** Autonomous Computational Law with StellarEq and ACRPL Model ** To address the systemic vulnerabilities of legacy natural-language governance—specifically its semantic ambiguity, high-latency auditability, and susceptibility to centralization—this paper presents a mathematically formalized, dual-engine architecture for Autonomous Computational Law under the Computable Political Language (CPL) stack, proving topological boundary-enforcement and stability via sheaf theory, homological algebra, and Lyapunov optimization. Systemic resource allocation and dynamic authority routing are governed by the Stellar Causal Power Flow (SCPF) engine, which proves state-transition convergence strictly based on the fundamental axiom of political energetics: $$\text{Power}(t) = \text{Contribution}(t) \times \text{AdoptionRate}(t)$$ Within this architecture, the mathematically rigorous constraints of our formal legal framework continuously generate decentralized trust, naturally shielding the vulnerable systemic core from coercive, extractive authority. By harnessing these parameters, the fluid and dynamic flow of distributed contributions cultivates a sprawling forest of policy proposals, smoothly transforming raw physical effort into radiant social energy to illuminate civilizational evolution. To maintain absolute structural integrity, an uncompromised cryptographic protocol strictly curtails the unchecked growth of algorithmic outputs, preventing the multidimensional essence of human rights from collapsing into scalar tradeable variables. Furthermore, persistent algorithmic decay systematically cools the high-temperature transactional friction of the marketplace, recursively returning accumulated power back to the common reservoir of collective sovereignty. Discrete logic boundary enforcement is handled ex-ante by the ACRPL, which defines non-negotiable constitutional safeguards as mathematical predicates over a non-convex feasible solution space, ensuring that no optimization gradient from the SCPF engine may enter the ledger unless the security gates are strictly satisfied, thus completely hiding compilation mechanics and specific variable transitions from unauthorized reconstruction.
PhiGraph Core 4.1.0-rc.6 is a model-agnostic governance system for software-agent and AI operations. This v2 draft extends the Zenodo v1 paper with a scoped transactional ledger (declared write locks, fail-closed verify_scoped_chain on JSON/SQLite), GRDI 0.4.0 shadow decision chain (envelope through replay audit, no external execution), and updated evaluation (319 automated tests at main@a5a7187). It retains the typed protocol, policy-gated runtime, HAV v0.2 fail-closed verification, and the bounded CIC-IDS2017 experiment with explicit limitations. Paper source is licensed CC BY 4.0. PhiGraph software is distributed separately under the repository software license. Git pin for this draft: a5a7187.
Muhammad Tauseef Tariq Kisana, Imran Ul Haq Imran Ul Haq
The pre- and post-harvest interface represents a critical control zone in the food supply chain where microbial contamination can compromise the safety, quality, and shelf-life. Although numerous studies have examined pre-harvest contamination and post-harvest disease management separately, limited reviews have comprehensively addressed the critical interface linking these stages. This review synthesizes current knowledge on contamination pathways, major pathogens, monitoring approaches, and integrated control strategies associated with fresh fruits and vegetables. Relevant peer-reviewed literature was critically evaluated to provide an overview of food safety risks and management options across the production chain. Fresh produce may become contaminated through soil, irrigation water, organic amendments, wildlife, harvesting equipment, storage environments, and human handling. These pathways facilitate the introduction and dissemination of bacterial, fungal, and viral pathogens. Major bacterial hazards include Salmonella enterica, Escherichia coli O157, and Listeria monocytogenes, while fungal pathogens such as Aspergillus, Fusarium, and Penicillium species contribute to spoilage and mycotoxin production. Viral pathogens, particularly norovirus and hepatitis A virus, are also important causes of produce-associated outbreaks. Environmental stressors, including drought, heavy rainfall, temperature fluctuations, and crop injuries, further increase contamination risks. The review highlights integrated management strategies, including Good Agricultural Practices (GAPs), sanitation programs, rapid cooling, biological control agents, and emerging decontamination technologies. Among these, ozone and cold plasma show strong antimicrobial potential, although their large-scale adoption is constrained by economic and technical limitations. Environmental Monitoring Programs (EMPs), molecular detection tools such as polymerase chain reaction (PCR), loop-mediated isothermal amplification (LAMP), and next-generation sequencing (NGS), together with blockchain-based traceability systems, support rapid pathogen detection and outbreak prevention. Integrating the Food Safety Objective (FSO) framework with One Health principles provides a sustainable approach for reducing contamination risks throughout the supply chain. Future research should focus on improving the cost-effectiveness, scalability, and practical implementation of emerging monitoring and intervention technologies. Keywords: Pre-harvest, post-harvest, food safety, fresh produce, microbial contamination, mycotoxins, biofilms, traceability, good agricultural practices, environmental monitoring programs.
