Softwarization of the Infrastructure of Internet of Things for Secure and Smart Healthcare
, Riya Pandey, Akshit Kalita Computer Science Dept, Ganpat University, India Corresponding author email:[email protected]
Abstract- Smart Healthcare is a very important application in the field of Internet of Things. It is going to revolutionize the Healthcare sector and help the Healthcare providers in decreasing their expenditure along with utilizing new technologies. The crucial step in this process is the acquisition, aggregation and the analysis of the data obtained through the various sensors present in the various devices which produce huge amounts of varied data which include text, audio, and video. Since the growth in technologies, various new kinds of sensors have been made to measure different types of data for the healthcare sector.
Generation of these large amounts of data will necessitate some type of processing to take place which can stand in the way as a big challenge along with the security concerns for the data. Thus, to address these challenges, a scalable, cost-effective, stable, and secure implementation is suggested in this chapter using an infrastructure which has been Softwarized that includes cloud and fog computing, Blockchain and Tor. The suggested system is a machine-to-machine messaging, rule-based beacon for seamless data management, and uses two types of data processing techniques, to facilitate smart-healthcare applications, which are explained further. Along with the proposed system various other terminologies like fog computing, Blockchain and Tor are explained which will make the idea of the system clearer.
Keywords:Cloud Computing, Smart Healthcare, Fog Computing, Internet of Things, Blockchain, Tor
With the growing technologies in the field of smart healthcare, it is very essential to introduce a system which will enhance the quality and efficiency of the equipment that provide the health analysis and perform various other tasks like alarming the doctors in case of an emergency, tracking the patients‘
health and reports regularly along with reducing the amount of latency and risks of privacy of the systems. Usage of real time data has increased in the field of smart healthcare and thus calls for real time analysis. Various IoT devices and sensors can be used for the purpose of collecting the data relating to the patient. The collected data must be transmitted to the devices which will perform the analysis on the data to produce the results.This task can be taken care of by Wi-Fi, Zigbee, a wired connection or Mobile networks like 3G/4G/LTE [1-5]. The next step is the analysis of data which can be done on the cloud, but this process introduces latency, thus fog computing can be used to help reduce that. Furthermore, various methods 1can also be used for providing anonymity of the user and security of the healthcare information that can otherwise be misused. 2
1The corresponding author Mayur Mistry is with Computer Science dept, Ganpat University, Riya Pandey and Akshit Kalita is with SIT, India.
Some methods for protection of the data explained in the chapter are Blockchain and Tor. The system proposed aims to softwarize the infrastructure of Internet of Things for Smart
Healthcare which will help meet all the requirements discussed so far with the highest level of efficiency[6-8].
II. System’s Architecture
The architecture of the system includes smart sensors of different types and sizes that track and monitor raw sensor data as well as patients‘ health-related metrics[9-10]. The sensors‘ transceivers use a wireless interface to communicate with the base stations and the most powerful base station acts as the data aggregator. The IOT gateways operate with a variety of devices and network protocols, allowing for widespread communication.
Figure 1: System Architecture
Now let us discuss the system architecture in detail along with all the aspects involved in it which are:1) Data sensors 2) Data Transmission3) Fog Computing4) Cloud computing5) Security and privacy (Blockchain, Tor)
A. Sensors and IoT Devices
Softwarization of sensor networks will allow them to be dynamically configurable which is not possible in the usual sensors which are mostly application specific[11-23]. Software-defined networking (SDN) will also be more economical along with improving the sensor networks‘ agility and flexibility and allows management of data-forwarding rules. This will also allow the user to easilymake commercially available off-the-shelf hardware SDN compliant and improve interoperability among communication protocols thus
also lowering the cost of network deployment and configuration. All the sensors that can be utilized for various activities in the domain of Health Sciences like diagnosis, treatment or monitoring are called medical sensors[24-35]. Based on the risk potential all the devices in the Healthcare sector are divided into 4 categories, Class 1 being the devices with lowest potential risk and Class 4 with highest potential risk. Sensors in the medical domain need to follow some standards and specifications which are discussed below:
IEC 60601-1 provides standards of safety to be followed by the medical equipment.
