Healthcare Information System Design & Wireless Security ... · Wireless biosensor networks can be...

Healthcare Information System Design & Wireless Security

Communication Implementation

Divyesh Patel, Dhaval Bhoi, Hardik Mandora,

U & P U. Patel Department of Computer Engineering

Chadubhai S. Patel Institute of Technology

[email protected], [email protected], [email protected]

Abstract:

Human health information from healthcare system can provide important diagnosis data and reference to doctors.

However, continuous monitoring and security storage of human health data are challenging personal privacy and big data

storage. To build secure and efficient healthcare application, Hadoop-based healthcare security communication system is

proposed. In wireless biosensor network, authentication and key transfer should be lightweight. An ECC (Elliptic Curve

Cryptography) based lightweight digital signature and key transmission method are proposed to provide wireless secure

communication in healthcare information system. Sunspot wireless sensor nodes are used to build healthcare secure

communication network; wireless nodes and base station are assigned different tasks to achieve secure communication

goal in healthcare information system. Mysql database is used to store Sunspot security entity table and measure entity

table. Hadoop is used to backup and audit the Sunspot security entity table. Sqoop tool is used to import/export data

between Mysql database and HDFS (Hadoop distributed file system). Ganglia is used to monitor and measure the

performance of Hadoop cluster. Simulation results show that the Hadoop-based healthcare architecture and wireless

security communication method are highly effective to build a wireless healthcare information system.

Introduction

Since the first biosensor was introduced in 1962 by Clark and Lyons [1], there has been increasing demand for such

analytical devices in real applications. Research initially focused mainly on detector principles and recognition elements;

however, to obtain a user-friendly and well-performing analytical device, many components have to be considered [1].

Biosensors have been developed for many years and research has become very popular in recent years. Advances in

microelectronics, material science, and wireless communication technology have led to the development of micro sensors

that can be used for the monitoring of bioinformation objects. So biosensors remain a subject of great popular interest [2].

Wireless sensor networks (WSN) [3, 4] have received significant attentions due to their widespread applications in

military and civilian environments. Sensors are low cost, low-power devices which have limited resources. A sensor node

typically contains a power unit, a sensing unit, a processing unit, a storage unit, and a wireless transmitter/receiver.

Wireless biosensor networks can be used in healthcare system: inside a healthcare system, biosensors are placed or

embedded in human body to monitor their blood pressure, body temperature, sugar level, heartbeats, and so forth.

Biosensors constitute a wireless network and periodically monitor the health information of their hosts. Health monitoring

involves collection of data about vital body parameters and making intelligent decisions. This information is required to

be transferred securely. Insecurity information transfer can lead to much risk. So security and privacy [5, 6] issues have

become critical research fields in wireless biosensor network.

JASC: Journal of Applied Science and Computations

Volume VI, Issue I, JANUARY/2019

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Development of an effective security scheme is challenged by the limited storage, computing ability, and energy. In

modern healthcare environment, it is important to design a security scheme based on small biosensor computing devices

and big cloud computing resource.

In this paper, a novel Hadoop-based wireless healthcare architecture is proposed to protect data communication security in

biosensor network. There are two main contributions in the paper. The first is that Hadoop-based biosensor wireless

healthcare information system architecture is proposed. The second is that ECC-based digital signature and security

communication method are implemented in Sunspot WSN. The rest of this paper is organized as follows: Section 2

reviews some related work on biosensor network and Hadoop-based healthcare system. Section 3 describes the system

architecture of Hadoop-based wireless healthcare system. Section 4 describes the security data communication method

based on small biosensor node and big cloud resource. Section 5 gives simulation results by Sunspot and Hadoop cloud

platform. The conclusion is drawn in Section 6.

