The Internet of Things holds immense promise for advancing sustainability by enabling intelligent management of resources in large-scale systems such as smart electrical grids. However, optimizing these complex systems often exceeds the capabilities of traditional classical computers. This research introduces the Quantum Nexus, a conceptual system architecture that integrates quantum computing with sustainable Internet of Things deployments. The proposed system leverages edge computing for initial data preprocessing and employs advanced post-quantum cryptographic techniques to ensure robust security against emerging computational threats. In simulations involving a smart grid network of one thousand nodes, a quantum-inspired algorithm called the Quantum Approximate Optimization Algorithm was compared against a classical Genetic Algorithm. The quantum approach reduced grid imbalance by eighteen point five percent and achieved solutions 3.2 times faster in complex scenarios. Although quantum computing involves substantial initial energy consumption, the overall operational energy savings demonstrate a net positive environmental impact in large-scale applications. The Quantum Nexus architecture thus offers a promising but challenging framework for sustainable Internet of Things systems, balancing optimization power with energy and security trade-offs.
The Internet of Things holds immense promise for advancing sustainability by enabling intelligent management of resources in large-scale systems such as smart electrical grids. However, optimizing these complex systems often exceeds the capabilities of traditional classical computers. This research introduces the Quantum Nexus, a conceptual system architecture that integrates quantum computing with sustainable Internet of Things deployments. The proposed system leverages edge computing for initial data preprocessing and employs advanced post-quantum cryptographic techniques to ensure robust security against emerging computational threats. In simulations involving a smart grid network of one thousand nodes, a quantum-inspired algorithm called the Quantum Approximate Optimization Algorithm was compared against a classical Genetic Algorithm. The quantum approach reduced grid imbalance by eighteen point five percent and achieved solutions 3.2 times faster in complex scenarios. Although quantum computing involves substantial initial energy consumption, the overall operational energy savings demonstrate a net positive environmental impact in large-scale applications. The Quantum Nexus architecture thus offers a promising but challenging framework for sustainable Internet of Things systems, balancing optimization power with energy and security trade-offs.
This chapter explores the transformative integration of Quantum Computing (QC) with the Internet of Things (IoT), highlighting significant advancements in security, optimization, and intelligent decision-making for cyber-physical systems. It provides a detailed review of quantum-enhanced algorithms, architectures, and sensing technologies designed for IoT ecosystems. Addressing the limitations of traditional methods in large-scale computation, network optimization, data analytics, and security, the chapter emphasizes core quantum concepts, i.e., superposition, entanglement, and interference. Techniques, including quantum annealing and the QAOA, enable efficient resource allocation, scheduling, and routing in emerging infrastructures like 6G and UAV networks. The use of hybrid quantum–classical models is examined for fault detection, cryptographic key generation, and real-time analytics across edge-to-cloud environments. QML has been shown to enhance adaptive learning and pattern recognition on resource-limited devices. To counter emerging cyber threats, quantum-resilient security solutions, including post-quantum cryptography, quantum random number generation, and quantum key distribution, are discussed. The chapter also highlights quantum sensors that utilize tunnelling, spin dynamics, and interference for applications in biomedical diagnostics, environmental monitoring, infrastructure evaluation, and autonomous navigation. Emerging use cases, including quantum-powered digital marketing and quantum-secured smart locks, are introduced. The chapter concludes by underscoring the transformative role of scalable hybrid QC–IoT architectures, which are poised to advance edge intelligence, smart infrastructure, healthcare, and logistics, while opening new directions for research and real-world deployment.
The emergence of quantum computing poses significant threats to classical cryptographic systems, particularly in smart urban environments powered by the internet of things (IoT). This chapter explores the sustainability aspects and future prospects of integrating post-quantum cryptography (PQC) into IoT-based infrastructures. It examines various PQC algorithms, their deployment challenges in resource-constrained devices, and the trade-offs between energy efficiency, performance, and security. The chapter also highlights quantum-specific threats, including communication vulnerabilities and supply chain risks, and emphasizes sustainable strategies such as lightweight cryptography, hybrid models, green computing paradigms, and AI-based optimization. Future research directions call for energy-efficient algorithm design, cryptographic hardware co-development, and multidisciplinary collaboration. The study underscores the importance of aligning cryptographic resilience with long-term sustainability goals in smart city systems.