They must satisfy the legal specifications that include various quality management standards, management of risk. The sensor must also ensure that it is effective in producing accurate response to the provided input. Safety during the operation of the device must also be considered.
It should provide a measurement that is very stable and quick along with fast response time.
Measurement of the sensors should be highly precise and accurate
They should give outputs as digital so that it can be directly connected to the microcontrollers/microprocessors.
Figure 2: Medical Sensor
There are many types of medical sensors that perform different kinds of functions and measurements.
Here, let us discuss some of the sensors which we can use in our system for getting the data along with their applications[36-43].
Temperature sensors (Thermometers): These are very commonly used sensors for the measurement of body temperature.
Airflow sensors: These are used to measure the flow of air or oxygen and thus these can be used in anesthesia machines, heart pumps etc.
Pressure sensors: These are used in medical diagnosis or for monitoring the blood pressure and in infusion pumps. These sensors are mostly integrated with embedded systems.
Implantable pacemaker: This is an embedded sensor system which is used to maintain the rhythm of the heart in real time. It delivers a synchronized rhythmic electric stimulus to the heart muscle to perform its task.
Oximeter: This sensor measures the ratio of hemoglobin that is saturated by oxygen to the total amount of hemoglobin in the person‘s blood.
Glucometer: It measures the concentration of glucose in blood.
Magnetometer: This sensor examines the magnetic field of the earth around the person and thus tells which direction he is standing in.
Electrocardiogram sensor (ECG sensor) : This sensor measures the heart‘s electrical activity.
Heart rate sensor: It keeps a count of heart contractions occurring in a minute.
EEG sensor (electro encephalo gram): This sensor is used for measuring the electrical activity of the brain.
Respiration rate sensing device: It measure the number of times a person‘s chest rises per minute.
So, the sensors discussed are some of the sensors that are used for the live analysis of the patient‘s health by the system proposed.
B. Data Transmission
Now that we have the data from the sensors, it is going to be analyzed on the cloud after which the analyzed data will be sent to the healthcare doctors or nurses [44-49]. For these processes data needs to be transmitted in some way. So, the following are some ways which are used for data transmission:
The sensing device is integrated with a processor which analyzes the data which is then uploaded to the cloud. In this way of data transmission wired Internet is required through which the Ethernet connection is attached. But there are a few problems with this method:
Wired Internet is not present at all places.
There is no radio link involved in this type of data transmission.
2) Cellular Technology
Cellular technologies have very high scalability and reliability and are thus preferable. The Cellular Internet of Things is popularly known as CIOT uses the already existing infrastructure and provides very good coverage. The hardware of CIOT has SIM cards and can thus connect to networks via 2G, 3G, or LTE connectivity[50-62]. Benefits of the Cellular technologies are:
Coverage: Nowadays Cellular technologies have very high coverage and can also reach to rural areas and underground spaces.
Security: The data while the transmission is kept secure as cellular technologies provide End-to-end security by the SIM credentials.
Bandwidth: Cellular technologies like 4G LTE have great bandwidths and a very high speed as fast as 1 Gbps. With the new 5G technology speeds are expected to go up to 10Gbps
3) ZigBee Data Transmission
Zigbee devices uses mesh networks of devices to transmit data over larger distances through these device network. Zigbee data packets have two types of transmission namely Unicast and broadcast transmissions. Unicast transmits the data from the source device to one target device. Whereas in broadcast transmissions the data packets are sent to all the devices in the network.