Implementation

Biosensors, nanosensors, and biochips have turned out to be famous as an apparatus for therapeutic diagnostics due to

their noninvasive or insignificantly intrusive nature [7]. A biosensor is a test that incorporates a natural part, for example,

an entire bacterium or an organic item, with an electronic segment to yield a quantifiable flag. It can recognize and

quantify convergences of explicit microscopic organisms or dangerous synthetic concoctions; it can likewise gauge

causticity levels (pH). Nanosensors give new and incredible assets to checking in vivo forms inside living cells [8].

Biochips are structured by consolidating coordinated circuit components, an electrooptics excitation/recognition

framework, and bioreceptor tests into an independent and incorporated small scale gadget.

The fast enhancement in chip and detecting material innovation has prompted an improvement of scaled down sensors

that can be embedded in the human body. The biosensor based way to deal with human services makes it considerably

more viable by decreasing the reaction time [9]. The biosensor arrange comprises of a gathering of biosensors embedded

inside the human body, outside gadget (control hub) set on the human body, and a base station, which are appeared in

Figure 1.



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Figure 1: System model of biosensor network [9]

A system is shaped by the biosensors among themselves and the control hub. The control hub is associated with an outer

base station, as appeared in Figure 1. There are three sorts of remote correspondence connects in the biosensor organize

based social insurance framework. They are the correspondence connects between the biosensors, the correspondence

interfaces between the biosensor and control hub, and the connection between control hub and the base station. All these

remote connections are viewed as shaky because of the way that the information is accessible on the channel. Thusly,

information trade utilizing any of these correspondence joins must be anchored. The set limitations experienced by the

biosensors make existing answers for sensor organize security unacceptable for biosensor security. Subsequently

biosensors security requires novel arrangements. The calculation utilized is a lightweight encryption calculation. They

utilize the mistake rectifying codes and the numerous biometrics for anchoring the key for the issues of estimation

blunders and haphazardness issues.

Poon et al. investigate the utilization of this conductor in the security instrument of BASN (body region sensor arrange)

[10], that is, by a biometrics approach that utilizes a natural normal for the human body as the confirmation personality or

the methods for anchoring the conveyance of a figure key to anchor between BASN interchanges. The strategy was tried

on 99 subjects with 838 fragments of concurrent accounts of electrocardiogram.



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Perrig et al. [11] have displayed a lot of conventions for accomplishing prerequisites of security in sensor organize. Their

design comprises of two hinders that are SNEP and μTesla. In SNEP they utilize symmetric keys to scramble the

information. Symmetric keys are likewise used to figure the Message Authentication Code (MAC). Both of these

arrangement of keys are gotten from an ace key which is shared by the hubs with the base station and are set in them

before being sent. μTesla is utilized to accomplish verified communicated by postponed key exposure. The keys are

processed from the ace predeployed key and the counter which is increased after each square.

New ages of human services frameworks for the most part keep running on a great many servers to meet the prerequisites

of a large number of clients [12]. Conventional social insurance information investigation frameworks are troublesome in

tackling the procedure issues with gigantic information. They propose a huge information the executives and investigation

arrangement dependent on Hadoop. They present information examination techniques dependent on MapReduce and

Hive. Analysis results demonstrate that Hadoop-based system enhances the execution of information transfer and

information inquiry. Hive-based information investigation strategy is appropriate for gigantic information examination

assignments.

Customary information stockpiling for patients isn't sufficiently adaptable for the expanding number of patients and

applications [13]. Distributed computing guarantees ease, high adaptability, and dependability which can be a potential

answer for putting away patients' therapeutic records. They examine the effect of distributed computing on enhancing

human services administrations. The compositional plan called "MedCloud" which uses and incorporates administrations

from Hadoop's environment is given for therapeutic frameworks advancement.

Kojima and Nagahashi propose the calamity help preparing framework utilizing the electronic triage tag [14]. They plan

the graphical UIs to build up the situations of harmed individuals data and transport data, for example, emergency vehicle.

