Article

# Human Arthritis Analysis in Fog Computing Environment Using Bayesian Network Classifier and Thread Protocol

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## Abstract

From the last few decades, old age persons and adults were facing the problem of arthritis. Regular monitoring of joints health and consultation from the specialist can help the patients to recover from this chronicle disease. As per the experts in medical research community, the ratio of orthopedic doctor to arthritis patient is low. Therefore, smart devices and ICT-based infrastructure can support the healthcare industry a lot. Motivated from these facts, in this paper, we propose an architecture to track the hand movements of the patient. To provide medical services to the arthritis patients, fog and cloud gateways for real-time response generation are used. Thread protocol and Bayesian network classifier have been included in the proposed architecture to achieve reliable communication and anomaly detection. To test the validity of the proposed scheme, a dataset of 431 arthritis patients is taken in real-time and simulated on OMNet++ simulator. Simulation results reveal that the packet delivery ratio is improved by 15-20%, the response time is reduced by 20-30% and packet delivery rate is improved by 25-35% in comparison to without fog and thread protocol.

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... To manage this particular immense information proliferation, a robust IoT process stack is needed, which works with each problem relevant to information transmission and processing at various phases. Using standardized protocols and levels, a structure could be created to perform the appropriate providers regarding IoT products [5]. The enumerations will be utilized within the automotive business to fulfill the computer users' needs and realize their business goals. ...
... Furthermore, BC holds a decentralized and immutable ledger that keeps all the information captured in economic transactions. It contains a sequentially connected chain to the time-frame blocks, collectively utilizing cryptographic hashes [5]. This enables an end-user to obtain a distributed peer-to-peer system, in which mistrust users could swap info through one another, without a reliable third party [17]. ...
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... • Security: Akrivopoulos et al. [61] assessed the functioning design, which gains ECG uses as a result of a personalized gadget making use of fog pathways for accurately revealing them to many other authorized components. The individuals are encouraged by this design to talk about information autonomously to their PCPs and also explain to them for display screen and problems of their health condition. ...
... • Privacy: There should be regular healthcare suggestions as well as strategy to actualize the watching framework to preserve the info safeguards. It decreased the interchanges overburden as well as protect the protection of affected people [61] wished for CC form healthcare administrations design to create actual well-being pieces of expertise while saving the protection by confirming delicate well-being data to the clients. In addition, Chakraborty et al. [11,63,64] launched dominant FC based that is enormous scope, and GIS conveyed, as well as dormancy sensitive heightened fitness level programming type for time-delicate uses. ...
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... It has three levels: The cloud 43 level, the fog level, and the IoT/end-users level. It has effective role in application 44 of TTH cure through biofeedback (Mittal et al. [45]; Mistry et al. [46]). 45 The chapter explains the drastic paradigm shift from Healthcare 1.0 to Healthcare 46 4.0 which is bringing a 180 degree shift in current scenario. ...
... It has effective role in application 44 of TTH cure through biofeedback (Mittal et al. [45]; Mistry et al. [46]). 45 The chapter explains the drastic paradigm shift from Healthcare 1.0 to Healthcare 46 4.0 which is bringing a 180 degree shift in current scenario. Different sensor based 47 devices like EEG, EMG, and GSR can be effectively used for this purpose. ...
Chapter
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In today’s world, people know so much about the world around them but most of them know so little about their own selves. The world gets more mysterious and enigmatic as one tries to know it. The reach and scope of the human mind may be infinite but the mental complexities generated result in the hampering of improvement and elevation in the personality. There are many cases of lack of knowledge of inner self and emotional instability in boys and girls of pre-adult age of 20–25 years which lead to various psychological imbalances. One can switch to proper meditation with positive attitude to find cure from all possible issues. It has been reflected by researchers that the complete personal effectiveness, social success, pleasant attitude, and work style efficiency of an individual are governed by the imaginations, emotions, and mental fitness.
... The conventional centralized systems such as cloud and fog [56] are always under the impact of security and privacy attacks. However, BC, a peer-to-peer (P2P) decentralized ledger, is a disruptive innovation in data security and privacy. ...
Article
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... Health is the most important aspect of human life, as, without good health, all the other aspects of human life will be affected [82]. Especially with the pandemic, human health is in jeopardy [83]. To protect the public from the rapid spread of the COVID-19 virus, various countries issued a lockdown. ...
Article
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... Cloudlets use WiFi with limited coverage, which does not offer ubiquitous computing. Meanwhile, fog computing is developed to overcome the issues of cloudlet as a central data server for the subsection of network located closer to the edge [41]. With the time-span, the European Telecommunications Standards Institute (ETSI) proposed the concept of Mobile Edge Computing (MEC) in the initial phase of 2014 that manages cloud computing and Information Technology (IT) capabilities in the proximity of mobile users [6]. ...
