Search Results (108)

The strain on the global food sector to reconcile environmental sustainability with operational efficiency has been intensifying. In a growing economy, this study investigates the revolutionary potential of integrated digital ecosystems that include blockchain, big data analytics, and IoT-enabled real-time monitoring on the performance of Green Supply Chain Management (GSCM). The research, that relies on the Technology–Organization–Environment (TOE) framework, utilizes a rigorous mixed-methods approach which utilizes Fuzzy-Set Qualitative Comparative Analysis (fsQCA) and Structural Equation Modeling (SEM) on data from food-processing firms in Pakistan. Green innovation is an important moderating catalyst, and SEM results confirm that digital integration significantly enhances waste reduction and energy efficiency, explaining 62% of performance variance. A further configurational analysis indicates causal equifinality and reveals 3 distinct paths to superior sustainability, from “Innovation-Driven Institutionalization” to “Government-Supported Scaling.” It demonstrates that various combinations of external support and internal readiness may ultimately contribute to success. The findings are supported by post-implementation evaluations, which show a 29% decrease in energy consumption and a 55% reduction in cold-chain losses. These findings offer novel insights for practitioners and policymakers, validating that environmental stewardship and commercial profitability are mutually reinforcing objectives in the digital age. Full article

The proliferation of Internet of Things (IoT) devices has created unprecedented challenges in cybersecurity, as billions of interconnected devices generate, process, and transmit sensitive data across diverse networks. This study addresses critical security vulnerabilities in IoT ecosystems, focusing on the development of a comprehensive security framework that encompasses device authentication, an attribute access control mechanism, and privacy preservation. This work introduces HADA, a proposed hybrid authentication method that combines the validation of unique credentials and trust value. For the authentication of the data owner and user, the following credentials are validated: identity, certificate, reconfigurable physical unclonable function (PUF), and trust. Differential privacy is used to secure the credentials during information exchange. Then, the newly developed dynamic attribute access control method selects the number of attributes and matches the attributes; these two processes are performed using the Bi-Fuzzy logic and graph neural network (GNN) algorithms, respectively. After matching the data, the user is allowed to access them from the cloud server. For data encryption, the lightweight SKINNY algorithm is implemented in Hyperledger Fabric blockchain. The proposed system performs better than existing methods in terms of throughput, latency, and resource utilization. Full article

This systematic review examines how digital technologies enable circular economy (CE) transitions across sectors and value chains. Analysing 266 peer-reviewed publications (2016–2025), we develop a comprehensive taxonomy of digital enablers—including IoT, AI, blockchain, cloud computing, additive manufacturing, and digital platforms—and map their applications to circular strategies such as reuse, remanufacturing, and recycling. Our findings reveal that data-driven technologies dominate CE implementation, with 89% of studies involving data collection, storage, analysis, or sharing functions. IoT emerges as the foundational technology for real-time tracking and monitoring, while AI and big data analytics optimise circular processes and predict maintenance needs. Blockchain ensures traceability and trust in circular supply chains, and cloud computing provides scalable infrastructure for collaboration. Manufacturing (41%) and construction (15.5%) are the most studied sectors, with strong European research leadership reflecting policy drivers such as Digital Product Passports. We identify three impact types: enabling (process optimisation), disruptive (business model innovation), and facilitating (ecosystem collaboration). Key barriers include technical complexity, organisational resistance, high implementation costs, and regulatory gaps. The review concludes with recommendations for integrated, multi-stakeholder approaches to realise a digitally enabled circular economy. Full article