They let the electronic triage tag produce crucial indications of harmed individuals always. By gathering and checking

those information at ordinary interims, they build fiasco alleviation preparing framework that empowers restorative staff

to lead a progressively handy preparing thinking about the adjustment in side effects of harmed individuals. They use

SunSPOT created by Sun Microsystems as an electronic triage tag.

They propose a technique to protect the protection and security of patients' versatile medicinal records in convenient

capacity media to maintain a strategic distance from any unseemly or accidental divulgence [15]. Following HIPAA rules,

the technique is intended to secure, recoup, and check patient's identifiers in compact EHRs.

IBM InfoSphere Guardium gives database action checking and inspecting abilities that empower client to coordinate

Hadoop information assurance into existing venture information security technique [16]. Client can arrange the

framework and use InfoSphere Guardium security approaches and reports for Hadoop situations. It doesn't include remote

sensor organize security correspondence.

HDSM is a Hadoop-based conveyed sensor hub the board framework, which utilizes Hadoop MapReduce structure and

disseminated record framework [17]. Every sensor hub copies DVR (advanced video recorder) for detecting video

information. All sensor hubs are associated with HDSM supervisor by means of gigabit ethernet. So HDSM isn't

appropriate to lightweight sensor hub and application. Cloudwave stage is proposed to access and question huge volumes of electrophysiological flag information utilizing the

HDFS stockpiling module. Cloudwave enables clients to scan for clinical occasions utilizing metaphysics and semantics

thinking [18]. Be that as it may, it doesn't include biomedical information security correspondence.



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Seen from the above investigation, how to construct security human services data framework with biosensors and

distributed computing is an incredible test to current medicinal services data framework plan and implemention.

System Design

To construct a biosensor medicinal services framework, multidiscipline learning and methods are required, for example,

electronic building, bioinformatics, software engineering, programming designing, correspondence procedure, and data

security. A tale design of Hadoop-based remote biosensor social insurance data framework is appeared in Figure 2.

Figure 2: Hadoop-based biosensor wireless healthcare information system architecture.

As appeared in Figure 2, human services data framework incorporates biosensors, Sunspot hub, Sunspot base station,

Mysql database server, and Hadoop distributed computing bunch server. They participate to satisfy social insurance data

accumulation, exchange, stockpiling, and preparing. Breathing, heartbeat, beat, pulse, and body temperature are critical

wellbeing parameters, which reflect human wellbeing status. Persistent checking and capacity of the wellbeing

information can give imperative determination references to specialists. Seen from Figure 2, biosensor can quantify

organic flag and changes over it into an esteem. The wellbeing parameters can be gathered by various types of biosensors;

the biosensor circuit sheets are associated with Sunspot remote hub by standard expansion interface. In Sunspot hub, there

is a 20-stick augmentation interface, in which it bolsters standard UART (Universal Asynchronous Receiver/Transmitter),

I2C, simple flag input, GPIO (General Propose Input Output), Vcc 3V, Vcc 5V, and GND. So extraordinary kinds of

biosensors can be associated with Sunspot hub; sequential port multiplexing method can be utilized to interface numerous

biosensors that utilization sequential correspondence. So the proposed methodology has great adaptability if there should

be an occurrence of increment of the quantity of biosensors.



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SunSPOT is a little remote sensor arrange gadget; it is programmable gadget dependent on Java. SunSPOT depends on a

32-bit ARM-9 CPU and 11 2.4 GHz radio channels. SunSPOT gadgets can speak with one another through the Zigbee

convention. So the deliberate esteem can be sent to base station by Zigbee convention. At that point the base station is

associated with medicinal services Mysql database server by USB interface. Every one of the information gotten by base

station are exchanged to the social insurance server and put away in Mysql Database. With the screen zones growing, the

quantity of checking hubs will increment enormously. So Hadoop-based enormous information stockpiling and process

are required.

