Iterative and Empirical Models for Quantum Blockchain in IoT Security: A Systematic Review and Performance Analysis

The expansion of Internet of Things (IoT) ecosystems has created a high demand for secure cryptographic solutions, even in a future with quantum computing capabilities. Traditional mappings, such as RSA or Elliptic Curve Cryptography (ECC), are expected to be broken by Shor's algorithm, driving the realization of post-quantum cryptography (PQC). This work presents a simulative and prototypical framework for assessing PQC integration in IoT communication. With the liboqs library and NS-3 network simulator, along with the Python scripting language, we implement both NIST-standardized algorithms, such as Dilithium (for signatures) and Kyber (for key encapsulation), as well as the classical RSA and ECC baselines. IoT networks of 50-500 virtual nodes are simulated based on IEEE 802.15.4/6LoWPAN with RPL routing. Performance attributes, including encryption/decryption time, key length, packet delivery ratio, latency, and energy consumption, are collected and evaluated. Preliminary results indicate that PQC incurs additional handshake and key overhead, but is relatively practical, with an average overhead of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$15-25 ~\text{ms}$</tex> per processed secure transaction. The effort provides a replicable testbed, showcasing what we consider the best and worst PQC has to offer for resource-constrained IoT. Targeted at master's students and researchers, this book provides an applied approach to investigate quantum-resilient security in IoT. It encourages future expansion to hardware-in-the-loop validation on embedded devices.

The Internet of Things (IoT) now underpins critical systems like healthcare monitors, smart home, smart grids, automotive V2V & V2X networks, and industrial controls but it still relies on classical cryptography, whose security assumptions are vulnerable to quantum computers. A large quantum computer could run Shor’s algorithm to break RSA/ECC and use Grover’s algorithm to accelerate brute-forcing of symmetric keys, undermining the confidentiality and authentication of data in these IoT applications. Researchers have proposed quantum cryptographic solutions. Quantum key distribution (QKD) and quantum random number generators (QRNGs) exploit quantum mechanics to detect eavesdropping and deliver provably secure key material. For example, chip-scale QRNGs can be embedded in IoT sensors to generate true random keys, boosting resistance to brute-force attacks. These quantum techniques provide information-theoretic secrecy, for instance, QKD-generated keys have been used to authenticate smart-grid SCADA. However, most IoT devices are resource-constrained, making direct deployment of conventional QKD hardware challenging. Post-quantum cryptography (PQC) complements quantum methods by providing algorithms such as lattice-based, hash-based that resist quantum attacks on existing hardware. Standardized PQC can be deployed via software updates on legacy IoT nodes, enabling scalable and quantum-safe security within current constraints. Researchers are exploring lightweight PQC for connected cars and e-health sensors. Hybrid approaches combining QKD/QRNG with PQC offer a layered defense. Since QKD protocols are regarded as ‘future-proof’ and PQC standards mature, their combined use will be essential for robust, quantum-safe IoT security. Miniaturizing quantum components such as integrated QKD/QRNG modules and optimizing lightweight PQC schemes for low-power IoT devices are future research directions to secure IoT networks.

A roadmap from classical cryptography to post-quantum resistant cryptography for 5G-enabled IoT: Challenges, opportunities and solutions

The emergence of quantum computing seriously threatens the security of legacy cryptographic protocols in consumer electronics devices based on the Internet of Things (IoT). These are essential components of mission-critical infrastructures, such as industrial control systems, smart grids, and healthcare. In particular, this work examines how to strengthen the resistance of the IoT to classical and quantum-based cyberattacks by combining deep learning methods with post-quantum cryptography (PQC). To protect key exchange, encryption, and authentication processes in IoT-consumer devices, we will continue to use quantum-resistant cryptographic algorithms, including lattice-based, hash-based, and code-based cryptography. To provide real-time anomaly detection and response capabilities, we suggest a hybrid security framework combining PQC with deep learning-based intrusion detection systems. Our methodology ensures secure communications and data exchanges linked to quantum attacks in the future within IoT systems while leveraging continuous learning over time to allow Intrusion Detection System (IDS) models to adapt to new attack vectors. We suggest optimized algorithms for PQC solutions that are lightweight and suitable for deployment in edge computing nodes and Internet of Things devices, considering the resource-constrained IoT situations where PQC implementation would be difficult. Through comparative studies and simulations, we demonstrate how our integrated PQC-deep learning framework offers an improved IoT security solution that could safeguard vital systems in a world powered by quantum technology. The results shows the key generation time which is 12.5ms, encryption/decryption time 25.3ms, latency 18.7ms, throughput 5000 operations/sec and energy consumption 2.4mJ.

