Research ArticleIntelligent Digital Currency and Dynamic Coding Service SystemBased on Internet of Things Technology

Shanshen Li 1 and Xin Jing 2

1School of Economics and Management, Xi’an Shiyou University, Xi’an 710065, Shaanxi, China2Law School, Shanghai University of Finance and Economics, Shanghai 200433, China

Correspondence should be addressed to Xin Jing; jingxin@163.sufe.edu.cn

Received 22 October 2020; Revised 28 November 2020; Accepted 30 November 2020; Published 22 December 2020

Academic Editor: Zhihan Lv

Copyright © 2020 Shanshen Li and Xin Jing. -is is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work isproperly cited.

-e amazing rise of digital currency is not only favored by investors but also attractive to lawbreakers for its anonymity anddecentralization.-is paper mainly discusses the intelligent digital currency and dynamic coding service system based on Internetof-ings technology. In this paper, the RDCAR algorithm is used to realize the routing discovery process of the wireless network.When the intermediate node receives the RREQmessage, first of all, to avoid the loop, it checks whether the same RREQmessagehas been introduced. If it has received it, it will discard it. Otherwise, it will cache the message and attach its own neighbor node listto the signal-to-noise ratio of the channel link, update the RREQmessage, and broadcast it.-e payment cipher is managed by thebank. When the user opens an account, the bank registers and sends it to the user. -e key is generated by the algorithm chip, andthe public key is kept in the bank background server. When the bill is delivered to the bank, the bank inputs all the elements on thebill on the counter terminal and transmits it to the verification machine for verification through the bank network. If theverification is correct, it indicates that the bill is indeed issued by the customer, and all bill elements are correct, and payment canbe made.-e node operation protocol of public chain and alliance chain maintains the operation of the Internet of-ings system.-e nodes of alliance chain generate new blocks according to the interval of 30 s. When the node fails to complete the blockgeneration within 30 s, it will rotate to the next node. -e mkfile command is used to generate 16b, 1 KB, 1MB, and 1GB files asinput. -e peak speed of the encoding service system is about 370mb/s. -e results show that the system designed in this study isrobust and suitable for complex trading environment.

1. Introduction

With the rise of the wave of digital currency in recent years,some underlying technologies related to it, such as block-chain technology and distributed accounting methods, alsoshow broad application prospects. Digital currency ismoving from theory to reality, and its feasibility and securityare being tested. As a typical application of the Internet of-ings in the financial economy, the Bitcoin system hasattracted much attention due to its decentralized, open, andtransparent characteristics. Various cryptocurrencies suchas Monero coins and dark coins emerge in endlessly. Inaddition, central banks and commercial banks in variouscountries have also used the underlying advanced tech-nology of Bitcoin as a reference, planning to carry out

research on legal digital currency, so as to improve thenational digital financial system. -is shows that the futuredevelopment of digital currency has broad prospects, but theaccompanying digital currency security and privacy issueshave become increasingly prominent.

-e amazing growth of encrypted digital currency is notonly favored by investors but its anonymity and decen-tralization are also quite attractive to criminals. Althoughsome countries have considered introducing digital currencyregulatory policies, the attitudes of different countries aredifferent. Facing the diversified needs of people’s life andwork, research and deployment of legal digital currency areurgently needed. However, unlike Bitcoin’s non-real-time,lightweight transaction information, small transaction vol-ume, and low sensitivity, legal digital currency is circulated

HindawiComplexityVolume 2020, Article ID 6647039, 16 pageshttps://doi.org/10.1155/2020/6647039

mailto:jingxin@163.sufe.edu.cnhttps://orcid.org/0000-0001-6526-4152https://orcid.org/0000-0002-8631-6115https://creativecommons.org/licenses/by/4.0/https://creativecommons.org/licenses/by/4.0/https://doi.org/10.1155/2020/6647039

throughout the country and the world, and its security andprivacy issues are more prominent, and legal digital currencyand its related applications should be suitable for morecomplex international environments. -erefore, how tostrengthen the robustness of the legal digital currency systemand build a harmonious credit society by learning from thesecurity and privacy protection measures of the Bitcoinsystem and Bitcoin wallet is the research direction of ourfuture work. Digital currency is backed by national credit,which can synchronize online and offline to the greatestextent, and maximizes the convenience and security oftransactions.

Utilizing the advantages of distributed architecture andproximity to end users, fog/edge computing can providefaster response and higher quality of service for IoT ap-plications. Lin et al. believed that the Internet of -ingsbased on fog/edge computing will become the future in-frastructure for the development of the Internet of -ings.In order to develop the IoT infrastructure based on fog/edge computing, they first studied the architecture relatedto the IoT, supporting technologies and issues, and thenexplored the integration of fog/edge computing and theIoT. -ey gave a comprehensive overview of the Internet of-ings in terms of system architecture, supporting tech-nology, security, and privacy issues and studied the inte-gration of fog/edge computing with the Internet of -ingsand applications. -eir research lacks data [1]. In order toovercome the scalability problem of the traditional IoTarchitecture, Sun et al. proposed a novel method for mobileedge computing. At the same time, they proposed a layeredfog computing architecture in each fog node to provideflexible IoT services while maintaining user privacy; eachuser’s IoT device is associated with the proxy VM (locatedin the fog node), and proxy VM collects, classifies, andanalyzes the raw data stream of the device, converts it intometadata, and then transmits the metadata to the corre-sponding application VM (owned by the IoT serviceprovider). Each application VM receives correspondingmetadata from a different proxy VM. -eir research lackspractice [2]. Yang et al. believed that the Internet of -ings(IoT) is ubiquitous in our daily lives. In order to protect thesecurity of IoT devices, they have conducted a lot of re-search work to deal with these problems and find betterways to eliminate these risks, or at least minimize its impacton user privacy and security requirements. -eir investi-gation consists of four parts. -e most relevant limitationsof IoT devices and their solutions will be discussed first.-en, the classification of IoT attacks will be introduced.Secondly, it will focus on the mechanism and architectureof authentication and access control. Finally, the securityissues in different layers will be analyzed. -eir researchprocess is too complicated [3]. Yaqoob et al. discussed thearchitecture of the Internet of -ings. In this case, first ofall, they investigate, focus on, and report on the recentmajor research progress in the IoT architecture and thenclassify the IoT architecture and design a taxonomy basedon important parameters (such as applications, supportingtechnologies, business goals, architecture requirements,network topology, and IoT platform architecture types).

-ey identified and outlined the key requirements of thefuture IoT architecture and discovered and introducedsome outstanding case studies on the Internet of -ings.Finally, they listed and outlined the future research chal-lenges. -eir research has no practical significance [4].

