mergingrfidandblockchaintechnologiestoacceleratebig...

Research ArticleMerging RFID and Blockchain Technologies to Accelerate BigData Medical Research Based on Physiological Signals

Xiuqing Chen Hong Zhu Deqin Geng Wei Liu Rui Yang and Shoudao Li

School of Medicine Information Xuzhou Medical University Xu Zhou 221000 China

Correspondence should be addressed to Xiuqing Chen xiuqingchen126com

Received 4 October 2019 Revised 20 December 2019 Accepted 16 January 2020 Published 14 April 2020

Guest Editor Liang Zou

Copyright copy 2020 Xiuqing Chen et alis is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

e proliferation of physiological signals acquisition and monitoring system has led to an explosion in physiological signals dataAdditionally RFID systems blockchain technologies and the fog computing mechanisms have significantly increased theavailability of physiological signal information through big data research e driver for the development of hybrid systems is thecontinuing effort in making health-care services more efficient and sustainable Implantable medical devices (IMD) are ther-apeutic devices that are surgically implanted into patientsrsquo body to continuously monitor their physiological parameters Patientstreat cardiac arrhythmia due to IMD therapeutic and life-saving benefits We focus on hybrid systems developed for patientphysiological signals for collection storage protection and monitoring in critical care and clinical practice In order to providemedical data privacy protection and medical decision support the hybrid systems are presented and RFID blockchain and bigdata technologies are used to analyse physiological signals

1 Introduction

e medical applications are continually increasing Forhandling physiological signals efficiently specific tech-nologies such as data gathering using RFID protocolsinfrastructures and distributed information storage basedon blockchain frameworks are required e hospitalsapplications are adopting physiological signals to realize aquicker way to visit these records e physiological signalsare responsible to offer patient care enhance the clinicalperformances and promote the clinical data research[1ndash5]

Since the fog computing solves the secure storage issuesof big data in the clinical data research withminimal cost thefog computing technology is customizable and economicaland offers infrastructure platform and software Physio-logical signalsrsquo analysis and migration have been proposedfor accessing and sharing physiological signal data by dif-ferent research labs and health-care experts which canenable exchange of physiological signals more rapid andsuitable by using RFID technologies and smart phone appplatforms e advantages of RFID protocols [6ndash9] the fog

computing and blockchain in the medical applicationsprovide security and privacy protection for storing andsharing physiological signal records It can provide doctorswith collaboration ways through IMD [10] and RFID to helppatients in case of emergencies mode e new model basedon blockchain can support medical background ruralhealthcare and analyse data for medicines and medical re-search [11ndash15]

It is urgent for different research institutions to share theencrypted physiological signals erefore privacy and se-curity problems of physiological signals are the data ownersand research institutionsrsquo primary focus when the physio-logical signals include a lot of sensitive information and theattackers are continually trying novel approaches to steal thephysiological signals In order to handle these problems themedical databases adapted blockchain and fog computingare proposed [16 17] e medical application ecosystemsallow the regulators to share and exchange physiologicalsignal data in Figure 1 e introduction of the blockchain-fog-RFID based on data ecosystems ensures that the indi-viduals take control over physiological signal informatione proposed sharing data-driven economy shares the

HindawiJournal of Healthcare EngineeringVolume 2020 Article ID 2452683 17 pageshttpsdoiorg10115520202452683

physiological signals for research and commercial purposesin Figure 1

In the paper we protect cardiac IMD against securitythreats by presenting a security scheme First we verify andclassify the IMDrsquos major security attacks Second we in-troduce blockchain and the RFID systems to extend the IMDarchitecture [10] and discuss the structures of the interop-erability in the medical environment as shown in Figure 2

e motivation of the blockchian-fog-RFIDmethod foraccelerating big data medical research based on physio-logical signal is as follows the method is becoming morecommon due to the application of powerful computers andthe availability of physiological signals from varioussources However although the complexity of physiolog-ical signals makes the complex methods particularly ap-plicable their application of physiological signals isgenerally considered earlier than in other fields Big datahas become a buzzword in medical innovation Rapidadvances in artificial intelligence particularly promise toreform medical practice from the resource allocation to thecomplex diseasesrsquo diagnosis However big data brings hugerisks and challenges including major questions aboutpatient privacy the importance of fairness consent and

patient management in data collection based on RFID datastorage based on fog computing and dealing with databreaches by using blockchian In the future we will discussthe methodrsquos applications in physiological signals researchbasic research disease management aetiology detectionand diagnosis health services research treatment devel-opment and treatment evaluation e possibilities of theblockchian-fog-RFID method for accelerating big datamedical research in physiological signals are enormous

e paper contribution consist of four parts as follows

(1) e security scheme is a low energy cost RFIDsystem in IMD e applied authentication protocolis implemented on the RFID circuit without energy

(2) e applied energy harvesting scheme uses the en-hanced WISP which performs computationalfunctions and uses the harvested energy to go beyondpassive RFID tags

(3) e presented authentication protocol enables theauthorized health-care professionals to obtain theaccess permission to cardiac IMD securely in theregular and emergency model which are determinedaccording to the patientrsquos ability to supply valid

National healthorganization

Contractresearch

organisation

Insirancecompanies

Doctors Patients

Hospitals

Universities

Privatecompanies

FDA

AI

Machinelearning

Physiologicalsignal date set

Blockchainlifedate

BlockchainDate

Figure 1 e flow of data from the individuals to the companies and research institutions

2 Journal of Healthcare Engineering

credentials thanks to a biometric key distributionscheme implemented

(4) e schemes generate and share a master key se-curely based on the physiological sets of the patientcollected by IMD Monitoring and ensuring dataintegrity during clinical trials is not always feasible incurrent research systems Blockchain makes the datacollected immutable traceable and probably moretrustworthy during clinical trials We also improvethe way we currently report adverse events

In conclusion we argue that the blockchain can improvethe management of clinical trial data enhance trust in theclinical research process and simplify regulatory oversightof trials Finally we evaluate the security solutionrsquos securityand performance

e proposed model covers the many aspects of thehealth industry such as doctors patients and pharmacies toinsurance suppliers and government e paper shows theapplications of using RFID blockchain technologies and fogcomputing for storing andmanaging the physiological signaldata A blockchain model for sharing physiological signals isproposed In the next section the combination of block-chain RFID and artificial intelligence (AI) technologies is

suitable for collecting storing and handling heterogeneousphysiological signal e proposed model can be used forphysiological signals management

2 Related Work

e industry of healthcare has changed dramatically becauseof the boom in clinical research for physiological signal datasharing We summarize the healthcare studies includingphysiological signal data patient information obtained byfog computing and improvements to blockchain technol-ogye health-care applications of physiological signal dataadopt big data and deep learning technologies and providewith data confidentiality and identity authentication so as tomaintain patientsrsquo privacy In order to more convenientlyserve big data medical analysis Rajan and Rajan [1] andFaust et al [2] proposed the importance of medical big dataprivacy and the impact of data analysis on medical care

