The rapid digitization of public services has positioned e-government as a cornerstone of modern governance, relying increasingly on advanced wireless and emerging technologies to support scalable, resilient, and data-driven operations. Despite extensive adoption efforts, a comprehensive investigation and systematic analysis of how emerging technologies collectively serve e-government across key domains remains limited. In particular, existing studies often address technologies in isolation, leaving gaps in understanding their integrated roles in smart cities, sustainability initiatives, cybersecurity frameworks, and evolving energy paradigms. This paper investigates the use of wireless and emerging technologies within e-government ecosystems and examines their employment and benefits across diverse public-sector applications. The study analyzes how these technologies contribute to service delivery, operational coordination, and policy execution, while critically discussing the technical, organizational, and regulatory challenges associated with their deployment. Furthermore, the impacts of these challenges on performance, security, and long-term viability are assessed and provided to guide researchers, system designers, and policymakers. By consolidating fragmented research and highlighting cross-domain interactions, this work offers a structured perspective on the role of wireless and emerging technologies in shaping the next generation of e-government systems.

A new age of technological and application improvements is about to dawn with the expected debut of Sixth Generation (6G) mobile technology by 2030. This launch will be a watershed moment in the history of wireless communication. There will supposedly be three-dimensional coverage for all things, everywhere, and at all times with 6G's ultra-high data speeds and nearly instantaneous communications. The 6G Radio Access Network (RAN)'s fronthaul links the pool of digital units (DUs) with the geographically distributed Remote Units (RUs). However, optical technologies continue to play a fundamental role in enabling 6G fronthaul, since they provide high-speed, low-latency, and reliable transmission that are needed to fulfil the required standards of 6G. This study is reviewing the fifth generation (5G) and the 6G optical fronthaul, which is describing the current research progress and discusses the important of 6G fronthaul technologies and system designs, It also covers the possible uses of each optical technology and their advantages in 6G fronthaul networks and the impact of the Artificial Intelligent (AI) on them. In order to help researchers and industry experts build future wireless networks that are both strong and efficient, this study seeks to provide a thorough overview of 6G optical fronthaul technologies and explore the future research and their current role in the area.

The building access control (BAC) is an emerging trend for the smart buildings that is expected to leverage the wireless technology such as hybrid Light-Fidelity (LiFi)/WiFi networks that provides high speed connection for access point (AP) assignment and data exchange purposes. Due to LiFi access points' short coverage areas and user mobility, such networks have a difficult handover (HO) process. So far, most of the existing APA studies in LiFi networks consider pedestrian users in indoor environment and no study considered APA in vehicle-to-building scenario. The orientation of the device due to the mobility of the vehicle, including speed and pausing behavior, can impact the HO process and therefore cause delay. This study proposes a novel access point assignment (APA) technique for hybrid LiFi/WiFi wireless networks that is suitable for BAC. Unlike mobile users in an indoor area, it considers varying speed with pausing (VSP) and the orientation-based random waypoint (ORWP), user location, user speed, and optical gain data rate for making the decision called mobility-behavior aware APA (MBA-APA).The proposed method is designed for multi-band network where RFID is added as an alternative AP if both LiFi and WiFi failed, and aims to eliminate the HO in such environment with optimal network selection. The proposed MBA-APA achieves better APA and eliminates HO in all cases compared with benchmark works.

This paper presents a dynamic, privacy-preserving context-aware geofencing system designed to support indoor climate data access and control in IoT-enabled buildings. The approach addresses two interrelated challenges: safeguarding sensitive indoor climate data in compliance with the General Data Protection Regulation’s (GDPR) data minimization and purpose limitation requirements, and enabling responsive environmental control based on user presence without relying on continuous tracking. During installation, each IoT device is registered via a mobile application that automatically collects spatial metadata, including GPS coordinates, which are clustered and aggregated into convex hulls representing building footprints. These geofences govern both data access and control logic, with evaluations occurring locally on users’ mobile devices to preserve privacy. The system architecture integrates a Java-Spring-MongoDB backend with an Angular-based dashboard and supports real-time visualization using Leaflet, OpenStreetMap, and Three.js. Evaluations at two case study sites, a mid-sized Danish university building and a large Malaysian library, demonstrate accurate spatial modeling, responsive access control enforcement, and successful actuation of climate control systems based on geofence crossings. Results show that anticipatory thermal preconditioning and energy-saving setbacks can be triggered reliably by geofence events, confirming the viability of location-based automation as a privacy-aware control mechanism for smart buildings.

