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Human Arthritis Analysis in Fog Computing Environment Using Bayesian Network Classifier and Thread Protocol
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.
... 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]. ...
Due to the tremendous growth of internet-connected devices and rapidly increased data traffic in high-speed networks, a new generation, namely 5G, has been advancing in the telecommunication industry. Moreover, the Industry Internet of Things (IIoT) performs pervasive computing, and industries rely on interconnecting devices for their daily tasks. The IoT connects many devices in various applications with blockchain (BC), including smart homes, smart cities/communities, agriculture, healthcare 4.0, industry 4.0, and SCM, which transfers high-speed information, in particular, the 5G-enabled manufacturing setting through data gathering and computing. The majority of the problems, such as an entry management mechanism, time to fetch the information from various protocols and devices utilized, and protection and privacy of information, have remedies based on the centralized structure. However, the centralized structure may have one disadvantage of computational overhead. There is a need for a distributed cloud access management mechanism for system-to-system interaction in different manufacturing areas. It has been found that BC and 5G-enabled IoT can revolutionize a lot of recent and upcoming manufacturing uses regarding various sectors by offering fine-grained decentralized entry management. Different database and transaction logs could be traced effectively for utilizing BC for consistency and privacy upkeep within the contribution of new processes in the manufacturing sectors. The sensory challenges and issues of 5G-enabled IIoT operation are also examined within this content.
... • 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. ...
In the communication era, through the help of the IoT, the healthcare industry grown to the next level, as the device gets smaller and data gets bigger, Patient Generated Health Data (PGHD) able to access millions of data sets available online intended for the utilization of the healthcare industry and also 50 billion Internet of Things (IoT) gadgets to be continuously interconnected by 2020. The healthcare industry has grown from the stage of doctor and process-focused 1.0, technology-focused 2.0, blend of technology human 3.0 and automation of healthcare 4.0 using IoT, AI, and cyber-physical systems it grows healthcare IoT (technology-focused) requires real-time processing, nearby storage, increase in data rate, low latency, and reliability, it will satisfy by fog computing architecture. It leads to an increase in smart medical devices, drawn-out dealing with such clients or patients alongside an expansion in the general consumption of treatment for patients. Be that as it may, with later innovative headways, for example, haze and distributed computing, these issues are moderated with a base capital venture on believing, what's more, process-focused identified with the information of the patients. Inspired from these realities, the study gives an investigation of the job of haze registering, distributed computing and Internet of Things to provide continuous setting mindful administrations, to the end clients and when required current cloud computing model is not competent to deal with enormous transmission capacity information because of its idleness, volume, and transfer speed necessities. The fog environment is created to address every one of the issues looked by the cloud computing model. It extends to handle cloud computing to keep the devices near to the data center and cloud storage; those devices are known as fog nodes; it acts as an intermediate between the proposed architecture. It provides limited information to the end-user or client for the appropriateness of haze gadgets and portals in the automation of healthcare, for present and future applications.
... 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. ...
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.
... New technologies such as cloud storage, IoT, big data mining, AI, data analytics, remote medical care [26], bioinformatics, predictive modeling [27], and more are becoming pervasive in health systems. Hence, these systems face new health data privacy, confidentiality, and security challenges. ...
... In clinical practices, several tools are equipped to diagnose CVDs. Auscultation is a basic diagnostic method in which doctors listen to the heartbeat sound from a patient's chest to make a diagnosis [7,8]. Auscultation can be achieved using a medical instrument known as a "stethoscope", invented by Laennec in 1816. ...
Cardiovascular diseases (CVDs) are a significant cause of death worldwide. CVDs can be prevented by diagnosing heartbeat sounds and other conventional techniques early to reduce the harmful effects caused by CVDs. However, it is still challenging to segment, extract features, and predict heartbeat sounds in elderly people. The inception of deep learning (DL) algorithms has helped detect various types of heartbeat sounds at an early stage. Motivated by this, we proposed an intelligent architecture categorizing heartbeat into normal and murmurs for elderly people. We have used a standard heartbeat dataset with heartbeat class labels, i.e., normal and murmur. Furthermore, it is augmented and preprocessed by normalization and standardization to significantly reduce computational power and time. The proposed convolutional neural network and bi-directional gated recurrent unit (CNN + BiGRU) attention-based architecture for the classification of heartbeat sound achieves an accuracy of 90% compared to the baseline approaches. Hence, the proposed novel CNN + BiGRU attention-based architecture is superior to other DL models for heartbeat sound classification.
... Recently, a well-designed application of IoT, i.e., wearable devices, has been preferred in the healthcare sector, which measures the blood oxygen, temperature, blood pressure, breathing rate, and glucose state for the remote diagnosis of the patient. It utilizes Artificial intelligence (AI) algorithms to analyze the above-mentioned measures and provide significant inferences about the patient [1,2]. Furthermore, information communication tools and technology advancements make wearable devices reliable and efficient. ...
