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In recent years, with the constant development and integration of Internet technology, Internet of Things technology, and intelligent terminal technology, to make people’s work and life more comfortable and convenient, these new technologies have been more and more widely used in daily home life such as social education, agricultural production, in...
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... The work of Tianheng Wang, Shuo Wang M ORE and more Internet of Things (IoT) devices are widely deployed to enable smart transportation, home, and medical applications [1]- [3]. The ultra-reliable low latency communications (URLLC) in 5G networks serve as an enabler for meeting the emerging latency-sensitive service requirements. ...
In this paper, we focus on buffer-aided wireless powered Internet of Things (IoTs) comprising of one wireless access point (AP) and multiple devices, where the AP provides energy to all devices via downlink radio frequency (RF) energy beams. All devices utilize the harvested energy to transmit their data to the AP in a time-division multiple access (TDMA) manner. Every device is assumed to be provisioned with energy storage and data buffer to store the collected energy from the AP and its data, respectively. The problem of minimizing the long-term average age of information (AoI) of the system is formulated in this paper. By solving the problem under the Lyapunov optimization framework, the AoI-aware adaptive transmission scheme is obtained, in which downlink RF energy beamforming, downlink energy transfer and uplink access, as well as transmit power and transmission rate by every device, will be jointly adjusted in order to minimize average weightede AoI according to the underlying channel state information (CSI), the buffer state information (BSI), the energy-consumption status information (ESI) of all terminals, as well as the AoI status information (ASI). Our analysis unveils that, the status update rate at devices has a significant impact on the achievable AoI performance, and the minimum average weighted AoI can only be realized at a reasonable status update rate, which is neither too high nor too low. Moreover, flexible AoI-aware scheme can be realized by adjusting either the AoI priority level or the AoI weighting coefficient.
... As such, [16] used the Narrow-band Internet of Things (NB-IoT) for reducing power consumption. The NB-IoT is suitable for patient healthcare monitoring, particularly remote observations and outdoor emergencies, because it was designed to strongly cover and support a large number of devices with low cost and low power communication [7,8,18]. The NB-IoT supports single-sensor nodes and multiple-sensor nodes [19,20], while the number of nodes communicating with a single base station can reach 50K [8]. ...
... The NB-IoT is suitable for patient healthcare monitoring, particularly remote observations and outdoor emergencies, because it was designed to strongly cover and support a large number of devices with low cost and low power communication [7,8,18]. The NB-IoT supports single-sensor nodes and multiple-sensor nodes [19,20], while the number of nodes communicating with a single base station can reach 50K [8]. However, the performance of NB-IoT in healthcare is affected by the following problems that limit the IoMT performance [2,4,6]: ...
... These devices are classified into single-sensor nodes where each sensor is considered as an individual terminal, e.g., temperature sensors, communicating with the base station [5], or multiplesensor nodes where sensors communicate with the base station through a gateway [6]. Although a large number of terminal devices supports efficient health monitoring, the devices generate a large amount of data that cause congestion, communication overload, and slow computations [2,7,8]. This also surges transmission delay because the delay increases when data size or packet size increases [4,9]. ...
This paper proposes a three-computing-layer architecture consisting of Edge, Fog, and Cloud for remote health vital signs monitoring. The novelty of this architecture is in using the Narrow-Band IoT (NB-IoT) for communicating with a large number of devices and covering large areas with minimum power consumption. Additionally, the architecture reduces the communication delay as the edge layer serves the health terminal devices with initial decisions and prioritizes data transmission for minimizing congestion on base stations. The paper also investigates different authentication protocols for improving security while maintaining low computation and transmission time. For data analysis, different machine learning algorithms, such as decision tree, support vector machines, and logistic regression, are used on the three layers. The proposed architecture is evaluated using CloudSim, iFogSim, and ns3-NB-IoT on real data consisting of medical vital signs. The results show that the proposed architecture reduces the NB-IoT delay by 59.9%, the execution time by an average of 38.5%, and authentication time by 35.1% for a large number of devices. This paper concludes that the NB-IoT combined with edge, fog, and cloud computing can support efficient remote health monitoring for large devices and large areas.
