1 Digital Business System and Challenges

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https://doi.org/10.1007/s13369-021-06057-w

RESEARCH ARTICLE - SPECIAL ISSUE - FRONTIERS IN PARALLEL PROGRAMMING MODELS FOR FOG AND EDGE COMPUTING INFRASTRUCTURES

Risk Handling and Vulnerability Assessment in IoT‑Enabled Marketing Domain of Digital Business System

Yi Liu¹ · Ziyan Tang² · Thota Chandu³ · Shanmugan Joghee⁴

Received: 11 May 2021 / Accepted: 30 July 2021

Abstract

Rapid Internet development and technology contribute to the emergence of digital business models, product life, utilities, etc. Internet of Things (IoT) also handles e-business and functionality, or automated business structures. As additional com- puters and hardware are necessary for clustering, monitoring and maintenance are difficult. While the IoT e-business model improves overall business prospects, it poses many difficulties, including accurate business knowledge, risk, vulnerability evaluation and problem interpretation and handling of a large array of business data. Hence, in this study, IoT-enabled digital business system (IoTE-DBS) has been suggested for risk handling and vulnerability assessment of the reliable business model. The research examines the implications of digital business models, proposes a conceptual structure, and discusses how digital business models impact IoT-based businesses, business results and markets. The proposed model’s efficiency is assessed using end-user satisfaction, business process accuracy, the Pearson correlation coefficient, and the relative error.

Keywords Risk handling · Vulnerability assessment · Internet of Things · Digital business system

1 Digital Business System and Challenges

Business environments are getting more dynamic and unpre- dictable as digital technology spread. Every new technology presents opportunities and challenges simultaneously, both in isolation and in tandem with other technologies [1]. The rising pace of innovations needs businesses to identify and respond to those changes more quickly. The outcome seems to have been a more fantastic dynamic [2] and uncertainty of value development and acquisition. The corporate model of a concentrating firm, the manner in which a corporation produces and offers consumers value, and the processes used to catch a share of that value, is just in time for a snapshot

[3]. In addition, since consumers’ demands for increasingly complicated matters and technology encourage businesses to cooperate more and more, developers see new interdepend- encies and ways of mutual production of value emerge [4, 5].

The evolution of the business system is part of the initiative to strengthen the goals of the company. In its components, business activities will evolve. The business method elements involve member classes, function, and systems [6];

events, actions, artifacts, and gateways [7]; message flow, association, and the series flow. Digital businesses use inno- vative technologies to generate value for their key market strategies, user interactions, and internal skills. The concept encompasses many emerging brands and established play- ers that use digital technology to change their companies [8–10]. Nowadays, more capital is invested online, moving the market focus on digital sales and digital platforms. The digital era has raised the perception of digital goods and services, which has prompted businesses to pursue new strategic advantages in the digital market [11]. Tailored to the unique convergence of digital and physical capital, digital companies build competitive edges. Digital business persons/organizations accomplish things that most people cannot do and create competitive advantages [12]. Digital businesses are distinguished from e-business by keeping

* Ziyan Tang

[email protected]

1 School of Economics and Management, Sichuan Conservatory of Music, Chengdu 610000, Sichuan, China

2 Business School, Sichuan University, Chengdu 610000, Sichuan, China

3 IBM Global Business Services, Hyderabad, India

4 School of Business, Skyline University College, Sharjah, UAE

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traditional business structures generally unchanged. The online application [13] for a credit card from our customers eliminates paper application inefficiencies.

Organizational resilience is one of the most important performance drivers in this digital age. In addition to the digital revolution, it is crucial for companies, from a digital initiative, to address risks implemented in the environment and their effect on the current ecosystem [14]. While the changing world poses all the difficulties and threats, companies should never neglect the possibilities presented by

‘moving into digital’ [15] and their tremendous effects.

Digitalizing businesses and automating processes produces rising security threats and vulnerabilities [16]. As a result of aggressive digitization, many advanced cyber safety functions on earth are beginning to turn their capacity into a business value chain through the three-dimensional use of quantitative risk analysis to make decisions [17]. These advancements allow the latest technology systems to integrate various technologies. Digital threats may be described as regulatory risks, safety risks, third-party risks, employee risks, data protection risks, and automation risks [18].

Moreover, in a single sector, these threats may not be identified at a time. It is crucial to design the components of the digital risk strategy effectively. The organizations must take urgent steps to ensure cyber protection, and traditional information safety and cybersecurity evaluations of the networks are the most straightforward solutions [19, 20]. The following queries come into the picture when we think about digital business and choosing risk management schemes; ‘Is this sufficient? Is cybersecurity the only challenge for a digital company?’ It is essential to consider vulnerable areas beyond standard risk for a thriving digital ecosystem to achieve the target [21]. For this cause, security is one of the most significant hurdles to digital transformation today and stresses traditional security technologies’ restrictions on IT leaders. Over the years, they have not applied their network’s security capabilities to face the current security threat raised by introducing emerging technology and their related elas- ticity, scalability, speed, and volume issues [22]. Above all, many conventional solutions are not the proactive protection platform to enable mobility, integration, and orchestration that they now need.