Digital transformation is central to circular economy (CE) strategies, yet the intersection between digital innovation and women’s entrepreneurship remains underexplored. We examine how IoT, AI, blockchain, data analytics and platform technologies are represented in CE-oriented management research and assess the visibility of gender-inclusive and women entrepreneurship perspectives. We merged Scopus and Web of Science records (2015–2025), removed duplicates, screened for relevance, and mapped themes and networks using bibliometrix (R) and VOSviewer. Digital-CE scholarship was found to rise after 2018, dominated by smart manufacturing, circular supply chains, digital product passports and blockchain traceability. Four clusters emerged: digital circular manufacturing, circular business model innovation, waste and resource management, and policy–social aspects. Gender-related terms appear in only 1.35% of the corpus, revealing a gap between academic research and EU policy priorities for inclusive digital and circular transitions. We integrate a gender-inclusive lens and outline an agenda positioning women entrepreneurs as critical yet overlooked actors in digital circular ecosystems. As a bibliometric review, this study maps scholarly attention rather than the prevalence of women-led circular ventures. Beyond mapping, we advance the paper’s primary contribution by proposing a governance-oriented synthesis that frames digital infrastructures as administrative mechanisms shaping who can participate in, benefit from, and influence digital circular ecosystems. Full article

Multi-tenancy is essential for scalable IoT–Cloud systems; however, it introduces complex security vulnerabilities at the intersection of shared cloud infrastructures and resource-constrained IoT environments. This systematic review evaluates next-generation security frameworks designed to enforce tenant isolation without violating the strict latency (<10 ms) and energy bounds of lightweight sensors. Adhering to PRISMA guidelines, we analyze selected high-quality studies to categorize intersectional threats, including cross-tenant data leakage, side-channel attacks, and privilege escalation. Our analysis identifies a critical, unresolved conflict: existing mitigation strategies often incur a 12% computational and communication overhead, creating a significant barrier for real-time applications. Furthermore, we critically analyze emerging technologies, including Zero Trust Architectures (ZTA), adaptive Artificial Intelligence (AI), blockchain, and Post-Quantum Cryptography (PQC). We find that direct PQC deployment is currently infeasible for LPWAN protocols due to key-size constraints (1.6 KB) that exceed typical payload limits. To address these challenges, we propose a novel multi-layer security design principle that offloads heavy isolation and cryptographic workloads to hardware-accelerated edge gateways, thereby maintaining tenant isolation without compromising real-time performance. Finally, this review serves as a roadmap for future research, highlighting federated learning and hardware enclaves as essential pathways for securing next-generation multi-tenant IoT ecosystems. Full article

The rapid expansion of the Internet of Things (IoT) has led to billions of interconnected devices generating and exchanging sensitive data across diverse domains, which introduces challenges in identity management (IdM) regarding privacy, scalability, and verifiability. While blockchain technology provides decentralization and tamper resistance, its transparency and increasing on-chain storage demands make it unsuitable for large-scale IoT identity ecosystems. To overcome these challenges, IoT-SBIdM is proposed as a lightweight, privacy-preserving, and stateless blockchain-based identity management framework designed for IoT environments. This framework incorporates Elliptic Curve Cryptography (ECC)-based accumulators and Zero-Knowledge Proofs (ZKPs) to facilitate selective disclosure, enabling entities to prove credential authenticity without exposing sensitive identity information. Furthermore, the framework adopts W3C-compliant Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs) to promote interoperability and user-controlled identity ownership. The experimental results indicate that IoT-SBIdM achieves efficient smart contract execution by reducing gas costs through optimized registry logic. Moreover, the system maintains a compact block size of only 45 MB at higher block heights, outperforming comparable schemes in storage efficiency by achieving a 55% reduction relative to recent models and an approximate 94% reduction relative to older systems, thereby demonstrating superior scalability and storage efficiency, making it suitable for identity management solutions for IoT environments. Full article

Ports are critical nodes in global supply chains and play a central role in sustainability transitions in trade and logistics. This study investigates how Artificial Intelligence (AI) contributes to sustainable innovation within port ecosystems, focusing on efficiency, transparency, resilience, and environmental performance. To address the research question—how has AI supported sustainability in maritime ports?—we conducted a systematic screening combined with bibliometric performance analysis and science mapping. A total of 80 peer-reviewed articles published between 2019 and 2025 (Scopus) were analysed. The results show a strong acceleration of publications in 2025, alongside a citation–time lag for recent studies. The findings indicate three dominant application streams: (1) operational efficiency and optimisation (terminal operations, forecasting, routing, scheduling); (2) digital and smart-port enablement through IoT and data infrastructures; and (3) governance, risk, and compliance (e.g., Port State Control, inspection analytics, cyber-resilience). The mapping also evidences increasing convergence of AI with complementary technologies—particularly IoT and, in a smaller but visible subset, blockchain—to enhance trust, accountability, and interoperability. By synthesising the field’s intellectual structure and thematic evolution, this study outlines research gaps and proposes future directions toward integrated frameworks for sustainable port ecosystems and Sustainable Commerce 4.0. Full article