Over the last few decades, the healthcare industry has continuously grown, with hundreds of thousands of patients obtaining treatment remotely using smart devices. Data security becomes a prime concern with such a massive increase in the number of patients. Numerous attacks on healthcare data have recently been identified that can put the patient’s identity at stake. For example, the private data of millions of patients have been published online, posing a severe risk to patients’ data privacy. However, with the advent of Industry 4.0, medical practitioners can digitally assess the patient’s condition and administer prompt prescriptions. However, wearable devices are also vulnerable to numerous security threats, such as session hijacking, data manipulation, and spoofing attacks. Attackers can tamper with the patient’s wearable device and relays the tampered data to the concerned doctor. This can put the patient’s life at high risk. Since blockchain is a transparent and immutable decentralized system, it can be utilized for securely storing patient’s wearable data. Artificial Intelligence (AI), on the other hand, utilizes different machine learning techniques to classify malicious data from an oncoming stream of patient’s wearable data. An amalgamation of these two technologies would make the possibility of tampering the patient’s data extremely difficult. To mitigate the aforementioned issues, this paper proposes a blockchain and AI-envisioned secure and trusted framework (HEART). Here, Long-Short Term Model (LSTM) is used to classify wearable devices as malicious or non-malicious. Then, we design a smart contract that allows only of those patients’ data having a wearable device to be classified as non-malicious to the public blockchain network. This information is then accessible to all involved in the patient’s care. We then evaluate the HEART’s performance considering various evaluation metrics such as accuracy, recall, precision, scalability, and network latency. On the training and testing sets, the model achieves accuracies of 93% and 92.92%, respectively.
... The network of all of these smart devices that are connected to sense, communicate, and interact within them and with the external environment on real-time data and make decisions by processing these data is collectively called the Internet of things or the IoT. All IoT devices are connected centrally to the cloud to compute the enormous amount of data that IoT devices collect [10,11]. The cloud also has a vast data storage capacity and is generally remotely located from the devices. ...
With the rapid growth in the data and processing over the cloud, the accessibility of
those data has become easier. On the other side, it poses many technical and security challenges to the users of those provisions. Fog computing makes these technical issues manageable up to some extent. Fog computing is one of the promising solutions for handling the big data produced by the IoT, which is often security-critical and time sensitive. Massive IoT data analytics by fog computing structure is emerging and requires extensive research for more proficient knowledge and smart decisions. Though advancement in Big Data Analytics is taking place, it does not consider Fog Data Analytics. But, there are many challenges, including heterogeneity, security,
accessibility, resource sharing, network communication overhead, real-time data processing of complex data, etc. This paper explores various research challenges and their solution using the next-generation Fog Data Analytics and IoT networks. We also performed an experimental analysis based on fog computing and cloud architecture. The result shows that fog computing outperforms cloud in terms of network utilization and latency. Finally, the paper is concluded with future trends.
... In this frame of reference, a VFL approach is used by a hospital and an insurance company that serves patients (same sample space) to collaboratively train an AI model for smart treatment choices utilizing their datasets, such as healthcare costs at insurance companies and previous medical records at hospitals. ML models such as classification [84], gradient descent computation, and linear regression are applicable in the case of such vertical divisions. In DFL, peer training strategies such as SecureBoost are proposed, where all peer nodes summate the user features to train their models [85]. ...
Recently, in healthcare organizations, real-time data has been collected from connected
or implantable sensors, layered protocol stacks, lightweight communication frameworks, and end
devices, termed as Internet-of-Medical-Things (IoMT) ecosystems. IoMT is vital in driving healthcare
analytics (HA) toward extracting meaningful data-driven insights. Recently, concerns have been
raised over data sharing over IoMT, and stored electronic health records (EHRs) forms over to
privacy regulations. Thus, with fewer data, the analytics model is deemed inaccurate. Thus, a
transformative shift has started in HA from centralized learning paradigms towards distributed or
edge-learning paradigms. In distributed learning, federated learning (FL) allows training on local
data without explicit data-sharing requirements. However, FL suffers from a high degree of statistical
heterogeneity of learning models, level of data partitions, and fragmentation, which jeopardizes its
accuracy during the learning and updation process. Recent surveys of FL in healthcare have yet to
discuss the challenges of massive distributed datasets, sparsification, and scalability concerns. Owing
to the gap, the survey highlights the potential integration of FL in IoMT, the FL aggregation policies,
reference architecture, and the use of distributed learning models to support FL in IoMT ecosystems.
A case study of a trusted cross-cluster-based FL, named Cross-FL, is presented, highlighting the
gradient aggregation policy over remotely connected and networked hospitals. Performance analysis
is conducted regarding system latency, model accuracy, and trust of consensus mechanism. The
distributed FL outperforms the centralized FL approaches by a potential margin, which makes it
viable for real-IoMT prototypes. As potential outcomes, the proposed survey addresses key solutions
and the potential of FL in IoMT to support distributed networked healthcare organizations.