... To overcome these problems, development is needed that is able to support and promise a far significant impact in the medical world [12]. The development carried out aims to be able to overcome the difficulties of problems in the medical field in a solution that can be adopted by medical parties in the future [13]. ...
Ground Glass Opacities (GGO) are a picture of abnormal lung conditions characterized by white or gray areas. This picture of GGO in the lungs could previously be detected based on the results of medical examinations such as Computerized Tomography (CT scan) and Magnetic Resonance Imaging (MRI) images of patients suffering from Covid-19. However, from the results of the examination, it can be seen that the CT scan and MRI images still have a noise level that is too high, causing difficulties in describing the distribution pattern of the GGO itself. The purpose of this study was to optimize the detection of GGO on MRI images using the Multipixel Interpolation technique. The detection process adopts several stages including image preprocessing, edge detection process, and gradient morphological segmentation. Image preprocessing is done to remove noise and improve the MRI input image. The edge detection process is carried out to detect lung organs automatically using the Canny method which is optimized with the multipixel interpolation technique. The final stage of the research is the segmentation process using a gradient morphology technique to see the spread of GGO in patients with Covid-19 contained in the MRI image. The results of this study present an overview of the GGO pattern with fairly good results. The results of the GGO pattern description will also measure the level of spread to see the severity of pneumonia. Based on the results presented, this research is useful as an alternative solution in the process of diagnosis and treatment of Covid-19 patients.
... Block query requires two steps. e first step is to construct a Bloom filter about all query indexes in each block, which is used to query whether there are spatial location attribute related data in the block; Part 2: Establish a double-layer combined Bloom filter of all query indexes in each block, which is used to query the exact location of the query index corresponding to the spatial location attribute [12]. Both query structures use hash function to construct encrypted query index, which can quickly query data during query operation. ...
In today’s rapid development of higher education, the reform of higher applied undergraduate institutions and higher education has become an effective way to improve the quality of higher education personnel training and improved the quality of teaching. With the continuous expansion of the scale of university laboratories, the number of managers is also increasing, but the management of laboratories is still in the exploratory stage. The current laboratory information management system of Internet of Things has high degree of data heterogeneity, poor data interoperability, and difficult guarantee of data privacy, all of which lead to the difficulty in mining the data value of Internet of Things. The decentralized and zero-trust architecture of blockchain subverted the traditional centralized system architecture and has been applied in many aspects. Blockchain technology was a good addition to IOT to improve the privacy security, versatility, and reliability of the network. This thesis developed an IOT information management system for smart labs based on blockchain technology and the Internet of Things. The data processing efficiency, accuracy, and system throughput are compared with the original IOT laboratory information management system. The results showed that compared with the current laboratory IOT information management system, this system had higher speed, higher efficiency, and better performance in processing the information and data.
... Cheng et al. developed an intelligent hospital information management system based on internet technology to meet the functions of patient reservation, viewing medical records and hospital news. At the same time, it can also manage the hospital more comprehensively [14]. Onasanya and Elshakankiri used Internet of things technology to build an intelligent health care system for cancer care, increase the choice of existing treatment, and provide more solutions for health care [15]. ...
Aiming at the problems of difficult medical treatment for urban residents, low efficiency of hospitals, imbalance of medical resources and difficulty to meet the demands of patients, this paper proposes an urban intelligent medical service system based on multiobjective decision-making optimization strategy. Firstly, it analyzes the medical service demands of urban residents and points out that the urban intelligent medical service system should provide comprehensive services for residents; the main demands include basic medical demands, expected medical demands, and health care medical demands. Secondly, based on demand analysis, an urban intelligent medical service system composed of government, community hospital, large hospital, and urban residents is designed. The system has three kinds of system functions: customer business services, basic services, and institutional information services. Then, the overall process of the system before and after treatment of urban residents is introduced. Finally, according to the optimization objectives of the system: low medical cost, fast treatment speed, and high service satisfaction, the corresponding optimization strategies are formulated to optimize the urban intelligent medical system, reduce medical cost, improve treatment speed, and improve customer satisfaction, to better serve urban residents.
... The distance-based radial basis kernel function (Cheng et al. 2021) has the following form: ...
