Fatemeh Stodt

Member, IEEE Mohammed B M Kamel
email: mkamel@inf.elte.hu@hs-furtwangen.de

Christoph Reich

Member, IEEE Fabrice Theoleyre

Senior Member, IEEE Peter Ligeti

Privacy-Preserving Shop Floor Blockchain

Keywords: IIoT, Blockchain, shop floor, Industry 4.0, Attribute-based verification, Privacy

The security of shop floor operations within the Industrial Internet of Things (IIoT) is of utmost importance, particularly when it comes to auditing processes. Given the interconnected nature of devices and systems in IIoT, ensuring data integrity and trustworthiness of the generated data by IIoT devices becomes crucial. In this context, blockchain technology can play a significant role as its immutable and decentralised ledger provides a transparent and tamper-resistant platform for securely recording and verifying transactions and events on the shop floor. In this paper, we provide a privacy-preserving approach that allows an auditor to verify the stored data as well as the IIoT device itself. Our results showed that it can be efficiently implemented in the Shop Floor to be provide a secure framework for auditing of both the involved devices and the generated data by the IIoT devices. Furthermore, it can withstand a wide range of adversarial assaults.

T HE manufacturing sector is presently experiencing a significant shift, moving away from conventional mechanical procedures and towards a new era of data-driven, intelligent production. Industry 4.0 serves as an example of this paradigm change, which incorporates Big Data analytics throughout the whole product lifecycle, including production, distribution, after-sales support, retail, and material supply. This integration has a huge impact and is revolutionising the conception, production, and delivery of products.

The transition to intelligent production is not without difficulties, though. As [START_REF] Yu | Iotchain: Establishing trust in the internet of things ecosystem using blockchain[END_REF] [START_REF] Yu | Iotchain: Establishing trust in the internet of things ecosystem using blockchain[END_REF] point out, the manufacturing industry struggles with the fragmented nature of data, which makes it more difficult to aggregate and analyse the enormous amounts of information created. To fully realise the potential of Industry 4.0, creative solutions are required to address the challenges posed by the fragmentation of data, which is impeding the efficacy and efficiency of production processes.

In this environment of swift transformation and intricacy, the shop floor the core of production processes becomes an essential focus point. Here is where raw materials are turned into finished goods, and this is also where there is a greater demand than ever for smooth coordination and real-time in-formation sharing. Any inefficiencies in data administration or information tracking might result in major operational failures, which will ultimately reduce the industrial entity's competitiveness.

Blockchain technology offers a decentralised, transparent, accountable, liable, and secure way to track and manage information on the shop floor, which makes it a possible answer to these problems. In order to solve this, this study investigates the creation and use of a blockchain solution that is especially tailored to the particular requirements and limitations of shop floor operations in the context of Industry 4.0. We want to provide answers to the following research issues through this investigation:

1) How can an auditor verify efficiently whether the stored information in the Blockchain has been generated by a valid IIoT node? 2) How can an auditor verify a set of given stored data have indeed generated by a purported IIoT device? The structure of the paper is the following. We introduce some related research in the area of industrial blockchain in Section II. We explain over background of paper and gives some information regarding attribute-based authentication and industrial blockchain on the IV. Our proposal is explained in Section V and analysed in section VI. In section VII we VOLUME 11, 2023 bring a use case to enlighten the proposal. Finally, Section VIII includes our conclusions.

II. RELATED WORKS

The combination of blockchain technology and the IIoT is revolutionising manufacturing processes and fostering a synergistic effect that enhances efficiency, security, and transparency in the quickly changing Industry 4.0 landscape. Bahga's [START_REF] Bahga | Blockchain platform for industrial internet of things[END_REF] introduction establishes the framework for a thorough investigation of the most recent developments in blockchain integrated IIoT, preparing the reader for a detailed examination of their combined effects on the manufacturing industry.

The digitization of the industry requires the confidentiality and integrity of critical data. Additionally, Blockchain technology steps ensures improving shop floor data security [START_REF] Stodt | Security audit of a blockchain-based industrial application platform[END_REF]. It also adds a level of transparency to the manufacturing process never seen before, providing all participants with an unambiguous and immutable record of each transaction and activity [START_REF] Mishra | Role of blockchain in achieving solutions in ambiguous supply chain operations[END_REF]. Processes are simplified, inefficiencies are reduced, and fresh opportunities for innovative business models are generated as a consequence [START_REF] Ghovanlooy Ghajar | Schloss: Blockchain-based system architecture for secure industrial iot[END_REF]. In the following, the state of art of blockchain-based IIoT (II-A) and lightweight blockchain-based approaches (II-B) are discussed.

A. BLOCKCHAIN-BASED INDUSTRIAL INTERNET OF THINGS (BIIOT)

One innovative solution to these issues is the Blockchainbased Industrial Internet of Things (BIIoT), which connects IIoT systems via peer-to-peer networks to improve interoperability. By dismantling silos and promoting a more connected environment, this makes it easier for information to be distributed seamlessly across different industrial sectors. To meet the needs of various industries in this environment, many decentralised blockchains are set up, each focused on a particular area and guaranteeing efficient and safe information management [START_REF] Joseph E Kasten | Engineering and manufacturing on the blockchain: A systematic review[END_REF].

The centralised nature of systems is one of the major issues with the present IIoT infrastructure, and this is most noticeable in the firmware upgrade procedure. Conventional techniques entail the manual installation of firmware upgrades on IIoT nodes after obtaining them from a central server. This procedure adds possible security flaws in addition to being expensive and time-consuming. More recent techniques implement a kind of distributed repository of firmwares [START_REF] Arakadakis | Firmware over-the-air programming techniques for iot networks -a survey[END_REF], where each node can provide certain versions to its neighbors. However, enforcing global security may be very challenging with these techniques. A blockchain-based solution that uses smart contracts to automate and secure the firmware upgrade process throughout the industrial network is introduced in the work published in [START_REF] Wan | A blockchainbased solution for enhancing security and privacy in smart factory[END_REF]. Using a decentralised approach improves the system's efficiency and security since nodes may independently evaluate and apply firmware changes.

The notable improvement in IIoT is the combination of blockchain technology with digital twins, as examined in Sasikumar A. et al.'s study [START_REF] Sasikumar | Blockchain-based trust mechanism for digital twin empowered industrial internet of things[END_REF]. In order to improve security, data privacy, and the authority of decentralised digital twin networks in IIoT applications, the authors suggest a Proof of Authority (PoA) trust mechanism based on blockchain technology. With the number of industrial Internet of Things (IoT) nodes increasing, this integration is essential for maximising network performance and guaranteeing safe data transfer.

