Blockchain Papers

Cailing Cheng, Hui Zhang, Li Fei, Can Liu · 6 authors

No abstract is available for this record.

Ahmed A. Jasim, Noor Riyadh Issa, Hisham A. Shehadeh, Fadhil Mukhlif · 6 authors

Abstract The Wireless Sensor Networks (WSNs), which are deployed in harsh environments, are extremely susceptible to localized battery exhaustion as well as intelligent inside routing threats, especially sinkhole and data falsification attacks. In this paper, we present ECHVM (Enhanced Cluster Head selection by Voting and an ECC-based Blockchain Mechanism), a novel, secure, and energy-efficient routing framework to achieve an optimal trade-off between network security and hardware resource efficiency. We present the ECHVM protocol that combines Elliptic Curve Cryptography (ECC) with a lightweight, local distributed ledger to mitigate risks of centralized authority by transferring the verification of crucial network events from a single, highly vulnerable centralized alert sink node to a decentralized, voting-based consensus among neighboring nodes. In this study, a weighted voting algorithm based on node and distance proximity metrics is applied to cluster head selection, where a 51% neighbor consensus rule is leveraged to validate local ledger transactions against malicious acts. Moreover, a local energy density and topological communication geometry-oriented energy-efficient cluster head (CH) selection algorithm is crafted to ensure balanced CH distribution in the high-density areas of the network structure so as to avoid the premature energy hole problem. Using the standard first-order radio energy model, quantitative simulations performed in MATLAB show that ECHVM achieves a malicious node detection rate of 98.2% and extends the network lifetime by 30% compared to state-of-the-art protocols (e.g., ELSO and SEC-HDT) because of proactive topology defense and rapid node sleeping. The results of statistical validation using the Wilcoxon Signed-Rank test confirm that both the reduction in energy consumption and network longevity offered by ECHVM are highly significant ( p = 0.019), justifying ECHVM as a mathematically sound, permanent, scalable security framework suitable for resource-constrained, dense Internet of Things (IoT) and smart sensing applications for next-generation communication systems.

P. Thanalakshmi, V. G. Kiruthika, J. C. Gokul Abinash, P. Saravanan · 6 authors

Wireless Sensor Networks (WSNs) are vulnerable to malicious nodes and sensor node failures, which compromise data integrity and network reliability. These threats result in incorrect decisions and reduce system trust. To address this, machine learning algorithms enable anomaly detection by identifying abnormal sensor nodes, while blockchain ensures secure and tamper-proof data storage. However, reliable consensus is essential before data validation in the blockchain. A hybrid framework combining ML, blockchain, and a modified HotStuff consensus algorithm with post-quantum cryptographic systems provides secure, fault-tolerant, and quantum-resistant consensus, ensuring trustworthy and resilient WSN operations.

B Srinivas, N Rukma Rekha, Subba Rao Y.V

Wireless Sensor Networks (WSNs) are widely used in critical applications such as environmental monitoring, healthcare, industrial automation, and military surveillance; however, their resource constraints, wireless communication, and unattended deployment make them highly vulnerable to node capture attacks.In such attacks, adversaries physically compromise sensor nodes to extract cryptographic keys and sensitive information, leading to key leakage, node impersonation, communication disruption, and large-scale network compromise.Existing key management schemes often rely on static key structures, they lack forward secrecy, and fail to identify structurally vulnerable nodes, resulting in weak resilience against progressive node capture attacks.To address these limitations, this paper proposes a threshold-based ECDHE-TSSS key management framework to detect vulnerable nodes and mitigate node capture attacks in WSNs.The proposed scheme introduces an attack matrix based on graph-theoretic metrics to identify high-risk nodes and provide adaptive protection through decentralized masking of secret shares.The Proposed Scheme integrates Elliptic Curve Diffie-Hellman Ephemeral (ECDHE) with Threshold Shamir Secret Sharing (TSSS) to achieve forward secrecy and strong resistance against node compromise while maintaining lightweight operations suitable for resource-constrained environments.A layered security architecture incorporating Schnorr-based Non-Interactive Zero-Knowledge Proof (NIZKP) authentication and distributed key revocation further enhances network resilience and secure communication.Simulation results demonstrate that the proposed scheme significantly reduces key compromise probability and improves overall network robustness compared with existing approaches.