... As a result, they achieved 71% accuracy in classifying the osteoporosis patients according to the risk of fracture. Further, the studies [36,37] proposed an AI-based remote patient monitoring system (especially for intensive care unit (ICU) patients) that provides readmission, vital sign assessment (body temperature, pulse rate, respiratory rate), and any abnormality in the patient routine care. Their proposed model outperforms others in terms of accuracy, i.e., 67.53% for readmission and 67.40% for abnormality. ...
A smart device carries a great amount of sensitive patient data as it offers innovative and enhanced functionalities in the smart healthcare system. Moreover, the components of healthcare systems are interconnected via the Internet, bringing significant changes to the delivery of healthcare services to individuals. However, easy access to healthcare services and applications has given rise to severe risks and vulnerabilities that hamper the performance of a smart healthcare system. Moreover, a large number of heterogeneous devices accumulate data that vary in terms of size and formats, making it challenging to manage the data in the healthcare repository and secure it from attackers who seek to profit from the data. Thus, smart healthcare systems are susceptible to numerous security threats and risks, such as hardware and software-based attacks, system-level attacks, and network attacks that have the potential to place patients’ lives at risk. An analysis of the literature revealed a research gap in that most security surveys on the healthcare ecosystem examined only the security challenges and did not explore the possibility of integrating modern technologies to alleviate security issues in the smart healthcare system. Therefore, in this article, we conduct a comprehensive review of the various most recent security challenges and their countermeasures in the smart healthcare environment. In addition, an artificial intelligence (AI) and blockchain-based secure architecture is proposed as a case study to analyse malware and network attacks on wearable devices. The proposed architecture is evaluated using various performance metrics such as blockchain scalability, accuracy, and dynamic malware analysis. Lastly, we highlight different open issues and research challenges facing smart healthcare systems.
... The Bayesian optimization algorithm (BOA) is a highly efficient framework for optimising black-box function globally without utilising the gradient information and grasping the function distribution through formalising the optimization problem [58,78]. BOA utilises the probabilistic model to capture the unrecognised functions, and for that, the Gaussian process is always the best choice for the model. ...
Air pollution is one of the biggest concerns in the world but it has not been paid much attention in developing countries. It is necessary to design models and methods to understand air pollution in developing countries to reduce the rate of pollution. This paper proposes an Internet of Things (IoT) and Artificial Intelligence (AI)‐based hybrid model to predict the Air Quality Index (AQI) with a practical case study of the public data sets. The sensor node is deployed in the city to collect air quality data. Moreover, this sensor node connects to the cloud server for collecting data at the firebase real‐time database through a WiFi/5G network embedded in the raspberry controller. Carbon monoxide (CO) and fine particular matter PM2.5 sensors are integrated within a sensor node to monitor the AQI of the regions. A Kalman fis also applied to remove unwanted noise from the data collected through the sensor node. Models namely Artificial Neural Network (ANN), Support Vector Machine (SVM), k‐nearest neighbour (k‐NN), Convolutional Neural Networks (CNN), Long Short Term Memory (LSTM), CNN‐LSTM, ensemble model, and a proposed model, that is, CNN‐LSTM‐Bayesian optimization algorithm (BOA) model, have been utilised to predict the AQI. The performance evaluation of models is done through statistical parameters, such as mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R²), and accuracy score on two different public data sets and compared with the baseline models. The performance of the CNN‐LSTM‐BOA model is better than baseline models in terms of above‐mentioned statistical parameters as the accuracy reported is more than 97 %.This study can help predict the Air Quality Index and provide sufficient time to generate warning signals in the location.
... 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]. ...
The emerging need for high data rate, low latency, and high network capacity encourages wireless networks (WNs) to build intelligent and dynamic services, such as intelligent transportation systems, smart homes, smart cities, industrial automation, etc. However, the WN is impeded by several security threats, such as data manipulation, denial-of-service, injection, man-in-the-middle, session hijacking attacks, etc., that deteriorate the security performance of the aforementioned WN-based intelligent services. Toward this goal, various security solutions, such as cryptography, artificial intelligence (AI), access control, authentication, etc., are proposed by the scientific community around the world; however, they do not have full potential in tackling the aforementioned security issues. Therefore, it necessitates a technology, i.e., a blockchain, that offers decentralization, immutability, transparency, and security to protect the WN from security threats. Motivated by these facts, this paper presents a WNs survey in the context of security and privacy issues with blockchain-based solutions. First, we analyzed the existing research works and highlighted security requirements, security issues in a different generation of WN (4G, 5G, and 6G), and a comparative analysis of existing security solutions. Then, we showcased the influence of blockchain technology and prepared an exhaustive taxonomy for blockchain-enabled security solutions in WN. Further, we also proposed a blockchain and a 6G-based WN architecture to highlight the importance of blockchain technology in WN. Moreover, the proposed architecture is evaluated against different performance metrics, such as scalability, packet loss ratio, and latency. Finally, we discuss various open issues and research challenges for blockchain-based WNs solutions.
... 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. ...
