Blockchain Model for Tracking Employees’ Location in the Company’s Premises ^†

Abstract

In the ever-evolving world full of technologies, blockchain proves itself to be the most secure way of dealing with tampering of data. This paper proposes an innovative model for tracking employees within facilities using RFID, IoT devices and blockchain technology implemented on the Hyperledger Fabric platform. The blockchain system supports a secure and tamper-proof recording of employee movement because it keeps the data in a decentralized system. Smart contracts automate activities like control access, generate alerts and create audit trails without the need for centralized management. This implementation shows a high level of security and efficiency, making it a good approach to improve monitoring and compliance within organizations.

1. Introduction

Reportedly, almost 33% of employers track their employees’ physical location [1]. The growing demand for secure and effective monitoring of employees within an organization’s premises has led to the investigation of innovative technical solutions. Conventional approaches, which mainly depend on manual input systems or centralized databases are prone to errors, manipulation and inefficiency. In order to address these challenges, this work presents an innovative employee tracking system that combines RFID technology with blockchain, particularly using the Hyperledger Fabric platform. The use of RFID cards enables real-time monitoring of employee movements, with data being securely stored on a distributed ledger.

The use of blockchain technology guarantees that the tracking data remains permanent, accessible and resistant to unlawful modifications. Furthermore, the implementation of smart contracts on Hyperledger Fabric enables the automation of particular operations, such as access control, alert generating in case of emergencies and maintenance of an audit trail. In order to minimize the possibility of errors and enhance response times, these contracts function according to predetermined circumstances, therefore guaranteeing the execution of crucial security and operational tasks without human involvement.

The present study provides a comprehensive account of the system’s conception and execution, with particular emphasis on its architectural framework, the function of smart contracts and the integration of RFID technology into the blockchain. In addition to improving security and accountability within the facility, the suggested system offers a scalable solution that can be tailored to other areas. By reducing the limitations of conventional tracking techniques, this strategy provides an innovative solution to current issues in employee management and facility security.

2. Related Works

Several studies have investigated the usage of RFID technology for the development of secure and efficient tracking systems, particularly in the healthcare [2,3,4]. Other studies are mostly concentrated on the combining of RFID with blockchain technology for inventory management and supply chain optimization [5,6], emphasizing its efficiency in real-time monitoring and data gathering. The advancement of blockchain technology prompted researchers to explore its capacity to tackle the security and data integrity issues that are inherent in centralized systems. Recent studies have focused on investigating the application of blockchain technology to improve the security and transparency of RFID-based systems [7,8]. Additionally, blockchain’s potential for recording professional growth and external training has been demonstrated in educational contexts, hinting at the broader applicability of this model for employee lifecycle and qualification management [9]. These studies have shown the advantages of decentralized ledgers in preventing data manipulation and unlawful entry. Moreover, the application of smart contracts has been investigated in various contexts, demonstrating their capacity to automate procedures and ensure adherence without the requirement of intermediaries. These investigations establish the basis for future advancements at the intersection of RFID and blockchain technology, namely in the areas of employee tracking and facility management.

The paper [10] explores the application of Radio Frequency Identification (RFID) technology in hospital settings to enhance the management and tracking of medical assets, patients and medications. It emphasizes the advantages of RFID, particularly UHF (Ultra High Frequency) RFID, over other technologies due to its superior range and minimal hardware requirements, making it ideal for scalable deployment in hospitals. The paper details a proof-of-concept implementation that successfully demonstrates how RFID systems can reduce human errors, optimize resource usage and integrate with mobile applications for real-time monitoring. However, it also highlights the challenges related to signal interference and the need for thorough coverage analysis to ensure the system’s effectiveness in various hospital environments.

The research [11] presents a system that also leverages RFID technology to automate and improve the accuracy of employee attendance tracking in organizations. By issuing RFID tags to employees, the system allows for real-time logging of attendance, automatic calculation of working hours and salary generation, all while reducing the risk of fraud and human error inherent in manual systems. The system also includes features such as real-time notifications for policy violations and the ability to track employee movements across different zones within the workplace. However, all data is stored in a central database making it vulnerable to attacks.

The paper [12] discusses the implementation of RFID and IoT technologies in healthcare. The focus is on how these technologies can enhance the efficiency and safety of patient care by reducing paperwork and errors and how RFID devices can track and monitor patients’ movements within a hospital while providing instant access to their medical history. The research focuses on the benefits of RFID in improving healthcare delivery, particularly in monitoring and tracking patients. Table 1 presents comparison of the related works.