Article
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... IoT data stored, processed, and accessed at a different server across the Internet (using cloud/fog computing), which is vulnerable to insecurity [10]. IoT data are susceptible to cyberattacks like data tampering and false data injection [11] and have a single-node-failure issue in existing cloud-based solutions [12,13] [14]. Typically, cloud-based solutions cannot fully ensure data availability, integrity, and security for IoT-based smart cities. ...
Article
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... Regular joint health monitoring and consultation by a physician will assist patients with this chronic disease. A WBAN-based framework is proposed by Tanwar et al. [30], to evaluate real-time health care for patients problem related to arthritis. To minimize false detections in the proposed architecture, the Bayesian network classifier is used. ...
Article
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... Although fog computing has overcome several limitations of cloud computing model, it still suffers some challenges and is therefore under developing phase. Several researchers have proposed various models for fog computing like HealthFog [16][17][18], which works for healthcare systems. In the literature, it has been noticed that fog computing has observed its widespread employment in HMS as it involves several sensors monitoring vital parameters of the human body. ...
Chapter
Automation of health monitoring has witnessed an unmatched transformation during the past decade owing to advancement in the IoT. In automated health monitoring system, patient is efficiently and precisely monitored using numerous sensing devices. These monitored parameters need to be forwarded and processed at cloud which aids medical expert in diagnosis and treatment. However, the transmission of this data to cloud necessitates a wide bandwidth and high speed networks as real-time monitoring generates a plethora of data. In order to address this issue, the computing resources are pushed to the edges of the network, known as fog computing. Fog computing eliminates the limitations of cloud computing as it has low bandwidth requirement and reduced latency time. Additionally, it also addresses the issue of scalability and thus caters to the demand of IoT-based computing environment further making it an appropriate choice for implementing any latency-sensitive and location-sensitive application, e.g., automated Health Monitoring System (HMS). In this chapter, the authors discuss the evolution in IoT, concept of cloud computing and related issues. Thereafter, the authors present the concept of fog computing along with associated constraints and challenges. Furthermore, the authors propose a secure fog computing architecture by integrating security aspect in the fog layer. In the proposed architecture, authors present two-step approach to maintain privacy and integrity of health data. The proposed architecture caters the demand of a secure automated HMS that advocates its widespread deployment in real life.
... The primary objective of a 5G network is to transform a standard cellular network into an intelligent network by incorporating AI, blockchain, edge computing, and IoT technologies. It also brings effective radio access techniques, such as massive multiple-input multiple-output (MIMO), device-to-device (D2D), millimeter-wave (mmWave), and ultra-densification connectivity, which prolongs the user scalability in WN [14,15]. However, the 5G network has abstracted design principles and is not appropriately documented; as a result, there is a high risk that malicious adversaries can maneuver the standards and regulations of a 5G network [5,16]. ...
Article
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... -Convolutional neural networks (CNN): It is the most frequently used DL classification technique for the datasets consisting of images and videos [61]. It has various layers which perform different tasks such as dimensionality reduction and conversion of the data to vector form [152,171]. -Recurrent neural network (RNN): It is an upgraded and modified version of the feedforward NN. It is recurrent because each layer depends on the output of the previous layer as opposed to the case in feedforward networks [148]. ...
Article
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The COVID-19 pandemic is rapidly spreading across the globe and infected millions of people that take hundreds of thousands of lives. Over the years, the role of Artificial intelligence (AI) has been on the rise as its algorithms are getting more and more accurate and it is thought that its role in strengthening the existing healthcare system will be the most profound. Moreover, the pandemic brought an opportunity to showcase AI and healthcare integration potentials as the current infrastructure worldwide is overwhelmed and crumbling. Due to AI’s flexibility and adaptability, it can be used as a tool to tackle COVID-19. Motivated by these facts, in this paper, we surveyed how the AI techniques can handle the COVID-19 pandemic situation and present the merits and demerits of these techniques. This paper presents a comprehensive end-to-end review of all the AI-techniques that can be used to tackle all areas of the pandemic. Further, we systematically discuss the issues of the COVID-19, and based on the literature review, we suggest their potential countermeasures using AI techniques. In the end, we analyze various open research issues and challenges associated with integrating the AI techniques in the COVID-19.
... The most popular unsupervised learning technique is clustering, which works by grouping the data points based on maximum group similarity or distance from other data points. The common procedure for this technique is to choose a representative (or data point) for each group and the new data point is being classified as a member of one of the groups based on the proximity from the representative data point 62 . There can be cases where few data points may not be classified as members of any group and hence, they are termed as outliers, which in turn help us to identify the anomalies from all the data points. ...