A coronavirus outbreak caused by a novel virus known as SARS-CoV-2 originated towards the latter half of 2019. COVID-19's abrupt emergence and unchecked global expansion highlight the inability of the current healthcare services to respond to public health emergencies promptly. This paper reviews the different aspects of human life comprehensively affected by COVID-19. It then discusses various tools and technologies from the leading domains and their integration into people's lives to overcome issues resulting from pandemics. This paper further focuses on providing a detailed review of existing and probable Artificial Intelligence (AI), Internet of Things (IoT), Augmented Reality (AR), Virtual Reality (VR), and Blockchain-based solutions. The COVID-19 pandemic brings several challenges from the viewpoint of the nation's healthcare, security, privacy, and economy. AI offers different predictive services and intelligent strategies for detecting coronavirus signs, promoting drug development, remote healthcare, classifying fake news detection, and security attacks. The incorporation of AI in the COVID-19 outbreak brings robust and reliable solutions to enhance the healthcare systems, increases users' life expectancy, and boosts the nation's economy. Furthermore, AR/VR helps in distance learning, factory automation, and setting up an environment of work from home. Blockchain helps in protecting consumers' privacy and securing the medical supply chain operations. IoT is helpful in remote patient monitoring, distant sanitizing via drones, managing social distancing (using IoT cameras), and many more in combating the pandemic. This study covers an up-to-date analysis on the use of blockchain technology, AI, AR/VR, and IoT for combating the COVID-19 pandemic considering various applications. These technologies provide new emerging initiatives and use cases to deal with the COVID-19 pandemic. Finally, we discuss challenges and potential research paths that will promote further research into future pandemic outbreaks.
... 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]. ...
The rampant and sudden outbreak of the SARS-CoV-2 coronavirus also called COVID-19 and its uncontrollable spread have led to a global crisis. COVID-19 is a highly contagious disease and the only way to fight it is to follow social distancing and Non-Pharmaceutical Interventions (NPIs). Moreover, this virus is increasing exponentially day by day and a huge amount of data from this disease is also generated at a fast pace. So, there is a need to store, manage, and analyze this huge amount of data efficiently to get meaningful insights from it, which further helps medical professionals to tackle this global pandemic situation. Moreover, this data is to be passed through an open channel, i.e., the Internet, which opens the doors for intruders to perform some malicious activities. Blockchain (BC) emerges as a technology that can manage the data in an efficient, transparent manner and also preserve the privacy of all the stakeholders. It can also aid in transaction authorization and verification in the supply chain or payments. Motivated by these facts, in this paper, we present a comprehensive review of the adoption of BC to tackle COVID-19 situations. We also present a case study on BC-based digital vaccine passports and analyzing their complexity. Finally, we analyzed the research challenges and future directions in this emerging area.
... Wang et al. [19] also use a crowdsourcing mechanism to collect the passing time cost of a road segment from users. Unlike the previous solution, it uses a local and global blockchain to reduce network communication overhead with computing nodes owned by individuals or edge routers functioning as miners competing via PoW [29] [27]. The global blockchain stores aggregated reports from local chains and can be queried by any user to receive a traffic report at any location. ...
With the rise in traffic congestion and associated costs, it becomes crucial to readily make available accurate traffic reports to the general public and also to predict the traffic levels to mitigate further congestion. Various tools and technologies have emerged to solve the aforementioned problem, which comprises secure and accurate data collection, storage, utilization of this data for prediction, and making required data available to the public. Motivated from the aforementioned discussion, in this paper, various approaches to solving this larger puzzle have been discussed and analyzed, and a holistic solution combining the power of blockchain, InterPlanetary File System (IPFS), and neural networks have been suggested. The goal is to leverage apt technology to solve the pertinent issue of traffic management. Simulation results show that an LSTM model with 50-time steps and 200 units in the hidden layer, followed by a dense layer leads to minimum Root Mean Square Error (RMSE) value, with a randomly generated but complete dataset. Security analysis of the proposed solutions shows its efficacy compared to state-of-the-art approaches.
... 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. ...
The next wave in smart transportation is directed towards the design of renewable energy sources that can fuel automobile sector to shift towards the autonomous electric vehicles (AEVs). AEVs are sensor‐driven and driverless that uses artificial intelligence (AI)‐based interactions in Internet‐of‐vehicles (IoV) ecosystems. AEVs can reduce carbon footprints and trade energy with peer AEVs, smart grids (SG), and roadside units (RSUs). It supports green transportation vision. However, the sensor information, energy units, and user data are exchanged through open channels, and thus, are susceptible to various security and privacy attacks. Thus, AEVs can be remotely operated and directed by malicious entities that can propagate false updates to the peer nodes in IoV environment. This can cause the failure of components, congestion, as well as the entire disruption of IoV network. Globally researchers and security analysts have addressed solutions that pertain to specific security requirements, but still, the detection and classification of malicious AEVs is a widely studied topic. Malicious AEVs exhibit an anomaly behavior that differentiates them from normal AEVs, and thereby, the detection of anomalous AEVs and classification of anomaly type is required. Motivated from the aforementioned facts, the survey presents a systematic outlook of AI techniques in anomaly detection of AEVs. A solution taxonomy is proposed based on research gaps in the existing surveys, and the evaluation metrics for AI‐based anomaly detection are discussed. The open challenges and issues in AI deployments are discussed and a case study is presented on anomaly classification through a weighted ensemble technique. Thus, the proposed survey is designed to guide the manufacturing industry, AI practitioners, and researchers worldwide to formulate and design accurate and precise mechanisms to detect anomalies.