Context The integration of artificial intelligence (AI) in the energy sector is pivotal for achieving Sustainable Development Goal 7 (SDG7). Within the European Union, the regulatory landscape, particularly the proposed AI Act, influences how organisations navigate responsible AI (RAI) adoption while addressing societal expectations, creating a critical need to examine how they communicate their commitment to RAI and sustainability. Objective This study uncovers how narratives employed in the public communications of EU energy stakeholders legitimise corporate efforts and signal alignment with RAI principles. Method A grey literature search of website pages, whitepapers, and reports was conducted. Thematic analysis, using inductive and deductive coding, was employed to identify emerging themes and evaluate how organisations frame their initiatives in response to regulatory and societal pressures. Result Analysis of 28 reports reveals that EU energy stakeholders predominantly frame AI as an inevitable technological advancement while lacking concrete strategies for RAI implementation. Communications focus on aspirational commitments rather than measurable actions. To address these gaps, this study develops the Responsible AI (RAI) Communication Model. This framework guides stakeholders in structuring their communication around three core pillars: (1) aligning AI initiatives with measurable sustainability goals and governance, (2) developing trustworthy and accountable narratives backed by concrete evidence, and (3) establishing organisational legitimacy through active stakeholder engagement. Conclusion By adopting this model, energy stakeholders can move beyond rhetorical narratives towards sharing demonstrable practices. This fosters greater trust, ensures effective communication of priorities like transparency and accountability, and promotes regulatory alignment.

UAV-assisted edge computing has emerged as a critical technology for real-time monitoring and control in smart city traffic management systems. A key challenge in these systems is efficiently distributing computational tasks across available edge nodes while ensuring system reliability and performance, particularly for resource-intensive applications like object detection. This paper aims to address these challenges by proposing CPFT-MOSA (Comprehensive Parallel Fault-Tolerant Multi-Objective Simulated Annealing), a novel task allocation optimization framework specifically designed for UAV-assisted edge computing in traffic management scenarios. Our approach makes three primary contributions: 1) a multi-objective optimization framework that minimizes active nodes, optimizes energy distribution, and reduces execution time while ensuring fault tolerance through parallel task execution; 2) an integrated YOLO-based real-time task prioritization system that dynamically adapts to traffic conditions under resource and bandwidth constraints; and 3) a feasibility-driven task assignment strategy that maintains computational balance while meeting system constraints. The proposed method decouples YOLO-based object detection tasks into several parallel modules within each UAV processing block and implements a dynamic resource management mechanism to balance processing efficacy and reliability. An experimental evaluation conducted across 17 nodes managing 45 full layers (comprising 270 total layer assignments) demonstrates that CPFT-MOSA performs superiorly to traditional methods. Our results show quantitative improvements in three key metrics: efficient resource utilization with an average of 16.8 active nodes; a 21% improvement in energy distribution efficiency with a value of 0.063; and an 8.5% reduction in execution time to 1.6 units compared to conventional approaches. The framework’s ability to balance multiple competing objectives while maintaining fault tolerance makes it particularly suitable for real-world traffic management applications where reliability and performance are crucial for time-sensitive decision-making in dynamic urban environments.

Speech recognition-based applications increased and developed as a result of artificial intelligence's rapid growth, particularly Machine Learning, which play a crucial role in many aspects of daily life, such as applications related to human-computer interaction, and natural language processing. The complexity and diversity of speech signals provides challenges in maximizing the rate of accuracy and efficiency of speech recognition systems. Hyperparameter tuning is a crucial step in machine learning that has a significant role in optimizing the performance and generalization by determining the optimal values for the model's hyperparameters. This paper employed the recently developed WAR Strategy optimization algorithm for optimizing the features related to the speech signal and tuning the hyperparameters of machine learning typical models for accurate and rapid speech recognition. Two types of features are extracted from the speech signal including the spectral feature using the Mel-Frequency Cepstral Coefficients (MFCCs) technique and the statistical features. Afterward these features are optimized using the WAR Strategy optimization algorithm to obtain the optimum features set that describe the speech signal important information. Finally, the hyperparameters of six classical machine learning models are tuned to serve as newly designed classifiers in the final classification phase of the proposed system. Three different language speech datasets are used to evaluate the proposed system (i.e. English, Arabic, Malaysian) to prove the high generalization property of the proposed system. The obtained recognition accuracy that was ranging from 98.38% to 100% in a training time between 0.001 to 19.8 second demonstrate the high effectiveness of the proposed speech recognition system in dealing with the many obstacles facing the recognition of speech signal within high accuracy, low resources requirements, and minimum training time.