In the same way, profiling for consumers is relevant to improve consumer service, and profiling should be consist- ent with protecting customer data privacy. Digital resilience is also essential—the systems’ availability is need- less because of its significant reliance on the infrastructure.

Various such scenarios address other sectors that may be considered across multiple businesses and activities [23]. In the Internet of Things (IoT) ecosystem, effective and intelligent business processes are highly based, where end-to- end optimization is key to the whole ecosystem’s success [24]. There are dynamic networks of heterogeneous devices,

including automotive, medical, and other applications [25].

The risk effect evaluation is getting more complicated at this point. The relationship between risk factors will increase the degree of vulnerability. The risk model has been developed to explain the dependencies of security control and the extension of vulnerability [26]. One of the emerging securing technology, blockchain technology, enables safe transactions and contracts to be made and registered as a distributed ledger technology [27]. Blockchain technology is a consensus-based and accurate record-keeping system that provides immaculate, immutable, and encrypted records [28]. It is built as a base for distributed, safe data stocking involving transactions and interactions and can be used from IoT to workflow modeling on any layer. This document pri- marily concentrates on using blockchain in the IoT-based digital business system, which turns business processes into secure digital business structures to control the transformation of the service workflow-based business process. Thus, for the risk-handling and vulnerabilities evaluation of a stable business model, the IoT-enabled digital business system (IoTE-DBS) has been recommended. This research explores the importance of digital business models, proposes a conceptual paradigm, and reflects how digital business models impact businesses, company success, and IoT-based markets. Businesses may utilize the always-on connection of IoT devices to establish a recurring income business or subscription model. Like the as-a-service business model for technology, an IoT subscription model enables it to offer continuous value to consumers for a monthly charge. Digital mobile and wireless technologies expand the user’s ownership model to the service by accessing product information and monitoring the operation of the devices deployed. The key to retaining customers is achieving and sustaining customer satisfaction.

The happiness of customers includes the trust element of the consumer when the business provides them with superior service. It decides the future sales for the company, supports growth and keeps the customers profitable. Service quality is directly related to the retention of users and loyalty to the brand. Other inherent advantages above that a company may profit from by using the IoT data. The aim is always to ensure a greater degree of pleasure for customers, resulting in customers being retained. The pleasure of customers is attributed directly to the commitment of customers and the customer experience. The advantages illustrated are those that directly affect the user experience and dominate user satisfaction. The remaining structure of this research report is as follows:

• Various suggestions on blockchain technology in IoT- enabled systems

• Research flow of this study with Proposed IoTE-DBS framework description

• Analysis and discussions

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2 Blockchain Technology in IoT

Blockchain has recently gained substantial recognition as a decentralized and distributed peer-to-peer public ledger technology. A related block structure is implemented to validate and preserve data. A trustworthy consensus process is used to synchronize data changes, enabling the development of a tamper-resistant automated data-speak- ing network [29]. Blockchain is believed to be used in numerous interactive communication channels such as the Internet of Things, identity management and supply chain systems [30]. This part discusses various researchers’ suggestions for risk and vulnerability handling through blockchain technology in IoT networks.

In the detailed review of the literature on IoT blockchain, Pavithran et al. [31] defined five main components and their requirements and obstacles to be taken into account when designing IoT blockchain architecture.

They identified loopholes preventing the implementation of a stable IoT blockchain platform. Their study had simulated two distinct forms of blockchain deployment and defined a comparatively more robust node-to-device architecture than a gateway. Miraz et al. [32] commented that IoT requires improving its safety features because blockchain possesses these features because of their wide- spread use of cryptographic frameworks. The reversed blockchain intended contributions from distributed nodes to its trusted peer-to-peer model, and the design of IoT rudimentarily embody them. Consequently, this article acutely discerned the feasibility of blockchain with IoT technology—which might induce the concept of the blockchain of Things (BCoT)—and the advantages that these integrations might bring.