-is research mainly discusses the intelligent digitalcurrency and dynamic coding service system based on theInternet of -ings technology. -is research is mainly basedon the RDCAR algorithm to realize the route discoveryprocess of the wireless network. When the intermediatenode receives the RREQ message, first, to avoid loops, checkwhether the same RREQmessage has been introduced. If it isreceived, discard it. Otherwise, buffer the message andappend its own neighbor node list, corresponding to thesignal-to-noise ratio of the channel link, and update RREQmessage and broadcast it.-e payment cipher is managed bythe bank. When the user opens an account, the bank reg-isters and sends it to the user, and the key is generated by thealgorithm chip, and the public key is stored in the bank’sback-end server. When the bill is delivered to the bank, thebank enters the various elements of the bill on the counterterminal and transmits it to the verification machinethrough the bank network for verification. If the verificationis correct, it indicates that the bill is indeed issued by thecustomer, and the bill elements are correct, so you can makepayment. -e public chain and alliance chain node opera-tion agreement maintains the operation of the Internet of-ings system.-e alliance chain node generates new blocksat a time interval of 30 s.When the node cannot complete theblock generation within 30 s, it will rotate to the next node.

2. Dynamic Coding Service System

2.1. Internet of +ings. -e IoT paradigm is expected tocompletely change the way we live and work through alarge number of new services based on the seamless in-teraction between a large number of heterogeneous devices.After decades of creation of the concept of the Internet of-ings, in recent years, various communication technol-ogies have gradually emerged, reflecting the diversity ofapplication fields and communication requirements. Atpresent, this heterogeneity and fragmentation of theconnectivity landscape hinder the full realization of thevision of the Internet of -ings by posing some complexintegration challenges. In this case, the emergence of 5Gcellular systems with truly ubiquitous, reliable, scalable,and cost-effective connection technologies is considered tobe a potential key driver of the yet-to-be-emerging globalInternet of-ings. Similar to how humans use the Internet,devices will become the main users of the Internet of-ings(IoT) ecosystem. -erefore, device-to-device (D2D)communication is expected to become an inherent part ofthe Internet of -ings. Devices will automatically com-municate with each other without any centralized controland cooperate in a multihop manner to collect, share, andforward information. -e ability to collect relevant in-formation in real time is the key to leveraging the value ofthe Internet of -ings because such information will betransformed into intelligence, which will help create a

2 Complexity

smart environment. Ultimately, the quality of the infor-mation collected depends on the intelligence of the device.In addition, these communication devices will operate withdifferent networking standards and may encounter inter-mittent connections with each other, andmany of themwillbe limited by resources. -ese features bring some net-working challenges that traditional routing protocolscannot solve. -erefore, devices will need intelligentrouting protocols to be intelligent. Nowadays, the devel-opment of traditional business models has become moreand more mature, and people use them to guide variouse-commerce activities [5, 6]. -e Internet of -ings (IoT) isan innovative revolution on the Internet and has become anew platform for e-commerce [7].-e flow of static timingconstraints is shown in Figure 1.

2.2. Positive Impact of Digital Currency. For many people,the concept of digital currency is abstract and confusing.Having confidence in intangible assets without governmentor precious metal support is a daunting task. However, therapid spread of smartphones and tablets, the rapid trans-formation of cross-border banking, and the emergence ofnon-card real-time payments have made digital paymentscommonplace. First of all, it has a positive impact. Digitalcurrency creates a relatively novel concept and model, whichcan improve transaction convenience and reduce transac-tion costs, promotes the progress and development of sharedfinance, and decentralizes the digital currency embeddedpayment system mainly from its own; the system allowsusers to directly carry out peer-to-peer transactions withoutresorting to financial units, which can improve transactionefficiency and reduce transaction costs. At the same time, itslower transaction costs will have an impact on traditionalpayment systems and promote banks and other financialinstitutions to continuously improve their service qualityand reduce transaction costs. Secondly, digital currency canallow people who have not created an account in a financialunit to carry out noncash payments, and the speed is veryfast, and the cost is low. -erefore, digital currency has apositive impact on the popularization of finance, especiallyfor those who are relatively backward in finance. In regionsand countries, the benefits it brings are also very large [8, 9].Digital currency can realize network transfer with the help ofmobile phones and so on, and the recipient only needs toobtain digital currency to exchange activities with it [10].When there is no code perception, it is shown in Figure 2,and when there is code perception, it is shown in Figure 3.

2.3. Verify Algorithm. -e Verify algorithm uses a layeredverification mechanism to verify whether the hiddentransaction amount is correct [11, 12]. Specifically, it will bestated in Verify-I that the payer has enough bitcoins; that is,the payeemust be convinced that the input of the transaction

is greater than the output.-en, the recipient verifies that thetransaction amount promised by the wallet is equal to theactual transaction amount in Verify-II. In the commitmentphase, the wallet will make a commitment to both the bitcoindeposit amount b1 and the bitcoin transaction amount b2(b< 2≤ b1). In addition, the wallet will also promise thedifference between b1 and b2. In the verification phase, thecorrectness of the commitment value is checked in two steps:(1) the difference between the input amount b1 and theoutput amount b2 is always positive and (2) the promisemade by the payee to the correct transaction amount isalways the same as the promise value c of the wallet [13].Online wallet calculates F1 and F2 and sends (F2, F3) to thepayee:

F1 � hm−a1 h2 mod n,

u(t) � ui(t − Δt)RL(Δt) + ui(t)

n

i�1(t + Δt)(t − Δt),

e0(λ, T) �5πHCλ6

1e

(hc/λKT) −hc

e.

(1)

Recipient calculation is as follows:

U �h1F3

� hβ1h

−rα2−q1α−q22 mod n,

F2 �1Q

ω∈Q

exp −U(z)

T δ(z − ω),

M � T[x, y, p(x, y), f(x, y)].

(2)

If the check passes, the recipient can trust m≥ α. At thisstage, my country’s main basis for this supervision is onlythis notice, and there is no other effective document [14, 15].Moreover, the measures of my country’s regulatory au-thorities are mainly aimed at preventing financial risks andmoney laundering crimes. -e protection of financialconsumers is mainly based on the principle of “risk your-self.” -e overall rules are relatively rough. -e filing systemfor trading platforms is not a licensing system.-is has led tothe obligation of financial institutions and digital crypto-currency trading platforms to be limited to popularizingindustry knowledge and risk disclosure, accepting irregularadministrative inspections, and so on [16] and make sub-stantive requirements for deeper content such as the entrybarriers of the trading platform, network security, infor-mation disclosure, and fund management. As a result, thechaos in the digital cryptocurrency market has not beencurbed, and even with the emergence of ICOs, lawsuits fordigital cryptocurrency continue to increase [17, 18]. Re-cipient calculation is as follows:

Complexity 3

c �c1

c2′� h1b1− b2′ h2r1− r2,

c1 � −2 rui − F

f�1pufqif

⎛⎝ ⎞⎠ + 2puf,

c2 �i∈I h1 − ru( × h2 − ru( ������������������������

i∈I rui − ru( 2i∈I rvi − rv(

2 .

(3)

Verify that c is equal to c′. If there is any error in theverification process, the payee returns 0 and rejects thetransaction [19]. If the verification is successful, the payeereturns 1, and then the online wallet broadcasts theencrypted transaction and sends the digital currency amountto the payee account [20, 21].

2.4. Code-Aware Routing Algorithm. In wireless networks,the quality of the channel is the key to the successfultransmission of data, which is generally measured by thesignal-to-noise ratio [22]. -e channel quality has great

differences in different links of the actual network[23, 24].

h � n

i�1coihmin +

n

i�1coi,

DK1 � P xi ∈ xk, yi � 1( �

i

x ∈ xk(i),

DK−1 � P xi ∈ xk, yi � −1( �

i

x ∈ xkD−1.

