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Efficient computation of quality of service (QoS) during medical data processing through intelligent measurement methods is one of the mandatory requirements of the medial healthcare world. However, emergency medical services often involve transmission of critical data, thus having stringent requirements for network quality of service (QoS). This p...
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Citations
... On the other hand, in Sodhro et al. [20], the authors contribute in three distinct ways. Firstly, they propose a novel Adaptive QoS Computation Algorithm (AQCA). ...
... Beberapa metriks QoS yang bisa diketahui melalui SpeedTest adalah throughput dan jitter [22]. Throughput adalah bandwidth aktual yang terukur tiap satuan waktu [23]. Throughput jaringan merupakan tingkat rata-rata keberhasilan pengiriman data melalui saluran komunikasi tiap satuan waktu [24]. ...
Daerah pegunungan kecamatan Pegantenan kabupaten Pamekasan, sampai saat ini memiliki jaringan seluler Global System for Mobile (GSM) yang kurang baik. Jaringa tersebut baik di sebagian daerah, dan buruk di daerah yang lain. Hal ini karena kontur tanah di daerah pegunungan yang tinggi dan rendah dan mengitari pegunungan serta ketersediaan Base Tranceiver Station (BTS) yang relatif kurang memadai. Padahal di era digital ini, seluruh warga masyarakat memilliki ketergantungan pada media dan peralatan digital, seperti hand phone, laptop dan sebagainya. Untuk mengatasi masalah itu, penggunaan repeater bisa dijadikan salah satu solusinya. Namun, jangkauan repeater sangat terbatas, dalam pengertian diperlukan teknik untuk meningkatkan dan memaksimalkan performansinya agar dihasilkan jangkauan semaksimal mungkin. Penelitian ini menguji dan mengoptimalisasi kinerja repeater sinyal GSM dengan metode High-Multi Serial agar penggunaan repeater menjadi optimal dan menghasilkan jangkauan lebih luas serta menghasilkan Quality of Service (QoS) sinyal yang lebih baik. Hasil penelitian menunjukkan bahwa ketinggian antena sangat berpengaruh pada jangkauan dan QoS dari sinyal keluaran repeater. Ketinggian optimal dari antena repeater internal yang digunakan adalah sekitar 8 m. Selain itu, penggunaan multi repeater yang disusun secara serial juga dapat meningkatkan jaungkauan maupun QoS.Daerah pegunungan kecamatan Pegantenan kabupaten Pamekasan,sampai saat ini memiliki jaringan seluler Global System for Mobile (GSM) yang kurang baik. Jaringan tersebut baik di sebagian daerah, dan buruk di daerah yang lain. Hal ini karena kontur tanah di daerah pegunungan yang tinggi dan rendah dan mengitari pegunungan serta ketersediaan Base Tranceiver Station (BTS) yang relatif kurang memadai. Padahal di zaman ini, seluruh warga masyarakat memilliki ketergantungan pada media dan peralatan digital, seperti hand phone, laptop dan sebagainya. Penggunaan repeater bisa dijadikan salah satu solusi untuk mengatasi permasalahan tersebut. Namun, jangkauan repeater sangat terbatas, dalam pengertian diperlukan teknik untuk meningkatkan dan memaksimalkan performansinya agar dihasilkan jangkauan semaksimal mungkin. Penelitian ini menguji dan mengoptimalisasi kinerja repeater sinyal GSM dengan metode High-Multi Serial agar penggunaan repeater menjadi optimal dan menghasilkan jangkauan lebih luas serta menghasilkan Quality of Service (QoS) sinyal yang lebih baik. Hasil penelitian menunjukkan bahwa ketinggian antena sangat berpengaruh pada jangkauan dan QoS dari sinyal keluaran repeater. Ketinggian optimal dari antena repeater internal yang digunakan adalah sekitar 8 m dengan jumlah bar rata-rata 3 bar, Signal to Noise Ratio rata-rata 5,25 dB, delay rata-rata 78,63 ms, jitter rata-rata 31,50 ms, throughput rata-rata 793,75 KBps dan packet loss rata-rata 1,38%. Selain itu, penggunaan multi-repeater yang disusun secara serial juga dapat meningkatkan jaungkauan maupun QoS dengan peningkatan kinerja mendekati n kali lipat.