Sixth-generation (6G) networks will operate with pervasive autonomy and minimal centralised control, imposing stringent requirements on security and trust. This short communication presents a hybrid trust evaluation approach that combines fuzzy inference for uncertainty management, bidirectional long short-term memory (BiLSTM) networks for temporal prediction, and blockchain for immutable verification. The pipeline first maps multi-source interaction and context metrics into linguistic trust values via fuzzy rules, then leverages BiLSTM to anticipate trust fluctuations under dynamic conditions, and finally anchors trust updates on a permissioned blockchain to ensure integrity and traceability. Using CIC-IoT2023, the proposed approach attains high accuracy and F1-score while reducing Execution Time (ET) and energy demands relative to a recent spatial-temporal trust model for 6G IoT. Results indicate that jointly addressing uncertainty, temporal evolution, and ledger-backed validation yields stable trust trajectories suitable for resource-constrained devices. The study outlines a practical path toward explainable, adaptive, and tamper-resistant trust management for 6G ecosystems. Full article

This study examines how technological innovation and sustainability jointly reshape contemporary public administration by integrating digital transformation with public value creation. Using a mixed-method approach, we compile a Scopus-based bibliographic dataset and conduct descriptive and network analyses on 199 articles to map publication trends, methodological patterns, and core keyword clusters. We then perform an in-depth qualitative content analysis of 83 papers, coding public sector domains, actors, technological innovations, and sustainability dimensions. Findings highlight a shift from early e-government, centered on administrative efficiency, toward a paradigm of “sustainable digital governance”, where AI, IoT, blockchain and data analytics drive the twin digital–green transition. Five conceptual clusters and several application domains show that public value increasingly emerges within collaborative ecosystems involving administrations, firms, universities, citizens and digital platforms. The study offers an integrated overview of this evolving field and clarifies technology’s role as an enabling factor in sustainable governance. Building on the review results, we propose the Sustainable Public Innovation Ecosystem (SPIE) framework, which links systemic enablers (technological and sustainability innovation) governance efficiency and sustainable public value through ecosystem dynamics and governance mechanisms. It also outlines a future research agenda on hybrid actors ethical and regulatory issues, and approaches to measuring sustainable public value, providing guidance for scholars and policymakers designing digitally enabled and sustainability-oriented public reforms. Full article

Unmanned Aerial Vehicles (UAVs) are increasingly integrated into Internet of Things (IoT) ecosystems for applications such as surveillance, disaster response, environmental monitoring, and logistics. These missions demand reliable and secure communication between UAVs and cloud platforms for command, control, and data storage. However, UAV communication channels are highly vulnerable to eavesdropping, spoofing, and man-in-the-middle attacks due to their wireless and often long-range nature. Traditional cryptographic schemes either impose excessive computational overhead on resource-constrained UAVs or lack sufficient robustness for cloud-level security. To address this challenge, we propose a dual-layer encryption architecture that balances lightweight efficiency with strong cryptographic guarantees. Unlike prior dual-layer approaches, the proposed framework introduces a context-aware adaptive lightweight layer for UAV-to-gateway communication and a hybrid post-quantum layer for gateway-to-cloud security, enabling dynamic cipher selection, energy-aware key scheduling, and quantum-resilient key establishment. In the first layer, UAV-to-gateway communication employs a lightweight symmetric encryption scheme optimized for low latency and minimal energy consumption. In the second layer, gateway-to-cloud communication uses post-quantum asymmetric encryption to ensure resilience against emerging quantum threats. The architecture is further reinforced with optional multi-path hardening and blockchain-assisted key lifecycle management to enhance scalability and tamper-proof auditability. Experimental evaluation using a UAV testbed and cloud integration shows that the proposed framework achieves 99.85% confidentiality preservation, reduces computational overhead on UAVs by 42%, and improves end-to-end latency by 35% compared to conventional single-layer encryption schemes. These results confirm that the proposed adaptive and hybridized dual-layer design provides a scalable, secure, and resource-aware solution for UAV-to-cloud communication, offering both present-day practicality and future-proof cryptographic resilience. Full article

