Benefits of IoT in Healthcare Domain

Discuss About The Security In Computing And Communication.

IoT is an emerging an emerging technology and has various advantages as well as disadvantages. Various domains have already started the use of IoT. One such domain is the healthcare domain. IoT is generally used in healthcare domain for monitoring the health remotely and provide notifications whenever there is an emergency [9]. The IoT devices used in healthcare for the purpose of monitoring the health might range from simple devices like blood monitoring devices or heart rate monitors to highly advanced monitoring devices which are implanted like the pacemakers, Fitbit electronic wrist bands or advanced hearing aids. However, along with the advantages there are various challenges that are faced by the IoT in the field of healthcare [1]. Healthcare is a complex sector, there exists different stakeholders who are having different objectives, and this structure differs in each country, as there exists different government regulations different countries [12]. IoT is still an unknown area and healthcare sector is one of the complex sector having involvement of the government. The current and the previous state of remote health monitoring has been presented.

Data which are collected from the IoT devices maignt include certain things like the vital sign’s of the patient, physical activities or glucose level while the patien is present at home and many more dose not travel typically to the electronic health record system [3]. It has also been seen that is most cases the data is not centralized and can be easily availale for the providers, which is initially responsible for limiting the information value as the adat is not presented to the provider always in context to the clinics. Along with this there exists certain electronic health recording systems where the patient is allowed to import the data directly into their record despite of this there still exists some limitations for few of the dominant EHR players [7]. Initially, this leaves many of the providers to remain uncertain about the way in which the information (outside their record system) is to be handled.

Security threats is a primary concern for the regulatory bodies present at the healthcare industry. The concern mainly includes the security of the privacy of the personal healthcare information, which are stored and conveyed by making use of the connected devices [2]. Many of the organizations associated with the healthcare makes sure that the soring of the sensitive data is done in a secure and encrypted manner. Along with they are also not having any type of control over the safety and the security for the data access points, which are used for the purpose of transmission of the data. Initially this acts as the significant threat, which increases gradually depending on the number of devices, which gets connected network [1].

Challenges Faced by IoT in Healthcare Domain

Integration of multiple devices stands out to be an obstacle in the path of success of IoT in healthcare. Most of the devices that are present in the hospitals along with the health devices need to be connected to the network for collecting data from the patients [3]. The most prominent challenge that exists is today’s world is that the manufactures of IoT devices for healthcare have not agreed upon any set of protocols or standards. Therefore, whenever multiple number of mobile devices is connected to the network for collecting the data then it becomes a very complicated process of grouping all the information collected. This is because the mobile devices are having different protocols [5]. Due to lack of homogeneity between the medical devices or the IoT devices used for medical process reduces the chances of success while implementation of the IoT technology in healthcare domain.

Different and numerous types of complexities are attached with the process of aggregation and collection of data. Despite of the fact that combined results helps in deriving of new conclusions by inferring the records of the patient, the results which comes up can be very much challenging and might not have been checked by any data expert or night not have undergone any type of analytic program so as to get refined [4]. Along with this, the identification of valuable as well as actionable data is one of the critical factor and this is because medical specialists and physicians find it very difficult to reach the conclusion regarding the growth of the data. There is lack of quality due to increased amount of data in the process of decision-making [6]. Besides this, the concern is becoming much bigger due to the involvement of new devices, which is connected to the network that is associated with continuous collection of the data along with the generation of big data as well.

More than one device is required by the patients for the collection of data that the provider needs. For this purpose, there might exist a need of more than one sensors and in most cases, it has been seen that this sensors is used along with a hub where the data gets pushed [8]. These pubs are designed for processing the information. It has also been seen that these hubs are not compatible with the different types of sensors, which are available, and lacks in common hardware or wireless connectivity. This will initially lead the patients to have an extensive hardware with them, which would be overwhelming as well as costly.

Data Collection and Aggregation Challenges

The patients prefer collection of different sets of data by making use of different types of medical devices. The usage of such medical devices depends on the purpose of each device or according to the instructions of the physicians. In many cases, it has been seen that the data, which is captured by the IoT device, stays within the boundaries of each system and the IoT vendors [8]. This collected information is not visible to any other systems. However, it is unfortunate that lack of wider adaptation of the adequate interoperability has led to the lock down of the data from different IoT devices in each individual system. This initially leads to the loss of potential values for the rest of the team associated with patients care. Current and previous methodologies