(4)

-e final strong classifier is as follows:

H(x) � sing T

t�1ht(x) − threshold⎛⎝ ⎞⎠. (5)

If you want to transmit under poor channel quality, youmust choose a relatively low transmission rate. -ere aremany broadcasting situations in network coding. Whenbroadcasting to various downstream nodes, the signal-to-noise ratio of each single link in the broadcasting link isdifferent [25]. Choosing a suitable broadcasting transmis-sion rate can make the throughput efficiency reach duringthis broadcast maximum. -e coding-aware routing algo-rithm based on rate selection is expressed as follows:

M � minl∈L

Ml|Ml �Hl ∗Hl′

Rl ,

MIQs(c) � MQs(c) + t∈c

MQs(i),

MIQd(c) � MQd(c) + t∈c

MQ(l),

CRMJ �1 + MIQd(l)

1 − Pt.

(6)

Among them, M is a unicast or broadcast link on theentire link L for data packet transmission. When the data

A

B D

C

a

b

Figure 2: No coding perception.

Design workenvironment

Read in thedesign table

Connectiondesign

Readinformation

file

Read inparameter file

Read inconstraint file

Check thetiming

Constraintreport

Figure 1: Flow of static timing constraints.

A

B D

C

a

b

Figure 3: Coding-aware.

4 Complexity

link layer successfully transmits a data packet, the routinglayer initiates a transmission [26].

Angle(a, r) � arc cosa•r

|a|∗ |r| ,

min E2 � N

k�1y′(k) − y(k) 2,

s � x1, y1 , x2, y2( , ..., xN, yN( .

(7)

-e probability of successful data packet transmission onthe data link layer is as follows:

pji �

5

k�11 − pj1i

k− 1p

j1i,

CRML � I∈L

CRMJ,

F2(s, v) �

1s

s

i�1Y N − v − Ns( + i − yv(i)

2,

Pn,j �pn,t

pn,t−1− 1,

Pj �pn,t

pn,t−1.

(8)

-at is, the probability of starting a routing layertransmission is pji . -e process of the Verify algorithmgenerating code is shown in Figure 4.

3. Dynamic Coding Service System Experiment

3.1. System Framework Design. -e system is divided intofour parts: the background server, the foreground man-agement system, the foreground business system, and thecipher. -e back-end server is equivalent to a certificationauthority CA, which stores the user’s certificate informationand completes the actual verification process. It is generallythe server of the head office. -e front-end managementsystem is the management program on the front-endcomputer of the branch, which manages the user accountinformation, the issuance and management of the userpassword, the management of the operator and the logquery, and so on. -e front desk business system is abusiness management program on the front-end computerof the branch. It uses the information on the check and thepayment password to verify the authenticity of the check andgenerally provides services for the bank transfer system. -ecipher is a handheld device used by the user, which is issuedto the user by the bank to implement the user-side algorithmin hardware, including the generation of the key and thegeneration of the payment password. -e system frameworkis shown in Figure 5.

3.2. Cipher. -e encryption chip integrates RSA and SHA-1algorithms and saves the user’s key. -ere are two types ofchips: A slice is mainly a public key algorithm, which is used

in the payment password verification subsystem, and B sliceis mainly a private key algorithm, which is used in thepayment cipher used by the account opening unit. -epayment cipher adopts the B-chip arithmetic chip to make ahandheld device to manage the user’s account and key andgenerate payment password.-e payment cipher is managedby the bank. When the user opens an account, the bankregisters and sends it to the user, and the key is generated bythe algorithm chip, and the public key is stored in the bank’sback-end server.

3.3. PasswordManagement. -e payment cipher is issued bythe bank to the customer who opens an account with thebank and downloads the account number of the customerwith the bank and the corresponding account key in it.When the customer uses the account to issue a bill, enter thebill number, bill type, amount, and other information on thepayment cipher and use the payment cipher to automaticallycalculate a string of numbers. -is number is closely relatedto the payer account number, receiver account number, billtype, bill number, amount, and date of signing and is calledthe payment password. -e customer fills in the number onthe bill as the digital seal of the bill. When the bill is deliveredto the bank, the bank enters the various elements of the billon the counter terminal and transmits it to the verificationmachine through the bank network for verification. If theverification is correct, it indicates that the bill is indeedissued by the customer, and the bill elements are correct, soyou can make payment.

3.4. Calculate the Plaintext Format of Payment Password.-e format of the 48-byte plaintext data PLAIN_TXT inputto the B slice when the payment password is generated isshown in Table 1. After the chip output is converted, it is a19-digit integer plus 1 identification bit to form a 20-digitpayment password output. -e plaintext format of thepayment password is shown in Table 1.

3.5. Design of the Alliance Chain. System initializationcompletes the generation of system parameters and theinitial state of the blockchain; transaction verification andforwarding are the process of sharing information amongalliance chain members, ensuring that nodes reach con-sensus on the same basis; the consensus process includes thespecific process of node interaction in CPBFT; transactionconfusion is responsible for after the transaction is con-firmed, and the transaction is processed before being sent tothe public chain node to remove the transaction relationshipinformation to protect transaction privacy; the final trans-action traceability is the process of internal supervision ofthe system. -e communication between nodes in the al-liance chain uses encrypted channels to prevent informationleakage when the communication transmits the plaintextand completes transactions.

3.6. Choice ofAlgorithm. -eVerify algorithm aims to checkthat the difference b1 − b2 between the input amount and the

Complexity 5

output amount of the transaction is always positive, and theoutput amount b2 is the transaction amount specified by thepayee. -e algorithm first confirms that the promised secretvalue is always positive. To this end, the payer commits to thedifference between the input amount and the output amountof the transaction. -en, use the range proof method toconvince the payee to believe that the promised secret valueis always positive. Secondly, because the recipient knows thecorrect transaction amount b2 in advance, he also needs touse the correct transaction amount and some auxiliaryevidence c1 and r2 to verify that the final commitment valueis equal to the commitment value c made by the wallet. Wecall this two-step verification method a layered verificationmechanism. If the algorithm returns 1, the protocol goes tothe next step.

3.7. Operation Process Design. -e peer relationship betweenpublic chain nodes and alliance chain nodes in the system isjust different in processing messages. Together, these two

parts can be regarded as servers. -e user’s wallet is the clientand sends operation requests to the server. After each part ofthe system starts running, it performs related functionsaccording to the protocol designed in chapter 4.When there isno user to send a transaction, the blockchain node auto-matically runs the consensus process in a loop to maintain theconsistency of the system and waits to process the senttransaction information. After the user sends the transaction,it is collected and processed by the alliance chain nodes,including verification transactions, packaged transactions,and transaction confusion, and finally the confirmed trans-actions are confused and broadcasted to the public chainnodes to complete the complete transaction confirmation andrecording process. When the system initiates the traceability,the entire process only occurs between the alliance chainnodes. -e traceability initiating node initiates a request toother alliance chain nodes including the supervision node.-e other alliance chain nodes in the figure include more thanone node, and the request is judged separately, and the su-pervised node obtains the user identity after recovering the

Table 1: Plaintext format of the payment password.