Mobile Ad Hoc Networks (MANETs) are decentralized, infrastructure-less wireless systems that rely heavily on efficient routing protocols due to their dynamic and resource-constrained nature. Dynamic Source Routing (DSR) is widely adopted for such environments; however, it suffers from notable limitations, including cache saturation, memory inefficiencies, and lack of link quality awareness. These weaknesses reduce performance in high-mobility or large-scale scenarios and limit the reliability of DSR in real-world applications like military or disaster recovery networks. Motivated by these challenges, this paper proposes a comprehensive enhancement to DSR through intelligent caching strategies to improve scalability, energy efficiency, and route reliability. The proposed model introduces a dynamic route compression technique that reduces route size by eliminating redundant intermediate nodes. It also incorporates a hierarchical caching scheme where clusters managed by energy-aware Cluster Heads (CHs) distribute cache responsibilities, thereby improving scalability and reducing overhead. In addition, link quality metrics such as residual energy and link duration are integrated into route selection, enabling more stable and energy-efficient communication paths. Simulations using NS-2 across varying network sizes (50, 75, 100 nodes) revealed that the enhanced DSR significantly improves key performance metrics such as Packet Delivery Ratio (PDR), routing overhead, cache hit rate, and energy consumption. Route compression alone yielded a 14.3 PDR improvement and 21% reduction in overhead, while the hierarchical approach further optimized network performance. These results confirm the feasibility and effectiveness of the proposed enhancements, addressing longstanding gaps in traditional DSR implementations and laying a foundation for scalable and intelligent MANET routing protocols.

The ophthalmologist uses various techniques to diagnose corneal abnormalities, including corneal topography and tomography devices. Currently generated color corneal elevation surface maps from topographic imaging devices are essential for detecting ocular diseases, while accurately classifying these maps to differentiate between different shapes remains an issue. This study aims to assess and compare parameters of the front and back corneal surface elevation map patterns of normal/abnormal corneas. Two hundred cases were randomly taken (100 normal and 100 abnormal) with a single normal reference image, and then an additional 25 cases were added later for the optimization process. The preprocessing of all images involved converting them from color to black and white images to highlight the elevation bowtie shapes. The correlation coefficient, as implemented in MATLAB, was used to identify similar shapes of bowties for both normal and abnormal cases and determine the elevation values at the thinnest location. Additionally, it was used to measure the elevation bowtie angle between the reference normal image (case) and all other cases for image classification. Individually, the cut-off value of maximum correlation (mean ± 2 standard deviation) was calculated to distinguish between normal/abnormal patterns for 200 cases, and the cut-off values (0.974418 ≥ correlation ≥ 0.575, 0.994548 ≥ correlation ≥ 0.537841) were used for elevation back and front, respectively. Normal angle has cut-off values of (≤ 25 and =90), while normal elevation values at the thinnest location are (≤ 15, ≤ 20) for front and back surfaces. This method is based on elevation back, achieving an accuracy of 74%, and an accuracy of 64% was achieved for elevation front. Our model’s final accuracy was calculated by combining both the elevation back and elevation front final results to achieve 75.5% accuracy. Adding more data will not further increase the accuracy output since it has reached its optimal level. The development of this method aims to assist ophthalmologists in diagnosing and confirming clinical decisions through the accurate detection of corneal disease.

As the world move beyond the 5G era, the emergence of 6G promises a significant integration with innovative communication paradigms and burgeoning technology trends, actualizing previously utopian concepts alongside increased technical complexities. Analytical models offer basic frameworks, but ML and AI now outperform them in solving complex problems, either by augmenting or supplanting model-based methodologies. The predominant focus of data-driven wireless research is on discriminative AI (DAI), which necessitates extensive real-world datasets. In contrast to DAI, Generative AI (GenAI) refers to generative models (GMs) that can identify the fundamental data circulation, patterns, and characteristics of the incoming data. Given these attractive characteristics, GenAI can either substitute or augment DAI methodologies in multiple contexts. This comprehensive tutorial-survey article begins with an overview of 6G and wireless intellectual ability by delineating potential 6G applications and services. The aspects presented in this paper support the internet of things integration with 6G networks with the support of the AI as intelligent systems. This review paper concentrates on fundamental wireless research domains, encompassing network optimization, organization, and management. It examines the foundational learning principles of DAI and its methodologies, the application of DAI in wireless networks, and the utilization of GMs in 6G networks. Due to its comprehensive nature, this paper will act as a crucial reference for researchers and professionals exploring this dynamic and promising field.