Surendran Swapna Kumar, Kalli Satyanarayan Reddy

Wireless sensor networks (WSNs) integrated with the internet of things (IoT) are hybrid technologies of interconnected systems. The IoT connects various devices, from sensors to smart gadget networks, and leverages a framework to provide secure solutions. This paper presents a lightweight adaptive proof-of-stake (APoS) blockchain framework design specifically for IoT-WSN. It focuses on efficient energy, scalability, and robust security. The proposed model integrates a hybrid APoS-delegated PoS (DPoS) consensus mechanism, trust-based routing, and a random forest (RF)-driven intrusion detection system (IDS). Extensive simulations of 100 to 10,000 nodes display energy usage of 0.018–0.019 mJ/node, breach of privacy rates of 0.02%, and throughput up to 9.92 tx/round for 1,000 nodes and 3.40 tx/round for GreenOrbs validation. The IDS achieves 94.21% accuracy for 1,000 nodes and 88.89% for GreenOrbs against distributed denial-of-service (DDoS), Sybil, and Jamming attacks. Validated using the GreenOrbs dataset, the framework ensures real-world applicability in resource-constrained WSNs. Future research has validated and verified the use of APoS and PoS hybrid models for broader decentralised IoT–WSN deployments.

P. Manju Bala, S. Usharani, A. Balachandar, A. Olukayode

No abstract is available for this record.

V. Samuthira Pandi, D Shobana, J Lakshmi Priya, Ala’a Al-Shaikh · 6 authors

Power transfer is a promising new technology Wireless charging networks are being deployed for a RES array, which will be used in electric vehicles, industrial automation, and biomedical implants. Yet, due to power losses, electromagnetic interference, as well as the potential of unauthorized access or malicious attacks, guaranteeing the reliability and security of WPT systems is still a big challenge. Conventional monitoring and defect detection mechanisms need to be more efficient, reactive in real-time, and prone to data tampering. Implementation of the blockchain is a promising usable solution to solve these issues due to decentralized, tamper-proofed and transparent data management characteristics of the technology. At the same time, the research presents results on the implementation of one of the blockchain-oriented technologies in terms of constant control of normal operating conditions and detection of defects in wireless power transfer (WPT) networks, thus improving the efficiency of the operation while ensuring the security of the entire system. This study presents a novel data integrity assurance and autonomous fault detection framework for WPT systems by integrating blockchain with real-time sensor networks and artificial intelligence (AI)-based analytics. Automated Responses: Smart contracts allow automatic reactions when anomalies are detected, minimizing downtime and maintenance costs. Moreover, it enables secure and transparent records of power transactions using distributed ledger technology (DLT), preventing unauthorized access to energy and enhancing accountability of the system. Results of simulation and experimental validation show the gain in defect detection accuracy, reduced fault reporting latency, and improved cyber-attack resilience of blockchain-enabled WPT networks. This research proposes a pioneering model that can utilize blockchain-based monitoring solutions to enhance the design of WPT networks, providing an innovative proof of concept that can address the limitations of such systems. In the future, research directions that address challenges such as optimizing consensus mechanisms specifically for low-power IoT devices and investigate hybrid blockchain models could improve scalability and increase transaction speed to facilitate potential real-world applications of WPT in practical cases.

