Introduction

The rapid advancement of smart technologies, coupled with the proliferation of real-time sensor networks, has significantly impacted numerous domains. Smart buildings represent a critical sector that necessitates effective data management and automation systems. However, these systems face critical challenges that severely constrain their widespread adoption and effectiveness. Traditional blockchain systems, while revolutionary in terms of decentralization and security, suffer from fundamental inefficiencies, including high transaction fees, processing delays, scalability constraints, and resource inefficiencies. These systems rely on complex consensus mechanisms and extensive computational resources to validate transactions, which results in increased costs and delays. In the context of smart building environments, where rapid and frequent data exchanges between HVAC systems, security sensors, energy management devices, and occupancy monitoring systems are crucial, these issues are exacerbated, making the deployment of comprehensive smart building networks economically unfeasible.

Moreover, smart buildings face significant security vulnerabilities due to centralized architectures and inadequate authentication mechanisms, with 37.8% of smart buildings having experienced cyberattacks according to Kaspersky security research1 . The proliferation of IoT devices has created an expanded attack surface, where connectivity between a wide range of Internet of Things devices is crucial for smart building operations, but one vulnerable IoT device allows hackers to gain access, and it may be months before any malware they’ve introduced is discovered. Traditional centralized Building Automation Systems (BAS) create single points of failure, while conventional access control systems struggle with the scale and complexity of managing millions of connected IoT devices2.

Previous approaches have attempted to address these limitations through various methods, each with significant drawbacks that limit their effectiveness3,4. Off-chain transactions and layer-two scaling techniques5,6 aim to reduce the burden on the primary blockchain by handling some transactions off the main chain. While these methods have shown promise in reducing costs and improving throughput, they often introduce architectural complexity and potential security trade-offs that undermine the inherent advantages of blockchain technology. Alternative consensus algorithms like Proof-of-Stake (PoS) and delegated Proof-of-Stake (DPoS)7,8 have been proposed to lower transaction fees and improve scalability by reducing computational load compared to Proof-of-Work, but they introduce centralization risks and varying security levels. Most critically, existing solutions fail to provide comprehensive performance metrics demonstrating their effectiveness in smart building deployments, with critical evaluation parameters such as operational costs, network overhead, encryption/decryption time, and throughput under varying building occupancy and environmental conditions remaining inadequately addressed.

To address these critical limitations in smart building environments, this research investigates several key questions: How can building sensor nodes be integrated with Distributed Ledger Technology (DLT) networks to reduce transaction fees and improve efficiency in smart building operations? How can encryption techniques be employed to secure data transmission between building automation systems and DLT networks? How can DLT contract-based systems be utilized to automate and optimize the lifecycle management of smart building sensor nodes and devices? What are the potential benefits and performance improvements of this integration in different smart building applications, such as energy management, security, and occupancy control? These questions form the foundation for developing a comprehensive solution that addresses both technical and economic barriers preventing widespread DLT adoption in sensor networks.

This research presents a novel approach that addresses the fundamental problems of high transaction fees and inefficiencies in traditional blockchain systems through innovative integration of sensor nodes with DLT networks. The major contributions include a zero-fee sensor-DLT integration architecture that directly integrates sensor nodes with the IOTA Tangle distributed ledger, eliminating transaction fees while maintaining interoperability with existing sensor systems and achieving a reduction of operational costs to zero for transaction processing. The research implements an automated lifecycle management system using contractual logic on IOTA Tangle for comprehensive sensor node lifecycle management, enabling automated registration, status tracking, and state transitions with high operational integrity while supporting complex state changes such as maintenance mode activation, compromise reporting, and proper decommissioning protocols.

Additionally, the solution incorporates a high-performance security framework employing the Fernet symmetric encryption scheme optimized for smart building IoT environments with comprehensive exception handling, maintaining security integrity under varying building operational conditions with minimal latency impact and robust vulnerability mitigation while preserving real-time performance characteristics required for building automation. This DLT based approach addresses the fundamental authentication vulnerabilities such as single point of failure, high transaction processing fee identified in current smart building systems by implementing decentralized identity management and eliminating reliance on centralized authentication servers that create single points of failure. The implementation provides a scalable smart building integration platform designed for intelligent building management and automated infrastructure systems, efficiently handling large volumes of building sensor data with linear scalability characteristics and offering a future-ready platform capable of adapting to emerging smart building technologies and energy management requirements.