ACCU 16 Account, compressed BCD codeService 4 Business type, 0× 31–0× 35Date 4 Date, compressed BCD codeTicket_ num 8 Voucher number, compressed BCD codeBalance 9 Amount, in minutes, compressed BCD code

1

7

2

6

3

5

4

aa

b

b

a + b

a + b

a

Figure 4: -e process of the Verify algorithm to generate code.

Datacurrency Cipher

Front officemanagement

system

Front officebusinesssystem

Backstageverification

machine

Receiving userVerification

resultResult

Transfer

Save thepublic key

Account

Figure 5: System framework.

6 Complexity

key.-e design values of running nodes are shown in Table 2.-e running process is shown in Figure 6.

3.8. Implementation of Routing Algorithm. -e route dis-covery process of RDCAR is as follows:

(1) -e source node initiates the route discovery processof the wireless network by broadcasting a routerequest (RREQ) message . In our algorithm, therouting request message needs to include the fol-lowing messages: neighbor nodes within the range ofthe source node, high channel quality signal-to-noiseratio, and the path that has been transmitted.

(2) -e intermediate node receives the RREQ message;first to avoid loops, check whether the same RREQmessage has been introduced and discard it if it isreceived; otherwise, it will buffer the message andattach its own neighbor node list, corresponding tothe signal noise of the channel link, and then updatethe RREQ message and broadcast it.

(3) When the RREQ arrives at the destination node, thedestination node sends a route reply request (RREP)message to the source node.-is is a unicast messagethat contains information on this unicast path.

(4) When the intermediate node receives the RREPmessage, it selects a value from the set of rates sup-ported in the 802.11 protocol, calculates the expectednumber of transmissions based on the stored signal-to-noise ratio information, selects the appropriaterate, updates the neighbor node list, and finally cal-culates the smallest metric value. -en, add this in-formation to RREP. Take out the RREQ path in thecache and compare it with the upstreampath set in theRREP. Use the conditions of the encoding node aboveto check whether there is an encoding opportunity forthe new stream. If it exists, calculate the metric valueof the broadcast link. Add the minimum metric valueto RREP, and continue forwarding the cached pathuntil it reaches each source node.

(5) Maintain routing regularly, update routing mes-sages, and reselect the most appropriate path for thecurrent distributed flow. -e realization of therouting algorithm is shown in Figure 7.

3.9. IoTNodeOperation. -e public chain and alliance chainnode operation agreement maintains the operation of theInternet of -ings system. -e alliance chain node generatesnew blocks at a time interval of 30 s. When the node cannotcomplete the block generation within 30 s, it will rotate to thenext node. Similar rules are also used in the public chain togenerate block rights maintenance system operation.

3.10. Realization of User Transfer Process. -e transfer be-havior takes place in the user’s wallet, and the user firstverifies his identity using the wallet. -e user’s login nameand corresponding password are stored locally, and the

password retains the value afterMD5 calculation. During thelogin process, compare whether it is the same as the pre-viously saved record. After the user logs in, the wallet willcheck whether the user has an identity certificate issued bythe authentication server in the system. Only transactionssent by users registered in the system will be received by thealliance chain node. When the user does not store theidentity certificate in the node, the wallet will prompt usercomplete the authentication. Users can use the wallet toinitiate transfers, and they can also query the status of theaccount. Transfers between users need to use the public keyas the address to receive the transfer. In fact, the public key isassociated with the payment transaction so that the currencyobtained in the payment transaction is used. Whenmaking apayment, you can prove your ownership of the currencythrough the private key corresponding to the public key andpass the cryptographic algorithm without the need to pass acentralized credit institution. -e generated payment ad-dress will be displayed, and the corresponding private keywill be stored in the file by the wallet and used in thetransaction. -e payment address can be sent to other usersin the digital currency system by means of communicationoutside the system.

After sending the transaction, the user needs to wait forthe alliance chain node and the public chain node to performa series of operations and finally store the relevant transfer-in and transfer-out transactions in the public chain block.-e wallet has stored the label and content of the completetransaction for a period of time. By comparing the datastored in the public chain, you can check whether thetransaction you initiated has been written to the block andconfirmed by multiple blocks. To initiate a transfer, usersneed to use their USOTas the payment method, provide thecorresponding private key, send currency to the public keyprovided by the other party, and provide one or more publickey addresses of their own as the change address. -e totalamount of currency in the USOTprovided by the transactioninitiator cannot be less than the payment amount. If thebalance cannot be exactly equal, the excess amount will besent to the change address and returned to the payer.

3.11. Traceability and Transaction Disclosure. Traceability isinitiated and completed internally by the alliance chain, andthe result of the transaction traceability can be seen in the end.-e goal of retroactive transactions is generally a transfer-outtransaction because the initiator of a complete transaction onthe transfer-out exchange is the owner of the transfer-outtransaction address, and the identity of the initiator can bequeried. If no one spends it for a transfer-in transaction, itcannot get the owner of the transaction.When searching for atransaction, according to the query transaction serial number,it traverses the plaintext of transaction information saved inthe block, finds the block containing the same serial number,decrypts the transaction, finally finds the transaction, anddisplays the complete record in the transaction.

3.12. System Test and Implementation. -is system runsunder Windows and uses Java language for programming.

Complexity 7

-e machine configuration for system testing is shown inTable 3. -is system uses the JPBC (Java Pairing-BasedCryptography ) library. JPBC is a Java package based on thepaired cryptography library function (PBC). JPBC com-pletely breaks away from the dependence of PBC, realizes thepair operation completely based on Java, and puts aside thelimitations of the platform itself. -is system uses it as thebasic support library for cryptography. In addition to thebasic upload and download function files, this systemmainlyincludes the following types of files that generate user keys,generate keyword ciphertexts and secret doors, and im-plement keyword encryption and search. CLPEKS.java isused to implement various algorithms. CLPEKS Pub Par-ams.java is used to store public parameters, Service andClient Key Interface java is an interface class for client and

server keys, CLPEKS Secret Key.java and CLPEKS ClientKey java store server and client keys, respectively, andCLPEKS Cipher text.java and CLPEKS Trap door.java storeciphertext and secret door, respectively.

4. Analysis of Dynamic Coding Service System

4.1. Algorithm Performance Analysis. -e performance testmainly includes two aspects. On the one hand, the algorithmperformance changes under different matrix sizes, that is,when N is different; on the other hand, the performancediffers between the test algorithm and the SM2 algorithm.First, test the performance of the algorithm when differentmatrix sizes are different, that is, when N is different. Whenthe testN is 4, 8, 16, 32, and 64, the number of signatures and

Requestmessage

Intermediatenode

Routemaintenance

Return data

Retrieve data

Download data

Return data

Figure 7: Implementation of the routing algorithm.

Table 2: Design values of operating nodes.

Constraint content Settings (ns)Input delay 3.1Output delay 3.0Clock jitter and skew 3.2Drive capability 3.2

1st Timinganalysis

Floorplan

Placement

2nd Timinganalysis

Public chainnode

Alliance chainnode

Route

Drivecapability

Figure 6: Running process.

