Abstract: The convergence of Vehicular Ad-Hoc Networks (VANETs) and the Internet of Things (IoT) is giving rise to the Internet of Vehicles (IoV), a key enabler of next-generation intelligent transportation systems. This survey provides a comprehensive analysis of the architectural, communication, and computing foundations that support VANET–IoT integration. We examine the roles of cloud, edge, and in-vehicle computing, and compare major V2X and IoT communication technologies, including DSRC, C-V2X, MQTT, and CoAP. The survey highlights how sensing, communication, and distributed intelligence interact to support applications such as collision avoidance, cooperative perception, and smart traffic management. We identify four central challenges—security, scalability, interoperability, and energy constraints—and discuss how these issues shape system design across the network stack. In addition, we review emerging directions including 6G-enabled joint communication and sensing, reconfigurable surfaces, digital twins, and quantum-assisted optimization. The survey concludes by outlining open research questions and providing guidance for the development of reliable, efficient, and secure VANET–IoT systems capable of supporting future transportation networks.

Abstract: This study deeply examines the object detection techniques in autonomous vehicles. Two requirements should be met by autonomous vehicles’ object detection algorithms: First, a high level of accuracy is required. Second is real-time detecting speed. As autonomous vehicle’s core is object detection, which enables self-driving cars to precisely sense their environment and react appropriately to objects they detect. But in practical settings, developing a reliable and extremely precise system still presents significant difficulties because of restrictions such fluctuating ambient conditions, sensor limitations, and computational resource constraints. Degradation of the sensor in bad weather or low light, for instance, can significantly reduce the accuracy of detection. Considering this we have proposed to in corporate predictive maintenance in object detection. We also highlighted the performance metrics used in the proposed framework. Further a detailed literature review is included regarding object detection in autonomous vehicles specially in adverse weather condition following with analysis on the current work.

Abstract: Fast fault handling is important for industrial IoT networks because short link breaks can interrupt sensors, robots, and shop-floor control. This work adds eBPF packet filters to a Go runtime so that routing can change on the device when a link fails. We tested several failure cases with mixed traffic. The setup switched routes in about 9.4 ms, while an SDN controller needed 22.8 ms. Packet loss during short breaks fell by 38%, and control traffic dropped by 27%. The design also lets users load simple rule updates to fit different field protocols without changing main code. These results show that placing fast reaction logic on edge devices can improve service stability in factory networks. The method fits small and mid-size systems, though wider field trials and long-term checks are still needed.

Abstract: Factory systems often include many field units that speak different message formats such as OPC-UA and MQTT. Many software bridges can link these units, but they often add long wait time, extra link steps, or slow return after faults. We built a small Go-based gateway that joins both formats and uses short worker groups to handle tasks at the same time. Tests on 102 devices with mixed data show that round-trip delay fell by about 21%, bridging cost dropped by about 35%, and link recovery time improved from 290 ms to about 170 ms. The gateway kept steady message flow during brief bursts and did not require changes to field units, which helps when old and new devices must work together. These results show that placing a compact tool near edge nodes can improve message time and lower setup work in plant networks. Limits came from small boards, where heavy traffic raised CPU use. Future work will test larger setups and add timing support such as TSN or QUIC.

Abstract: The emergence of quantum computing threatens the security of classical cryptographic algorithms such as RSA and ECC. Post-quantum cryptography (PQC) offers mathematically secure alternatives, but migration is a complex, multi-year undertaking. Unlike past transitions (AES, SHA-2, TLS 1.3), PQC migration requires larger parameter sizes, hybrid cryptographic schemes, and unprecedented ecosystem coordination. This paper estimates migration timelines for small, medium, and large enterprises, considering infrastructure upgrades, personnel availability, budget constraints, planning quality, and inter-enterprise synchronization. We argue that realistic timelines extend well beyond initial optimistic estimates: 5–7 years for small enterprises, 8–12 years for medium enterprises, and 12–15+ years for large enterprises. PQC migration is not a siloed technical upgrade but a global synchronization exercise, deeply intertwined with Zero Trust Architecture and long-term crypto-agility. These timelines are contextualized against expected arrival windows for fault-tolerant quantum computers (FTQC), projected between 2028 and 2033 [1–3]. We further analyze the “Store Now, Decrypt Later” threat model, crypto-agility frameworks, and provide comprehensive risk mitigation strategies for enterprises navigating this unprecedented cryptographic transition.