The novelty of this research lies in its integrated approach that simultaneously addresses transaction costs, processing efficiency, and security requirements through a unified DLT-sensor architecture specifically tailored for smart building environments. Unlike previous solutions that focus on individual aspects, this work provides a comprehensive framework that demonstrates measurable improvements across all critical performance metrics, including network overhead and encryption/decryption time in building automation contexts. By combining building sensor networks with DLT technology and implementing advanced encryption, this solution not only enhances the performance of smart building systems but also sets a new standard for DLT integration in real-time building management applications. The impact of this research extends beyond technological advancements, providing a practical solution for transforming data management in smart buildings and intelligent infrastructure.

Related works

Recent research in blockchain-IoT integration has focused on several key areas, with security and privacy frameworks emerging as a primary concern. Padma and Ramaiah (2024)9 introduced the SecPrivPreserve framework, which employed OTPs, encryption, and hashing mechanisms to enhance IoT data security in smart city applications. While their permissioned blockchain approach demonstrated improved responsiveness and encryption quality, its restricted nature limits broader application. Building on this foundation, Eghmazi et al. (2024)10 developed a Blockchain as a Service (BaaS) solution utilizing Hyperledger Fabric v2.x, implementing a novel architecture with public-private key encryption. Though effective for data security, their solution faced challenges in deployment complexity and ecosystem integration. Chaira et al. (2024)11 took a different approach by combining local and public blockchains for IoT security, particularly focusing on device authentication, though the dual blockchain management introduced additional complexity considerations. The integration of machine learning and artificial intelligence with blockchain-IoT systems represented another significant research direction. Mishra and Chaurasiya (2024)12 developed a hybrid model combining LSTM and SVM algorithms for transaction security, achieving high accuracy rates despite limitations in preprocessing requirements. Chen et al. (2024)13 extended this concept to traffic management, creating a comprehensive system that integrated TD3 reinforcement learning with blockchain for urban traffic optimization. Similarly, Li et al. (2024)14 proposed a reputation-based sharding scheme enhanced with deep reinforcement learning, demonstrating improved throughput while acknowledging potential scalability challenges in complex environments. Domain-specific applications have emerged as a crucial area of blockchain-IoT research, with several notable implementations. Kharche et al. (2024)15 focused on developing Intelligent Transportation Systems in India, imposing blockchain and IoT for traffic optimization and resource management. Medina and Rojas-Cessa (2024)16 introduced AMI-Chain, a specialized solution for smart-city power grids that innovatively used IPFS for off-chain storage, supporting over 7.7 million smart meters while maintaining high data durability. Garg et al. (2024)17 explored the integration of Drones-as-a-Service with blockchain and IoT, though their work highlighted significant implementation challenges in real-world scenarios. Cryptographic innovations and consensus mechanisms have also seen substantial development. Ma et al. (2024)18 investigated the integration of NTRU and McEliece cryptosystems with blockchain, offering quantum resistance capabilities while struggling with computational overhead challenges. Padma and Ramaiah’s second study (2024)19 introduced a confidence-based consensus mechanism utilizing Grey Wolf Optimization, achieving a 12.5% increase in throughput and an 18.3% reduction in energy consumption, though questions remained about its effectiveness in large-scale deployments. Architectural approaches have evolved significantly, with a notable distinction emerging between traditional blockchain implementations and IOTA-based DLT in IoT environments. The IOTA platform’s DAG topology offers distinct advantages through its elimination of mining requirements and transaction fees, making it particularly suitable for IoT applications. This architectural evolution was further explored in Sisi and Souri’s (2024)20 systematic review, which categorized blockchain approaches into private, public, consortium, and hybrid models, providing a comprehensive framework for understanding different implementation strategies. The comparative analysis of these approaches revealed several key insights. Performance metrics varied significantly across implementations, with solutions like AMI-Chain demonstrating impressive scalability in specific applications, while others showed improvements in energy efficiency or security resilience. Architectural trade-offs between permissioned and permissionless systems, on-chain and off-chain storage, and various consensus mechanisms continue to influence system design decisions. Implementation challenges persist across all approaches, particularly in areas of scalability, integration complexity, and resource constraints, suggesting that while significant progress has been made, opportunities for improvement remain in creating fully robust and scalable blockchain-IoT solutions. Table 1 discusses the comparative analysis of related Blockchain-IoT integration approaches. While existing blockchain security frameworks suffer from high computational overhead and slow response times, the proposed DLT approach with IOTA Tangle shows performance with low latency (0.0168s) and fast cryptographic operations (0.000174s encryption, 0.000162s decryption). This represents a performance improvement, achieving sub-millisecond cryptographic processing and reducing response times compared to traditional blockchain approaches, while maintaining power consumption of 2.4190W for the IoT gateway coordinator under normal network conditions. These results directly address the critical scalability and energy efficiency challenges inherent in traditional blockchain-based IoT deployments.