The EdgeShare architecture, which was presented by Yang et al. [START_REF] Yang | Edgeshare: A blockchain-based edge data-sharing framework for industrial internet of things[END_REF], demonstrates the critical role that edge computing plays in this integration. Blockchain-based data-sharing framework offers a safe, portable, and effective edge datasharing solution By fusing blockchain technology with edge computing, the framework ensures safe transaction recording and auditing while enhancing the effectiveness of data exchange.

Stodt et al. [START_REF] Stodt | Trust management system for hybrid industrial blockchains[END_REF] detail a specialized blockchain-based Trust Management System (TMS) for IIoT, which enhances the security and interoperability of industrial networks. Unique to this approach is its decentralized nature, leveraging smart contracts to manage trust among devices dynamically. This system stands out by autonomously adapting to the varying trustworthiness of devices, ensuring robust and scalable security tailored to the diverse needs of the industrial internet landscape. By preempting potential breaches, this TMS represents a significant step forward in securing the burgeoning connectivity inherent to IIoT.

To sum up, incorporating blockchain technology into the IIoT represents a noteworthy advancement in the direction of smart manufacturing. Developments in edge computing, trust management systems, and blockchain-based digital twins highlight the integration's potential to optimise industrial processes and guarantee safe and effective operations in the age of smart manufacturing.

B. LIGHTWEIGHT BLOCKCHAIN-BASED APPROACHES

In the IoT domain, the resource constraints of devices necessitate specially crafted, lightweight blockchain solutions. Traditional blockchains with their Proof of Work consensus are notoriously computationally intensive and thus unsuitable for such environments [START_REF] Schinckus | Proof-of-work based blockchain technology and anthropocene: An undermined situation?[END_REF]. In order to address this, we evaluate a variety of cutting-edge blockchain solutions that guarantee performance without putting a strain on resources.

Two methods stand out for their resource efficiency and scalability. To improve transaction throughput with the least amount of performance overhead, Tikiri makes use of a realtime transaction architecture in conjunction with shardingbased replication [START_REF] Bandara | Tikiri-towards a lightweight blockchain for iot[END_REF]. In order to specifically lower the processing and storage requirements necessary in industrial IoT scenarios, LightChain uses a Synergistic Multiple Proof consensus and an Unrelated Block Offloading Filter [START_REF] Liu | Lightchain: a lightweight blockchain system for industrial internet of things[END_REF]. The priorities of these techniques differ: While LightChain is primarily concerned with minimising the resource footprint, Tikiri is transaction-centric.

In terms of security, AEchain uses lightweight cryptographic algorithms to achieve strong security and efficiency [START_REF] Khan | Aechain: A lightweight blockchain for iot applications[END_REF]. Likewise, Fusion Chain uses the Interplanetary File System for storage and a Practical Byzantine Fault Tolerance consensus to guarantee data privacy and a smaller blockchain size [START_REF] Na | Fusion chain: A decentralized lightweight blockchain for iot security and privacy[END_REF]. AEchain focuses on providing high-throughput authentication services, whereas 'Fusion Chain' considers everything. To address privacy preservation and anomaly detection, Selvarajan et al. [START_REF] Selvarajan | An artificial intelligence lightweight blockchain security model for security and privacy in iiot systems[END_REF] provide a system that combines a lightweight blockchain with a Lightweight Consensus Proofof-Work.

For performance and usability, Allouche et al. [START_REF] Allouche | Lightweight blockchain processing. case study: scanned document tracking on tezos blockchain[END_REF] present a load-balancing structure that synergizes with lightweight processing paradigms, allowing complex processes to be offloaded, yet preserving the blockchain's core operations onchain. Addressing identity management, Bouras et al. [START_REF] Mohammed Amine Bouras | A lightweight blockchain-based iot identity management approach[END_REF] develop a consortia blockchain solution, confronting the dual challenge of scalability and usability in IoT identity ecosystems.

In conclusion, these diverse blockchain technologies illustrate a significant stride towards tailoring blockchain applications for IoT. They pivot away from traditional, resourceheavy mechanisms towards more nuanced, context-specific solutions that respect the limitations and necessities of IoT devices. While each offers distinct advantages, their collective contributions suggest a future where IoT can benefit from blockchain's security and trust assurances without the associated resource strain.

III. BLOCKCHAIN-BASED IIOT CHALLENGES

The convergence of blockchain technology and the IIoT or BIIoT represents a paradigm shift towards an industrial ecosystem that is more transparent and safe. Nonetheless, this integration creates specific difficulties. To fully realise the potential of BIIoT, this subsection explores the open research issues and challenges that must be resolved.

1) Privacy Leakage

Although blockchain technologies use a number of safeguards to protect transaction confidentiality, they are not infallible. For example, Bitcoin transactions use IP addresses to mask user identities, thereby offering some anonymity [START_REF] Rathee | A review on blockchain and its necessitate in industrial iot[END_REF]. These precautions, however, are not always successful since transaction patterns can disclose user identities [START_REF] Tiago M Fernández-Caramés | A review on the use of blockchain for the internet of things[END_REF]. Moreover, confidentiality violations may result from the full storage of transaction data on the blockchain.

2) Security Vulnerability

Security is improved by integrating blockchain with IIoT through the creation of signatures and cryptographic techniques. However, security risks associated with vulnerabilities in IIoT and blockchain systems are substantial [START_REF] Giang | Blockchain-based system for secure data storage with private keyword search[END_REF]. Industrial wireless networks are vulnerable to security lapses like eavesdropping and jamming [START_REF] Derhamy | Iot interoperabilityon-demand and low latency transparent multiprotocol translator[END_REF]. Furthermore, the viability of heavy encryption techniques is limited by the resource constraints of IIoT nodes [START_REF] Li | Consortium blockchain for secure energy trading in industrial internet of things[END_REF], and it is difficult to manage cryptographic keys in decentralised environments. These security problems are made worse by smart contract vulnerabilities, as the DAO attack showed when it stole $50 million worth of Ethereum by taking advantage of these flaws [START_REF] Teslya | Blockchain platforms overview for industrial iot purposes[END_REF].

3) Resource Constraints

IIoT nodes, which include RFID tags and sensors, are generally limited in terms of their processing power, storage capacity, and energy availability. The resource-intensive Proof of Work (PoW) consensus algorithm used in blockchain technology may not be appropriate for low-energy IIoT nodes [START_REF] Liu | Performance optimization for blockchain-enabled industrial internet of things (iiot) systems: A deep reinforcement learning approach[END_REF]. The deployment of blockchain data across IIoT nodes is complicated by its large size since these nodes frequently function in environments with erratic network connections [START_REF] Rathee | A review on blockchain and its necessitate in industrial iot[END_REF].