... -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]. ...
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 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. ...
Network management for unmanned aerial vehicles (UAVs) is challenging, keeping in view the high mobility of the vehicles. Hence, for smooth execution of various operations such as rescue, surveillance, and crowdsensing in a UAV environment, softwarization of UAV SGI networks becomes essential, which separates control functions from hardware, i.e., data from the control plane. Using softwarization, various complex operations in the UAV environment can be executed with an increase in UAVs. However, with an increase in the complexity of UAV network management, secure communication among UAVs becomes a tedious task as most of the communication among UAVs takes place using an open SGI network, i.e., the Internet. Software-defined networking (SDN) and network function virtualization (NFV) are the key softwarization enabling techniques in fifth-generation (5G) networks, which are used to manage secure network services with reduced capital and operating expenditures. However, different softwarization layer may suffer from controller hijacking, user authentication, access control, and resource consumption attack. Till date, many solutions reported in the literature for this problem are centralized controlled that suffers from single-point of failure and also prone to various security attacks. Motivated from this, in this paper, a systematic and comprehensive survey is presented, which is based on blockchain (BC)-envisioned secure and trusted softwarized UAV network management. We also propose a BC-based softwarized UAV architecture to make the communication network secure and easily manageable. It can offer flexible and dynamic decision capabilities for network management services even in open 5G-enabled UAV networks. Finally, we analyzed the research challenges posed and future challenges in this area.
... 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 ...
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.
... 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. ...
The Internet of things (IoT) connects multiple devices worldwide. It is a growing field in the healthcare system such as health monitoring and tracking, fitness program, and remote medical assistance. With the advent of IoT based technologies in healthcare, it can alleviate the pressure on healthcare systems and can reduce the healthcare cost, and increase the computing and processing speed. Cloud computing was introduced to manage larger and complex healthcare data in the IoT environment. Cloud computing uses centralized cloud data centers. The central server manages the data for all the IoT devices. The integration of IoT with the cloud has some major issues such as latency, bandwidth overuse, real-time response delays, protection, and privacy. So the concept of edge computing and fog computing came into existence to overcome these issues. This paper review the IoT-Fog-based system model architectures, similar paradigm, issues, and difficulties in the area of cloud computing and finally, the performance of some of these proposed systems is assessed using the iFogSim simulator.
... 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. ...
Nowadays, increasing urbanization has necessitated the social, environmental, and economic development of cities to enhance the Quality of Life (QoL) significantly and introduces the ``Smart City" concept. It integrates Information and Communication Tools (ICT), Internet of Things (IoT), and other technologies to resolve urban challenges. The key goal is to make the most acceptable use of available resources and technologies to develop smart cities. An IoT-enabled application plays a crucial role here, but it has various security, privacy, latency, and reliability issues with a single-point-of-failure problem. The evolving technology blockchain can handle the aforementioned security and privacy issues and provide high-quality services due to several features like transparency, trust-free, decentralization, immutability, and others. The 6G communication network takes care of latency and reliability issues in the smart city with their unique characteristics such as latency ($10-100 \mu s$) and reliability (99.99999\%). Motivated by these facts, in this paper, we presented a comprehensive review for blockchain technology and IoT together functional to smart cities. First, state-of-art-the works and contextual information are introduced. Then, we proposed a blockchain-based decentralized architecture for IoT-integrated smart cities covering different application perspectives, such as smart grid, Intelligent Transportation System (ITS), and healthcare 5.0 underlying 6G communication networks. Next, we describe the challenges of the proposed architecture respective to each application, as mentioned above. Finally, we collated the open research issues and future direction to efficiently integrate blockchain into IoT-envisioned smart cities.
... 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]. ...