Abstract: In blockchain ecosystems, maintaining transparency and privacy has become an ethical dilemma. This is because, while certain specific information of the user is shared to ensure transparency of transactions across networks, such information could be detrimental to the user, as there is a possibility of it being tampered with. For instance, in the Catalyst voting process in Cardano, users can still see the amount of ADA tokens being held by other users, which can influence their voting options, especially when large ADA holders vote in support of certain ideas or proposals. To discourage such challenges as voter manipulation and vote buying, this study proposed the implementation of zero-knowledge proof (ZKP) in blockchain ecosystems to enhance the transparency of the catalyst voting process and enhance efficiency and speed of result release. Using survey questionnaire and a multivocal literature review, this study was able to proof that ZKP cannot only be applied in the catalyst voting process to enhance its transparency, but also addressed potential challenges to its applications such as scalability, encourage trust and fairness of the voting system, and improve voter participation due to its user-friendliness. Mathematical models emphasize scaled voting as optimal for balancing inclusion and plutocratic control.

Abstract: Driven by the technology innovation, service diversification, and the evolution and defects of current networks, the 6th generation (6G) network architecture is starved for research. One of the challenges is that the architecture design should take into account multiple factors of customers, operators, and vendors. For service-oriented and network-oriented design requirements, this article proposes a data-driven distributed autonomous architecture towards 6G with three-layer-four-plane logical hierarchy. The architecture is simplified as four network function units and the interactions among which are carried on the dual-bus interfaces, i.e., service-based interface and data channel interface. In addition, it takes user data-centric as the fundamental principle and rendered as distributed autonomous domains with different scales to better adapt to customized services. We further provide the network simplification evaluation by going through several signaling procedures of the 3rd generation partnership project (3GPP), inspiring the advanced research and subsequent standardization of 6G network architecture.

Abstract: Wireless sensor networks (WSNs) including Software Defined Wireless Sensor are partic-ularly vulnerable to Denial-of-Service (DoS) attacks. Trust models are widely acknowl-edged as an effective strategy to mitigate the threat of a successful DoS attacks in WSNs. However, existing trust models commonly rely on threshold configurations that are based on the network administrator’s experience and leaving the challenging task of weight allocation for various trust metrics to network users. This limits the widespread application of trust models as a WSN defense mechanism. To address this issue, this study proposes and analyses theoretically an Adaptive, Threshold-Free, and Automati-cally Weighted Trust Model (ATAW-TM) for SDWSNs. The model architecture is aligned with the layered centralized management architecture of SDWSNs, which makes it flex-ible and enhances its responsiveness. The proposed model does not require manual threshold configuration and weight allocation, and allows for a rapid trust system re-covery. It has significant advantages compared to to existing trust models, and is po-tentially more feasible to implemented on a large scale.

Abstract: Digital twins (DT) have emerged as transformative tools for smart city management, enabling urban planners and administrators to monitor infrastructure, simulate scenarios, and optimize operations through virtual representations of physical systems. As urban populations approach five billion people by 2030 and cities confront escalating challenges around congestion, housing, environmental quality, and resource management. A monitoring-only paradigm proves increasingly inadequate for enabling the proactive, adaptive, and participatory governance that future urban systems require. This paper articulates a vision for metaverse-enabled DTs that fundamentally reconceives urban management by transforming passive observation into immersive collaboration and automated action. We present a four-layer Metaverse-Enabled DIGital Twins Enterprise (MEDIGATE) architectural framework that creates capabilities that no isolated technology can achieve, including real-time information fusion across urban domains, immersive interfaces supporting collaborative decision-making among distributed stakeholders, anticipatory response to emerging challenges before they escalate, and continuous learning that improves system performance over time. Recognizing that comprehensive urban-scale implementation presents significant complexity and risk, we introduce the microverse concept as a practical pathway for incremental deployment through domain-specific immersive environments that generate immediate value while building toward eventual integration. We examine healthcare as an illustrative domain demonstrating how immersive DTs transform reactive service delivery into proactive wellness management through multi-modal information fusion and automated intervention. The paper addresses technical challenges around privacy, security, data reliability, computational requirements, and interoperability. We conclude by articulating a research agenda spanning technical development, social innovation, and policy frameworks necessary to realize genuinely proactive smart cities that anticipate needs, engage citizens meaningfully, and deliver equitable outcomes for all urban residents.