4) Scalability

One major obstacle to blockchain technology's use in largescale IIoT networks is its scalability. The low transaction throughput of existing blockchain systems, such as Bitcoin, limits their applicability to IIoT applications with high transaction volumes [START_REF] Bueno Da Silva | Blockchain and industry 4.0: overview, convergence, and analysis[END_REF]. Blockchain scalability improvements are necessary to handle the high volume of transactions and nodes in IIoT networks.

5) Big Data

The massive volumes of data generated by IIoT call for efficient big data analytics in order to glean insightful information. However, because blockchain data is encrypted and IIoT nodes have limited resources, traditional big data analytics systems are not appropriate for BIIoT [START_REF] Fraga-Lamas | A review on blockchain technologies for an advanced and cyber-resilient automotive industry[END_REF]. Although cloud computing presents a viable solution, it also raises issues with latency and privacy.

In summary, while blockchain offers promising solutions for enhancing IIoT, addressing these challenges in Table 1 is crucial for its successful integration and adoption in industrial settings.

IV. PRELIMINARIES

The foundations for the an attribute verification protocol and industrial blockchain are intricately laid out, providing a solid framework for understanding the seamless integration of these technologies for the proposed architecture. In this section, the main building blocks of our solution have been discussed.

A. ATTRIBUTE VERIFICATION PROTOCOL

Attribute verification protocol [START_REF] Mohammed | Attribute verifier for internet of things[END_REF] is a distributed protocol that can be utilised as a building block in a system to verify the participants based on their attributes in privacy-preserving approach. It has many applications as data validation [START_REF] Mohammed | D3vn: Decentralized abe-based distributed data validation network[END_REF] and network security [START_REF] Mohammed | 3l-aodv: Three layer security protocol for grayhole attack mitigation in manet[END_REF]. Three roles with specific responsibilities exist:

• Issuer: The issuer acts as the authority and the responsible entity for attributes. An issuer is responsible for giving the verifier the public key of attributes and providing the attributes to the prover (user).

VOLUME 11, 2023 • Verifier: The verifier's role is to check and confirm the attributes of the prover (user). In a zero-knowledge scenario, the verifier challenges the prover for proof. • Prover: The prover is the entity trying to prove that it possesses certain attributes. It first validates its ownership of the proven attributes with the issuer. Once a prover has confirmed possession of a claimed attribute, the issuer sends a secret key for that attribute to the user. Then, the prover will be able to respond to the challenges posed by the verifier.

The verifier in the attribute verification protocol decides which attributes will be used during the verification process. The protocol supports two main verification modes:

• 1-out-of-n verification mode: In this mode, the verifier defines a set of attributes, and the verification process of the prover passes as long as at least one of its attributes is in the set of defined attributes by the verifier.

• n-out-of-n verification mode: this mode provides exhaustive verifications since the prover's verification process ensures that all defined attributes pass the verifier's tests.

It is noteworthy to mention that in the 1-out-of-n verification mode, the privacy of the prover is preserved as the prover does not need to reveal any of its attributes other than the fact that it has one of the defined attributes by the verifier.

B. INDUSTRIAL BLOCKCHAIN

The inherent decentralisation and immutability of blockchain technology provide significant benefits for applications in the industrial sector. Assuring accuracy and confidence in data, a uniform ledger view is made available to all authorised network members, hence elevating transparency.

1) Properties

A fundamental change in the recording and sharing of information is brought about by blockchain technology. Blockchain is a distributed ledger that keeps an everexpanding list of data records safe from alteration and manipulation. Every record, referred to as a block, has a timestamp and is connected to all other blocks in a chronological sequence [START_REF] Nofer | [END_REF].

Instead of being kept in one place, the ledger is duplicated and dispersed among a network of computers. All nodes in the network receive updates from the blockchain whenever a new block is added. The decentralised nature of the ledger enhances its transparency and reliability by enabling all network participants to verify, store, and transmit data.

The information on a blockchain is thought to be unchangeable. Network agreement is necessary because once a block's data is recorded, it cannot be changed without also changing all blocks that come after it. A fundamental component of the blockchain's security mechanisms is its immutability, which guarantees that once a transaction is entered, it cannot be removed or altered.

Consensus algorithms are the procedures used to arrive at a consensus on transactions. These techniques ensure that every copy of the distributed ledger is identical [START_REF] Mingxiao | A review on consensus algorithm of blockchain[END_REF]. Two popular methods are PoW and Proof of Stake (PoS). PoW needs a lot of processing power to validate transactions and create new blocks, whereas PoS chooses validators based on how much Bitcoin they possess. Furthermore, trust in a distributed network is addressed by Practical Byzantine Fault Tolerance (PBFT), which enables the system to operate properly and establish consensus even in the event that some nodes malfunction or behave maliciously.

While not the main emphasis of every industrial blockchain, smart contracts offer the chance to further automate and optimise industrial processes where they are suitable.

2) Layers of an industrial Blockchain

Different from their public counterparts, industrial blockchain networks are usually permissioned, meaning that only authorised businesses are allowed to participate in the network. The integrity and dependability of information are vital in the industrial, energy, and supply chain management sectors, where this design decision is especially important for handling sensitive data and vital activities.

Fig. 1 provides a graphic representation of the complex multi-layered blockchain architecture that interfaces with the Internet of Things (IoT). By facilitating a safe and smooth information flow between various applications, this integration makes sure that the digital and physical domains work together harmoniously.

At the base, the Perception Layer collects ambient data using sensors and actuators to act as a link between the phys- ical and digital worlds. Accurate real-time data capturing is the job of this layer; the Communication Layer then securely transmits the data. To ensure data accuracy from the source to the Blockchain Composite Layer, this next layer uses a variety of communication protocols.

Anchoring the whole system, the Blockchain Composite Layer is the central component of the architecture. It is an intricate web of layers, each with a distinct function. The composite layer is where all of the blockchain's essential features come together. It is responsible for controlling data storage and retrieval, monitoring network connections, confirming transactions using strong consensus techniques, and encouraging network involvement.

Finally, the Industrial Applications Layer shows how blockchain is really being used in a variety of businesses. This layer covers the end-use applications, demonstrating the flexibility of the blockchain and its potential to transform industrial processes, whether it is optimising energy distribution in smart grids, improving supply chain traceability, or guaranteeing the integrity of data in healthcare.