Increasing demand for automation and instant solution leads the technological world towards massive applications such as the Internet of drones, Autonomous Vehicles (AVs), border surveillance, telesurgery, Augmented Reality (AR), which requires a vast upgrade in technology with improved processing and computation power. Centralized Cloud Server (CSS) provides the facility to compute critical tasks at the central data server, but it consumes more time in case of the remote distance between CSS and edge device. In this article, we have discussed the advantages of edge computing over cloud computing to overcome latency and reliability issues in critical applications. Moreover, the idea of processing and analyzing massive applications at the edge comes up with the requirement of building intelligence at the edge to compute complex tasks within a negligible time. Edge intelligence provides intelligence at the edge to process large datasets for critical computations and to overcome storage issues. Also, the performance tolerant connectivity and low-speed rate issues with 4G and 5G can be solved using a 6G wireless network. 6G connected edge intelligence application offers ultra-low-latency, security, and reliability mechanisms that could be helpful in COVID-19 pandemic situations. We have also discussed a demonstration of the aforementioned massive application in the form of a case study on combating COVID-19 situations using 6G-based edge intelligence. The case study depicts the benefits of using 6G (latency: 10 − 100µs) over 4G (latency: < 10ms) and 5G (latency: < 5ms) communication networks. The proposed 6G-enabled scheme is compared against the traditional 4G and 5G networks to designate its efficiency in terms of communication latency and network mobility. Eventually, we then analyzed various open issues and research challenges in this emerging research area for future gains and insights.
Smart devices contributes unique and improved functionality to the smart healthcare system, a smart device is in possession of a substantial quantity of sensitive patient data. In addition, the many aspects of healthcare systems are now interconnected through the use of the internet, which has resulted in substantial shifts in the manner in which individuals receive healthcare services. Having simple access to healthcare services and applications, on the other hand, has resulted in the emergence of significant risks and vulnerabilities that hinder the performance of a smart healthcare system. In addition, a huge number of heterogeneous devices generate data with varying sizes and formats. This makes it difficult to manage the data in the healthcare repository and protect it from attackers that want to make money off of the data. Therefore, intelligent healthcare systems are vulnerable to a wide variety of security risks and threats, such as assaults based on hardware and software, attacks directed at the system level, and attacks directed at the network, all of which have the potential to put patients’ lives in jeopardy. In this paper, AI, big data analytics and cognitive computing role in healthcare sector is explained. At the end variety of unresolved concerns and research obstacles that are specific to smart healthcare systems.KeywordsSmart healthcare systemsArtificial intelligenceMedical devicesBig data analyticsCognitive computing
(Asymmetric) Password-based Authenticated Key Exchange ((a)PAKE) protocols allow two parties establish a session key with a pre-shared low-entropy password. In this paper, we show how Encrypted Key Exchange (EKE) compiler [Bellovin and Merritt, S &P 1992] meets tight security in the Universally Composable (UC) framework. We propose a strong 2DH variant of EKE, denoted by 2DH-EKE, and prove its tight security in the UC framework based on the CDH assumption. The efficiency of 2DH-EKE is comparable to the original EKE, with only \(O(\lambda )\) bits growth in communication (\(\lambda \) the security parameter), and two (resp., one) extra exponentiation in computation for client (resp., server).We also develop an asymmetric PAKE scheme 2DH-aEKE from 2DH-EKE. The security reduction loss of 2DH-aEKE is N, the total number of client-server pairs. With a meta-reduction, we formally prove that such a factor N is inevitable in aPAKE. Namely, our 2DH-aEKE meets the optimal security loss. As a byproduct, we further apply our technique to PAKE protocols like SPAKE2 and PPK in the relaxed UC framework, resulting in their 2DH variants with tight security from the CDH assumption.Keywords(Asymmetric) PAKEUC FrameworkTight Security
Clinical decision support systems (CDSSs) symbolize a significant transformation in healthcare delivery. CDSS enhances healthcare delivery by enabling personnel in medical institutions to handle complex decision-making processes with great speed and high accuracy. Decision support systems are developed using a knowledge-driven or data-driven approach, although both approaches seem to complement each other. For instance, while data-driven is an objective approach, the knowledge-driven approach is subjective. The objective of the chapter is to elaborate on the integration of data-driven and knowledge-driven methodologies for clinical decision support systems. An overview of data-driven and knowledge-driven approaches is presented with a review of both current and dated literature on the subject with numerous viewpoints to support the discussion. Based on the findings, a promising methodology is proposed that integrates data-driven and knowledge-driven approaches and is believed to overcome the challenges of the individual approaches.
Currently, a facial analysis system for human–computer interfaces is presented and employed extensively. The increasing computer and digital speed, accuracy, and low-cost webcams that users can use bring computer vision systems more and more popular. These systems can assist people with alternative, hand-free communication to computers with human–computer interfaces. Hence, in this paper, computer vision-based face analysis model (CVFAM) has been suggested for human–computer interaction. This paper can determine the mouth and eyes position and use the facial centre to estimate the head's pose. In the suggested model, the face location is extracted from the central image by a cascade and skin detector and then is sent to the recognition phase. In the recognition phase, the threshold condition is examined, and the extracted face and gaze will be predictable. To learn about computer systems that automatically analyzes images, this paper CVFAM mode has suggested recognizing the user's face sitting in front of the system and recognizing user hand gestures and facial expressions, therefore providing interfaces for users HCI. The simulation results show that the suggested CVFAM model enhances the accuracy ratio of 93.5%, the detection rate of 94.2, the location analysis ratio of 94.5%, the recognition rate of 83.2, and the average delay ratio of 22.1% to other existing models.