W. Zhang, Zhixi Yun, Miao Du, Xin Guo · 5 authors

As a data-free knowledge transfer paradigm, federated learning (FL) provides a novel solution for knowledge fusion in smart cities, especially in the field of Internet of Vehicles (IoV). However, the bandwidth bottleneck in the IoV limits the efficiency of federated collaboration, while trust issues associated with aggregation servers reduce users’ willingness to collaborate. To address these challenges, this article proposes a multiple collaborative iterative distillation-based swarm learning (CISL) framework for IoV. CISL leverages multiple collaborative iterative distillations to transform federated collaboration into serverless cross-device and cross-decentralized autonomous organization (DAO) knowledge transfer and fusion, enabling trustworthy swarm collaboration under bandwidth-constrained conditions. Moreover, it adaptively adjusts the inheritance and elimination of shared knowledge (SK) to enhance model adaptability and improve single-vehicle performance. Specifically, CISL proposes a collaborative iterative distillation mechanism that progressively integrates knowledge of other vehicles within the DAO, achieving cross-device SK fusion. Meanwhile, CISL introduces a multisage collaborative distillation mechanism, enabling each DAO to collaboratively distill and integrate SK from other DAOs, thereby expanding its knowledge domain. Additionally, CISL employs a dynamic balancing strategy to adaptively regulate the inheritance and elimination of SK, optimizing local models and enhancing their performance. Comprehensive experiments conducted on six benchmarks across two scenarios demonstrate that, compared to state-of-the-art methods, CISL exhibits superior adaptability and robustness across different datasets and task scenarios.

Yenlik Begimbayeva, Temirlan Zhaxalykov, Amir Akhtanov, Ruslan Pashkevich · 6 authors

This research focuses on enhancing the security of decentralized quantum key distribution (QKD) networks, where the absence of a central authority creates significant challenges such as malicious node infiltration, undetected key leakage, and unauthorized re-entry of revoked participants. Traditional authentication and trust models are insufficient for fully distributed QKD topologies, which remain highly vulnerable to insider threats and persistent compromise. To address these risks, let’s propose a layered security framework composed of three integrated components: Challenge-Response Authentication (CRA), Dynamic Trust Scoring (DTS), and Blockchain-Based Access Control (BBAC). CRA verifies node legitimacy through randomized quantum-state interactions, significantly reducing impersonation and quantum replay attacks. DTS implements real-time trust evaluation using anomaly detection to dynamically downgrade compromised nodes based on their behavioral deviations. BBAC maintains an immutable and tamper-proof trust ledger to block revoked nodes from re-entering under falsified identities and resists Sybil attacks using post-quantum cryptographic primitives. Simulation results confirm that the system improves detection rates of covert threats, ensures authentication latency under 10 ms, and reduces re-entry success to zero. The proposed architecture ensures long-term scalability and resilience, making it applicable to critical domains such as finance, national infrastructure, and military communication. This work contributes a novel, verifiable, and scalable solution to one of the most pressing open problems in distributed quantum networks

Tianxun Liang, Zhihai Rong

In this paper, we explored the Transaction Network on Ethereum from August 7, 2015 to November 22, 2024 with the perspective of complex network science. We aim to identify key network characteristics, such as disassortativity and core nodes stability. Our analysis reveals that high-degree nodes, typically mining pools and exchanges, preferentially connect with lowdegree nodes and rarely connected with each other. Additionally, we find that the ETN core structure remains robust over time. These findings contribute to blockchain network regulation and offer insights for future research into cryptocurrency transaction dynamics.

Suale Yakubu, Agnes Mindila, Peter K. Kihato

ABSTRACT The emergence of wireless technology brought about enhanced communication across various devices, resulting in the demand for efficient and reliable wireless networks, like wireless mesh networks (WMNs) and mobile Ad‐hoc Networks (MANETs). MANETs are known for their decentralized nature, rapid deployment, infrastructure‐less operation, adaptability, and ease of use in several applications and outdoor events. Despite their flexibility, they often face challenges relating to security vulnerabilities, together with blackhole and grayhole attacks, and trade‐offs in terms of performance relating to reliability and integrity. This paper proposes an improved, innovative routing protocol for Ad‐hoc On‐Demand Distance Vector (AODV) by infusion of blockchain's proof of stake (PoS) consensus mechanism named PoSAODV, whose objective is to enhance security, energy‐efficiency, and adaptability while reducing packet loss rate, routing overheads, and increasing throughput. Smart contract‐based validator selection was utilized to ensure fairness and reduce blackhole and grayhole attacks. The result obtained through simulation demonstrates that PoSAODV outperforms the original AODV by reduced latency of 0.79 ms , average throughput of 45 Mbps , and packet delivery ratio of 80%–100% in both unsafe and safe environments. This makes PoSAODV suitable for resource‐constrained ad‐hoc networks with dynamic topologies.