Rajan and Rajan [1] proposed a physiological signalmonitoring scheme by using the Internet of ings (IoT)Our schemes use IoT to improve the access method ofphysiological signals and the real-time dynamic monitoringmethod of the remote monitoring system which enhancesthe efficiency of the remote monitoring systems Faust et al

e android medicaldevice records the

patientrsquos status

Lab assistant queries theblockchain to access

the order does the workand report to the record Reader Reader

Doctor 1 creates an orderwhich receives an unique ID

called as hash which points toa record in the blockchain

Doctor 2 may replace thedoctor 1 during his absence

and need to observe thepatientrsquos record

Reader

Blockchain

TAG

IMD

Figure 2 Blockchain in the medical environment

Journal of Healthcare Engineering 3

[2] summarized the application of deep learning algorithmsin physiological signals and pointed out that deep learningmethods performed better than classical analysis and ma-chine classification methods for large and diverse datasetsShanthapriya and Vaithianathan [3] proposed the healthmonitoring system for human regional network e steg-anography technologies monitor patientsrsquo health safety andprovide patients with data confidentiality and identity au-thentication Orphanidou [4] reviewed big data applicationsof physiological signals pointed out how the applicationsuse physiological signals to provide real-time support formedical decision making in both clinical and family settingsand need to be overcome in clinical practice Tartan et al [5]proposed a heart rate monitoring system based on mobiledevices and geographical location which can monitorphysiological signals and send alarm information whenabnormal heart rate changes

e health-care systems [6ndash9] are data-distributiondomains where many physiological signals are generatedstored scattered and accessed daily by using RFID Yurialvarez et al [6] described that the contribution of RFIDtechnology can improve medical services can offer hospitaltracking of patients drugs and medical assets and canimprove the efficiency and safety of electronic medicalapplications Martinez Perez et al [7] used RFID technologyin the ICU (information management system) to track ICUpatientsrsquo admission nursing plan life monitoring pre-scription and drug management process improving thequality of patientsrsquo care during hospitalization Adame et al[8] proposed the monitoring systems for intelligenthealthcare which provides location status and tracks patientsand health-care assets Omar et al [9] proposed the reliablesecure and privacy-based medical automation and orga-nizational information management system that can providereal-time monitoring of vital signs of patients during hos-pitalization for intelligent patient management

e literatures [11ndash15] have been tremendous concen-tration in blockchain applications Xu et al [11] provided adecentralized resource management framework based onblockchain by studying resource management issues Aiqingand Xiaodong [12] proposed a blockchain-based securityand privacy protection sharing protocol to improve thediagnosis of electronic health systems e private block-chain is responsible for storing personal medical informa-tion (PHI) while alliance blockchain keeps the secure indexrecord of PHI Dubovitskaya et al [13] proposed a frame-work for sharing EMR data for cancer patients based on theblockchain and implemented Lebech et al [14] used mul-tisignature blockchain protocol for diabetes data manage-ment and access control as well as sharing and encryptione new approach helps to share diabetes data more ef-fectively in different institutions Yue et al [15] proposed themedical data gateway (HGD) architecture based on block-chain which enabled patients to safely own control andshare the data without infringing privacy

When different research institutions share the physio-logical signals the issues of privacy and security are theprimary focus of research institutions because the physio-logical signals include the sensitive information and the

attackers are continually trying novel approaches to stealinformation In order to meet the privacy needs and dealwith the security problems medical databases which useblockchain and fog computing technology are proposed

e enhanced trusted sharing physiological signalsmodel features highly secured data encryption and de-cryption schemes e model requires permission from theblockchain network to share patient information amongmedical staff e proposed model encrypts and analyzes thephysiological signals through the blockchain network bigdata analysis technology and AI technologies Kamel et al[16] pointed out that blockchain technology is becomingmore and more important in the research of medicine andmedical care proposed eight solutions of blockchain ap-plication in medical care and predicted that blockchain andAI solve various medical problems in the future Jen Hunget al [17] used blockchain in the drug supply chain to createtransparent drug transaction data prevent counterfeit drugsand protect public health

e abovementioned research findings do not applyblockchain to RFID systems However the protocol [18]proposed the RFID system based on blockchain and did notapply fog computing to medical fields It is our innovativework to propose RFID protocol based on fog computing andblock chain technology in medical systems

RFID protocol framework based on fog computing andblockchain is used for medical big data collection and dataprivacy protection [19ndash21] Gu et al [19] proposed a securityand privacy protection solution for fog computing whichdesigns a framework for security and privacy protectionusing fog computing and a privacy leakage based on context-based dynamic and static information to improve health andmedicine infrastructure Silva et al [20] proposed a medicalrecords management architecture based on fog computinge architecture used blockchain technology to providenecessary privacy protection and to allow fog nodes toexecute authorization processes in a distributed mannerGuan et al [21] discussed data security and privacy issues infog computing ey pointed out that the data security andprivacy challenges posed by fog layers and data protectiontechnologies in cloud computing cannot be directly appliedto fog computing Patel added the fog computing in theoriginal blockchain medical data sharing sequence model[22] Tang et al [23] proposed a new game theory frameworkto improve the mining efficiency of blockchain network andmaximize the total benefits of blockchain network In orderto improve the diagnosis of an electronic medical systemZhang and Lin [12] proposed a security and privacy pro-tection based on the blockchain PHI sharing (BSPP) schemee consensus mechanism (private blockchain and jointblockchain) is constructed by designing a blockchain datastructure

3 Mutual Authentication Protocol Using IMDs

e presented mutual authentication protocols for theWISPhave two modes the regular mode shares the IMD and thesame credentials the emergency mode is initiated when oneof the following status appear e IMD credentials are not


shared by the programmer the patients cannot communi-cate with the shared credentials and the credentials con-figured are expired

31 e reats and Its Influence on the Medical Recorde threats and its influence on physiological signals are asfollows privacy equity consent and patient governance inhealth information collection discrimination in informationapplications and handling data breaches

Because of newly developing data collection and storagetechnologies to collect and analyse vast amounts of data thetechnologies (RFID blockchain and artificial intelligence)enable more human experience While strict clinical testingis still required for handling data breaches the technologieswill fuel a new age of precision medicine in various methodsas shown in Table 1

32 Physiological Signals Data Privacy Rules While physi-ological signals are the lifeblood of todayrsquos digital societynumerous people are not fully aware of appropriate datacollection and processing e privacy issues are the con-cerns in the process of generating data It is more significantto be considered privacy protection in healthcare wherepersonal physiological signals consist of a large percentage ofthe data e rules and regulations guide the process of datageneration transmission access and exchange e privacystorage rules are as follows entitles patients more controlover physiological signals establishes boundaries of physi-ological signalsrsquo use and release protects the privacy ofphysiological signal enables patients to make choices wiselyand enables patients to be aware of methods for preventingdata leakage It is completely important to maintain thesecurity and privacy of physiological signals by using RFIDfog computing and blockchain

33 Security Attacks and Requirements for IMDs is partshows IMDsrsquo main security attacks [10] and discusses thesecurity requirements in Figure 3 Table 2 explains thesymbols and definitions of all the authentication protocols

34 Mutual Authentication Scheme in the Emergency Modee IMD and programmer can securely produce and offerthe major key which is extracted from the patientrsquos data byexecuting the presented mutual authentication protocolrsquosemergency mode in Figure 4

Step1 the reader initiates the presented mutual au-thentication protocolrsquos emergency mode by transmit-ting the synchronization request M1 (IDR NR andflag) to the IMDStep2 WISP computes features VRandPermute (FWcup Fprime W) and sends V to the readerStep3 the reader computes Kbio H (Q) and sendsM3 (IDR I HMAC (Kbio I|Q|IDR)) to WISPStep4 if the number of matching characters is greaterthan the predefined threshold the WISP calculates

KprimebioH (Q) and verifies Kprimebio Kbio If the key issuccessfully confirmed WISP generates NW and com-putes KH (Kbio |NW) and KprimeH (K |NW) WISP ad-mits the reader by transmitting M4 ((NW IDW)KbioHMAC (Kbio NR|NW|IDW))Step5 in order to determine (NW IDW) the readerdecodes the messagersquos first part using Kbio After that itverifies the authenticity of (NW IDW) by employingHMAC function and comparing the result to the re-ceived messagersquos second section If they are equal thereader calculates KH (Kbio |NW) and KprimeH (K |NW)and then sendsM5 (Seq1 HMAC (Kprime NW |Seq1))ereader sends messages (Kprime Seq1) to the programmerStep 6 WISP verifies the session keysrsquo equality IMDcollects the key of session and the relevant sequencenumber

Two modes (emergency mode and regular mode) havethe same shortcomings First neither model talks about howto store large amounts of data on the database Second bothmodels have secret key leakage attacks and tracking attacksird neither model uses cloud storage technology orblockchain technology

35 Attacks for Mutual Authentication Protocol in theEmergency Mode

351 e Reader Impersonation Attacks e reader com-putes Kbio H (Q) and then sends M3 (IDR I HMAC(Kbio I|Q|IDR)) to WISP

In order to simplify the analysis steps the steps 3ndash6 inFigure 4 are omitted here e tracing attacks in theemergency mode have three phases