Purpose The purpose of this paper is to introduce a novel deep learning model for translating sign language in the Web-based e learning platforms. The increased utilization of Web-based e-learning systems highlighted the need for an accurate, rapid and highly generalized system for recognizing sign language to facilitate effective communication for deaf/mute diverse learning communities. Design/methodology/approach In this paper, a novel lightweight hybrid deep learning model has been introduced, called a multi-branch convolutional LSTM fusion network (MB-ConvLSTM), that integrates multiscale convolutional branches with long short-term memory (LSTM) units. The model uses modern deep learning methods and optimizes computing efficiency to integrate smoothly with Web-based e-learning systems, therefore, improving accessibility while promoting diversity in virtual classrooms. Findings The presented deep model has a unique property of capturing both spatial and temporal features and provides a highly accurate recognition for sign language in different environmental conditions and positions of variant hand shapes. The sign image will pass through multiple preprocessing stages, and the features will be extracted using two methods (i.e. linear discriminant analysis and gray-level co-occurrence matrix) before the final classification phase. The high generalization and the computational efficiency of the proposed system have been proved by assessing it in recognizing three public data sets of sign language from variant cultures, including American, Arabic and Malaysian. The proposed work outperforms the existing state-of-the-art models in terms of precision (100%), recall (100%) and F1-measure (100%) for American and Malaysian sign language and (99.7%) for Arabic sign language; moreover, the recognition time also decreased, and the lowest time was equal to 732 ms for Malaysian sign language, which makes it ideal for real-world applications. Research limitations/implications The lightweight architecture of the proposed model ensures scalability for deployment on cloud-based Web services, whereas its low latency supports live interactions in multimodal learning environments. The proposed model surpasses current state-of-the-art models in accuracy (100%), precision (100%), recall (100%) and F1-measure (100%) for American and Malaysian data sets, and (99.7%) for Arabic sign language. Furthermore, the inference time was reduced, with the minimum inference time recorded at 732 ms from recognizing the Malaysian sign language, making it suitable for practical applications. Originality/value A novel lightweight hybrid deep learning model has been introduced, called MB-ConvLSTM, that integrates multiscale convolutional branches with LSTM units. The presented deep model is able to capture both spatial and temporal features and provides highly accurate recognition results in different environmental conditions and positions of variant hand shapes. The model uses modern deep learning methods and optimizes computing efficiency to integrate smoothly with Web-based e-learning systems, therefore, improving accessibility while promoting diversity in virtual classrooms.

The retrofitting of existing buildings with building management systems presents significant challenges, primarily due to the need for labor and cost efficiency. Wireless technology offers a promising solution to these challenges by minimizing the need for extensive wiring and structural alterations. However, achieving retrofitting in a cost-effective manner necessitates the use of low-cost wireless technologies. This paper introduces a framework for constructing a Zigbee gateway using open-source tools combined with low-cost hardware. The proposed architecture addresses large-scale IoT deployments within the Zigbee ecosystem. By leveraging edge computing with the robustness and scalability offered by Zigbee technology, this architecture significantly reduces the economic barriers to retrofit buildings with building management systems. The results underscore the potential of open-source Zigbee technology in aligning with sustainability goals, providing a cost-effective pathway for retrofitting buildings into smart, energy-efficient living environments.

In the contemporary era, smart buildings, characterized by their integration of advanced technologies to enhance energy efficiency and user experience, are becoming increasingly prevalent. While these advancements offer notable benefits in terms of operational efficiency and sustainability, they concurrently introduce a myriad of privacy concerns. This review article delves into the multifaceted realm of privacy issues associated with energy-efficient smart buildings. We commence by elucidating the potential risks emanating from data collection, storage, and analysis, highlighting the vulnerability of the personal and behavioral information of inhabitants. The article then transitions into discussing the rights of occupants, emphasizing the necessity for informed consent and the ability to opt-out of invasive data collection practices. Lastly, we provide an overview of existing regulations governing the intersection of smart buildings and privacy. We evaluate their effectiveness and present gaps that necessitate further legislative action. By offering a holistic perspective on the topic, this review underscores the pressing need to strike a balance between harnessing the benefits of technology in smart buildings and safeguarding the privacy of their occupants.