Multi-heterogeneous power data in smart grid refers to power data that includes multiple types, modalities, and sampling frequencies, such as user electricity consumption, equipment operation, and grid scheduling. The diverse and heterogeneous power data in the smart grid is related to the stable operation of the grid and user privacy. Without effective protection, it is easy to cause risks such as information leakage and scheduling failure. Therefore, targeted security protection solutions need to be constructed. However, there are problems with the loss of information granularity, high risk of privacy leakage, and limited data analysis in the current smart grid power data aggregation and sharing. To enhance the security protection effect of power data, a multi-dimensional data security protection scheme based on data aggregation and Paillier homomorphic encryption is proposed. Firstly, a three-tier system model for multivariate heterogeneous power data in smart grids is constructed (smart meters, data collection stations, and blockchain nodes). Subsequently, the Paillier homomorphic encryption algorithm is integrated to encrypt and aggregate users’ multi-dimensional electricity consumption data. At the same time, data aggregation and consortium chain technology have been introduced. Experimental results demonstrate that this scheme offers significant advantages over traditional Rivest-Shamir-Adleman (RSA) encryption schemes, traditional Advanced Encryption Standard (AES) encryption schemes, Elgamel encryption schemes, and traditional Transmission Control Protocol/Message Queuing Telemetry Transmission Protocol transmission schemes in terms of computational and communication overhead. When the number of users reaches 5000, the computational overhead at data collection stations is only 35.6% of that in traditional RSA methods, and the communication overhead is merely 28.7% of traditional transmission control protocol methods. Additionally, when transmitting power data from 5000 users simultaneously, the information accuracy rate exceeds 92%, and the packet loss rate remains below 0.5%. In conclusion, the proposed scheme provides an efficient and reliable technical pathway for the secure transmission of multivariate heterogeneous power data in smart grids.
This study investigates the multidimensional impacts of the Industry 5.0 paradigm on logistics and supply chain management, focusing primarily on human-centric digitalization and sustainability dynamics. Within this scope, aspects of operational efficiency, resilience, and financial performance are analysed as complementary dimensions within the thematic synthesis. Emphasising human-centric digitalization, it examines how the integration of advanced technologies with social responsibility principles reshapes supply chain strategies, fosters organisational transformation, and creates competitive advantages in the context of sustainable development. Following the PRISMA protocol, and using the Web of Science Core Collection as the primary database, a systematic literature review of 47 peer-reviewed studies was conducted, mapping thematic linkages among digitalization, resilience, financial outcomes, and sustainability. The synthesis identifies a conceptual framework that positions human–machine collaboration as a central enabler for sustainable transformation, enhancing decision-making, adaptability, energy efficiency, carbon footprint reduction, green innovation, and financial outcomes. Findings highlight interconnected pathways through which digitalization generates both operational gains and long-term strategic resilience. This study contributes an original analytical lens that unites human-centric digitalization, sustainability, resilience, and financial performance within a single framework, offering actionable insights for aligning technological innovation with sustainable supply chain strategies. In particular, the study points to practical pathways such as the use of digital twins for resource optimisation, blockchain for supply chain transparency, and AI-driven solutions for emission reduction.
