Applications

Ometov, Aleksandr; Kozyrev, Dmitry; Rykov, Vladimir; Andreev, Sergey; Gaidamaka, Yuliya; Koucheryavy, Yevgeni

doi:10.1155/2017/9625687

Cited by 21 publications

(8 citation statements)

References 52 publications

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“…For instance, multiple wearable sensor nodes collaborate to monitor an individual's overall health. Whereas, WIoT devices are often standalone ones currently being utilized for a wide range of applications, including health monitoring, observing human activities through localization and tracking, and various gaming and entertainment gadgets [8], [9]. Moreover, wearables also assist consumers in carrying out their everyday tasks more conveniently and efficiently employing visual and auditory stimuli [10], e.g., responding to incoming calls and messages, being notified of weather updates, and visualizing the timely vital information and many others [11]- [13].…”

Section: Figure 1: Forthcoming Wearable Integration Scenariomentioning

confidence: 99%

Towards Energy Efficiency in the Internet of Wearable Things: A Systematic Review

et al. 2020

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Personal mobile devices such as smartwatches, smart jewelry, and smart clothes have launched a new trend in the Internet of Things (IoT) era, namely the Internet of Wearable Things (IoWT). These wearables are small IoT devices capable of sensing, storing, processing, and exchanging data to assist users by improving their everyday life tasks through various applications. However, the IoWT has also brought new challenges for the research community to address such as increasing demand for enhanced computational power, better communication capabilities, improved security and privacy features, reduced form factor, minimal weight, and better comfort. Most wearables are battery-powered devices that need to be recharged-therefore, the limited battery life remains the bottleneck leading to the need to enhance the energy efficiency of wearables, thus, becoming an active research area. This paper presents a survey of energy-efficient solutions proposed for diverse IoWT applications by following the systematic literature review method. The available techniques published from 2010 to 2020 are scrutinized, and the taxonomy of the available solutions is presented based on the targeted application area. Moreover, a comprehensive qualitative analysis compares the proposed studies in each application area in terms of their advantages, disadvantages, and main contributions. Furthermore, a list of the most significant performance parameters is provided. A more in-depth discussion of the main techniques to enhance wearables' energy efficiency is presented by highlighting the trade-offs involved. Finally, some potential future research directions are highlighted.

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Section: Figure 1: Forthcoming Wearable Integration Scenariomentioning

confidence: 99%

Towards Energy Efficiency in the Internet of Wearable Things: A Systematic Review

et al. 2020

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“…However, the feasibility of such approaches in the context of a contact tracing solution is yet to be investigated. Generally, the user devices are equipped with short-range wireless communication technology, e.g., Bluetooth Low Energy (BLE) or IEEE 802.11 (a.k.a., WiFi) [12], [13]. Commonly, the user equipment transmits the beacons with timestamps along with the anonymized user IDs, thus, other users in the range of the emitter can 'hear' and store these anonymized IDs and timestamps of neighboring nodes on their device.…”

Section: Contact-tracing Applicationmentioning

confidence: 99%

Technical Perspectives of Contact-Tracing Applications on Wearables for COVID-19 Control

Shubina

Ometov

Lohan

2020

2020 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

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The wearables' market is rapidly evolving, with applications ranging from healthcare and activity monitoring to emerging domains such as drones and haptic helmets. Wearablebased contact tracing is gaining increased attention in the COVID-19 era for more efficient disease prevention. Therefore, it is of timely relevance to identify the leading existing wireless contact-tracing solutions and their suitability for wearables. Existing trade-offs of contact-tracing applications require a thorough analysis of technical capabilities, such as accuracy, energy consumption, availability, sources of errors when dealing with wireless channels, privacy challenges, and deterrents towards a large-scale adoption on the wearables market. Based on extensive literature research, we conclude that decentralized architectures generally offer a better place in a trade-off in terms of accuracy and user eagerness to adopt them, taking into account privacy considerations, compared to centralized approaches. Our paper provides a brief technical overview of the existing solutions deployed for contact tracing, defines main principles that affect the overall efficacy of digital contact tracing, and presents a discussion on the potential effect of wearables in tackling the spread of a highly contagious virus.

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“…Data are then processed by applications of the User Terminals (UT). UTs may be connected with the gateway via short-range communication protocols, e.g., Bluetooth Low Energy [ 26 , 27 ]. The gateway is connected with the cloud or medical service, where the collected data are stored or processed.…”

Section: Underlaying Conceptsmentioning

confidence: 99%

Decision Support Algorithm Based on the Concentrations of Air Pollutants Visualization

Svertoka

Bălănescu²,

Suciu³

et al. 2020

Sensors

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As medical technologies are continuously evolving, consumer involvement in health is also increasing significantly. The integration of the Internet of Things (IoT) concept in the health domain may improve the quality of healthcare through the use of wearable sensors and the acquisition of vital and environmental parameters. Currently, there is significant progress in developing new approaches to provide medical care and maintain the safety of the life of the population remotely and around the clock. Despite the standards for emissions of harmful substances into the atmosphere established by the legislation of different countries, the level of pollutants in the air often exceeds the permissible limits, which is a danger not only for the population but also for the environment as a whole. To control the situation an Air Quality Index (AQI) was introduced. For today, many works discuss AQI, however, most of them are aimed rather at studying the methodologies for calculating the index and comparing air quality in certain regions of different countries, rather than creating a system that will not only calculate the index in real-time but also make it publicly available and understandable to the population. Therefore we would like to present a decision support algorithm for a solution called “Environmental Sensing to Act for a Better Quality of Life: Smart Health” with the primary goal of ensuring the transformation of raw environmental data collected by special sensors (data which typically require scientific interpretation) into a form that can be easily understood by the average user; this is achieved through the proposed algorithm. The obtained result is a system that increases the self-awareness and self-adaptability of people in environmental monitoring by offering easy to read and understand suggestions. The algorithm considers three types of parameters (concentration of PM10 (particulate matter), PM2.5, and NO2) and four risk levels for each of them. The technical implementation is presented in a step-like procedure and includes all the details (such as calculating the Air Quality Index—AQI, for each parameter). The results are presented in a front-end where the average user can observe the results of the measurements and the suggestions for decision support. This paper presents a supporting decision algorithm, highlights the basic concept that was used in the development process, and discusses the result of the implementation of the proposed solution.

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Reliability-Centric Analysis of Offloaded Computation in Cooperative Wearable Applications

Cited by 21 publications

References 52 publications

Towards Energy Efficiency in the Internet of Wearable Things: A Systematic Review

Towards Energy Efficiency in the Internet of Wearable Things: A Systematic Review

Technical Perspectives of Contact-Tracing Applications on Wearables for COVID-19 Control

Decision Support Algorithm Based on the Concentrations of Air Pollutants Visualization

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