Achieving high performance in energy systems is crucial for sustainability. Energy economy optimization (EEO) models offer transparent analysis for energy policy decision-making. However, evaluating and benchmarking these models is a complex multicriteria decision making (MCDM) problem. Challenges include multiple criteria, data variation, and the importance of diverse criteria. This study develops an integrated MCDM approach to evaluate and benchmark EEO models. The methodology involves three phases. First, 12 commonly used EEO models and five evaluation criteria (software licenses, public source code, redistribution, public source data, and commercial software) are identified to create an evaluation decision matrix. Second, the fuzzy-weighted zero-consistency method (FWZIC) is used to evaluate and assign weights to the criteria. These weights are utilized in the benchmarking phase. Third, individual and group fuzzy decision by opinion score method (FDOSM) techniques are integrated to benchmark the EEO models based on the weights acquired. The FWZIC weighting reveals that the public source code criterion has the highest weight (0.3347), while redistribution has the lowest weight (0.1021). The group FDOSM results show that the OSeMOSYS model ranks first with the highest score (0.1595), while the DNE21+, MARIA, and MESSAGE models have the lowest score (0.0646), ranking them last. Systematic ranking, sensitivity ranking, and comparative analysis verify the proposed evaluation and benchmarking framework.

In recent years, the building sector has experienced an increasing legislative pressure to reduce the energy consumption. This has created a global need for affordable building management systems (BMS) in areas such as lighting-, temperature-, air quality monitoring and control. BMS uses 2D and 3D building representations to visualize various aspects of building operations. Today the creation of these visual building representations relies on labor-intensive and costly computer-aided design (CAD) processes. Hence, to create affordable BMS there is an urgent need to develop methods for cost-effective automatic creation of visual building representations. This paper introduces an automatic, metadata-driven method for constructing building visualizations using metadata from existing smart building infrastructure. The method presented in this study utilizes a Velocity Verlet integration-based physics particle simulation that uses metadata to define the force dynamics within the simulation. This process generates an abstract point cloud representing the organization of BMS components into building zones. The developed system was tested in two buildings of respectively 2,560 m2 and 18,000 m2. The method successfully produced visual building representations based on the available metadata, demonstrating its feasibility and cost-effectiveness.

Since the number of recorded instances of COVID-19 and the number of deaths linked with the virus are both on the rise, it is crucial to maintain an appropriate level of social distance and wear masks to prevent the transmission of coronavirus. The goal of the Social Distancing is to determine if a given pair of persons is keeping a suitable amount of space from one another. Quarantining infected areas and limiting the spread of viruses in both social and physical settings have been made easier with the help of a number of promising robotic technologies that aid people and healthcare experts in the tracking of virus symptoms, the care of infected individuals, and the avoidance of further infection and spread. In this study, we examine the most up-to-date strategies, structures, and tools for utilizing drones in non-social contexts. Additionally, difficulties, problems, and restrictions within the existing literature are addressed. The primary goal of this study is to aid and educate developers and readers interested in deploying drones and robots' technology to battle COVID-19, and to expose the challenges observed in the previous studies to allow the researcher to improve their methods and systems.

This systematic literature review critically examines the current state of cache improvement strategies in the routing protocol for Ad-Hoc Networks. Despite the growing significance of routing protocols in facilitating dynamic communication in decentralized networks, they might face substantial challenges regarding cache management, directly impacting network efficiency and performance. This review aims to consolidate existing research findings, identify best practices, and highlight gaps in the literature that warrant further investigation. The methodology for this review involved a comprehensive search across multiple academic databases, employing specific keywords related to routing protocols such as Ad-Hoc Networks, caching, and Ad-Hoc Networks. Studies were selected based on predetermined inclusion and exclusion criteria, ensuring relevance and quality. The review process emphasized peer-reviewed articles, conference papers, and significant technical reports published in the last decade. Key findings indicate a diverse range of caching strategies, each with unique implications for network performance, scalability, and energy efficiency. This review reveals that while some strategies significantly enhance the efficiency of cache usage in routing, others offer improvements in managing network resources and adapting to dynamic network topologies. The analysis also uncovers a critical gap in research related to the security implications of caching strategies in routing protocols and their impact on energy consumption in mobile nodes. This review’s findings are significant for researchers and practitioners in the field, providing a consolidated resource on routing cache improvement strategies and identifying fruitful avenues for future research. It underscores the need for more comprehensive studies addressing the balance between caching efficiency, resource management, and security in Ad-Hoc Networks. The implications of these findings extend beyond academic interest, offering practical insights for developing and enhancing more robust, efficient, and secure Ad-Hoc Networks using routing protocols.