Chinese Wushu is the traditional sports of the Chinese nation. The spirit of Wushu is based on Wushu. With the passage of time, its connotation and value are deepening. This is the source of strength and inspires past Wushu practitioners to overcome themselves. The health management system mainly serves mid-to-high-end health management institutions, and develops a health information management system in accordance with health management and service procedures. It mainly includes physical examination workstation, health assessment, intervention system, charging center, green channel, external marketing system, CRM management, inventory management, decision analysis, system maintenance and other modules. We are trying to make progress. Based on the Internet of things medical system management model, this paper studies the health characteristics of Wushu routine sports, in order to better detect the health status of Wushu Routine Athletes by using the Internet of things medical system, so as to better promote the development of Wushu routine sports in China. This paper puts forward the research methods related to the Internet of things, kinematics and surface electromyography, This paper analyzes the body composition indexes of Wushu routine athletes, and establishes the intelligent health management model of the medical Internet of things, so as to study the health characteristics of athletes more timely and transparently. The experimental results show that there is no significant difference in the absolute value of decline rate, the relative value of decline rate and the relative value of decline rate BMI among first-class athletes (42.56%, 11.2%/kg, 3.21% / BMI), second-class athletes (43.26%, 0.92%/kg, 2.12%/BMI) and non class athletes (39.23%, 0.82%/kg, 1.82/BMI) (P > 0.05).
... Healthcare is boosted as an application domain that appears to be very promising for enhancing service quality of the patients due to the Internet of Things (IoT), cloud computing, and Wireless Body Area Network (WBAN) technologies, as well as the interactions among those [2]. Some wireless technologies (e.g., ZigBee, Bluetooth, LoRa, Sigfox) are used in healthcare applications; however, some recent studies [3,4] have already revealed that the Narrow Band IoT (NB-IoT) is more suitable for healthcare applications in terms of licensing policy, long-range data transmission performance, energy-efficiency, etc., than other technologies. The fact that the NB-IoT is a Low Power Wide Area Network (LPWAN) technology facilitating a long-range and deep indoor coverage makes it perfect for a typical smart hospital context [5,6]. ...
... According to their analysis, a licensed frequency supported cellular network (e.g., NB-IoT) ensures reliable and efficient communication in the healthcare system. An intelligent medical plan was based on NB-IoT technology and a smart hospital information management system was developed and explored in [4]. ...
Narrowband Internet of Things (NB-IoT) is a promising technology for healthcare applications since it reduces the latency necessary in acquiring healthcare data from patients, as well as handling remote patients. Due to the interference, limited bandwidth, and heterogeneity of generated data packets, developing a data transmission framework that offers differentiated Quality of Services (QoS) to the critical and non-critical data packets is challenging. The existing literature studies suffer from insufficient access scheduling considering heterogeneous data packets and relationship among them in healthcare applications. In this paper, we develop an optimal resource allocation framework for NB-IoT that maximizes a user’s utility through event prioritization, rate enhancement, and interference mitigation. The proposed Priority Aware Utility Maximization (PAUM) system also ensures weighted fair access to resources. The suggested system outperforms the state-of-the-art works significantly in terms of utility, delay, and fair resource distribution, according to the findings of the performance analysis performed in NS-3.
Athlete monitoring has evolved significantly, with biometric data analysis emerging as a crucial component in enhancing performance, preventing injuries, and optimizing training strategies. This research paper investigates the advancements, applications, and implications of biometric data analysis in the realm of athlete monitoring, with a specific focus on the integration of machine learning techniques. The study reviews the historical context of athlete monitoring, surveys existing literature on the importance of biometric parameters, and explores cutting-edge technologies for real-time and predictive analytics. The applications of biometric data analysis are examined in the context of injury prevention, performance optimization, and recovery monitoring. Furthermore, the paper delves into various machine learning algorithms employed to interpret biometric data, offering case studies to demonstrate their efficacy. Challenges and considerations, including ethical implications, are addressed, laying the groundwork for a discussion on the future directions and emerging trends in this field. Real-world case studies showcasing successful implementations of biometric data analysis in professional sports settings are presented. The paper concludes with a summary of key findings, recommendations for the continued integration of biometric data analysis, and suggestions for future research, providing a comprehensive exploration of the role of biometric data analysis in athlete monitoring.