8 Complexity

verifications per second by the algorithm and the experi-mental results retain two significant digits. -e performanceof the algorithm at differentmatrix sizes is shown in Table 4. Itcan be seen from Table 4 that with the increase in the matrixsize, the performance of algorithm signature and verificationis gradually decreasing.-e reason is that the larger thematrixsize, the more cycles in the GeneratePrivateKey and PublicKey Derivation GeneratePublicKey methods. At the sametime, the performance of the verification algorithm dropsfaster because the loop of the GeneratePublicKey method inthe verification process contains the accumulation of ellipticcurve points which takes longer. However, although theperformance of the algorithm decreases as the matrix size Nincreases, the security of the algorithm will increase as Nincreases. Next, test the performance difference between thisalgorithm and the standard SM2 signature algorithm. Whenthe test matrix size N� 64, the number of signatures andverifications per second of this algorithm and the SM2 al-gorithm are the same. -e initialization of the private keymatrix SKM and the public key matrix PKM will only beperformed once, so this part of the time is not calculated.From an algorithm perspective, the performance differencebetween the two algorithm signatures (or signature verifi-cation) is the time it takes to derive the key from the matrix.-e performance difference of the signature of the two al-gorithms is greater than the performance of the verification.-is is because the calculation of the private key corre-sponding to the public key in the private key derivation al-gorithm (the private key class contains the public key) willinvolve the elliptic curve dot product operation. -e dotmultiplication operation takes a long time. It can be seen fromTable 4 that when the matrix size is N� 64, the signature andverification efficiency of this algorithm are both close to half ofthe efficiency of the SM2 algorithm, and the performancedifference is due to the secret key of the proxy signatureprivate key and the proxy verification public key derived fromoverhead. On the whole, performance has been lost, butaccording to the safety analysis of this algorithm, the overallsafety limit of the system has been increased from 1 to 33.After that, the SM2 hash algorithm and this research algo-rithm are tested for performance comparison. Use the mkfilecommand to generate 16B, 1KB, 1MB, and 1GB files insequence as input. After 10 tests, according to the test results,as the length of the input message increases, the computa-tional efficiency of SM2 and this research show an upwardtrend first and then reach a peak.-e peak algorithm speed ofSM2 is around 100MB/s, and the peak algorithm speed ofRDCAR is around 370MB/s. On the whole, the efficiency ofthe algorithm in this study is higher than that of SM2. -is is

not because of the gap in the algorithm itself but because thebottom layer of the Go algorithm is implemented in assembly,which is more efficient. -e performance analysis of the al-gorithm at different matrix sizes is shown in Figure 8. Beforethe simulation is carried out, it is necessary to specify specificvoltage values for the power signal and ground as shown inTable 5.

4.2. Dynamic Coding Analysis. Use simulation tools tosimulate the RDCAR routing algorithm. We take the net-work model of the grid graph and distribute 25 nodes in therange of 100×100m2, and the communication range of eachnode is 20m. 11 streams are randomly generated in thenetwork. -e purpose of our experiment is to compareRDCAR, DCAR protocol, and COPE coding protocol. Sincethere is no DCAR protocol and COPE coding protocol, thereis no rate-aware algorithm, so the rate of DCAR and COPEcannot be automatically adjusted. Let’s take a look at thecoding opportunities and throughput. Under differentspeeds and signal-to-noise ratios, compare the coding op-portunities and throughput of RDCAR. -e rates of COPEand DCAR we used here are the same, so the difference incoding opportunities is enough to reflect the difference inthroughput. At the same time, the increase in coding op-portunities will lead to the increase in throughput, so thenumber of coding opportunities can reflect the size ofthroughput.-e simulation shows the comparison of codingopportunities at different rates in a low channel capacityenvironment. We can see that in the actual situation of about5 to 10 dB, the channel quality is very poor and the packetloss rate is quite high. DCAR and COPE use a minimum of12mbps, and DCAR and COPE use 24mbps for codingopportunities. Our RDCAR uses a rate selection algorithm,so it will use a lower transmission rate to ensure the numberof successes on a poor link and use a higher transmission rateon a better link to increase throughput. We found thatDCAR adopts coding-aware routing scheme and can activelydiscover coding opportunities. -erefore, under the samecircumstances, no matter what transmission rate is used, it isbetter than COPE. When the channel environment of 5 to10 dB is poor, compared with the two cases of 12mbps and24mbps, using 12mbps to guarantee the transmission rate,the coding opportunity and throughput are better than thoseof 24mbps. -e medium quality channel ranges from 10 to20 dB. Since under medium channel quality, a certaintransmission success rate can be guaranteed even when48mbps is used again, and we can see that as the trans-mission rate increases, under the premise of ensuring a

Table 3: Machine configuration during the system test.

Project ValueOperating system Microsoft Windows 7 UltimateVersion 6.1.7601 Service Pack1 internal version 7601System type x64-based PCProcessor Intel8CoreTM i3-2310MCPU 2.10GHzRAM capacity 8GB

Complexity 9

certain transmission probability, the coding of COPE andDCAR opportunities and throughput gradually approachRDCAR. Due to the high channel quality, the success rate ofinformation transmission is very high, and the main factorthat affects coding opportunities and throughput is thetransmission rate. -erefore, we can see that in Figure 9,when DCAR and COPE only use low-speed channels, thelow transmission rate guarantees success rate is no longerapplicable but greatly affects the transmission efficiency,making the coding opportunity and throughput smaller.DCAR and COPE use medium-speed channels. It can beseen that the coding efficiency of RDCAR is still far behindwhen using 24mbps. -e transmission rate of RDCAR inour actual simulation is about 48mbps. -erefore, we foundin subsequent experiments that if 48mbps high-speedtransmission is used, the coding opportunities of DCAR andRDCAR are quite close, and there is a certain gap betweenCOPE and DCAR. -e coding situation monitored bydifferent transmission rates is shown in Table 6.-e learningstep length of the RDCAR algorithm is shown in Table 7.-eoriginal high-frequency coded signal collected by using theoscilloscope is shown in Figure 10.

4.3. FunctionAnalysis. -emodel proposed in this study notonly includes the antitampering, traceability, and decen-tralization characteristics of transactions in the existingdigital currency system but also adds supervisable attributesto the system to enhance the system’s ability to protect userprivacy.-e performance test is divided into two aspects. Onthe one hand, it tests the performance changes of the al-gorithm under different matrix sizes, that is, N; on the otherhand, it tests the performance comparison between thisalgorithm and the standard SM2 signature algorithm.During the test, first create a plaintext byte array and assignvalues, then create a key byte array and assign values, createan encryption instance by calling sm4.NewCipher (key), andthen call c.Encrypt to calculate the encryption result. -en,call c.Decrypt to calculate the decryption result. -e per-formance of the algorithm changes when the test matrix sizeN is different. When the test N is 8, 16, 32, and 64, calculatethe number of signature tests and verification times of thealgorithm per second. -e result of functional analysis isshown in Figure 11. Since authorization generation andauthorization verification will only occur once when thesystem is initialized, this part of the time is not calculated. In

1 2 3 4 5 6 7 8 9 10 11 12Groups

SignatureCheckOther

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Figure 8: Algorithm performance analysis at different matrix sizes.

Table 5: Before the simulation, you need to specify specific voltage values for the power signal and ground.