The evolution of the medical instruments is evolving at a slow pace. There exists the need of regulatory approval as well as training for the medical personnel so as to use to new equipment’s and the measuring devices. This initially results in limiting the rate of growth of the new innovations. According to Moore’s law, the rate of development of the electronic is growing at a much faster rate and is generally dictated by the economic considerations. The wearable sensors generally represents a much more dynamically evolving set of measurements devices than the conventional medical instruments. Along with the addition of new sensor modules updated sensors and obsoleted ones a heterogeneous mix is to be deployed at any point of time. There is a need of further development in the machine learning process so as to deal with the heterogeneous sensory inputs which are continuously developing. There is also a need of coping up with the streaming data data of varying dimensionality and semantics as sensor designs change over time and inevitably missing values of the data by the analytics which are done on the data gathered from the wearable sensors. Operating in this type of environment makes the learning task face significant challenges despite of the advances made in this area along with the emergence of big data applications. Big Data consists of massive volumes of high-dimensional observations, which are often available at the modes of streaming. The development of sequential algorithms have taken place in both domains that is in the primal and dual domains. This are generally associated with targeting the online supporting vector machines. This type of algorithms are not designed for the purpose of dealing with various feature dimensionalities which varies according to time, the incomplete vectors due to the absence of the features or failure of the acquisition and in case if this is not treated properly then it might lead to serious impairment of the classified performance.  It is possible to input the missing values so as to cope up with the missing data by making use of the linear or non-linear functions of the features which are available. This is followed by proceeding with the clairvoyant learning scheme which is based on the full data.

Security Challenges

Second of all as the data of the sensors are plentiful and they are completely untagged. So there exists the need of getting this data associated with the “ground truth schematics”(diagnosis of the physician) so as to make them usable in the process of training for machine learning algorithms. However it is infeasible while requiting for the additional inputs from the overloaded physicians. So the need of new creative method arises which would be acting as an alternative for this. One of the attractive possibility is the ability to leverage the clinical records and this is becoming readily accessible by the deployment of the Electronic Health Record System.

The figure provided above shows the framework of the current data analytics. The advantage of creating the link with the clinical record is that all the ongoing clinical process would be helping in providing data for the training related to machine learning.

This section of the report generally consists of the literature review of the various issues addressed by different researchers. The main issues includes the issues related to chronic diseases, Artificial intelligence in the field of healthcare, IoT in healthcare and many more. 

Islam et al., in the year of 2015 discussed about the use of IoT for the purpose of remote monitoring of the patients having a chronic disease [11]. The patients generally requires a regular follow-up about their conditions. This reduce face-to-face visits with the doctors.

According to Shima Okada et al. who mainly focused on the body movements during sleep as they considered that movements of the body is generally responsible for sleep wke cycle [10]. In their work, they proposed a model for the purpose of measuring the body movements of an individual while sleeping by making use of different image processing. For the purpose of validating their research they compared the different image processing’s with the sleep stages that were measured by the PSG along with this they also made use of video monitoring for the purpose of characterizing the different body movements while sleeping in normal children’s as well as in ADHD.

Tracy S, Barger et al., was associated with developing a Smart-house venture which was customized so as to monitor the different movements of an individual inside a house by making use of various sensors [12]. The prototype for the design they provided is under test so as to see the outputs. The researchers have examined if the system is capable of detecting the behavioral patterns of the individuals inside the house and are discussing on the results of the work.

Integration Challenges

A novel “Brain-Computer Interface system” has been presented by Darius Adam Rohani et al., which generally aims at rehabilitation of the attention deficit hyperactive disorder in the children’s [13]. This makes use of the P300 potential in a series of feedback games for the purpose of improving the attention of the subject. A “Support Vector Machine “has also been applied by them by making use of the temporal and template based on the various features so as to detect various type of disorders.

Dong-Hwan Park and Hyo-Chan Bang conducted their research which was mainly focused upon the fact that how technology are responsible for contributing toward the improvement of the Interpol ability between various IoT devices as well as how to make the use of IoT device easy [14]. “Semantic-based IoT information services” and “semantic interoperability of IoT devices” is afforded by the anticipated platform technology. Along with this the service platform is also applicable for a lot of semantic IoT services and this mainly includes collection of invisible information present in the tangible environment by making use of various smart devices. This initially results in providing a smart life service by the process of sharing, distributing the open sensing information and many more.

The researchers named Chayan Sarkar and Akshay Uttama Nambi for various IoT devices presented a unified schematic knowledge base, which is associated with making use of the ontology as the building blocks [15]. Most of the ontologies are for the IoT mainly focuses on the available resources, services as well as on the information of the location. Both this researchers were associated with building upon the existing state-of-ontology as as to provide knowledge regarding the contextual information’s as well as on the set of policies so as to execute the various services. Several ontologies are contained in the Knowledge and this mainly includes the resources, location, policies, ontology of the service and context and domain.