The introduction of sensor technology in our daily lives has brought comfort, convenience, and improved health over the past few decades. Technological advances further expanded the use of medical sensors by reducing their size and costs. Medical sensors improve the intelligence and capabilities of healthcare services including, remote health monitoring, surgical procedures, therapy, and rehabilitation. We present a comprehensive review of medical sensors in the last 50 years focusing on their deployment in healthcare applications. The review also discusses the role of Internet of Things (IoT) technology in enhancing the capabilities of sensor technologies for the healthcare domain. Moreover, we also investigate the benefits and challenges of various integrated architectures which have been proposed recently to seamlessly integrate heterogeneous medical sensors with emerging technologies and paradigms that include edge, Mobile Cloud Computing (MCC), fog, and cloud computing technologies. Finally, we identify future challenges that must be addressed to achieve the maximum potential and benefits of medical sensor technologies and ultimately provide robust, scalable, reliable, and cost-effective healthcare delivery.
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.
In Smart Grid (SG), energy management in residential houses has gained widespread popularity with the increasing population and demand for energy. Residential Energy Management System (REMS) can lead to higher efficiency and lower operating costs for the multi-carrier energy supply (i.e., gas and electricity). Several approaches exist for single carrier REMS, however, it is quite challenging to develop a multi-carrier REMS due to the dynamic consumption environment. So, this paper proposes a MultiAgent-based Residential Energy Management scheme, i.e., MA-REM for a multi-carrier energy system. It benefits different Demand Response Program (DRP) for each energy component (i.e., gas and electricity), which accelerates by employing Reinforcement Learning (RL) methodology. The proposed MA-REM scheme uses Q-learning based on a dynamic pricing mechanism for optimal energy management in REMS to reduced energy cost and energy consumption. The effectiveness of the proposed MA-REM scheme is evaluated by comparing it with existing approaches in terms of energy consumption and energy cost for electricity & gas.
Machine-to-Machine (M2M) communication refers to autonomous communication among devices that aims for a massive number of connected devices. M2M communication can support ubiquitous communication and full mechanical automation, and it will change everything from industry to ourselves. Recent developments in communication technology make Long Term Evolution (LTE)/Long Term Evolution-Advance (LTE-A) a promising technology for supporting M2M communication. LTE can support the diverse characteristic of M2M communication due to its IP connectivity, coverage area, and scalability. Therefore, the LTE schedulers should satisfy the need for M2M communication. Motivated by these facts, in this paper, we present a survey on the classification of LTE / LTE-A scheduling methodologies from the perspective of M2M communication. We classify the schedulers based on their objectives, such as energy efficiency, spectrum efficiency, group-based, and Quality-of-Service (QoS) support for Machine Type Communication Devices (MTCDs). We also highlight the scope of future research direction for the scheduling work.
The paper proposes a sixth-generation (6G)-enabled
cellular vehicle-to-anything (C-V2X)-based scheme, Res6Edge,
that supports high-data ingestion rate through artificial intelligence
(AI) models at edge nodes, or Edge-AI. Through Edge-AI
in 6G supported C-V2X, we address the research gaps of earlier
schemes based on fifth-generation (5G) resource orchestration.
6G improves decision analytics and real-time resource sharing
among C-V2X ecosystems. The scheme operates in three phases.
In the first phase, a layered network model is proposed for
V2X communication based on 6G-aggregator and core units.
Then, based on the proposed stack, in the second phase, 6G
resource allocation is proposed through a macro base station
(MBS) units. MBS ensures channel gain and reduces energy
loss dissipation. Finally, in the third phase, an intelligent edge-
AI scheme is formulated based on deep-reinforcement learning
(DRL) to support responsive edge-cache and improved learning.
The proposed scheme is compared to 5G baseline services
in terms of parameters like- throughput, latency, and DRL
scheme is compared to random allocation approaches. Through
simulations, Res6Edge obtains a V2X user throughput of 43:24
Mbps, compared to 0:7 Mbps for 4 x 10^8 connected ACV
sensors. The reduced latency is approximately 13:84 times of 5G. DRL
learning algorithm achieves a satisfaction probability of 0:5 for
500 vehicles, compared to 0:35 using conventional schemes. The
obtained results indicate the viability of the proposed scheme.
Index Terms—6G, Resource sharing, Resource allocations,
Edge-AI, Vehicle-to-anything.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Ambient assisted living provides an environment to aid regular activities (such as exercise, medical diagnosis etc.) for well being of users. In this direction, posture correcting mechanisms can be developed to have a real-time detection and correction system, which allow doctors and caregivers to monitor the patients’ health without worrying about potential falls and backaches. Most of the data related to patient health resides on the cloud environment to be accessed from anywhere by different users. However, the existing cloud computing infrastructure does not support a quick feedback for posture correction by the patients located geographically separated locations.However, with the advent of 5G networks, a new networking paradigm of Tactile Internet emerges having low latency communication for ubiquitously connecting heterogeneous devices at the edge of the network. Keeping focus on these issues, we propose, TILAA, a framework for Tactile Internet-based Ambient Assistance Living in fog environment in this paper. It provides a collaborative communication feedback to the patients and doctors with reduced delay. TILAA consists of a micro controller, sensing devices such as-accelerometer and gyroscopes, and a battery holder. The straps on the chest wearable consist of actuators, which send vibrations as a Tactile feedback to the doctors or to the hospitals for taking proactive measures in case of emergency. Performance evaluation of TILAA shows that it has superior performance for the back posture of all patients as compared to pre-existing expensive detection and posture schemes.