Article
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... It is a three-party protocol, which includes the healthcare provider, the user and the verifier. Here, it is assumed that the healthcare provider makes no deliberate blunders in issuing the certificate; cryptographic systems handle the rest for authentication and verification [120]. ...
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... The records of the wearable devices (see Fig. 20.5) can help the users to encourage them to do better on the following day. By using a diary with the training times, sleep rates, and health parameters, the users can get more insights about their lifestyle [28]. Many users of these applications find ...
Chapter
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Internet of Things (IoT) provided many solutions in the healthcare area. IoT opened many new fields to use tools and technologies that can help users remotely. Many researchers used machine learning algorithms, artificial intelligence, and data science to get the power of the streaming healthcare data. Nowadays, using 5G network applications may help people to employ the data in the healthcare area to provide valuable services for healthcare providers to make better decisions on demand. This chapter covers healthcare applications based on 5G technology. It presents new challenges and techniques in the healthcare area.
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Chapter
Time passes away and the objects of the world are tried communicated between themselves automatically due to Internet of Things (IoT) and its communication process. This creates an unbelievable environment where the reaction reflected instantly after things happened. This is only possible because of IoT-based sensor devices and cloud computing. The IoT-based sensor devices receive the signals from the environment and send it to the cloud for processing and transmitting. But a number of challenges are come up when the system try to achieve the speed. Such challenges are to minimize the latency time, maximum use of memory having less capacity of storage and provide services when breakdown occurred in a network. To overcome these challenges CISCO designed an optimistic computing technology coined as fog computing. This computing provides facilities of computation, storage and network services by staying between IoT-based devices and cloud computing data centers. Today’s healthcare sector always tries to establish a rocky relationship with this upcoming computing technology and IoT-based sensor devices for providing the Quality of Services (QoS) to their patients and staffs. In the field of healthcare huge amount of patient data are generated through the IoT-based sensor devices. Collection, analysis, visualization and utilization of these data in a proper and meaningful way are the big challenges from the point of view of a patient care. The IoT-based sensor devices are usually ubiquitous in nature and highly acceptable in the field of health care for data collection. On the other hand, the cloud computing is widely acceptable by healthcare organization for storage and processing of patient data. Fog computing makes these process more efficient from the point of view of cost, penetration and social benefit. This chapter at first tries to present the working environment and integrated architecture of fog computing with IoT. The author tries to present the importance of fog computing with IoT in healthcare sector with the help of different services and applications. At last with the help of a case study different issues and challenges are also highlighted for the purpose of research in near feature.
Chapter
Nowadays, healthcare industry is leveraging the technical innovations for providing better facilities to the patients. A number of high quality medical devices are available to record a patient’s health based on numerous parameters. Such sensor-based health monitoring devices generate high volume of data which is analyzed to provide the appropriate treatment. Such monitoring requires the storage and analysis of data on a remote cloud. Though cloud-based services provide efficient storage, they suffer from the delays incurred while sending the data and retrieving the analysis. Fog computing has proven to be an efficient solution to this problem. A fog node can be considered as an edge node, network device, healthcare equipment, etc., having a limited computation power. These devices are located in proximity to the sensor nodes. Fog nodes can be used to perform data analysis in a distributed manner without adding network delay. However, without any proper infrastructure, it is difficult to identify a fog node having sufficient resources to analyze a set of data. This problem can be addressed by using publish/subscribe paradigm over distributed hash tables (DHTs). Publish/subscribe system provides an event triggered approach which can be used to identify a fog node capable to service a data processing request. Further, a DHT is a peer-to-peer overlay network which is used for efficient resource sharing among the peer nodes. In this chapter, a DHT-based peer-to-peer network of fog nodes is proposed. The objective of the proposed networking infrastructure is to create an overlay of physical fog nodes to provide efficient resource discovery. It is achieved by using publish/subscribe communication and peer-to-peer overlays enabling the nodes to share their computation capabilities with each other.
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Fog computing is an architecture that uses edge devices to perform computation, storage, and communication locally and globally through routing over the internet. Healthcare industry has grown up from 1.0 to 4.0 generation. Healthcare 3.0 was a hospital centric, where long-lasting disease patients endured a great deal for their regular check-ups due to several visits to hospitals. To overcome some of the drawbacks of healthcare 3.0, we are discussing the healthcare 4.0 and its several challenges and future implications. The healthcare 4.0 track will be focusing on topics such as the use of technology and systems to improve patient safety, health outcomes, and the patient experience.
