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As the population is increasing worldwide, huge need arises to provide proper health-care services. With the advent of modern technologies the need-gap may be augmented. Sensor is one such technology which can be used to enable Internet of Things based health-care monitoring system. In this paper, implementation of such a system is described. In a...
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The Internet of Things (IoT) is an emerging and challenging field for researchers. IoT is a network of general objects which are embedded with technologies that helps to communicate and interact within themselves and external environment. This in-turn provides intelligence to the objects to make people life comfortable. In this paper we discussed I...
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... If the health sensing device is made to communicate with portable devices like smart phones and tablets etc., communicating with the cloud is possible. People have access to these portable communication devices which are now becoming cheap [5]. The healthcare industry has made patient care more reliable. ...
Internet of Things (IoT) is becoming the most promising and life changing technology in todays world. It is a computing process in which the communication is made or aided with sensors. In the past few years, IoT has become most productive in the area of healthcare, to improve the quality of care to the patient’s. As there is a rise in health issues, providing health assistance to each and every member is important. People nowadays are busy with their lives and even they forget about their health problems. Some people do not even take care of their health. When the healthcare system has started communicating with IoT devices, it only started to maintain the digital identity of the patient. Today, IoT in healthcare has become more productive because the communication between doctor’s and patients has been improved with mobile apps. These apps are developed by the companies so that the doctors can monitor the patient’s health. If any problem has occurred to the patient, then the doctor approaches the patient and gives the appropriate treatment. In this thesis, particular focus is given to infant healthcare, because the greatest fear of parents is that they would lose their infants at any time. Therefore, in this thesis, the traditional care methods for infants are reviewed first. Then, an experimental setup is proposed and implemented which is capable of monitoring the patient health. In this review a commercial device has been identified which monitors the real-time information about the infant’s heart rate, oxygen levels, sleeping position, etc. If anything happens to the baby, the information will get to the mobile application which has already been developed by a company and is commercially available. Then, by doing an example field test for the baby, the information which is recorded is analyzed. The information that has been analyzed is sent to the mobile which is aided through a base station.
... If the health sensing device is made to work with portable devices like smart phones and tablets then cloud communication is theoretically possible. These portable communication gadgets, which are increasingly affordable, are accessible to anyone [4]. Patient treatment is now more dependable thanks to the healthcare sector. ...
The IoT is quickly emerging as the most revolutionary exciting technology available today. It is a computer procedure in which sensors are used to create or support communication. IoT has become particularly effective in the healthcare sector in recent years, helping to raise the standard of patient care. Giving every member health help is crucial because health problems are becoming more prevalent. Due to their hectic lives, people today often neglect to take care of their health. Some individuals don't even care for their health. The patient's digital identification has been maintained by the healthcare system, which only recently began to communicate with IoT devices. Because mobile apps have increased doctor-patient communication, IoT in healthcare has been increasingly effective in recent years. Companies create these apps so that doctors can keep tabs on their patients' health. If any problems emerge, the doctor approaches the patient and provides the necessary treatment. Parents' top worry is that their children will pass away at any moment; hence this thesis lays a specific emphasis on neonatal healthcare.Therefore, the traditional approaches to baby care are first explored in this thesis. The patient's health can then be monitored using an experimental setup that is then designed and put into action. A commercial product that continuously monitors a baby's heartbeat, oxygen levelsand sleeping position has been found during this evaluation. If the infant has any issues, the information will get to the smartphone application, which was developed by a company and sold. The recorded data is then examined by conducting a sample field test on a baby. The mobile device receives data after analysis, and with help of a base station, processes it.
... According to the report, the major research aim should be to improve the quality and efficacy of healthcare in order to respond to large public health emergencies via IoT data management. ( [7] shows a real-time health-care monitoring system that connects multiple sensors to a local data processing unit and accommodates a range of channel access needs. Every sensor gets the needed bandwidth since channel access is discrete. ...
Smart healthcare serves as an innovative way for integrating sensors, Internet of things (IoT) and large scale analytics. A better patient monitoring reduces the costs of medical services to great extent, which opens up new frontiers or strategies for the sustainability of mankind in terms of data services, remote diagnosing/monitoring or the new kinds of treatments. However, Cloud computing is an on-demand service that provides storage, network, power to do intensive computing, intensive sharing of resources, but users should also deal with the privacy-related issues to the data stored in the cloud through data breaches. The current health care systems implemented on the cloud poses threat to the privacy of the data of the patients, since data breaches cost millions to billions of dollars to the health care institutions every year this problem can be taken care with the Fog computing (FC) based health care systems, where the fog technology offers more additional advantages like low latency, resource management, less power consumption, etc. The proposed system demonstrates the use of IoT, computing platforms (cloud/Fog) with Machine Learning (ML) algorithms. Fog layer extracts the attributes and filters the data collected about heart diseases, whereas cloud layer estimates the level of disease detection using the classifier algorithms Random forest and Naive Bayes. In terms of accuracy, precision, recall, and f-measure, the results suggest that Random Forest outperforms Naive Bayes.