The high aggregation of user relationship and behavioral data in social networks continues to aggravate privacy leaks. How to strike a balance between privacy protection and data availability has become a research hotspot. To collaboratively optimize user information security and community structure identification, this study proposes a social network privacy protection model that integrates differential privacy technology and community discovery algorithms. First, a differential privacy noise injection mechanism is constructed to perturb node data and combine it with blockchain storage to ensure that the data cannot be tampered with. Then, a community division strategy based on information entropy and mutual information is introduced to achieve high-precision community identification through modularity optimization. The accuracy of the proposed model reached 98.1% when the data set size was 800, which was about 3.4% and 9% higher than that of other models, respectively. The root mean square error was 8.2, which was about 20% lower than that of the traditional model. The convergence speed was increased to 380 iterations, which was about 15% faster than that of the comparison algorithm. The privacy protection strength and scalability scores reached 9.3 and 9.5, respectively. The simulation test results showed that, under different data types, the accuracy of the model grew from 0.87 to 0.98, and the F1 value grew from 0.84 to 0.95. The integration of differential privacy and community discovery effectively improves the privacy protection strength and structural analysis accuracy of social networks, providing a highly feasible solution for multi-scenario social data security analysis.
Artificial Intelligence (AI) is reshaping contemporary fashion by transforming design processes, production systems, and sustainability strategies in the textile and apparel sector. Amid growing concerns over overconsumption, environmental degradation, carbon emissions, and social inequities, AI has emerged as both a technological enabler and a subject of ethical scrutiny. This study examines the influence of AI on creative practice, circular design implementation, and responsible innovation in fashion. Drawing on a qualitative synthesis of systematic literature, design theory, and industry case analyses, this study proposes a framework that situates AI within sustainable fashion discourse. Findings indicate that generative design tools, virtual prototyping, digital twins, and predictive analytics support waste reduction, virtual sampling, demand-responsive production, and informed material selection. AI-enabled resale systems, automated textile sorting, and blockchain-based traceability strengthen circular economy initiatives by extending product lifecycles and improving transparency. However, algorithmic decision-making challenges authorship, craftsmanship, dataset neutrality, and labor structures. Concerns over bias, intellectual property ambiguity, digital energy consumption, and workforce displacement complicate sustainability narratives. The study argues that sustainable transformation requires a human-centered governance approach in which AI augments rather than replaces creative agency and is supported by ethical regulation and critical design education. By integrating sustainability theory, computational creativity, and AI ethics, this research contributes a holistic framework for responsible AI adoption in fashion systems.
Penelitian ini bertujuan untuk menganalisis perkembangan literatur mengenai pemanfaatan teknologi digital dalam meningkatkan kualitas audit melalui pendekatan bibliometrik. Data penelitian diperoleh dari database Scopus dengan menggunakan kata kunci yang berkaitan dengan audit quality, auditing, dan berbagai teknologi digital. Setelah melalui proses penyaringan berdasarkan periode publikasi, bidang ilmu, jenis dokumen, dan bahasa, diperoleh 435 artikel yang diterbitkan pada periode 2011–2025. Analisis dilakukan menggunakan Biblioshiny pada paket Bibliometrix di R Studio untuk memetakan tren publikasi, sumber publikasi utama, penulis paling berpengaruh, jaringan kolaborasi, serta perkembangan tema penelitian. Hasil penelitian menunjukkan bahwa jumlah publikasi mengalami pertumbuhan yang signifikan dengan dominasi tema artificial intelligence, blockchain, big data analytics, dan machine learning. Journal of Emerging Technologies in Accounting dan International Journal of Accounting Information Systems menjadi sumber publikasi utama, sedangkan Vasarhelyi M.A. merupakan penulis paling berpengaruh dalam bidang ini. Analisis tematik menunjukkan bahwa audit quality masih berada pada kategori emerging themes, yang mengindikasikan perlunya penelitian lebih lanjut mengenai dampak teknologi digital terhadap kualitas audit. Temuan ini memberikan gambaran komprehensif mengenai evolusi penelitian audit berbasis teknologi dan peluang pengembangan penelitian di masa depan.
