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Blockchain implementation in green agricultural supply chains can be undertaken independently by manufacturers or retailers or jointly by both in industrial practices. However, most existing studies predesignate implementation entities artificially and largely ignore firms' endogenous economic willingness, which restricts the practical explanatory capability. Addressing this research gap, this study systematically explores firms' implementation willingness and the optimal selection of blockchain implementers in green agricultural supply chains, thereby offering targeted decision-making insights for blockchain policymakers and industrial practitioner. Four game-theoretic models are constructed involving entities: none, retailer-only, manufacturer-only, and both. By comparing the equilibrium outcomes of different game models, this study employs the dual thresholds of deployment costs and operational costs to quantitatively evaluate the economic willingness of supply chain entities to implement blockchain. Different from prior studies that treat blockchain implementers as exogenous and only consider a single cost dimension, this paper endogenizes firms' blockchain implementation decisions based on economic intentions and establishes a dual-cost threshold framework to categorize and identify the optimal implementer under different scenarios, and illustrate the results with a case study. The key findings are concluded as follows. First, economic willingness varies within thresholds. Manufacturers and retailers may take the initiative to implement blockchain regardless of their partners' behavior, or act reactively by following their partners; beyond the thresholds, they refuse to implement it. Second, entity selection varies across scenarios. Under moderately high dual costs, independent retailer implementation is optimal; under balanced costs, manufacturers may implement alone. Extremely high operational costs lead to no willing implementer, while extremely high deployment costs require joint implementation. Third, the likelihood of implementation modes differs substantially within thresholds. The most likely scenario is retailers' independent implementation, the least likely is manufacturers' independent implementation, with joint implementation in between. Beyond these thresholds, no entity implements it at all, a situation accounting for over 50% in the case study. Methodologically, this paper applies global optimization rather than the local optimization strategy with fixed designated implementer settings in previous studies, effectively capturing the internal economic motivation driving voluntary blockchain implementation in green agricultural supply chains.

Electricity markets depend on centralized clearing mechanisms that require participants to trust that submitted bids are preserved and accurately incorporated into the market-clearing process. Current blockchain-based energy market solutions either decentralize the auction mechanism or utilize the blockchain solely as a transaction log, lacking verifiable assurances that off-chain clearing employs the complete and unaltered set of submitted bids. This work introduces a hybrid blockchain-based governance architecture that enables verifiable bid integrity for centralized electricity market clearing while maintaining conventional off-chain clearing procedures. The architecture records cryptographic commitments of submitted orders on a permissioned Hyperledger Fabric blockchain, stores clear-text bids in restricted private collections, and anchors settlement outputs on-chain via an oracle interface. This design allows independent post-clearing verification that the orders used in clearing correspond precisely to those committed before auction closure, without disclosing confidential bid information. The system is evaluated using a real intraday electricity market dataset containing 46,643 orders from a full trading day in the Spanish market. Experimental results show that the architecture maintains one-to-one correspondence between submitted orders and on-chain commitments, enforces correct market lifecycle transitions, and detects inconsistencies between committed bids and the inputs used during clearing, providing tamper-evident guarantees of input integrity in adversarial scenarios. Performance benchmarking against a centralized database baseline shows that the blockchain implementation introduces additional latency and achieves 176.5 transactions per second under the evaluated configuration, reflecting a throughput-limited regime while remaining compatible with realistic intraday auction time windows. These findings demonstrate that blockchain technology can serve as a practical governance layer for electricity markets by shifting trust from unverifiable operator actions to cryptographically auditable input integrity, without requiring modifications to existing clearing algorithms. The approach does not verify the correctness of the clearing algorithm itself but ensures that its inputs are cryptographically auditable.