Internet of things provides interaction with billions of objects across the world using the Internet. In Internet of thing era, Healthcare Industry has grown-up from 1.0 to 4.0 generation. Healthcare 3.0 was hospital centric, where patients of long-lasting sickness suffered a lot due to multiple hospital visits for their routine checkups. This in turn, prolonged the treatment of such patients along with an increase in the overall expenditure on treatment of patients. However, with recent technological advancements such as fog and cloud computing, these problems are mitigated with a minimum capital investment on computing and storage facilities related to the data of the patients. Motivated from these facts, this paper provide an analysis of the role of fog computing, cloud computing, and Internet of things to provide uninterrupted context-aware services to the end users as and when required. We propose a three layer patient-driven Healthcare architecture for real-time data collection, processing and transmission. It gives insights to the end users for the applicability of fog devices and gateways in Healthcare 4.0 environment for current and future applications.
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.
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.
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.
The rapid development of information technology and computer networks make them part of almost everything in our daily life and it became impossible to abandon their use. One of the main and important applications of technology in homes is home automation including controlling and automation of electronic and electrical machines remotely. Wireless Home Automation Networks (WHANs) are used in homes to connect the different devices together and to the Internet. In order to control home devices remotely, there are many popular protocols such as INSTEON, ZigBee, and Home Plug. In this paper, we focus on the relatively new protocol called Z-Wave protocol and we discuss it development and applications in smart homes. This wireless protocol has many advantages over the popular and widely used ZigBee protocol as it provides better reliability, low radio rebirth, easy usage, and easy Interoperability.
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.
The public IPv4 address space managed by IANA (http://www.iana.org) has been completely depleted by Feb 1st, 2011. This creates by itself an interesting challenge when adding new things and enabling new services on the Internet. Without public IP addresses, the Internet of Things capabilities would be greatly reduced. Most discussions about IoT have been based on the illusionary assumption that the IP address space is an unlimited resource or it is even taken for granted that IP is like oxygen produced for free by nature. Hopefully, the next generation of Internet Protocol, also known as IPv6 brings a solution. In early 90s, IPv6 was designed by the IETF IPng (Next Generation) Working Group and promoted by the same experts within the IPv6 Forum since 1999. Expanding the IPv4 protocol suite with larger address space and defining new capabilities restoring end to end connectivity, and end to end services, several IETF working groups have worked on many deployment scenarios with transition models to interact with IPv4 infrastructure and services. They have also enhanced a combination of features that were not tightly designed or scalable in IPv4 like IP mobility, ad hoc services; etc catering for the extreme scenario where IP becomes a commodity service enabling lowest cost networking deployment of large scale sensor networks, RFID, IP in the car, to any imaginable scenario where networking adds value to commodity. For that reason, IPv6 makes feasible the new conception of extending Internet to Everything. IPv6 spreads the addressing space in order to support all the emerging Internet-enabled devices. In addition, IPv6 has been designed to provide secure communications to users and mobility for all devices attached to the user; thereby users can always be connected. This work provides an overview of our experiences addressing the challenges in terms of connectivity, reliability, security and mobility of the Internet of Things through IPv6 in order to reach the Internet of Everything. This describes the key challenges, how they have been solved with IPv6, and finally presents the future works and vision that describe the roadmap of the Internet of Everything in order to reach an interoperable, trustable, mobile, distributed, valuable, and powerful enabler for emerging applications such as Smarter Cities, Human Dynamics, Cyber-Physical Systems, Smart Grid, Green Networks, Intelligent Transport Systems, and ubiquitous healthcare.
Zigbee wireless communication technology is a kind of newly arisen wireless network technology; the characteristic is short distance communication, low speed, low power dissipation, and low cost. It, application of Zigbee wireless communication technology, makes that inconvenient wire repeat can be avoided in the area of home, factory, hospital, etc. With the rapid development of IT industry and the strong functional expansion of SCM, Zigbee wireless communication technology will play an important role in wireless sensor network (WSN). In this paper, Zigbee wireless communication technology and the process of establishing Zigbee network are introduced, the application of Zigbee wireless communication technology is studied in the real world.
Case study on comparison of wireless technologies in industrial applications
- abinayaa
V. Abinayaa, Anagha Jayan, "Case Study on Comparison of
Wireless Technologies in Industrial Applications" International
Journal of Scientific and Research Publications, vol. 4, no. 2,
pp. 1-4, 2014.
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