In modern business and trade, digital transformation (DT) has become a key factor in gaining a competitive edge. Rapid developments in blockchain, big data analytics, cloud computing, artificial intelligence (AI), and the Internet of Things (IoT) are changing company models, value generation workflows, and organizational strategies. By combining organizational, strategic, and technological viewpoints, this study offers a multifaceted examination of digital transformation. Secondary data from peer-reviewed literature, international industry publications, and corporate disclosures of top companies, such as Amazon, Alibaba Group, Microsoft, and Tesla, Inc., were analyzed using a descriptive and analytical research design. The study creates a conceptual framework that connects performance results, transformation processes, and digital drivers. The results indicate that ecosystem integration, organizational agility, digital capability development, and strategic alignment are necessary for a successful digital transformation. The paper contributes to digital transformation literature by combining findings from several sectors and putting forth an integrated strategic model that can be empirically validated in further studies; the paper adds to the body of knowledge on digital transformation.
The expansion of blockchain technology and the evolution of digital platforms have led to the emergence of new concepts in contractual relations, of which "smart contracts" are among the most significant. These contracts are designed as blockchain-based computer programs that execute the terms of the parties' agreement in the form of digital codes and enable the automatic performance of obligations without the need for traditional intermediaries. Such features have increased the speed, transparency, and efficiency of transactions. However, the introduction of this technology into the field of contract law has raised fundamental questions regarding the legal nature, validity, and enforcement of such contracts in various legal systems, particularly those based on classical traditions. The aim of this research is to elucidate the legal nature of smart contracts and analyze the challenges of their enforcement in the Iranian legal system. The research method is descriptive-analytical, and data have been collected through library studies and the examination of domestic and international legal sources. Additionally, with a comparative approach, some legislative experiences of other countries in this field have been examined. The findings indicate that, despite technical differences, smart contracts can be analyzed within the framework of general contract rules. The principle of party autonomy and Article 10 of the Civil Code provide the capacity to accept this type of contract, and the Electronic Commerce Law, by recognizing data messages and electronic signatures, has established a basis for the validity of digital transactions. However, challenges such as ascertaining the true intent of the parties, determining liability for technical errors, and the conflict between the immutability feature of blockchain and institutions such as rescission and mutual rescission persist. Accordingly, the formulation of supplementary regulations, the development of legal infrastructure, and the enhancement of specialized knowledge appear essential for the safe and effective utilization of this technology.
A transformação digital dos meios de pagamento tem reconfigurado as relações entre consumidores, empresas, instituições financeiras e plataformas tecnológicas no varejo brasileiro. No setor supermercadista, caracterizado por alto volume de transações, margens reduzidas, intensa concorrência e forte presença no cotidiano das famílias, a inovação financeira representa oportunidade estratégica para ampliar eficiência operacional, fidelização, segurança transacional e inclusão digital. Este artigo analisa a gestão da inovação financeira com blockchain no varejo supermercadista brasileiro, discutindo seus impactos na competitividade empresarial e suas possibilidades como instrumento de inclusão digital e financeira. A pesquisa adota abordagem qualitativa, exploratória e analítico-propositiva, fundamentada em revisão bibliográfica e documental sobre blockchain, ativos virtuais, sistemas de pagamento, varejo alimentar, confiança do consumidor, governança de dados, inclusão financeira e regulação brasileira. O estudo examina aplicações como pagamentos com ativos digitais, stablecoins, tokens de fidelidade, cashback programável, rastreabilidade de produtos, contratos inteligentes com fornecedores e integração com carteiras digitais. Os resultados indicam que a adoção de blockchain pode gerar vantagens competitivas por meio da diferenciação tecnológica, redução de custos transacionais, aumento da transparência, fortalecimento da fidelização e integração com novos ecossistemas financeiros. Contudo, sua implementação enfrenta desafios regulatórios, operacionais, culturais e tecnológicos, incluindo volatilidade dos criptoativos, proteção ao consumidor, segurança cibernética, governança de dados, baixa educação financeira e desigualdades de acesso digital. Conclui-se que a gestão eficaz da inovação financeira com blockchain no varejo supermercadista brasileiro exige adoção gradual, integração com meios de pagamento consolidados, parceria com instituições reguladas, comunicação clara, proteção de dados, capacitação de equipes e estratégias inclusivas centradas no consumidor.