... In addition, authors in [82][83][84][85][86][87][88] presented several interesting and emerging research challenges related to the QoS, medical QoS, and QoE and then proposed various frameworks, methods, and algorithms. The effective and adaptive QoS optimization methods are proposed in [89][90][91][92][93]. The AI-and ML-driven classification and regression methods are the key role players in medical QoS and end-user perception analysis [94][95][96][97][98]. ...
Smart healthcare is altering the delivery of healthcare by combining the benefits of IoT, mobile, and cloud computing. Cloud computing has tremendously helped the health industry connect healthcare facilities, caregivers, and patients for information sharing. The main drivers for implementing effective healthcare systems are low latency and faster response times. Thus, quick responses among healthcare organizations are important in general, but in an emergency, significant latency at different stakeholders might result in disastrous situations. Thus, cutting-edge approaches like edge computing and artificial intelligence (AI) can deal with such problems. A packet cannot be sent from one location to another unless the “quality of service” (QoS) specifications are met. The term QoS refers to how well a service works for users. QoS parameters like throughput, bandwidth, transmission delay, availability, jitter, latency, and packet loss are crucial in this regard. Our focus is on the individual devices present at different levels of the smart healthcare infrastructure and the QoS requirements of the healthcare system as a whole. The contribution of this paper is five-fold: first, a novel pre-SLR method for comprehensive keyword research on subject-related themes for mining pertinent research papers for quality SLR; second, SLR on QoS improvement in smart healthcare apps; third a review of several QoS techniques used in current smart healthcare apps; fourth, the examination of the most important QoS measures in contemporary smart healthcare apps; fifth, offering solutions to the problems encountered in delivering QoS in smart healthcare IoT applications to improve healthcare services.
... The results show that the proposed scheme can reduce data dispersion, thus improving the accuracy and data quality. Sodhro et al. (2020) [15] studied the adaptive process of medical data processing in intelligent medical applications, and proposed a data processing framework in physical layer, media access control layer and network layer by monitoring the performance indicators in the medical data processing process. The results show that the proposed intelligent medical data processing method can improve the quality of network services. ...
... The results show that the proposed scheme can reduce data dispersion, thus improving the accuracy and data quality. Sodhro et al. (2020) [15] studied the adaptive process of medical data processing in intelligent medical applications, and proposed a data processing framework in physical layer, media access control layer and network layer by monitoring the performance indicators in the medical data processing process. The results show that the proposed intelligent medical data processing method can improve the quality of network services. ...
With the rapid development of Internet of Things (IoT) and information technology, there are more and more data storage about graphic signals in digital devices. Therefore, enterprises need to improve the efficiency of graphic signal processing in digital devices. The purpose of this paper is to process the graphic signal data in enterprise digital equipment and improve the efficiency of algorithm task processing in the system. Firstly, the background of informatization process and intelligent data processing is analyzed. Through data information mining and model analysis, a data processing framework is proposed in the physical layer, media access control layer, and network layer. In the process of model processing, through the optimization of cloud computing resource allocation strategy, the optimization and design of Parallel Neural Network Algorithm (PNNA) are put forward, and finally the results of model processing are stored in the management and operation mode library. The results show that the signal reception intensity of all models will increase with the increase of the number of users. When the number of users increases to 250, the signal strength received by the Wireless Local Area Network-1 (WLAN-1) model is -60 dBm, the signal strength received by the WLAN-2 model is -57.5 dBm, and the signal strength received by the PNNA model is -52.5 dBm. Therefore, the proposed intelligent parallel algorithm has a remarkable effect on data signal enhancement. It has important reference value for studying the intelligent processing of graphic signal data.
... The reproducible acquisition of EEG signals is a basic requirement for an effective analysis and for the monitoring of brain activity and abnormalities that are formed [30,31]. Over the years, many methods and techniques have been reported based on the size, frequency and shape of EEG signals and associated signals for the diagnosis of various diseases and other applications [32,33]. ...