(1) e testing phase the attacker chooses the target tagRlowast monitors the first round (1M1 1M2 1M3) to Rlowastand obtains the outputs keys 1Kbio H (Q) and thereader applies 1M3 (IDR I HMAC (Kbio I|Q|IDR))to WISP

(2) e reader impersonation attacks phase the attacker(the counterfeit reader Rprime) chooses the monitoredinformation 1M1 e attacker monitors the outputinformation (2Kbio H (Q) 2M3 (IDR I HMAC(Kbio I|Q|IDR))) in the second round

(3) e decision phase the adversary obtained the values(1Kbio 1M3) and (2Kbio 2M3) If (1Kbio 1M3)ne (2Kbio2M3) and the attacker confirms that Rlowast is not Rprimewiththe probability 1 if (1Kbio 1M3) (2Kbio 2M3) theattacker makes sure that Rlowast is the counterfeit Rprimeerefore the protocol does not meet the weakindistinguishability property and suffers from thereader impersonation attacks

352 Reducing the Calculation Cost of Reader and WISPIn order to reduce the computation of the whole systems theHASH computational expense of the reader and WISP arehigh the proposed protocol uses the PRNG function toreplace HASH function


36Mutual Authentication in the RegularMode e regularmode ensures the secure data exchange as shown inFigure 5

Step1 the reader sends Mprime1 (NR IDR flag HMAC (K

NR |IDR)) in the regular modeStep2 WISP can confirm the received requestrsquosfreshness and the readerrsquos authenticity If the organizedprimary key has not run out the received request fromthe keys is authenticated by the WISP By contrary theWISP rejects access by sending the denial messageStep3 WISP computes KprimeH (K |NW) and sendsMprime2 ((Nbr NW IDW) K HMAC (K NR| NW|IDW)) to

readerStep4 when receiving themessages the reader decodes thefirst part of the messages to obtain (Nbr NW and IDW)Step5 after verifying successfully the reader calculatesthe key value Kprime using NW and sends the messagesMprime3 (Seq1 HMAC (Kprime NW | Seq1))

Step6 WISP can confirm the messagersquos freshness andthe keysrsquo equality computed on both sides WISP in-crements the Nbr parameter which represents the totalnumber of session keys which originated from theprimary keyStep7 WISP delivers the messages (Kprime Seq1 Nbr) toawaken IMD antenna

e attacks for mutual authentication protocol in theregular mode

361 Secret Key Disclosure Attacks e attackers monitorthe delivery messages and reveal the secret keys as follows

In Step1 Mprime1 (NR IDR flag HMAC (K NR |IDR)) the

attacker discloses IDR

In Step3 Mprime2 ((Nbr NW IDW) K HMAC (K NR|

NW|IDW)) the attacker discloses IDWIn Step7 (Kprime Seq1 Nbr) the attacker discloses Kprime

Table 2 Symbols and definitions of the enhanced RFID system privacy protection authentication protocol

Symbols DefinitionsCi TIDi

T COUNT Challenge from the DB to reader temporary identity countRi Rilowast Ns Response for the reader RioplusNs random number generated

Ress CRP (CiRi) Ki h(COUNT + 1RiRlowasti )) ith challenge-response ith keyPUFTh () oplus (|) PUF for the tag T one-way hash function XOR concatenationKH KP KD Hospital patient doctor

Table 1 Audiences and influence functions of medical record

Audiences Influence functionsPatients Promote diagnoses and identification of physiological signals facilitate preventive care and reduce costsDoctors e rigorous diagnosis treatment choices monitoring disease progression therapy response and patient susceptibilityResearchers Perform large-scale disease modelling and efficacious therapies

Clinics Risk estimation forecasting relapse possibility designing criteria for dischargereadmission predicting mortality andconveying potential crisis episodes

Batterydepletion

attack

Attacks

Unsecure accessduring

emergencysituations

Trafficcapture and

analysis

Desynchronization

attacks

Attackstargeting

authenticationprotocl

Relayattack

Energypreservation

Data reliability

Renewablecredentials

Secure access inemergency situations

Protection against batterydepletion attacks

Perfect forward secrecy(PFS) property

Figure 3 Security attacks and requirements for secure IMDs


362 e Tracing Attacks In order to simplify the analysisprocess the steps 3ndash6 in Figure 5 are omitted here etracing attacks have three phases

(1) e testing phase the attacker chooses the target tag Tlowasten shehe monitors the first round (1M1 1M2 1M31M4) to Tlowast and obtains the outputs keys (1IDR 1IDW)

(2) e tracing attacks phase we assume that the tag set(T0 T4 Ti) includes Tlowast and the counterfeit tag Tprimee attacker monitors the keys (2IDR 2IDW) in thesecond round

(3) e decision phase the adversary obtained the values(1IDR 1IDW) and (2IDR 2IDW) If (1IDR1IDW)ne (2IDR 2IDW) the attacker confirms that Tprime isnot Tlowast with the probability 1 if (1IDR 1IDW) (2IDR2IDW) the attacker makes sure that Ti is Tlowast (thecounterfeit tag Tprime) erefore the original protocol inthe regular mode does not meet the weak indistin-guishability property and suffers from the tracingattacks

363 Medical Framework Based on RFID Blockchain andArtificial Intelligence At present amounts of patients havethe comprehensive datasets which consist of clinical history(the genetic lifestyle data drug and blood biochemistry) Inaddition the consumer companies and the pharmaceuticalare willing to pay much money for the vast personalphysiological signal data applied to train their AI model viausing the machine learning We proposed the medicalframework based on RFID blockchain and artificial in-telligence as in Figure 6

Previous researches based on RFID blockchain andartificial intelligence mainly focused on the medical ap-plication respectively e studies improve the timeproficiency of physiological signal data processing andcontribute to medical data management by combiningthree technologies e effectiveness of the medicalframework involves low resource usage large computationtime more energy less power and low memory con-sumption (Algorithm 1)

IMD WISP

Record ECG

Create vault

Compute keyKbio = H (Q)

Compute keyK = H (Kbio | NW)Kprime = H (Kbio | NW)


Select NW

Verify Kprime

Secure communication using Kprime

Identify common features Qcompute key Kbio = H (Q)

Record ECG

RFID reader

M1 ⟨NR IDR flag⟩

M5 ⟨Seq1 HMAC (Kprime NW | Seq1)⟩

⟨Kprime Seq1⟩

⟨Kprime Seq1⟩

M3 ⟨IDR I HAMC(Kbio |Q| IDR)⟩

M2 ⟨Vault V⟩

Programmer

M4 ⟨NW IDWKbio HAMC(KbioNR | NW | IDW)⟩

Figure 4 Mutual authentication protocol in the emergency mode (protocol 1)


Applicationreverse

engineering

Featureextraction

Machinelearning

classification

TAG

Datacollection

ReaderBlockchain

Patient

Figure 6 e medical framework based on RFID blockchain and artificial intelligence

IMD

Request

Nbr

WISP

Key K

[Invalid key] ⟨Deny flag⟩


[Valid key]

Check remainingkey lifetime

Select randomNW

Compute session keyKprime = H(K | NW)


RFID reader Programmer

Mprime1 ltNR IDR flag HAMC(K NR | IDRgt

Mprime3 ltSeq1 HMAC (Kprime NW | Seq1)gt

Verify Kprime

update Nbr

ltKprime Seq1gt

ltKprime Seq1 Nbrgt

Mprime2 ltNbr NW IDWK HAMC(K NR | NW | IDW)gt

Figure 5 Mutual authentication in the regular mode (protocol 2)


4 Security and Performance Analysis ofProtocol 3 and Protocol 4

e protocol 3 and protocol 4 are more suitable to storephysiological signals in medical applications

41 Security Analysis for Protocol 3 Scheme 3 overcomes theweaknesses of protocol 1 and the protocol 4 overcomes theweaknesses of protocol 2