An example of an existing working non-blockchain employee tracking system is Time Clock Wizard [13], a cloud-based solution widely adopted by small to mid-sized businesses. It offers essential functionalities such as digital time tracking, GPS and photo verification, shift scheduling, payroll integration and customizable alerts. Its intuitive and accessible user interface facilitates rapid adoption, and its mobile application particularly benefits remote and field employees. However, the centralized nature of its data storage presents inherent vulnerabilities, making it susceptible to potential security breaches and data manipulation. Moreover, the system’s scalability may pose challenges for larger organizations with complex operational structures. Thus, while Time Clock Wizard effectively addresses basic workforce management needs, it highlights areas where blockchain-based solutions could significantly enhance data integrity, transparency and security.

Table 1. Comparison of the related works.

Paper	Purpose	Technology	Blockchain
[2]	Tracking patients	RFID/IoT	No
[3]	Tracking patients	RFID/WSN	No
[4]	Tracking patients	RFID/WSN	No
[14]	Patient monitoring	IoT/Blockchain	Ethereum
[15]	Tracking employees	Mobile phone/Android	No
[16]	Monitoring patients	IoT	Yes
[17]	Tracking assets	IoT/Blockchain	Ethereum
[18]	Tracking employees	GPS	No
[19]	Tracking employees	Mobile phone/Android	No

Table 1. Comparison of the related works.

Paper	Purpose	Technology	Blockchain
[2]	Tracking patients	RFID/IoT	No
[3]	Tracking patients	RFID/WSN	No
[4]	Tracking patients	RFID/WSN	No
[14]	Patient monitoring	IoT/Blockchain	Ethereum
[15]	Tracking employees	Mobile phone/Android	No
[16]	Monitoring patients	IoT	Yes
[17]	Tracking assets	IoT/Blockchain	Ethereum
[18]	Tracking employees	GPS	No
[19]	Tracking employees	Mobile phone/Android	No

Despite the promising advancements demonstrated by blockchain-integrated models, practical implementation often encounters challenges such as increased initial infrastructure costs, the complexity of managing distributed ledger networks and the requirement for robust training programs for system administrators. Potential scalability concerns in larger multi-branch environments might also arise, requiring further research into optimization strategies and efficient blockchain architectures tailored for high-volume operations.

3. Traditional Model of Tracking Employees

The traditional method of employee tracking within organizational premises typically involves a centralized system architecture, combining conventional identification technologies, manual data entry and administrative oversight (Figure 1). These systems, while functional, face critical limitations in terms of security, scalability, data integrity and automation.

3.1. Centralized Access and Attendance Systems

In a standard implementation, employees use identification cards (often magnetic stripe or barcode-based) to check in and out of work. Card readers are stationed at entry and exit points. Each scan logs the employee’s ID and timestamp into a centralized database maintained on-site or on a corporate server. This system may also support basic attendance features, such as:

• Logging entry and exit times;
• Calculating daily presence duration;
• Triggering alerts for late arrivals or early departures.

However, this data can be altered by administrators or compromised due to centralized vulnerabilities, leading to concerns over integrity and auditability.

3.2. Manual Attendance Reconciliation and Payroll Processing

Human resources (HR) departments typically manage employee timesheets and reconcile attendance data manually or via automated scripts. These records are pulled from the centralized database and used for:

• Verifying work hours;
• Calculating monthly attendance;
• Processing payroll.

Still, errors may occur due to database inconsistencies, incorrect card usage, or manual overrides. In cases of technical failure (e.g., power outage, server malfunction), data loss or duplication is common, affecting payroll accuracy and audit trails.

3.3. Emergency Protocols and Safety Limitations

In emergency scenarios (e.g., fire, evacuation), the system’s ability to identify personnel within the premises is limited. Traditional systems lack real-time zone-based monitoring. If an employee has not scanned out, it is unclear whether they remain inside or simply missed checkout. This absence of live positional tracking increases risk during critical incidents, as safety personnel (e.g., firefighters) lack updated location data.

Some organizations attempt to supplement this with CCTV footage or manual headcounts—methods that are time-consuming and less reliable.

3.4. Role of Organizational Departments

Human resources (HR) departments are responsible for maintaining attendance records, processing payroll and conducting audits. Their access to data is direct and modifiable.

IT departments maintain the database and ensure the uptime of card readers and servers. Troubleshooting access points or rectifying data discrepancies falls under their purview.

Security teams might have access to entry/exit logs but do not typically interact with work zone movement data unless integrated with surveillance systems.

These departments operate independently with minimal cross-verification. Audit trails are prone to tampering or accidental deletion, undermining accountability.