Table 1 Comparative Analysis of Blockchain-Based Security Frameworks.
Full size table

Methods

This section discusses the proposed methodological approach for this study, as illustrated in Fig. 1. The figure provides a visual representation of the research design and procedural framework that can be employed to address the issues.

Fig. 1
figure 1

Architecture of Proposed Work.

Full size image

Sensor nodes in smart building

Smart buildings have revolutionized the way people interact with their built environment, employing IoT technologies to improve efficiency, comfort, and security21. In the heart of these systems lies a network of sensor nodes, including temperature, humidity, occupancy, light, air quality, and energy consumption sensors22. These nodes collect data, perform local processing, communicate wirelessly, and manage energy consumption. However, increasing concerns about data privacy and security require robust measures to protect sensitive information and maintain secure communication channels23. To ensure secure communication, smart building systems employ various strategies. Strong encryption algorithms are used to protect data both in transit and at rest, with end-to-end encryption implemented for particularly sensitive information. Additionally, privacy preservation is crucial in smart building systems24 due to the sensitive nature of data collected by sensor nodes, which can include occupancy patterns, energy usage, and even biometric information. To evaluate the efficacy of smart building systems in a cost-effective manner, this study utilized a simulation setup environment. This compact and affordable setup served as a prototype for larger-scale implementations, allowing the collection and analysis of environmental data crucial to smart building operations.

Local gateway and device registration

Local gateways serve a crucial function in the integration of IoT devices with DLT networks by acting as intermediaries that bridge technological disparities25. They facilitate the connectivity between resource-constrained IoT devices and complex DLT systems, performing tasks such as protocol translation, data aggregation, and security implementation26. During the device registration process, gateways assume the responsibility of managing device identities, initiating registration transactions, and attesting to the authenticity of devices27. Additionally, they enhance resource efficiency by offloading DLT-related computational tasks from IoT devices and provide offline functionality when required28. Through the execution of these essential functions, local gateways enable even elementary IoT devices to engage with and gain advantages from DLT networks, thereby utilizing the security, transparency, and decentralization offered by these systems29. The local gateway functions as an intermediary between the IoT sensor node and the DLT network. When an unregistered device attempts to connect, the local gateway initially identifies it as unknown and initiates the onboarding process. It temporarily isolates the device in a secure network segment to prevent unauthorized access to the primary network. The gateway subsequently attempts to authenticate the device utilizing any pre-shared credentials or certificates that may have been provided during the manufacturing process. Should this attempt fail, it may require manual intervention, requiring an administrator to verify and approve the device. Upon successful authentication, the gateway assigns a temporary identifier to the device and collects its metadata, including the device type, firmware version, and capabilities. Thereafter, it initiates the formal registration process with the DLT network, establishing a new identity for the device and submitting a registration transaction.

Security and privacy

Encryption is crucial for ensuring the security of communication between sensor nodes and the IOTA Tangle. It helps address certain important security issues that are inherent in distributed systems30. Encryption primarily guarantees the confidentiality of data, effectively preventing unauthorized individuals from accessing sensitive information while it is being transmitted. This is especially important in situations involving personal, financial, or proprietary data. Furthermore, encryption plays a crucial role in ensuring the integrity of data by enabling recipients to confirm that the information has not been modified during transmission, thus preserving the reliability of the IoT network. Within the framework of the IOTA Tangle, where data immutability is a fundamental characteristic, guaranteeing the precision of data prior to its permanent recording is of utmost importance. This study focuses on integrating and using the Fernet encryption technique with IOTA. The use of Fernet encryption31, which is a type of symmetric authenticated cryptography, offers an effective method to ensure the security of data in the sensor nodes that communicate with the IOTA Tangle. In Fernet encryption, a combination of the Advanced Encryption Standard (AES) in Cipher Block Chaining (CBC) mode, coupled with a Hash-based Message Authentication Code (HMAC) using SHA256, provides data security in the decentralized system. This study uses fernet encryption to secure the communication between the sensor node and the authentication system before transmitting data to the IOTA Tangle. In the first step, keys are generated. The Fernet key K is a 32-byte (256-bit) key, composed of a 16-byte signing key (SK) and a 16-byte encryption key (EK)32.

Fernet Encryption and Decryption Process

Key Generation

$$\begin{aligned} K = SK \parallel EK \end{aligned}$$
(1)

where \(\parallel\) denotes concatenation, SK is the signing key, and EK is the encryption key.

Encryption process Let M be the plain text message from the sensor node.

  1. 1.

    Generate a random 128-bit Initialization Vector (IV):

    $$\begin{aligned} IV \in \{0,1\}^{128} \end{aligned}$$
    (2)
  2. 2.

    Generate a current timestamp (T):

    $$\begin{aligned} T = \text {current\_time()} \end{aligned}$$
    (3)
  3. 3.