Blockchain has gained attention for its potential to verify health insurance records, allow secure data sharing, and protect patient privacy. Despite these benefits, blockchain adoption in Egypt remains limited, and little research has examined its drivers and barriers. This study contributes to the limited research on blockchain adoption in Egyptian public hospitals by extending the TOE model with context-specific factors and examining adoption decisions at the organizational level. The study was conducted in public hospitals operating within the Universal Health Insurance System (UHIS) across six governorates. A quantitative approach was used, with data collected from a stratified random sample of 228 senior management and IT professionals across 53 public hospitals, analyzed using PLS-SEM. The results show that relative advantage, financial capability, and perceived trust positively affect adoption intention, whereas perceived risk and complexity negatively affect it. Security and privacy, as well as government support and regulations, significantly enhance perceived trust but do not directly affect adoption intention. Top management support and hospital readiness also strengthen perceived trust. The model explains 67.1% of the variance in blockchain adoption intention (R2 = 0.671) and shows predictive relevance (Q2 = 0.662). Perceived trust positively influences adoption intention and mediates selected relationships in the model, particularly for security and privacy, as well as government support and regulations, which indirectly influence adoption through trust. The proposed model provides practical insights for Egyptian public hospitals, blockchain providers, the Ministry of Health, and policymakers in designing strategies that may facilitate blockchain adoption in public hospitals in Egypt.

The purpose of this study is to investigate the factors that affect Blockchain adoption in governmental operations in Sri Lanka and to propose a comprehensive adoption framework for Blockchain technology in the Sri Lankan governmental operations. The Technology- Organization-Environment (TOE) framework is utilized due to its capacity to capture the complexities of technological adoption in the public sector, addressing both internal (organizational) and external (environmental) factors that influence the adoption process. Given the structural, regulatory, and data sensitivity challenges of governmental settings, the TOE framework integrates employee insights from technological, organizational, and environmental perspectives, making it adaptable to the public sector's needs and scalable across various government entities. It also reflects the regulatory and operational requirements specific to the Sri Lankan public institutions, including essential compliance areas such as data privacy, security regulations, and government workflows, thereby offering a practical pathway for Blockchain adoption within the local context. This study employed statistical methods to ensure the validity and reliability of data collected through a structured questionnaire distributed to Grade I-IT Directors to capture their perceptions and experiences with Blockchain technology. Using the structural equation modelling (SEM), the study finds that all the technological, organizational, and environmental dimensions of the TOE framework are significantly associated with intention to adopt Blockchain technology in the Sri Lankan governmental operations. At a more specific level, trust, compatibility, security, higher authority support, monetary resources, rivalry pressure, and regulatory support were identified as significant predictors of adoption intention, while relative advantage, IT resources, and business partner pressure were not statistically significant, and firm size showed only weak support. These findings provide an empirically grounded framework for understanding Blockchain adoption intention in the Sri Lankan public sector and offer implications for future policy and implementation planning.

The interaction of blockchain technologies with additive manufacture (AM) processes enabled by IoT, specifically VAT Photopolymerization (VPP), offers innovative means for increased security, transparency, and traceability in industrial production data. The VPP-IoT system discussed in the present study is based on sensor data for monitoring and mitigating VOC emissions. To ensure secure flow and management of sensor data in this system, we implemented a tamper-proof and transparent access system for stakeholders based on blockchain-decentralized and immutable technology. Hyperledger Fabric was used to set up a private blockchain network that worked with IoT sensors to keep track of VOC emissions in real time. Smart contracts were designed to automate data validation, storage, and alert generation processes to maintain compliance with environmental and safety regulations. The blockchain-based system demonstrated high-performance metrics, such as an average transaction throughput of 487.2 tps and data integrity verification success of 99.99%. The results point toward blockchain-IoT integration's potential to improve regulatory compliance, predictive maintenance, and real-time monitoring in VPP additive manufacturing for safer and more sustainable industrial practices. While experimental results established a strong positive correlation between VOC emissions and key printing parameters, such as exposure duration and light intensity, layer thickness showed little effect. This study has broader implications as it promotes the application of Industry 4.0 concepts in a more practical context for VPP additive manufacturing.

