Fog Computing for Healthcare 4.0 Environments

Health issues (concerning human being) are critical nowadays. Due to heavy workload and less time, human beings do not have sufficient time to consult a doctor regarding their health. Healthcare industry has a different generation like healthcare 1.0 to healthcare 4.0. Healthcare 3.0 is focused on hospitals, where patients have to visit multiple hospitals for their routine examination, making them suffer through long-lasting sickness. It turns a patient into a lengthy process of examination and also it increases the overall budget of treatment. However, with the help of Fog Computing (FC), the above-said problem can be minimized by investing less money on computing and storage facility in respect of data related to patients. Healthcare 4.0 is working on FC platform. FC extends cloud computing platforms with additional computing, storage and networking resources, placed near end-user devices. FC deploying fog nodes throughout the network is deployed in target areas like cars and offices etc. When an IoT device generates the data, then it will be analyzed by one of the fog nodes without sending back to the cloud. The main aim of this chapter is to provide a systematic view of the technology used for FC in healthcare 4.0. This chapter also gives a comparative study of the different version of healthcare with current version 4.0. Further, different researchers view about healthcare industry is discussed in detail. This chapter also discussed the importance of FC in healthcare with the help of some case studies for better understanding in solving health-related issues.

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.

Fog computing environment is geographically dispersed and diverse heterogeneous devices are associated ubiquitously to it towards the end of a system so as to give cooperatively variable and adaptable communication, storage devices, and computation. Fog computing has numerous recompenses and is well-matched for the applications, wherein time-sensitivity, higher response time, and lower latency are absolutely important factors, particularly healthcare applications. These applications also have lot of challenges such as need of remote monitoring of patients, need of preventive instead of reactive care, etc. In many studies, cloud computing was shown to be well suited for healthcare applications, but with advent of fog computing, fog computing imposes more advantages as compared to cloud computing. Many studies showed that fog computing is well-matched for healthcare applications as it facilitates low latency, higher response time, reliability, scalability, location awareness, better security and privacy of health data, fault tolerance, etc. This study is divided into collection of frameworks developed for healthcare application with respect to fog computing and collection of proposed architectures and implemented systems for the same. Researchers have shown through simulations and experiments that the main factor in healthcare application is reduced latency which should be achieved by means of fog computing.

The wide use of Internet-enabled devices has not left the healthcare sector untouched. The health status of each individual is being monitored irrespective of his/her medical conditions. The advent of such medical devices is beneficial not only for patients but also for physicians, hospitals, and insurance companies. It makes healthcare fast, reliable, and hassle-free. People can keep an eye on their blood pressure, pulse rate, etc., and thus take preventive measures on their own. In hospitals too, the Internet of Things (IoT) is being deployed for various tasks such as monitoring oxygen and blood sugar levels, electrocardiograms (ECGs), etc. IoT in healthcare also reduces the cost of various ailments through fast and rigorous data analysis. Prediction of diseases through machine learning techniques based upon the symptoms has become a promising concept. There may also be a situation where real-time analysis is required. In such a latency-sensitive situation, fog computing plays a vital role. Establishing communication every time with the cloud is not required with the introduction of fog and thus the latency is reduced. Healthcare is a latency-sensitive application area. So, the deployment of fog computing in this area is of vital importance. Proper analytics and research may lead to better care, improved treatment, and enhanced patient satisfaction. The chapter discusses the relevance of fog computing in the area with its issues and challenges. Later, the security issues of fog computing in the area have also been highlighted.