Since the Internet of Things (IoT) will be entwined with everything we use in our daily life, the consequence of security flaws escalates. Smart objects will govern most of the home appliances and car engines yielding potential disaster scenarios. In this context, successful attacks could lead to chaos and scary scenarios (www.darkreading.com). Unprotected personal information may expose sensitive and embarrassing data to the public and attacks may threaten not only our computers and smart devices, but our intimacy and perhaps our lives too. Because persons and objects will be bonded with each other, user consent becomes critical. Therefore, thing, object, and user Identity will be the focus of future IoT security solutions, yielding a Trust, Security, and Privacy (TSP) paradigm, which may constitute the Achilles' heel of IoT. While security issues are quite straightforward, mainly from background knowledge, privacy issues are far more complex. Privacy constitutes a rather challenging task, even for the m...

Safeguarding IoT networks: Mitigating black hole attacks with an innovative defense algorithm

Background: Internet of Things (IoT) is an important technology that promises a smart human being life, by allowing a communications between objects, machines and every things together with peoples. Trust is an important parameter which is closely related IOT with respect to sending a message source to destination. Objective: In this model the Internet of Thing (IOT) system to pledge with misbehaving nodes whose status are non deterministic. This paper also presents an overview of trust management model in IoT. The accuracy, robustness, and lightness of the proposed model is approved through a wide arrangement of recreations. Methods: In order to achieve the desired objective, the following four contributions has been proposed to improve the trust over internet of thing (IoT):1) End-to-end packet forwarding ratio (EPFR) 2) AEC 3) Packet Delivery Ratio 4) Detection Probability. Results: In this paper we calculate the performance of TM-IoT in term of End-to-End Packet Forwarding Ratio (EPFR) 2) AEC 3) Packet Delivery Ratio 4) Detection Probability. The exploratory result analysis shows the efficiency of proposed model as compared to existing work. Conclusion: The proposed model TM-IoT show the better exploratory result as compared to existing work in terms of End-to-End Packet Forwarding Ratio (EPFR) , AEC, Packet Delivery Ratio, Detection Probability.

The Internet of Things (IoT) is evolving in various sectors such as industries, healthcare, smart homes, and societies. Billions and trillions of IoT devices are used in e-health systems, known as the Internet of Medical Things (IoMT), to improve communication processes in the network. Scientists and researchers have proposed various methods and schemes to ensure automatic monitoring, communication, diagnosis, and even operating on patients at a distance. Several researchers have proposed security schemes and approaches to identify the legitimacy of intelligent systems involved in maintaining records in the network. However, existing schemes have their own performance issues, including delay, storage efficiency, costs, and others. This paper proposes trusted schemes that combine mean and subjective logic aggregation methods to compute the trust of each communicating device in the network. Additionally, the network maintains a blockchain of legitimate devices to oversee the trusted devices in the network. The proposed mechanism is further verified and analyzed using various security metrics, such as reliability, trust, delay, beliefs, and disbeliefs, in comparison to existing schemes.

