This paper introduces an LLM-driven design-time workflow for Internet of Things (IoT) and network management system development that combines the generative and summarization capabilities of Large Language Models with the formal rigor of Model-Driven Engineering (MDE). The key novelty lies in grounding LLM-assisted topology design and network management, including reasoning about configuration code to formally verifiable models, enabling security- and safety-aware decisions by design with improved trust and explainability compared with black-box runtime solutions. The approach relies on activity-diagram-based models that provide formal semantics for capturing control flows, decision points, and interactions among IoT devices, edge nodes, and network management components, supporting systematic functional safety validation. Design-time security analysis is realized through MDE combined with Object Constraint Language (OCL) rules, allowing explainable detection of misconfigurations, policy violations, and potential vulnerabilities before deployment. The workflow is evaluated using representative IoT and mobile network management scenarios, demonstrating enhanced effectiveness and up to 15 times reduction in detection and corrective action time for critical tasks.

The fast adoption of Internet of Things (IoT) devices, autonomous systems and latency-sensitive applications has increased the need to have effective real-time data processing architectures. The paper will provide a detailed comparative analysis of cloud-native and edge computing systems in processing real-time data with a systematic literature review (SLR) and empirical benchmarking experiments. On the basis of a PRISMA-directed review of 87 papers (22 of which have been ultimately included) found after screening, we evaluate latency, throughput, energy consumption, scalability, fault tolerance, and security profiles of both paradigms. The experimental findings show that edge computing has a mean latency of 8.3 ms compared to cloud-native deployment of 142.7 ms, and the cloud-native architecture has higher availability at 99.95% and is scaled 3.8× times horizontally. It is suggested to use a hybrid framework, combining edge inference with cloud orchestration that is 94.2% times faster and has the same cloud-grade reliability. The ANOVA, regression modelling, and multi-criteria decision analysis (MCDA) data analysis shows that the choice of the optimal architecture is determined by application specific latency tolerance (α), data locality requirements and the budget constraint in the infrastructure. These results are applicable to the system architects operating in such sectors as smart healthcare, industrial IoT, autonomous vehicles, and smart grid management.

The growing deployment of the Internet of Things (IoT), especially in critical infrastructure, has increased the need for identity systems that are scalable and robust against attacks. However, existing centralized systems have fundamental weaknesses, especially where adversaries use artificial intelligence (AI)-based techniques, such as generative spoofing, model poisoning, and deepfakes to create fake identities. In this paper, we present a novel blockchain-based IoT security system that combines decentralized identity verification, zero-knowledge proofs, Byzantine-resistant federated learning, and formal verification of smart contracts. The proposed architecture eliminates single points of trust, allows device registration while preserving privacy, and provides defense against AI-driven attacks through formally modeled state transitions. Experimental results show that this method shows significant improvements over previous frameworks, including a 48% reduction in false acceptance rate during GAN-based spoofing and speedup the ZKP verification. This work provides a blockchain-enabled identity management system for IoT to encounter AI-based threats and maintain a balance between performance and security with the help of adversarial simulation, symbolic execution, and threshold cryptography.

Enhancing energy management for internet of things enabled smart grids with the LEO-QCGNN approach

Vehicular ad hoc networks (VANETs) and the Internet of Things (IoT) are fundamental components of intelligent transportation systems (ITS). The evolution toward 6G‐enabled vehicular networks and the integration of heterogeneous sensors in modern vehicles have created new opportunities for improving routing efficiency, mobility management, scalability, and security in vehicular IoT (VIoT). However, highly dynamic topologies and strict quality‐of‐service requirements introduce significant technical challenges. Machine learning (ML), particularly neural network–based approaches, has emerged as a promising solution for addressing these issues. Despite the rapid growth of ML‐driven VIoT research, a structured and up‐to‐date systematic review remains limited. This paper presents a comprehensive systematic review of ML applications in VIoT, analyzing 30 selected studies and categorizing them using a newly proposed taxonomy based on training mechanisms and data utilization strategies. The reviewed works are comparatively evaluated in terms of application domains, performance metrics, challenges, and future research directions. The findings demonstrate that ML techniques enhance routing performance, enable accurate traffic congestion prediction, and support intelligent decision‐making. Nevertheless, security, scalability, and real‐world deployment remain open challenges requiring further investigation.