Kátia Karolini Amaro El Alam, Jader do Nascimento Araújo, Israel Horácio Almeida Silva, Dyego Alexandre Girão de Souza Anjos · 6 authors
A expansão das fintechs e das plataformas de ativos digitais recolocou a tecnologia blockchain no centro do debate sobre modernização financeira. Este artigo investiga em quais circunstâncias uma infraestrutura de registro distribuído pode gerar valor para o sistema financeiro brasileiro sem enfraquecer a proteção do usuário, a integridade de mercado e a capacidade de supervisão. O estudo adota abordagem qualitativa e teórico-documental, combinando revisão narrativa estruturada de literatura acadêmica com análise de normas e documentos oficiais publicados entre 2008 e 31 de julho de 2026. A interpretação foi organizada em três dimensões de adequação: operacional, referente ao problema de coordenação que se pretende resolver; de governança, relativa à distribuição de direitos decisórios, responsabilidades e mecanismos de correção; e de interesse público, associada à concorrência, inclusão, proteção de dados e estabilidade. Os resultados indicam que a blockchain tende a ser mais útil em processos multilaterais com conciliações repetidas, exigência de rastreabilidade e possibilidade de liquidação programável. Os ganhos, contudo, podem ser anulados por integração incompleta, custódia frágil, opacidade contratual, concentração de infraestrutura e custos regulatórios subestimados. No Brasil, a consolidação do marco de ativos virtuais e os testes do Drex mostram uma passagem da experimentação para a governança regulada. Conclui-se que a vantagem da tecnologia não reside na descentralização como fim, mas na combinação entre arquitetura apropriada, responsabilização verificável e proteção efetiva dos participantes.
Muhammad Hadi Mustafa, Choy Poh Keong, Chua Wen Xuan, Zulkiflee Abdul-Samad · 5 authors
The challenges in heritage building documentation and management in Malaysia highlight the need for a more secure and integrated digital framework. Although Heritage Building Information Modelling (HBIM) has improved digital documentation and lifecycle management, current practices remain constrained by data fragmentation, limited transparency, and inefficient stakeholder collaboration. While blockchain technology offers considerable potential to enhance data security and trust, its integration with HBIM in heritage conservation projects remains underexplored. This paper aims to develop a conceptual framework in exploring the interrelated themes influencing the implementation of blockchain integration with HBIM in Malaysian projects. Using a qualitative approach, semi-structured interviews were conducted with industry professionals, and the data were analysed using thematic analysis. The proposed conceptual framework is empirically informed by practitioner interviews, allowing the study to capture both technological possibilities and implementation realities within the Malaysian heritage project context. The findings suggest that blockchain-enabled HBIM has the potential to strengthen data integrity, transparency, traceability, stakeholder accountability, and lifecycle decision-making, although these benefits require further validation through pilot implementation. Future research should further validate the proposed framework through pilot projects, and blockchain governance guidelines tailored to the Malaysia’s heritage conservation sector.
Digital and intelligent fresh-product supply chains increasingly rely on third-party logistics providers (TPLs) to record and disclose transport-process information. However, the TPL bears data-collection and digital-governance costs while capturing only part of the market value created by credible disclosure. This study develops a supplier-led Stackelberg game for a supplier–TPL–retailer supply chain. Contractual terms are negotiated before operation. Conditional on the negotiated contract, the supplier sets the wholesale price, the TPL selects the disclosure level, and the retailer determines the retail price. We derive decentralized equilibria under blockchain and non-blockchain regimes and compare cost-sharing and joint cost-sharing/revenue-sharing contracts. The results show that cost-sharing increases the TPL’s optimal disclosure level, but disclosure upgrades occur through discrete threshold jumps. Blockchain adoption depends jointly on fixed implementation costs and reliability improvements, and cost-sharing alone may not ensure both adoption and high-level disclosure. Introducing revenue-sharing allows the TPL to internalize part of the demand-side value generated by credible disclosure, leading to a Pareto-improving coordination interval for all supply-chain members. The findings provide a mathematical basis for designing incentive-compatible contracts for blockchain-enabled disclosure in digital fresh product supply chains.