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The great technological advances and rapid growth in the physical objects being connected to the Internet have led to the emergence of the term “Internet of Things” (IoT). IoT has an impact on almost all areas like construction, business, data analytics, e-commerce, agriculture, transportation, and healthcare. Maintenance of such a system can be done by the cloud computing but due to issues like long processing times, slow responses, and privacy issues, it is not preferred in real-time systems. IoT with its integration with fog computing can resolve problems like slow responses, delays, privacy, and security issues in healthcare systems. This chapter discusses the IoT and fog computing, their architecture, their application domains, and their integration and importance in healthcare. A literature survey involving all the works that include fog and IoT is discussed. Case studies involving fog and IoT in healthcare systems are also presented to provide light on how fog and IoT eliminate pressures on healthcare systems that require real-time processing.
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Almost one million people worldwide die by suicide each year. One of the main reasons is due to depression. Life stresses, high depressions, and anxiety are commonly prevalent in mental health problems. Early detection and recognition are in need to allow better treatment and prevention on mental disorders as well as other complications. However, some patients skip their checkup routine due to multiple hospital procedures and long-waiting process. Motivated with the fog computing as a recent technological advancement, this chapter aims to facilitate a new version of an online system on electronic Mental Assessment and Self-Treatment System (e-MAST) for all patients. This system provides patients with stress questionnaires, anxiety questionnaires, and depressive symptoms questionnaires generated using rule-based techniques. Besides, the sum of answers from the patients will be calculated using weighted sum method. This system offers life stress controls and self-treatment techniques while awaiting professional help. This system helps to increase the scientific community’s awareness of mental health and creates an opportunity to embrace a healthy generation of people. Also, this system can be used at all times, anywhere, and can be benefited by all toward smart hospital ideas.
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There is considerable interest in the knowledge of the bees with the -OM symbol, the word OM is considered the beginning and end of the past and future. OM’s motto is the reality of the world and the human body, a subtle understanding of the mind, emotions, thoughts, and beliefs in our lives.
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The revolution in the healthcare domain was originated with the emergence of modular IT system in healthcare (Health 1.0) to the healthcare extension of Industry 4.0 (Health 4.0) integrated with Internet of Things (IoT), Cyber Physical Systems, Artificial Intelligence (AI), Cloud Computing, Big Data, Bioinformatics, Robotics, Precision Medicine, to cite a few. Applying IoT in healthcare 4.0, massive amount of patients’ data is generated by the sensors and this data is accessible to the doctors at any time and at any place for analysis and for appropriate line of treatment. The sensors in the healthcare domain of IoT need to be wearable and wireless to monitor the patients on large scale. In addition, the analysis of data and decision of treatment should be done and communicated in as little amount of time as possible. Thus, the aggregation, storage, analysis, and maintenance of data should be such that the data is continuously available, portable, consistent, accurate, scalable, secure, and quickly transferable. These challenges constraint the energy, memory, communication, and processing capacity of the end devices (sensors) used. Hence, instead of relying entirely on remote data centers using Cloud computing, the gap is bridged by means of fog computing (near the healthcare premises). The factors affecting the architecture of fog computing in healthcare domain are location of patient, latency requirements, geographic distribution, heterogeneous data, scalability, real-time vs batch processing, mobility of end devices, etc. On the other side, use of fog computing in the healthcare has substantial challenges for researchers and organizations including application-oriented architecture prototype, modeling and deployment, infrastructure and network management, resource management, mobility of patients and hence data mobility, security and privacy of patients’ data, scalability, easy incorporation of various healthcare professionals’ proficiency with intelligent devices and sensors, and minimum latency time in case of life threatening situations. This chapter discusses background and research challenges of fog computing in Healthcare 4.0 with an aim to guide the researchers and stakeholders for the overall improvement in the functioning of the healthcare domain.
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As healthcare industry is growing the major concerns are storage of healthcare data including medical and nonmedical data, accessing of data, and data security. The healthcare sector is one of the fastest developing sectors that focus on medical and nonmedical entities of the system like patients and doctors, medical equipment and drugs manufacturers, medical insurance facilities providers, etc. In parallel, it also includes multiple sectors. This chapter discussed the amalgamation of fog computing, blockchain, and Internet of Things (IoT) in healthcare. Fog computing extends the capability of cloud computing that works between the cloud and end user devices called IoT devices to perform operations such as computation, storage, and communication over the Internet. It provides better data storage facilities with real-time access, lower latency, higher response, better fault tolerance, secure and conceal environment. In IoT, conglomerate devices are interconnected and fragments IoT system into five layers such as fog, access, data interface, application, and security layers. To provide better security of the data in healthcare environment, we discussed blockchain technology and consensus mechanism. This research focuses on the usefulness of technologies for existing patients and normal users and improves the services of healthcare industry.