In today's competitive environment, emerging technologies such as the Internet of Things and blockchain can enhance transparency, traceability, and information management in supply chains. Based on this perspective, a multi-period and multi-product virtual closed-loop supply chain optimization model is developed. In this model, IoT and blockchain are incorporated as traceability and information-management infrastructures that facilitate product tracking and information transparency across the supply chain. The model is formulated as a mixed-integer linear programming model with a four-echelon forward structure and three-echelon reverse structure. Demand uncertainty is managed using a fuzzy approach. The problem is solved for small- and medium-scale instances using GAMS. For large-scale instances, due to NP-hard nature of the problem, the Grey Wolf Optimizer algorithm is used. To examine the role of Internet of Things and blockchain technologies in the supply chain, two comparative studies are conducted: the first study analyzes the simultaneous implementation of these two technologies. The second study focuses on the role of blockchain. The results of the comparative studies indicate that integrating the Internet of Things and blockchain enhances supply chain sustainability, transparency, and profitability.

Transaction verification is essential to blockchain security. As blockchain data continue to grow, resource limited nodes may be forced to operate as non-full nodes, which weakens independent verification and may increase centralization risk. To address this issue, the stateless blockchain technology has been proposed, which uses the accumulators to combine the UTXO set into one fixed-size commitment. However, they suffer from two critical limitations: (i) the inability to support script validation duo to the lack of scriptSig, and (ii) the absence of an outsourcing mechanism to ensure that task executors reliably provide the appropriate witness for the nodes just recovered from failures. We propose LSTVS, a lightweight stateless transaction verification architecture for UTXO based blockchains, which extends RSA accumulator based stateless verification with script-based authorization verification, cache assisted stale proof tolerance, and outsourced witness updates. First, we incorporate UTXO fields associated with transactions into the membership witness to enable digital signature verification. Second, we reconstruct the transaction data format to prevent the exponential growth of transaction reference fields. Finally, we introduce an outsourcing mechanism to improve the transaction verification rate while minimizing computational resource consumption. Experimental results show that the proposed architecture supports the core validation dimensions of UTXO based stateless verification, including existence verification, unspent status verification, and script-based authorization verification, while avoiding UTXO scale dependent proof growth and introducing input dependent transient witness overhead. Compared with existing state of the art RSA accumulator-based schemes, LSTVS improves the transaction verification rate and reduces the local witness update overhead for intermittently online nodes.

Healthcare insurance fraud causes substantial financial losses, operational inefficiencies, and reduced trust among patients, providers, and insurers. Conventional fraud detection approaches often rely on centralized infrastructures and opaque machine learning models, limiting transparency, adaptability, and data integrity. To address these challenges, this paper proposes a secure and explainable healthcare fraud detection framework that integrates blockchain technology with machine learning. A permissioned blockchain provides decentralized, tamper-resistant storage and an immutable audit trail for claim-related evidence, while a stacking ensemble architecture based on LightGBM and XGBoost identifies suspicious provider behavior from healthcare claims data. Experiments conducted on a real-world dataset comprising more than 500,000 outpatient claims, 40,000 inpatient claims, and 138,000 beneficiary records demonstrate effective performance under severe class imbalance. Given the imbalanced nature of fraud detection, Average Precision (AP) was adopted as the primary evaluation metric. Among the evaluated models, the Voting classifier achieved the highest AP score of 0.746, indicating the strongest precision-recall balance for identifying fraudulent providers. The proposed stacking framework achieved competitive overall performance with an accuracy of 0.940, AUC of 0.951, AP of 0.733, precision of 0.702, recall of 0.630, and F1-score of 0.664. Model predictions are further interpreted using SHAP (SHapley Additive exPlanations), providing stable feature-level explanations across multiple validation runs. The blockchain layer securely anchors hashed prediction artifacts and explanation records with minimal latency, enabling verifiable and tamper-resistant auditing. These findings demonstrate the potential of integrating explainable machine learning with blockchain-based auditability to support trustworthy healthcare fraud detection.