In sharded blockchains, peers are divided into smaller groups (shards) that generate and verify blocks in parallel, offering enhanced throughput and reduced delays. These properties make sharded blockchains a promising solution for secure data management in Internet of Things (IoT) systems. Particularly, geolocation-based sharded blockchains assign geographically proximate peers to the same shard, enabling faster IoT transaction processing. Yet, peers in each shard can easily collude to falsely accept/reject blocks. To resolve this issue, in this paper, we propose a robust reputation-based shard inspection framework. The framework adopts the shard inspection mechanism where a group of inspectors selected from the most reputable peers randomly verify blocks in each shard. This enables avoiding collusion attacks and enhancing the security of each shard. However, additional block verifications during the inspection process can incur significant block delays and energy overheads. To reduce these overheads, we formulate an optimization problem that jointly determines the number of inspectors and the inspection interval to maximize the system utility, which is proportional to the blockchain throughput and energy consumption. We then develop a distributed algorithm that enables dividing the optimization problem into sub-problems solvable independently by each shard. Experimental results show that our framework can maximize the system utility, while maintaining high levels of security in each shard.

With the deepening of the transformation and upgrading of the construction industry and high-quality development, the intrinsic relationship and optimization path between Construction Quality Management (CQM) and Organizational Performance (OP) have become a core issue of concern for both the industry and academia. The traditional quality management system faces many challenges in responding to increasingly complex engineering requirements and urgently needs to integrate emerging technologies to achieve strategic upgrades. This study focuses on exploring the empirical correlation between construction quality management practices and organizational performance (covering multiple dimensions such as cost, schedule, safety, satisfaction, etc.), and deeply analyzes the intelligent technology driven path centered on the deep integration of Building Information Modeling (BIM) and Internet of Things (IoT) technology, aiming to construct a strategic framework of "quality technology performance" linkage. This study reveals the correlation mechanism between construction quality management (QC) and organizational performance (OP) through empirical analysis, and proposes a performance upgrade path by integrating BIM IoT technology. Using a mixed research method (quantitative questionnaire+qualitative case), the significant impact of QC elements on OP was verified (β=0.78, p<0.01), and a "technology management performance" strategic framework was constructed. The results show that BIM IoT improves quality and efficiency through data collaboration (reducing design changes by 30%), real-time monitoring (reducing rework rates by 25%), and decision optimization, ultimately driving OP growth of 18% -24%. The research provides a feasible strategic paradigm for the digital transformation of the construction industry, verifying the necessity of technology integration and institutional innovation to drive organizational sustainable competitiveness. This study not only reveals the close relationship between construction quality management and organizational performance from an empirical perspective, but also innovatively proposes a quality management upgrade path with BIM IoT technology linkage as a strategic lever, providing theoretical basis and practical guidance for construction enterprises to break through quality management bottlenecks and achieve performance leaps. The study ultimately emphasizes that promoting the deep integration of intelligent technologies such as BIM IoT and quality management, and building an organizational process and performance evaluation system that is compatible with them, is a key strategic choice for the construction industry to achieve high-quality, efficient, and sustainable future development.

With the rise of Industry 4.0, horizontal Internet of Things (IoT) platforms are becoming a standardized approach for managing interoperability within complex and heterogeneous IoT systems. Horizontal IoT platforms are software solutions that provide overall IoT system orchestration and management. They work to facilitate IoT services and resources, where security management remains one of the main challenges. This article provides a survey and comparison of security management in IoT systems using horizontal IoT platforms. For this purpose, we first define and compare vertical and horizontal IoT platforms. Although vertical IoT platforms provide solutions to many industries, horizontal IoT platforms improve system connectivity by interconnecting multiple vertical domains. We then describe the security management functionalities of horizontal IoT platforms. With these in mind, we perform a comparative study on the current state of security management approaches of existing horizontal IoT platforms. Particularly, we survey and compare the security management features of the selected standard-based reference implementations. Through discussions, we cover concerns that researchers and developers should be aware of when selecting specific reference implementations for their works. Finally, we identify open issues in the existing security management principles of these reference implementations to be addressed in future studies and practical implementations.