Sai Kiran P. et al., were associated with providing an proposal for the system responsible remote monitoring of the health. This system consisted of a composed medical data, which was gathered form, the biomedical sensors and after this, the data was conveyed to the adjacent gateway for the processing related to auxiliary [16]. Subsidizing of the transmitted data takes place up to a substantial amount of the depleted power. The transmitters and the upsurges that takes place in the network traffic are responsible for this. This metrics are responsible for discarding of the analysis of performance, which helps in saving of power, reduces the network traffic up to a certain extent. The rule engine proposed by these researchers contributed a lot in substantial reduction in the consumption of energy and the generated network traffic.

Conclusion

A general framework was framed by Iuliana Chiuchisan et al., for the healthcare system so as to monitor the different type of threats that existed in the smart intensive care units [17]. The system was associated with providing counseling and providing of real time updates about the patients to the medical assistants or to the doctors. The updates mainly included the vigorous constraints or the movement of the patients or some important changes that occurs in the environment. This was done so as to take certain preventive measures.

Other researchers like Boyi Xu et al., also proposed sematic model so as to store and interpret the data in the IoT. This was followed by the designing of the Data accessing method. This was developed so as to acquire along with practicing the use of use of IoT data at an universal basis. This was done so as to improve the accessibility of the resources fr IoT data [9]. Lastly they also presented an IoT-based system for providing services in cases of medical emergency and they also demonstrated the way in which the collection, integration and interoperation of the IoT data is to be done.

Numo Vasco Lopes et al proposed an IoT architecture for the disabled persons and this anticipates in describing and identifying the furthermost appropriate IoT technologies and the international criterions, which would help in stacking of the architecture proposed by them [18]. Particularly they discussed about the empowering IoT technologies along with the feasibility of the IoT devices for the peoples who are disable. Lastly, they considered two use cases, which were formerly being deployed for the peoples.

Various types of algorithms have been provided for the purpose of providing authentication to the IoT devices. Yang, Hao and Zhang (2013) in their study provided an enhanced mutual authentication mosel for the IoT environment [22]. Along with this they also provided some improvements to the algorithm for the authentication related to Challenge-response based RFID authentication protocol. This was done for the distributed database environment. Due to this reason the IoT control system became much more suitable. Their approach mainly included three major steps and this includes the adding of backup for each terminal device which has been used for controlling, addition of monitoring device in order to take a follow up and monitor the terminal device and lastly the adding of push in the alarm mechanism in order to get alarm for any kind of authentication process [23].

Literature Review

Porambage et al. (2014) in their study proposed a Tw0-Phase Authentication Protocol for the Wireless sensor network in distributed IoT applications. This approach can be considered as a certificate based authentication approach [24]. Along with this the two phase authentication is also associated with allowing both of the IoT devices and the control stations in order to authenticate and recognize each other. This would lead to an establishment of a secure connection and the transferring of the data would be done in a secure way. Besides this they also made use of the protocol support resource limitation of the sensor nodes ad also considered the network stability as well as the heterogeneity. For issuing the certificates the Certificate Association was used. This made the existing nodes capable of moving and changing their location once they get their own certificates. The CA is also associated with validating the identity of the sensors along with favoring the communication between different entities present in the network. The network members firstly connect with the CA in order to confirm their destination identity this would help them in initializing a connection. Due to all this reasons it is considered that the approach is an end-to-end application layer authentication approach and also depend on the security features of the other lower layers.

Kalra and Sood (2015) in their study provided a scheme for secure authentication for the IoT devices and the cloud servers. This schema was mainly dependent on the Elliptical Curve Cryptography (ECC) based algorithm [25]. This is associated with supporting a better security solution as compared to the other Public Key Cryptography (PKC) algorithms. This is mainly due to the small size of the keys [26]. The authentication protocol is associated with using the EEC for the devices which are embedded to it and makes use of the HTTP protocol. It is a novel approach to use the cookies present in the HTTP. There is a need of configuring this device with the TCP/IP. The authentication protocol which has been proposed is designed for uing the cookies in the HTTP which are then implemented in order to fit the embedded devices which are having a constrained environment and is mainly controlled by the cloud servers. There exists three major phases in  the propsed protocol and this includes the registration phase, pre-computed phase and the login phase. The embedded devices register themselves in the registration phase with the various cloud servers. This is followed by sending of cookies and these cookies are stored in the embedded devices. The pre-computation and the login phase is associated with sending of request regarding the login request and it is to be done before connecting to the server. And lastly in the authentication phase the embedded devices as well as the cloud servers are associated with mutually authenticating each other by making use of the EEC algorithm. However, the size of the encrypted message is increased significantly despite of having a small encryption key [27]. The EEC is much more complex and faces a lot of difficulty while implementing than the other cryptographic algorithms and also requires a lot of resources for processing.