Net name Voltage value (V)AWCC1V8 1.8DD RYTT 075GND 1.2YCC1V2 1.8YCC1V8 33

Table 4: Algorithm performance at different matrix sizes.

Algorithm N� 4 N� 8 N� 16 N� 32 N� 64Signature 221.72 262.47 251.89 239.81 219.78Check 245.10 227.27 224.72 189.39 155.28

10 Complexity

terms of privacy protection, the complete transaction rec-ords are encrypted and stored. Only after initiating thetraceability of the transaction and voting by the members ofthe alliance chain, can the transaction plaintext be viewed bythe special members of the alliance chain. After completing aconsensus, the alliance chain broadcasts a batch of transfer-in and transfer-out transactions, hiding multiple pieces ofinformation belonging to the same complete transaction in alarge amount of similar information and avoiding theleakage of user privacy in the process of information in-teraction.-e public transaction records stored in the publicchain for query, because of truncation and confusion, losethe traceable transaction relationship information and havethe unlinkability of the transaction output required by thedigital currency public chain data. -e data stored in thealliance chain node have the most restrictive restrictions onaccess rights. During the normal operation, the alliancechain node only has the write permission, and the verifi-cation function in the system is completed by the data in the

public chain block. In terms of supervisable attributes, whenthere is a need for the system to trace the transaction, thealliance chain node initiates a transaction traceability ap-plication, and the nodes can understand the reason forinitiating the traceability outside the system, and then thesupervisory authority node is responsible for restoring thekey and decrypting the traceability. Transaction recordsfinally obtain the identities of relevant transaction partici-pants through the identity verification server. But, only aftera USOT is spent, the consortium chain can obtain its owner’sidentity signature through a transaction request from apublic chain user. If a USOT has not been spent, its ownercannot be known. In order to increase the supervisableproperties of the system, this research adds three parts of thesupervision agency, identity authentication server, andencrypted storage to the Internet of -ings system. Whenthere is no need to initiate traceability, the supervisoryagency only participates in the consensus process as aparticipant in the alliance chain, and only when it needs to

Table 7: Learning step size of the RDCAR algorithm.

Number of learning samples Number of test samples Number of hidden nodes Learning step100 50 8 0.005, 0.005100 50 8 0. 005, 0. 005

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Number of test groups

0102030405060708090

100

Com

pare

resu

lts

RDCARDCARCOPE

Figure 9: Comparison of encoding opportunities at different rates.

Table 6: Coding situation monitored by different transmission rates.

Project Infeasible area Feasible region Near real POF5 dB (0.10, 0.20) (0.40, 0.55) (0.30, 0.45)10 dB (0.10, 0.10) (0.70, 0. 70) (0.60, 0.64)12mbps (0.10, 0.20) (0.40, 0. 50) (0.24, 0.27)24mbps (0. 10, 0.20, 0.10) (30, 0.7, 0.25) (0.12, 0.31, 0. 67)36mbps (0.20, 0.50, 0 60) (70, 0.7, 0. 50) (0.50, 0.77, 0.37)48mbps (0.20, 0.50, 0.60) (70, 0.7, 0.50) (0.50, 0.77, 0.37)

Complexity 11

trace the transaction, can it act as a trusted secret sharedshare collector to decrypt data. -e identity authenticationserver issues identity certificates to registered users, whichcan trace the transaction to a clear user and achieve thor-ough supervision. -e addition of supervisory attributesreduces the degree of decentralization of the system, but itdoes not damage the Internet of -ings from the user’sperspective. -e original intention of the design is that themembers and data of the alliance chain and the public chaindo not rely on centralized credit, and it can also achievebetter protection for users. When a user loses an account ortransaction certification document such as an identity cer-tificate, he can protect his account through the identityauthentication server and the system. Encrypted storage is toprevent consortium chain members from leaking transac-tion information after being attacked. -e function of theregulatory agency has affected the decentralized structure,and it has also become a security weakness that the systemmay be attacked. -e use of secret sharing to a certain extentprevents the possibility of stealing transaction data with theregulatory agency as the target, and only alliance chainmembers agree to restore only when the key is the super-visory authority which has the ability to decrypt data. -etest results after changing the maximum number of learningtimes are shown in Table 8. -e regulatory error is shown inTable 9.

4.4. Security Analysis. -e issuance of legal digital currencyrequires the transformation of the payment system infra-structure. As a digital form of currency, legal digital currencyneeds to adopt extremely high technology in any link ofcirculation to reduce the degree of operational risk. Once thelegal digital currency infrastructure is destroyed, it will causethe entire financial system to suffer losses. -e system se-curity results are shown in Figure 12. -e analysis of legaldigital currency has to deal with a large number of trans-actions, and the most mature distributed ledger technologycannot fully meet the requirements of the central bank’s

payment system. In its fiat digital currency project, the Bankof Canada uses distributed ledger technology (DLT) to builda payment system, but the performance of distributed ledgertechnology is not optimistic. According to the report, it isdifficult to use distributed ledger technology for transactionsystems to process a large amount of instantaneous trans-action data. After the official issuance of legal digital cur-rency, it faces far more requirements than Bitcoin in terms ofimportance and transaction scale. -e payment infrastruc-ture established by the central bank must meet the trans-action needs of the society. -e issuance of legal digitalcurrency will have a profound impact on the paymentsystem of the entire country. -erefore, the construction ofthe payment system must consider the requirements ofscalability, compatibility, and transaction throughput. -isarticle divides the attacks faced by the supervisable digitalcurrency model into three types according to their sources:attacks from outside the system, attacks from alliance chainnodes, and attacks from public chain nodes. In terms of themost basic security of digital currency transactions, thesystem uses identity certificates and signatures to ensure thatattackers cannot forge identities to steal other people’s assets;transactions in the system are based on USOT and use aunique public key as an address. -e corresponding privatekey can be unlocked for payment. -e purpose of the attackfrom outside the system is to destroy the function of thesystem and make the digital currency system unable tooperate normally. -e target of the attack may be a node inthe system or a communication network. Due to the dis-tributed nature of the blockchain, attacking a node in thesystem will not affect the operation of the system, but whenan attacker has the ability to attack multiple nodes, thealliance chain as the core of the system must ensure that thenode that stops working cannot exceed 1/3 of the system.Since the public chain uses DPoS as a consensus mechanism,during the election cycle, those nodes that are made publicdue to the operation of the system are likely to becometargets for system attackers. -erefore, in order to ensurethat the system does not stop running, it is necessary to

Frequency = 10HZFrequency = 40HZFrequency = 20HZ

Frequency = 50HZFrequency = 30HZFrequency = 60HZ

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2 4 6 8 10 12 140Number of groups

Figure 10: -e original high-frequency coded signal collected by using an oscilloscope.

12 Complexity

N = 8N = 16

N = 32N = 64

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2 4 6 8 10 12 14 160Groups

Figure 12: System security results.

1 2 3 4 5 6 7 8 9 10 11 12 13 14Node

N = 8N = 16

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Figure 11: Functional analysis results.

Table 8: Test results after changing the maximum number of learning.

Maximum number of studies Best accuracy rate (%) Worst accuracy rate (%) Average accuracy (%)10000 66 56 6020000 66 60 62

Table 9: Regulatory errors.

