These days, satellite remote sensing data is employed for different drone applications. The main goal is to provide imaginary information about electromagnetic locations and patterns of geolocations insight into Earth. The Internet of Drone Things (IoDT) exploits remote sensing data to deliver medicine from source to destination. However, many existing medicine delivery systems based on drones need longer execution times and more efficiency in delivering medicine to the right destinations. This paper presents transfer learning, which empowers a spatiotemporal remote sensing data training system for medicine delivery in edge cloud networks based on IoDT applications. The objective is to deliver the medicine to the original destination with the highest score and process all drone tasks based on their given deadlines. We present the offloading spatiotemporal training and scheduling (OSPTS) algorithm methodology that completes the data collection process and medicine delivery in different locations. Therefore, we solve the problem as a combinatorial problem and find the optimal solution based on searching and convolutional neural networks (CNN). Transfer learning and convolutional neural networks are sub-schemes of the OSPTS that train the remote sensing data on edge nodes and point clouds for optimal medicine delivery. Simulation results show that the OSPTS obtained the highest score for medicine delivery in the correct position with less processing time than existing systems.

To overcome vendor lock-in and reliability issues in single-cloud deployments, organizations increasingly adopt multi-cloud environments. However, scheduling Spark workflows across heterogeneous clouds under simultaneous deadline and budget constraints remains challenging due to resource diversity, variable pricing, and cross-cloud data transfers. We propose the Deadline Budget Spark Workflow Scheduling to Multi-Cloud (DB-SWSMC) algorithm, a novel scheduling algorithm combining heuristic initialization with simulated annealing optimization to: (1) efficiently allocate resources while balancing cost-time tradeoffs, (2) handle intra/inter-cloud data dependencies, and (3) rigorously enforce constraints. Evaluations across five workflows and compared against existing algorithms (HBDCWS, DBCS, and BDHEFT). Experimental results demonstrate that DB-SWSMC outperforms existing algorithms by 20-40% in cost efficiency and 15-80% in success rates, especially under tight budget and deadline constraints.

In cloud computing environments characterized by on-demand resource provisioning and elastic scalability, scheduling massive hybrid heterogeneous DAG workloads must reconcile complex task dependencies, intertwined deadline urgency and priority, and stringent latency constraints. At the same time, the imperative of sustainable computing demands energy- and resource-aware mechanisms that minimize waste under high concurrency. To address this challenges, we propose multi-factor Feature and multi-dimensional Reward-driven Adaptive Scheduling Policy (FRASP), an adaptive framework that first employs a Multi-factor feature Representation Graph Attention Network (MGAT) to embed dependency structure, remaining time, and priority into rich node representations, enabling precise identification of critical tasks. It then leverages a Multi-dimensional dynamic rewards Group Relative Policy Optimization (MGRPO) to generate and evaluate candidate schedules in parallel, simultaneously optimizing time-priority penalty, resource utilization, and deadline satisfaction rate. Moreover, FRASP incorporates a preemptive scheduling mechanism to dynamically interrupt lowerpriority tasks and allocate resources to high-priority taskses, ensuring rapid responsiveness in computing. Experimental results demonstrate that FRASP reduces average job completion time, increases deadline satisfaction, and enhances resource efficiency by approximately 5%, confirming its superiority in multiconstraint cloud environments while contributing to sustainable resource use.

The rapid growth of Internet of Things (IoT) devices in edge cloud networks has resulted in a surge of decentralized data generation, necessitating advanced methods for secure and efficient data processing. This paper introduces a novel secure federated learning (FL) framework designed specifically for IoT edge cloud environments, addressing key challenges such as data privacy, communication efficiency, and scalability. The proposed framework incorporates hybrid secure offloading and scheduling aggregation schemes (HSOSA) to manage real-time big data workloads across multiple nodes. These workloads are processed and securely aggregated at centralized nodes, ensuring streamlined execution while minimizing resource usage and security risks. The framework also integrates innovative offloading and scheduling techniques to optimize the distribution and scalability of data workloads. The framework integrates the different workload benchmarks, such as healthcare (patient, doctor, and disease history), runs them on different nodes and aggregates their results in the cloud. We considered the three decentralized nodes, such as Internet of Things (IoT), edge, and cloud nodes, to divide the workload executions and share their results with the aggregated node (e.g., cloud) for storage. Simulation results demonstrate that HSOSA significantly reduces execution time, resource consumption, and potential security vulnerabilities during workload processing, making it a robust solution for decentralized IoT systems in edge cloud networks.