V. PROPOSED ARCHITECTURE

The architecture proposed in this paper aims to integrate blockchain technology within heterogeneous network environments. An efficient blockchain framework ensures both operational speed, transparency, legal liability, accountability, and privacy. The architecture is a hierarchical approach, depicted in Fig. 2, and is utilised to interconnect subnetworks isolated by VLANs, through the novel introduction of "middle nodes" which are pivotal in executing computational tasks and maintaining network efficiency.

A. ARCHITECTURE PRELIMINARIES

The architecture employs a multi-tiered network structure, each tier with distinct roles and responsibilities:

• Local Nodes (LNs): Devices with limited computational capacity that perform data collection and actuation tasks, such as sensors and actuators. They rely on MNs for secure communication and data storage.

B. ARCHITECTURE DESCRIPTION

The architecture facilitates a harmonious collaboration where LNs transmit data to MNs. At the core of our proposed system is a constellation of MNs that not only facilitate computation offloading but also act as local data custodians, significantly improving fault tolerance. The employment of a PBFT consensus mechanism by FNs ensures the reliability and security of transactions across this distributed architecture. FNs and MNs are strategically chosen based on criteria such as computational capability, network connectivity, and trust level of them based on historical performance.

The operation of the architecture may be comprehended in the following steps: 6) Blockchain Append: FNs combine the candidate blocks after verification and add them to the blockchain as new blocks, which makes the data permanent and unchangeable. Even the LNs, such as simple sensors, may transfer data safely and reliably because of this complex design. The MNs in between serve as buffers, which are especially important when the network is unstable. They guarantee that data is never lost and is constantly prepared for blockchain integration.

C. ARCHITECTURE WORKFLOW

Our proposed architecture has been designed considering the privacy of the participants. Privacy within the network is ensured through the utilised attribute verification protocol that operates in conjunction with the blockchain in 4 phases: 1) Setup Phase, 2) Registration Phase, 3) Generation Phase, and 4) Validation Phase as described in the next sections. MNs handle the encryption of data using keys generated by HSMs, ensuring secure transaction initiation. LNs participate by providing hashed data to MNs, adding another layer of privacy. But before participating in the subnetwork, it is needed to pass the preregistration step.

1) Setup Phase

The setup phase is a very critical pre-registration step in network configuration, during which a node is identified and integrated into the network. In this phase, and during registering a new node, the responsible LN assigns a unique identifier to guarantee its unique location inside the network. This is a dual-faced unique ID (ID n ) derived from the node's serial number (Sn) and MAC address (MAC) as in Equation 1. Whereas the MAC address is unique and identifiable in the digital world, the serial number is like an unchangeable imprint from genesis.

ID n = H (Sn ∥ MAC) (1)
Here, H represents a cryptographic hash function and ∥ denotes concatenation. MAC, inherently unique and recognisable in the digital realm, serves as a reliable hardwarebased identifier. In contrast, Sn acts like an immutable tag assigned at the time of manufacture. This dual-component approach to node identification enhances security, as it couples a physically unalterable attribute Sn with a digitally unique identifier MAC. The setup phase (see Algorithm 1) is rigorously conducted only once for each node to maintain the integrity of these identifiers. Since the identifiers are unique return node 5: end function and immutable, they play a pivotal role in the network's overall security architecture. These components work together to provide a strong identification system, which is essential for safe and effective network operations.

2) Registration Phase

Following the identity assignment in the setup phase, nodes are further characterised by a set of configurable attributes. These attributes play a crucial role in dictating their operational behaviour and interaction within the network. Without loss of generality, we discuss four primary attributes as follows:

• Logical Network Sector (SEC): This attribute defines the operational scope of a node within the network topology. It determines the node's functional area and its interaction with other segments of the network, aiding in effective network segmentation and management. It is noteworthy that these attributes are not static. They can be updated or expanded upon during the lifecycle of the system by the Main Nodes (MNs). This dynamic adaptability is crucial in a network environment that may evolve or require adjustments in response to new operational demands or technological advancements. We have adopted the n-outof-n verification mode, as described in Section IV-A. The process of validation and tokenization of nodes based on their attributes is described as follows.

1) Attribute Verification Function: we can represent the verification process of a node's attributes by the Main Nodes (MNs) as follows:

V attr (LN ) = 1 if SEC, INS, POW is valid 0 otherwise (2)
Where V attr (LN ) represents the verification function for the LN attributes. The function returns 1 if all the attributes (SEC, INS, POW, TRA) are successfully verified by the MNs, and 0 otherwise. 2) Token Issuance Function: After the verification, tokens are issued to certify the readiness of the LNs for network participation.

T issue (LN ) = Token if V attr (LN ) = 1 No Token if V attr (LN ) = 0 (3)
In equation The Generation Phase is a pivotal process in the lifecycle of blockchain data management. It encompasses the creation and preparation of data for entry into the blockchain. This phase consists of several sequential steps to ensure data integrity and security, as described below:

1) Data Generation: Initially, raw data is generated by LNs. This data could represent transactions, sensor outputs, user actions, or any relevant information that needs to be recorded on the blockchain.

D = GenerateData(Raw Inputs) (5)
Where D denotes the generated data from raw inputs. 2) Data Digest Creation: Each piece of data D is then processed to create a cryptographic hash digest. This digest serves as a unique fingerprint of the data.

H (D) = Hash(D) (6)
Here, H (D) is the hash digest of data D. 3) Attribute Digest Generation: Concurrently, all relevant attributes of the data are hashed to ensure that every characteristic is accounted for and secured.

H (A) = Hash(A 1 , A 2 , . . . , A n ) (7)
With H (A) representing the combined hash of attributes A 1 , A 2 , . . . , A n . 4) Double Hash Formation: To further enhance security, a double hash is formed, which is the hash of the hash digest and the attribute digest.

H 2 (D, A) = Hash(H (D) ∥ H (A)) (8)
H 2 (D, A) symbolises the double hash, where || denotes concatenation. 5) Commit to Public Blockchain: The final step is committing the double hash to the public blockchain. This action immutably records the data and its attributes, ensuring traceability and verifiability.

B = CommitToBlockchain(H 2 (D, A)) ( 9
)
Where B is the blockchain record containing the double hash H 2 (D, A). Each step in the Generation Phase (see Algorithm 3) is designed to be repeatable and scalable, ensuring that as data generation increases, the system can handle the additional load without compromising on security or integrity. The process also emphasises the importance of repeatability, allowing for consistent data generation and recording in an efficient and secure manner. By Generation Phase, the blockchain return B 8: end function system fortifies the data against tampering and unauthorised alterations, thus upholding the principles of decentralisation and trust that are central to blockchain technology.