Dharma Teja Valivarthi, Dede Kurniadi

The rapid development of the Internet of Things (IoT) and its widespread applications in fog computing environments have underscored the urgent need for secure, scalable, and energy-efficient data exchange mechanisms. This study introduces a hybrid consensus architecture designed to address these challenges by combining Delegated Proof of Stake (DPoS) and Whale Optimization Techniques (WOT). The primary objective of this model is to optimize resource allocation, enhance security, and minimize energy consumption while ensuring scalable and efficient data sharing within fog-based IoT networks. The proposed methodology utilizes DPoS to limit node validation to a select group of trusted delegates, reducing computational overhead and improving scalability by streamlining the consensus process. Meanwhile, WOT enhances decision-making by mimicking the bubble-net feeding behavior of humpback whales, allowing for dynamic and efficient optimization of resource allocation. The integration of these two techniques significantly boosts system performance. Empirical results demonstrate that the hybrid model achieves a 95% increase in security and a 94% improvement in energy efficiency compared to conventional IoT consensus methods. Additionally, the model optimizes processing times, increases data throughput, and minimizes latency, facilitating real-time, low-latency communication that is essential for IoT applications. This combination of DPoS and WOT balances resource utilization and effectively addresses the trade-offs between security, energy efficiency, and scalability. Consequently, the hybrid DPoS-WOT consensus model emerges as a robust and practical solution for secure, efficient, and scalable IoT data sharing in fog computing environments.

Konduru Venkateswara Raju, P. Jayasri, Ranjith Kumar Painam, K. Reddy Madhavi · 6 authors

No abstract is available for this record.

Mahmuda Akter Keya, S. Mandal, Swarojani Dhar, Homaira Tahsin · 8 authors

The Internet of Things (IoT) is now causing a massive wave of digitization, creating vast amounts of data in the Internet of Everything era. Distributed ledger technologies like blockchains and IOTA are essential to IoT data services. The IOTA Foundation has completely redesigned distributed ledger technology to enable secure cash and data exchange, fee-free microtransactions, and scalable network growth for IoT device networks. It provides an approach and transaction confirmations to enable smart device microtransactions. The quicker the network uses the IOTA Tangle, the more transactions are verified. However, IOTA’s maximum transaction rate of approximately seven transactions per second (TPS) limits its potential. In May 2020, the community network achieved 600 confirmed Transactions Per Second (CTPS), showcasing progress. Motivated by the need to enhance IoT scalability and efficiency, this research proposes a methodology to integrate Tangle into IoT blockchains, leveraging Tangle as the backbone for IoT devices. The approach addresses message flooding, reducing overhead while offering a distinct web interface cost-cutting strategy to minimize transaction time and storage for microtransactions. Experimental results demonstrate that the framework improves transaction throughput, substantially reducing processing time and resource usage. These findings underscore its efficacy in enabling efficient and scalable IoT microtransactions. The research concludes that the proposed integration of Tangle enhances IoT transaction efficiency and sets a foundation for future innovations in secure, lightweight IoT data exchange.