    Encrypt the message:

    $$\begin{aligned} C = \text {AES-128-CBC-PKCS7}(EK, IV, M) \end{aligned}$$
    (4)
  4. 4.

    Construct the versioned ciphertext (VC):

    $$\begin{aligned} VC = 0x80 \parallel T \parallel IV \parallel C \end{aligned}$$
    (5)
  5. 5.

    Calculate HMAC:

    $$\begin{aligned} H = \text {HMAC-SHA256}(SK, VC) \end{aligned}$$
    (6)
  6. 6.

    Final Fernet token (F):

    $$\begin{aligned} F = \text {Base64}(VC \parallel H) \end{aligned}$$
    (7)

The sensor node transmits F to the authentication system.

Decryption process at authentication system

  1. 1.

    Decode the received Fernet token:

    $$\begin{aligned} VC \parallel H = \text {Base64}^{-1}(F) \end{aligned}$$
    (8)
  2. 2.

    Verify HMAC:

    $$\begin{aligned} \text {If } \text {HMAC-SHA256}(SK, VC) \ne H, \text { reject the message} \end{aligned}$$
    (9)
  3. 3.

    Extract components:

    $$\begin{aligned} 0x80 \parallel T \parallel IV \parallel C = VC \end{aligned}$$
    (10)
  4. 4.

    Verify timestamp:

    $$\begin{aligned} \text {If } |\text {current\_time}() - T| > \Delta t, \text { reject the message} \end{aligned}$$
    (11)

    where \(\Delta t\) is the allowed time difference

  5. 5.

    Decrypt the message:

    $$\begin{aligned} M = \text {AES-128-CBC-PKCS7}^{-1}(EK, IV, C) \end{aligned}$$
    (12)

The authentication system serves as a gatekeeper for sensor node interactions with the DLT network. It verifies the sensor node’s metadata against authorized records. Only sensor nodes with matching metadata are granted permission to communicate with the DLT network. If a mismatch is detected, the sensor node is denied access to the network.

Distributed ledger technology

Distributed Ledger Technology (DLT) is a decentralized system that allows the recording and synchronization of data between numerous nodes in a network, eliminating the need for a central authority33. This technology serves as the foundation for different blockchain implementations and cryptocurrencies, although its uses go beyond these specific areas. DLT employs cryptographic techniques to guarantee the integrity of data and uses consensus procedures to verify and record transactions. The IOTA Tangle provides significant advantages over traditional blockchain technologies through its groundbreaking feeless transaction structure, enabling cost-effective deployment of IoT sensor networks.

IOTA tangle integration

The IOTA Tangle represents a significant innovation within the field of DLT, offering a distinct alternative to traditional blockchain architectures. Developed by the IOTA Foundation, the Tangle utilizes a DAG structure, where each transaction validates two previous transactions, creating a web-like network of interconnected data points34. For this study, IOTA Tangle is integrating with sensor nodes in smart buildings using the distinctive structure of Tangle to improve security and efficiency. IOTA utilizes a low-complexity cryptographic hash function known as Curl-P, which is specifically engineered for devices with limited resources. This enables effective encryption on sensor nodes. IOTA employs the Masked Authenticated Messaging (MAM) protocol for authentication. MAM is a data communication protocol that operates on a second layer and enables the transmission of encrypted and authenticated data streams over the Tangle35. The integration procedure commonly demands including IOTA libraries into the firmware of sensor nodes, thereby facilitating direct contact with the Tangle. This method enables the safe and unalterable storing of sensor data and simplifies machine-to-machine communication without relying on centralized servers36. Nevertheless, there are still obstacles to overcome in order to enhance the energy efficiency of cryptographic operations on sensor nodes with restricted resources, as emphasized by this study37 in their comparative analysis of DLT solutions for IoT. Notwithstanding these difficulties, the absence of fees and the ability to handle large amount of data make the IOTA Tangle an appealing choice for implementing extensive sensor networks in smart city contexts, where ensuring the accuracy and secure transmission of data are of utmost importance38.

Data storage and management

In the data storage and management, sensor data undergoes a multi-stage process before integration into the Tangle. Initially, raw sensor data is preprocessed, typically involving noise reduction and aggregation to optimize transmission efficiency. Before storage, the data is encrypted using the Fernet symmetric encryption scheme. This encryption step ensures the confidentiality and integrity of the sensor data, crucial in IoT environments where data sensitivity is most important. The encrypted payload is then encapsulated within an IOTA transaction, which includes metadata such as the sensor’s unique identifier and timestamp. This transaction is subsequently attached to the Tangle through the node selection and Proof-of-Work processes intrinsic to IOTA’s consensus mechanism39. For data retrieval, authorized entities can query the Tangle using the transaction hash or MAM channel ID, subsequently decrypting the Fernet-encrypted payload using the appropriate key. This approach not only ensures end-to-end security but also facilitates scalable and tamper-resistant data management, particularly beneficial in large-scale IoT deployments where data integrity and access control are critical40.