The detailed literature survey identified that conventional applications use unified data processing architectures, subject to reduced scalability, lack of clarity, and failure. Xiong et al. [33] deployed blockchain as a distributed ledger to support the transparent data storage method for IoT networks where IoT data are saved for future use, for instance, in the implemented blockchain for recovery and audit. And thus, it presented an overall architecture that combines blockchain and IoT. However, they proposed this model to perform a case study on a system for allocating learning capital to support intelligent data management. IoT devices’ resources could be restricted by transferring data from IoT devices through the blockchain network. The blockchain model had proved itself to be a reliable transaction platform in a confidently shared network that can overcome IoT-intrinsic security vulnerabilities. The consensus is therefore computationally taxing across a blockchain network and takes a lot of resources. In addition, traditional transactions using blockchain-based

implementations are sluggish, making IoT systems with blockchain technologies much more challenging. A low- consensus algorithm called proof-of-authentication (PoAh) was used in Maitra et al. [34] work for resource-restricted IoT edge nodes and latency and energy consumption. The consensus algorithm adopted reduced block validation latency to 29.35 ms and energy consumption to 44.31 mJ per transaction and showed promise and ability, integrated blockchain technology in IoT systems efficiently.

QinWang et al. [35] proposed creating a modern blockchain strategy to guarantee decentralized and distributional connectivity for Industry 4.0. The decentralization and the communication of information in this system influenced IoT and industrial IoT (IIoT). They presented the basic framework, key features, and security specifications in the blockchain and outlined the IoT and Industry 4.0 implementation requirements. They were then discussed using encryption tools and technologies to adapt blockchain to the IoT for Industry 4.0. They identified the most critical blockchain- based IoT applications to support blockchain technologies’

functions and benefits on IoT and IIoT networks. Eventually, guidelines to help prospective researchers and entrepreneurs in the blockchain were suggested. IoT architectures usually focus on data transfer for processing on central cloud servers. Even though IoT computing, processing, and communication capacities were expected to be improved by cloud providers, this approach yields isolated data silos and calls for trust from third parties running the cloud system, which is the only point of failure. Centralized cloud-based systems often lack transparency and allow undetected exploitation and the disguise of IoT data. To address these downsides, Lockl et al. [36] built and validated a blockchain IoT data logging and tracking system using a theoretical approach to architecture analysis. Their contribution to the IoT knowledge base was being asserted by applying ideas and management and technical recommendations.

All the above references had the highest performance in risk handling for various IoT applications integrated with blockchain technology. These results motivated this study to formulate the research hypothesis using a blockchain integrated IoT framework for secured digital business systems.

Therefore, this research develops an IoT-enabled digital business system (IoTE-DBS) to efficiently handle risks and vulnerabilities in the digital business system’s marketing domain. The following parts of the document focus on the research methodology adopted and the subsequent discussions in detail. Influent elements for the buying intention of the blockchain traceability. The results show that the advantage and familiarity seen through customer confidence have significantly favorable effects on acquiring blockchain items. Perceived risk has a significant impact on consumer confidence and buying intent, influencing consumers’ confidence in blockchain product traceability factors, including

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certification of third-party products, platform reputation and technology-related cognitive level, moderation of third-party certification influences, knowledge, consumer sentiment, and governmental consumer sentiment confidence controls.

3 Research Methodology

Vulnerability assessment and risk handling are the core approach in the risk management process. In the past, an asset-driven approach was carried out to determine the risk. There exist several challenges in doing such risk assessment models. There are several issues with this method.

Many assets owned by the company, carried out on all the company’s assets, contribute to high-risk management costs.

Many risk analyses are performed independently of each asset irrespective of its connection to other assets or business processes. Accounting only considers the cost parameter variable in the risk evaluation, irrespective of its value for business processes. For its contribution to business operations, an investment with low value on the cost metric may have a high value. Therefore, the risk value of this asset could also be increased. This research contributes to the novel approach with blockchain supported IoT-enabled digital business model for risk handling and vulnerability assessment in the market domain. Hence, this section start with the scope of IoT technology in the digital business system. The manual intervention requirements in the aggregation, modi- fication and exchange of data can be reduced and regulatory reporting and auditing documents easier and less manual processing can become essential. This would allow staff to concentrate on value-added tasks only. The deployment of a blockchain system can assist reduce the overall cost by cutting transaction prices considerably. Cryptocurrency pay- ments are made through peer-to-peer networks and need not be centrally verified. This means that a small business may accept payment in bitcoin or another blockchain settlement platform and pay less trading costs.

3.1 Digital Business Systems and IoT

The Internet of things (IoT), utilizing sensors and other peripheral equipment and networks, is changing businesses’

activity. Internet links people via commercial and social networking or via corporate transactions such as Internet banking or digital business through networks. However, the emerging IoT deals with connecting machines and systems through sensors and actuators to collect meaningful information and improve human productivity and efficiency. It is about IoT because we will be concerned about the explosion of linked devices between about a billion-plus today and over 50 billion in the next decade. IoT thus brings the importance of interconnectedness to a whole new degree. Although this

technology offers a means of eliminating pollution, costs, and inconveniences while improving productivity, this technological transformation’s key attraction is to make life environmentally safer, more efficient, and healthier. With the growing use and increasing IoT networks, the sensors and linked devices cost will decrease. To further minimize costs, the production of low-bandwidth, low-power consum- ing appliances will be a new direction. Upon recognizing the IoT region’s business potential, new goods, services, and sales models will emerge, attracting investments and thereby generating employment within the IoT area. Perhaps it improves import and export demand for goods and solutions such as these, which in turn could improve economies close to what IT services have achieved. Furthermore, this contributes to developing auxiliary or backbone industries such as smart and connected computer manufacturing, tracking and measuring systems, decision-making control, and analytics and security technologies that guarantee the secure use of IoTs and resolve privacy concerns.