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Fog computing is the most recent buzzword in the field of cloud computing and is an emerging technology closely associated with the Internet of Things, abbreviated as IoT. Fog Computing or Edge Computing is just a slight variant or a logical extension of the traditional cloud computing technology. As the IoT is slowly gaining momentum, the need for a robust and reliable infrastructure for data transmission becomes a necessity. So, with providing reliable connectivity, handle unprecedented amounts of data along with securing the data source is what fog computing intends to do. As said, the main highlight of the fog computing is that the data lies somewhere between its source of origin and the cloud and the sole objective of this is to provide agile and reliable communication to the IoT devices connected via what is termed as “Fog Nodes”, which are nothing but decentralized and distributed nodes that work in tandem to connect the plethora of IoT devices. The implications of fog computing are cross domain and with the imminent prevalence of the IoT devices in the near future, it is becoming even more important. Mostly, the benefits of the cloud computing have been reaped by many of the large and small technological firms, by providing services like data and file storage, hosting websites, etc. but with the advent of the IoT devices and fog computing, the doors are open for a wide variety of disciplines and perhaps one of the most exciting and important domain is that of Healthcare.
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The increasing pervasiveness of the Internet of Things (IoT) has led to more connected remote devices, which entails exponentially more data and computation. This creates the need for the phenomenon of cloud computing. Cloud computing uses third-party hardware in geographically remote locations, which communicate via the internet. And extension to cloud computing is fog computing, which brings the cloud closer to the user, thus decreasing the computation and storage time. However, the geographically distributed fog network nodes are vulnerable to different types of security attacks. This chapter studies the reason for the susceptibility of fog nodes. The security systems and concerns in cloud and fog computing are compared in the chapter. The chapter also examines the various types of attacks to which the fog network is vulnerable. These types include man in the middle attacks, authentication threats, distributed denial of service and others. Finally, the chapter aims at investigating the different methods to handle these types of attacks. The first approach is the prevention of security attacks, which includes techniques like identity authentication, access control and cryptographic schemes. The second approach for data privacy and security handling is detection. The chapter delves deeper with methods like intrusion detection, data integrity check and network traffic analysis. The last approach covered by the chapter is recovery, which covers recovery schemes suited to tackle specific attacks. Thus, the chapter intends to impart an intensive and multi-faceted understanding of data security and privacy in fog data analytics.
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Electrocardiogram (ECG) is a cardiac test that records the timing and strength of electrical signals primarily responsible for heartbeats. An ECG data is used to gain an insight into irregularities in the heart rhythms. A Fetal Electrocardiogram (fECG) is extracted from the abdominal electrocardiogram signal of pregnant women. This is a non-invasive method for recording fECG during the early weeks of pregnancy and is an effective diagnostic tool used by clinicians to regularly evaluate the foetus health status. The aim of this paper is to put forward architecture for continuous monitoring of fetal electrocardiogram from maternal ECG to avoid any kind of acute condition caused to the newborn child at the time of birth. The continuous acquisition of fECG will lead to a very large amount of data to send over the cloud for further examination by the doctor, this data has to be pre-processed before moving it to the cloud for a much faster and efficient evaluation. The proposed architecture along with the Healthcare 4.0 environment and mobile fog computing; will have a potential to extend, virtualize new and efficient healthcare processes for fetal health monitoring.
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With the introduction of the concept of Internet of things, a centralized cloud computing was born to counter the lack of processing power and complexity of design of the IoT sensor environment station devices. This worked for a while, but, because of the physical limitations of data transfer speeds over long distances, it has recently been failing to deliver positive results in real time latency-based services. To compliment the cloud’s drawbacks and to bring computation close to the nodes (also called Edge Computing) while utilizing every bit of computational power offered by the complete network, fog computing has emerged as a compromise between cloud and edge reducing wastage of resources and increasing communication, relay speeds for the transmission and exchange of Data. Fog Data Analytics is the analysis of the mechanisms and collaborations developed in the network for communication and computation between Edge, Fog, and Cloud layers. The relevance of customization of grounds of comparison for the implementation in different fields warrants a generalization of the judgement criteria i.e., there is no single ideal approach for implementation and thus standardizing the architecture to be implemented is shortsighted and hence a broader explanation is important. This book chapter gives an introduction to Fog Data Analytics, an explanation of the advantages and drawbacks present in the existing system i.e., the motivation for incorporating Fog and methods used to solve some of the issues.