The adoption rate of Internet of Things (IoT) devices has posed new challenges to computing systems, particularly to energy efficiency, data processing, and real-time decision making. Traditional cloud-centric frameworks face severe bottlenecks of high communication latency, bandwidth limitations, and energy costly data transmission. While offering unparalleled computational and storage capabilities, these frameworks suffer from high cloud-centric latency bottlenecks. Conversely, edge-centric frameworks process data nearer to the source, providing lower latency but suffering from limited processing capacity, memory, and scalability. These opposing concerns highlight the need for a collaborative framework. This paper proposes a dynamically adaptive task allocation algorithm, Hierarchical Edge-Cloud Collaborative AI Algorithm which seeks to balance workloads between edge nodes and cloud servers based on task intensity, latency, and energy constraints. The framework utilizes machine learning for workload forecasting to predict computational demand and reinforce task allocation in real-time to dynamically improve workload distribution efficiency. The proposed framework helps maintain the desired responsiveness and quality of service with optimal energy consumption by adaptively balancing workloads. Results from tests performed confirm the new algorithm outperformed the conventional IoT Management systems significantly. As indicated in the results, intelligent offloading decisions can provide up to 45% energy savings and also reduce latency by 30%. Enhancements in both operational efficiencies and user experience can be achieved with such offloading decisions. Furthermore, the system demonstrates strong scalability and retains performance with an increasing number of connected devices. These results demonstrate the promise of sustainability and practicality in managing IoT infrastructures using ubiquitous computing with collaborative hierarchical AI.

This study addresses the critical challenge of ensuring secure data transmission and management in Internet of Things (IoT) systems by proposing a blockchain-based architectural framework. Traditional IoT security models often lack fine-grained architectural validation and user-centric evaluation, leading to gaps in trust, data integrity, and operational transparency. To overcome these limitations, the research introduces a novel framework that integrates Transmission Nodes, Inspection Nodes, Forwarding Nodes, and a Blockchain Security Service to secure sensor data from source to destination. The study employs a mixed-method approach, combining conceptual modeling with subjective evaluation from 32 domain experts across development, administration, and IoT service delivery sectors. Key findings indicate that components like the Blockchain Security Service and Transmission Node scored highly in terms of security effectiveness, data integrity, and reliability, while Inspection Nodes revealed varied perceptions, highlighting areas for improvement. The contributions of this study are fourfold: (1) introducing a user-informed performance assessment model for blockchain-based IoT architectures, (2) validating an operational case scenario using real-world transmission flows, (3) offering a detailed architectural breakdown with defined roles for each node, and (4) establishing a multi-metric evaluation framework incorporating integrity, latency, scalability, and privacy. The findings provide both theoretical and practical insights for enhancing trust and performance in decentralized IoT environments.

In today's world, the Internet of Things (IoT) plays an increasingly important role, allowing various devices and objects to interact in real time. However, with an increase in the number of connected devices, there is a need to ensure reliable control of these systems. The Internet of Things (IoT) is the concept of a network of devices equipped with sensors and the ability to connect to the internet, which allows them to communicate and interact without direct human participation. IoT makes a significant contribution to improving efficiency, automation and improving the quality of life. In this paper, we systematically consider and analyze the current state of trust management for IoT. We propose a classification based on the tools, methods and technologies used to form trust management methods (collection of information for the formation of trust, calculation and storage of trust values). The article addresses the main issues of security and management in IoT, and also provides solutions that ensure the reliability and security of devices and data. In conclusion, it is concluded about the importance of trust management to ensure the safety and efficiency of IoT devices. We will try to help the reader understand current issues in this area, build a reliable management system and navigate the literature.

The advancement of energy efficiency in the Internet of Things (IoT) and wireless sensor networks (WSNs) is an important research effort, given their rapid application expansion across smart cities and homes, healthcare, agriculture, and industrial automation. This paper conducted a comprehensive survey of existing innovative solutions to challenges focusing on hardware-based, software-driven, and network optimization approaches, alongside artificial intelligence-driven and demand-side energy management, and security-enhanced frameworks. 82 peer-reviewed journal articles and conference papers published between 2021 and 2025 were reviewed, using sources such as IEEE Xplore, ScienceDirect, Web of Science, SpringerLink, and Google Scholar. It identifies significant developments in energy-efficient techniques, including ultra-low-power hardware, adaptive scheduling, bio-inspired clustering, and energy harvesting. Others include intelligent optimization methods(e.g. machine, quantum-inspired heuristics), and blockchain-enhanced security. A structured evaluation process is implemented, following PRISMA guidelines, categorizing studies, and synthesizing findings to highlight technological progress, challenges, and future research directions. The findings show a growing trend towards integrated, multi-objective routing and cross-layer energy optimizations, with significant progress in minimizing energy use, network lifetime and improving security mechanisms. However, challenges like scalability, computational overhead and real-world deployment issues persist. Our study offers valuable insights for sustainable energy management in IoT and WSNs and helps guide future development toward more resilient, adaptable and sustainable energy-aware systems.