Biosensor Information Secure Communication Process

From the system security perspective, information correspondence between Sunspot remote hub and Sunspot base station

is defenseless against assault, for example, information altering assault. So we propose a lightweight advanced mark and

confirmation strategy to ensure information correspondence security. As we probably am aware, biosensor has

constrained figuring and capacity asset; ECC (Elliptic Curve Cryptography) calculation is a lightweight open key

cryptography calculation which is reasonable for WSN. So we use ECC uneven cryptography calculation (upheld elliptic

bend SECP160R1) to execute computerized mark and confirmation. Biosensor data secure correspondence is appeared in

Figure 3.

Figure 3: Biosensor information secure communication and process.



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As appeared in Figure 3, biosensor data secure correspondence is isolated into three process steps. The initial step is to

produce open/private key match; the remote hub creates its open/private key combine. In Sunspot programming API

determination, there are Java class-ECPublicKeyImpl, ECPrivateKeyImpl, and ECKeyImpl to perform ECC open/private

key sets age; the technique genKeyPair (publicKey and privateKey) in ECKeyImpl class is utilized to create people in

general/private key sets. At that point it exchanges its open key to the base station, which stores people in general key out

of sight Mysql database server. Since the progression is executed before information correspondence, it is free of

information transmission; aggressor thinks that its hard to get the key and the biosensor information at the same time. So

information secure conveyance is given in our correspondence procedure. To keep assailants from messing with general

society keys put away in Mysql database, people in general keys are upheld up to Hadoop distributed storage. Sqoop are

utilized to execute the assignment of trading information between Mysql database and Hadoop document framework.

General society keys from Mysql database will be sponsored up to HDFS stockpiling framework by Sqoop import

guidance. The general population key can be recouped from Hadoop to Mysql database by Sqoop trade guidance.

In the second step, when the remote hub gets the medicinal services data from biosensors, it executes ECC computerized

signature calculation to sign and ensure the biosensor measure esteems. In Sunspot programming API determination, there

is a Java class-mark to perform ECC mark and confirmation, which incorporates initSign, refresh, sign, and check

strategies. A 160-piece standard agreeable elliptic bend (secp160r1) is utilized to actualize ECC signature by calling

Sunspot programming API.

In the third step, when the base station gets the message which incorporates measure esteem, base station inquiries the

Sunspot ID and peruses its open key from foundation Mysql database; at that point base station utilizes the general

population key of Sunspot to check the message. On the off chance that the message can be confirmed effectively, the

biosensor measure esteem will be put away out of sight Mysql database. The specialists can check and dissect the

wellbeing data to do insightful determination. On the off chance that the message is altered by an assailant, it can't be

checked effectively; the message including measure esteem will be disposed of.

Normal execution time in Sunspot hub is utilized to quantify the overhead of the ECC mark and check. The message is

processed by SHA1 calculation to get the Hash esteem; at that point the Hash esteem is marked by ECC signature

calculation. Mark and check time are utilized to quantify the overhead of ECC mark and confirmation forms in Sunspot

remote hub. The analysis results are appeared Table 1.

Table 1: Execution time of ECC signature and verification on SunSPOT Sensor (ms).



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We utilize normal execution time as the overhead of ECC mark and confirmation forms on SunSPOT Sensor. Seen from

Table 1, the overhead can be acknowledged, on the grounds that the normal execution time is millisecond level. The

normal execution time of ECC mark and confirmation is close, while the season of mark is somewhat more than that of

check. With the expansion of message length, the normal execution time of mark and confirmation increments at the same

time. To a large portion of mark application in remote sensor organize, the overhead of ECC mark and confirmation

procedures can be acknowledged on the part of normal execution time.