For all the nodes to receive the data, all the routers receiving the data packets retransmits the packet three times and each time an entry is created in the local broadcast table to make sure that the packets do not keep on getting transmitted for an endless time. The Zigbee stack holds buffer space for every transmission as the packet can be retransmitted by the next node[63-77]. This process takes up a lot of network and causes network congestion. As all the devices in the device network transmits the data packets 3 times, broadcast transmissions are used only in few scenarios to avoid the network traffic.
In this type of transmission, the data is sent from one device to another device directly. If the target device is a neighbour of the source device, then the data is directly sent. But if the destination device is not a neighbour of the source, then the data needs to be sent along a multiple hop path that requires some means to establish the route between the source and target.
C. Fog Computing
The cloud causes a very high level of latency which cannot be tolerated in healthcare applications.
Therefore, Fog computing is sometimes used which decreases the distance between the cloud and the users and brings them closer, thus reducing latency. Fog nodes in our proposed system have lesser resources than the cloud nodes and are smaller than them. But the fog nodes are much more efficient and powerful as compared to IoT devices and gateways. Thus, as the latency is decreased and the performance is improved, the system becomes more efficient and starts to analyze and bundle localized data efficiently while reducing avoidable traffic to the cloud. Fog computing allows short-term analytics at edge devices where data is generated and collected, thereby complementing cloud computing which performs long- term, resource-intensive analytics. Edge devices alone do not have the resources required to perform machine learning and advanced analytics tasks [70-78]. Cloud computing possesses this power but the distance between the data generation and the cloud prevents processing and responding to the data on time. Additionally, connecting all the endpoints to the cloud and sending raw data over the internet can result in negative security, privacy and legal consequences, especially with respect to sensitive data [79- 91]. Smart grids, vehicle networks, smart buildings, smart cities and software-defined networks are
Figure 3: Applications of Fog Computing
Benefits of Fog computing
Fog computing has various benefits and thus we have used it in our proposed system also as it reduces latency and is thus very ideal to use for real time applications. Following are some of its‘ advantages:
Bandwidth conservation: Bandwidth consumption and related costs are reduced since Fog Computing leads to a reduced volume of data being sent to the cloud [92-94].
Improvement in response time: since preliminary data processing takes place near the source of the data, there is a reduction in latency and an improvement in overall responsiveness. The aim is to achieve almost real time responsiveness [95-97].
Network independent: The network that is used for Fog Computing can either be wired or wireless and thus it is not network dependent [98-99].
Drawbacks of Fog Computing
Fog computing has some disadvantages too, some of which are stated below:
Location Restriction: Since it is restricted to a physical location, Fog Computing does not provide the anytime/ anywhere flexibility associated with Cloud Computing.
Security risks: Fog Computing is susceptible to IP Spoofing and Man in the middle attacks.
Expensive: Since this technology requires both cloud and edge resources, the hardware costs can prove to be expensive.
Ambiguity: Despite having existed for years, this technology is ambiguous as its definition differs from vendor to vendor.
Fog Computing Workflow based on time sensitivity of Data Category Nearest Fog Nodes to
Aggregating Fog Nodes Cloud Time taken to Respond[101-
Milliseconds Seconds or a few minutes Minutes, days or even weeks
M2M communication Haptics, including
telemedicine and training
Visualization Simple Analytics
Analysis of Big data Displaying
Duration of IOT Data[106- 109] storage
Momentary Hours, days or a few weeks Months or years Area Coverage Maximum one city block Comparatively more Worldwide
Table 1: Fog computing workflow
The data that is most sensitive to time is analysed on the node nearest to the source. For example, verifying protection and control loops is most time sensitive in a Smart Grid Distribution network. Thus, the closest fog nodes to the grid sensor are able to detect dysfunctionalities and avoid them by transmitting instructions to the actuators. Data which does notrequire real time analysis and can wait a few minutes is transmitted to a node which performs aggregation for analysis. Less time sensitive data is transmitted for analysis and storage to the cloud [110-121].