411e Reader Impersonation Attacks Resistance In orderto resist the reader impersonation attacks the readercalculates Kbio

prime PRNG (Q||NR) using NR Even if the at-tacker monitors the output information (2Kbio PRNG (Q||NprimeR) 2M3(IDR I HMAC (2Kbio I||Q||IDR))) using the

new nonce Nprime R in the second round the attacker cannotcounterfeit the original reader

412 Key Leak Attack Resistance In order to resist the keyleak attacks WISP calculates B NW oplus IDW the readercalculates K PRNG (Kbio||NW) and Kprime PRNG (K||NW)

413 Provision of Data Integrity Verification In order tomeet data integrity the protocol 3 has used HMAC hashcalculation to protect the integrity of messages (K1 Seq1)

414 Provision of Scalability and Efficiency In order tosatisfy the scalability each tag identifier does not match thecorresponding key in DB erefore the identifications oftag keys do not match one by one in DB of the improved

e proposed protocol in the emergency mode (Figure 7) is as follows(1) Step 1(2) e reader initially generates the random numbers (NR IDR flag 1)(3) Calculate ANRoplus IDR(4) Broadcast M1(5) Step 2(6) Compare IDR(7) if IDR ne AoplusNR then(8) Process termination(9) else(10) (11) set up VRandPermute (FW cupFW

prime )(12) Send M2 to reader(13) M2 in V(14) Step 3(15) for each fi

r do(16) if fi

r FR then(17) e reader and the tag match each other(18) Calculate Kbio H (Q ||NR)(19) Send the message M3 (IHMAC(Kbio I|Q|IDR))(20) Step 4(21) If the number of matched characteristics is greater than the predetermined threshold in WISP(22) Calculate Kbio

prime H (Q||NR)(23) if Kbio

prime Kbio then(24) if HMAC(Kbio

prime I|Q|IDR) HMAC(Kbio I|Q|IDR)(25) Verify success generate random numberNW Calculate B NW oplus IDW(26) Calculate KH (Kbio |NW) and new key KrsquoH (K |NW)(27) Send S1 HMAC(Kbio NR| NW|IDW) M4 lt NW IDW1113864 1113865Kbio

HMAC(Kbio NR|NW|IDW)gt(28) Step 5(29) if Kbio (reader)Kbio (tag) obtain (NW IDW)(30) Calculate S2 HMAC(Kbio NR| NW|IDW)(31) if S2 S1 then(32) Calculate (K Kprime) KH (Kbio | NW) KrsquoH (K |NW)(33) Send lt Seq1 HMAC(Klsquo NW | Seq1)gt to WISP(34) Step 6(35) WISP verifies the session keysrsquo equality calculated by both sides (WISP reader)(36) If the session keys calculated on both sides are equal(37) WISP records (Kprime Seq1) to awaken the IMD antenna(38) When IMD detects the request begins to collect (Kprime Seq1) and employs them to exchange data securely with the programmer(39)

ALGORITHM 1 e suggested mutual authentication protocol in the emergency mode


protocol which guarantees the efficiency of tag authenti-cation and satisfies the scalability property

415 Replay Attacks Resistance e attacker replays themessages to authenticate by monitoring the previous in-formation In order to resist replay attacks all messages areencrypted by using the random numbers (Nbr NW and NR)and combined with PRNG function

416 Provision of Data Integrity Verification In order toachieve the property of data integrity we have usedPRNG calculation Kprime PRNG (K|NW) to protect the in-tegrity of Kprime

42 Security Analysis for Protocol 4

421 Secret Key Disclosure Attacks Resistance In order toachieve anonymous and privacy requirements in improvedprotocol 4 the protocol uses the XOR function to encryptthe transmitted keys as follows

B IDWoplusNW K1Kprime oplusNR K2Kprime oplusNW

422 Tracing Attacks Resistance e key updating mecha-nism Kprime PRNG (K|NW) involves the ith keys and the nonces(NW K) e ith key Ki cannot be cracked by the (i+1) th keysKi+1 and the ith sessions e reasons are that PRNG functionsprotect the parameters by the encrypted messages ereforethe enhanced protocols resist the tracing attacks

IMD WISP RFID reader

Step 11M1 ltNR A flaggt

Step 21M2 ltVault Vgt

Step 31M3 ltI HMAC (Kbio I | Q | IDR)gt

Step 41M4 ltNW BKbio HMAC (Kbio NR | NW | IDW)gt

Step 51

Step 62

M5 ltSeq1 HMAC (Kprime NW | Seq1)gt


ltK1 Seq1gt

A = NR IDR

Step 1 Record ECG

Step 2 Record ECGcreate vault

Step 3 Identify commonfeatures Q compute keyKbio = PRNG (Q || NR)

Step 61

Programmer

+

Step 4 Compute keyKbio = PRNG (Q || NR)

SelectB = IDW NW+

Compute keysK = PRNG (Kbio | NW)Kprime = PRNG (K | NW)

Step 6 Verify KprimeK2 = Kprime NW+

ltK2 Seq1gt

Step 5 Compute keysK = PRNG (Kbio | NW)Kprime = PRNG (K | NW)K1 = Kprime NR+

Figure 7 e proposed mutual authentication protocol in the emergency mode (protocol 3)


423 Availability and Desynchronization Attacks ResistanceIn order to provide anonymity the communication com-ponents (tag and DB) update the shared messages aftercompleting the conversation If the opponents destroy theupdating process the authentication scheme is subjected todesynchronization attacks In order to guarantee the con-fidentiality and anonymity of K the messages synchronouslyshould be updated In addition the attacker knows theshared key Kprime during the updating processes which isprotected by the random numbers (NW NR) e improvedprotocol is desynchronization resistance

43 e Comparisons of Security and Performance AnalysisTable 3 lists the computational cost for five protocols ecomputational costs of tags in protocol 3 are 3PRNG+Xorand the computational costs of tags in protocol 4 are2PRNG+Xor e safety performances of the enhancedprotocols are superior to other schemes Compared with theoriginal protocol 1 and protocol 2 the improved protocolssupport the security enhancements and ensure the functionsuch as integrity efficiency and user privacy

5 Blockchain Framework for Security andPrivacy Storage and Sharing

A framework is developed to share physiological signalsrsquocross domain and build the radiological studiesrsquo ledger andpatient-defined access permissions by applying the block-chain as the distributed data store Relative disadvantages ofthe framework include the privacyrsquos complexity and securitymodels Ultimately the large-scale feasibility of the approachremains to be demonstrated

e peculiar health-care technologies are requiredsuch as parallel processing distributed data networkscalable storage frameworks and infrastructures e fogcomputing is economical and customizable since fogcomputing handles these complex problems in the virtualenvironment and only needs to pay for the used servicesand resources

e sharing physiological signals systems are importantin different medical institutions but the current infra-structure for transmitting physiological signals relies onthe trust third-party intermediaries We propose theframework of cross-domain sharing image where the

IMD WISPRFIDreader

Key K

Programmer

Request

Nbr

[valid key]

[Invalid key] ltDeny flaggt

Step 12Check remaining

key lifetime

Step 3 Compute session key

Step 31Mprime3 ltSeq1 HMAC (Kprime NW | Seq1)gt

Step 42ltK2 Seq1 Nbrgt Step 41

ltK1 Seq1gt

Step 21Mprime2 ltNbr NW BK HMAC(K NR | NW | IDW)gt

Step 11Mprime1 ltNR A flag HMAC(K NR | IDR)gt

Step 2 Select random NWcompute B = NW + IDW

Step 1 A = IDR + NR

Kprime = PRNG (K | NW)K2 = Kprime NW+


Kprime = PRNG (K | NW)K1 = Kprime NR+

Figure 8 e proposed mutual authentication protocol in the regular mode (protocol 4) (Algorithm 2)


blockchain is used as the distributed data storage to es-tablish patient-defined access rights e blockchainframework is verified to eliminate the access permission ofthe third-party to protected physiological signal infor-mation meets many standards of the interoperable medicalsystem and easily generalizes to fields beyond physio-logical signal We summarize the framework based onblockchain to allow patients to securely grant electronicaccess permission to their physiological signal data anddescribe the advantages and disadvantages of the approach

e actual transmission of physiological signals re-quires the physiological signals receiver who transmits thesigned request to the URL endpoint e individual ser-vice is the requesting entity that the access permission ofthe physiological signals study is authorized to by theowner (patient) e studies of all patientsrsquo physiologicalsignals result in the huge blockchain far too large todownload store and validate for nodes running onmobile devices e size of the blockchain has been provento be the limiting element for chains storing the trans-actional data