Evaldas Drasutis

This paper introduces a novel architecture for a distributed ledger, commonly referred to as a "blockchain", which is organized in the form of directed acyclic graph (DAG) with UTXO transactions as vertices, rather than as a chain of blocks. Consensus on the state of ledger assets is achieved through the cooperative consensus: an profit-driven behavior of token holders themselves, which is viable only when they cooperate by following the "biggest ledger coverage rule", akin the "longest chain rule" of Bitcoin. The cooperative behavior is facilitated by enforcing purposefully designed UTXO transaction validity constraints. Token holders are the sole category of participants authorized to make amendments to the ledger, making participation completely permissionless - without miners, validators, committees or staking - and without any need of knowledge about the composition of the set of all participants in the consensus. The setup allows to achieve high throughput and scalability alongside with low transaction costs, while preserving key aspects of high decentralization, open participation, and asynchronicity found in Bitcoin and other proof-of-work blockchains, but without huge energy consumption. Sybil protection is achieved similarly to proof-of-stake blockchains, using tokens native to the ledger, yet the architecture operates in a leaderless manner without block proposers and committee selection.

Alexandr Kuznetsov, Emanuele Frontoni, Marco Arnesano, Kateryna Kuznetsova

Blockchain-based sensor networks offer promising solutions for secure and transparent data management in IoT ecosystems. However, efficient set membership proofs remain a critical challenge, particularly in resource-constrained environments. This paper introduces a novel OR-aggregation approach (where “OR” refers to proving that an element equals at least one member of a set without revealing which one) for zero-knowledge set membership proofs, tailored specifically for blockchain-based sensor networks. We provide a comprehensive theoretical foundation, detailed protocol specification, and rigorous security analysis. Our implementation incorporates optimization techniques for resource-constrained devices and strategies for integration with prominent blockchain platforms. Extensive experimental evaluation demonstrates the superiority of our approach over existing methods, particularly for large-scale deployments. Results show significant improvements in proof size, generation time, and verification efficiency. The proposed OR-aggregation technique offers a scalable and privacy-preserving solution for set membership verification in blockchain-based IoT applications, addressing key limitations of current approaches. Our work contributes to the advancement of efficient and secure data management in large-scale sensor networks, paving the way for wider adoption of blockchain technology in IoT ecosystems.

Maciej Kalka, Marek Kirejczyk

Transport Layer Security (TLS) protocol is a cryptographic protocol designed to secure communication over the internet. The TLS protocol has become a fundamental in secure communication, most commonly used for securing web browsing sessions. In this work, we investigate the TLSNotary protocol, which aim to enable the Client to obtain proof of provenance for data from TLS session, while getting as much as possible from the TLS security properties. To achieve such proofs without any Server-side adjustments or permissions, the power of secure multi-party computation (MPC) together with zero knowledge proofs is used to extend the standard TLS Protocol. To make the compliacted landscape of MPC as comprehensible as possible we first introduce the cryptographic primitives required to understand the TLSNotary protocol and go through standard TLS protocol. Finally, we look at the TLSNotary protocol in detail.

Nimisha Pichlerová

Wireless Sensor Networks (WSNs) play a crucial role in modern distributed systems, supporting applications such as smart cities, healthcare, and industrial automation. However, their decentralized and resource-constrained nature makes them highly vulnerable to malicious node attacks, including data manipulation, packet dropping, and routing disruption. Traditional detection techniques based on rule-based or statistical methods are inadequate for handling dynamic and complex attack patterns. Recent advancements in Artificial Intelligence (AI), particularly Vision Transformers (ViTs) with cross-attention mechanisms, have significantly improved malicious node detection by capturing global dependencies and contextual relationships in network data. Simultaneously, blockchain technology has emerged as a robust solution for secure, decentralized, and tamper-proof data storage in WSNs. Blockchain-based WSN architectures enhance data integrity, transparency, and trust through distributed ledgers and smart contracts. Studies show that blockchain-integrated detection frameworks can achieve near-perfect classification accuracy while ensuring secure data transmission. Furthermore, hybrid AI-blockchain systems combine intelligent detection with secure storage, improving resilience against attacks. This survey reviews recent methods, compares architectures, identifies research gaps, and highlights future directions for developing secure and scalable WSN systems.