Objectives: The key objectives are to enhance the performance of Internet of Things (IoT) networks to attain a high packet delivery ratio (PDR), efficient residual energy (RE) usage, minimum energy consumption, and decrease packet loss during data transmission. Performance metrics are used to discover and choose high, top-notch, and long-living paths, increasing network lifetime and optimizing resource management by preventing the participation of non-essential nodes and inefficient paths in the network routing process. Methods: To devise an energy-efficient technique and refine it with decision-making for selecting an optimal path, which employs a composite metric path quality index (PQI) to enhance path quality, comprising RE for priority of nodes with maximum remaining energy, link quality score (LQS), packet delivery ratio (PDR) and hop count (HC) to minimize the path length must be integrated into the routing process to make data transmission more efficient. Hence, intermediate nodes ensure path quality, determine if packet flow is good or bad, eliminate unnecessary nodes, and optimize the whole routing by deciding on a path. The proposed technique simulates IoT network scenarios in which nodes dynamically compute these metrics and select optimal paths for transmitting data to the sink node. Findings: The proposed technique achieves a much higher PDR and an energy-proficient routing mechanism by balancing the quality of the optimal path, the residual energy, and the number of hops, which can not only prolong the network lifetime but also ensures that resources are being used efficiently, in turn all leading to a better network performance than existing methods. Simulation results show that the proposed technique significantly improves the PDR over the existing methods and reduces overall energy consumption. In this work, the validated results show that metrics PQI, RE, LQS, and HC collectively improve data delivery and ensure higher survivability of the network by optimizing the path choosing and eliminating the unnecessary nodes from the source to the target. The proposed technique simulation showed a 32.15% increase in network RE and a 36.44% improvement in PDR. Novelty: The technique presented in this paper introduces a routing approach designed with the unique integration of RE, LQS, PDR, and HC, deriving PQI metric, which will improve the path selection in IoT networks. Unlike existing methods, this method considers energy efficiency, path quality instead of link quality, and path length together to avoid over-utilizing energy-draining nodes while favoring stable, efficient paths. The approach fills a gap in existing IoT environments by focusing on energy-wise efficiency and path reliability and reducing the number of nodes unnecessarily concerned with the dynamic optimal path selection. As IoT deployments increase, so does the need for energy-efficient data transmission, and the proposed technique supports the sustainability of IoT networks. Keywords: Energy Efficiency, Path Quality Indicator, Residual Energy, Hop Count, Quality of Service

The rapid expansion of the Internet of Things (IoT) has introduced unprecedented cybersecurity challenges, as traditional cryptographic methods struggle to provide both efficiency and quantum resistance in resource-constrained environments. This research introduces Adaptive Quantum Cryptography (AQC), a novel security framework that integrates Quantum Key Distribution (QKD), Post-Quantum Cryptography (PQC), Quantum Random Number Generators (QRNGs), and AI-driven dynamic adaptation to safeguard IoT networks against evolving cyber threats. Unlike classical encryption techniques such as RSA and ECC, which are vulnerable to quantum attacks, AQC leverages quantum mechanical principles—such as superposition, entanglement, and the no-cloning theorem—to ensure provably secure key exchange and data encryption. The proposed AQC system dynamically adjusts cryptographic protocols based on real-time network conditions, computational resources, and threat intelligence, optimizing security without imposing excessive overhead. Experimental evaluations demonstrate that AQC achieves a 70% improvement in key exchange efficiency, a 50% reduction in computational load, and a 95% resilience rate against quantum attacks compared to conventional encryption methods. Additionally, the integration of QRNGs enhances cryptographic key entropy, while AI-driven anomaly detection enables proactive threat mitigation. This framework is particularly suited for next-generation IoT applications, including smart grids, autonomous vehicles, and industrial automation, where security, scalability, and energy efficiency are critical. By combining quantum-resistant algorithms with adaptive security mechanisms, AQC provides forward secrecy, resistance to quantum adversaries, and robust authentication, ensuring long-term protection for IoT ecosystems in the post-quantum era. The findings underscore AQC’s potential as a foundational security architecture for future quantum-safe IoT networks.