Adopting IoT contributes to developing and implementing adequate data and analytical technologies that provide insight into practical decision-making. In conjunction with a large number of devices and high volume, speed, and structure of IoT data, data analysis, servers, and the data center, it will generate opportunities for security and data storage management. Besides understanding industry-specific usage habits, client actions, and groundbreaking marketing strategies, this involves expertise such as knowledge in business research, mathematics, and statistic; imaginative concept for end-user visualization; large-size data structures, programming, and architecture of large-scale and modular systems and equipment. Many of these advantages are, however, correlated with safety risks and privacy violations. Smart meters, and trackers of actions or people’s activity in work, maximize energy consumption and shut down efficient machines while nobody is at work or in occupied space.

Protected service can be jeopardized if specific documen- tation of our movements or lack of a house fell into the wrong hands. This invasive observation of people can often result in unintended social consequences and behavioral adjustments. There are also worries about privacy, who has access to it, and how the information obtained is being used. It is unwarranted to have such data protection and security issues regarding future technologies. The following describes blockchain technology in the digital business system.

3.2 Digital Business System and Blockchain Technology

During the existing enterprise space, a growing range of applications of blockchain is tested and applied in many sectors, including banking, insurance, and supply chain

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management. A digital transaction ledger is managed without a central broker, stable, immutable, and tamper-proof, and can be spread to all parties without a critical broker. A majority of parties have to authorize a new transaction before a new transaction is registered to verify a new transaction.

Blockchain should serve as a useful replicated transaction data store and replace centralized brokers operated by trustworthy individual authorities. Even though blockchain technology’s utility facilitates the revolutionary digital business system to improve the elimination of central intermediary operations, it provides restricted values in the digitalization and the automation of business processes just by benefitting from blockchain technology. To gain the benefit, it takes various techniques, including service-oriented Architecture, service workflow, and IoT. The value-driven digital business system architecture offered an explanation of blockchain technology that helps organization improvement. Digital business systems perform tracking of service characteristics that also ensure effectiveness in real-time. Instead, with the range of resources available, businesses face the task of choosing preferable services for the composition of workflows. Workflows for services promote this by offering simulation and reengineering business processes to simplify and automate processes according to workflow criteria. Algo- rithms for conformity monitoring immediately validate the specification. Service arrangements are considered part of company workflows. The implementation complexity for further research to turn the criteria definition into an intelligent contract and be used in the ledger. Blockchain technology is still in its development stage, whereby various platforms are innovating and developing their protocols in different directions.

Such protocols allow the sharing of data between the blockchain impossible. Digital enterprise systems include numerous distributed services and can be combined with several blockchains to fulfill their objectives. For instance, Oracle’s real-world data can be stored in various blockchains with varying data formats. Oracle blockchain Information is checked and processed on a blockchain to use Smart Con- tracts about real-world activities. Data security is a major public concern of blockchain, which guarantees no privacy of information. There is a deal between interoperability and anonymity in the situations of distributed process implementation. For example, whether or not the credit score of services located in a blockchain database affects the interoperability of services in the workflow phase. To mitigate this issue, entry, authority control, and management can be implemented and high overhead, particularly in the global context. All blockchain network members are, however, still able to obtain the information stored on the blockchain. The great diversity and plurality of linked services, particularly IoT-based services, are another security solution. Encryption technology can be used, yet again with high overhead. So

the proposed IoTE-DBS, along with blockchain technology, evolved. The following discusses the scope of blockchain technology and IoT in the digital business system.

3.3 Blockchain Technology and IoT in Digital Business System

In many fields, blockchain is being tested and often iden- tifies a link within the particular industry between blockchain and IoT. Smart arrangements and the enhancement of many systems, such as claim processing, are the primary usage cases of blockchain insurance. It is fascinating to understand the convergence of blockchain and the IoT in blockchain and IoT in businesses, going beyond the mere telematics paradigm to connect real-time IoT data in various perspectives for different smart, digital business systems. To address safety and security issues in the Internet of Things, blockchain systems are the connecting element. The golden bubble needed for the IoT-based business systems be the integration of blockchain technology. This allows billions of connected devices to be monitored, purchases processed, and system collaboration made available for the IoT ecosystem manufacturers. Such a collaborative solution can reduce failure points, providing a more durable environment for devices to operate. Blockchains will make user data private because of their cryptographical algorithms. The blockchain ledger is manipulative and cannot be exploited by hostile agents since it occurs in no area. Man-in-the-center attacks cannot be carried out since no single interceptive thread of communication exists. Blockchain allows reliable messages from peer-to-peer feasible. By cryptocurrencies like bitcoin, blockchain has already shown its value in the field of financial markets, offering secure payment services without the need for third-party brokers.