Gossip protocols propagate information through peer-to-peer networks analogously to epidemic spreading, yet this analogy has remained informal. Here, we formalise it for blockchain consensus by means of a phase-encoding under which the dynamics reduce, at leading order and in a weak-coupling regime, to coupled-oscillator synchronisation on complex networks. Block preferences correspond to oscillator phases, communication latencies to natural frequencies, and network topology to the coupling graph. The resulting order parameter (a synchronisation measure from statistical physics) tracks simulated network consensus with a correlation of [Formula: see text] and represents it as a phase transition. Consensus disruptions then appear as phase-coherence disturbances, providing candidate anomaly signals for node isolation, network partitions, and block withholding (three typical attacks on blockchains). On real blockchain data, where continuous phase dynamics are not observable, we construct a static phase proxy from per-pool block-attribution statistics; applied to Bitcoin, this proxy-based detector identifies the 2013 chain fork at [Formula: see text] significance on unmodified data. Validation on a second protocol family confirms that the phenomenology generalises. This framework expands physicists' reach into adversarial systems, provides epidemic modellers with an empirical testbed, and offers blockchain operators a complementary, consensus-layer anomaly signal.

Rapid advancements in digital healthcare technologies have created new opportunities in nephrology, but they have also increased the fragmentation of datasets, complexity of data sharing, data privacy and security risks, and a lack of data verifiability and reliability (provenance). While several technological solutions have been developed to address these concerns individually, blockchain has been suggested as a unified solution for secure and efficient data management through several features. These include immutability, interoperability, and programmability, which provide a powerful framework that extends the utility of blockchain beyond data governance in nephrology. Blockchain also has potential applications in pharmaceutical and biobanking quality assurance and in the reliability of medical devices through auditable maintenance logs, making it a promising candidate to address several challenges in patient care and nephrology research. Despite this promise, practical challenges arise between the features of blockchain, such as immutability, and legislation, including the General Data Protection Regulation (GDPR). Furthermore, the value of real-world adoption in nephrology remains uncertain due to scalability, computational overheads, and implementation costs that may outweigh its advantages in many settings.

Path planning for cloud based autonomous systems such as smart transportation, Internet of Things (IoT) installations and robot fleets need to be secure, energy efficient, time efficient and fulfil privacy constraints. Current reinforcement learning (RL) techniques mainly consider optimisation of single objective, centralised or loosely secured model updates which are susceptible to data poisoning, privacy breach and adversarial model updates. We present Blockchain enabled Energy and Time efficient Multi Objective Reinforcement Learning (BlockE2T MORL) a new decentralised approach for secure, cloud assisted path planning. BlockE2T MORL has three main components: (i) a dynamic multi objective reward function that reduces energy, travel time and security threat; (ii) a lightweight blockchain inspired trust mechanism that assigns continuous trust values to agents, and is incorporated in the reward function to punish dishonest or malicious agents; and (iii) a hybrid actor critic learning strategy that facilitates exploration and exploitation in dynamic environments. Unlike conventional blockchain systems, our approach incurs low computational overhead ([Formula: see text] instead of [Formula: see text] for validation) and operates without heavy consensus protocols. We evaluate BlockE2T-MORL on a simulated grid-based cloud environment with up to 100 agents and varying adversarial ratios. After 200 training episodes (5 independent runs), the proposed framework achieves: (i) energy consumption&#x2009;=&#x2009;124.3&#x2009;&#xb1;&#x2009;8.7&#xa0;J (23.4% reduction vs. standard RL, p&#x2009;<&#x2009;0.01), (ii) latency&#x2009;=&#x2009;45.2&#x2009;&#xb1;&#x2009;3.8 ms (17.4% improvement, p&#x2009;<&#x2009;0.05), and (iii) trust score&#x2009;=&#x2009;0.87&#x2009;&#xb1;&#x2009;0.04 (67% improvement, p&#x2009;<&#x2009;0.001). The framework converges faster (210&#x2009;&#xb1;&#x2009;25 episodes in the final optimized configuration, compared with &#x2265;&#x2009;520 episodes for baseline methods). BlockE2T-MORL offers a scalable, privacy-preserving, and computationally lightweight solution for next-generation intelligent path planning in cloud-based autonomous systems.