Mahalle, Prasad, and Prasad (2014) in their study proposed the Threshold Cryptography based Group authentication scheme for the IoT devices. This model is associated with providing an authenticity for all the IoT devices which are based upon the group communication model [28]. This TCGA is also designed for the purpose of implementing this in the Wi-Fi environment. A secret channel or a session is created for authenticating each group. This can also be used for the purpose of group applications as well. There exists a group head for each group and is responsible fo generation of the keys and distribution of this keys whenever a new member is added to the group. This is done in order to preserve the leakage of the group key and this head of the group is often referred to as the authority of the group. There are five major models in the proposed algorithm and this includes the distribution of keys, updating of the keys, generation of the group credits, listener of the authentication and lastly the decryption of the messages.

Moosavi et al (2015) proposed the SEA or the Secure and Efficient Authentication and Authorization Architecture for the IoT based healthcare by making use of the Smart Gateways [29]. This architecture was dependent on the credit based DTLS handshake protocol. The following things are included in the architecture: Medical sensor network which is responsible for the collection of the information from a patient’s body or rooms in order to help them in getting proper treatment and medical diagnosis. The second one involves the components present in the Smart e-Health gateways and this are responsible for enabling the various types of system communication which would be acting as the intermediate for the MSN and the internet. And lastly it includes the bank-end system which is associated with receiving, processing and storing the collected information.

Jan et al. (2014) proposed a lightweight mutual authentication schema which is associated with validating the identity of the IoT devices which generally takes part in the network [30]. Along with this they also proposed a decreased communication overhead as well. CoAP or the Constrained Application Protocol has been chosen as a under layer protocol in order to establish communication between various IoT devices. The authentication has been completed by making use of the 128 bit Advanced Encryption Standard or the AES. Firstly the identification of the identity of the clients and server is done. Which is followed by providing of various types of resources to the clients which are generally based upon the specific conditions which are determined according to the request. The conditional specific data transmission is associated with minimizing the transmitted packets number which ultimately results in the reduction of the energy consumed and computation. The utilization of the bandwidth in the communication is also decreased as well.

Mietz, Abraham and Ro?mer (2104) in their study provided a new CoAP option [31]. The CoAP which is associated with working in the application layer generally provides the ability in order to retrieve the data from the devices and this might include the metadata or the measurements of the sensors. This information is also used by the real-time applications. Despite of this it has been seen that sometimes the information is not a security requirement to not retrieve raw communication data. But only the abstractions, mainly includes the high level state of the observed entities. Additionally, the nature of the resource constrained device can also be access by anyone on the internet, reduction mechanism for the consumption of the energy also plays a very important role. The mechanism which has been proposed has greatly helped in meeting the two major requirements and this results in a creation of a high-level state of the readings from the raw sensors. The proposed option has also helped in the reduction of the number of messages while observing the resources of the sensor. This would ultimately result in the reduction of energy consumption and would also be increasing the lifetime of the devices.

The figure provided below helps in understanding how it is possible to infer with the information as the knowledge regarding the chronic disorders from the wearable health care devices. The tier 1 shows the raw and unrefined data can be acquired from the wearable smart IoT devices and this devices consists of sensors like the “ECG Sensor, accelerometer and a skin temperature sensor and many more. The tier 2 section consists of the section where the information is inferred from the data by means of filtering, processing, categorizing, condensing and contextualizing the data. Elimination of the irrelevant and reductant information is also done in this stage. The tier 3 section of this figure shows the analysis or prediction phase. There is a need of designing algorithms so as to predicting the purpose of the chronic disease. This would be done by application of various mining techniques like the Constraint based mining, periodic pattern mining. Once the data has been gathered some valid conclusion is reached which would help in making of decisions and catalogues which would include situations of the patient in Ideal, Normal, With Symptoms in real time basis. So as to reach a specified objective knowledge is inferred by various organizations and structuring information’s and is ultimately put into action.