In this era, digital technologies such as artificial intelligence, the Internet of Things (IoT) and blockchain are gaining popularity in research and academia. The supply chain management application is the key to achieving many benefits from AIoT and blockchain technology. However, these technologies have many issues, such as sustainability, a green environment, and multiconstraints (e.g., time, energy, cost, and CO2) for supply chain management applications. This article presents sustainable, secure blockchain-assisted AIoT and green multiconstraint supply chain systems. Initially, we present a secure and sustainable methodology that securely validates the supply chain management system data. For the green environment, we consider the problem a combinatorial problem consisting of different constraints such as time, energy, cost, and carbon dioxide (CO2). To solve this problem for supply chain management jobs, we present a multiconstraint genetic algorithm deep convolutional neural network (MCGA-DCNN) algorithm methodology. The objective is to reduce total processing time, total processing energy consumption, cost, and the CO2 environment as a green environment for supply chain management jobs. The genetic algorithm is evolutionary, where the fitness function optimizes the multiconstraint weights at the runtime based on DCNN and provides the optimal solutions for jobs. Simulation results show that MCGA-DCNN minimized the time, energy, cost, and CO2 and securely validated all transactions for all supply chain management jobs compared to existing schemes.

ABSTRACT Background The rapid evolution of Internet of Things (IoT) technologies has driven innovations across domains such as robotics, autonomous systems, and environmental control. However, effectively learning graph‐based representations within these dynamic and heterogeneous systems remains a significant challenge, especially when scalability and adaptability are required. Aims This study aims to develop and evaluate a novel meta‐reinforcement learning (meta‐RL) framework that combines Deep Q‐Networks (DQNs) with Graph Convolutional Networks (GCNs) to learn adaptive and efficient representations of graph clusters. The primary objective is to enhance cluster‐based representation learning by integrating reinforcement learning with graph aggregation policies. Methods We propose a cluster policy‐GNN model that formulates optimal graph aggregation as a Markov Decision Process (MDP). The framework incorporates a cluster meta‐policy to guide node‐specific aggregation strategies and utilizes a combination of DQN and GCN for adaptive graph representation. Training involves clustering nodes based on policy‐determined hops and batching to ensure efficient GNN training. A custom reward function drives the reinforcement learning process to prioritize computational focus on the most informative subgraphs. Results Our experimental results, benchmarked on real‐world graph datasets, demonstrate that the proposed framework significantly outperforms existing state‐of‐the‐art methods, including static GNNs, alternating graph‐regularized networks, and causal‐aware neural architecture search models. The learned cluster policies effectively enhance representation learning by dynamically adjusting to the structural heterogeneity of input graphs. Improvements were observed across various domains and scales, validating the flexibility and generalizability of the method. Conclusion The proposed meta‐RL framework with integrated DQN and GCN modules offers a powerful and scalable approach for graph cluster representation learning. By introducing adaptive, node‐specific aggregation strategies guided by reinforcement learning, the method effectively captures complex graph structures and surpasses current techniques. Future work may explore real‐time adaptation and deployment in more dynamic IoT‐based applications.

Modern smart city services necessitate complex technological infrastructure with heterogeneous compute servers, networks, and communication protocols. However, there are many research issues in heterogeneous computing infrastructure for Intelligent transport systems (ITS) when using the services in the network. Therefore, the main objective of this paper is to intelligent transportation services and their underlying infrastructure, built on an amalgamation of the Internet of Things (IoT), cloud and fog computing, and associated technologies. Specifically, this paper investigates the challenging issues of security, processing costs, and communication delays that frequently occur during the communication of data and messages. We propose novel, secure, and cost-effective schemes based on blockchain-assisted homomorphic encryption techniques. A Secure, Cost-Optimal Workload Assignment (SCWA) algorithm and a blockchain scheme made possible by Partially Hashing Homomorphic Encryption and Decryption (PHHE/D) are designed to distribute workload efficiently. We developed a simulator, MOTEL, that simulates the different functions of the proposed schemes and all the necessary components. Using MOTEL and data sets from real transport companies, the proposed approach is tested and evaluated using various experiments. The results demonstrate that, compared to existing solutions, the proposed approach significantly reduces processing costs and delays while maintaining an appropriate level of security in transport services.