The high rate of electric vehicles (EVs) development has motivated the issues of peak load congestion, data privacy, scalability, and secure energy coordination in smart electric mobility networks. The traditional centralized EV charging management systems have weaknesses of privacy leakage, single point failure, lack of real time flexibility and lack of trust in the transaction. This paper proposes a Privacy-preserving Edge -Trust -Adaptive Learning Framework (PETAL-Grid), an AI-based federation blockchain model to support adaptive and privacy-preserving energy coordination. The key goal of this study is to attain scalable, real-time and secure EV charging coordination through the integration of federated artificial intelligence, edge-based demand intelligence and blockchain enabled trust management. The proposed framework allows joint demand learning without the need to exchange raw data, real-time adaptive charging based on edge intelligence, and transparent and tamper-proof energy transactions based on smart contracts. The PETAL-Grid workflow comprises of local data collection, edge-based demand forecasting, federated model aggregation, adaptive load coordination, and blockchain-based transaction validation. The results of the simulation show that PETAL-Grid can attain 18% peak load reduction, 17% efficiency of energy utilization, and 98-99% transaction security, which are better than the centralized and the baseline models. The results validate that PETAL-Grid is a scalable, reliable and dependable solution to sustainable smart electric mobility networks.

Industrial Internet of Things (IIoT) systems face growing demands for low-latency, energy-efficient, and trustworthy operation under heterogeneous devices, mobility, and renewable energy variability. Existing fog-cloud approaches typically optimize isolated objectives and lack integrated mechanisms for sustainability and verifiable coordination. This paper presents the Energy-Aware Hierarchical Green Fog (EAHGF) framework, which introduces a unified reinforcement learning (RL) orchestration layer that explicitly incorporates residual energy, renewable energy availability, spatial proximity (via BLE), and task deadlines into hierarchical fog-cloud decision-making. A lightweight Proof-of-Stake blockchain provides immutable auditability of allocations with minimal overhead. A stochastic multi-layer queuing model captures system dynamics, while RL-based scheduling and proximity-aware offloading jointly optimize energy and latency. Extensive OMNeT++/INET simulations with up to 3,000 heterogeneous IIoT devices (Poisson arrivals &#x3bb;&#x2009;=&#x2009;0.5-2 tasks/s, random waypoint mobility 1-5&#xa0;m/s, 70% renewable offset on fog nodes) demonstrate that EAHGF achieves a workload acceptance rate of ~&#x2009;92%, reduces energy consumption by approximately 28%, and improves latency by ~&#x2009;22% compared to baseline fog frameworks and FogNetSim++. The integrated PoS blockchain maintains&#x2009;~&#x2009;100 ms confirmation latency while providing blockchain-assisted accountability, traceability, and trust in resource allocation decisions. EAHGF thus offers a scalable, sustainable, and trustworthy foundation for next-generation Green IIoT deployments, preserving&#x2009;~&#x2009;65% residual energy versus ~&#x2009;45% in conventional systems.

Autonomous driving systems (ADSs) increasingly rely on LiDAR sensors for perception. However, the resulting high-volume data places a strain on storage systems and network bandwidth and raises data-privacy concerns. We propose an IoT data engineering framework for processing, transmitting, storing, and retrieving high-volume LiDAR sensor data in in-vehicle systems that combines error-bounded compression and blockchain-based storage over in-vehicle Time-Sensitive Networking (TSN). With IEEE 802.1Qbv-based TSN scheduling, our framework supports deterministic delivery within the evaluated setup. It combines AES-GCM encryption, blockchain smart contracts, and InterPlanetary File System (IPFS) storage to support confidential, tamper-evident archival under the stated trust and threat model. Experimental evaluation on the KITTI dataset demonstrates that our BEDM framework reduces LiDAR data volume by 75.4%, contributing to a total network bandwidth reduction of 53.7%. The results demonstrate the feasibility and effectiveness of the integrated framework within the evaluated KITTI-based setup and single-switch TSN abstraction, and cross-scene and TSN traffic-sensitivity analyses further characterize its robustness.