Two tables are planned in the substance relationship graph (ERD) of database; the main table is Sunspot security element

table; it incorporates the Patient ID, Sunspot ID, and Sunspot open key, in which Patient ID and Sunspot ID are joined to

be essential key; Sunspot open key can be utilized to check biosensor measure esteem. The security substance table can be

upheld up to the Hadoop HDFS to keep open key from altering assault. The second table is Sunspot measure element

table, which records biosensor wellbeing measure esteems that are estimated from the patients. It incorporates the Sunspot

ID, Biosensor ID, measure timestamp, and measure esteems, in which measure timestamp is the essential key of the

Sunspot measure substance table. In each measure time point, the measure esteem is one of a kind. The element

relationship model of the two tables is indicated is Figure 4. The two elements are associated by confirmation connection.

Figure 4: Entity-relationship model of Sunspot security entity and measure entity.

Seen from Figure 4, the connection between Sunspot security substance and Sunspot measure element is confirmation.

Just if the measure esteem is confirmed effectively, the measure esteem can be recorded in the Sunspot measure substance

table, generally the measure esteem will be disposed of. The uprightness of measure esteems can be ensured by the

Sunspot security substance. The Sunspot ID of Sunspot security substance is the remote key of Sunspot measure element.

The two elements can be associated by the Sunspot ID to execute joint question.

In our plan, the Sunspot security element table is supported up to Hadoop HDFS stockpiling to guarantee the recuperation

of open key. One Sunspot hub is identified with one record in Sunspot security substance table, in which the information

sorts of Patient ID and Sunspot ID are 32-bit unsigned long whole number; the information kind of Sunspot open key is

160-piece character string. So the storage room of one record in security substance table is 28 Bytes (4 Bytes + 4 Bytes +

20 Bytes), and one Sunspot hub needs 28 Bytes in Hadoop HDFS stockpiling. On the off chance that the distributed

storage measure is 100 G Bytes, the size of the biosensor arrange is 3.571428 * 109.



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Sunspot Platform Simulation Research To approve biosensor based medicinal services framework and data secure correspondence method, we use Sunspot stage

to reenact the safe correspondence process. The recreation situation is appeared in Figure 5.

Figure 5: Sunspot based healthcare communication system simulation.

Seen from Figure 5, there are 4 Sunspot hubs in the reproduction situation. Two source hubs gather medicinal services

data by biosensors and send their information to the middle of the road hub, the halfway hub transmits the measure an

incentive to base station, and base station gets the message. Sunspot hub 1 executes ECC computerized signature

calculation to shield measure an incentive from biosensors, while Sunspot hub 2 does not utilize ECC calculation to

secure the measure esteem. We guess that the moderate hub perhaps be assailant, so the middle hub can mess with the

measure esteem which is transmitted by it. At the point when the altered message is gotten by base station, if the message

is ensured by ECC computerized signature calculation, the messed with message can't pass the confirmation; it will be

disposed of; the altering assault conduct can be identified in human services secure correspondence framework; generally,

if Sunspot hub 2 can't execute ECC signature calculation, base station can't check the message; as the altered message isn't

right, it might mislead the specialist and mischief the patient.



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Using Hadoop to Store and Audit Data Hadoop is an open source distributed computing and huge information stockpiling stage. It is utilized to store and review

the information estimated by Sunspot in this paper. Sqoop is a product instrument which can exchange information

between Hadoop HDFS (Hadoop dispersed record framework) and organized databases, for example, Mysql.

In our Hadoop stockpiling tests, five PC hubs are utilized to construct distributed storage framework. One of them is

namenode; the other four hubs are datanodes. The import guidance of Sqoop is utilized to occasionally import information

tables from Mysql database to Hadoop HDFS. The information tables incorporate Sunspot security substance table and

Sunspot measure element table. Just security substance table is traded to Hadoop HDFS. To guarantee the protected

review, just security managers are conceded access to the review information put away in the Hadoop HDFS. On the off

chance that the security head questions that the security substance table is altered, he can check the information on the

Hadoop HDSF to recognize the altering assault conduct. Ganglia device is utilized to screen Hadoop's running status,

when the table is foreign made/sent out by Sqoop. The checking consequence of Hadoop is appeared in Figures 6 and 7.