Modern smart grids demand robust computational solutions to manage real-time data processing, complex optimization, and fault resilience, especially with the integration of distributed and renewable energy resources (DERs). This study introduces a hybrid classical-quantum architecture tailored for these needs, leveraging classical computing for routine processing while employing quantum computing for high-complexity tasks. Experimental simulations demonstrate that the hybrid architecture reduces computation time by 40–41% and enhances energy efficiency by approximately 30% compared to traditional configurations. Additionally, its task allocation system improves scalability and resilience, making this architecture a viable solution for optimizing dynamic, large-scale smart grids. The findings offer a pathway toward a scalable and sustainable grid management system, bridging classical reliability with quantum advancements.
The relationship between quantum computing and the Internet of Things (IoT) is becoming a powerful pathway to develop secure, intelligent and sustainable network systems – however, present-day IoT architectures experience scalability challenges, as well as energy and data vulnerability, due to classical models in cryptography. This paper presents a Next Generation Quantum-IoT Communication Framework (QIoT-CF) that addresses these issues with quantum-based encryption, entanglement-assisted routing, and dynamic sustainability optimization for communication in the framework. The architecture uses Quantum Key Distribution (QKD) to ensure quality data security that is unbreakable, and it uses reinforcement learning–based queuing to balance energy consumption and communication reliability with heterogeneous IoT nodes. The simulations suggest that latency decreased by 45%, energy consumption improved by 43%, and the quantum bit error rate (QBER) improved by 52% relative to current IoT systems. The sustainability controller will change transmission policies based on carbon-aware energy metrics providing an acceptable trade-off between quantum performance and sustainability at-scale. The framework represents a critically significant advancement toward eco-intelligent quantum communication security situated within the 6G vision and beyond.
The intersection of Quantum Computing (QC) and the Internet of Things (IoT) represents a paradigm shift in meeting the challenges of classical computing for large-scale, data-intensive IoT environments. IoT produces massive streams of data in real-time across several arenas, including healthcare, smart cities and industrial automation. The data produced is confronted with significant data processing, security, and resource utilization challenges. The principles of QC such as superposition and entanglement, provides incomparable computational capabilities, consenting to real-time processing of high-dimensional IoT data, quantum-secure cryptography, and sophisticated optimization methods. While the potential of QC is path-breaking, integration with IoT involves enormous challenges, including scalability, energy efficiency, hybrid architecture, and cost. This paper explains the collaborative prospects of QC-IoT integration, pointing to major challenges and research directions to exploit its capabilities towards greater efficiency, security, and sustainability.
Integrating the Internet of Things (IoT) in smart grids has revolutionized the energy sector, enabling real-time data collection and efficient energy distribution. However, this integration also introduces significant security challenges, particularly data encryption. Traditional encryption algorithms used in IoT are vulnerable to various attacks, and the advent of quantum computing exacerbates these vulnerabilities. To address the above challenges, this paper proposes a novel encryption mechanism, the Quantum-Resistant Hybrid Encryption for IoT (QRHE-IoT), designed to enhance the security of communications in IoT-enabled smart grids. QRHE-IoT combines the strengths of symmetric and asymmetric encryption algorithms and incorporates quantum-resistant algorithms to provide robust security. This paper explains the QRHE-IoT mechanism, its theoretical basis, and how it addresses the security challenges in smart grid communications. It also presents the results of tests conducted to evaluate the effectiveness of QRHE-IoT in a simulated smart grid environment. The proposed QRHE-IoT mechanism presents a promising solution to these challenges, offering robust security for smart grid communications in the face of emerging threats, including quantum computing.