The application of blockchain technology in streamlining cross border trade is quite pertinent within the African context, due to the underlying border delays and cumbersome documentation processes. This exploratory research focuses on examining, through case studies, how ten African nations have been applying blockchain technology in comparison to their BRICS counterpart. The study further examines the significance of blockchain technology in streamlining cross-border trade and rules of origin documentation within the context of the African Continental Free Trade Area (AfCFTA). The following nations, such as Egypt, Tunisia, South Africa, Botswana, Mauritius, Rwanda, Ethiopia, Kenya, Nigeria, and Ghana, were selected because they are actively participating in the African Continental Free Trade Area (AfCFTA)'s Guided Trade Initiative (GTI). The other aspect of this research examines the significance of the deployment of distributed ledger technology in BRICS. The significance of this study is to glean lessons from BRICS on the adoption of BCT. Critical realism is the philosophical underpinning of the research. This study applies the inductive approach, qualitative research design, and a case study research strategy. By examining existing case studies of blockchain applications in these selected African and BRICS nations, key lessons are learnt to enhance policy formulation. The result of this research is to enhance policy development on adoption of Block chain technology to build intra-African trade.
How do multiple possible action trajectories progressively narrow into one failing line of activity? While escalation of commitment scholars have long puzzled over this question, their tendency to conflate escalation with persistence and treat commitment as primarily cognitive compromises the concept’s theoretical precision and practical relevance. Adopting a communicative-relational ontology, we reconceptualize escalation as a process of communicatively constituted, ventriloquial attachment. We draw on an in-depth study of an interorganizational team to trace how such an attachment to the idea of working with blockchain technology emerged and progressively intensified in members’ interaction, pulling the team toward a single line of activity while alternatives were increasingly sidelined to a point where detaching became seemingly impossible. Our findings introduce the concept of escalating attachment , emphasizing how escalating processes are dynamic, relational accomplishments that both pull action forward along select lines of activity and hold it in place. This, we claim, resonates with practitioners’ lived experiences of how they not only hold onto an idea but also become progressively held by that very same idea. Further, our insights contribute to the communication as constitutive of organization (CCO) literature by lifting scholars’ focus away from situated (inter)action and toward how (inter)actions connect.
Large groups can make decisions via techniques like voting, referendums, and elections. During the realization and evaluation of these events, time cost, count honesty, and voting reliability are crucial activities. Users can create their own polls, votes, and surveys using the interfaces created in this study. On these designed election processes, they can cast an electronic ballot. The CNN machine learning system evaluates the votes, or the counting process, with great accuracy, preventing manipulation and bias errors. Blockchain and zero-knowledge proof-based infrastructures support evaluation processes concurrently, enhancing voting privacy, data security, and transparency procedures. Users can create their own polls, votes, and surveys using the interfaces created in this study. On these designed election processes, they can cast an electronic ballot. The CNN machine learning system evaluates the votes, or the counting process, with great accuracy, preventing manipulation and bias errors. Blockchain and zero-knowledge proof-based infrastructures support evaluation processes concurrently, enhancing voting privacy, data security, and transparency procedures. The values obtained from the results of the obtained CNN algorithm and data privacy criteria are quite satisfactory.
Blockchain technology has emerged as a transformative tool for secure data sharing across decentralised systems, particularly in finance, healthcare, and governance. However, despite its promise, the widespread adoption of blockchain platforms remains constrained by unresolved security threats and architecture-specific vulnerabilities. This paper presents a systematic literature review (SLR) that critically evaluates the security risks and countermeasures associated with blockchain-based data sharing models. The review focuses on three widely referenced platforms, Ethereum, Hyperledger Fabric, and MedRec, chosen for their relevance to public, permissioned, and healthcare-oriented blockchain deployments, respectively. The review analyzed 30 peer-reviewed publications from 2018 to 2025 sourced from IEEE Xplore, SpringerLink, ScienceDirect, and other digital libraries. Empirical insights from the reviewed literature indicate that Sybil attacks remain prevalent on public blockchains, although adaptive Proof-of-Stake protocols are reported to reduce their success rate considerably. Front-running and Miner Extractable Value–related behaviors are frequently observed in Ethereum-based decentralised finance ecosystems, often resulting in significant financial losses. Unauthorised access persists as a major concern, particularly for software wallets, which are commonly exposed to phishing and malware attacks. The findings underscore unique trade-offs across platforms: Ethereum supports transparency but is prone to transaction manipulation; Hyperledger ensures strong access control yet faces insider threat challenges; and MedRec enhances patient privacy but lacks robust mobile integration. This study provides a structured synthesis of existing threats, platform-level responses, and design trade-offs, offering guidance for stakeholders aiming to strengthen security in blockchain-based data-sharing infrastructures.