Considering all of these factors sharing the physiologicalsignals by using blockchain helps the interoperable healthsystem and has greater ability to access patientsrsquo physio-logical signals electronically

51 Physiological Signals Data Sharing Model Based onBlockchain [22] Intelligent contract based on blockchain isused to promote the security analysis and management ofmedical sensors Intelligent device invokes intelligent con-tract and writes records of all events on blockchain eintelligent contract systems support real-time patientmonitoring and medical intervention by sending notifica-tions to patients and medical professionals e provider ofmedical records can modify the physiological signals but itneeds patientrsquos consent and the patient can assign accessauthority to medical records

When applying blockchain to the construction of thecredit system we promote the collection and supervision ofcredit information in the medical field and build the newrelationship platform It is significant to the improvement ofthe credit system construction According to the unifiedevaluation criteria the credit rating is evaluated the result ofthe rating level is publicized on the platform of block chainthe credit rating is rewarded and the violation of credit ispunished so as to strengthen the construction of the creditsystem in the medical field in the real sense

e asymmetric information encryption methods needtwo keys public key and private key After the physiologicalsignals are encrypted with public key only the corre-sponding private key can be used for decryption On the

e proposed mutual authentication protocol in the regular mode is in Figure 8 as follows(1) Step 1(2) e reader generates (NR IDR flag 0)(3) Calculate A IDRoplusNR and KH (NR|IDR)(4) Transmit M

prime1 ltNR IDR flag HMAC(K NR | IDR)gt

(5) When WISP receives the request it confirms that the primary key is expired and verifies that how many session keys whichoriginated from the primary key exceeds the predetermined threshold

(6) if tltT thenIf the primary key has not expired WISP receives the messages

(7) else the key expired access denied(8) Step 2(9) After WISP successful authentication the random numberNW is generated(10) Calculate KrsquoH (K |NW) B IDWoplusNW(11) Transmit M

prime2 lt Nbr Nw I Dw k HMAC(K NR|NW|IDw)gt

Calculate S1 HMAC(K NR|NW|IDw)(12) Step 3(13) After receiving the messages the reader starts to parse the first part of the message through the key K to obtain (Nbr NW

IDW)(14) Calculate S2 HMAC(K NR|NWIDw)

If S2 S1 thene message is trueCalculate Krsquo PRNG (K |NW) K1 KrsquooplusNR

(15) Transmit Mprime3 Seq1 HMAC(KprimeNw|Seq1)

(16) Step 4(17) Based on the received HMAC WISP can confirm the timeliness of the message and the equality of the session keys calculated on

both sides(18) After verifying successfully Nbr++ K2 KrsquooplusNW(19) WISP records (K2 Seq1 Nbr) to awaken the IMD antenna(20) When IMD detects the request collects (K1 Seq1) and employs them to exchange data securely with the programmer

ALGORITHM 2 e proposed mutual authentication protocol in the regular mode


contrary if the private key is used to encrypt data only thecorresponding public key can be used for decryption If theblockchain can be grafted scientific research institutionsunderstand the probability of disease occurrence the oc-currence of accidents the level of hospital management andclaims cases and other actual situations

(1) Use the fog-based blockchain and fog warehouse tostore medical data as shown in Figure 10

(1) List of medical research and patients in eachinstitute

(2) Patients are authorized to access the entity set ofeach study e entities are represented by thecommon part of the asymmetric key pair on theblockchain

(2) Definition study the transaction builds the patient asthe master of a UID which is the specific uniqueidentifier and the source as the creator Tuples storedin block chains are transactions with double signa-tures similar to documents with signatures frompatients and hospital representatives e patientsclaim that the definition study has received the

medical diagnosis in the hospital which confirms thestatement and promised to provide the study in theprevious block e patientrsquos signature declaration isobtained through themobile application which sharesand stores the values required allowing access to thetransaction in the future en the hospital signs thefollow-up information of the patients and broadcaststhe transaction to the blockchain

(3) Allow access the transaction allows the owner of themedical information research to authorize the otherparty to retrieve its medical data Patient KP signs atransaction to grant the function to doctor KD esigned verification blocks are embedded in block-chains As shown in Figure 11 patients publish thetransaction after verifying the key with the doctorthrough the APP platform e patient can be au-thorized to the legitimate doctor or institution and thedoctor can associate anymedical information receivedwith the correct local medical record number

e middle column (Block Chain Medical Data SharingSequence) describes the interaction between entities andjudgments in each stage and reflects the sharing medical

IMD

Request

[Valid key][Invalid key] ltDeny flaggt

Nbr

WISP RFID reader

Select randomNW

Kprime = PRNG(K | NW)

Programmer


IDR = C NR+

Verify Kprimeupdate Nbr

+ NbrK1 = Kprime

⟨K1 Seq1 Nbr⟩

Secure communication using K1

Mprime1 ⟨NR C flag HMAC (K NR | IDR)⟩

Mprime3 ⟨Seq1 HMAC (Kprime NW | Seq1)⟩

Key KC = IDR + IDR

+ NbrKprime = PRNG (K | NW)K1 = Kprime

⟨K1 Seq1⟩

Mprime2 ⟨Nbr NW IDWk HMAC (K NR | NW | IDW)⟩

Figure 9 Secure communication protocol between the IMD and the programmer (protocol 5) (Algorithm 3)


information by supporting distributed block chains and out-of-block transactions

e actual medical data transmission requires themedical data receiver to deliver the signature request to themedical sourcersquos URL endpoint which creates the researchBoth requests and responses are transmitted through thesecure link of the transport layer to prevent eavesdroppinge effective blocks are generated in the timely manner bygenerating the distributed database with access permissionsand stimulating the block generator in some way Only thosenodes with security deposits can participate in the expansionof the chain and any node with misconduct will be forced toabandon its investment e nature of blockchain providesthe direct audit of the activity of each node such as thenumber of blocks generated and the failure status of theblocks generated e node operator can prove the nodeownership by using the private key which is corresponded tothe identity public key of the node to sign the message eenhanced model adds the fog computing in the originalblockchain medical data sharing the sequence model [22]

which is used to construct the blockchain for medical datasharing

We have showed the technology fundamentals ofblockchain and provided a summarization of the blockchainapplication that can be used as a tool to allow the patient-controlled physiological signalrsquos cross-domain sharingwithout the central authority In particular we highlightedthe way blockchain satisfies many requirements of the in-teroperable health system However these technologies alsohave several important limitations and the relative merits ofexisting alternatives must be considered before any large-scale and blockchain-based application for sharing physi-ological signals

When receiving query request the physiological signaldata source verifies the correctness of the signature ensuresthat the hashed data matches the previously published datafor KP-owner via Block B and confirms that the KP-ownerhas allowed the requestor access to these physiological signaldata via Block C If meeting all the conditions the responsecontaining the physiological signal study is returned from

e programmer can use the session key calculated by the protocol to establish the secure communication after IMDauthenticates the programmer in Figure 9

(1) Step 1(2) e reader initially generates (K C IDRoplusNR) and transmits the values M

prime1 (NR C flag HMAC (K NR | IDR)) to the WISP

(3) Step 2(4) e IMD returns Nbr and updates IDR CoplusNR(5) Step 3(6) If the key is valid then(7) e WISP selects NW and transmits the values M

prime2((Nbr NW IDW) HMAC (K NR| NW|IDW)) to the reader

(8) else(9) e WISP transmits the sequences (Deny flag) to the reader(10) Step 4(11) e WISP updates KprimePRNG (K |NW) and the reader updates KprimePRNG (K |NW) and K1KrsquooplusNbr e reader sends the

value M3 (Seq1 HMAC (Kprime NW Seq1)) to the WISP and sends the messages (K1 Seq1) to the programmer(12) Step 5(13) e WISP identifies Kprime by comparing the value Kprime of the WISP with the Kprime value of the reader e WISP updates Nbr