4) Validation Phase

The Validation Phase in a blockchain-based network is a critical multi-tiered process that ensures data integrity, privacy, and compliance with network protocols. This phase involves several key steps:

1) Initial Verification by MNs: The MNs first verify LNs properties against their pre-registered attributes. This verification is essential to ensure that each LN adheres to the network's standards and policies. The process can be represented as follows:

V MN (LN ) = 1 match pre-registered values 0 otherwise ( 10
)
Where V MN (LN ) is the validation function performed by the MNs on the LNs. 2) Consensus Process by FNs: FNs, utilising the PBFT consensus mechanism, further scrutinise the blocks containing encrypted data. This step is crucial to maintain the integrity and trustworthiness of the data in the blockchain. The consensus can be represented as:

C PBFT (Block) = n i=1 V FNi (Block) n (11)
Where C PBFT (Block) is the consensus function, V FNi (Block) is the validation function performed by each FN on the block, and n is the total number of FNs participating in the consensus process.

3) Random Audits for Data Integrity and Privacy:

Post-verification, independent auditors conduct random checks to assure data integrity and validate the effectiveness of privacy-preserving measures (cf. Fig. 3). This layer adds an additional level of security and compliance verification as it sh By integrating privacy protocols with an efficient blockchain architecture, the system achieves a robust privacy framework that does not compromise the network's speed and efficiency. This balanced approach is critical for blockchain adoption in sensitive and high-stakes environments, such as IIoT networks, where data privacy and rapid processing are paramount. The Validation Phase algorithm (see Algorithm 4) is defined as:

VI. ANALYSIS A. IMPLEMENTATION

To verify the efficiency and applicability of the proposed architecture, the registration, generation and validation phases have been implemented in a setup containing one middle node and seven local nodes implemented on devices with 1.8 GHz processing unit, as shown in Figure 4. To implement the attribute verification protocol, we have utilised Charm framework [START_REF] Joseph A Akinyele | Charm: a framework for rapidly prototyping cryptosystems[END_REF]. The step 5 of the generation phase has been ignored during efficiency measurement as it depends on the time constrains and properties of the specific adopted Blockchain technologies [START_REF] Cézane | Privacy-preserving blockchain technologies[END_REF]. The middle node in our setup if not Verify(MNs, LN ) then return True or False 16: end function is responsible for four attributes as described in Section V-C1. The attributes have been set as follows: As mentioned earlier, during the registration phase we assumed that a single MN is responsible for the four available attributes in the system, namely SEC, INS, POW, and TRA. Therefore, each of the LNs will get four tokens from the MN, to be used later during the validation phase.

B. SECURITY ANALYSIS

Our approach addresses the problems of privacy leaking, security flaws, and the intrinsic resource limitations of IIoT devices. Our solution improves also scalability and massive data management. By using cutting-edge encryption techniques that make user identities and transaction data opaque to VOLUME 11, 2023 unauthorised investigation, our system by design protects user privacy. Our method lowers the possibility of data breaches since, in contrast to traditional systems, it does not need the storing of all transaction data. In addition, we strengthen the security framework against known vulnerabilities by using strong key management techniques and addressing smart contract flaws. Furthermore, the architecture is carefully designed to withstand a wide range of adversarial assaults in the context of security analysis. Probabilistic polynomial-time (PPT) complexity limits the adversary's capabilities, and trust anchors are safely positioned in servers and intermediary nodes. Sensitive data digests (hashes) that are encrypted at numerous layers are the only thing that are distributed, making passive assaults to obtain sensitive data useless. With resilience techniques in place, active attacks including Denial of Service (DoS) are anticipated. The system can continue to function even in the event that one of the intermediate nodes fails. During such situations, redundant systems are used to preserve the registration of new local nodes. However, this poses a problem with consistency, which we solve with a new synchronisation mechanism.

Through the integration of these processes, our blockchain approach that we have provided emerges as a robust framework that can thwart most of the threats that represent a danger to traditional IIoT infrastructures. We can fix current problems and usher in a new age of safe and scalable IIoT ecosystems thanks to this complex fusion of technology advancements.

C. PERFORMANCE ANALYSIS

Consensus techniques are essential for striking a balance between transaction speed, energy consumption, and system security when it comes to optimising blockchain technology for IIoT applications. Despite being one of the original and most secure algorithms, PoW has been criticised for using a lot of energy and having a limited transaction throughput, which makes it less suitable for IIoT applications with huge transaction volumes. Block creation durations are typically about 10 minutes, and because solving cryptographic challenges requires a lot of computing power, energy costs are very expensive [START_REF] Yun | Analysis of mining performance based on mathmatical approach of pow[END_REF].

A more energy-efficient option is Proof of Stake (PoS), in which the generation of new blocks is decided by a user's stake in the network as opposed to processing power. This significantly lowers the amount of energy used. Depending on the exact implementation, PoS can give a moderate to high level of security with a medium to high transaction throughput. For blockchain networks that place a higher priority on scalability and resource efficiency than PoW, PoS offers a more affordable option [START_REF] Cao | Performance analysis and comparison of pow, pos and dag based blockchains[END_REF].

Blocks may be created via the PBFT mechanism in a matter of seconds, and it is known for its strong transaction throughput. Because of this characteristic, it is the best option for systems that require fast transaction processing [START_REF] Sukhwani | Performance modeling of pbft consensus process for permissioned blockchain network (hyperledger fabric)[END_REF]. However, in the context of the IIoT, where devices frequently encounter resource limitations, the conventional PBFT paradigm, which demands the participation of every node, may result in inconsistent performance. This is especially true when taking into account the various capacities that nodes in the network have.

By redefining the function of resource-limited local nodes, our proposed system addresses these issues and improves efficiency (cf. Table . 3). These nodes safely generate data, which enhances network security even if they are not directly involved in the consensus process. The full nodes are responsible for creating blocks by consensus; they include the safely stored information from the local nodes into the candidate blocks. Thus, the suggested system can both accommodate the resource constraints seen in IIoT contexts and preserve the high throughput and low latency features of PBFT.

PBFT is reasonably balanced in terms of cost and energy, with reasonable consumption and expenditure. On the other hand, the range of security assurance provided by PBFT is significantly impacted by the node majority's dependability. Because of this heterogeneity, incorporating PBFT into IIoT situations where security is a non-negotiable requires caution.

The comparative study shows that although PBFT designed for full nodes and Proof of Stake (PoS) are efficient and scalable, making them suitable for widespread IIoT application, PoW has a strict security framework that is too strong to ignore, particularly in high-stakes situations where security breaches are unacceptable. Finally, our model takes advantage of the speed and scalability of PBFT and strengthens the architecture with strong privacy and security guarantees by using Attribute Verification Protocol.