The blockchain’s autonomous, separate, and secure capa- bility renders it a perfect part of an IoT solutions base with its benefits, as shown in Fig. 1. The figure shows the various platforms and tasks depending on IoT and blockchain- enabled digital business systems. The approach reduces the collision and tampering risks and builds trust between the parties/devices. It reduces costs by removing overhead associated with intermediaries. It can accelerate transactions to a great extent by eliminating settlement time in days to instant.

It is not shocking that IoT technology from the company soon became one of the early backers in the blockchain.

The blockchain records the history of intelligent machines in an IoT network. It permits smart devices to work without central supervision independently. This opens up the door to multiple IoT situations, shockingly complex or perhaps unworkable without them. IoT solutions allow safe, stable communication between devices within an IoT network through blockchain.

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The blockchain manages messages over a bitcoin network in the given paradigm, similar to financial transfers, among equipment. To allow note exchanges, devices use intelligent contracts that form the relationship between both sides. The most exciting feature of blockchain is the prospect of main- taining the autonomous and trusted master of all IoT network transactions. It is necessary to be some unified model for the various compliances and regulatory specifications of Industrial IoT (IIoT). By offering a trustworthy sharing service where knowledge is secure and traceable, blockchain will enrich IoT. It is easy to classify data sources at any moment, and information remains constant over time and improves its protection. If IoT information can be exchanged safely by several participants, this integration would be a key revolution. Data leakage in some chain sections could lead to fraud and delay the infection quest processes that can significantly affect people’s lives and cost businesses, industries, and countries a great deal of money. Blockchain can also be used to connect secure and encrypted details to the IoT. It acknowledges that the application blockchain is the secret to addressing the IoT paradigm’s scalability, safety, and reliability issues. While implementing a blockchain, these connections’ position must be determined: the hybrid nature of IoT and blockchain within the IoT.

The fundamental facilities, structures and services on which the remaining business is constructed are business infrastructures. Infrastructure is commonly thought of as physical items the infrastructure may be considered fundamental software and services. A financial model is

a summary based on specific characteristics of a firm performance that helps predict future financial success. In other words, it allows a firm to view the probable financial results of choice quantitatively. A customer service model is a method to establish how companies handle unhappy consumers and complaints. The strategy should describe what is to be done in some scenarios. Com- munity-based business models acquire traction because enterprises seek to strengthen their customer participation activities with relevant and excellent customer experi- ences. This community business model mostly relies on community assistance for the acquisition and provision of customer support. All decisions come from the same site from centralized entities. Centralized governance is the organizational structure in which a limited few people decide on an enterprise. The environmental activity allows companies to celebrate their clients. If a firm incorporates its prospects into its sustainability processes, it makes consumers part of and feels closer to the company’s broader mission.

The blockchain in IoT network enterprise solutions has been radically different from public/crypto blockchains.

However, the integration of components and algorithms varies greatly, including consensus forming, ordering, etc., the basic principles remain the same. These solutions are used in a variety of processes by IoT and commercial IoT applications. Figure 2 shows the overall layout of blockchain integration in IoTE-DBS.

It creates transactions containing data or information and can be exchanged within or outside the local network by other IoT nodes. The membership service provider’s keys, signatures, credentials, and configurations are provided by the administrator and credential authority to each node. Whereas the peers in Fig. 2 are the IoT nodes with ample resources. They can run consensus algorithms and manage the distributed directory. The other participant, named orderer, is a node, which gathers all supported/

approved trades into a newly formed block. Lastly, the chain code is applied to peer nodes to verify transaction agreements among various IoT devices. The IoT devices in the digital business systems build transactions through special channels that are a private subnet between two or more of the participant in the form of previously used chain code/smart contracts. Any verified chain code exchange is deposited in a ledger as a part of a block made by a buying firm. The orderer waits for a certain period of batch time or blocks time for the new legitimate sector. The orderer shuts down the block and assigns the new block to all link pairs at the batch timeout. Each peer node checks his credentials and updates his respective directory. In this case, there is a basic presumption that the Certificate Authority and the Orderer have faith and protection.