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The information and communication technology transforms the industry 1.0 to interconnected industry 4.0. This transformation affects the health care also, while healthcare 1.0 is man oriented and doctor centric. The things are changed and healthcare 2.0 evolved where manual records are converted to electronic records which is called by electronic health records. The technology transforms this sector into healthcare 3.0 and 4.0 are evolved from patient-centric health solutions which are based on central server to cloud and fog-based solutions. When the healthcare solutions are transformed then various devices, sensors are installed and the data can be monitored on regular basis and further various actions will be taken accordingly using Internet of Things (IoT). To collect and process the data, there are various technologies like cloud and fog computing. But the security and scaling are two basic concerns which can be handled. This chapter is important to understand the concepts of data security and privacy functions in fog computing for healthcare 4.0.
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Fog computing is well known for its low edge to edge delay and service latency while providing better performance than cloud computing. It offloads the cloud servers and shifts the computation facility toward the edge of network. However, with the increasing demand of technology, security and privacy concerns are also increasing. As the new security mechanisms are devised, attacker’s approach toward getting access to information is also modified. Nowadays, attackers are more advanced in terms of attacking strategies. Therefore, there is a dire need of advanced security mechanisms for such advancing technology. While discussing about security, individual biometric seems to be a promising approach from the last few decades. There are a lot of biometric-based techniques that have been developed using face, palm print, fingers eyelids, etc. But there are a few works in the field of typing behavior characteristics. To this end, we design a security strategy for fog computing by analyzing user’s typing behavior pattern. We deployed nine behavior parameters for this study. We first implemented this strategy using four parameters and then added new parameters while evaluating error rates at each step. Results also show that Crossover Error Rate (CER) reduces to 2% for the final stage using the proposed strategy. The proposed scheme is validated by a simulator designed for registering new users and identifying the user requesting for service.
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The recent decade has seen considerable changes in the way the technology interacts with human lives and almost all the aspects of life be it personal or professional has been touched by technology. Many smart devices have also started playing a vital role in many fields and domains and the internet of things (IoT) has been the harbinger of the advent of IoT devices. IoT devices have proven to be monumental in imparting ‘smartness' in the otherwise static machines. The ability of the devices to interact and transfer the data to the internet and ultimately to the end-user has revolutionized the technological world and has brought many seemingly disparate fields in the technological purview. Out of the many fields where IoT has started gaining momentum, one of the most important ones is the healthcare sector. Many wearable smart devices have been developed over time capable to transmit real-time data to hospitals and doctors. It is essential for tracking the progress of the critically ill patients and has opened the horizon for attending patients remotely using these smart devices.
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A study by the Harvard University conducted in 2019 suggested that in India, nearly five million deaths occur on an annual basis due to lack of healthcare support services (Yan et al. The design and implementation of the elderly healthcare information mining platform, in 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Kansas City, MO, 2017, pp. 1501–1506). Out of the quoted figure, mortality rate of those patients suffering from fatal disorders who require treatment in their early stages is also quite significant. Only 12.5% of the people suffering from cancer receive an early treatment causing 70% of the cancer deaths in India with primary reason as latency in identification. Not only this, about 80% of all the serious medical errors involve miscommunication during care transitions to the different care units. With the population growing at each step and the health services being limited, E-health revolution became necessity and care services embedded with technological innovations need of the time. Elderly people play a major role in the expansion of the E-healthcare sector as this section of the society is usually unaware and not comfortable with technology platforms supporting E-healthcare. In addition, changes in lifestyle have also led to the outburst of diseases which in turn has generated potential and diversified areas of research (Verma and Khanna, Int J Prev Med 4(10):1103–1107, 2013; Jiang and Xu, How to find your appropriate doctor: An integrated recommendation framework in big data context, in 2014 IEEE Symposium on Computational Intelligence in Healthcare and e-Health (CICARE), Orlando, FL, 2014, pp. 154–158). Work proposed is a blockchain-assisted app-based system, supported by cloud environment for elderly healthcare system which is an effort to provide a convenient, adaptable, and efficient platform to address healthcare issues of the elderly. Architecture proposed targets at facilitating necessary medical services to the user with the features like prescription, diet plans, and medicine intake details from the doctor’s end. Patient’s records are added to the database with the help of QR code scan on patient’s Aadhaar, patient’s medical history with his previous visits to the different doctors, symptoms observed on that visit, and prescriptions given that would be well maintained and easily accessible for future reference by any doctor or patient by simply scanning a QR code on Aadhaar. Prescriptions, timely reminders, clinical reports, maps with distance to nearby hospitals, specific medicinal information, health tips, first aid tips, COVID help center, and a chatbot facility are proposed at a single platform for assistance. Owing to the confidentiality and sensitive nature of the data, security becomes a prime concern, to address the issue a hybrid blockchain model is employed for communication, and a model is proposed with 5G communication platform to reduce latency and thus mortality rate.
