1. Introduction
In the present era, the Internet of Things (IoT) and blockchain technologies are in great demand across all sectors of lifestyle and living. The Internet of Things is typically a group of services that are decentralized in nature and the capability of the services are a result of the server performance and utilization factors. Thus, it becomes a typical task for the performance enhancement of IoT devices to connect and collaborate seamlessly with computing resources. To overcome these issues, blockchain technology provides decentralization with high reliability and security. Thus, IoT based on blockchain technology may become an effective option for building a secure IoT system. With technology advancements, many healthcare devices have been used to help patients stay in touch with their physicians and track their health during day-to-day activities. Most wearable devices use IoT technologies, which ultimately forms several networks of various sizes that aim to process patient data to provide a helpful decision about patient health. The Internet of Things (IoT) networks are intelligent networks that could connect billions of objects that communicate digitally to share information and integrate devices through standard protocols. IoT is a commonly used term that means locating, tracking, monitoring, and managing things. IoT healthcare applications implement the latest communication technologies to connect healthcare providers and patients through value-added services, such as remote monitoring the health status of patients and providing data analysis applications from sensors to help doctors and patients. Therefore, IoT has an important role towards the medical and healthcare information realm and transcription process.
While healthcare systems utilize the benefits of IoT networks to monitor and track patients, several pre-processing issues remain open, including handling extensive stream data and the real-time tracking of patient’s records. Such a problem is a common issue for people who suffer from chronic diseases affecting people’s quality of life [
1]. Often, IoT applications are used to collect various human body symptoms and factors such as blood pressure or temperature. Often various wearable devices operated by sensors are used to measure the vital signs and some applications can be used to help patients with their living activities. Therefore, the main issue is how data are gathered, processed, and saved, given the different types of data: textual, video, and continuous data.Researchers showed that implementing an Internet of Things platform based on a centralized approach to enhance data transfer from these sensors is a significant challenge [
2]. According to [
3], the main challenges in connecting traditional and Internet of Things applications in healthcare are dealing with big data at very high speed as well as the need to solidify the basic infrastructure involved in this process. Therefore, the cost of analytic data platforms, the ever-increasing number of Internet devices, and standardized standards for collecting data from IoT devices are driving the further adoption of this technology, which requires computerizing huge amounts of data collected from sensors as well as ensuring the security and privacy of these data [
3].
The traditional model of the IoT that generates large amounts of data sent to the information center to process and store is no longer efficient for healthcare systems. Therefore, with limited computation at IoT networks (edge computing), alternative options are sought to deal with the computation challenge. In this era, and add-on layer called fog computing, in association with the cloud layer, aims to drive the sensor’s data processing and storage features close to the data source to ensure a rapid and reliable response to emergency monitoring applications and the safe handling of privacy-sensitive data [
4]. In the meantime, the fog computing model of IoT healthcare applications is increasingly being investigated as a means to conduct complex computing and time-sensitive processing locally [
5]. Furthermore, above fog computing, when processing Big data, such as providing data to various subscribers, cloud computing becomes an alternative, where data could be collected from different providers and shared across hospitals, physicians, and insurance companies with restricted privacy and sharing policies.
The manuscript is divided into five sections. The background comprises information about block chain technology and its allied components such as security, the Ledger, sharing and distribution. Further, the discussion over Internet of Medical Things devices and their applicability in healthcare is discussed. The data collected from these IoMT devices is sent over the Internet with the help of fog, edge and cloud gateways. The issues related to fog and edge gateways are discussed sequentially.
Section 3 contains the research. The motivations under which some of the Allied architectures fall are discussed. A variety of designs and implementations, submitted by various authors, are expressed in this section.
Section 4 contains the system design and the methodology. The proposed a system architecture comprising of the three stages of processing is explained in this section. Various layers at which the data are accumulated and submitted to the cloud computing environment for further processing are explained. Furthermore, after the data acquisition is over, data analytics is expressed for decision-making module. In the next
Section 5, the experiment and observations are represented. The prototype model created for test networks is explained in the experiment portion. This section also expresses some of the hardware specifications along with analysis of results for the data-set collected from an open source repository. The analysis is also depicted in the form of graphs and tables. At the end of the section, a block chain network is deployed in the test network to demonstrate the exchange of smart contracts between two peer nodes. Each and every section contains a detailed description of all the points mentioned.