Demographic bias in AI-driven clinical decision support systems (CDSS) represents one of the most consequential and least-addressed risks in healthcare AI. Large Language Models (LLMs) applied to emergency triage can silently perpetuate or amplify existing health disparities across thousands of patient decisions. This paper introduces FairGuard, a continuous bias detection and governance framework that embeds demographic fairness auditing directly into the LLM inference pipeline using four integrated mechanisms: (1) an equity-enforcing consent gate that applies demographic-blind access control; (2) a RAG corpus bias analyzer that traces class-directional misclassification to retrieval layer composition; (3) per-subgroup confusion matrix stratification computing &#x394;F1 across demographic groups; and (4) a blockchain-anchored continuous monitoring layer that enforces a &#x394;F1 &#x2264; 0.05 governance threshold across every inference batch. Evaluated on the MIMIC-IV Full Emergency dataset (N=300-1,000), FairGuard achieves a gender &#x394;F1 of 0.020, well within threshold, while identifying RAG corpus composition as the root cause of conservative triage bias (50.9% Urgent &#x2192;&#xa0;Non-Urgent misclassification), establishing that the observed bias is class-directional rather than demographically concentrated. FairGuard provides the first continuous, blockchain-enforced fairness governance mechanism for LLM-based emergency triage CDSS.

Suitable vaccines for individuals are suggested by the vaccine recommendation system regarding certain criteria. Nevertheless, the existing studies didn't augment the vaccine recommendation system centered on users' symptoms and medical history among several geographical locations in the hyperledger fabric blockchain. Thus, in this paper, Krichevsky Dirichlet Trofimov-based latent Dirichlet allocation (KDT-LDA) and federated learning-Expcos bidirectional distillation long short-term memory (FL-EBiDLSTM)-based vaccine recommendation systems using symptoms and medical history are presented. Primarily, the vaccine symptoms dataset is taken. Then, the pre-processing is done based on named entity recognition, tokenization, and stemming. Later, by employing NSR-KMeans, the pre-processed data is grouped. Later, KDT-LDA-based symptoms and medical history modeling and adversarial debiasing-ClinicalBERT-based word embedding are carried out. Simultaneously, from the pre-processed data, the polarity score is identified. By utilizing the Cauchy Cubic-based fuzzy inference system, the labelling is performed regarding the polarity score. After that, the labelling outcomes are trained by the EBiDLSTM-based sentiment nature identification. Then, the natural language processing features are extracted from the symptoms and medical history modeling outcomes. After that, by using Spearman rank correlation, feature correlation is performed. Lastly, the appropriate vaccine is predicted based on FL-EBiDLSTM. Here, to solve the issue of training the patient data among various locations, FL is included. In real-time, vaccine demand users register with the hyperledger fabric blockchain and upload their medical history. Later, the vaccine recommendation system suggests the vaccines concerning the history. As per the outcomes, the proposed model achieved a high accuracy of 99% and outperformed prevailing techniques.