D. Cloud Computing
Cloud Computing is the delivery of different IT resources such as data storage, computing power, servers, databases, networks and other services through the internet. By opting to keep files on a cloud-based storage rather than a local storage device, the user has remote access to the data as long as he has an electronic device which has access to the web. Few types of Cloud Computing are - 1. Software as a Service (SaaS) 2. Infrastructure as a service (IaaS) 3. Platform as a Service (PaaS) 4. Functions as a Service (FaaS)5. Integration Platform as a service (IPaaS) definition 6. Private Cloud Definition 7.Hybrid Cloud Definition [122-127].
Cloud Computing Benefits and Drawbacks:
Some advantages of Cloud Computing are -
Agility - Cloud Computing makes deploying technological services possible in a matter of minutes given the broad range of technologies it gives you access to. It gives the user the freedom to test and experiment with new ideas to transform their business.
Elasticity - Cloud Computing allows you to provision only those resources which you need and avoids over-provisioning of resources. These provisions can be scaled up or down depending on the user‘s requirements.
Saving Costs - Since the user only accesses the resources he/she requires, the cost of unnecessary resources are eliminated, thereby reducing overall costs.
Some Drawbacks of Cloud Computing are -
Data confidentiality risks - Without cloud protection, users‘ data is susceptible to access by individuals with malicious intent.
Connection dependent - Cloud Computing mandatorily requires a stable internet connection to function.
In places with spotty or irregular connection, the access to cloud resources is essentially cut off and the services are rendered unusable.
Reliance on Technical Support- If users encounter problems using Cloud services, they have no choice but to contact customer support who may not be available 24/7. Users do not have the freedom to troubleshoot all problems themselves as they would in their local machines.
E. Security & Privacy
The ability to protect sensitive data and information about personally identifiable health care information is called Privacy. The primary areas of focus with respect to privacy are establishing authorization requirements and formulating policies ensuring that patient‘s personal details are gathered, exchanged and employed in the correct ways. On the other hand, Security deals with the protection of data from malicious attacks and theft [128-133]. A few methods that fall under the umbrella of Privacy and Security are as follows –
Tor is an open-source software which enables users to anonymously communicate by bouncing internet users‘ and websites‘ traffic through more than seven thousand relays operated by volunteers globally, making identifying and locating the source of the information extremely difficult. This counters network surveillance threats. Tor can be utilized between the cloud and the fog nodes since it introduces unpredictability and communication delays. The trade-off is the increase in latency which can pose problems for applications that require real time analysis .
Tor provides data encryption and transmits data via a virtual circuit consisting of several Tor Relays which are randomly selected. Each relay decrypts an encryption layer, revealing the next relay the data has to be passed on to. This method is known as ‗Onion Routing‘. The innermost layer contains the main data and the address it has to be sent to which the final relay decrypts. This method prevents any single point of communication being susceptible to network surveillance since surveillances relies upon having the knowledge of the source and destination. One must keep in mind, however, that Tor does not wipe out tracks completely but merely reduces the chances of data and actions being traced back to the operator .
ii. Blockchain Technology
Blockchain technology secures the records of the patients as it tracks and maintains authorization to all the records and confidential information. A Blockchain is a system that makes it impossible to hack or cheat the system as it stores all transactions together in groups, it is available to participants who have access or authentications to the system. As all the participants can see all the transactions, it makes it impossible to manipulate or change the transactions without being noticed.Blockchain technology validates all the blocks and ensure that each transaction happening is legit and true. The data produced by blockchain has very high security as it is based on the principles of cryptography, decentralization, and consensus. Each block of the blockchain can contain one or more than one transaction [135-137].
The three main principles of Blockchain are:
1. Cryptography: This is a method to secure the data by converting it into a form that can only be understood by the sender and the receiver like encryption which is discussed further in the chapter.
2. Decentralization: Blockchain has participants from a distribute network which enables decentralization. A single point of failure cannot be found in this kind of a network. A single user does not have the right to change the record of transactions.