K1 KrsquooplusNbr and sends (K1 Seq1 Nbr) to the IMD

ALGORITHM 3 Secure communication protocol between the IMD and the programmer

Table 3 e comparisons of the performance analysis and safety performance

Performance Protocol 1 Protocol 3 Protocol 2 Protocol 4 Protocol 5F0 No Yes No Yes NoF1 3H+Xor 3PRNG+Xor 2H+Xor 2PRNG+Xor 1PRNG+2XorF2 No Yes No Yes YesF3 No Yes No Yes YesF4 No Yes No Yes YesAttack types Protocol 1 Protocol 3 Protocol 2 Protocol 4 Protocol 5R1 No Yes No Yes NoR2 Yes Yes Yes Yes YesR3 Yes Yes Yes Yes YesR4 Yes Yes Yes Yes YesR5 No Yes No Yes YesR6 No Yes Yes Yes YesF0 provision of scalability and efficiency F1 storage cost (tag) F2 blockchain-enabled F3 cloud computing-enabled F4 fog computing-enabled R1 keyleak attacks resistance R2 replay attacks resistance R3 desynchronization attacks resistance R4 reader impersonation attacks resistance R5 tracking attacksresistance R6 tag impersonation attacks resistance


the source In order to prevent eavesdropping the requestsand responses are sent to prevent eavesdropping e spe-cific steps of blockchain medical data sharing sequencediagram are as follows

Step 1 for hospital (KH-owner) KH-owner will servicephysiological signals retrieval requests at https by usingon-blockchain transaction and off-blockchaincommunication

Blockchain and fog warehouse

Hash history Blockchaintechnology

Structured data types ofphysiological signal

The serialnumber ID of the patient in hospital

MRI ECG Heart Rate Signal Image

My webpage com

User authentication layer

Fraction

Websites

Public andprivate keys

Figure 10 Blockchain based on fog warehouse

Blocktime

Fogcomputing

A B C D

kH kp kH kp kD kDquery

kH

Actors

Assertions

On-blockchaintransaction

Physiological signals Physiological signals

Step2Patient e physiological

signals were acquired for Kp-owner by Kh-owner

Step3Hospital Kp-ownerrsquos

assertion above is accrate andKh-owner will share these data

at its establised end point

Step4Physician I am Kd-owner

and need to review imaging fromyour hospitalization

Step5Patient Agreed I am Kp-owner

and will allow access

Step6Patient As the Kp-owner

I permit the Kd-owner to access thephysiological signals that wereacquired for me by Kh-owner

Step7Physician uses the information in

blocks (AC) and to submit an queryrequest for physiological

signals signed by KdStep8

Hospital valid at the Physicianrsquossignatureuses the information in

blocks (BC) and to confirmauthorization and transmits the

physiological signals study in an queryresponse

Off-blockchaincommuication

Step1Hospital kH-owner will

service physiological signalsretrieval request at https

Figure 11 Blockchain medical data sharing sequence diagram based on fog computing


Step 2 for the patient (KP-owner) the physiologicalsignals are acquired for KP-owner and KH-ownerStep 3 for hospital KP-ownerrsquos assertion is accrate andKH-owner shares the physiological signals at theestablished endpointStep 4 for physician (KD-owner) KD-owner reviewsthe physiological signals from the hospitalizationStep 5 for patient if the patients agree they are KP-owner and will allow accessStep 6 for patient the patient permit KD-owner toaccess the physiological signals that were acquired byKH-ownerStep 7 physician uses the information in blocks (A C)to submit the query request for physiological signalssigned by KDStep 8 hospital valid at the physicianrsquos signature usesthe data in blocks (B C) to confirm authorization andtransmits the physiological signals study in the queryresponse e requests are sent by the KD-owner attimepoint D

e ecosystem is consisted of the blockchain nodes andfog storage For example one of the main reasons for in-corporating fog storage technology into the ecosystem is tosupply the offline storage solution especially for largephysiological signals For security and privacy the client sidewould encrypt the physiological signals uploaded to the fogstorage With the maturity of the fog storage personalstorage may be replaced by it

Most significantly blockchain technology can create thephysiological signal-driven marketplace where patients canget real return by offering their data to research institutionspharmaceutical and consumer companies the applicationdevelopment community and producing new physiologicalsignal data

6 Conclusions

We extend the architecture of the IMD with blockchainRFID and WISP which increases the physiological signaldatarsquos confidentiality and authenticity e enhanced RFIDprotocols provide protection against tracking attacksreadersrsquo impersonation attacks and secret disclose attacks

e physiological signal records have proved the im-portance for the patients and sharing and acquiringphysiological signals is essential for intelligent and advancedmedical services e blockchain application of e-commercehas proven that trusted and auditable transaction in peer-to-peer networking is possible In the paper we have intro-duced a blockchain-based architecture model for physio-logical signal data on fog computing environment Ourcontributions are mainly consisted of the proposed solutionand introduction to future medical data directions inblockchain e paper proposes the outline to show theframework and schemas for dealing with heterogeneousphysiological signals Once the hybrid technologies are in-tegrated big data systems and AI technology have the po-tential to offer privacy protection and data sharing and

transform healthcare management In the future we willfocus on heterogeneous physiological signal data issuesthrough fog computing blockchain and AI technology inthe realistic medical environment

Data Availability

e paper gives an outline about the framework and in-ternal working and protocols for handling heterogeneousphysiological signal data Once the hybrid technologies areintegrated big data systems and AI technology have thepotential to offer privacy protection and data sharingtransform healthcare management

Conflicts of Interest

e authors declare no conflicts of interest

Acknowledgments

is work was supported in part by Jiangsu PostdoctoralScience Foundation (Grant nos 1701061B and 2017107007)Xuzhou Medical University Affiliated Hospital PostdoctoralScience Foundation (Grant nos 2016107011 18382253120225 and 53120226) Xuzhou Medical University Ex-cellent Persons Scientific Research Foundation (Grant nosD2016006 D2016007 and 53591506) the Practice InovationTrainng Program Projects for the Jiangsu College Students(Grant nos 20161031308H and 201610313043Y) the NaturalScience Foundation of the Jiangsu Higher Education In-stitutions of China (Grant no 16KJB180028) and 333Project of Jiangsu Province (no BRA2017278)

References

[1] J P Rajan and S E Rajan ldquoAn internet of things basedphysiological signal monitoring and receiving system forvirtual enhanced health care networkrdquo Technology and HealthCare vol 26 no 2 pp 379ndash385 2018

[2] O Faust Y Hagiwara T J Hong O S Lih andU R Acharya ldquoDeep learning for healthcare applicationsbased on physiological signals a reviewrdquo Computer Methodsand Programs in Biomedicine vol 161 no 1 pp 1ndash13 2018

[3] R Shanthapriya and V Vaithianathan ldquoECG-based securehealthcare monitoring system in body area networksrdquo inProceedings of the 2018 Fourth International Conference onBiosignals Images and Instrumentation (ICBSII) pp 206ndash212IEEE Montreal Canada October 2018

[4] C Orphanidou ldquoA review of big data applications of phys-iological signal datardquo Biophysical Reviews vol 11 no 1pp 83ndash87 2019

[5] E O Tartan and C Ciflikli ldquoAn android application forgeolocation based health monitoring consultancy and alarmsystemrdquo in Proceedings of the IEEE 2018 IEEE 42nd AnnualComputer Software and Applications Conference (COMPSAC)pp 341ndash344 IEEE Computer Society Tokyo Japan July 2018

[6] L Yuri alvarez F Jacqueline N Guillermo alvarez et alldquoRFID technology for management and tracking e-healthapplicationsrdquo Sensors vol 18 no 8 pp 2663ndash2678 2018

[7] M Martınez Perez C Dafonte and A Gomez ldquoTraceabilityin patient healthcare through the integration of RFID


technology in an ICU in a hospitalrdquo Sensors vol 18 no 5pp 1627ndash1641 2018

[8] T Adame A Bel A Carreras J Melia-Seguı M Oliver andR Pous ldquoCUIDATS An RFIDndashWSN hybrid monitoringsystem for smart health care environmentsrdquo Future Gener-ation Computer Systems vol 78 no 2 pp 602ndash615 2016