Algorithm 1
figure a

IoT Device Lifecycle Management on IOTA Tangle.

Full size image

Device lifecycle management using contractual logic on the IOTA tangle

The contract-based device lifecycle management system using the IOTA Tangle represents a promising approach to IoT device governance. The IOTA Tangle, a DAG-based distributed ledger, provides a feeless and scalable infrastructure that is particularly well-suited for IoT applications. In this context, the contractual logic is implemented to interact with the Tangle, storing transaction records as immutable data while keeping business rules in the application layer41. This implementation enhances device lifecycle management by focusing on core state transitions throughout a device’s existence.

During the onboarding process, this system enables automated registration with comprehensive record-keeping. During the operational phase, it facilitates status tracking, state management, and activity logging. The implementation maintains an immutable record of device status changes and critical events on the Tangle. If security issues arise, the system supports transitioning devices to maintenance or compromised states, with appropriate handling protocols. During the decommissioning phase, it records the termination of device operation and maintains the complete historical record.

To ensure system integrity, this study implementation incorporates robust security measures that protect against vulnerabilities. A strict state transition validation mechanism ensures that devices can only move between predefined states (e.g., from REGISTERED to ACTIVE, but not directly from REGISTERED to MAINTENANCE), with each transition verified against an explicitly defined transition matrix. This controlled state management prevents unauthorized state changes that could compromise the device ecosystem. Additionally, all operations implement comprehensive exception handling to maintain system integrity during failures.

The implementation incorporates dual verification through a mechanism that validates device status across both local database records and the immutable Tangle ledger, automatically resolving inconsistencies by prioritizing the Tangle record as the source of truth. Each transaction includes timestamps and is published to the Tangle with transaction data. The system’s comprehensive logging creates an audit trail of all operations, including failed attempts, providing transparency for security analysis. The Tangle-based registry maintains device history through the entire lifecycle, enabling forensic analysis when security incidents occur, while also providing a foundation for auditing and lifecycle verification through the transparent and immutable nature of the Tangle ledger.

Workflow of the proposed approach

Algorithm 1 presents a detailed framework for managing the lifecycle of IoT devices on the IOTA Tangle, organized into three distinct phases. In the initial Device Registration Phase, foundational security is established through the creation of a secure client connection, the initialization of a database, and the configuration of the device registry alongside the lifecycle contract. The device undergoes a formal registration process, specifying its deviceType and associated metadata, with Fernet encryption mechanisms being employed to ensure secure data exchange. Upon activation, the Operational Phase focuses on continuous monitoring of the device by gathering sensor data and verifying its validity. This phase integrates encryption protocols for secure data transmission, considers network latency variables, and keeps the registry updated with timestamps of device activity. The process of response management encompasses decryption tasks and the revision of performance metrics. It includes conditional branches that address maintenance requirements and identify security violations, with the latter potentially triggering decommissioning actions when warranted. The final analysis and reporting phase produces comprehensive performance reports based on accumulated metrics and retrieves the complete device history, facilitating thorough analytical assessments. This algorithmic approach provides a robust, security-centered framework for the management of IoT devices throughout their operational lifecycle within the distributed ledger architecture of the IOTA Tangle.

Results

In this section, the experimental setup and experimental outcomes are discussed as follows:

Experimental setup

An experimental setup was implemented to evaluate the proposed IoT security framework under realistic network conditions. Table 2 outlines the comprehensive configuration parameters used throughout the evaluation process.

Table 2 Experimental Setup and Configuration.
Full size table

Results and analysis

The performance of the proposed system was evaluated using several key parameters, which are discussed as follows:

Encryption and decryption time

Figures 2 and 3 present a comparative analysis of encryption and decryption performance at selected transaction points (10, 50, 200, and 500) under both normal and congested network conditions. The most notable observation is the significant performance deviation in normal network encryption at transaction 1, where execution time peaks at 0.000402 seconds, approximately 131% higher than the average encryption time of 0.000174 seconds. This substantial initial overhead likely corresponds to the just-in-time (JIT) initialization phase of the cryptographic protocol, involving key schedule computation, memory allocation, security parameter initialization, and cryptographic context establishment. A noteworthy insight presented in Fig. 2 is the consistent performance across most transaction points, with encryption and decryption times remaining relatively stable after the initial setup phase. The consistency of both encryption and decryption performance under normal network conditions at transactions 50, 200, and 500 (ranging between 0.000148-0.000173 seconds) indicates algorithmic stability following the initialization phase. Figure 3 explains the temporal dynamics of cryptographic operations throughout the entire sequence of 500 transactions. Under normal network conditions, both encryption and decryption operations demonstrate higher initial overhead followed by performance stabilization, with execution times predominantly ranging from 0.00015 to 0.00025 seconds after initialization. Both processes exhibit similar performance characteristics throughout the transaction sequence, typically requiring between 0.00015 and 0.00025 seconds per operation. The lower graph in Fig. 3, representing congested network conditions, reveals similar behavioral patterns to normal conditions. While initial performance shows the same initialization overhead, the steady-state performance remains consistent throughout the transaction sequence. Both encryption and decryption maintain stable performance around 0.00015-0.00025 seconds, suggesting that network congestion has minimal impact on cryptographic operation performance. The first measurement points marked with red stars in both panels highlight the initialization overhead, confirming the JIT optimization hypothesis. The rapid stabilization of both encryption and decryption performance after the first few transactions in both network conditions indicates efficient system resource allocations and cryptographic context establishment. Despite potential congestion effects, the system maintains excellent and consistent operational performance, demonstrating IOTA’s cryptographic resilience capabilities and providing valuable insights for optimizing deployment strategies in different operational environments.

Fig. 2
figure 2

Encryption and Decryption Time at different transaction numbers and scenarios.

Full size image
Fig. 3
figure 3

Normal and Congested Network: Performance over Transactions.

Full size image

Network transaction throughput and latency

Figures 4 and 5 provide a comprehensive comparative analysis of IOTA network performance metrics under normal and congested network conditions, revealing significant performance disparities between these operational states. Figure 4 demonstrates transaction throughput measurements, showing that under normal network conditions, the system maintains remarkably consistent performance with mean and median values of approximately 0.331 transactions per second, exhibiting minimal variation across all statistical measures. In contrast, congested network conditions reveal substantially reduced throughput performance, with mean and median values dropping to approximately 0.106 and 0.108 transactions per second, respectively, representing a performance reduction of approximately 68% compared to normal conditions. Figure 5 presents network latency measurements that demonstrate an inverse relationship with throughput performance. Under normal network conditions, latency exhibits exceptionally low values with mean and median measurements of 0.0168 and 0.0165 seconds, respectively, maintaining consistent performance with maximum deviation reaching only 0.0262 seconds. However, congested network conditions show dramatically elevated latency values, with mean latency increasing to 0.3735 seconds and median latency reaching 0.2374 seconds. The maximum latency under congestion peaks at 2.4245 seconds, representing approximately a 93-fold increase compared to normal conditions. The substantial difference between mean and median latency values under congestion (0.3735 vs 0.2374 seconds) indicates a right-skewed distribution, suggesting the presence of extreme latency events that significantly impact overall network performance. The performance relationship between normal and congested conditions reveals critical insights into IOTA’s network behavior under stress. The disproportionate impact on latency compared to throughput suggests that network congestion primarily manifests as increased transaction confirmation delays rather than complete processing failures. Under normal conditions, the system demonstrates optimal performance with throughput values around 0.33 transactions per second and latency measurements below 0.02 seconds. The congested network maintains functional operation despite performance degradation, processing transactions at reduced rates while experiencing significant latency increases.

Fig. 4
figure 4

Throughput at different Transaction Numbers and Scenario.

Full size image
Fig. 5
figure 5

Network Latency at different Transaction Numbers and Scenario.

Full size image

Power consumption

Figure 6 presents a comprehensive analysis of power consumption measurements across normal and congested network conditions, revealing important insights into IOTA’s energy efficiency characteristics under varying network loads. The data demonstrates that congested network conditions result in measurably higher power consumption compared to normal operations, with mean consumption increasing from 2.4190 watts under normal conditions to 2.7139 watts under congestion, representing an approximately 12.2% increase in energy utilization. The median values follow a similar pattern, showing an increase from 2.4166 watts to 2.7142 watts, while maximum power consumption peaks reach 2.8086 watts during congested conditions compared to 2.4748 watts under normal operation. The elevated power consumption during network congestion can be attributed to several factors inherent to distributed ledger operations under stress conditions. During congested periods, nodes must maintain active processing states while handling increased retry attempts, packet retransmissions, and extended timeout periods. The system continues to consume computational resources for transaction validation, cryptographic operations, and network communication protocols even when effective throughput is significantly reduced. This sustained activity level prevents the system from entering lower-power operational modes that might otherwise be available during periods of reduced transaction volume. Additionally, the increased network communication overhead associated with congestion management protocols, including consensus mechanisms and node synchronization processes, contributes to the elevated power consumption observed in the measurements. When analyzing power efficiency in conjunction with the performance metrics demonstrated in the previous figures, a significant disparity emerges between normal and congested network operations. Under normal conditions, the system achieves approximately 0.137 transactions per second per watt (0.331 transactions/second ÷ 2.4190 watts), while congested conditions result in dramatically reduced efficiency of approximately 0.039 transactions per second per watt (0.106 transactions/second ÷ 2.7139 watts). This represents a 72% decrease in energy efficiency during congestion, indicating that the network not only experiences reduced performance but also operates at substantially higher energy cost per transaction processed. The combination of increased power consumption and decreased throughput during congestion highlights the importance of network optimization strategies for maintaining both performance and energy efficiency in IOTA implementations.