The coronavirus (COVID-19) has emerged as one of the most serious issues. Researchers and officials are considering the implementation of several social distancing methods to detect potentially contaminated individuals. Nevertheless, limited social distancing methods have been discovered for tracking, scheduling, and monitoring vehicles for smart buildings. In these methods, people are tested on a regular basis in testing facilities every few days. This suggests that there may be untested infected individuals exhibiting active symptoms. Furthermore, since pandemics comparable to COVID may exhibit a range of symptoms that fluctuate throughout the day. For this reason, each time a vehicle requests entry into the facility, a real-time test or check must be performed. This study proposes a real-time vehicle social distancing decision system for managing the number of vehicles (RT-VSDD) that adds an additional testing method besides the traditional testing phase (test reports from test centers) which is a real-time vital health check during the building access request phase in order to reduce the risk of unidentified infected individuals. The concept of low-risk area and high-risk area in the building is introduced in this study where the method classifies the vehicles based on the risk levels and sends them to the targeted area. The system proposed in this study is identified as vehicle social distancing (VSD) system and is designed specifically for COVID pandemic. The performance evaluation of the proposed work has been performed using MATLAB simulations. 100 vehicles were assumed in the presented scenario with 5% untested, 20% positive, 75% negative, 30% high temperature, and 70% low temperature. When compared with the benchmark work, 40% of vehicles were classified as high risk and 55% were low risk by the proposed system, and 20% and 75% by the benchmark work. Only 5% of vehicles were denied access using the proposed system and 25% by the benchmark work. The total waiting vehicles rate was 25% and 11% in favour of the proposed work for a total waiting time of 100 minutes. The threshold value for the maximum vehicle allowed was reached 26 times by the proposed work against 13 times only by the benchmark work. 95% of vehicles were allowed access using the proposed technique, while only 75% were able to access the building using the benchmark technique. It is anticipated that the suggested system design will facilitate a reduction in the infection rate within buildings, reduces the negative economic impact, and manage the building access effectively for various industries and government sectors.

Agriculture 4.0 plays a crucial role in shaping sustainable cities and societies by revolutionizing urban food systems. By incorporating advanced technologies like precision farming, vertical gardening, and data analytics, Agriculture 4.0 improves local food production, reduces food transportation, and optimizes resource utilization. This paper introduces an innovative approach using Multi-Criteria Decision Making (MCDM) to assess Agriculture 4.0 Decision Support Systems (ADSS), contributing significantly to the selection of optimal systems that can drive sustainability in smart agriculture. The novelty of this research lies in developing a comprehensive evaluation framework that extends the hyperbolic fuzzy-weighted zero-inconsistency method for criteria weighting, combined with the combinative distance-based assessment method for benchmarking ADSS. The assessment matrix evaluates 13 ADSS across eight key criteria, including "accessibility," "re-planning," "expert knowledge," "interoperability," "scalability," "uncertainty and dynamic factors," "prediction and forecast," and "historical data analysis". Results from the hyperbolic fuzzy-weighted zero-inconsistency approach highlight "re-planning" (0.143) and "prediction and forecast" (0.140) as the most significant criteria, while "expert knowledge" ranked lowest (0.113). In the combinative distance-based assessment, the system labelled "OCCASION" achieved the highest score (3.843), positioning it as the most favourable ADSS, whereas the "MOLP-based beef supply chain" system scored lowest (-3.519). Sensitivity analysis, conducted using varying sets of weights, confirms the robustness and reliability of the proposed approach. This research provides a powerful decision-making tool that can guide stakeholders in selecting the best ADSS, ultimately promoting sustainability and resource optimization in Agriculture 4.0. The findings have important implications for farmers, agribusiness, and smart agriculture, demonstrating the potential of the methodology to enhance decision-making processes in a critical sector.