The integration of Software-Defined Networking (SDN), blockchain (BC), and machine learning (ML) has emerged as a promising approach to securing Internet of Things (IoT) and Industrial IoT (IIoT) networks. This paper conducted a comprehensive review of recent studies focusing on multi-layered security across device, control, network, and application layers. The analysis reveals that BC technology ensures decentralised trust, immutability, and secure access validation, while SDN enables programmability, load balancing, and real-time monitoring. In addition, ML/deep learning (DL) techniques, including federated and hybrid learning, strengthen anomaly detection, predictive security, and adaptive mitigation. Reported evaluations show similar gains in detection accuracy, latency, throughput, and energy efficiency, with effective defence against threats, though differing experimental contexts limit direct comparison. It also shows that the solutions’ effectiveness depends on ecosystem factors such as SDN controllers, BC platforms, cryptographic protocols, and ML frameworks. However, most studies rely on simulations or small-scale testbeds, leaving large-scale and heterogeneous deployments unverified. Significant challenges include scalability, computational and energy overhead, dataset dependency, limited adversarial resilience, and the explainability of ML-driven decisions. Based on the findings, future research should focus on lightweight consensus mechanisms for constrained devices, privacy-preserving ML/DL, and cross-layer adversarial-resilient frameworks. Advancing these directions will be important in achieving scalable, interoperable, and trustworthy SDN-IoT/IIoT security solutions. Full article

The temperate Himalayan states of Jammu and Kashmir, Himachal Pradesh, Uttarakhand, Ladakh, Sikkim, and Arunachal Pradesh in India face unique agro-ecological challenges across agriculture and allied sectors, including pest and disease pressures, inefficient resource use, post-harvest losses, and fragmented supply chains. This review systematically examines 21 critical problem areas, with three key challenges identified per sector across agriculture, agricultural engineering, fisheries, forestry, horticulture, sericulture, and animal husbandry. Artificial Intelligence (AI) and Machine Learning (ML) interventions, including computer vision, predictive modeling, Internet of Things (IoT)-based monitoring, robotics, and blockchain-enabled traceability, are evaluated for their regional applicability, pilot-level outcomes, and operational limitations under temperate Himalayan conditions. The analysis highlights that AI-enabled solutions demonstrate strong potential for early pest and disease detection, improved resource-use efficiency, ecosystem monitoring, and market integration. However, large-scale adoption remains constrained by limited digital infrastructure, data scarcity, high capital costs, low digital literacy, and fragmented institutional frameworks. The novelty of this review lies in its cross-sectoral synthesis of AI/ML applications tailored to the Himalayan context, combined with a sector-wise revenue-loss assessment to quantify economic impacts and guide prioritization. Based on the identified gaps, the review proposes feasible, context-aware strategies, including lightweight edge-AI models, localized data platforms, capacity-building initiatives, and policy-aligned implementation pathways. Collectively, these recommendations aim to enhance sustainability, resilience, and livelihood security across agriculture and allied sectors in the temperate Himalayan region. Full article