3. Consensus: This is a decision-making process which takes place between a group. In Blockchain, this mechanism is used to make all the participants come to a single conclusion for making the decisions regarding approval of a transaction or any other required decision.
However, different blockchain networks can have different security aspects defining who participates in the network and who cannot access the data. Networks are categorised as two types depending on this labelled as either private or public,describing who can participate in the network, if the network requires specific permissions or not to add new participants and how new participants can enter the network.
Figure 4: Blockchain Healthcare Technology
In Public blockchain networks anyone can join and become a participant and be anonymous as well. To achieve consensus and validate transactions in public blockchain computers with internet connections are used.Bitcoin is the most prevalent example of a public blockchain and consensus in bitcoin is achieved through bitcoin mining. In the bitcoin network, the computers, also known as the miners, perform tasks to solve the cryptographic problem and then validates the transactions.
In Private blockchains participants have to disclose their identity which is then used to validate the individual and only then can he become the participant. This type of network typically allows only known organizations to become participants. This network achieves consensus through a process called selective endorsement. In this process only the known users verify the transactions. Members who have special permissions and access can maintain the transaction ledger. This network type is more secure and has greater access controls.According to one‘s business goals, the type of network is decided. Private blockchains are mostly used when tight control and regulations are required. Whereas Public networks are used when high level of decentralization needs to be achieved.While establishing a private network, a secure infrastructure and good technology choices are very important because lack of these can lead to security risks.
Understanding blockchain network risks and managing them is very crucial for the Blockchain Technology. Thus, a blockchain security model is implemented which is just a plan to implement security
to the network. For developing the security model, administrators create another model which contain all types of risks like business, technology, process, and governance risks. This is known as a risk model.
Next, they make a threat model which contains all the threats to the network. And finally, they specify the security controls that should reduce the threats and risks, which were stated in the models.
iii. Data Encryption
Data Encryption is the process of using an algorithm to convert data into ciphertext which is undecipherable by the everyday user. This is a popular method that adds an added level of security incase an attacker were to gain access to data. In order to decrypt the data and view it in its original form, a decryption key is required which is basically a number. Cryptography can be symmetric or asymmetric in nature. Symmetric-key algorithms use the same key for encrypting or decrypting a file. This method is much faster than its asymmetric counterpart but requires the sender to exchange the key with the receiver once the data has been delivered. Asymmetric cryptography uses a public key to encrypt files and a private key to decrypt them. As the names suggest, the public key may be accessible to everyone whereas the private key must be protected.
One of the most popular public key cryptosystems is the Rivest Sharmir-Adleman (RSA) algorithm. The user using the RSA approach generates and releases a public key which is done on the basis of two large, protected prime numbers as well as an additional value. Anyone can encrypt these messages using the public key but they can only be decrypted using the aforementioned prime numbers. No approaches to defeat this system have been published if the key is big enough in size. Since it is a slow algorithm, data is not directly encrypted with it, rather it is used to send the keys for symmetric cryptography which can later be utilized for encryption-decryption in bulk.
One method of decrypting an encrypted file is to use the brute force method of trying all possible combinations of keys until one works. This method usually requires the attacker to have huge access to huge amounts of computational power and is therefore very inefficient. The length of the key determines the possible number of keys and hence a lengthy key may render the success of this kind of attack implausible. Most encryption software use asymmetric algorithms to exchange these secret keys after using a symmetric algorithm to encrypt the data.
II. Data Aggregation
For analytics, applications, and services, users must collect and process data from the IoT sensor network in the field of medicine and healthcare. For example, considering an application like a health monitoring system for a person. The sensor network can be large, and it contains various sensors including sensors for body temperature, pulse rate, room temperature, and humidity. This application requires data to be logged into the system in real time to the cloud so that analysis can take place and quick alerts can be generated if health problems are detected. Thus, the monitoring system requires collaboration among all the sensors.