3.5. Data Privacy and Integrity Concerns

All employee movement data is stored in a centralized server, making it vulnerable to unauthorized access, internal manipulation or data breaches.

Unlike decentralized systems, there is no immutability or verifiable history of actions. The lack of cryptographic validation mechanisms renders the data vulnerable and insecure from both an internal governance and external compliance standpoint.

4. Proposed Model

The proposed blockchain-based employee tracking model is specifically tailored for businesses with a fixed number of employees who can consistently use RFID-enabled devices (such as badges or wearable bands). The model assumes a low concurrency rate, where employees pass through controlled access points individually, ensuring accurate recording of transitions between different zones. RFID readers are strategically placed at clearly defined entrances and exits within enclosed office environments, rather than open areas with multiple entry and exit points (e.g., parking lots, recreational zones).

Moreover, the tracking capability provided by the model is limited exclusively to the company’s premises (offices or branch locations) and does not extend to adjacent external areas such as parking zones, restrooms or designated smoking zones. The current implementation records only employee presence and movement. It does not capture physiological or psychological metrics such as heart rate or blood pressure, which could otherwise offer insights into individual workloads and stress levels.

Additionally, the model defines the scope and granularity of data recording: data is collected exclusively via RFID sensors installed at specific locations, at the exact moments employees pass through designated checkpoints. These data points are used primarily for attendance tracking, payroll calculation, emergency response planning and internal auditing purposes. Recorded data retention periods and their specific usage objectives must be clearly defined by organizational policies and compliance standards. Future access and reuse of these data are limited strictly to authorized departments (such as HR, IT or Security) within the organization, ensuring responsible and secure handling aligned with privacy regulations and organizational governance.

The proposed employee tracking system consists of three primary components: RFID readers, IoT-enabled data transmission and blockchain technology. These components work together to provide a transparent, tamper-proof and automated solution for managing employee work hours and ensuring their safety during emergency situations. Figure 2 shows visual interpretation of the proposed model and it is explained further.

4.1. RFID and IoT Integration

RFID readers are strategically placed at key points throughout the premises, including entrances, exits and different working zones. Each employee or visitor is issued an RFID badge (permanent, temporary or one-time), which is read by the RFID readers when passing these checkpoints. The data collected from these readers, including the employee ID and timestamp is transmitted via IoT devices to a cloud-based system. This data is continuously updated to ensure real-time tracking of employee and visitor movements.

4.2. Blockchain Technology and Smart Contracts

The core of the system is built on blockchain technology, ensuring the security and immutability of the data. Each time an employee or visitor enters or exits the premises, the corresponding RFID data is recorded on the blockchain. Smart contracts are programmed to calculate working hours automatically, using the timestamps from RFID reads to track entry and exit times. These contracts can also manage payroll processing by triggering salary payments on a specified date, based on the accumulated working hours of each employee.

Furthermore, smart contracts are used to enforce emergency protocols. For example, if an employee has not checked out by the specified end of the working day, the smart contract automatically records the employee’s last known location on the blockchain. In the case of an emergency event, such as a fire, the system records the last known location of all employees, ensuring that responders have up-to-date information on the locations of personnel.

4.3. Role of the Organizational Departments

Various organizational departments participate in the blockchain network, acting as stakeholders with specific permissions. For instance, the Human resources (HR) department can access working hour data for audits and payroll verification, while the IT department monitors the system’s functionality and ensures the correct operation of RFID readers and IoT devices. All interactions with the system are logged on the blockchain, providing a secure audit trail that enhances transparency and accountability.

According to the hierarchical logistic management model proposed in [20], the employee tracking system outlined in this paper is situated primarily at the lowest level—the transaction processing system (Figure 3) [20]. This layer is defined by its handling of high-frequency, real-time data and includes core activities such as delivery of materials, production, warehouse management and internal logistics operations. In this context, the employee tracking model aligns directly with the processing of routine, yet essential data: entry and exit timestamps, movement across zones and last known locations of personnel. These functions are executed via RFID and IoT technologies and are logged immutably on the blockchain, reflecting the same granularity and operational focus described for this base layer in the hierarchical model.

Moreover, the employee tracking model also relates to the third level, the decision support system, through its integration of smart contracts that calculate working hours, analyze attendance patterns and support safety-related decisions. The system enables subsystems such as working time analysis and personnel availability reporting, both of which are explicitly listed under this layer in the proposed model. These analytics inform higher-level decision-making processes by top management, especially in emergency response planning and compliance monitoring.

Although the system operates mostly within the operational and analytical scopes of the lower and middle layers, it does not extend to the second layer (management information system), where the focus is on tactical planning such as inventory levels or employee productivity metrics at an aggregated scale. Likewise, it does not reach the strategic management system (fourth level), which deals with macro-level tasks like network optimization or market expansion strategies.