The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT), known as Artificial Intelligence of Things (AIoT), has emerged as a transformative paradigm for enabling intelligent, data-driven, and context-aware decision-making in urban environments to reduce the carbon footprint of mobility and industry. This review examines the conceptual foundations, and state-of-the-art developments of AIoT, with a particular emphasis on its applications in smart cities and its relevance to climate change management. AIoT integrates sensing, connectivity, and intelligent analytics to provide optimized solutions in transportation systems, energy management, waste collection, and environmental monitoring, directly influencing urban sustainability. Beyond urban efficiency, AIoT can play a critical role in addressing the global challenges and management of climate change by (a) precise measurements and autonomously remote monitoring; (b) real-time optimization in renewable energy distribution; and (c) developing prediction models for early warning of climate disasters. This paper performs a literature review and bibliometric analysis to identify the current landscape of AIoT research in smart city contexts. Over 1885 articles from Web of Sciences and over 1854 from Scopus databases, published between 1993 and January 2026, were analyzed. The results reveal a strong and accelerating growth in research activity, with publication output doubling in the most recent two years compared to 2023. Waste management and air quality monitoring have emerged as leading application domains, where AIoT-based optimization and predictive models demonstrate measurable improvements in operational efficiency and environmental impact. Altogether, these support faster and more effective decisions for reducing greenhouse gas emissions and ensuring the sustainable use of resources. The reviewed studies reveal rapid advancements in edge intelligence, federated learning, and secure data sharing through the integration of AIoT with blockchain technologies. However, significant challenges remain regarding scalability, interoperability, privacy, ethical governance, and the effective translation of research outcomes into policy and citizen-oriented tools such as climate applications, insurance models, and disaster alert systems. By synthesizing current research trends, this article highlights the potential of AIoT to support sustainable, resilient, and citizen-centric smart city ecosystems while identifying both critical gaps and promising directions for future investigations. Full article

The proliferation of Internet of Things (IoT) applications in safety-critical domains, such as healthcare, smart transportation, and industrial automation, demands robust solutions for data integrity, traceability, and security that surpass the capabilities of centralized databases. This paper analyzes how blockchain technology can be integrated with core IoT service functions—including data management, security, device management, group coordination, and automated billing—to enhance immutability, trust, and operational efficiency. Our analysis identifies practical use cases such as consensus-driven tamper-proof storage, role-based access control, firmware integrity verification, and automated micropayments. These use cases showcase blockchain’s potential beyond traditional data storage. Building on this, we propose a novel framework that integrates a permissioned distributed ledger with a standardized IoT service layer platform through a Blockchain Interworking Proxy Entity (BlockIPE). This proxy dynamically maps IoT service functions to smart contracts, enabling flexible data routing to conventional databases or blockchains based on the application requirements. We implement a Dockerized prototype that integrates a C-based oneM2M platform with an Ethereum-compatible permissioned ledger (implemented using Hyperledger Besu) via BlockIPE, incorporating security features such as role-based access control. For performance evaluation, we use Ganache to isolate proxy-level overhead and scalability. At the proxy level, the blockchain-integrated path achieves processing latencies (≈86 ms) comparable to, and slightly faster than, the traditional database path. Although the end-to-end latency is inherently governed by on-chain confirmation (≈0.586–1.086 s), the scalability remains high (up to 100,000 TPS). This validates that the architecture secures IoT ecosystems with manageable operational overhead. Full article

This review examines the impact of data analytics powered by the Internet of Things (IoT), edge computing, and artificial intelligence (AI) on improving energy efficiency in smart environments, with a focus on smart factories, smart cities, and smart territories. Advanced AI, machine learning (ML), and deep learning (DL) techniques enable real-time energy optimization and intelligent decision-making in complex, data-intensive systems. Integrating edge computing reduces latency and improves responsiveness in IoT and Industrial Internet of Things (IIoT) networks, enabling local energy management and reducing grid load. Federated learning further enhances data privacy and efficiency by enabling decentralized model training across distributed smart nodes without exposing sensitive information or personal data. Emerging 5G and 6G technologies provide the necessary bandwidth and speed for seamless data exchange and control across energy-intensive, connected infrastructures. Blockchain increases transparency, security, and trust in energy transactions and decentralized energy trading in smart grids. Together, these technologies support dynamic demand response mechanisms, predictive maintenance, and self-regulating systems, leading to significant improvements in energy sustainability. Case studies of smart cities and industrial ecosystems within Industry 4.0/5.0/6.0 demonstrate measurable reductions in energy consumption and carbon emissions through these synergistic approaches. Despite significant progress, challenges remain in interoperability, scalability, and regulatory frameworks. This review demonstrates that AI-based edge computing, supported by robust connectivity and secure IoT and IIoT architectures, has a transformative potential for creating energy-efficient and sustainable smart environments. Full article