Processing of Data
Two main approaches of data processing are:
1. Data fusion: The final decision, for example indicating the health parameters of a patient, is made by a gateway of sensor-network or a base station that acquires data from each sensor, analyses it and then
makes the decision. This base station then forwards its decision to the IoT gateway for reporting or taking necessary actions.
2. Decision fusion: Smart sensors are used in this kind of approach. The sensors locally process their data and make decisions about the health of the patient. The individual sensors then pass on its decision to the sensor‘s gateway which is further connected to the IoT gateway. The Internet of Things gateway receives the individual decisions, aggregates them, then makes a final decision about the patient.
Advantages and disadvantages of the two approaches of data processing:
1. The first approach that is data fusion gives out high volume of raw data and thus takes up higher power and bandwidth.
2. The second approach that is Decision fusion is usually less accurate as the sensors locally process the data which may not show very accurate processing capabilities whereas in Data fusion, the computations are executed on the sensor network gateways that have higher power capability by the system.
Agile IoT Platform
A platform which uses a gate array that can be programmed along with hardware and software parts which allow personalized patient care by transmitting location and other services with the help of M2M communication is proposed. Advantages of the platform:
Very flexible. It can work with various types of hardware, thus almost all types of actuators or sensors can be used. This is a very useful attribute as it helps the user configure the system as per their requirement and provides access to remote control services.
Enables seamless data aggregation and analytics. It also lets the user control the application as well as the devices.
Reduced Latency and improved data collection and aggregation because of the use of M2M communication and FPGA hardware.
Reduced cost of providing softwarized IoT for smart healthcare. The platform also offers efficient management and no loss of accuracy.
A. IoT Softwarization:
For providing ultra-low latency and for allowing the users to communicate with the system via their mobile devices, Softwarized IoT devices would need to seamlessly integrate with 5G wireless technology.
On comparing the current technologies with 5G systems, a lot of challenges come up including an absence of a standard 5G definition.
Some other issues that Softwarization technologies must address, include:
1. Managing transmission control, bandwidth, and other resources.
2. Connecting network computers, transceivers, and physical elements in the most efficient way possible.
3. The key indicators for assessing the efficiency of softwarized components and applications must be established.
4. To guarantee the scalability, designing and managing distributed controllers and network functions.
5. Organizing network functions and resources autonomously through the softwarized middleware B. Privacy & Security:
Since any blockchain member can see all transactions, it is critical to use established communication protocols with the IoT devices. Homomorphic encryption or zero knowledge proofs can also be used for the security purpose depending on the technical capabilities of the devices.Blocked Transactions that usually occurs because of an absence of agreement among blockchain members to have a requested transaction can be minimized using proper logical blockchain implementations based on enforceable smart contracts. Further including legalities in the smart contracts can enforce various required laws or regulations and control misconduct which necessitates procedures that produce a hash of the smart contract‘s legal component ensuring its confidentiality.
Applications in the sector of Smart Healthcare are enormous, and it will help the mankind with a lot of applications in the field of healthcare some of which include real time patient monitoring, medical-device actuation, improving healthcare quality using data analytics, improving patient experience and lower the cost. The system proposed in the chapter offers an economical, secure, private, and flexible solution. In the future, a new FPGA platform for high efficiency, reduced latency, and the local implementation of user defined flow rules can be proposed. In addition, an M2M transmitter-receiver and microcontroller can be envisioned for data integration and agile deployment of smart healthcare applications and services.
Through this system we learnt all about the medical sensors, their important features, their types, devices which can be used for data transmission, fog computing, how it works and its types, cloud computing, its types and various challenges and advantages of the same. As we move to the era where data is becoming enormous and is increasing at a very high rate, the privacy concerns come into picture which were discussed in the chapter and some ways which can be used to overcome those concerns. Next, we saw data aggregation and various types of methods for data processing. Lastly, we defined some of the challenges present in the field of IoT.
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