In summary, the employee tracking model fits within the first and third layers of the proposed hierarchical logistic management model—functioning as a transactional subsystem that also contributes to higher-level decision support regarding workforce presence, security and compliance.

5. Implementation of the Model as a Hyperledger Fabric Network

The Hyperledger Fabric platform is used for the implementation of the model. All peers hold a copy of the channels to which they have access (in this case, there are two channels). Peers initiate transactions and send them to the nodes that store a copy of the blockchain and the current state of the data.

Figure 4 illustrates the implementation of the proposed model as a HF network. The dashed lines represent access to the channel in read-only mode, while the solid lines indicate that peers have both read and write access.

5.1. Org. 1—Company

Peer 1 and peer 2 are IoT devices aggregating data from Entry/Exit RFID readers. The time of entry and the time of exit are directly written in Channel 1. Smart contracts can be used to calculate monthly working time and trigger salary payment.

Peer 3, 4 and 5 are IoT devices aggregating data from RFID readers located on different zones at the organization premises. The physical location of the employees is not directly written on Channel 2, due to the large amount of data that can be generated, but a smart contract triggers the writing when a certain event or emergency occurs (for example in case of a fire). This way only the last known location of the employee will be written on the blockchain. Another example of an event is when the worker has not checked out at the exit and the working time is over. The smart contract then writes the last known location of the employee.

While combining entry/exit and internal location tracking in fewer peers could reduce complexity, we separated these functions to optimize resource utilization and ensure performance. Entry/exit peers handle continuous, small-volume transaction writes, whereas internal location peers selectively record data during critical events to avoid excessive ledger growth.

This IoT devices (entry/exit or location endpoints) primarily act as transaction submission entities. They are lightweight client applications (Fabric-SDK-Go) that sign and submit transactions to a nearby “edge-gateway” peer installed on commodity hardware with sufficient SSD capacity. The gateway peers maintain the full ledger and the client applications keep only their X.509 certificate.

Peer 6 and peer 7 represent different departments of the organization where monitoring is needed. For example, Human resources department can monitor working times of the employees, perform audits or IT department can monitor the proper working of the system.

5.2. Org. 2—Insurance Company

Peer 8 is the insurance company which can retrieve the needed data in case of an insurance event, being sure that the information is unaltered, because of the decentralized and immutable nature of blockchain.

The endorsement policy requires validation from multiple organizational departments, enhancing security and trust. The Orderer node, crucial for transaction ordering and consensus, is deployed redundantly to ensure high availability and reliable communication. MQTT over TLS or similar protocols can secure reliable real-time communications, particularly critical during emergencies.

6. Extension of the Proposed Model for a Multi-Branch Organizational Structure

The model presented in this work, while initially developed for deployment within a single organizational unit, can be extended to suit a multi-branch organizational architecture involving a central headquarters and numerous distributed branches (Figure 5). This extended model enables the implementation of decentralized monitoring and data capture at each branch, combined with centralized auditing, coordination and analytics at the corporate level.

Each branch operates as a separate organization within the Hyperledger Fabric network, equipped with its own set of RFID readers, IoT devices and peers responsible for recording employee activity. These peers write to dedicated branch-specific blockchain channels, such as branch_A_channel, branch_B_channel and so forth. Local data includes entry and exit times, movement through internal zones and event-triggered records for emergency cases or abnormal conditions.

At the same time, a central office (represented as a distinct organization in the network) is granted read-only access to all channels. This architecture preserves data autonomy at the branch level, while enabling the central authority to supervise processes across the organization. The central office may operate its own dedicated channel, central_channel, to aggregate selected data from each branch. This data could include processed summaries such as monthly working time reports, unclosed exit events or emergency location logs.

Smart contracts (chaincode) continue to play a key role at both levels:

• At the branch level, smart contracts handle:

  ○

  Automated calculation of working hours;

  ○

  Real-time emergency alerts and anomaly detection;

  ○

  Writing employee movement logs in case of events (e.g., failure to check out).

• At the central level, a different set of smart contracts is deployed to:

  ○

  Monitor branch-level compliance with attendance policies;

  ○

  Generate cross-branch reports for human resources;

  ○

  Perform analytics on workforce efficiency and safety protocol responsiveness.

To support employee mobility across branches, the system maintains a pseudonymous identity registry in which each employee is represented by a salted hash of their internal employee number. The linkage between the hash and personally identifiable information (PII) resides exclusively in the off-chain HR database. This pattern aligns with GDPR guidance on minimizing personal data on ledger and can be further strengthened with decentralized identifiers (DIDs) and verifiable credentials.