Electroencephalography (EEG) is a technique of Electrophysiology used in a wide variety of scientific studies and applications. Inadequately, many commercial devices that are available and used worldwide for EEG monitoring are expensive that costs up to thousands of dollars. Over the past few years, because of advancements in technology, different cost-effective EEG recording devices have been made. One such device is a non-invasive single electrode commercial EEG headset called MindWave 002 (MW2), created by NeuroSky Inc that cost less than 100 USD. This work contributes in four distinct ways, first, how mental states such as a focused and relaxed can be identified based on EEG signals recorded by inexpensive MW2 is demonstrated for accurate information extraction. Second, MW2 is considered because apart from cost, the user's comfort level is enhanced due to non-invasive operation, low power consumption, portable small size, and a minimal number of detecting locations of MW2. Third, 2 situations were created to stimulate focus and relaxation states. Prior to analysis, the acquired brain signals were pre-processed to discard artefacts and noise, and band-pass filtering was performed for delta, theta, alpha, beta, and gamma wave extraction. Fourth, analysis of the shapes and nature of extracted waves was performed with power spectral density (PSD), mean amplitude values, and other parameters in LabVIEW. Finally, with comprehensive experiments, the mean values of the focused and relaxed signal EEG signals were found to be 30.23 µV and 15.330 µV respectively. Similarly, average PSD values showed an increase in theta wave value and a decrease in beta wave value related to the focus and relaxed state, respectively. We also analyzed the involuntary and intentional number of blinks recorded by the MW2 device. Our study can be used to check mental health wellness and could provide psychological treatment effects by training the mind to quickly enter a relaxed state and improve the person's ability to focus. In addition, this study can open new avenues for neurofeedback and brain control applications.
Supplementary information:
The online version contains supplementary material available at 10.1007/s11277-022-09731-w.
... In addition, since the content of data transmitted in MIoT is privacy-sensitive, network security features such as confidentiality, integrity, and availability of medical data are challenging. Moreover, since MIoT is considered a QoS-restricted environment, especially in terms of the Packet Loss Ratio (PLR) and delay [14], at this level, ensuring the medical-related QoS requirement is a demanding issue. ...
The adverse impacts of using conventional batteries in the Internet of Things (IoT) devices, such as cost-effective maintenance, numerous battery replacements, and environmental hazards, have led to an interest in integrating energy harvesting technology into IoT devices to extend their lifetime and sustainably effectively. However, this requires improvements in different IoT protocol stack layers, especially in the MAC layer, due to its high level of energy consumption. These improvements are essential in critical applications such as IoT medical devices. In this paper, we simulated a dense solar-based energy harvesting Wi-Fi network in an e-Health environment, introducing a new algorithm for energy consumption mitigation while maintaining the required Quality of Service (QoS) for e-Health. In compliance with the upcoming Wi-Fi amendment 802.11be, the Access Point (AP) coordination-based optimization technique is proposed, where an AP can request dynamic resource rescheduling along with its nearby APs, to reduce the network energy consumption through adjustments within the standard MAC protocol. This paper shows that the proposed algorithm, alongside using solar energy harvesting technology, increases the energy efficiency by more than 40% while maintaining the e-Health QoS requirements. We believe this research will open new opportunities in IoT energy harvesting integration, especially in QoS-restricted environments.
... In addition, it can overcome the illusion caused by respiratory movement. Research on the evaluation criteria of hepatic fibrosis in CT images using the grey co-occurrence matrix method has clarified the differences in spatial distribution patterns between normal liver tissue and fibrous liver tissue, thus providing a scientific quantitative basis for clinical diagnosis [13,14]. ...
Hepatic fibrosis is the body’s response to chronic liver disorders caused by various causes. Ultrasonic examination using an intelligent algorithm is increasingly important for the diagnosis of hepatic fibrosis. The purpose of this study was to analyse the preliminary application of artificial neural networks (ANNs) combined with ultrasonic quantitative diagnosis of hepatic fibrosis. In this study, 93 patients with hepatic fibrosis in our hospital were enrolled. The ultrasound image data of patients with chronic liver disease and the normal control group were collected, and 10 ultrasound examination indexes were quantified. The liver fibrosis grade of patients with chronic liver disease was confirmed by ultrasound-guided liver biopsy. After statistical analysis of all the ultrasonic data, the meaningful indexes were selected as the input layer, and the pathological grading results of liver fibrosis were selected as the output layer. The results showed that grades 2 and 4 hepatic fibrosis were diagnosed by ANN. The sensitivity, specificity and accuracy of the artificial neural network model were 95.4%, 96.2% and 95.8%, respectively. The preliminary artificial neural network model of ultrasound diagnosis of early liver cirrhosis has high sensitivity and specificity. In this study, the accuracy and specificity of ultrasound diagnosis of hepatic fibrosis were greatly improved. It is concluded that this is very helpful for the treatment of the disease.