Prevention of the resource exhausting in the IoT environment is one of the major concern of the various developing approaches. The restrictions in the resources of the IoT environment requires various authentication mechanism which are fitted to the limited amount of memory, processing and the energy of the IoT devices. This mechanism would be using the CoAP and ECC [32]. CoAP has been designed IETF working group of the CoRE or the Constrained Restful Environment. Main goal of the CoRE includes the providing of the efficient architecture for the network nodes which are highly constrained. CoAP is associated with providing these constrained nodes in order to implement the transfer of web and this can be used for the purpose of IoT communication [33]. The figure provided below shows the different protocol stack which has been used with the IoT environment associated with the new protocols. This has been mainly designed for the purpose of fitting the limited resources of the IoT environment and this mainly includes the 6LoWPAN, CoAP, MQTT and the XMPP [34].

There exists a lot of difference between the CoAP and the HTTP protocol. CoAP is associated with allowing the machines to act as a client as well as a server. It is aslo associated with exchanging of the meassages in an asynchronous nature. This is followed by transferring of the messages over the datagram oriented transport protocol like the UDP. Besides this an optional Request or Response layer is added to the CoAP messaging in order to provide the reliable communication like the TCP which has been shown below. The use of optional layer can be used for the purpose of dealing with both UDP as well as for the asynchronous interactions. The packet overhead shown in the figure can be minimized by enforcement of the 4 byte header field. CoAP is also associated with providng same HTTP method which might include the GET, POST, PUT and DELET. Along with this it is also associated with providng the same types of response code in order to reflec the statuc of execution which is generally based upon the requests of the client.

CON Messgae: This generally refres to the “Confirmable” requests. Whenever an CON request is send by the source nodes then the recipient has to respond to the message with an ACK message.

NON Message: This generally refers to the “Non-Confirmable” requests which menas that whenever a source node is associated with sending a NON request then the recipient dosenot have to respnde back.

ACK message: This message refers to the “Acknowledgement” messages which is generally sent back as a response to the CON message. Then the processing is completed tehn the receipent of the CON message should respond with an ACK message. The ACK message might also contain the results of the processings.

RST message: This generally refers to the “Reset message” which is generally sent back whenever a recipient of the message faces an error and dose not understand the message or has no intrest over the sender of the message.

The EEC or the Elliptic Curve Cryptography algorithm is associated with implementing security features which are similar to the RSA cryptography system but has a smaller key size [35].

In order to receive the similar type of security restrictions the EEC is associated with utilizing a smaller key size which also provides the security of high level as compared to the asymetric cryptographic techniques which are existing. Those features consists of larger key sizes for example the 256-bit symmetric key which must be protected by using more than 15999-bit RSA. Contararily the ECC makes use of the assmetric key size which is around 512 bits and this ensures the fact that there exists an equivalent amount of security. The reduced key size has greatly helped in the reduction of the cost and has also helped in the implementation of a compact design. The smaller chips and the nodes halps in the running of cryptographic process at a faster and effceint way. This features are very much suited for the environemnt which have a constarined resource. The authentication mechanism is passed through multiple atges and those stages includes the following:

Stage 1: This is the Initialization phase where the Control system is associated with generating a private key and a public key in order to communicate by making use of the ECC.

Stage 2: This is the registration phase and mainly includes the pre-authentication process over the CoAP where the IoT device is checkedin order to see if it is authenticated or not.The Control station is associated with checking the device ID and finds out if tehre exists any type of corresponding entry or not. In case of absence then the the ID is used with the private control key in order to generate the encrypted password and store it in the IoT device agin.

Stage 3: This is the mutual authentcation stage and in this stage the IoT makes use of the password in order to generate the authentication key followed by sending it back to the control system whenever it tries to connect it. Ths control syatem is associated with checking the keys by making use of the IoT entries which are stores at the control system.

Stage 4: In this stage all the traffic passes between the IoT devices and the control stations. This would be hlping in the encryption and securing of the devices from any type of attack.

The figure provided below shows the proposed authentication mechanism.

Conclusion

The proposed authentication architecture would be implemented on the ECC and the authentication mechnaism which is present over the CoAP connection. When this two approach

s would be combined then an optimized overhead would be added to the IoT network. This would be initially leading to the minimization of the communication and processings which requires authenticating IoT devices ad would also achive and powerful security level.

This report has been divided into different sections the first section of the report consists of the introduction. In the introduction portion, the general definition of IoT is provided which is followed by the use of IoT in Healthcare. IoT has been applied in different sections of our life so as to make our lifestyle and our works much easier. The section also consists of the current and past methods used for solving of the problems related to the usage of the IoT in healthcare.

Followed by the introduction section is the literature review section. In this section of the report consists of the discussion about the various works done by different researchers by application of the IoT in the field of healthcare.

Lastly, a methodology is proposed for the further research.

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