Fig. 6
figure 6

Power Consumption at different Transaction Numbers and scenerio.

Full size image

Discussion

In this section, the proposed system’s key findings and how it follows the industry standards and practical implementation are presented.

Observations

Based on the experimental analysis and comparative analysis, several key observations highlight the strengths and contributions of our approach:

  1. 1.

    The experiments in this work indicated distinct performance characteristics for cryptographic operations. Under typical conditions, both encryption and decryption processes exhibited similar and consistent processing durations ranging from 0.00015 to 0.00025 seconds, particularly demonstrating stable performance post-initialization. Both processes displayed similar initialization overhead at the first transaction, with encryption showing the most significant initial peak at 0.000402 seconds. After initialization, both encryption and decryption maintain consistent performance with minimal variability, indicating a well-balanced cryptographic implementation rather than asymmetric behavior.

  2. 2.

    The system demonstrates significant initialization overhead, notably observed during the first transaction with encryption reaching 0.000402 seconds (approximately 131% above average). Performance quickly stabilizes after the initial few transactions, with both encryption and decryption maintaining consistent performance throughout the remaining transaction sequence. The rapid stabilization of performance trajectories within the first 10-20 transactions signifies efficient system resource allocation where JIT optimizations achieve equilibrium.

  3. 3.

    The power consumption analysis reveals significant differences between network conditions, with congested networks consuming substantially more energy than normal operations. Normal network conditions maintained a mean power consumption of approximately 2.4190 watts, while congested conditions increased consumption to approximately 2.7139 watts, representing a 12.2% increase in energy utilization. This elevated power consumption, combined with dramatically reduced throughput (from 0.331 to 0.106 transactions per second), results in a 72% decrease in energy efficiency during congested conditions, demonstrating the substantial energy cost of network congestion in distributed ledger systems.

  4. 4.

    Although the experimental results indicate relatively stable performance across most metrics under normal conditions, it is vital to recognize prospective challenges in practical applications. The system’s performance may be significantly affected by external variables, such as differing network infrastructures, geographical dispersion of nodes, and heightened transaction volumes. The dramatic performance degradation observed under congested conditions (68% reduction in throughput and up to 93-fold increase in maximum latency) underscores the importance of additional testing within a range of operational scenarios to thoroughly assess the system’s robustness and scalability potential.

  5. 5.

    The performance metrics collectively demonstrate a system that operates efficiently under normal conditions but experiences substantial degradation during network congestion. The comprehensive analysis reveals that congested networks exhibit markedly inferior performance across all measured parameters: reduced transaction throughput, increased latency (with maximum values reaching 2.4245 seconds), and elevated power consumption. These findings highlight the critical importance of implementing effective congestion management strategies and network optimization techniques to maintain both performance and energy efficiency in IOTA implementations under varying operational conditions.

Compliance with industry standards and practical implementation

In this work, the proposed system aligns with several established IoT security standards and protocols as follows:

  1. 1.

    Encryption, Device Identification and Monitoring: The implementation of Fernet symmetric encryption (AES-128 in CBC mode with PKCS7 padding) provides authenticated encryption that aligns with OWASP security recommendations48, preventing unauthorized data access or manipulation. UUID4 generation for device identification follows OWASP guidelines on secure device authentication, using random numbers to ensure uniqueness and reduce collision risks in distributed systems. Comprehensive logging and monitoring directly addresses OWASP’s “Security Logging and Monitoring Failures” vulnerability by enabling threat detection, supporting forensic investigations, and ensuring regulatory compliance.

  2. 2.

    Cyber Resilient System: The systematic implementation of real-time resource monitoring (CPU, memory, power consumption) exemplifies the Analytic Monitoring technique advocated in NIST SP 800-160 Vol. 249, enabling continuous surveillance of operational parameters to identify security anomalies. Establishing performance thresholds for anomaly detection aligns with NIST’s Anomaly Detection principle, supporting the cyber resiliency objective of early adverse condition identification through baseline deviation analysis. The collection of detailed encryption/decryption operation metrics corresponds to NIST’s performance monitoring sub-objective, facilitating comprehensive security assessment through quantitative analysis of cryptographic performance patterns, resource utilization efficiency, and operational integrity indicators.