3. Research Motivation
Massive amounts of data are streamed on the IoT platforms by various sensor-based devices and circuits. The connection of these devices via the internet makes it possible to stream the data over the wires or wireless medium to intended centralized servers for further processing. Various concerns arise when the data are streamed on the internet and they are indeed very important consider. The security of the data, privacy matters and congestion of the network transfers are some very strong issues that still require much attention. The server efficiency, performance and its latency are indeed more important factors that should be studied before information floats on the internet-based computational units. However, blockchain technology has resolved these issues up to a larger extent. The use of such information in a secure decentralized environment makes it suitable to handle the biggest issue of data privacy and security. Therefore, to improve connectivity in the IoT environment, edge–fog–cloud computing has been adopted to overcome scalability, latency and computing efficiency issues, and blockchain technology is capable of handling and dealing with security issues. Therefore, the result of this work is an Edge–IoT-enabled framework based on the blockchain.
Cloud platforms are typically hosted in centric and large-scale data located at the edge of the Internet backbone [
11]. IoT devices interact with each other in surrounding environments, which leads to the generation of a massive amount of data and, hence, processes, storing the data at a central server [
12]. However, the storage of data at a single central server can be exposed to privacy leakage, if there are no appropriate defensive mechanisms adopted [
12]. The centralization of the data in various data centers raises the probability of the data to be located at a distance from the user and all the services that are available for the user vary as per the demography of the region in which the data center is located. The concern for larger bandwidth as well as the data center management is another concern in this regard. The limitations that are specified in this study can be leveraged with the help of fog and edge computing. These technologies have emerged as a great means for providing low latency as well as guaranteeing higher bandwidth for the handling of the system. The approach that we are proposing in this study leads us to bring the next generation of data processing with the help of edge resource layers that are used for the real-time decision making.
Reference [
13] presented a very comprehensive comparison of existing surveys on the research gaps of secure communication among devices connected in the IoT networks. The authors proposed that the convergence of blockchain at different levels can help in improving the security but they also commented that this will not eliminate the use of existing security approaches. They proposed a security and blockchain model for healthcare which provides authentication, privacy and trust in the devices that are deployed in the healthcare system. The proposed model has not been deployed; therefore, the actual impact is unknown. The collection of data from IoMT is a challenge [
14], as most of the devices consume low power and transmitting huge amounts of data is very limited. This limitation can be handled by the help of 5G and Low-Power Wide-Area Network (LP-WAN), which is currently in the deployment phase, at a very rapid pace [
15].
Reference [
16] proposed a framework of blockchain-enabled Internet of Medical Things (IoMT) named BCeMT. They have also provided some that are efficient during the pandemic situation. The proposed framework by the authors was used for prevention of the disease including contract tracing as well as the management of the injectable medicine supply chain. The proposed framework BCeMT improves interoperability and preserves privacy. It also assures security by using cryptography-based hash function and bit-wise XOR operation. Reference [
29] also presented a very robust architecture for the use of blockchain in healthcare units. The framework suggested by [
30] made use of artificial intelligence and IoT devices for a sustainable city healthcare unit.
The proposed framework [
17] has only added an additional layer of blockchain into the IoMT layering architecture. The IoMT has three layers inclusive of the perception layer, followed by a data management layer and finally the medical services layer, which is responsible for taking decisions. The layer which is added in BCeMT is added in parallel to the existing three layers of I0MT in such a way that it serves all the three layers. Thus, it provides a mechanism that all the communication is protected by blockchain technology. The proposed framework is facing some challenges such as resource constraints because the information produced by the medical devices is enormous and the size of the data will grow continuously. Another issue is also related to the size of the chain in blockchain, because all the peers are responsible for duplicating the data, which is done by a participating and broadcasting node. The nodes in this network also have limited processing capability. Therefore, there is a need to have a cloud storage in place that can store huge amounts of data. The data that are not very critical can be placed on the clouds and the critical data can be passed through the blockchain. In this way, the load on the participating nodes can be reduced to a great extent. There are also some governance issues, as the healthcare data are very sensitive and there are multiple medical institutes and hospitals involved that are part of one network. Therefore, some legal measures need to be taken. Further areas that can be further explored are (I) the need for policies and privacy concerns related to the medical data; (II) obstacles that arising related to the sharing of the data in the medical realm, for which there is no guarantee that they will be exploited or leaked; (III) scalability will also be challenged as the size of the network can grow exponentially and the data flow will be enormous when the hospitals are overloaded [
16].