[9] H Q Omar A Khoshnaw and W Monnet ldquoSmart patientmanagement monitoring and tracking system using radio-frequency identification (RFID) technologyrdquo in Proceedings ofthe Biomedical Engineering and Sciences vol 1 no 2 pp 1ndash12IEEE Kuala Lumpur Malaysia December 2016

[10] N Ellouze S Rekhis N Boudriga and M AlloucheldquoPowerless security for cardiac implantable medical devicesuse of wireless identification and sensing platformrdquo Journal ofNetwork and Computer Applications vol 107 no 1 pp 1ndash212018

[11] C Xu K Wang and M Guo ldquoIntelligent resource man-agement in blockchain-based cloud datacentersrdquo IEEE CloudComputing vol 4 no 6 pp 50ndash59 2018

[12] Z Aiqing and L Xiaodong ldquoTowards secure and privacy-preserving data sharing in e-health systems via consortiumblockchainrdquo Journal of Medical Systems vol 42 no 8pp 140ndash154 2018

[13] A Dubovitskaya Z Xu S Ryu et al ldquoSecure and trustableelectronic medical records sharing using blockchainrdquo inProceedings of the AMIA Annual Symposium proceedingsAMIA symposium vol 1 no 1 pp 1ndash13 AMIA SymposiumWashington DC USA January 2017

[14] C S Lebech S M Nibe K omas P Vestergaard andO Hejlesen ldquoHow to use blockchain for diabetes health caredata and access management an operational conceptrdquoJournal of Diabetes Science and Technology vol 13 no 2pp 1ndash14 2018

[15] X Yue HWang D Jin M Li andW Jiang ldquoHealthcare datagateways found healthcare intelligence on blockchain withnovel privacy risk controlrdquo Journal of Medical Systemsvol 40 no 10 pp 218ndash242 2016

[16] B M N Kamel J T Wilson and K A Clauson ldquoGeospatialblockchain promises challenges and scenarios in health andhealthcarerdquo International Journal of Health Geographicsvol 17 no 1 pp 1ndash19 2018

[17] T Jen-Hung L Yen-Chih C Bin and L Shih-wei ldquoGov-ernance on the drug supply chain via gcoin blockchainrdquoInternational Journal of Environmental Research and PublicHealth vol 15 no 6 pp 1055ndash1067 2018

[18] K Harleen A M Afshar J Roshan A K Mourya andC Victor ldquoA proposed solution and future direction forblockchain-based heterogeneous medicare data in cloud en-vironmentrdquo Journal of Medical Systems vol 42 no 8pp 156ndash167 2018

[19] J J Gu R C Huang L Jiang G Qiao X Du andM GuizanildquoA fog computing solution for context-based privacy leakagedetection for android healthcare devicesrdquo Sensors vol 19no 5 pp 1ndash16 2019

[20] C A Silva G S Aquino S R M Melo and D J B Egıdio ldquoAfog computing-based architecture for medical records man-agementrdquo Wireless Communications and Mobile Computingvol 2019 no 1 pp 1ndash16 2019

[21] Y Guan J Shao G Wei and X Mande ldquoData security andprivacy in fog computingrdquo IEEE Network vol 32 no 5pp 1ndash6 2018

[22] V Patel ldquoA framework for secure and decentralized sharingof medical imaging data via blockchain consensusrdquo HealthInformatics Journal vol 1 no 1 pp 1ndash14 2018

[23] C Tang C Li X Yu Z Zheng and Z Chen ldquoCooperativemining in blockchain networks with zero-determinantstrategiesrdquo IEEE Transactions on Cybernetics vol 2 no 3pp 1ndash6 2019


physiological signals for research and commercial purposesin Figure 1

In the paper we protect cardiac IMD against securitythreats by presenting a security scheme First we verify andclassify the IMDrsquos major security attacks Second we in-troduce blockchain and the RFID systems to extend the IMDarchitecture [10] and discuss the structures of the interop-erability in the medical environment as shown in Figure 2

e motivation of the blockchian-fog-RFIDmethod foraccelerating big data medical research based on physio-logical signal is as follows the method is becoming morecommon due to the application of powerful computers andthe availability of physiological signals from varioussources However although the complexity of physiolog-ical signals makes the complex methods particularly ap-plicable their application of physiological signals isgenerally considered earlier than in other fields Big datahas become a buzzword in medical innovation Rapidadvances in artificial intelligence particularly promise toreform medical practice from the resource allocation to thecomplex diseasesrsquo diagnosis However big data brings hugerisks and challenges including major questions aboutpatient privacy the importance of fairness consent and

patient management in data collection based on RFID datastorage based on fog computing and dealing with databreaches by using blockchian In the future we will discussthe methodrsquos applications in physiological signals researchbasic research disease management aetiology detectionand diagnosis health services research treatment devel-opment and treatment evaluation e possibilities of theblockchian-fog-RFID method for accelerating big datamedical research in physiological signals are enormous

e paper contribution consist of four parts as follows

(1) e security scheme is a low energy cost RFIDsystem in IMD e applied authentication protocolis implemented on the RFID circuit without energy

(2) e applied energy harvesting scheme uses the en-hanced WISP which performs computationalfunctions and uses the harvested energy to go beyondpassive RFID tags

(3) e presented authentication protocol enables theauthorized health-care professionals to obtain theaccess permission to cardiac IMD securely in theregular and emergency model which are determinedaccording to the patientrsquos ability to supply valid

National healthorganization

Contractresearch

organisation

Insirancecompanies

Doctors Patients

Hospitals

Universities

Privatecompanies

FDA

AI

Machinelearning

Physiologicalsignal date set

Blockchainlifedate

BlockchainDate

Figure 1 e flow of data from the individuals to the companies and research institutions







2 Related Work











Reader

Blockchain

TAG

IMD

















































Batterydepletion

attack

Attacks


emergencysituations

Trafficcapture and

analysis

Desynchronization

attacks

Attackstargeting


Relayattack

Energypreservation

Data reliability













IMD WISP

Record ECG

Create vault




Select NW

Verify Kprime



Record ECG

RFID reader



⟨Kprime Seq1⟩

⟨Kprime Seq1⟩


M2 ⟨Vault V⟩

Programmer




Applicationreverse

engineering

Featureextraction

Machinelearning

classification

TAG

Datacollection

ReaderBlockchain

Patient


IMD

Request

Nbr

WISP

Key K



[Valid key]


Select randomNW






Verify Kprime

update Nbr

ltKprime Seq1gt

ltKprime Seq1 Nbrgt















r do(16) if fi




















Step 51

Step 62



ltK1 Seq1gt

A = NR IDR

Step 1 Record ECG



Step 61

Programmer

+


SelectB = IDW NW+



ltK2 Seq1gt










IMD WISPRFIDreader

Key K

Programmer

Request

Nbr

[valid key]



key lifetime




ltK1 Seq1gt




Step 1 A = IDR + NR


































IMD

Request


Nbr

WISP RFID reader

Select randomNW


Programmer


IDR = C NR+


+ NbrK1 = Kprime

⟨K1 Seq1 Nbr⟩




Key KC = IDR + IDR


⟨K1 Seq1⟩




























My webpage com


Fraction

Websites



Blocktime

Fogcomputing

A B C D


kH

Actors

Assertions




























6 Conclusions




Data Availability




Acknowledgments


References
































2 Related Work











Reader

Blockchain

TAG

IMD

















































Batterydepletion

attack

Attacks


emergencysituations

Trafficcapture and

analysis

Desynchronization

attacks

Attackstargeting


Relayattack

Energypreservation

Data reliability













IMD WISP

Record ECG

Create vault




Select NW

Verify Kprime



Record ECG

RFID reader



⟨Kprime Seq1⟩

⟨Kprime Seq1⟩


M2 ⟨Vault V⟩

Programmer




Applicationreverse

engineering

Featureextraction

Machinelearning

classification

TAG

Datacollection

ReaderBlockchain

Patient


IMD

Request

Nbr

WISP

Key K



[Valid key]