In recent technological era, the healthcare industry has been gaining momentum toward service-oriented facilities to the customers. The primary aim of the Healthcare 4.0 is to provide healthcare services anytime and anywhere. This is possible, as Healthcare 4.0 targets integration with current technologies like Internet of Things (IoT), Cloud Computing, Big Data, and Machine Learning. However, the integration of Healthcare 4.0, with IoT and cloud computing through Internet has several challenges in handling real-time applications such as access latency, cost, and lack of service availability. On the other hand, the fog computing (FC) is able to overcome these challenges using fog devices, that are capable of optimizing the delay in information gathering and processing. The primary advantage of the fog computing system is the geographical location of fog devices within proximity of patient and IoT healthcare systems. This enables fog computing system to perform computation, storage and networking services with lesser delay and jitter. However, the major issue in fog computing is to handle the voluminous healthcare data generate from different IoT healthcare edge systems. Hence, this chapter targets to present on various fog-based data processing and data analysis (FDPA) mechanisms in fog computing solutions toward achieving the objectives of Healthcare 4.0. This chapter is divided into five major sections namely architecture of fog data processing and analytics, applications of FDPA, data processing algorithms in fog computing and data compression mechanisms and data analysis mechanisms in Fog computing toward Healthcare 4.0. The fog data architecture discusses various layers namely sensing layer, fog gateway layer, fog-based data processing and data analysis layer, cloud layer, and service layer. Here, the process of sensing of healthcare data, maintenance of data, and various methods to analyze healthcare data are discussed. Further, the healthcare data gathered from sensor devices are raw and redundant in data, hence they are needed for various data processing algorithms for fog-based healthcare systems. Hence, various data processing techniques such as Dynamic Time Warping, Clinical Speech Processing are discussed. Next, in fog computing, the data compression techniques are required for optimizing the bandwidth consumption and energy efficiency are presented. Next, data analytics mechanism such as real-time decisive analysis, real-time control and context analysis, and real-time data analysis are presented.

The integration of IoT and ML led to the development of 4.0 technology. With the help of 4.0 technology, we can deliver the benefits of IoT and ML to Healthcare industry. Healthcare 4.0 focuses on precise, timely, and effective medicine using IoT and ML. Data analysis and data processing play a crucial role in managing and exertion of this unstructured data being generated by device interactions. The two primary challenges with performing data analysis on cloud are burdensome load on cloud and the slow response time. To overcome these issues, the fog or edge computing (FC) is introduced. FC uses edge devices to perform considerable amount of computation, storage, and communication for data that needs to be immediately processed. It overcomes issues of costly bandwidths by offloading network traffic from the main channel, overcomes limitations of computing power, and also protects the sensitive IoT data.

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.

Fog computing offers few major benefits in comparison to the cloud computing approaches such as low latency, privacy, and resiliency against cloud inevitability. Fog computing adds an additional layer of computing power between the device and the cloud, keeping critical analytics closer to the device, thus reducing the amount of time it takes from request to reply. The aim of role of the Internet of Things in the field of healthcare is to make it easier for patients to stay connected to their providers and for their providers to provide their communities with transparent, value-based care. Fog computing can be the basic infrastructure for turning IoT from innovation to practice in healthcare. In this chapter, we are addressing the challenges of healthcare 4.0. The challenges are regarding the data (data collection and analysis), security and privacy and e-healthcare services and also we will discuss how the taxonomy of fog computing can be a better solution to healthcare 4.0.

The healthcare industry has come a long way with a series of revolutions starting from Healthcare 1.0 up to Healthcare 4.0 where innovation made efforts to save human lives better than the past. Today IoT devices around the world and especially in healthcare domain generate a huge amount of data with 3Vs of big data, i.e., velocity, variety, and volume. This is where fog computing and cloud computing have come up in utilizing those data which can be utilized for analytics and modeling purposes which can keep records as well as predict the patients’ health by performing analysis. In the age of IoT and AI, which is bringing amazing possibilities and intelligence in various industries, it has brought revolutions in healthcare as well by giving real-time analytics on patient’s data without fail with help of fog computing and cloud computing. In this chapter, the role of IoT, fog computing, and cloud computing has been described along with applications of machine learning and big data that runs on these paradigms has been explained. Issues related to cloud computing and motivation behind bringing fog computing paradigm have also been explained in detail. Several architectures of fog computing are also discussed in this chapter along with their application and comparison. Application of big data and machine learning modeling has also been explained in later part of the chapter. Lastly, case studies related to fog computing, big data, and machine learning in healthcare has been explained and compared.