Krzysztof Gogol, Benjamin Kraner, Malte Schlosser, Tao Yan · 6 authors

Liquid staking has become the largest category of decentralized finance protocols in terms of total value locked. However, few studies exist on its implementation designs or underlying risks. The liquid staking protocols allow for earning staking rewards without the disadvantage of locking the capital at the validators. Yet, they are seen by some as a threat to the Proof-of-Stake blockchain security. This paper is the first work that classifies liquid staking implementations. It analyzes the historical performance of major liquid staking tokens in comparison to the traditional staking for the largest Proof-of-Stake blockchains. Furthermore, the research investigates the impact of centralization, maximum extractable value and the migration of Ethereum from Proof-of-Work to Proof-of-Stake on the tokens' performance. Examining the tracking error of the liquid stacking providers to the staking rewards shows that they are persistent and cannot be explained by macro-variables of the currency, such as the variance or return.

D. Sumathi, T. Poongodi, Ramasamy Lakshmana Kumar, Balamurugan Balusamy

Consensus algorithms play the significant role in any blockchain system since it has to prove its performance and security. Hence, there is a need to identify the curbs of various consensus algorithms. The features of several kinds of blockchain systems rely on the consensus techniques. A detailed analysis of the algorithms has been done which might throw the limelight to researchers to devise a novel consensus algorithm. Issues due to the adoption of the consensus and solutions to overcome have been discussed. The main contribution of this work is to provide a deep insight into the consensus algorithms and its properties. There are several metrics considerations for evaluating the performance of consensus protocols. Additionally, the energy consumption of Bitcoin and Ethereum of several countries has also been presented in this work. Finally, the most commonly used consensus algorithm has been analysed along with its variant information.

Reza Soltani, Marzia Zaman, Darshana Upadhyay, Achin Jain · 5 authors

Wireless Sensor Networks (WSNs) are essential for data collection across various domains but face growing risks from replication attacks, which introduce new vulnerabilities and security challenges. To address this issue, we propose a novel hybrid approach that integrates Distributed Ledger Technology (DLT) with adaptive Machine Learning (ML) methods, aiming to bolster both security and trustworthiness within WSNs. Specifically, our approach utilizes DLT to secure voting records and manage rewards, while adaptive ML models detect replica nodes by analyzing network parameters, including location, signal strength, and transmission rate. We present and evaluate three ML-based models for detecting replication attacks: 1) Random Forest Model (RFM), 2) Adaptive Weighted Random Forest Model based on Predicted Replica Nodes (AWRFM-PRN), and 3) Adaptive Weighted Random Forest Model based on Predicted Good and Replica Nodes (AWRFM-PGRN). The AWRFM-PRN and AWRFM-PGRN models enhance detection accuracy through iterative weight adjustments based on previous predictions. Our simulations show that the hybrid approach significantly improves detection performance compared to traditional methods. We evaluated our models by increasing the dataset size with varying proportions of replica nodes across ten subsets. We found that the AWRFM-PGRN model achieved around 71% accuracy when replica nodes comprised 50% or more of the network. Meanwhile, the AWRFM-PRN model demonstrated high effectiveness with accuracy ranging from 80% to 99% for replica nodes constituting 15% to 40% of the network. Furthermore, all models delivered nearly 99.9% accuracy when the proportion of replica nodes was between 5% and 10%. This innovative integration of DLT with adaptive ML modeling establishes a benchmark for robust and tamper-proof security in WSNs, offering significant enhancements over traditional ML techniques such as RFM, particularly in scenarios with high replica node counts.

