Blockchain-Based Secure Localization against Malicious Nodes in IoT-Based Wireless Sensor Networks Using Federated Learning

Gebremariam, Gebrekiros Gebreyesus; Panda, Jeebananda; Indu, S.

doi:10.1155/2023/8068038

Cited by 29 publications

(4 citation statements)

References 72 publications

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“…Looking ahead, as seen with the emergence of artificial intelligence and ML, other promising technologies such as blockchain [129] and quantum computing [130] will further improve the performance and robustness of IoT localization systems. These advances promise not only to increase system performance but also to enable new LBS and applications previously thought to be infeasible.…”

Section: Discussionmentioning

confidence: 99%

Trustworthy Localization in IoT Networks: A Survey of Localization Techniques, Threats, and Mitigation

Pettorru,

Pilloni,

Martalò

2024

Sensors

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The Internet of Things (IoT) has revolutionized the world, connecting billions of devices that offer assistance in various aspects of users’ daily lives. Context-aware IoT applications exploit real-time environmental, user-specific, or situational data to dynamically adapt to users’ needs, offering tailored experiences. In particular, Location-Based Services (LBS) exploit geographical information to adapt to environmental settings or provide recommendations based on users’ and nodes’ positions, thus delivering efficient and personalized services. To this end, there is growing interest in developing IoT localization systems within the scientific community. In addition, due to the sensitivity and privacy inherent to precise location information, LBS introduce new security challenges. To ensure a more secure and trustworthy system, researchers are studying how to prevent vulnerabilities and mitigate risks from the early design stages of LBS-empowered IoT applications. The goal of this study is to carry out an in-depth examination of localization techniques for IoT, with an emphasis on both the signal-processing design and security aspects. The investigation focuses primarily on active radio localization techniques, classifying them into range-based and range-free algorithms, while also exploring hybrid approaches. Next, security considerations are explored in depth, examining the main attacks for each localization technique and linking them to the most interesting solutions proposed in the literature. By highlighting advances, analyzing challenges, and providing solutions, the survey aims to guide researchers in navigating the complex IoT localization landscape.

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Section: Discussionmentioning

confidence: 99%

Trustworthy Localization in IoT Networks: A Survey of Localization Techniques, Threats, and Mitigation

Pettorru,

Pilloni,

Martalò

2024

Sensors

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“…According to the findings of this survey, adoption of blockchain revolutionizes IoT applications, particularly in terms of calculating as well as resolving existing issues. In a thorough research of blockchain-based IoT for smart city applications, author [17] examined how 6G networks and blockchain could help with issues such a single point of failure, security, and privacy in IoT applications, particularly those for smart cities.Author [18] also covered importance of blockchain in applications for smart cities in a similar spirit. Blockchain's importance in tackling the data and privacy problems with IoT applications was explored by author [19].In addition, the authors emphasized the advantages blockchain brings to IoT ecosystem security.…”

Section: Related Workmentioning

confidence: 99%

Soft computing in secure social internet of things based on post-quantum blockchain with federated learning

Bishnoi

Parikh

Kumari

et al. 2023

Preprint

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A system's compliance with a specified security model, security standard, or specification is the focus of a security evaluation. The process of selecting the appropriate model for security assessment is determined by the type of cryptosystem. There are various models available. By enhancing data authentication and security for soft computing, this study proposes a novel technique for secure social internet of things (SSIoT) privacy analytics using post-quantum blockchain federated learning with encryption and trust analysis, the privacy analysis and data authentication. In terms of latency, QoS, energy consumption, packet loss rate, and other parameters, the experimental analysis is carried out. The purpose of the security analysis and performance evaluations is to demonstrate that the proposed scheme can satisfy the security requirements and enhance the FL model's performance. Federated learning is able to carry out effective machine learning (ML) with multiple participants while maintaining privacy of terminal personal data. Our proposed combination of federated learning as well as blockchain provides a solid foundation for future industrial Internet, as demonstrated by the numerical results.the proposed technique attainedenergy consumption of 55%, packet loss rate of 59%, QoS of 79%, Latency of 72%, network security analysis of 82%.

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“…And it simultaneously reveals the questions of personal data privacy and also possibility to inter-fere with traffic flow or the operation of vehicles through the manipulation of data. The heterogeneity of the technologies coupled with the trend adds yet another layer of com-plexity [10]. Smart road networks integrate a host of different technologies, standards, and protocols, which provides a wide opening for the attackers, making it challenging to estab-lish the uniform security measures.…”

Section: Introductionmentioning

confidence: 99%

“…Smart road networks integrate a host of different technologies, standards, and protocols, which provides a wide opening for the attackers, making it challenging to estab-lish the uniform security measures. With the increase of diversity, a security framework that is flexible and adaptable in nature is the need of the hour as this security system can keep threats under check and flawlessly connect different systems and devices when needed [11].…”

Section: Introductionmentioning

confidence: 99%

Next-Generation Protection: Leveraging Federated Learning and Blockchain for Intrusion Detection in Smart Vehicle Network

Javaid Ahmad Malik

2024

pst

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Smart Car's era ushers in new challenges, some of which are in the field of information security such as wise cyber-attack prevention. Traditional IDS systems would lose everything in such a dynamic environment with their centralized architectures, and this approach could create single points of failure and privacy issues. Along with interconnectedness, cyber security becomes an inevitable problem as smart vehicles are incorporated into a daily life. Traditional security mechanisms usually lack scalability and privacy, which brings about the need to develop alter-nate or innovative methods. This research demonstrates a mixed security system that combines both federated learning and blockchain technologies to improve intrusion detection in smart vehicular networks. We evaluated the effectiveness of this framework using four machine learn-ing models as respectively; Support Vector Machine (SVM), Decision Tree, Neural Network, and Random Forest. Empirical results show that SVM had the highest accuracy of both 93.88% in training and 91.84% in validation, which is higher than Decision Tree, Neural Network, and Random Forest models. These findings evidently demonstrate that the federated learning and blockchain are a strong solution for the plausible security of smart vehicle networks; with SVM being employed mostly in complex security scenarios. DOI: https://doi.org/10.52783/pst.353

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Blockchain-Based Secure Localization against Malicious Nodes in IoT-Based Wireless Sensor Networks Using Federated Learning

Cited by 29 publications

References 72 publications

Trustworthy Localization in IoT Networks: A Survey of Localization Techniques, Threats, and Mitigation

Trustworthy Localization in IoT Networks: A Survey of Localization Techniques, Threats, and Mitigation

Soft computing in secure social internet of things based on post-quantum blockchain with federated learning

Next-Generation Protection: Leveraging Federated Learning and Blockchain for Intrusion Detection in Smart Vehicle Network

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