Fig. 1 IoT and blockchain in digital business system

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3.4 Blockchain Design

It must be noted that blockchain represents a relatively young strategy, with very few real-world implementation projects. Figure 3 demonstrates the IoTE-DBS framework architecture suggested for blockchain adoption into an IoT network. Hyperledger Fabric is known as the basis for adoption. However, the paradigm laid out here is not limited to it and can even be combined with other solutions.

The entire blockchain procedure in IoT involves transaction origin, authentication and authentication, and commitment as its three stages. The initial IoT system access to blockchain nodes serves as peers or future supporters, as shown in Fig. 3. The trustworthy credential authority often exits the system with an administrator as part of a broad membership service provider (MP).

A device is an IoT device that can produce or receive blockchain transactions/trades within the proposed framework. An intelligent smartwatch, Mounted sensors, intelligent IoT decision-making, etc., are examples of IoT devices in the digital business system. The blockchain central network contains a node (or a peer). An instrument that will perform the consensus process and archive the ledger can even be considered. The IoT devices are attached to the nodes that process the trades from them. The nodes are classified into N1, N2, N3,…, N_n and Peers P1, P2, P3,…, P_n . The users in the IoTE-DBS are individuals who engage in the system; however, direct human intervention never exists in the proposed architecture except for the manager. Every IoT network can be controlled/operated by a human user, even

though there are no user interactions with the extraction, authentication, or storing of transactions in the blockchain process. As seen in Fig. 3, the administrator has a design to initiate the system and manage MP.

3.4.1 Stage 1: Transaction Origin

In a manufacturing system in smart environments, wearable IoT devices from various manufacturers produce data in different formats. For instance, temperature sensors or other controls on the vital automation devices may have numerous modules. Multi-structured data can be received and then conformed in a chain code executable format. Furthermore, such machines are resource-restricted and cannot thus operate themselves as blockchain nodes. They are therefore connected with a node N3, serving as a blockchain node. N3 can be preconfigured in any given configuration, or IoT devices may be programmed to find and secure a connection to the nearest. For electing the N3 node, it must have sufficient resources to perform the desired blockchain functions. IoT device applications gather data to be shared as a transaction/

trade. The software development kit is then formatted for chain code execution. The present trade proposal contains commercial data, including system signature, public address, and corresponding certificates, as payload. A quality management system on an assembly line, for example, may produce trades and include product statistics in blockchain data.

Therefore, the devices’ application interfaces with a certificate authority create the blockchain network’s registration certificates.

Fig. 2 Basic layout of blockchain integration in IoTE-DBS

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An administrator is the only authority that can accept integrations of IoT devices with chain codes, whereas all credentials are created by the certificate authority, as shown in Fig. 3. This credential information is issued for all the entities, including administrators, nodes, applications, and devices. A trade proposal is sent through the channel for execution since each system has a blockchain node connector.

Each developed application has a channel, which serves as a logical tunnel of communication between the application and the node. Transaction authorization and authentication are purely bound by the channel, meaning that the computer cannot view or perform trades forbidden on any specific channel. The channel initialization and management are the responsibility of the MP. This flow is worth remembering that several IoT devices are attached to one node. Each node is restricted to one trade to prevent double expenditure in one particular node.

3.4.2 Stage 2: Authorization and Authentication

The correct identification of devices and consumers, privileges, and so on depends on all incoming transaction

authorizations. Authentication is the next level after testing if trades are accredited according to the smart contract or chain code terms. Authentication is done for credentials, such as eCertificate and transport layer security certificate for entry. Two kinds of components, such as nodes and devices, are concerned in the IoT-DBS. The administrator seems to be a trustworthy authority with eCertificate, digital signature, transport layer, smart certificates, keys, and Certificate authority credentials. When the network is instantiated, it is created. To register new nodes/devices, the administrator object provides eCertified software. It further connects with the certification authority to join new nodes that can query or connect blocks to the ledger. It ensures that no unidentified user or computer can enter the network without proper authentication. On an administrator node, chain code is installed and instantaneously installed on a channel that fulfills the instantiation policy, including name and Version. The installation and initialization of a chain code in Hyperledger follow the same trade flow as the ordi- nary invocation, namely support, request, validation, and commitment. After implementation, modifications can cause a big security problem because commercial validation relies

Fig. 3 IoTE-DBS framework

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directly on the smart contract. In the whole process, chain code plays a key role as it enforces trade policy. Such regula- tions/policies mainly apply rules on the execution of trade among the participating devices. The proposal represents the same consensus as for particular firms with every adjustment to the intelligent contract. The above assumes that a smart contract transition takes place in at least 60% of nodes. Entry privileges are specified mainly by the channel and afterward the following smart contract file. The devices can communi- cate only with nodes over a channel where the MP-generated credential has to be used by both devices and nodes. When an administrator assigns the channel, nodes/devices that are not attached cannot be reached. The smart contract deter- mines which and how many neighbors search an exchange in current market blockchains.