... A study in one of the hospitals in London location that 15 percent of in-patient coronavirus status were certainly or possibly gained from the hospital asymptomatic team members was found to be a potential exporter of contagions [2]. Traditional health monitoring systems have significant disadvantages compared to IoT-aided remote health monitoring systems [3]. The Web is the most popular hypermedia system currently [4]. ...
... Scheduling sensors technique for nullify interference in IoT healthcare environment is proposed by Dhar et al. [83]. The proposed technique aims to keep the interference away among different sensors in to guarantee the validity of health data and to ensure the efficiency of transferring data. ...
The IoT revolution reshapes contemporary healthcare systems by incorporate economic, social, and technological prospects. It is progressing from conventional healthcare systems to more personalized healthcare systems, where patients can be monitored, diagnosed and treated effortlessly. Radiomics is a sub-field of machine learning (ML) that mines quantitative features from radiological images relying on an image-based approach with ML models, which procure information surpassing orthodox medical imaging analysis as diagnosis, prognosis, prediction and response to therapy. The upsurge in the number of radiological images increases the workload of radiologist which in turns decreases their performance, thus they can only detect and evaluate a small portion of information present in images within a short-time. Hence, there is need for a better method for the increase in radiological image selection, detection and evaluation processes thereby reducing the workload of experts. Therefore, this chapter discusses the different types and sources of radiological data, feature extraction and selection method for image analysis. The chapter also presents different ML models ideal for the radiomics and parameter tuning. The challenges, applicability and limitations of Radiomics are also described in this chapter. The radiomic process involves radiological image gathering, segmentation, feature extraction and selection, model building and evaluation. Each of the stage of the process workflow is carefully evaluated for development of a reliable, effective and robust model to be shifted into medical practice for disease diagnosis and prognosis response to treatment.
... Scheduling sensors technique for nullify interference in IoT healthcare environment is proposed by Dhar et al. [83]. The proposed technique aims to keep the interference away among different sensors in to guarantee the validity of health data and to ensure the efficiency of transferring data. ...
In recent years, the continuous growth in global infectious disease coupled with population growth and the associated increase in expectancy lead to the search for new ways of making the most use of limited resources. Automated disease monitoring, diagnosis, prediction, and treatment of patients are not only for fast data but also to get reliable service at reduced cost and accurate results from medical experts. The combined wearable devices and Internet of Things (IoT) have been reformed the medical system. This has minimized the response time in monitoring, diagnosis, prediction, and treatment by thrives toward the omnipresence of the healthcare services. However, the integration and design of IoT-based wearable sensors present many challenges especially in the areas of data exchange, monitoring, and diagnosing of patients. Therefore, the chapter proposes a framework for IoT-WBN-based with a machine learning algorithm (ML). The data collected from different wearable sensors like body temperature, glucose sensors, heartbeat sensors, and chest were transmitted through IoT devices to the integrated cloud database. To select the most useful features from the capture data, the ML was used, and the sensor signal is analyzed using ML for diagnosis of patient data. The proposed framework can be widely used in a remote area to monitor and diagnose patient health conditions to reduce, and eliminate medical faults, reduce healthcare costs, minimize pressure on medical experts, increase productivity, and enhancing patient satisfaction.
... In this part, we present three different IoT aspects in terms of networks and communications, which are: (i) data handling techniques which focused on the communication protocols that are used and employed to transfer health data, scalability and fault tolerance, and scheduling; (ii) energy consumption which focused on the possibilities of improving the power consumption, decreasing overall delay, and [76,77] Network communication protocols [78][79][80] Scalability and fault tolerance [81,82] Scheduling [83,84] Energy consumption [85,86,86,[86][87][88][89] Combined technologies [90][91][92][93][94][95][96] enhancing the overall reliability of IoT systems; (iii) combined techniques which applied various approaches to manipulate health data measurements such as gathered data, sending, storing, and analyzing stages. Table 4 presents the techniques that focus on network and communication techniques. ...
