Mobile Computing Solutions for Healthcare Systems

The use of a Software-Defined Network (SDN) approach improves the control and management processes of the complex structured wireless sensor network. Also, it provides higher flexibility and a dynamic network structure. SDN is introduced to efficiently and opportunistically use the limited spectrum to minimize the spectrum scarcity issues. The LEACH protocol is self-organizing and is characterized as an adaptive clustering protocol that randomly distributes energy load among nodes. By using cluster heads and data aggregation, excessive energy consumption is avoided. SDN is often placed in an open environment and is susceptible to various attacks. The routing is based on multihop’s flawless hauling range data transmission between the base station and cluster heads.The advantage of LEACH is that each node has the same probability of being a cluster head, which makes the energy efficiency of each node relatively balanced. Massive multiple-input multiple outputs (MIMO) play a polar role within the fifth generation (5G) wireless networks. However, its performance heavily depends on correct synchronization. Although timing offset (TO) can be avoided by applying orthogonal frequency division multiplexing (OFDM) with an adequate length of cyclic prefix (CP), carrier frequency offset (CFO) is still a challenging issue. Especially in the uplink of multiuser massive MIMO systems, CFO compensation can impose a substantial amount of computational complexity on the base station (BS) due to many BS antennas. However, to the best of our knowledge, no study looks into the joint estimation of CFOs and wireless channels in orthogonal frequency division multiplexing (OFDM) based massive MIMO systems. In this project, we propose a low-complexity CFO compensation technique to resolve this problem. In our paper, to traumatize this issue, we tend to propose a low-complexity frequency synchronization technique with high accuracy for the transmission of multiuser orthogonal-frequencydivision multiplexing-based large MIMO systems. First, we propose a carrier frequency offset (CFO) estimation whose process complexity will increase linearly concerning the quantity of base station (BS) antennas. We then propose a joint CFO compensation technique that is performed when combining the received signals at the BS antennas. As a result, its machine complexity exceeds the number of BS antennas. As a third contribution, the impact of the joint CFO estimation error is studied, and it is tested that by applying our planned joint CFO compensation technique, the joint CFO estimation error causes a continuing section shift solely. We tend to propose an algorithm to expeditiously calculate and take away the estimation error. Our simulation results testify to the effectiveness of our planned synchronization technique. As it is incontestable, our planned synchronization technique results in a bit of error rate performance that is the one for an asynchronous system. This leads to a considerable saving in the computational cost of the receiver. Numerical results are presented to verify the performance of our proposed joint CFO compensation technique and to investigate its computational complexity.

The recent outbreak of Severe Acute Respiratory Syndrome Corona Virus (SARS-CoV-2), also called COVID-19, is a major global health problem due to an increase in mortality and morbidity. The virus disturbs the respirational process of a human being and is highly spreadable. The current distressing COVID-19 pandemic has caused heavy financial crashing and the assets and standards of the highly impacted countries being compromised. Therefore, prediction methods should be devised, supporting the development of recovery strategies. To make accurate predictions, understanding the natural progression of the disease is very important.

The developed novel mathematical models may help the policymakers and government control the infection and protect society from this pandemic infection. Due to the nature of the data, the uncertainty may lead to an error in the estimation. In this scenario, the uncertainty arises due to the dynamic rate of change based on time in the infectious count because of the different stages of lockdowns, population density, social distancing, and many other reasons concerning demography. The period between exposure to the virus and the first symptom of infection is large compared to other viruses. It is mandatory to follow the infected persons.

The exposure needs to be controlled to prevent the spreading in the long term, and the infected people must be in isolation for the above-mentioned period to avoid short-term infections. Officials need to know about the long-term scenario as well as the shortterm for policymaking. Many studies are focusing on long-term forecasting using mathematical modelling. For the short-term prediction, this paper proposed two algorithms: 1) to predict next-day count from the past 2 days data irrespective of population size with less error rate and 2) to predict the next M days based on the deviation of the rate of change in previous N-days active cases.

The proposed methods can be adopted by government officials, researchers, and medical professionals by developing a mobile application. So that they can use it whenever and wherever necessary. The mobile health (M-Health) App. helps the user to know the status of the pandemic state and act accordingly.