In the rapidly evolving landscape of the Internet of Things (IoT), managing the vast volumes of data generated by connected devices presents significant challenges, particularly in B5G IoT environments. One key issue is data redundancy, where identical data is stored several times because it is captured by multiple sensors. To address this, we introduce “FODIT”, a filter-based module designed to optimize data storage in IoT systems. FODIT leverages probabilistic data structures, specifically filters, to improve storage efficiency and query performance. We hypothesize that applying these structures can significantly reduce redundancy and accelerate data access in resource-constrained IoT deployments. We validate our hypothesis through targeted simulations under a specific and rare configuration: high-frequency and high-redundancy environments, with controlled duplication rates between 4% and 8%. These experiments involve data storage in local databases, cloud-based systems, and distributed ledger technologies (DLTs). The results demonstrate FODIT’s ability to reduce storage requirements and improve query responsiveness under these stress-test conditions. Furthermore, the proposed approach has broader applicability, particularly in DLT-based environments such as blockchain, where efficient querying remains a critical challenge. Nonetheless, some limitations remain, especially regarding the current data structure used to maintain consistency with the DLT, and the need for further adaptation to real-world contexts with dynamic workloads. This research highlights the potential of filter-based techniques to improve data management in IoT and blockchain systems, contributing to the development of more scalable and responsive infrastructures.

The rapid expansion of the Internet of Things (IoT) has resulted in a substantial increase of diverse data from distributed devices. This extensive data stream makes it increasingly important to implement robust and efficient real-time anomaly detection techniques that can promptly alert about issues before they could escalate into critical system failures. Anomaly detection in data is essential in today’s interconnected landscape, as it facilitates the early identification of deviations from established baseline behavior that may indicate system malfunctions, security vulnerabilities, or operational inefficiencies. By promptly identifying these deviations, organizations can reduce downtime, optimize performance, and safeguard critical assets. This article provides a comprehensive review and comparative analysis of modern methods for detecting anomalies in distributed IoT systems. It examines a wide range of techniques, including traditional statistical approaches, distance-based methods, machine learning models, deep learning architectures, and explainable AI frameworks. Each category is evaluated with respect to detection accuracy, computational efficiency, and interpretability. Real-world examples – ranging from predictive maintenance in industrial IoT and energy management in smart grids to fraud detection in financial networks – demonstrate the broad practical applications of these techniques. The review further identifies current challenges and promising future research directions, including active learning-based approaches, which offer potential solutions to improve adaptability and reduce the reliance on large labeled datasets. The insights from this review provide a strong foundation for future research aimed at developing hybrid anomaly detection models that integrate advanced techniques to further enhance system adaptability and security in distributed IoT environments.

The innovative city network integrates numerous computational and physical components to develop real-time systems. These systems can capture sensor data and distribute it to end stations. Most solutions have been presented based on the centralized computing paradigm, which effectively and systematically increases data flow; however, distributed wireless technologies and heterogeneous network services continue to raise significant research problems. These challenges lower the optimization criteria and reflect communication structure around the network edges. In this research, we proposed a sustainable development for smart networks using efficient big data management with collaborative decisions for network devices. It differs from most existing work in the mentioned aspect. It applies computational lightweight intelligence for forwarding collected data using mobile collectors and reduces the congestion flow between devices on the limited bandwidth of wireless links. Moreover, the energy load is efficiently managed with edge-driven methods, and the incorporation of trusted devices ensures the integrity of the smart network. It also tackles potential communication threats smartly. Based on the experiments conducted in Network Simulator (NS-3), the proposed model enhances the efficacy of smart networks for performance metrics with reliability and effective management of resources in Internet of Things (IoT) network.

The integration of the Internet of Things (IoT) in retail management and supply chain operations has transformed business processes by enabling real-time data collection, automation, and data-driven decision-making. This study conducts a bibliometric analysis using Scopus-indexed literature and VOSviewer to map research contributions, key themes, and emerging trends at the intersection of IoT and retail management. The analysis identifies inventory management, supply chain transparency, automation, and AI-driven analytics as dominant research areas, with growing interest in blockchain-enhanced security, sustainability, and predictive analytics. Citation and co-authorship analyses highlight key influential studies and international collaboration networks, with India, China, and the United States emerging as major research hubs. Despite advancements, challenges remain in IoT security, interoperability, ethical considerations, and regulatory frameworks, necessitating further research on 5G-enabled IoT, edge computing, and resilient supply chains. This study provides a comprehensive overview of IoT-driven retail research, offering valuable insights for academics, industry professionals, and policymakers to drive innovation and efficiency in the digital retail landscape.