Abstract: The utilization of secure bootloaders in embedded systems represents a fundamental security mechanism to ensure device integrity and prevent unauthorized firmware tampering. Secure bootloaders leverage cryptographic validation techniques to establish a chain of trust from the hardware root of trust through the boot process, allowing only authenticated and unmodified firmware to execute. This approach mitigates risks such as persistent attacks, intellectual property theft, and system compromise by verifying firmware authenticity using digital signatures and cryptographic hashes. Implementing secure bootloaders effectively combines hardware trust anchors with public-key cryptography to protect embedded devices in diverse applications such as automotive, industrial, IoT, and medical sectors. This paper details the principles, architecture, and cryptographic mechanisms behind secure bootloaders, highlighting their role in preventing rollback attacks and ensuring firmware integrity over the device lifecycle.

Abstract: In recent years, there has been fast development within the area of vehicular ad hoc networks (VANET). In the future, VANET communication will play a first-rate position in improving the protection and performance of the transportation system. If security isn't always furnished in VANET, then it may result in apparent misapplication. One of the dangerous or risky attacks in VANETs is the Sybil, which forges fake identities inside the network to disrupt or compromise the communication among the network nodes. Sybil attacks have an effect on the carrier transport associated with road safety, traffic congestion, multimedia entertainment and others. Thus, VANETs claim for a security mechanism to prevent Sybil attacks. Within this context, this paper proposes a mechanism, known as Sybil Attack Prevention and Detection Mechanism in VANET based on Multi-Factor Authentication (SAPDMV), to detect Sybil attacks in VANETs based on Multi-Factor Authentication. The proposed system works based on the principle of registration, and use identification number, status, Maximum and minimal threshold value and security key for the verification. The paper proposes a Sybil Attack Prevention and Detection Mechanism in VANET (SAPDMV) based on multifactor authentication. The mechanism uses vehicle identification, status, security key, and both minimum and maximum speed thresholds to authenticate nodes and detect Sybil attacks. Implemented and tested using Network Simulator-2.35, the system demonstrates an improved detection rate, reduced false positive and false negative rates, and enhanced network performance metrics such as end-to-end delay, throughput, and packet delivery ratio. The simulation result shows our proposed algorithm enhances detection rate, false positive rate, and false negative rate. The proposed solution is improved to 96%, 5%, and 4%, respectively, compared with the Sybil attack-AODV and existing/old work. The approach is scalable and effective in real-world VANET environments, making it a promising framework for future intelligent transportation systems.

Abstract: We present a comprehensive synthesis of how innovations developed during the Noisy Intermediate-Scale Quantum (NISQ) era are reducing the resource requirements for future fault-tolerant quantum attacks on elliptic curve cryptography (ECC). While pure Shor’s algorithm requires N_L = 2, 330 logical qubits and ∼ 1.29 × 10¹¹ Toffoli gates for NIST P-256—well beyond current NISQ capabilities—we demonstrate that NISQ-era innovations could reduce future fault-tolerant quantum computer (FTQC) requirements by factors of 1.5–2.3×. A critical engineering challenge remains: the memory-to-computation gap. Google’s Willow processor achieves exponential error suppression for quantum memory, offering a 2.14× improvement for each increase in code distance. Yet, by October 2025, new demonstrations on this platform show that technological progress is rapidly narrowing this gap. IBM’s roadmap projects 200 logical qubits by 2029 and scaling to 2,000 qubits by 2033+, targets now validated by DARPA’s Quantum Benchmarking Initiative, which establishes 2033 as a formal government milestone for utility-scale fault-tolerant quantum computing. Our analysis reveals projections with varying probabilities of technological success, grounded in convergent external validation from multiple independent authoritative sources: Conservative (high probability): N_L ∈ [1, 800, 2, 200] with timeline 2033–2035; Realistic (moderate probability): N_L ∈ [1, 200, 1, 600] with timeline 2031–2033; Optimistic (lower probability): N_L ∈ [900, 1, 100] with timeline 2029–2031. These projections are directly validated by the convergence of three independent industry and government roadmaps, providing robust external validation for our timeline projections.