Select randomNW






Verify Kprime

update Nbr

ltKprime Seq1gt

ltKprime Seq1 Nbrgt















r do(16) if fi




















Step 51

Step 62



ltK1 Seq1gt

A = NR IDR

Step 1 Record ECG



Step 61

Programmer

+


SelectB = IDW NW+



ltK2 Seq1gt










IMD WISPRFIDreader

Key K

Programmer

Request

Nbr

[valid key]



key lifetime




ltK1 Seq1gt




Step 1 A = IDR + NR


































IMD

Request


Nbr

WISP RFID reader

Select randomNW


Programmer


IDR = C NR+


+ NbrK1 = Kprime

⟨K1 Seq1 Nbr⟩




Key KC = IDR + IDR


⟨K1 Seq1⟩




























My webpage com


Fraction

Websites



Blocktime

Fogcomputing

A B C D


kH

Actors

Assertions




























6 Conclusions




Data Availability




Acknowledgments


References









































































Batterydepletion

attack

Attacks


emergencysituations

Trafficcapture and

analysis

Desynchronization

attacks

Attackstargeting


Relayattack

Energypreservation

Data reliability













IMD WISP

Record ECG

Create vault




Select NW

Verify Kprime



Record ECG

RFID reader



⟨Kprime Seq1⟩

⟨Kprime Seq1⟩


M2 ⟨Vault V⟩

Programmer




Applicationreverse

engineering

Featureextraction

Machinelearning

classification

TAG

Datacollection

ReaderBlockchain

Patient


IMD

Request

Nbr

WISP

Key K



[Valid key]


Select randomNW






Verify Kprime

update Nbr

ltKprime Seq1gt

ltKprime Seq1 Nbrgt















r do(16) if fi




















Step 51

Step 62



ltK1 Seq1gt

A = NR IDR

Step 1 Record ECG



Step 61

Programmer

+


SelectB = IDW NW+



ltK2 Seq1gt










IMD WISPRFIDreader

Key K

Programmer

Request

Nbr

[valid key]



key lifetime




ltK1 Seq1gt




Step 1 A = IDR + NR


































IMD

Request


Nbr

WISP RFID reader

Select randomNW


Programmer


IDR = C NR+


+ NbrK1 = Kprime

⟨K1 Seq1 Nbr⟩




Key KC = IDR + IDR


⟨K1 Seq1⟩




























My webpage com


Fraction

Websites



Blocktime

Fogcomputing

A B C D


kH

Actors

Assertions




























6 Conclusions




Data Availability




Acknowledgments


References

































IMD WISP

Record ECG

Create vault




Select NW

Verify Kprime



Record ECG

RFID reader



⟨Kprime Seq1⟩

⟨Kprime Seq1⟩


M2 ⟨Vault V⟩

Programmer




Applicationreverse

engineering

Featureextraction

Machinelearning

classification

TAG

Datacollection

ReaderBlockchain

Patient


IMD

Request

Nbr

WISP

Key K



[Valid key]


Select randomNW






Verify Kprime

update Nbr

ltKprime Seq1gt

ltKprime Seq1 Nbrgt















r do(16) if fi




















Step 51

Step 62



ltK1 Seq1gt

A = NR IDR

Step 1 Record ECG



Step 61

Programmer

+


SelectB = IDW NW+



ltK2 Seq1gt










IMD WISPRFIDreader

Key K

Programmer

Request

Nbr

[valid key]



key lifetime




ltK1 Seq1gt




Step 1 A = IDR + NR


































IMD

Request


Nbr

WISP RFID reader

Select randomNW


Programmer


IDR = C NR+


+ NbrK1 = Kprime

⟨K1 Seq1 Nbr⟩




Key KC = IDR + IDR


⟨K1 Seq1⟩




























My webpage com


Fraction

Websites



Blocktime

Fogcomputing

A B C D


kH

Actors

Assertions




























6 Conclusions




Data Availability




Acknowledgments


References



























Applicationreverse

engineering

Featureextraction

Machinelearning

classification

TAG

Datacollection

ReaderBlockchain

Patient


IMD

Request

Nbr

WISP

Key K



[Valid key]


Select randomNW






Verify Kprime

update Nbr

ltKprime Seq1gt

ltKprime Seq1 Nbrgt















r do(16) if fi




















Step 51

Step 62



ltK1 Seq1gt

A = NR IDR

Step 1 Record ECG



Step 61

Programmer

+


SelectB = IDW NW+



ltK2 Seq1gt










IMD WISPRFIDreader

Key K

Programmer

Request

Nbr

[valid key]



key lifetime




ltK1 Seq1gt




Step 1 A = IDR + NR


































IMD

Request


Nbr

WISP RFID reader

Select randomNW


Programmer


IDR = C NR+


+ NbrK1 = Kprime

⟨K1 Seq1 Nbr⟩




Key KC = IDR + IDR


⟨K1 Seq1⟩




























My webpage com


Fraction

Websites



Blocktime

Fogcomputing

A B C D


kH

Actors

Assertions




























6 Conclusions




Data Availability




Acknowledgments


References






































r do(16) if fi




















Step 51

Step 62



ltK1 Seq1gt

A = NR IDR

Step 1 Record ECG



Step 61

Programmer

+


SelectB = IDW NW+



ltK2 Seq1gt










IMD WISPRFIDreader

Key K

Programmer

Request

Nbr

[valid key]



key lifetime




ltK1 Seq1gt




Step 1 A = IDR + NR


































IMD

Request


Nbr

WISP RFID reader

Select randomNW


Programmer


IDR = C NR+


+ NbrK1 = Kprime

⟨K1 Seq1 Nbr⟩




Key KC = IDR + IDR


⟨K1 Seq1⟩




























My webpage com


Fraction

Websites



Blocktime

Fogcomputing

A B C D


kH

Actors

Assertions




























6 Conclusions




Data Availability




Acknowledgments


References







































Step 51

Step 62



ltK1 Seq1gt

A = NR IDR

Step 1 Record ECG



Step 61

Programmer

+


SelectB = IDW NW+



ltK2 Seq1gt










IMD WISPRFIDreader

Key K

Programmer

Request

Nbr

[valid key]



key lifetime




ltK1 Seq1gt




Step 1 A = IDR + NR


































IMD

Request


Nbr

WISP RFID reader

Select randomNW


Programmer


IDR = C NR+


+ NbrK1 = Kprime

⟨K1 Seq1 Nbr⟩




Key KC = IDR + IDR


⟨K1 Seq1⟩




























My webpage com


Fraction

Websites



Blocktime

Fogcomputing

A B C D


kH

Actors

Assertions




























6 Conclusions




Data Availability




Acknowledgments


References

































IMD WISPRFIDreader

Key K

Programmer

Request

Nbr

[valid key]



key lifetime




ltK1 Seq1gt




Step 1 A = IDR + NR


































IMD

Request


Nbr

WISP RFID reader

Select randomNW


Programmer


IDR = C NR+


+ NbrK1 = Kprime

⟨K1 Seq1 Nbr⟩




Key KC = IDR + IDR


⟨K1 Seq1⟩




























My webpage com


Fraction

Websites



Blocktime

Fogcomputing

A B C D


kH

Actors

Assertions




























6 Conclusions




Data Availability




Acknowledgments


References























































IMD

Request


Nbr

WISP RFID reader

Select randomNW


Programmer


IDR = C NR+


+ NbrK1 = Kprime

⟨K1 Seq1 Nbr⟩




Key KC = IDR + IDR


⟨K1 Seq1⟩




























My webpage com


Fraction

Websites



Blocktime

Fogcomputing

A B C D


kH

Actors

Assertions




























6 Conclusions




Data Availability




Acknowledgments


References



















































My webpage com


Fraction

Websites



Blocktime

Fogcomputing

A B C D


kH

Actors

Assertions




























6 Conclusions




Data Availability




Acknowledgments


References






























6 Conclusions




Data Availability




Acknowledgments


References



























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