  3. 3.

    IOTA Protocol Integration: The integration of IOTA distributed ledger technology within the system architecture exemplifies an advanced approach to secure data management. By implementing IOTA’s Tangle framework through the testnet shimmer network, the system establishes immutable record-keeping capabilities that fundamentally prevent retrospective data manipulation. This immutability characteristic ensures complete data integrity across the temporal spectrum of system operations. Furthermore, the adoption of IOTA’s distributed ledger technology represents alignment with contemporary decentralized security standards, effectively eliminating single points of failure through the distribution of trust across multiple nodes within the network ecosystem. This architectural decision enhances overall system resilience against targeted attacks while simultaneously reducing dependency on centralized authentication mechanisms.

Table 3 discussed about the existing IoT vulnerabilities and how the proposed solution will beneficial to overcome these issues. This study identified eight critical security domains in IoT systems, encompassing authentication and access control through to real-time monitoring. The findings demonstrate that DLT, coupled with corresponding security mechanisms, presents potential solutions to these challenges. Specifically, cryptographic device signatures and immutable ledger records effectively mitigate authentication vulnerabilities and data manipulation risks, while distributed architectures enhance network resilience and system scalability. The integration of IOTA’s architecture, alongside optimized resource management and automated security policies, facilitates the implementation of sustainable security measures without compromising system performance. These findings suggest that a DLT-based approach to IoT security effectively addresses contemporary vulnerabilities while ensuring scalability and regulatory compliance. Future research directions should examine the practical implementation challenges of these solutions across diverse IoT environments.

Table 3 IoT Vulnerabilities and Proposed Solution Benefits.
Full size table

Operational boundaries

  • The experiments were carried out in a controlled environment, which might not reflect the full range of challenges present in real-world IoT scenarios, such as network congestion and the geographical spread of devices.

  • While the framework shows potential in managing a large number of devices, further testing is needed to confirm its scalability in extremely large and varied IoT networks, especially those involving millions of connected devices.

  • Although the solution improves latency, the consensus mechanisms could be further refined to ensure better performance under heavy loads and more complex operational conditions.

  • The framework requires quantum-resistant cryptographic upgrades for future security and extensive real-world testing to confirm scalability across large, distributed IoT networks under varying operational conditions.

Use cases

This section explores diverse real-world applications and implementation scenarios of the proposed framework across multiple industry sectors as discussed in Table 4.

Table 4 Real-World Applications and Implementation Scenarios of the Proposed Approach.
Full size table

Conclusion

This research addressed the critical challenges of optimizing cost-efficiency and security in Internet of Things (IoT) systems, which have become increasingly fundamental to smart city infrastructures and complex urban environments. The exponential growth of IoT device deployment has precipitated significant challenges in operational costs and security vulnerabilities, particularly in the authentication and management of large-scale data generation. To address these challenges, we proposed a novel decentralized framework integrating IOTA-based Distributed Ledger Technology (DLT) with sensor nodes. The implemented solution demonstrates robust device authentication protocols and secure communication channels while significantly reducing network isolation and minimizing message overhead during device association processes. The proposed framework effectively mitigates the inherent inefficiencies commonly associated with traditional blockchain architectures, thereby presenting a more economically potential solution for securing IoT ecosystems. The empirical evaluation revealed compelling results across multiple performance metrics. The encryption mechanisms demonstrated consistent and predictable performance parameters, while decryption processes, despite exhibiting minor variability, maintained acceptable operational thresholds. Furthermore, the system exhibited exceptional network throughput capabilities and minimal latency, validating its capacity to accommodate the demanding requirements of IoT deployments. The observed stability in memory utilization further proves the system’s optimization for typical IoT workload scenarios. The experimental results reveal that while the system maintains stable cryptographic performance with processing times of 0.00015-0.00025 seconds under normal conditions, network congestion significantly impacts overall efficiency, reducing throughput by 68% and increasing power consumption by 12.2%, emphasizing the critical need for robust congestion management strategies in IOTA-based IoT deployments. While this research establishes a robust foundation, several promising directions for possible future investigation. Primary among these is the potential for further performance optimization to accommodate increasingly demanding IoT environments, despite the current system’s success in reducing operational costs and processing overhead. Additionally, comprehensive real-world testing across diverse operational scenarios remains essential to validate the system’s scalability and economic viability. The development of standardized interoperability protocols across various blockchain platforms represents another critical area for future research, potentially yielding enhanced system flexibility and scalability, ultimately leading to improved cost-efficiency and operational effectiveness.