Electronic health records are distributed across different hospitals that work on powerful AI models but cannot be shared due to HIPAA and GDPR regulations. Federated learning (FL) avoids raw data sharing, yet lacks tamper-evident consent governance, adversarial robustness, and verifiable differential privacy (DP) accounting leaving regulatory compliance undemonstrated. To develop and externally validate BlockFedMed, a blockchain-orchestrated FL framework providing cryptographically verifiable consent, model-update integrity, and on-chain DP audits for multi-site ICU mortality prediction, and to quantify its operational clinical impact beyond algorithmic performance. BlockFedMed integrates Hyperledger Fabric v2.5 with a federated bidirectional LSTM and Gaussian DP (&#x3b5;=3.2, &#x3b4;=10-5). Three smart contracts govern consent (CMC), integrity (Mic), and incentive (Idc). The Byzantine fault-tolerant aggregator FedMed-Bft accepts only Mic-verified updates. Design-phase training used MIMIC-IV (n=52,167 ICU admissions). External validation used the entirely independent eICU Collaborative Research Database (n=200,859; 208 hospitals), unseen during model development. On external eICU validation, BlockFedMed achieved an AUROC of 0.841 (95% CI: 0.828-0.854) for in-hospital mortality, which was 7.4 points above Local-Only (p<0.001) and within 3.1% of the regulatory-prohibited centralised upper bound. Simulated consent-management latency fell 71% (from 28.3 min to 8.2 min per cohort) under controlled workflow conditions; prospective clinical measurement remains as future work. The Fabric network sustained 1240 TPS at 1.83 s latency. FedMed-Bft maintained AUROC &#x2265;0.836 under six simultaneous Byzantine participants, all correctly flagged on-chain. BlockFedMed delivers externally validated ICU mortality prediction with cryptographically auditable privacy and consent governance, demonstrating that blockchain-FL provides strong promise for meeting both clinical performance and regulatory compliance requirements simultaneously, pending prospective multi-centre deployment validation.

Electro-medical waste (EMW) management presents critical challenges related to traceability, regulatory compliance, and operational safety in healthcare environments, where improper handling can pose serious risks to public health and the environment. To address these challenges, this work proposes a blockchain-integrated Internet of Things framework, termed BIOT-EMW, which combines IoT sensing, blockchain-based auditability, and edge-level intelligence to enable secure and transparent EMW lifecycle management. A convolutional neural network-based computer vision module is deployed at the edge to automate EMW classification and reduce manual intervention. The performance evaluation of the proposed BIOT-EMW system emphasizes system-level operational metrics, including latency, delay, bandwidth, power consumption, resource utilization, and scalability, rather than exhaustive model-level benchmarking. Experimental results show that edge-level processing latency ranges from 3.45 to 9.33&#xa0;ms, end-to-edge communication delay from 6.43 to 12.11&#xa0;ms, bandwidth usage from 24.54 Mbps to 34.87 Mbps, and device-level power consumption from 6.5 to 13.31&#xa0;mW, with resource utilization between 78 and 92% and scalability reaching up to 97%. In a prototype-scale experimental testbed, the BIOT-EMW framework shows feasible and efficient operation with promising scalability for automated electro-medical waste management in smart healthcare facilities.

Internet of vehicles (IoV) allows real-time connectivity between automobiles, roadside infrastructure, and edge computer nodes for intelligent transport systems. In dynamic scenarios, secure message delivery, cooperative routing, and attack-resilient vehicle coordination need trust relationships. Dynamic topology change, malicious node insertion, and dynamic vehicle interactions make static scoring or machine learning trust management approaches fail. This impairs safe and adaptive trust assessment in decentralized IoV environments. An Adaptive Blockchain-Oriented Trust Management architecture with Proximal Policy Optimization (ABTM-PPO) may solve these difficulties. Vehicle data streams are processed by dynamic trust state encoder (DTSE) to capture temporal interaction patterns, node behavioral fingerprints, and contextual communication needs. To avoid reputation manipulation and fraudulent trust escalation, a blockchain-validated reputation consensus module (BRCM) maintains trust data in an immutable distributed ledger. PPO-driven adaptive trust optimization engines (PATOEs) adapt reward-guided reinforcement learning trust rules to network states and hostile situations. Trust assessment baselines are outperformed by the proposed technique in diverse traffic volumes and coordinated assaults. Malicious node detection is 97.1% and trust classification 98.4%. In high-mobility, packed communication intervals, it cuts decision reaction time by 21.6% while maintaining performance. These results show that the trust-aware security management system for next-generation intelligent vehicular networks is scalable and robust.