Nevertheless, blockchain’s key idea is to create unity across a wide variety of nodes. Forty percent of total nodes in our proposed structure as nodes based on the exchange submission in a session retain overhead support just under crypto chains and are much more stable than those of the existing corporate business chains. Trades are recognized as legal trades in the current digital market networks by approved neighbors. In compliance with their timeout of lots, the purchaser receives simultaneous transactions from several nodes and generates an additional block. The endors- ing nodes N3 support the exchange by checking their signatures and intelligent certificates, as shown in Fig. 3. No validation or denial happens on the closure of a block, which means if a malicious node delivers a forged trade to the customer/orderer, it is attached to the block. Crypto coins and related models apply a consensus algorithm during block construction, which guarantees no malicious dealing in the ledger. However, as stated earlier, the transaction rate in IoT real-time systems cannot allow long delays in creating consensus. In the proposed model, IoT devices attached to the same node are not part of global consensus. For the transactions/trade between devices connected to different nodes, the participating nodes first support them, and perhaps several nodes engaged in the certain blocking session form the consensus.

Correlations give a good framework for managing effect sizes, describing the strength of the connection between two directional variables. Table 2 shows the values of effect size obtained from the studies considered. As the effect magnitude and value fall inside the range of − 1:0 to + 1:0, Pearson’s correlation "r" is employed. However, given the significant variation in sample size (N) for studies, the weighted average of correlations is chosen as a more suitable approximation. Thus, the sample size of the investigation is considered to be its weight for further analysis and the population correlation, variance and error owing to sampling were evaluated using conventional formulas as follows.

Population correlation estimate

As found in Eq. (1), population correlation has been identified, where O is an estimation of population correlation with weighted average, O_j is a j th sample of observed correlation, M_j is a j th sample of the amount of individuals, ∑M_j is sum of total individual samples.

Therefore, research results conclude that the utilization of the system and user satisfaction are positively connected. The strength of the connection is rather moderate. The variance observed is determined using a weighted sum of the squared to estimate real variance. Weighted variance sample (observed variance)

As shown in Eq. (2), observed variance has been deliber- ated, where T_O² is an average squared weighted frequency and the sampling error feature is a sample size.

As determined in Eq. (3) where 𝛼²

f is a sampling error estimation and L is a amount of results in this analysis. By sub- tracting variance estimates from the sampling error observed, the residual variance may be computed. The ‘residual difference’ is the difference due to the real discrepancies between several research outcomes.

As calculated in Eq. (4), residual variance has been identified. The estimated variation due to sampling error in this study (Form 3) was then removed from the difference seen and provided a residual variance (Formula 4). The difference between correlations might be viewed as sampling failures when the residual variance is sufficiently modest.

For O_l (where O_d is a mean effect size O for l studies, O_d is the main mean effect size o which may be deemed theoretic or practically relevant), the formula Hunter and Schmidt will be used to determine the number of ‘lost’ studies.

As suggested in Eq. (5), where L is a denotes the number of meta-analysis and Y explores the amount of analysis required for among O_l, O_d.

(1) O=∑ [

M_jO_j]

∕∑ M_j

(2) T_O² =

(

∑M_j(

O_j−O)²)

∕∑ M_j

(3) 𝛼²

f =

(( 1−O²

)² L

)

∕∑ M_j

(4) Residual Variance=T_O²−𝛼²

f

(5) Y =L(

O_l∕O_d−1)

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3.4.3 Stage 3: Commitment

The last stage of trade processing is commitment. This stage is entirely ready to be spread to all nodes in the trade network and added to its ledgers when the consensus completes a block. Whenever the consensus algorithm returns true, the orderer accepts this new block, distributed along with the orderer’s signature to all network-connected nodes. The signature is verified by all nodes, and blocks are added to the ledger, and the added block is synced with the leader, and the status of the entire business/transaction is finally updated.

4 Case Study Analysis

The evaluation of the proposed model IoTE-DBS was performed by analyzing a marketing business domain. The IoT device-based business model actively tracks customers’ preferences and gathers different data from consumers or end-users to ensure that customers access reliable e-services to mitigate sensitive circumstances. The intro- duced IoTE-DBS platform was evaluated using a remote control mechanism to handle and facilitate the ordering and promotion of consumers and service providers. The IoTE-DBS discussion was used to gather customers’ data relating to the IoT device for creating an optimized business model according to the business processes. Since the selected Marketing Domain has an IoT system for record- ing information in a consumer body. According to customer and service provision specifications, the gathered information is processed through remote control. The efficiency of the IoTE-DBS model for improved market functionality is discussed in this section. With NetBeans’

implementation tool, the generated business method is

built and the proposed conceptual model efficiency is addressed using various metrics and compared with various business models such as business to business (B2B), business to customer (B2C), customer to customer (C2C).