VII. CASE STUDY: IMPLEMENTING BLOCKCHAIN ON A HYDRAULIC MACHINE SHOP FLOOR

This case study looks at how the suggested blockchain architecture might work in an actual setting a shop floor with large hydraulic machinery, to be exact. Because hydraulic machines can provide enormous quantities of power for diverse operations, they are indispensable in many industrial applications. They do, however, also have to deal with issues like operating efficiency, energy usage optimisation, and maintenance schedule.

A. PROBLEM STATEMENT

The absence of a strong data management system and unplanned maintenance caused a large amount of downtime for the chosen shop floor. The unpredictability of equipment breakdowns resulted in higher expenses and lower productivity. To improve operational efficiency and predictive maintenance capabilities, a system that could deliver secure, immutable, and real-time data was obviously needed.

B. IMPLEMENTATION OF PROPOSED ARCHITECTURE

There was no interruption to the shop floor's current infrastructure throughout the integration of the blockchain-based architecture. In order to monitor performance metrics, middle nodes were created to act as a link between the full nodes and the local nodes that were integrated into the hydraulic equipment. A new degree of communication between the machines and the management system was made possible by this network arrangement. Every hydraulic device had a special identification that connected to its local node within the blockchain. This identification guaranteed traceability and security in data transactions. Each node had parameters pre-registered for things like sector code, installer signature, power consumption, and transmission pattern, which helped to tailor the network to the unique requirements of a highpower-demand environment. Pressure, temperature, and fluid level data were gathered by the local nodes; these characteristics were essential for the hydraulic machinery to function.

After that, the intermediary nodes safely received the data, processed it at first, and temporarily stored it. Middle nodes combined the data into transactions, which were subsequently signed and sent to complete nodes. In order to reach a consensus, the full nodes carried out the PBFT protocol. They then added the validated transactions to the blockchain, guaranteeing that the data was unchangeable and easily accessible for examination.

VIII. CONCLUSION

In this paper we proposed a privacy preserving approach to secure the shop floor auditing process, utilizing the attribute verification protocol as the main building block and the Blockchain technology. The proposed approach provides two main services: First, it provides a mechanism for an auditor to verify that the stored data in the Blockchain has been generated by a valid IIoT node in the sytem. Secondly, it allows an auditor to verify a set of given data has been previously generated by a specific IIoT node withing the system. The provided case study shows how the proposed approach can be implemented on hydraulic machine shop floor to improve the operational efficiency and security.

There are several open problems regarding proposed approach. As the proposed approach involves heavy computations that requires relatively powerful devices, the future research directions include proposing approaches that can be directly implemented on resource-constrained devices. Additionally, the implementation of the adopted attribute verification protocol on IIoT devices can be further analysed.

FIGURE 1 .

 1 FIGURE 1. Industrial Blockchain Architecture and Layers.

•

  Middle Nodes (MNs): They operate as facilitators within the network to optimise data processing and storage and as bridges within subnetwork entities. These nodes perform the majority of processing operations. They are equipped with Hardware Security Module (HSM)s to ensure the generation of cryptographic keys, maintaining the security and privacy of data transactions.• Full Nodes (FNs): They represent the backbone of the blockchain, responsible for maintaining consensus and appending validated blocks to the chain. Positioned across the network, FNs execute the PBFT algorithm to maintain consensus and ensure the integrity of the blockchain.

Algorithm 1 2 :

 12 Setup Phase 1: function SetupPhase(node) ID n ← HashFunction(node.Sn ∥ node.MAC) 3: node.unique_ID ← ID n 4:

FIGURE 3 .

 3 FIGURE 3. Generation and Validation Phases

Algorithm 4

 4 Validation Phase 1: function ValidationPhase(LN , MNs, FNs, auditors) 2:

•

  Logical Network Sector (SEC=xxx) • Installer Signature (SEC=xxx,INS=xxx) • Power Consumption (SEC=xxx,POW=xxx) • Transmission Pattern (SEC=xxx,TRA=xxx)

FIGURE 4 .

 4 FIGURE 4. Implementation of the Proposed Framework

TABLE 1 .

 1 Issues in Blockchain-based IIoT

	Challenge Description
	Privacy
	Leakage
	Security
	Vulnera-
	bility
	Resource
	Con-	• IIoT nodes are resource-constrained
	straints	• Resource-intensive blockchain consensus algorithms
		are infeasible
		• Large blockchain data size and unstable network con-
		nections are challenging
	Scalability

• User identities can be revealed through analysis • Complete transaction data storage leads to potential breaches • Resource constraints limit encryption techniques • Challenges in key management and smart contract vulnerabilities • Low transaction throughput in current systems • Not suitable for high-transaction IIoT applications • Need for enhanced blockchain scalability Big Data • Vast amounts of data generated by IIoT • Cloud computing offers solutions but introduces privacy and latency issues

Pattern (TRA): Describes the

  This records the entity responsible for commissioning and configuring the node.

	The INS is pivotal for traceability and accountability, en-
	suring that any modifications or installations are reliably
	logged.
	• Power Consumption (POW): This metric reflects the
	energy usage of the Local Node (LN), which is es-
	sential for evaluating the network's sustainability and
	efficiency. Monitoring POW helps in optimising energy
	consumption and managing the environmental footprint
	of network operations.
	data com-
	munication behaviour of the node, which is crucial for
	managing network traffic, load balancing, and optimiz-
	ing bandwidth usage. The TRA helps in predicting and
	shaping the network's data flow, enhancing overall per-
	formance.

• Installer Signature (INS): • Transmission

  U attr (LN , new_attr) denotes the attribute update function, where LN , new_attr represents the new attributes to be assigned to the LN. This function reflects the dynamic adaptability of the network's attributes. Moreover, these attributes, once verified and tokenized, enable more granular control over the network. They allow network administrators to implement policies based on specific node characteristics, enhance security protocols, and optimise network performance. The registration phase (see Algorithm 2), therefore, is not just a procedural step but a critical component in establishing a robust, efficient, and secure network infrastructure.

		3 , T issue (LN ) represents the token issuance
		function. A token is issued if the verification function
		V attr (LN ) returns 1, indicating successful verification
		of the node's attributes.
	3) Dynamic Attribute Update Function: we can model
		the ability of MNs to update the attributes of LNs over
		time:
		U attr (LN , new_attr) =	Updated Attr Unchanged Attr otherwise if update
					(4)
	Here, Algorithm 2 Registration Phase
	1: function RegistrationPhase(node)
	2:	Define	node.attributes	←
		{"SEC": None, "INS": None, "POW": None, "TRA": None}
	3:	UpdateNodeAttributes(node)
	4:	return node
	5: end function
	6: function UpdateNodeAttributes(node)	▷ Update
		attributes based on network policies
	7: end function
	3) Generation Phase

TABLE 2 .