ABSTRACTThe Internet of Things (IoT) is a paradigm‐shifting concept that helps realize an acquisition, processing, and analytical global network, digitizing tangible entities to enhance efficiency and safety in various smart cities, healthcare, and Industry 4.0 domains. However, whereas IoT scales, with several heterogeneous devices and diverse, varied capabilities with service demands, cloud resource management in IoT usually faces the challenge of intricate complexity in efficiently allocating resources despite the demand for varied quality of service (QoS). Hence, this paper systematically reviews nature‐inspired metaheuristic algorithm applications in IoT resource allocation for solving NP‐hard problems. We summarize recent advances in metaheuristic methods, including comparisons against traditional methods. We also discuss practical feasibility and scaling issues in real‐world IoT scenarios. Further, we have highlighted a few gaps in the current literature and provided recommendations on specific topics for future research, thereby indicating how to develop scalable, efficient resource allocation solutions to meet IoT's ever‐evolving demands.

ABSTRACT With the rapid growth of the Internet of Things (IoT), wireless sensor networks (WSNs) are becoming increasingly vital in advancing information and communication technologies. These networks are widely integrated with the Internet across numerous applications. In WSN‐assisted IoT, ensuring energy efficiency and security is critical for Quality of Service (QoS), but remains challenging due to the networks' open and resource‐constrained nature. This paper introduces a novel solution: a secure entropy‐based Jaccard similarity integrated with a fuzzy C‐means (EJS‐FCM) clustering approach for dynamic WSN‐IoT networks. Cluster heads (CHs) are selected using a boosted grasshopper optimization (BGO) algorithm, and an Adler‐32 hashing‐based scheme authenticates each sensor node. Secure hash elliptical curve cryptography (SHECC) then encrypts data collected from the CHs, enhancing IoT data security through hashing functions in both encryption and decryption. The proposed approach significantly improves security, QoS, and energy efficiency, as evidenced by enhanced network lifetime, throughput, packet delivery ratio, delay, and encryption time.

The advent of the Internet of Things (IoT) and the concomitant development of smart systems has rendered context-aware computing an emerging field of research. The IoT facilitates the large-scale integration of Machine-to-Machine (M2M) communication systems, largely independent of human intervention. The context of a situation, encompassing factors, such as mood, location, and activity, is typically taken into account by humans in an implicit manner, influencing their subsequent actions. Similarly, IoT based smart systems require context data acquired through the use of sensors. The primary challenge lies in the adaptation of context information through the proper modeling and analysis of the vast and heterogeneous sensor data. The phases of context acquisition, modeling, reasoning, and dissemination are collectively referred to as the context management life cycle. The principal aim of this paper is to provide a comprehensive overview of the current state of the art in each phase of the context management life cycle. This study presents a comprehensive review of the tools, techniques, algorithms, and architectures documented in the relevant literature, with a focus on research papers and articles published between 2010 and 2024. The discussion and open issues section at the end of the paper offer insights for future researchers engaged in the study, development, implementation, and evaluation of techniques and approaches for context management in IoT.

The exponential growth of the Internet of Things (IoT) necessitates robust, scalable, and secure identity management solutions to handle the vast number of interconnected devices. Traditional centralized identity systems are increasingly inadequate due to their vulnerabilities, such as single points of failure, scalability issues, and limited user control over data. This study explores a decentralized identity management model leveraging the IOTA Tangle, a Directed Acyclic Graph (DAG)-based distributed ledger technology, to address these challenges. By integrating Decentralized Identifiers (DIDs), Verifiable Credentials (VCs), and IOTA-specific technologies like IOTA Identity, IOTA Streams, and IOTA Stronghold, we propose a proof-of-concept framework that enhances security, scalability, and privacy in IoT ecosystems. Our implementation on resource-constrained IoT devices demonstrates the feasibility of this approach, highlighting significant improvements in transaction efficiency, real-time data exchange, and cryptographic key management. Furthermore, this research aligns with Web 3.0 principles, emphasizing decentralization, user autonomy, and data sovereignty. The findings suggest that IOTA-based solutions can effectively advance secure and user-centric identity management in IoT, paving the way for broader applications in various domains, including smart cities and healthcare.