The efficient review of user specifications and business procedures thoroughly matches end-user requirements as shown in Fig. 4. The IoTE-DBS successfully fulfills the needs and requirements of its customers as it perfectly anticipates all business needs operations, core business elements, interfaces and delivery channels that minimize the complexity of the business process compared to other conventional processes, as shown in Fig. 4.

The proportion of IoTE-DBS value used in e-commerce gives accurate results. Figure 4 clearly shows that the proposed IoTE-DBS explores several components in the requested framework without extracting all details from each user request in different applications to optimize the overall market process. In contrast, other traditional management models are based solely on business customer- oriented concerns.

In contrast with other business process models such as B2B (86.31%), B2C (87.53%), and C2C (90.35%), the proven IoTE-DBS model guarantees 96.52% reliable business processes. A good analysis of customer specifications and business processes meets the end-user requirements in full, as seen in Fig. 5. Since the IoTE-DBS framework effectively forecasts the market domain, the information obtained must be close to the user-determined cri- terion calculated by the coefficient of Pearson correlation, which is computed as shown in Eq. (1):

(1) p_A,B= C(A, B)

S.D_A∗S.D_B

Fig. 4 End-user satisfaction

90 91 92 93 94 95 96 97 98

B2B B2C C2C IoTE-DBS

END-USER SATISFICATION RATIO

BUSINESS MODEL

(11)

where C(A, B) denotes covariance of the expected business process requirement values in the above Eq. (1). The standard deviation from requirement A is denoted by S⋅D_A , whereas S⋅D_B as the standard deviation from requirement B. A market requirement that is more pertinent to consumer demand predicted by the Pearson correlation coefficient is successfully evaluated by an IoT-E3 value model, and the result achieved is shown in Fig. 6

Therefore, a value of 0.97 Pearson correlation coefficient ratio was obtained by implemented the IoP-DBS model, most applicable to consumer specifications, as shown in Fig. 6. Compared with other business systems, including B2B (0.78), B2C (0.83) and C2C (0.87) business models, the corresponding value of IoTE-DBS value is higher. Furthermore, attempts to build the business model are required to accomplish a fruitful phase of growth. The

associated relative error ∈_r is then determined based on the following Eq. (2).

Fig. 5 Business process accuracy

80 82 84 86 88 90 92 94 96 98

B2B B2C C2C IoTE-DBS

YCARUCCASSECORPSSENISUB

BUSINESSMODELS

Fig. 6 Pearson correlation coefficient ratio

0.82 0.84 0.86 0.88 0.9 0.92 0.94 0.96

B2B B2C C2C IOTE-DBS

0.89

0.87

0.9

0.96

PEARSON CORRELATION COEFFICIENT RATIO

BUSINESS MODEL

Table 1 Relative error

Bold indicates the relative error comparison of four business process models

Business models Relative error

B2B 0.3123

B2C 0.2534

C2C 0.1132

IoTE-DBS 0.0543

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where the parameter E_a is the actual effort and E_e is the estimated effort. Based on Eq. (1), attempts are made to improve the business model to diminish the error present. Table 1 then explains the efficiency of the proposed framework.

In comparison, Table 1 shows how various business process models are reasonably error-free, in which each model predicts the exact precision of this business process.

Still, each model generates variation inaccuracy when designing the method. The gap from the real effort to the expected effort forecasts an error. The proposed IoTE- DBS model ensures that 0.0543 of the business process’s relative error value contrasts with other business models.

The experimental results show the IoT-DBS for high performance and increased cost-effectiveness to achieve the high user satisfaction, accuracy, Pearson correlation coefficient and relative error compared to the customer to customer, business to customer, business to business methods.

5 Conclusion

This paper analyzes the potential of business models, offers a conceptual context and discusses how do digital business models impact the IoT-based businesses, business success and markets. And eventually, this paper also analyzes the security aspect of the digital business system using IoT.

Therefore, blockchain technology’s scope and advantages on IoT-based Digital Business systems were documented in this article. The study proposed an integrated approach named IoTE-DBS to handles the risks and vulnerabilities in digital business systems. The case study analysis was performed by analyzing the marketing domain. IoTE-DBS model gathers business data according to IoT devices by gathering multiple forms of data used to analyze business processes effectively. Different characters, functions, and device values for handling user specifications are specified from the collected IoT data. The performance of the proposed model was then evaluated and compared with existing business models B2B, B2C, and C2C. In the future, it is planned to integrate machine intelligence and fog computing in IoTE- BDS. Provides a direction for future study to examine the mediation variables that may influence user satisfaction and system utilization. The links between system utilization and user satisfaction are better explained by resolving conflicting results in the prior study.

(2)

∈_r= E_a−E_e E_a

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