IoT sensor deployments increasingly export measurement streams to edge gateways and content-addressed storage such as IPFS, but access control decisions must be enforced without disclosing sensor owner policies, requester attributes, or stale data versions. Existing blockchain, CP-ABE, and zero-knowledge approaches reduce parts of this leakage, yet they can still expose public policy structure, accept stale Merkle proofs after sensor stream updates, overload provers when policies grow, or leave IPFS gateways vulnerable to bandwidth abuse. This paper proposes zk-Guard-R, a policy-hidden and replay-safe zk-SNARK access control framework for privacy-preserving IoT sensor data sharing. zk-Guard-R replaces public sparse policy matrices with MiMC-Merkle policy commitments verified inside the proof, separates long-lived logical sensor policy roots from frequently updated physical IPFS data roots, binds every proof to an on-chain nonce, and decouples attribute possession from policy interpretation through a bounded stack-based policy interpreter. Numeric sensor-access predicates are represented through committed values and range check gadgets, while an off-chain verification gateway couples accepted proofs with payment channel vouchers before releasing encrypted IPFS chunks. The design contribution is separated from the measured prototype: the full protocol specifies a bounded policy interpreter, whereas the present gnark prototype evaluates the core committed policy, committed attribute, range check, data root, nonce, Solidity verifier, and gateway-metering mechanisms. We implement a gnark BN254/Groth16 research prototype and benchmark it against a matrix-public zk-Guard prototype, a blockchain ABAC baseline, an IoT token/HMAC baseline, and a CP-ABE-style cryptographic-work proxy. For 128 attributes, the zk-Guard-R prototype with MiMC-Merkle commitments uses 425,574 R1CS constraints, generates proofs in 3.12 s, verifies in 0.73 ms, and uses 641 MB peak Go heap allocation. A three-run repeat of the 128-attribute configuration gives a proof-generation mean of 2.80 s with a 0.54 s standard deviation on the same local host, illustrating the runtime variability of prover measurements. We also deploy the generated Solidity verifier on a local Anvil EVM and measure 241,942 gas for a successful verification transaction, and we evaluate a local Kubo/IPFS gateway under valid, replayed, and voucher-limited flood requests. The results show that zk-Guard-R shifts substantial but measurable work to the prover while improving policy confidentiality, freshness, and gateway metering for IPFS-backed IoT sensor data sharing.
Over the past few years, Wireless Sensor Networks (WSNs) have been increasingly deployed for numerous sensing and monitoring purposes in environmental monitoring, industrial automation, health monitoring, military surveillance, smart agriculture and disaster management among others. The inherent limitations in terms of processing power, memory, communication bandwidth and energy of sensor nodes make WSNs highly susceptible to malware attacks. A wide variety of malware such as sensor network worms, Trojans, viruses, botnets and ransomware can easily propagate in a network through inter node communication. Such malware can cause serious damage to communication, compromise sensitive data, consume energy of the infected nodes thereby reducing the lifetime of network among others. In the last decade, numerous approaches have been proposed for the detection of malware infecting sensor nodes. These approaches range from traditional signature-based detection and behavior-based detection to more advanced approaches such as machine learning (ML)-based, deep learning (DL) -based, blockchain-based, trust management-based and federated learning-based detection. Most of the existing approaches for malware detection in WSNs have been designed to work on WSNs and have not been tested on real scenarios. Most of the approaches have their own strengths and weaknesses and the most suitable approach for a given application depends on various factors. In this paper, we present a comprehensive review of approaches for the detection of malware infecting sensor nodes in WSNs. We present a taxonomy of reviewed approaches for detection of malware. We also present a discussion on approaches for modeling malware propagation in a WSN as well as review on various categories of malware that have been designed to attack sensor nodes in WSNs along with detection frameworks for different categories of malware. We also present a comparative study of approaches used for the detection of malware in WSNs on the basis of various parameters such as detection accuracy, computational complexity, energy efficiency, scalability, detection latency and deployability. The review and taxonomy presented in this paper will be highly beneficial for researchers and practitioners designing approaches and systems for the detection of malware in WSNs. Various open research challenges in this area have also been discussed in this paper including detection of zero-day malware, designing of intelligent models to be light enough to be deployed on sensor nodes, use of explainable artificial intelligence for detection of malware in WSNs, designing approaches for privacy-preserving collaborative learning in WSNs and designing adaptive security approaches for WSNs.