Abstract: This paper presents the development and evaluation of a simulation framework simV2X, designed to model the interaction between vehicles, roadside infrastructure, and network components within Intelligent Transportation Systems (ITS). The study focuses on the integration of fog and edge computing paradigms in V2X communication architectures to improve reliability, scalability, and responsiveness in urban environments. The modular architecture of simV2X, implemented in Python using FastAPI and OpenLayers, supports step-by-step simulation, data visualization, and real-time event logging via an SQLite database. Key performance indicators such as SNR, BER, PER, PDR are computed to evaluate communication quality under different propagation conditions. A specific simulation scenario examines the efficiency of a mobile fog node (mRSU) acting as a relay between an OBU and a stationary RSU. The results show that under non-line-of-sight (NLOS) conditions, direct OBU–RSU communication experiences rapid signal degradation, while the use of a mobile relay significantly extends the coverage area and maintains stable connectivity. The conducted experiments confirm that mobile fog nodes can enhance network reliability in dense urban environments, provided that line-of-sight connections are preserved. The proposed simV2X framework enables reproducible simulation of V2X communication and can serve as a foundation for developing adaptive algorithms for relay selection and predictive mobility management in cooperative, connected, and automated mobility (CCAM) systems.

Abstract: This work presents an energy efficient implementation for UAV-based systems over 5G networks with on-boarded accelerated processing capabilities and provides a preliminary evaluation analysis of the integrated solution. A two-fold comparative study focused on connectivity and edge processing for UAVs, realizes two discrete deployment scenarios, where standard 5G configuration with Artificial Neural Networks processing is evaluated against 5G RedCap connectivity paired with Spiking Neural Networks. Both proposed alternative energy efficient solutions, are designed to offer significant energy saving, and this paper examines if they are fit candidates for energy stringent environments, i.e., UAVs, and also quantify the impact on the overall energy consumption of the system. The integrated solution with 5G RedCap/SNN realizes energy-use reductions approaching 60%, which translated to approximately 35% of increased flight time. The experimental evaluations were performed in a real-world deployment using a 5G equipped UAV with edge processing capabilities based on NVIDIA’s Jetson Orin.

Abstract: Underwater Wireless Sensor Networks (UWSNs) are fac- ing the critical challenges in energy efficiency because of limited battery capacity and the difficulty of node replacement in aquatic environments. This paper is proposing DTEAR (Digital Twin-Enhanced Adaptive Routing), which is a novel energy-efficient routing protocol that leverages digital twin technology for real-time network state optimization. Our protocol is integrating virtual network modeling with adaptive depth-based routing to minimize energy consumption while it is maintaining high packet delivery ratios. Through comprehensive simulations using the adapted C-Town network benchmark with 399 nodes, DTEAR is demonstrating 27.3% improvement in network lifetime, 18.5% reduction in energy consumption per packet, and 95.2% packet delivery ratio when compared to state-of-the-art protocols. The digital twin synchronization mechanism is ensuring routing decisions are based on accurate network states, and it is achieving convergence in 38 seconds on average. Results are validating the effectiveness of combining digital twin technology with underwater acoustic communication for enhanced network performance. This work is contributing to recent advances in digital transformation for distributed systems and energy-efficient underwater communication protocols.

Abstract: This research presents a Proposed Hybrid routing protocol for Vehicular Ad-hoc Networks (VANETs), designed to address the performance trade-offs inherent in purely reactive Ad hoc On Demand Distance Vector (AODV) and proactive Optimised Link State Routing (OLSR) routing paradigms. The purpose of the research is to seamlessly integrate the strengths of AODV and OLSR into a single, context-aware framework. A significant finding is the co-design of a dynamic transmission power control mechanism that works in concert with the routing logic to adapt to fluctuating vehicle densities in real-time, effectively mitigating intermittent connectivity and the high latency characteristic of large-scale Intelligent Transportation Systems (ITS). Rigorous evaluation under high-fidelity mobility scenarios (using NS-3 and SUMO with real-world traffic patterns) confirms the protocol's efficacy. The significant findings demonstrate substantial performance enhancements over established baseline protocols, consistently achieving a Packet Delivery Ratio (PDR) exceeding 90%, maintaining an end-to-end delay below the critical 40ms threshold, and realising per-node energy savings of up to 60%. The conclusion is that this work provides a validated foundation for a highly reliable and efficient communication fabric, significantly enhancing the dependability of mission-critical ITS services and promoting the development of scalable, sustainable next-generation transportation networks.