Hadoop load and memory insights results are appeared in Figure 6.

Figure 6: Hadoop cluster load and memory statistic results.



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Figure 7: Hadoop cluster CPU and network statistic.

Seen from Figure 6(a), the time scope of measurement results is from 10:20 AM to 11:20 AM. At the point when Hadoop

group sets up at about 10:26, the quantity of running procedures increments immediately when Hadoop instates the bunch

framework. At the point when security element table is foreign/traded from 11:05 AM to 11:15 AM, the quantity of

process expands a bit. It demonstrates that import/trade tasks of Sqoop just utilize less process asset.

Seen from Figure 6(b), when security element table is imported, the memory use rate builds a bit. It demonstrates that

import task of Sqoop involves a little memory asset. At the point when Hadoop group sets up, the bunch memory nearly

keeps consistent; it suggests that the underlying procedure of Hadoop nearly involves less memory asset. The

measurements results demonstrate the distinction between Hadoop introduction and Sqoop import/send out task.



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The CPU and Network insights results are appeared in Figure 7.

Seen from Figure 7(a), when information table is foreign made/traded among Mysql and Hadoop from 11:05 AM to 11:20

AM, there are just little waves in User CPU use bend; the normal User CPU utilization is about 10% between 11:05 AM

and 11:20 AM. Hold up CPU utilization bend just waves a little from 10:20 AM to 11:00 AM.

Seen from Figure 7(b), there are 4 crests from 11:05 AM to 11:20 AM in Hadoop bunch organize insights diagram. We

execute Sqoop import guidance for multiple times and Sqoop send out guidance just once amid 11:05 AM– 11:20 AM,

and the statures of three pinnacles are diverse in light of the fact that the measures of import information are

extraordinary. In the second import task, the measure of import information is most extreme. The last pinnacle is the least,

since information send out activity involves less system data transfer capacity.

Conclusion As of late, biosensor based social insurance data frameworks pull in numerous analysts and specialists' considerations

with the advancement of microelectronic, Bioinformatics science, inserted figuring, remote correspondence, and

distributed computing. Instructions to utilize various types of biosensors to fabricate an effective social insurance data

framework is a test issue to flow scientists. A tale Hadoop-based remote human services framework design is proposed in

this paper; Sunspot remote hubs are utilized to fabricate the remote biosensor arrange. A lightweight ECC computerized

signature calculation is utilized to give secure correspondence between remote hub and base station. Hadoop cloud stage

is utilized to reinforcement and recoup Sunspot security element table. Sunspot reproduction stage and Hadoop bunch are

utilized to approve remote medicinal services framework and secure specialized strategy. The fundamental commitments

of this paper are the accompanying three perspectives:

(1) An epic Hadoop-based biosensor Sunspot remote system engineering is proposed to construct human medicinal

services data framework. Multidiscipline learning is utilized to build a mind boggling social insurance framework, for

example, bioinformation science, remote sensor system, cryptograph and data security, security correspondence, database

and data framework, human-machine cooperation, and distributed computing.

(2) To guarantee the information correspondence security in social insurance framework, a lightweight ECC computerized

signature calculation and Hadoop-based information reinforcement and recuperation strategy are proposed to verify

Sunspot remote hub and ensure Sunspot key.

(3) Sqoop instrument is utilized to import/send out information between Mysql database and Hadoop HDFS distributed

storage; security head can utilize it to ensure and oversee key information.

Reproduction and checking results demonstrate that our human services data framework design and secure specialized

technique are exceedingly compelling to counter potential information altering assaults. Later on, more sensor types, for

example, Beidou position sensor will be incorporated in remote hub to give patient's exact position data. To guarantee the

security of measure esteem, lightweight encryption calculation will be utilized to ensure the secrecy of measure esteem. In

the meantime, more data will be transported in from Mysql database to Hadoop HDFS to enhance the security of

medicinal services data framework.



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