... Scheduling sensors technique for nullify interference in IoT healthcare environment is proposed by Dhar et al. [83]. The proposed technique aims to keep the interference away among different sensors in to guarantee the validity of health data and to ensure the efficiency of transferring data. ...
The Internet of Things (IoT) is a novel technology that makes smart devices the essential building blocks in the growth of smart pervasive frameworks. Health-related problems are considered as one of the major issues that directly affect the quality of the life. Among other applications enabled by the IoT, healthcare technologies might be the most important one. Although several pieces of research are developed for the IoT healthcare field, there are still considerably demanding research and projects of exploiting the benefits of IoT in the healthcare domain. This chapter surveys current advances in IoT-based health care focused in the aspects of privacy and security techniques, e-health and m-health, system design and architecture, cloud, fog, and evolutionary computing, as well as network and communication techniques. This chapter will directly support researchers and healthcare professionals in their understanding of developments in the domain of IoT-based healthcare technologies, thus enabling them to spark further research in this area.
... Optimal duty-cycle scheduling technique [7] and DeTAS [8] consider traffic load of devices in terms of queue size to determine the duty-cycle length. Interference aware scheduling discussed in [9] and [7]. These approaches to satisfy the varying quality of service demands of multiple applications and their demands [5]. ...
Devices in Internet of Things (IoT) network are connected in a systematic way to transfer data over the network. In automation it is required to process and schedule response without human or high processing power computer assistance. Devices in IoT network have unique identification (UID) and they are compact with other objects, people or animal. Wireless Sensor Network (WSN) supports the topology and transfer of information. Nodes are connected to the gateway and some cases there are same type nodes to perform same type functionality. In some automation there are huge tasks in same domain. Scheduling of tasks and response from the right node ensure performance of the network. Group of selecting sensors can participate for censing to produce better throughput. Using optimal resource in wireless sensor network is also a challenge. In this paper we propose combination Ant Colony Optimization (ACO) and priority scheduling algorithm optimal resource planning and job scheduling in wireless sensor network.
... 31 Novel paradigm is developed to seek whether any interference-aware senor-process scheduling could possibly use under IoT-based ecosystem. 32 Edge computing is integrated with the deep learning methods for deployment in the hierarchical orientation. 33,34 Good quality air is essential for healthcare, thus pervasive monitoring of PM2.5 particles were investigated for IoT-based use case. ...
Bio‐sensor data streaming and analytics is a key component of smart e‐healthcare. However, existing Internet of Things (IoT) ecosystem is unable to materialize the real‐time bio‐sensor data streaming and analytics within resource constrained environment. Moreover, traditional solutions fail to mitigate the edge‐cloud integration within a single sub‐system under IoT periphery which lead to investigate how edge‐cloud hybridization could be realized via similar set of tools. The objective of this article is to implement an integrated dual‐mode edge‐cloud system to serve streaming and analytics in real‐time. This study aims to achieve the aforesaid goal by presenting two different experiments that deals with the real‐time pulse sensor data streaming and analytics while utilizing light‐weight IoT‐supported JavaScript frameworks that includes Node.js, Johnny‐Five, Serialport.js, Plotly client, Flot.js, jQUERYy, Express Server, and Socket.io. Firstly, a standalone IoT‐edge system is developed and later, an integrated IoT‐based edge‐cloud system is developed to compare between the effectiveness of the systems. The implementation results show near correlation between the standalone edge and dual‐mode edge system. However, the dual‐mode edge‐cloud system provides more flexibility and capability to counter the bio‐sensor data streaming and analytics services within the constrained framework. Edge‐Cloud Amalgamation in the IoT‐based Healthcare
... Hadoop framework has been used for implementation and using IoT based cloud solutions, alerts are being generated to the doctor in real-time. In (Dhar et al., 2014), the paper proposes a solution so that the sensors used for monitoring the health of a patient work together. To achieve this, interference among the sensors and distortion of health care data. ...
This chapter discusses a deep learning and IoE (Internet of Everything) based analytical model for disease detection, prediction and correct treatment for the patient would be proposed. In the proposed model, all the stakeholders, namely doctors, patients, medical staff within a clinic, hospital or a medical institute, would be embedded with micro-sensors. The sensors would in turn sense and capture the information gathered from these sources and the surrounding environment and then send it to a single repository, a base or a server, where it would be stored for further processing. These sensors produce massive amounts of data, which needs to be encrypted as well. Then, in order to improve the effectiveness and accuracy of prediction from the data received from these sensors, deep learning methods are used. Further, the advantages of the proposed model would be explored. To conclude, the limitations, opportunities and future applications of deep learning techniques would be discussed in this chapter.