... Not only the recorded content is more prominent, but also more important points can be covered internally. In particular, it can effectively avoid errors caused by an incomplete handover and ensure the objectivity and integrity of nursing records afterward [15]. e optimization of real-time nursing mainly includes objective data, nursing observation, nursing measures, and other optimization, objective data streamlined, optimized; nursing observation more diverse, remote video, more humanized. ...
The medical Internet of Things (IoTs) can bind intelligent sensing devices with urinary nursing recipients and integrate information into various hospital information systems through network communication, so as to realize the intelligent perception, data collection, remote monitoring, information sharing, and other functions of urinary real-time nursing recipients. The urinary real-time nursing model can complete the expansion of hospital information system data to the bedside and the instant exchange of terminal data with the system through the medical IoTs and wireless local area network. Based on the summary and analysis of previous research results, this paper expounds on the research status and significance of the urinary real-time nursing model, elaborates on the development background, current status, and future challenges of medical IoTs, introduces the methods and principles of medical IoTs level evaluation system and transformation rules-based optimization algorithm, conducts urinary real-time nursing model design based on medical IoTs, analyzes model hardware design based on medical IoTs, performs model software design based on medical IoTs, proposes the urinary real-time nursing model optimization based on medical IoTs, explores the front-end function optimization of the urinary real-time nursing, implements the system program optimization of the urinary real-time nursing, discusses the hierarchical architecture of the urinary real-time nursing model, and finally carries out the role function analysis of the medical IoTs in the urinary real-time nursing model. The study results show that the urinary real-time nursing model based on medical IoTs can accurately and efficiently identify, manage, and integrate clinical nursing procedures and data such as patients, diagnoses, drugs, and can optimize nursing workflow, strengthen quality control, and improve nursing efficiency and provide patients with more convenient nursing services. The research results of this paper provide a reference for further research on the design and optimization of urinary real-time nursing model based on medical IoTs.
... e presented model is adaptive to a variety of operation modes of quality of service (QoS) and prediction accuracy based on user demands. Sodhro et al. [43] presented an efficient and intelligent monitoring and measurement approach for medical healthcare applications by transmitting critical patient data with good QoS through wireless networks. Alabdulatif et al. [44] discussed the main concept of a smart health IoT surveillance system in real time for cloud medium. ...
Smart health surveillance technology has attracted wide attention between patients and professionals or specialists to provide early detection of critical abnormal situations without the need to be in direct contact with the patient. This paper presents a secure smart monitoring portable multivital signal system based on Internet-of-Things (IoT) technology. The implemented system is designed to measure the key health parameters: heart rate (HR), blood oxygen saturation (SpO2), and body temperature, simultaneously. The captured physiological signals are processed and encrypted using the Advanced Encryption Standard (AES) algorithm before sending them to the cloud. An ESP8266 integrated unit is used for processing, encryption, and providing connectivity to the cloud over Wi-Fi. On the other side, trusted medical organization servers receive and decrypt the measurements and display the values on the monitoring dashboard for the authorized specialists. The proposed system measurements are compared with a number of commercial medical devices. Results demonstrate that the measurements of the proposed system are within the 95% confidence interval. Moreover, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Relative Error (MRE) for the proposed system are calculated as 1.44, 1.12, and 0.012, respectively, for HR, 1.13, 0.92, and 0.009, respectively, for SpO2, and 0.13, 0.11, and 0.003, respectively, for body temperature. These results demonstrate the high accuracy and reliability of the proposed system.
... e presented model is adaptive to a variety of operation modes of quality of service (QoS) and prediction accuracy based on user demands. Sodhro et al. [43] presented an efficient and intelligent monitoring and measurement approach for medical healthcare applications by transmitting critical patient data with good QoS through wireless networks. Alabdulatif et al. [44] discussed the main concept of a smart health IoT surveillance system in real time for cloud medium. ...