 2 Implementation Time

	Phases	No. of devices Time (ms)
	Registration	7 LNs	138
	Generation	1 LN	0.65
	Validation -challenge	1 LN	38
	Validation -response	1 LN	29

TABLE 3 .

 3 Comparative Analysis of Consensus Mechanisms

	Mechanism	Throughput Block Time	Energy	Security
	PoW	Low	10 min	High	Very High
	PoS	Med-High	Var	Low	Med-High
	PBFT	High	Secs	Mod	Low-High
	Our Proposal	High	Secs	Mod	High / ABS

 VOLUME 11, 2023

 VOLUME 11, 2023

 VOLUME 11, 2023

 VOLUME 11, 2023

VOLUME

[START_REF] Stodt | Trust management system for hybrid industrial blockchains[END_REF] 2023

VOLUME 11, 2023

 VOLUME 11, 2023

This research was funded by Project no. TKP2021-NVA-29 implemented with the support provided by the Ministry of Culture and Innovation of Hungary from the National Research, Development and Innovation Fund, financed under the TKP2021-NVA funding scheme and by ERC Advanced Grant "ERMiD" and the Federal Ministry of Education and Research (BMBF) of Germany under reference number COSMIC-X 02J21D144, and supervised by Projektträger Karlsruhe (PTKA).


Anne Raingeard
email: a.raingeard@brgm.frpa.reninger@brgm.fr

Pierre-Alexandre Reninger

Aurélie Peyrefitte
email: a.peyrefitte@brgm.fr

Guillaume Martelet
email: g.martelet@brgm.fr

Bertrand Aunay
email: b.aunay@brgm.frarnauld.malard@isska.ch

Arnauld Malard

AEM imagery down to one kilometer depth: New constraints for geological and hydrogeological modeling in volcanic contexts

Keywords: Airborne electromagnetics, Time Domain ElectroMagnetics, aeromagnetic, volcanic island, geological modelling, hydrogeological modelling

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

In order to bring fresh water to the northern part of La Réunion volcanic island (Indian Ocean), the County Council of La Réunion (CD974) requested an assessment of the geological and hydrogeological risks related to the drilling of a new water drainage gallery at great depths (300 to 1000 m) below the Plaine des Fougères. This new "GANOR" gallery will be connected to the existing deep GSAM gallery, which is covered by up to 1000 meters of volcanic materials. To assess the risks, BRGM has established a 3D geological model. This model then supported a 3D hydrogeological model in order to identify potential hydrogeological risks of drilling in the surrounding of highly permeable aquifers under high pressure. [START_REF] Peyrefitte | 71628-28: Aide à l'implantation d'une gallerie souterraine (GANOR): acquisition et valorisation de données géophysiques héliportées pour la caractérisation profonde géologique et hydrogéologique de la Plaine des Fougères[END_REF] present this work in a technical report. The local geological map exhibits very monotonous surface information, mainly with lava flows and tuffs PN3 and PN4 (Figure 1). The few existing soundings in the area never go deeper than a few dozen of meters.

DATA ACQUISITION

In 2014, an airborne electromagnetics (AEM) survey was conducted using the SkyTEM 304 system over the entire La Réunion island. This survey gave an image of the first 300 meters (line path in Figure 1). A first study creating a 3D geological model combining this AEM dataset with magnetic modelling was performed. To allow a preliminary geological/ hydrogeological interpretation, the lack of resistivity information at great depths, but also the lack of rock magnetization data, resulted in a preliminary understanding of the deep hydrogeology. This result was promising, but still highly hill-constrained.

Two surveys were then flown in 2021 over the Plaine des Fougères area, using SkyTEM 306HP and SkyTEM 312HP systems in order to extend the imagery, down to 1 000 m depth. These two new AEM datasets and the previous one are presented in Table 1. Figure 4 is an example of South-North resistivity profiles, illustrating the increase of investigation depth: SkyTEM 304 images resistivity up to 200-400 m depth,

SUMMARY

We present the integration of airborne magnetic data and five different airborne electromagnetics data sets spaning from 3 000 NIA up to 1 000 000 NIA magnetic moments (three different AEM systems were used) in La Réunion volcanic island. Subsequently, a 3D geological model of the first kilometer beneath the Plaine des Fougères was built, in order to constrain 3D hydrogeological modeling.

This approach allowed for the correlation of different datasets, providing a comprehensive image of the subsurface and enabling a greater hydrogeological understanding. It was used to position the route of a deep water drainage gallery and has great potential for applications in other areas.

whereas SkyTEM 306 HP and 312 HP system reach 800 -1000 m depth in this area, down to a maximum of 1200 m. Combining the three AEM datasets, a high-resolution / deep penetration 3D resistivity block of the Plaine des Fougères was generated from the surface down to 1000 m depth. The careful preprocessing of each dataset, their inversion and their combination into a single resistivity model is explained below.

DATA PROCESSING

BRGM processing methodology of AEM data is presented in [START_REF] Reninger | Processing methodology for regional AEM surveys and local implications[END_REF], it includes the use of the singular value decomposition [START_REF] Reninger | Singular value decomposition as a denoising tool for airborne time domain electromagnetic data[END_REF]. Also, to increase the signal/noise ratio, this processing includes an adaptative stack that changes its span according to the noise level, optimising resolution and depth of investigation. A manual editing of the residual noise was also achieved to complete the processing of each EM dataset. Such a processing strategy has proven to be essential for imaging resistivity contrasts up to one kilometer.

A joint smooth SCI inversion [START_REF] Viezzoli | Spatially constrained inversion for quasi 3D modelling of aem data[END_REF] was run considering all AEM datasets. This inversion resulted at the location of each AEM data in a 1D-resistivity model, made of 40 layers, having a fixed thickness and variable resistivity with depth.

A 3D resistivity model was then generated by the interpolation of the 304 and 306HP 1D resistivity models for the near surface (the first 200 m) and the interpolation of 306HP and 312HP 1D resistivity models for greater depths (from 200 to 1000 meters); the estimated depth of investigation was taken into account for interpolating data. The resulting 3D resistivity model takes full advantage of the resolution of the 304 and 306 HP systems for the 200 upper meters and of the depth of investigation of the 306HP and 312HP systems for mapping deeper geological bodies, allowing for greater modelling of the geological structure and understanding of the hydrogeology of the Plaine des Fougères.