Reference [
17] highlighted some very important research directions which can help in pandemic situations such as COVID-19. In this paper, they presented a comprehensive review of blockchain technology and IoT for smart cities. They have also proposed a decentralized architecture for the IoT devices integrated into smart cities. The proposed architecture [
18] is divided into three layers: the Energy Generation and Distribution Layer, the Communication Layer and the Consumer–Producer Layer. The first is responsible for the management of the energy requirements of the network. The second is responsible for maintaining a reliable communication link using the 6G communication medium between the network components. The third is uses the Ethereum client, which is able to perform a P2P-level trading in the blockchain environment. Each entity in this layer has its own wallet to record all the energy-related transactions. All the transactions are recorded using the framework and are reported back in the decentralized blockchain network. This P2P network results in the duplication of the data across all the nodes in the chain irrespective of the demography of the blockchain.
The authors of [
18] summarize three major challenges: (1) The security of the devices that are a part of the IoT realm is not adequate. (2) Privacy: protecting the privacy of the user data. (3) Centralization: centralized methods for IoT and bringing out some challenges such as failure of the node in between, traffic issues during broadcasting and the reduction in the scalability of the entire solution.
Methodology
The proposed methodology that is used in this manuscript is the structured analysis and design technique. The hierarchy design is explained step-by-step in an incremental fashion. The initial stage for designing the system begin from the collection of data from healthcare workers or hospitals and trees with the help of IoMT devices. Once the data are retrieved, that information is flown from the devices to the internet with the help of edge and fog Gateway devices. This devices at the second stage sends some information to the cloud network. The second stage of processing in the proposed methodology comprises yet another system function that relates to decision making based on cloud analytics. The third step, which is responsible for the blockchain and smart contract, is also designed. The methodology used in this is case helpful for the upgrade of a system and identifying its shortcomings.
5. Experiment and Observations
The experimental setup for this study makes use of IoMT devices, IoT Hub, Cloud Service Provider, Field Gateway devices, Edge Gateway Devices, Analytic Services, and blockchain network formation with smart contracts.
IoMT Devices: the main emphasis in this study is on the detection of COVID-19. In order to identify the virus and verify whether the patient is suffering from this chronic disease, X-ray data-sets were taken from smart gateway devices. The result, which is given by these data-sets, was analyzed with the help of deep learning algorithms and TensorFlow analysis using Keras framework.
Azure IoT Hub: Azure IoT Hub is a service which can be used to enable the communication between millions of IoT devices. It is a managed service that provides access of the bidirectional flow of information with the help of protocols such as HTTP, AMQP and MQTT. It is very effective to practically monitor and maintain the use of any type of IoT device connected across the network. It also available as easy to use SDK in various languages such as C#, C, Node.JS, etc. In the simplest case of cloud-based IoT solution, the IoT devices send data directly to the cloud and the IoT data persists there. However, it is not possible to manage the IoT data all the time in the cloud. Sometimes there is a requirement for a quicker response, possibly a near-real-time response, especially for critical applications. For these applications, the high latency of the cloud applications can prove to be highly detrimental for the end users as it results in increased response times. Edge computing helps move the computing capability closer to the data source. This results in the movement of workloads from the cloud to the edge which in turn resolves the problem of latency and response times.
Apart from offloading the computing, edge devices also provide the advantage of deploying artificial intelligence near to the data source. Machine learning models can be trained in the cloud and then deployed in the edge. IoT Edge also enables offline operations and enhanced security for IoT applications. Any IoT application implemented using the cloud has three major parts. All the devices which collect data from sensors are plugged inside the network directly or indirectly with the help of a field gateway. The edge intelligence is imparted to these devices that are connected to the gateway. The transportation of the data packets which are not in proper format is restricted at the back-end of the device and local-level decision-making is done at the edge level. The four key concepts that are available in edge computing relative to the connectivity and transportation of the information from the sensor devices to the central hub are:
For all the devices having a valid device ID and IP address, direct connectivity is provided to the cloud gateway.
For the devices that follow certain industry standards and are used for transferring information from a range of existing technologies such as BLE or ZigBee [
25], the connectivity is provided with the help of a field gateway. These devices need to be registered once before they are enrolled in the transaction processing system at Ledger level in the blockchain.
Some devices require the installation of specific device drivers or consensus protocols translations that empowers them to be used in the communication network. These devices, after proper installation of the protocol, can be used with the help of a customized cloud gateway to send the transaction or information from one point to another.
The connectivity to other devices is approved and provided with the help of a field gateway or any other custom cloud gateway. This enables the manual installation and approval system to handle all such devices inside the broadcasting network.