Target location Right in front Upper left Top right Right rearMaximum positioning error of X-axis (cm) 1 1 1 1Maximum positioning error of Y-axis (cm) 2 1 1 2

Complexity 13

ensure that the system attackers are all before destroying thecurrent witness node, complete a new round of witness nodevoting. Attacks from within the system are generally throughthe creation of system inconsistencies such as the fork of theInternet of-ings to achieve double-spending by tampering orcanceling transaction records. -e CPBFT consensus mecha-nism adopted by the alliance chain does not have the possibilityof forks of the Internet of -ings. When the number of col-luding attackers does not exceed the threshold, it can preventdouble-spending attacks. Although there may be forks in thepublic chain, only attackers account for more than 50% of thetotal number of nodes to ensure the success of the attack. -ereal-name registration mechanism in the background alsoreduces the possibility of launching and succeeding from insidethe system. Digital currency can record and check transactioninformation and information of both parties, can accuratelyreflect the implementation of monetary policy, and canstrengthen financial management. -e transaction record ofdigital currency cannot be tampered with, can completelyrecord the transactions of each participant, and can form aunified distributed ledger in the entire digital currency system.-rough the review of transaction information and the su-pervision of digital currency circulation, the national regulatoryagency can accurately grasp the monetary policy and creditpolicy in real time, and then scientifically and comprehensivelycalculate the policy implementation status, and adjust relevantpolicies in time according to changes in the situation; It canpromote the publicity, openness, and integrity of digital cur-rency transactions as a whole by establishing an overlyingpublic credit system.

4.5. Efficiency Analysis. Intermediaries such as digitalcurrency trading platforms or traditional financial insti-tutions and certain nonfinancial industries which are likelyto participate in the flow of digital currency transactionsshould keep records and report suspicious transactions.

-e specific methods include conducting due diligence oncustomer identity and storing customer identity infor-mation. -e public key address, account, transaction na-ture, date, and amount involved in the transaction arehelpful for monitoring transactions, recording suspicioustransaction information, and combining the informationon the blockchain ledger for more accurate recording. Inorder to prevent unqualified financial institutions fromparticipating in the payment and settlement system, theaccess system sets specific conditions so that only paymentand settlement participants who meet the correspondingconditions are allowed to be the counterparty of paymentand settlement. -e efficiency analysis result is shown inFigure 13. As the transparency and intensity of supervisionhave been greatly improved and the legal digital currencyrelies on advanced Internet technology, it can betteridentify the relevant conditions of financial institutionsinvolved in payment and settlement and strengthen pru-dential supervision. -e consensus mechanism used in thepublic chain part of the system is DPoS, and the consortiumchain part uses CPBFT. Both consensus mechanisms arebased on voting. -e system state is determined accordingto the choice of the majority of nodes in the system, withoutthe need for additional proof of work. -e computationaloverhead of the system mainly occurs in the verificationand encryption of transactions. In the process of verifyingthe block, each alliance chain node needs to encrypt andcompare the transactions packaged into the block. -isprocess uses a public key cryptographic algorithm, whichhas a high time complexity. Each node has basically thesame demand for computing power, and there will be nosystem security problems and decentralized performancecaused by the concentration of computing power. Toachieve consistency within the alliance chain, nodes need tobroadcast multiple times to achieve information interac-tion between the two. -e communication complexity isO(n), where n is the number of nodes. -e system is

1 2 3 4 5 6 7 8 9 10 11 12 13 14Groups

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Figure 13: Efficiency analysis results.

14 Complexity

designed so that each participant can control multiplenodes proportionally. -e more the number of nodes, thestronger the antiattack ability of the system, but thecommunication overhead will also increase. -e systemmaintains two blockchains at the same time. Comparedwith a single chain system, the communication betweensome nodes of different systems is increased. If the nodes ofthe alliance chain and the public chain communicate witheach other, the complexity of the communication isO(n, m), where n is the number of nodes in the alliancechain, mm is the number of nodes in the public chain, andm may be multiples of n, so the pair can be appropriatelyrelaxed. -e communication requirements between twoblockchain nodes can reduce the communication com-plexity to O(m). -e supervisable digital currency systemuses CPBFT and DPoS improved in this research. -echaracteristics of these two consensus mechanisms are thatthe block generation interval is short and the systemtransaction throughput is high, so there is no systembottleneck caused by increasing the system scale in theconsensus mechanism.-e correlation coefficient matrix ofthe independent variables is shown in Table 10. -e unitroot test results of the time series are shown in Table 11.

5. Conclusion

-is research mainly discusses the intelligent digital cur-rency and dynamic coding service system based on theInternet of -ings technology. To a certain extent, digitalcurrency can help save currency issuance and circulationcosts, improve the effectiveness of monetary policy, accel-erate the pace of development to a cashless society, andadopt effective methods for promotion and application. It isexpected that digital currency will be widely used by allpeople, thereby contributing to building a robust and effi-cient new financial system. At the same time, the current

status of the use of digital currencies at home and abroad isalso analyzed. -e digital currency market is frequentlytraded. According to the traditional trading market, digitalcurrency is an active market, but the currency value stabilityof digital currency is poor.

-is research is mainly based on the RDCAR algorithmto realize the traceability of the route discovery process of thewireless network initiated and completed within the alliancechain, and finally the results of the transaction traceabilitycan be seen.-e goal of retroactive transactions is generally atransfer-out transaction because the initiator of a completetransaction on the transfer-out exchange is the owner of thetransfer-out transaction address, and the identity of theinitiator can be queried. If no one spends it for a transfer-intransaction, it cannot get the owner of the transaction.Whensearching for a transaction, according to the query trans-action serial number, it traverses the plaintext of transactioninformation saved in the block, finds the block containingthe same serial number, decrypts the transaction, finallyfinds the transaction, and displays the complete record in thetransaction.

When the intermediate node receives the RREQ mes-sage, first, to avoid loops, check whether the same RREQmessage has been introduced. If it is received, discard it.Otherwise, buffer the message and append its own neighbornode list, corresponding to the signal-to-noise ratio of thechannel link, and update RREQ message and broadcast it.-e payment cipher is managed by the bank. When the useropens an account, the bank registers and sends it to the user,and the key is generated by the algorithm chip, and thepublic key is stored in the bank’s back-end server. When thebill is delivered to the bank, the bank enters the variouselements of the bill on the counter terminal and transmits itto the verification machine through the bank network forverification. If the verification is correct, it indicates that thebill is indeed issued by the customer, and the bill elementsare correct, so you can make payment. -e public chain andalliance chain node operation agreement maintains theoperation of the Internet of -ings system. -e alliancechain node generates new blocks at a time interval of 30 s.When the node cannot complete the block generation within30 s, it will rotate to the next node.

Data Availability

No data were used to support this study.

Conflicts of Interest

-e authors declare that they have no conflicts of interest.

Acknowledgments

-is work was supported by the Social Science Foundation ofShaanxi Province of China under grant no. 2019D009 andthe Shaanxi Province of China, Department of EducationProject of Philosophy and Social Sciences Key Research Baseunder grant no. 19JZ052.

Table 10: -e correlation coefficient matrix of the independentvariables.