Ahmed A. Jasim, Noor Riyadh Issa, Hisham A. Shehadeh, Fadhil Mukhlif · 6 authors
Abstract The Wireless Sensor Networks (WSNs), which are deployed in harsh environments, are extremely susceptible to localized battery exhaustion as well as intelligent inside routing threats, especially sinkhole and data falsification attacks. In this paper, we present ECHVM (Enhanced Cluster Head selection by Voting and an ECC-based Blockchain Mechanism), a novel, secure, and energy-efficient routing framework to achieve an optimal trade-off between network security and hardware resource efficiency. We present the ECHVM protocol that combines Elliptic Curve Cryptography (ECC) with a lightweight, local distributed ledger to mitigate risks of centralized authority by transferring the verification of crucial network events from a single, highly vulnerable centralized alert sink node to a decentralized, voting-based consensus among neighboring nodes. In this study, a weighted voting algorithm based on node and distance proximity metrics is applied to cluster head selection, where a 51% neighbor consensus rule is leveraged to validate local ledger transactions against malicious acts. Moreover, a local energy density and topological communication geometry-oriented energy-efficient cluster head (CH) selection algorithm is crafted to ensure balanced CH distribution in the high-density areas of the network structure so as to avoid the premature energy hole problem. Using the standard first-order radio energy model, quantitative simulations performed in MATLAB show that ECHVM achieves a malicious node detection rate of 98.2% and extends the network lifetime by 30% compared to state-of-the-art protocols (e.g., ELSO and SEC-HDT) because of proactive topology defense and rapid node sleeping. The results of statistical validation using the Wilcoxon Signed-Rank test confirm that both the reduction in energy consumption and network longevity offered by ECHVM are highly significant ( p = 0.019), justifying ECHVM as a mathematically sound, permanent, scalable security framework suitable for resource-constrained, dense Internet of Things (IoT) and smart sensing applications for next-generation communication systems.
Perkembangan Artificial Intelligence (AI) telah mendorong transformasi dalam Supply Chain Management (SCM) melalui peningkatan efisiensi operasional, kualitas pengambilan keputusan, dan ketahanan rantai pasok. Penelitian ini bertujuan untuk mengidentifikasi perkembangan penelitian AI dalam SCM, teknologi AI yang dominan, manfaat dan tantangan implementasinya, serta peluang penelitian pada periode 2021–2026. Metode yang digunakan adalah Systematic Literature Review (SLR) dengan menganalisis sepuluh artikel yang relevan dari berbagai sumber ilmiah. Data dianalisis menggunakan pendekatan narrative synthesis untuk mengidentifikasi pola, persamaan, dan perbedaan hasil penelitian. Hasil kajian menunjukkan bahwa Machine Learning merupakan teknologi AI yang paling banyak diterapkan, terutama pada demand forecasting, inventory management, optimasi logistik, dan manajemen risiko. Selain itu, perkembangan penelitian juga mengarah pada pemanfaatan Deep Learning, Computer Vision, Natural Language Processing, Digital Twin, dan integrasi AI dengan Internet of Things serta Blockchain untuk meningkatkan transparansi, fleksibilitas, dan ketahanan rantai pasok. Meskipun implementasi AI memberikan manfaat yang signifikan, masih terdapat tantangan berupa kualitas data, kesiapan infrastruktur digital, keamanan siber, dan kompetensi sumber daya manusia. Penelitian ini memberikan gambaran mengenai tren perkembangan AI dalam SCM sekaligus menjadi referensi bagi pengembangan penelitian dan implementasi AI pada berbagai sektor industri.