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Blockchain and deep learning are promising future technologies. Blockchain promotes decentralized services in the distributed systems, with enhanced security, privacy, transparency, reliability, and robustness. The deep learning provides the intelligent optimized solution to uncertain, complex problems. The empowerment of deep learning techniques to blockchain technologies can enhance the enactment of various upcoming technologies. In this chapter, we tide over the gap for deep learning techniques and outline its application for resource management in blockchain-empowered future generation cellular networks, IoT, and edge computing. We provide a brief background of the above technologies and explored the deep learning techniques for resource management in the upcoming technologies – future generation cellular networks, IoT, and edge computing. After that, we discuss the current deep learning techniques potential to facilitate the efficient deployment of deep learning with blockchain onto upcoming emerging technologies. We provided the encyclopedia review of deep learning techniques. In the end, we conclude the analysis by pinpointing the current research challenges and directions for future research.
Conference Paper
The 6G-based spectrum bands allocation to telecom providers would guarantee ultra peak rates, high availability, and extremely low-latency for various user applications. However, the spectrum allocation still suffers from the limitations of fair allocation process, delays in auction process, and collusive bidding due to inherent centralization. Thus, this paper proposes a scheme, Block6Tel, that integrates blockchain (BC) in 6Genvisioned spectrum allocation to ensure secure and trusted band allocation among telecom providers, and ensure transparency among telecom stakeholders. The scheme operates in two phases. First, a 6G-based protocol stack model is proposed that leverages a cell-free communication infrastructure. Then, in the second phase, a BC-based auction algorithm is proposed for interoperator spectrum allocation, and resource allocations among service providers are finalized. Finally, smart contracts (SC) are executed among telecom providers as bidders, and government authorities (GA) as auctioneers. Through extensive simulations, we prove the superiority of Block6Tel compared with traditional static allocation approaches, in terms of parameters like- resource utilization, requests overhead, and allocation fairness. The results demonstrate that the proposed scheme outperforms the traditional schemes using various parameters.
Article
With the dramatically increasing deployment of IoT (Internet-of-Things) and communication, data has always been a major priority to achieve intelligent healthcare in a smart city. For the modern environment, valuable assets are user IoT data. The privacy policy is even the biggest necessity to secure user’s data in a deep-rooted fundamental infrastructure of network and advanced applications, including smart healthcare. Federated learning acts as a special machine learning technique for privacy-preserving and offers to contextualize data in a smart city. This article proposes Blockchain and Federated Learning-enabled Secure Architecture for Privacy-Preserving in Smart Healthcare, where Blockchain-based IoT cloud platforms are used for security and privacy. Federated Learning technology is adopted for scalable machine learning applications like healthcare. Furthermore, users can obtain a well-trained machine learning model without sending personal data to the cloud. Moreover, it also discussed the applications of federated learning for a distributed secure environment in a smart city.
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Fog computing is the upcoming face of the technology revolution that could shape the future of IoT devices. Fog computing though similar to the cloud, has a variety of contrasting features, as this technology transpires new security, and privacy questions also turn up along with those left by cloud computing. There exist several vulnerabilities in fog computing which directly or indirectly affect the lives of individuals, particularly in the healthcare domain. Implementation of a hacker fog node that pretends to be legal could breach the privacy of a user. For the given purpose, a trust and management scheme is required hence boycotting these types of nodes. Likewise, social issues also play a significant role in the implementation of fog computing. Geographical access rates create security as well as forensics problems, which were not discussed before in cloud security. Fog can be seen as a bridge between IoT deployment and the unprivileged population of a fast-growing country like India. Capital requirements to make this link come into play are also huge. In this chapter, we discuss the ethical, legal, and social issues arising with the growth of healthcare data and personal records. Apart from the location of the cloud servers and gateways that have been set up based on the industry 4.0 architecture, this chapter also provides an integrated model for the adoption of gateways, fog nodes, IoT devices in their respective areas, with a view of reducing the total installation cost, given maximum request capacity, latency time, devices in use, and reportage area.
Article
Full-text available
The Internet-of-Things (IoT) has taken over the business spectrum, and its applications vary widely from agriculture and health care to transportation. A hospital environment can be very stressful, especially for senior citizens and children. With the ever-increasing world population, the conventional patient-doctor appointment has lost its effectiveness. Hence, smart health care becomes very important. Smart health care can be implemented at all levels, starting from temperature monitoring for babies to tracking vital signs in the elderly.