Internet of Things (IoT), cloud computing, fog computing, and other new technologies are expected to transform the healthcare industry among other. This chapter discusses the use of an automated system based on IoT, cloud and fog computing for constant monitoring of the patient’s health, dispensing medicinal dosage in a timely manner and other comprehensive function for the well-being of both sick and healthy individuals. The wearable embedded devices can capture the patient’s physiological signals including body temperature, blood pressure, electrocardiogram (ECG), oxygen saturation (SpO₂), pulse rate, stress, sweating and send them to the cloud server for processing. The processors in individual devices can also communicate and make necessary decisions through fog computing. The medicine dispensing system can monitor the patient medicine details and timings. The real-time captured information can be processed and analyzed to check drug effectiveness and adverse effects on patients. Based on the analysis report, physicians can take decision to continue to use the same drug or change it. It can also help to reduce medication errors by the doctors, nurses, and pharmacists as all the drugs will be identified and recorded by the medicine dispensing system. The system can also improve the medication adherence and critical care based on the real-time medication and physiological signals notifications to the patients, doctors, and family members. The IoT-based system exhibits the ability to achieve objectives for continuous health monitoring through embedded devices with IoT capability and connected to cloud computing and fog computing.

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.

Fog computing is a new trending technology which adds on the capability and efficiency to cloud computing network as it is interposed between cloud and device. Fog devices are able to compute large quantity of data locally, portable, and may be installed on heterogeneous system. It is significantly suitable for healthcare due to its real-time processing and event responses. Such huge variety of traits emerges new security and privacy issues. In the field of healthcare, security poses additional challenges due to the secure transfer, arrival and access, availability of medical device. Ultimately human well-being is the superior necessity. It has become more vulnerable due to its features like mobility, heterogeneity, decentralized and additional challenges sensitive health information records, interoperability of medical IoT device. Therefore, Fog computing demands exclusive way for security and privacy measurements rather than existing measures for cloud computing. Also, as the number of access point increases it is open for more vulnerability. Implanted devices are more critical as if it is not properly secured, then they may put patient in a critical situation. This chapter discusses about basic security and privacy issues which state the need for security in Fog-based medical devices. Different possible attacks and threats are covered with the scenario of implanted medical device. Security challenges for different segments of Fog computing like device, network, and data have been discussed with in-depth analysis for security challenges, privacy, and trust issues in the relation of healthcare 4.0.

With the advancement in the aging of world’s population and increments of people having chronic diseases, resulted in high demand for expensive medical treatment and care. In this view, the usage of latest technology solutions has been utilized at wide stage in order to improve the health of patient. One of the most prominent solution in this regard is the usage of cloud computing technology for the storage and process of patient health record. The medical data such as CT scan, MRI, X-rays, heart or kidney transplantation videos, and other health information should be available in digital format and such type of huge multimedia big data needs to be kept in the cloud. But, this usage of cloud computing can introduce delay while processing the data which is not tolerable. To deal with this problem, fog computing is used, which allows the data storage and its processing near to the data source. But it also brings with itself many security challenges such as data availability, security, privacy, performance, and interoperability, which requires high consideration. This chapter concentrates on these issues, i.e., how patient data can be retrieved for monitoring while reducing the latency and securing the private data of patient. A pairing-based cryptography technique such as an elliptic curve Diffie–Hellman key agreement protocol and a decoy technique are used to access and store data more securely along with the help of some cryptographic algorithms. In this chapter, we have also exasperated to gather some of the security matters which may stand up in the healthcare sector, and also discuss existing resolutions and emergent threats.

Sensors play an essential role in different applications such as medicine, manufacturing, climate, smart transportation, and smart city. Wearable or implantable body sensors are necessary for the human body to collect patient information. Such tools produce a massive amount of data, and to collect useful information, it is more difficult to secure such data from intruders, process, and interpret it. In this chapter, we are improving such a big data health monitoring system by leveraging the fog computing principle at smart gateways, offering advanced network edge techniques and services. In particular, as a case study, we chose electrocardiogram (ECG), because it plays an important role in the diagnosis of many heart diseases. The experimental results show that fog computing helps to reduce the encrypt and decrypt time compared to other traditional algorithms, and the information will be transmitted more safely using the algorithm with less computational overhead.