The internet of things (IoT) is making its impact in every possible field like agriculture, healthcare, automobile, traffic monitoring, and many others. Especially in the field of healthcare, IoT has numerous benefits. It has introduced the concept of remote monitoring of patients with the help of IoT devices. These devices are turning out to be a game-changer and are helping healthcare professionals monitor patients and suggest recommendations with the help of data obtained from connected devices or sensors. Telemedicine, which helped provide remote medical services to patients, has gained importance, especially during this COVID-19 pandemic. It has helped the patients have online consultations with the doctor during the lockdown period, decreasing the need for unwanted hospital visits during pandemic times. Since these IoT-related networks are used daily, from health monitoring wearables to smart home systems, they must be protected against security threats. Thus, intrusion detection System is significant in identifying intrusions over an IoT network. intrusion detection Systems can be deployed by utilizing Machine Learning, and deep learning approaches. This paper aims to implement various algorithms on the BoT-IoT dataset. Moreover, their performance measures are compared and analyzed.

The headway of man-made reasoning techniques overlays the methods toward shrewd medical services by growing new ideas, for example, Machine learning. This part presents an outline of Machine learning procedures applied to brilliant medical services. AI procedures are regularly applied to brilliant well-being to empower Artificial knowledge based on a current innovative improvement to medical care. Moreover, the section likewise presents difficulties and openings in Machine adapting, especially in the medical services space and near examination of different AI techniques.

Mobile Cloud Computing (MCC) is a highly complex topic that encompasses several information security issues. The authentication area of the various entities involved has been extensively discussed in recent years and shown a wide range of possibilities. The use of inadequate authentication processes leads to several problems, which range from financial damage to users or providers of Mobile Commerce (M-Commerce) services to the death of patients who depend on Mobile Healthcare (M-Health) services.

The design of reliable authentication processes that minimize such issues involves the use of non-intrusive authentication techniques and continuous authentication of users by MCC service providers. In this sense, biometrics may satisfy such needs in various scenarios.

This research has explored some conceptual bases and presents a continuous authentication protocol for MCC environments. Such a protocol is part of a cyberphysical system (CPS) and is based on the monitoring of physiological information interpreted from users’ electrocardiograms (ECG). Machine learning techniques based on the Adaptative Boost (Adaboost) and Random Undersampling Boost (RUSBoost) were used for the classification of the cardiac cycles recognized in such ECGs.

The two ML techniques applied to electrocardiography were compared by a random subsampling technique that considers four analysis metrics, namely accuracy, precision, sensitivity, and F1-score. The experimental results showed better performance of RUSBoost regarding accuracy (97.4%), precision (98.7%), sensitivity (96.1%), and F1- score (97.4%).

This paper provides an analysis of IoT protection and privacy problems, as well as current security strategies, as well as a list of open topics for potential study. The most significant inventions are those that vanish. They become indistinguishable from the structure of daily existence when they weave themselves through it. This Internet of Things idea has begun to transform our modern environment, including a common man's daily existence in society, a world in which machines of all shapes and sizes are produced with “smart” capabilities that enable them to connect and interact not only with other devices but also with humans, share data, make autonomous decisions, and perform useful tasks based on predetermined conditions. With its many implementations, the Internet of Things is now a well-known phenomenon across both horizontal and vertical industries. 

Medical information is maintained in a digital format, like scanned images along with patient information. In the mobile health care system, digitized medical information is transmitted to remote specialists for diagnosis purposes. The remote specialists verify the patient medical information using mobile or other devices and suggest the treatment. During medical data transmission, through unsecured media, there is a chance to modify the medical data by the attacker. It leads to the wrong diagnosis and affects the patient's entire life. So there is a need for secure medical data transmission in mobile healthcare to protect medical information from unauthorized users or intruders. The medical image watermarking technique is required to protect medical information in mobile healthcare. To withstand various medical image watermarking attacks, this chapter discusses two different types of robust medical image watermarking techniques in mobile healthcare. First, an intelligent-based medical image watermarking technique is discussed to protect the medical data in a secured manner during the electronic patient information embedding part. After embedding the patient information in the medical image, the generated watermarked medical image looks like an original medical image. So the attacker knows the visual existence of the medical data during its transmission. To avoid this, the second technique, i.e., the visual medical image encryption technique, is discussed. The mobile healthcare system uses the intelligent medical image watermarking technique and visual medical image encryption for the secure transmission of medical information. 