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This article investigates the use of depth sensors in applications created to preserve the quality of life in older adults. The increase in the numbers in the elderly population, as well as the popularization of structured-light-based sensors, motivate the investigation on how these devices can be used to promote quality of life to the elderly, especially in the prevention of falls. Initially, we present a comprehensive review of the applications of depth sensors in several areas, varying from surveillance and interactive systems to computer-vision applications and three-dimensional (3-D) modeling. Next, we focus on the use of depth sensors in applications to prevent, detect, and address the consequences of falls among the elderly. Our conclusion is that structured-light depth sensors are a viable alternative to develop applications that promote quality of life among the elderly, provided that physical and cognitive issues introduced by aging are considered.
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Medical care, supported by modern technology and methods, will play an important role in our global society. An increasing average age and the need to keep standards in care lead to the introduction of several solutions often described as ambient assisted living (AAL). However, most people feel uncomfortable with these solutions, describing a sense of secret observation. To alleviate this sense of secret observation, the sensor platform is placed in clear view of the end user. This is accomplished through the involvement of robotics in this domain, and both caregivers and the patient are offered support for several tasks. The Robots in Assisted Living Environments: Unobtrusive, Efficient, Reliable, and Modular Solutions for Independent Ageing (RADIO) project targets this topic but differs from other solutions by its specific concepts, which are especially the unobtrusiveness and inclusion into home automation. This article presents the RADIO approach with its solutions for combining robotics and home automation for use in AAL environments.
Purpose In this article authors argue how embedding of self-powered wireless sensors into cloud computing further enables such a system to become a sustainable part of work environment. Design/methodology/approach This is exemplified by an application scenario in healthcare that was developed in the context of the OpSIT-Project in Germany. A clearly outlined three-layer architecture, in the sense of Internet of Things, is presented. It provides the basis for integrating a broad range of sensors into smart healthcare infrastructure. More specifically, by making use of short-range communication sensors (sensing layer), gateways which implement data transmission and low level computation (fog layer) and cloud computing for processing the data (application layer). Findings A technical in depth analysis of the first two layers of the infrastructure is given to prove reliability and to determine the communication quality and availability in real world scenarios. Furthermore, two example use-cases that directly apply to a healthcare environment are examined, concluding with the feasibility of the presented approach. Practical implications Finally, the next research steps, oriented towards the semantic tagging and classification of data received from sensors, and the usage of advanced Artificial Intelligence based algorithms on this information in order to produce useful knowledge, are described together with the derived social benefits. Originality/value The work presents an innovative, extensible and scalable system, proven to be useful in healthcare environments.
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In this paper, we present a novel low-cost computationally efficient method to accurately assess human Gait by monitoring the 3D trajectory of the lower limb, both left and right legs (i.e. 6 segments - feet, tibias and thighs, and 6 joints - ankles, knees and hips). Our method utilises a network of miniaturized wireless inertial sensors, coupled with a suite of real-time analysis algorithms and can operate in any unconstrained environment. Firstly, we adopt a modified computationally-efficient, highly accurate and near real-time gradient descent algorithm to compute the direction of the gyroscope measurement error as a quaternion derivative in order to obtain the 3D orientation of each of the 6 segments. Secondly, by utilising the foot sensor, we successfully detect the stance phase of the human gait cycle, which allows us to obtain drift-free velocity and the 3D position of the left and right feet during functional phases of a gait cycle (i.e. heel strike to heel strike). Thirdly, by setting the foot segment as the root node we calculate the 3D orientation and position of the other 2 segments as well as the left and right ankle, knee and hip joints. We then employ a customised kinematic model adjustment technique to ensure that the motion is coherent with human biomechanical behaviour of the leg. Pearson’s correlation coefficient (r) and significant difference test results (P) were used to quantify the relationship between the calculated and measured movements for all joints in the sagittal plane. The correlation between the calculated and the reference was found to have similar trends for all six joints (r > 0:94; p < 0:005). Our method is low-cost, robust to measurement drift and can accurately monitor human gait outside the lab in any unconstrained environment.
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For learning a Bayesian network classifier, continuous attributes usually need to be discretized. But the discretization of continuous attributes may bring information missing, noise and less sensitivity to the changing of the attributes towards class variables. In this paper, we use the Gaussian kernel function with smoothing parameter to estimate the density of attributes. Bayesian network classifier with continuous attributes is established by the dependency extension of Naive Bayes classifiers. We also analyze the information provided to a class for each attributes as a basis for the dependency extension of Naive Bayes classifiers. Experimental studies on UCI data sets show that Bayesian network classifiers using Gaussian kernel function provide good classification accuracy comparing to other approaches when dealing with continuous attributes.
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