Healthcare 4.0 standards propose a personalized and precise medicine for effective therapy based on patient’s genetic, environmental, and lifestyle parameters. Healthcare 4.0 standards promote a patient-centric healthcare service delivery at his doorstep. This system enables patient’s healthcare data sharing online among the doctors, hospitals, and other healthcare service providers to leverage the efficiency in healthcare services management. The foolproof authentication mechanism forms a gateway to any security system to ensure integrity, confidentiality, and authorization to prevent any intrusions into the healthcare systems. Today biometric security mechanisms are gaining significance in the Internet of Things (IoT) network security domain. Biometric technology analyzes an individual end-user’s physiological, behavioral, or morphological traits such as the face, fingerprint, iris, retina, voice, and handwritten signatures for authentication purposes. Authors have reviewed the relevant literature on biometric authentication systems and carried out a comparative study of the various biometric techniques, results, and applications. The national and international status of healthcare data protection acts and tools used for biometric authentication was discussed. This chapter deals with a complete design process of multi-mode biometric-based security layer to provide secure authentication to access healthcare data at the edge devices deployed in hospitals and patient’s smart homes. Authors have discussed the prototype design for authentication of end-users of healthcare data and carried out a face recognition experiment for authentication.

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.

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 commonest type of primitive headache is Tension- Type Headache (TTH). The focus of complete research work is to compare the impression of EMG, GSR and EEG integrated biofeedback on stress due to headache and quality- of- life of the subjects under consideration. Electromyography (EMG) biofeedback (BF) and GSR (Galvanic Skin Resistance) are considered an effective therapy for headaches. There are no such comparative effects of visual and auditory EMG biofeedback for headache.

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.

Microwave imaging provides the solution to detect the cancer across the globe using utilizing the antenna design for medical and Internet of things. Breast tumor is one of the majority identically occurring cancers found in females. It results in death, if not initially diagnosed. Existing breast cancer detection techniques are not efficient to detect the breast cancer in its former phase. As per the world health organization in 2018, 2.09 million peoples were diagnosed and 627,000 deaths were reported due to the breast tumor. The endurance rate for the patients diagnosed with breast cancer is 66% in India and in 2018, 162,468 breast cancer new cases were covered as per the Cancer India organization. In this study, hexagonal T-slot micro strip antenna, operating at 1.75 and 4.04 GHz, is simulated to spot the breast tumor. The scanning antenna generates a real-time multiple data, which makes it easy for a physician to identify the breast cancer in an early phase of development. Simulation results such as current density, yield loss, electric field, gain, radiation pattern are analyzed and investigated for breast tumor and normal breast tissue.

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.

According to Indian philosophy, OM is a spiritual symbol called Atman Brahman (Reality, World, Truth, God, Supreme Spirit, Cosmic Principles, and Knowledge). This research has the symbol of global OM. The purpose of this study was to evaluate the impact of Gayatri Mantra recitation and Om recitation on human health. Gayatri mantra is a highly potential mantra, which is mentioned in Rig Veda. The study/Yagyopathy Experiment was performed on some patients (Male = 4) and (Females = 7) with an age range of 44–70 years. All patients were trained for reciting Gayatri mantra for 3 days. The baseline data were used. The participants participated in Gayatri mantra and Om recitation about 15 min for two consecutive days. The sequence of the session was assigned randomly to the participants. This preliminary study shows that both Gayatri mantras and sitting comfort have led to increased attention, as measured by the stroop task. However, Gayatri’s mantra effect was significantly greater than Om’s recitation. During the meeting on yagyopathy chaired by Shraddhey Dr. Sahab at Shantikunj on 23 Nov 18, Prof. Rohit Rastogi volunteered to experiment its effects on himself. I consulted Dr. Vandana Srivastava and was advised following treatment: yagyopathy twice a day, at sunrise and sunset, with havan samagri for asthma and normal samagri in 3:1 ratio, recitation of Surya Gayatri mantra 24 times and nadi-shodhan pranayama for 30 min. Kwath of mixed hawan samagri twice a day. In order to measure the efficacy of Yagyopathy, I undertook Lung Function Test (LFT) on 18 Dec 18 and started yagyopathy as above instructions (once a day) since 24 Dec 18. Following, three important parameters are measured during LFT: Forced Vital Capacity (FVC): a measure of lung capacity/volume of air can open after Deep inhalation. Forced expiratory volume 1 s (FEV1): Breath measurement. FEV1/FVC: The ratio of lung size (FVC) that expires in seconds. The results obtained after undertaking yagyopathy treatment once a day for approximately 2 1/2 months indicated considerable improvement in lung function parameters. Results can be further improved with increasing treatment doses to twice a day.

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.

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.

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.