5.4. Hardware and Software
During the development of techniques for the analysis and detection of COVID-19, we used deep learning approaches and TensorFlow and Keras libraries for the X-ray data-set, but, as we know, it required a large amount of the data-set for the training of models, so we used Image Data Generator techniques, which helps to increase the data volume and is sufficient to train the deep learning models. We used CNN and Dark-net Algorithms for the large amount of data. When we used the “Image Data Generator” approaches, which has a few features such as re-scaling the images, sheering images, zooming in on or out of images and horizontally flipping the images. It gives a very large volume of images. We used TensorFlow and Keras libraries for the deep learning model executions. In this technique, we used the few images for the testing and used already-trained models for the same data-set. Due to the small data-set, we did not require any high-specification system, we used our laptop to execute the whole code and test the model as well.
Hardware: PC (RAM 32 GB, SSD 1 TB), Intel(R) Core (TM) i9-9900k CPU, Dual NVIDIA Ge-Force RTX 2070 SUPER;
Software: Anaconda using Jupiter Notebook. Lighter libraries to use deep learning concepts.
As per the experimental setup, the analysis of information was done with the help of software programs performing deep learning analysis. The power of artificial intelligence is to help to predict the nature of the disease which a patient might be suffering.
Figure 11 below shows the graphical representation of the training accuracy and test accuracy, which are high and prove that it is a good model. We set 100 epochs and obtained very low values against training loss and test loss. The analysis is done for four different types of parameter and the findings are identified as below.
This metrics in
Figure 12 below show the evaluation values to train the model for 100-image data, which show the best values. These images are taken from IoMT devices. The analysis of the image values was done with the help of AI-powered algorithms. Keras and Anaconda were used to analyze the data-set.
After the analysis is completed for all the images in the data-set (
Figure 13), it is identified that 10 and 30 images give the maximum evaluation metrics values. This observation is important, as it can predict the nature of a disease that a patient might be suffering from. In this case, the accuracy increases after 30 images in the analysis engine. There is no ‘Image Generator or Augmentation’ applied because we have only a few images’ data. The model is already trained and we used the previously trained models during the testing of models, such that when we pass any image for the testing, first it calculates the feature vector and then compares it with the trained models. We used the CNN model including the VGG16 flavor with deep learning libraries such as TensorFlow and Keras. We did not use the Fastai.vision library here because it takes a very large number of computations and it is useful for large amounts of data, and for training purposes only.
Figure 14 below shows that larger data-set or observations from the IoMT devices results in better accuracy.
We set parameters such as only using 10 epochs, and used dropout libraries to shorten the execution time.
Figure 15 is a graphical representation that aims to provide the detail about the three trained data-sets. Time elapsed is measured in minutes. Once we have completed the analysis at the edge level and cloud level, the information which is finally analyzed is reported back to the main stakeholder. In the proposed model, the first organization comprises of the healthcare unit and the second organization belongs to the patient whose diagnostics reports are generated. The IoT Central hub, after the final analysis of the information, sends the processed data to the blockchain network.
In the experimental setup, we have deployed our own hyperledger fabric at the local network. The docker images from the
Figure 16 below shows that, inside the test network, two organizations are trying to communicate with each other. These organizations can vary depending upon the nature of blockchain established between them. The hyperledger fabric makes it possible for the two peers to accept smart contract between them. When the analyzed data block is finally ready to be deployed in the blockchain network, smart contract is floated in the docker images for both organizations to accept. As soon as the organization accepts the smart contract, the data block comprising the final analytic of the patient history and information is submitted on the blockchain network to all the nodes.
Figure 17 and
Figure 18 below show the console results after the two parties accept the Smart Contract in the prototype model in the test network. This makes it possible for the P2P network to accept the data block for a registered patient ID. The information inside the data block is valid and close across the network from registered IoMT devices. This type of communication makes it very simple for the healthcare unit to make use of valid and authentic information. At the same time. The privacy of the user whose information is propagated across the P2P network is also maintained.
Author Contributions
Conceptualization, W.A.S.; software, R.M.; validation, K.A.; formal analysis, K.A.; investiga-tion, N.J., N.A.K.; resources, J.A.; data curation, S.M.A.; writing—original draft preparation, N.A.K.; writing—review and editing, N.A.K.; visualization, W.A.S.; supervision, S.M.A.; project administration, R.M.; funding acquisition, J.A. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Data Availability Statement
Not applicable.
Acknowledgments
The authors would like to thank the Deanship of Scientific Research at Shaqra University for supporting this work.
Conflicts of Interest
The authors declare no conflict of interest.
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