Takuma Yoshioka, Toru Nakanishi, Teruaki Kitasuka

A system of zero-knowledge proofs on graph signatures has been proposed, where a graph can be signed, and the owner of the graph signature can prove a graph relation such as the connectivity and isolation of any two vertexes on the graph without disclosing all information about the graph. The correctness of the graph information is guaranteed by the signature. One of the applications is a virtualized infrastructure, where an infrastructure provider manages a distributed system, and each tenant is allocated a specific portion of this infrastructure for use. Tenants need to check with the provider that their resources are properly connected (connectivity) and that their resources are properly separated from the resources of other tenants (isolation). On the other hand, the provider cannot simply disclose the entire infrastructure topology to each tenant. Using the zero-knowledge proof system on graph signatures, both requirements can be addressed. Previously, an efficient zero-knowledge proof system on graph signatures using a bilinear-map accumulator has been proposed, where the verification time and the size of the proof data do not depend on the number of graph vertexes and edges. However, this system has two problems. First, since the proof does not include labels, it is not possible to prove the connectivity considering network bandwidth and cost. Second, since it assumes undirected graphs, it cannot handle applications on directed graphs such as network flows. In this paper, we extend the previous system and propose a zero-knowledge proof system of the connectivity for directed graphs where each edge has labels. We implemented our system on a PC using a pairing library and evaluate it by measuring the processing times. Compared to the conference version of this paper, we show the formal definitions and the security proofs of our proposed system, and add implementation-based evaluations reflecting the application to the virtualized infrastructure.

T. Gobinath, Sanjay Kumar Sonkar, Vinod N. Alone, C. Thiripurasundari

As a distributed and decentralized ledger that ensures secure and transparent transactions, blockchain technology has attracted considerable interest. In the context of wireless sensor networks (WSNs), where nodes with limited resources conduct transactions, ensuring efficient and trustworthy validation becomes a challenge. Using random forests, this paper proposes a novel method for enhancing blockchain transaction validation in WSNs. The proposed method enhances the accuracy and efficiency of transaction validation in WSNs by leveraging the ensemble-learning capabilities of random forests. The random forests model is trained with transaction content, originating node information, and network metrics extracted from WSN transactions. Experimental results indicate that the proposed method improves transaction validation precision and decreases validation time in comparison to conventional methods. In addition, the random forests model is resistant to multiple types of attacks, assuring the security and integrity of WSN transactions. The results demonstrate that random forests are a promising technique for improving blockchain transaction validation in wireless sensor networks.

Zahoor Ali Khan, Sana Amjad, Farwa Ahmed, Abdullah M. Almasoud · 6 authors

Over the past few years, great importance has been given to wireless sensor networks (WSNs) as they play a significant role in facilitating the world with daily life services like healthcare, military, social products, etc. However, heterogeneous nature of WSNs makes them prone to various attacks, which results in low throughput, and high network delay and high energy consumption. In the WSNs, routing is performed using different routing protocols like low-energy adaptive clustering hierarchy (LEACH), heterogeneous gateway-based energy-aware multi-hop routing (HMGEAR), etc. In such protocols, some nodes in the network may perform malicious activities. Therefore, four deep learning (DL) techniques and a real-time message content validation (RMCV) scheme based on blockchain are used in the proposed network for the detection of malicious nodes (MNs). Moreover, to analyse the routing data in the WSN, DL models are trained on a state-of-the-art dataset generated from LEACH, known as WSN-DS 2016. The WSN contains three types of nodes: sensor nodes, cluster heads (CHs) and the base station (BS). The CHs after aggregating the data received from the sensor nodes, send it towards the BS. Furthermore, to overcome the single point of failure issue, a decentralized blockchain is deployed on CHs and BS. Additionally, MNs are removed from the network using RMCV and DL techniques. Moreover, legitimate nodes (LNs) are registered in the blockchain network using proof-of-authority consensus protocol. The protocol outperforms proof-of-work in terms of computational cost. Later, routing is performed between the LNs using different routing protocols and the results are compared with original LEACH and HMGEAR protocols. The results show that the accuracy of GRU is 97%, LSTM is 96%, CNN is 92% and ANN is 90%. Throughput, delay and the death of the first node are computed for LEACH, LEACH with DL, LEACH with RMCV, HMGEAR, HMGEAR with DL and HMGEAR with RMCV. Moreover, Oyente is used to perform the formal security analysis of the designed smart contract. The analysis shows that blockchain network is resilient against vulnerabilities.