Abstract: Privacy is one of the bottlenecks that hinder the applications (e.g. vehicle navigation) of blockchain-UCAN. A sharded blockchain protects the privacy of vehicle data to a certain extent. However, the unbalanced shard loads leads to low throughput, and the feature extraction in blockchain-UCAN is still poor. In this paper, an optimal image binarisation method OTSU-GK is proposed to enhance image features while reducing the amount of uploaded data to further improve the throughput. Specifically, OTSU-GK employs a Gaussian kernel method, where the parameters are optimized through grid search, to optimize the calculation of the threshold. In addition, we design a Node Load Score-based (NLS) sharding blockchain, which considers the number of historical transactions, transaction types, transaction frequency and other metrics, to balance the sharding loads and further improve the throughput. The experimental results show that OTSU-GK pproximately a 50% improvement in metrics and 83% improvement in throughput compared to other methods.

Abstract: Energy efficiency and prolonged network lifetime remain key challenges in wireless sensor networks. Clustering, cluster head selection, and routing are central to addressing these issues since they directly affect energy consumption, data delivery, and overall network stability. In this work, we introduce a novel hybrid protocol named PUMA-GRID, which uniquely integrates the recent Puma Optimization Algorithm with a grid-based multi-hop routing framework. Unlike traditional schemes, PUMA-GRID adaptively balances exploration and exploitation during cluster head selection while learning optimal data forwarding paths through grid-based routing. This combination provides improved adaptability, scalability, and load balancing, key strengths that distinguish it from earlier AEO, LEACH, and static PUMA variants. The fitness function for cluster head election incorporates intra cluster distance, distance to the base station, and residual energy, with adjustable weights that allow flexible adaptation to deployment scenarios. Simulation experiments were performed under different base station placements and weight configurations to assess the influence of each factor. The results show that the effect of the weights depends strongly on base station location, and that careful tuning is required to balance efficiency and fairness. Across all scenarios, PUMA-GRID demonstrated superior performance compared to LEACH, AEO based schemes, and other PUMA variants Overall, PUMA-GRID demonstrates an effective and scalable solution for sustainable and energy-aware operation of wireless sensor networks.

Abstract: This paper addresses the industrial adoption gap of 5G/6G by presenting a verti-cal-agnostic Methodological Assessment Framework (MAF) that bridges network KPI with user-centric User-KPI and User-KVI to quantify techno-economic and societal value propositions from an end-user perspective. First, a detailed description of the MAF and its underlying principles is given, explaining how a use case’s value proposi-tion can be captured. Second, the MAF is applied to three different use cases from the verticals manufacturing, construction, and automotive utilizing the individual User-KPI and User-KVI for the quantification of the individual value propositions. The results show that the use of 5G can lead to enhanced process capability and reproducibility as well as increased insights into different processes. In addition, it is shown that the MAF objectively quantifies user value across diverse verticals, strengthens inter-disciplinary alignment, and can be employed to support investment decisions, based on the calcu-lated value propositions.

Abstract: Large Vision-Language Models (LVLMs) have achieved strong results in general visual understanding but remain limited in fine-grained visual reasoning. This paper introduces LVLM-GR, a framework designed to improve detailed visual grounding and robust multimodal reasoning. The proposed Visual Concept Quantizer (VCQ) encodes images into discrete visual tokens through context-aware pooling and a semantic hierarchical codebook, effectively preserving fine-grained semantics. These visual tokens are then aligned with language via a lightweight Grounded Reasoning Adapter (GRA) based on LoRA-tuned adaptation atop a frozen LLaVA 1.5 13B backbone. Experiments on GQA, RefCOCO+, and A-OKVQA show that LVLM-GR achieves superior performance in fine-grained visual understanding, reasoning, and grounding, highlighting its potential for complex multimodal reasoning tasks in material-level and detailed visual analysis.