ABSTRACT
Mobile computing is one of the significant opportunities that can be used for various practical applications in numerous fields in real life. Due to inherent characteristics of ubiquitous computing, devices can gather numerous types of data that led to innovative applications in many fields with a unique emerging prototype known as Crowd sensing. Here, the involvement of people is one of the important features and their mobility provides an exclusive opportunity to collect and transmit the data over a substantial geographical area. Thus, we put forward novel idea about Quality of Information (QOI) with unique parameters with opportunistic uniqueness of people's mobility in terms of sensing and transmission. Additionally, we propose some of the viable improved ideas about the competent opportunistic data collection through efficient techniques. This work also considered some of the open issues mentioned by previous related works.
ABSTRACT
The Internet of Things (IoT) is considered an effective wireless communication, where the main challenge is to manage energy efficiency, especially in cognitive networks. The data communication protocol is a broadly used approach in a wireless network based IoT. Cognitive Radio (CR) networks are mainly concentrated on battery-powered devices for highly utilizing the data regarding the spectrum and routing allocation, dynamic spectrum access, and spectrum sharing. Data aggregation and clustering are the best solutions for enhancing the energy efficiency of the network. Most researchers have focused on solving the problems related to Cognitive Radio Sensor Networks (CRSNs) in terms of Spectrum allocation, Quality of Service (QoS) optimization, delay reduction, and so on. However, a very small amount of research work has focused on energy restriction problems by using the switching and channel sensing mechanism. As this energy validation is highly challenging due to dependencies on various factors like scheduling priority to the registered users, the data loss rate of unlicensed channels, and the possibilities of accessing licensed channels. Many IoT-based models involve energy-constrained devices and data aggregation along with certain optimization approaches for improving utilization. In this paper, the cognitive radio framework is developed for medical data transmission over the Internet of Medical Things (IoMT) network. The energy-efficient cluster-based data transmission is done through cluster head selection using the hybrid optimization algorithm named Spreading Rate-based Coronavirus Herding-Grey Wolf Optimization (SR-CHGWO). The network lifetime is improved with a cognitive- routing based on IoT framework to enhance the efficiency of the data transmission through the multi-objective function. This multi-objective function is derived using constraints like energy, throughput, data rate, node power, and outage probability delay of the proposed framework. The simulation experiments show that the developed framework enhances the energy efficiency using the proposed algorithm when compared to the conventional techniques. © 2022 the Author(s)
ABSTRACT
BACKGROUND: Olfactory dysfunction is a cardinal symptom of COVID-19 infection, however, studies assessing long-term olfactory dysfunction are limited and no randomised-controlled trials (RCTs) of early olfactory training have been conducted. METHODOLOGY: We conducted a prospective, multi-centre study consisting of baseline psychophysical measurements of smell and taste function. Eligible participants were further recruited into a 12-week RCT of olfactory training versus control (safety information). Patient-reported outcomes were measured using an electronic survey and BSIT at baseline and 12 weeks. An additional 1-year follow-up was open to all participants. RESULTS: 218 individuals with a sudden loss of sense of smell of at least 4-weeks were recruited. Psychophysical smell loss was observed in only 32.1%; 63 participants were recruited into the RCT. The absolute difference in BSIT improvement after 12 weeks was 0.45 higher in the intervention arm. 76 participants completed 1-year follow-up; 10/19 (52.6%) of participants with an abnormal baseline BSIT test scored below the normal threshold at 1-year, and 24/29 (82.8%) had persistent parosmia. CONCLUSIONS: Early olfactory training may be helpful, although our findings are inconclusive. Notably, a number of individuals who completed the 1-year assessment had persistent smell loss and parosmia at 1-year. As such, both should be considered important entities of long-Covid and further studies to improve management are highly warranted.
ABSTRACT
INTRODUCTION: It is clear that a proportion of patients continue to suffer long-lasting symptoms following acute infection with coronavirus disease 2019 (COVID-19). Persistent olfactory dysfunction is one of the commonest complaints reported in the condition colloquially known as long COVID (now known as post-acute sequelae of SARS-CoV-2 infection (PASC)). The prevalence, risk factors and clinical course of long COVID olfactory dysfunction are not yet well understood. At present, the main stay of treatment is olfactory training. Quantitative olfactory testing and impacts on patient quality of life have not been widely studied. This study describes our experiences at Wrightington, Wigan and Leigh Teaching Hospitals, UK (WWL) of establishing a COVID-19 smell clinic, along with preliminary data on patient demographics, baseline smell test scores and quality of life questionnaire scores before olfactory training. METHODS: We piloted a COVID-19 smell clinic. We recorded patient demographics and clinical characteristics then performed clinical assessment of each patient. Quantitative measurements of olfactory dysfunction were recorded using the University of Pennsylvania Smell Identification Test (UPSIT). We measured the impact of olfactory dysfunction on patient quality of life using the validated English Olfactory Disorders Questionnaire (eODQ). RESULTS: 20 patients participated in the clinic. 4 patients were excluded from analysis due to missing data. Median age was 35 years. 81% (n=13) of the participants were female. 50% (n=8) of patients suffered with a combination of anosmia/ageusia and parosmia, whilst 43% (n=7) of patients suffered with anosmia/ageusia without parosmia. Almost all the patients registered UPSIT scores in keeping with impaired olfaction. Patient scores ranged from 22 to 35, with the median score at 30. All patients reported that their olfactory dysfunction had an impact on their quality of life. The median eODQ score reported was 90, with scores ranging from 42 to 169 out of a maximum of 180. CONCLUSION: We have demonstrated that it is simple and feasible to set up a COVID-19 smell clinic. The materials are inexpensive, but supervised completion of the UPSIT and eODQ is time-consuming. Patients demonstrate reduced olfaction on quantitative testing and experience significant impacts on their quality of life as a result. More research is needed to demonstrate if olfactory training results in measurable improvements in smell test scores and quality of life.