MQRP: Mobile sinks-based QoS-aware data gathering protocol for wireless sensor networks-based smart grid applications in the context of industry 4.0-based on internet of things

Cryptographic algorithms are crucial for secure communication in the Internet of Things (IoT). Quantum computers threaten public key cryptography algorithms like RSA (Rivest, Shamir, Adleman) and Diffie-Hellman (DH) key exchange schemes, which are widely in use today. As cryptographic algorithms are the foundation of secure internet communication, the development of Quantum Key Distribution (QKD) and Post-Quantum Cryptography (PQC) is essential to protect transactions over the internet. However, solutions like Quantum Key Distribution require heavy infrastructure. So, it is not feasible for the Internet of Things with fewer resources in computing power and form factor. Post-Quantum Cryptography is helpful on these devices. But, it needs further developments and improvements. This paper analyzes cryptography algorithms and protocols of IoT with different categories. Integrated PQC of National Institute of Standards and Technology (NIST) third level finalists on Raspberry Pi 4 (RPI4) board with Linux operating system. The performance of Post-Quantum Cryptography schemes for IoT has been evaluated. Finally, a Post-Quantum Cryptography scheme has been suggested for the Internet of Things based on the performance analysis.

Internet of Things (IoT) is a connection of people and things at any time, in any place, with anyone and anything, using any network and any service. Thus, IoT is a huge dynamic global network infrastructure of Internet-enabled entities with web services. One of the most important applications of IoT is the Smart Grid (SG). SG is a data communications network which is integrated with the power grid to collect and analyze data that are acquired from transmission lines, distribution substations, and consumers. In this paper, we talk about IoT and SG and their relationship. Some IoT architectures in SG, requirements for using IoT in SG, IoT applications and services in SG, and challenges and future work are discussed.

The rapid advancement and implementation of Internet of Things (IoT)-based technologies have permitted numerous opportunities for technical innovations in diverse aspects of life. IoT technology has enabled the streamlining of processes in numerous fields, ensuring better efficiency of systems to improve the quality of life. This review paper emphasizes the significant research works that concentrate on the application of IoT to smart grids and summarizes the current work done. The paper also discusses the various challenges and opportunities in smart grids while implementing IoT, addressing the pioneering methods used in IoT along with their relevant application in different fields. The main outcomes of this paper are to provide 1) identification of feasible solutions to overcome the implementation challenges; 2) a clearer understanding of the existing technological advancements in IoT application areas; and 3) spread awareness among researchers and professionals in the field of smart grids and IoT.

The Internet of Medical Things (IoMT) has revolutionized healthcare by bringing real-time monitoring and data-driven treatments. Nevertheless, the security of communication between IoMT devices and servers remains a huge problem because of the inherent sensitivity of the health data and susceptibility to cyber threats. Current security solutions, including simple password-based authentication and standard Public Key Infrastructure (PKI) approaches, typically do not achieve an appropriate balance between security and low computational overhead, resulting in the possibility of performance bottlenecks and increased vulnerability to attacks. To overcome these limitations, we present an intelligent two-phase dual authentication framework that improves the security of sensor-to-server communication in IoMT environments. During the registration phase, our framework is based on Elliptic Curve Diffie-Hellman (ECDH) for rapid key exchange, and during real-time communication, our framework uses the Advanced Encryption Standard Galois Counter Mode (AES-GCM) to encrypt data securely. The efficiency of the proposed framework was rigorously tested through simulations that evaluated encryption-decryption time, computational cost, latency, and packet delivery ratio. The security resilience was also evaluated against man-in-the-middle, replay, and brute force attacks. The results show that encryption/decryption time is reduced by over 45%, overall computational cost by 45.38%, and latency by 28.42% over existing approaches. Furthermore, the framework achieved a high packet delivery ratio and strong defense against cyber threats for maintaining the confidentiality and integrity of the medical data across IoMT networks. However, the dual authentication approach doesn’t affect the functionality of medical IoT devices while enhancing IoMT security, which makes it an ideal integration option for existing healthcare systems.