X variableY variable INF LNC LNY R P V

INF 1.000 −0.129 −0.132 0.730 0.180 −0.470LNC −0.129 1.000 0.999 −0.473 −0.363 0.876LNY −0.132 0.999 1.000 −0.473 −0.352 0.878.R 0.730 −0.473 −0.473 1.000 0.707 −0.670P 0.180 −0.363 −0352 0.707 1.000 −0.287V 0.470 0.876 0.878 −0.670 −0.287 1.000

Table 11: Time series unit root test results.

Variable (c, t, n) ADF inspectionvalueCriticalvalue Conclusion

lny (0, 0, 0) 6.962 −1.966 SmoothΔ lny (c, 0, 0) −2.998 −1.956 NonstationaryInf (c, 0, 0) −1.627 −1.956 NonstationaryΔinf (c, 0, 0) −3.809 −1.956 Smoothp (c, 0, 0) −0.182 −1.956 NonstationaryΔp (c, 0, 0) −3.898 −1.956 Smooth

Complexity 15

References

[1] J. Lin, W. Yu, N. Zhang, X. Yang, H. Zhang, and W. Zhao, “Asurvey on internet of things: architecture, enabling technol-ogies, security and privacy, and applications,” IEEE Internet of+ings Journal, vol. 4, no. 5, pp. 1125–1142, 2017.

[2] X. Sun and N. Ansari, “EdgeIoT: mobile edge computing forthe internet of things,” IEEE Communications Magazine,vol. 54, no. 12, pp. 22–29, 2016.

[3] Y. Yang, L. Wu, G. Yin, H. Zhao, and L. Li, “A survey onsecurity and privacy issues in internet-of-things,” IEEE In-ternet of +ings Journal, vol. 4, no. 5, pp. 1250–1258, 2017.

[4] I. Yaqoob, E. Ahmed, I. A. T. Hashem et al., “Internet of thingsarchitecture: recent advances, taxonomy, requirements, andopen challenges,” IEEE Wireless Communications, vol. 24,no. 3, pp. 10–16, 2017.

[5] O. Bello and S. Zeadally, “Intelligent device-to-device com-munication in the internet of things,” IEEE Systems Journal,vol. 10, no. 3, pp. 1172–1182, 2016.

[6] M. M. Zuberi and R. Levin, “Schumpeter’s revenge: the gale ofcreative destruction: digital currencies and blockchain tech-nology,” Banking & Financial Services Policy Report, vol. 35,no. 5, pp. 1–8, 2016.

[7] M. I. Mehar, C. L. Shier, A. E. Giambattista et al., “Under-standing a revolutionary and flawed grand experiment inblockchain,” Journal of Cases on Information Technology,vol. 21, no. 1, pp. 19–32, 2019.

[8] D. S. Gong, “Financial transactions in ATM machines usingspeech signals,” International Journal of Engineering Researchand Applications, vol. 7, no. 1, pp. 25–28, 2017.

[9] F. Balo, “Internet of things: a survey,” International Journal ofApplied Mathematics Electronics and Computers, vol. 4,no. 2016, pp. 104–110, 2016.

[10] M. R. Palattella, M. Dohler, A. Grieco et al., “Internet of thingsin the 5G era: enablers, architecture, and business models,”IEEE Journal on Selected Areas in Communications, vol. 34,no. 3, pp. 510–527, 2016.

[11] A. V. Torsner and R. Buyya, “Fog computing: helping theinternet of things realize its potential,” Computer, vol. 49,no. 8, pp. 112–116, 2016.

[12] M. A. Razzaque, M. Milojevic-Jevric, A. Palade, and S. Clarke,“Middleware for internet of things: a survey,” IEEE Internet of+ings Journal, vol. 3, no. 1, pp. 70–95, 2016.

[13] J. Singh, T. Pasquier, J. Bacon, H. Ko, and D. Eyers, “Twentysecurity considerations for cloud-supported internet ofthings,” IEEE Internet of +ings Journal, vol. 3, no. 3,pp. 269–284, 2016.

[14] A. Mosenia and N. K. Jha, “A comprehensive study of securityof internet-of-things,” IEEE Transactions on Emerging Topicsin Computing, vol. 5, no. 4, pp. 586–602, 2017.

[15] M. Amadeo, J. C. Campolo, A. D. MolinaroCorujo,R. L. Aguiar, and A. V. Vasilakos, “Information-centricnetworking for the internet of things: challenges and op-portunities,” IEEE Network, vol. 30, no. 2, pp. 92–100, 2016.

[16] Y. Iera and J. Wen, “-e IoT electric business model: usingblockchain technology for the internet of things,” Peer-to-PeerNetworking and Applications, vol. 10, no. 4, pp. 983–994, 2017.

[17] Z. Longchao, Y. X. Jianjun, and Y. Limei, “Research oncongestion elimination method of circuit overload andtransmission congestion in the internet of things,” Multi-media Tools and Applications, vol. 76, no. 17, pp. 18047–18066,2017.

[18] J. W. Xue, X. K. Xu, and F. Zhang, “Big data dynamiccompressive sensing system architecture and optimization

algorithm for internet of things,” Discrete and ContinuousDynamical Systems-Series S, vol. 8, no. 6, pp. 1401–1414, 2017.

[19] H. S. Dhillon, H. Huang, and H. Viswanathan, “Wide-areawireless communication challenges for the internet of things,”IEEE Communications Magazine, vol. 55, no. 2, pp. 168–174,2017.

[20] A. Ouaddah, A. A. Abou Elkalam, and A. Ait Ouahman,“FairAccess: a new blockchain-based access control frame-work for the internet of things,” Security and CommunicationNetworks, vol. 9, no. 18, pp. 5943–5964, 2016.

[21] M. Nitti, G. V. Pilloni, and L. Atzori, “-e virtual object as amajor element of the internet of things: a survey,” IEEECommunications Surveys & Tutorials, vol. 18, no. 2,pp. 1228–1240, 2016.

[22] J. Ni, K. Zhang, X. Lin, and X. S. Shen, “Securing fog com-puting for internet of things applications: challenges andsolutions,” IEEE Communications Surveys & Tutorials, vol. 20,no. 99, pp. 601–628, 2018.

[23] D. Zhang, L. T. Yang, M. Chen, S. Zhao, M. Guo, andY. Zhang, “Real-time locating systems using active RFID forinternet of things,” IEEE Systems Journal, vol. 10, no. 3,pp. 1226–1235, 2016.

[24] K. Sood, Y. S. Yu, and Y. Xiang, “Software-defined wirelessnetworking opportunities and challenges for internet-of-things: a review,” IEEE Internet of +ings Journal, vol. 3, no. 4,pp. 453–463, 2016.

[25] S. P. Mohanty, U. Choppali, and E. Kougianos, “Everythingyou wanted to know about smart cities: the internet of thingsis the backbone,” IEEE Consumer Electronics Magazine, vol. 5,no. 3, pp. 60–70, 2016.

[26] J. Bernal Bernabe, A. F. Hernandez Ramos, and A. F. SkarmetaGomez, “TACIoT: multidimensional trust-aware accesscontrol system for the internet of -ings,” Soft Computing,vol. 20, no. 5, pp. 1763–1779, 2016.

16 Complexity