This extension ensures that the model remains scalable and modular, supporting not only organizational growth but also regulatory compliance and multi-site coordination. It enhances the traceability and accountability of human presence across a wide operational network while avoiding centralized data bottlenecks or single points of failure.

In the context of the proposed hierarchical logistic management model, this multi-branch extension positions each branch-level deployment within the Transaction Processing System layer, where detailed real-time data is captured. Simultaneously, the central office assumes responsibilities associated with the Decision Support System and Strategic Management System layers by conducting organization-wide workforce analysis, developing predictive strategies and responding to aggregated risk factors across sites.

Through this multi-level integration, the model maintains the core objectives of security, transparency and automation, while enabling full compatibility with enterprise-scale operational structures. Future improvements may focus on integrating machine learning modules at the central level for predictive risk analysis and optimizing resource allocation based on historical movement data and shift trends.

7. Test Results

As mentioned earlier, the model is implemented as a Hyperledger Fabric network. The peers are realized as Docker containers (Figure 6).

An administrative Docker container is used to test the functionality of the proposed model. The implementation of the business logic is carried out by a smart contract, which in the terms of Hyperledger is called “chaincode”. Every peer is implemented as Docker container. Figure 7 shows the successful execution of the invoke function which is used for writing information into the blockchain. “Result: status 200” proves the completion of the task.

As previously said, only certain peers have “Write” privileges in the channel. Most of them can only read data from the blockchain. Figure 8 shows executing a request for reading data from it.

The test results prove the functionality of the proposed model.

8. Test Results of the Extension of the Proposed Model

The tests are focused on the functionality of the extended model for employee tracking using Hyperledger Fabric. Three new peers are added to the network representing different branches presented in Figure 5 (Figure 9).

A new smart contract, daylog_1, has been developed. The contract is successfully installed and committed on the branch-a-channel, where it is tested for both recording and querying workday data (Figure 10).

For the simulation, several workday records were created for an employee with ID E101, with realistic check-in and check-out times (Figure 11).

The contract is then queried to calculate the total work duration for the month of April 2025, aggregating the hours worked based on multiple entries (Figure 12).

The system returned the expected total work time, confirming that the extended model functioned correctly in both recording and reporting scenarios (Figure 13).

To ensure a seamless workflow, extensive testing was performed to verify the accurate retrieval of aggregated work hours for any given employee, based on their recorded workdays within a specified month. This simulated process aligns with real-world applications of time tracking and reporting within decentralized networks.

9. Conclusions

The research presented introduces robust and secure blockchain-based architecture for tracking employees within organizational premises. By integrating RFID technology with IoT-enabled data transmission and utilizing the Hyperledger Fabric platform, the proposed model ensures data immutability, real-time monitoring and automation of critical tasks such as attendance tracking, payroll processing and emergency management. Hyperledger was chosen to demonstrate the proposed model. However, other blockchain technologies that support the implementation of smart contracts, such as IOTA, can also be used.

The architecture is further strengthened by the implementation of smart contracts which automate operations like working time calculations and location logging during anomaly events (e.g., unregistered check-outs or emergency situations). The system’s design reflects a practical shift from vulnerable, centralized solutions to decentralized, tamper-proof infrastructure that enhances organizational transparency, security and efficiency.

Notably, the paper moves beyond theoretical modeling and demonstrates the feasibility of the solution through a functional prototype built using Docker containers simulating peers in a Hyperledger Fabric network. Functionalities such as writing and querying blockchain data, calculating monthly working hours and tracking across multiple branches were successfully tested. The extension to a multi-branch model is particularly impactful, showing how decentralized monitoring at individual locations can be combined with centralized oversight and analytics—ideal for large-scale enterprises.

Future studies will include comprehensive performance evaluations of the proposed blockchain model, involving systematic stress-testing scenarios to evaluate key performance metrics such as transaction latency and throughput. This process will encompass deploying the Hyperledger Fabric network across multiple machines to realistically simulate distributed operational environments, thereby assessing system robustness under high transaction volumes and identifying potential limitations and areas for optimization in real-world scenarios. Moreover, future research will explore the integration of wearable sensors for monitoring employee vitals and stress levels during work. This approach will enable the system to distinguish between periods of activity and rest more accurately, facilitating enhanced productivity tracking. Such advancements aim to extend the model’s applicability beyond mere physical tracking, embedding it into the second layer of the hierarchical logistic management model, thereby broadening the system’s scope towards comprehensive workforce well-being, operational efficiency and productivity optimization.