During the AEM surveys, airborne magnetic data were also acquired. They were all combined to obtain a high -resolution magnetic map. Given that the surveys were conducted with the same magnetic instrumentation and parameters (acquisition height, diurnal removal, the assembly was quite simple: data points measured along flight lines of the surveys were checked for consistency and subsequently gathered in a co mmon database. A high resolution 100 m magnetic anomaly grid was derived using a simple interpolation of this new combined dataset (Figure 2).

Oriented rock sampling was made by geologists over the area from May to June 2021. A sample magnetisation library was compiled, combining laboratory measurements (performed on oriented samples with a JR5-A magnetometer) and field magnetic susceptibility (with a kappameter). This library provides rock magnetization constraints for deposits of each activity period of the Piton des Neiges (the volcano on which is located the study area). A total of 75 rock samples were used as reference for further magnetic modelling.

Taking into account the volcanic history, 2D magnetic modeling was conducted, using the geological map, the AEM resistivity model as background preliminary geometry and rock sample magnetic measurements to constrain the geological magnetic responses. The obtained modelling generated geologically realistic cross-sections (example in Figure 5). The resistivity model provided the overall geometry of the volcanic layers and their degree of alteration. The magnetic data refined the geological model in depth, thereby highlighting distinct geological units within relatively homogenous resistivity layers.

The integration of the different datasets had the added benefit of providing a more detailed geological understanding of the area and allowing for more accurate interpretations of the hydrogeology.

RESULTS

Combining all the available geological information with series of 2D geophysically constrained cross -sections, a 3D geological model was derived all over the Plaine des Fougères on a depth range of -1000 m below the topography (1500 meters above sea level). We used GeoModeller® software which previously proved to allow the successful integration of geophysics in complex 3-D geological models (e.g. [START_REF] Martelet | Integrated 3D geophysical and geological modelling of the Hercynian Suture Zone in the Champtoceaux area (South Brittany, France)[END_REF][START_REF] Calcagno | Geological modelling from field data and geological knowledge: Part I. Modelling method coupling 3D potentialfield interpolation and geological rules[END_REF]. Figure 3 displays the resulting 3D geological model, built up upon geological units classified by their increasing age, ranging from PN0 to PN4. Based on the interpretation of resistivity signatures, some of the modelled units are differentiated based on their alteration or water saturation. The 3D geological model was developed to assess the hydrogeological risk of different scenarios of gallery route. The 3D model was therefore implemented in a hydrogeological model performed in Visual KARSYS, a free software designed for assessing hydrogeological risk [START_REF] Malard | Visual KARSYS, a web-platform for the documentation of karst aquifers including online geological modelling[END_REF]. The 3D geological geometries were used as input (as well as springs, geological sounding…) to simulate the hydrogeological conditions within the 3D model. The analysis of this hydrogeological model was focused along several possible gallery routes. Taking into account the expected lithological and hydrological characteristics of the modelled geological layers the hydrogeological risk was assessed, along the different gallery routes, depending on the proximity of overlying aquifers and hydraulic head.

CONCLUSIONS

Combining high-resolution near surface and deep AEM surveys with aeromagnetic data, geological knowledge and rock sample measurements, it was possible to generate a comprehensive 3D geological model down to a depth of 1000 meters below the Plaine des Fougères. The deep AEM investigation was possible with the combination of a high power emission and a resistive underground (mainly above 300 Ohm.m in the first 400 meters). The generated 3D geological model allowed for the characterisation of the subsurface materials, as well as a better understanding of the geological structure and hydrogeological system of the area. This made it possible to map geometry of hydrogeological bodies within the first kilometer below the surface, and thus reduce the risk associated with the drilling of a deep gallery. Since, previous deep tunnelling in the area faced enormous difficulties (and loss of money), anticipating the geological and hydrogeological environment of tunnelling is of utmost importance to mitigate the geological risks prior committing heavy construction operations.

Figure 1 :

 1 Figure 1: Geological map over the Plaine des Fougères. GSAM gallery is represented with a white line, the possible area of the future gallery in a white polygon and in blue, the aquifers crossed during the GSAM drilling. Airborne acquisitions are represented in black (304 system), light orange (306 HP system) and green (312 HP system).

Figure 2 :

 2 Figure 2: Magnetic anomaly map made with the fusion of 2014 and 2021 surveys (green lines show 2021 flight path).

Figure 3 :

 3 Figure 3: 3D geological model of Plaine des Fougères.

Figure 4 :

 4 Figure 4: South-North resistivity profiles acquired with 304 (a), 306HP (b) or 312HP (c) systems in the Plaine des Fougères area. (a) is oriented S/N, (b) and (c) are overlapping, SW/NE. Dotted blue line shows a point where lines are intersecting.

Figure 5 :

 5 Figure 5: Example of an interpolated resistivity profile along the existing underground Salazie amont gallery, with geologica l interpretation. PN0 to PN4 represent old to recent volcanic formations.

Table 1 : Comparison of acquisition parameters during the 3 AEM surveys used in this study.

 1

	Parameters	304 (2014)	306 HP (2021)	312 HP (2021)
		LM	HM	LM	HM	HM
	Used time gates	6.8 µs to 5.53 e -4 s 60.42 µs to 8.9 ms	6.6 µs to 4.8e-	351 µs to 1.02e-	1.06 to
				4 s	2 s	13.79 ms
	On Time	800 µs	10 ms	1 ms	5 ms	5 ms
	Off Time	738 µs	10 ms	0.5 ms	15 ms	15 ms
	Number of gates used	20	28	41	26	27
	Number of turns	1	4	1	6	12
	Loop surface	340.82 m²	340.82 m²	342 m²	342 m²	342 m²
	Repetition frequency	325 Hz	25 Hz	333.33 Hz	25 Hz	25 Hz
	Flight line spacing	400 m	400 m	400 m	400 m	400 m
	Kilometers of data	489 km used in this	489 km used in	232 km	232 km	226.5 km
		project	this project
	Average height	82.8 m	82.8 m	51.1 m	51.1 m	54.2 m
	Average current	9.2 A	117 A	8.72 A	240.9 A	229.7 A
	Cut-off duration	3.34 µs	50 µs	6 µs	272 µs	985 µs
	~ Magnetic moment	3 000 NIA	160 000 NIA	3 000 NIA	500 000 NIA	950 000 NIA

th International Airborne Electromagnetics Workshop, 3-7 th September 2023, Fitzroy Island

ACKNOWLEDGMENTS

This project is funded by the County Council of Réunion (CD974) and the Water Office of La Réunion. We thank the University of La Réunion for cooperation in sampling volcanic rocks.