Water quality plays an important role in human health. Contamination of drinking water resources causes waterborne diseases like diarrhoea and even some deadly diseases like cancer, kidney problems, etc. The mortality rate of waterborne diseases is increasing every day and most school children get affected to a great extent. Real-time monitoring of water quality of drinking water is a tedious process and most of the existing systems are not automated and can work only with human intervention. The proposed system makes use of the Internet of Things (IoT) for measuring water quality parameters and recurrent neural networks for analysing the data. An IoT kit using raspberry pi is developed and connected with a GPS module and proper sensors for measuring pH, temperature, nitrate, turbidity, and dissolved oxygen. The measured water quality data can be sent directly from raspberry pi to the database server or through the mobile application by QR code scanning. Recurrent Neural Network algorithms namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are used for forecasting water quality. Results show that analysis made using GRU is much faster than LSTM, whereas prediction of LSTM is slightly more accurate than GRU. The data is categorized as poor, moderate, or good for drinking and it can be accessed using smartphones through mobile application. In general, the proposed system produces accurate results and can be implemented in schools and other drinking water resources.

COVID-19 is an infectious disease that has spread globally, and the best way to slow down transmission is to maintain a safe distance. Due to the COVID-19 spread, social distancing has become very vital. Furthermore, the formation of groups and crowds cannot be left unseen. Even when the necessary regulations have been implemented by governments worldwide, people tend not to follow the rules. We wanted to make it possible for authorities in areas like schools, universities, industries, hospitals, restaurants, etc., to monitor people breaking social distancing rules and take appropriate measures to control the virus from spreading. To monitor and control the crowd, society requires a system that does not put other people's lives at risk. Therefore, it is critical that we stop it from spreading further. Initially, the government imposed a lockdown to control the spread of the virus. Due to the lockdowns, the economy had experienced some negative effects. Due to the economic slowdown, people were allowed to go out and carry on with their regular tasks, leading to crowding in many places, intentionally or unintentionally. The research work aims to make a crowd detection and alert system in public places like hospitals, schools, universities, and other public gathering events. The proposed idea has two modules; a deep CNN CrowdNet people counting algorithm to detect the distance between humans in highly dense crowds and an IoT platform for sending information to the authorities whenever there is a violation. Image processing is carried out in two parts: extraction of frames from real-time videos using YOLO CV, and the second is processing the frame to detect the number of people in the crowd. The crowd counting algorithm, along with the vaccination, will enforce safety rules in people-gathering places and minimize health risks and spread. The image processing YOLO model mainly targets people not following social distancing norms and standing very close by. The data for the violations are sent online to the IoT platform, where the value is compared to a threshold. The platform aids in sending alerts to the concerned authorities in case of significant violations. Warnings are sent through e-mail or personal messages to the concerned authorities and the location. This model prevents the presence of an official to check whom all are violating the rules. There is no need for human intervention and risking their lives; direct messages can be sent through the IoT platform to authorities if there is a crowd formation. Data analytics can help find out the peak hours of crowding and help control the crowd much more efficiently. CrowdNet, a deep CNN algorithm, will estimate the number of humans in a given frame to classify the locations where most people communicate and check whether the safe distance is not reached and the number of times it is not reached. Our system sends the number of people available in the frame at that moment and whether they are maintaining social distancing or not. The Deep CNN algorithm will filter the objects by capturing high-level semantics required to count only the humans and calculate the distance between the humans alone. The base neural network is Alexnet to estimate whether it is safe or not and then send it to the respective authority. This proposed idea using CrowdNet CNN and IoT combination will help find out peak hours of crowding and help control the spread of the disease during social distance violations without human intervention. Thus, social distancing in public places is automated using the real-time deep learning-based framework via object detection, tracking, and controlled disease.

Stroke recovery is the subsequent goal of stroke medicine. Rehabilitation and recovery research is exponentially increasing. However, several impediments impede the progress in the design of neurorehabilitation technology for stroke patient recovery. The conventional rehabilitation techniques for stroke recovery have some limitations like the absence of standardized terminology, poorly described methods, lack of consistent time frames and recovery biomarkers, reduced participation, and inappropriate measures to examine outcomes. Stroke recovery is challenging for many survivors. They require highly functioning and quick treatment accompanied by a gradual acceptance of brain improvement and human behavior. Therefore, there is an immediate need for neurorehabilitation technology to improve the quality of activities of daily life (ADLs) of those disabled. The method adopted is the design of neurorehabilitation technology using game-based systems that enhances the motor activities of hemiparesis patients. 

In this chapter, we discuss the technological developments that have led to the clinical utility of smart wearable body sensors. Smart wearable sensors can enhance the physician-patient relationship, promote remote monitoring techniques, and their impact on healthcare management and expenditure. We explore how continuous health status monitoring can be achieved with the help of wireless sensors, wireless communication, microprocessors, and data processing algorithms. Furthermore, we also discuss the impact of using wearable sensor systems by infants and aged persons to alert parents/caretakers/clinicians. We also explore integrating smart wearable sensors and IoT to enhance the automatic monitoring and alerting systems for health care improvement.