These days, the usage of body area network enabled healthcare services has been increasing widely in practice. The objective is to monitor healthcare in real time from different remote services. However, the existing body area network architecture for healthcare services is not sufficient and optimal. In this paper, we present a Meta- Verse-based Healthcare Body Sensor Network Architecture to offer seamless healthcare services to different subjects. The architecture consisted of metaverse technology, healthcare sensors, and fog nodes to offer seamless services to the subjects. In architecture, we consider the hetero-geneous devices such as medical sensors (e.g., heart rate, oxygen, blood pressure, and temperature) to be connected with the mobile devices and metaverse fog nodes to process the healthcare application tasks of subjects. This paper presents the meta verse body area network offloading scheduling (MBANOS) based on a convolutional neural network (CNN) for all healthcare sensor data and their tasks. Simulation results show that MBANOS outperformed and obtained the highest score of data processing on different nodes to maintain the subject healthcare in practice.

To fulfill their expanding computational demands, businesses are using cloud computing more and more these days. However, cloud systems alone may not always suffice. Consequently, multi-cloud systems, which provide more scalable storage and computing resources, are becoming more popular. This paper focuses on scheduling Spark workflow tasks in a multi-cloud environment. It addresses the challenges posed by different pricing models, dynamic resource provisioning, inter and intra transmission time, and the instability of resource performance. To tackle these issues, in this work, we propose a heuristic-based solution that considers factors such as VM instances, precedence constraints, transmission times, and the impact of performance uncertainty aiming to minimize rental costs while ensuring that workflow deadlines are met. The results show that the proposed method is effective in scheduling Spark workflow tasks in a multi-cloud environment while considering performance uncertainty.

This paper proposes a novel Be-in and Be-Out (BIBO) automated ticketing system based on blockchain mobile sensors for public transport modes (e.g., metro, tram, train, and bus). The existing Bluetooth and GPS-integrated BIBO suffering from fraud may involve users deliberately turning off their Bluetooth connection during a trip to deceive the system into charging a reduced fare. Even if Bluetooth is turned off or there is a loss of mobile ticketing connection, the system can still gather sufficient location data to compute fares. However, this becomes impossible if a user's phone is powered off. This paper tackled these issues and suggested a blockchain-enabled BIBO mobile sensors-enabled automated system based on socket programming for public transport. We aim to generate BIBO automated ticketing based on mobile sensors without Bluetooth and GPS while boarding public transport. This paper presents the blockchain mobile ticketing system (BMTS-BIBO) algorithm, which consists of different steps to solve the problem. We trained the ticketing model based on a deep convolutional neural network (DCNN) and integrated it with socket programming based on the application. Simulation results show that the BMTS-BIBO algorithm outperformed and obtained 98% BIBO ticketing without GPS and Bluetooth.

Industrial cyber–physical systems (ICPS) are emerging platforms for various industrial applications. For instance, remote healthcare monitoring, real-time healthcare data generation, and many other applications have been integrated into the ICPS platform. These healthcare applications encompass workflow tasks, such as processing within hospitals, laboratory tests, and insurance companies for patient payments, which necessitate a sequential flow. The external wireless, fog, and cloud services within ICPS face security issues that impact end-users’ healthcare applications. Blockchain technology offers an optimal solution for ICPS-enabled applications. However, blockchain technology for the ICPS platform is still vulnerable to cyberattacks, while microservices are essential for executing applications. This paper introduces the novel “Pattern-Proof Malware Validation” (PoPMV) algorithm designed for blockchain in ICPS. It exploits a deep learning model (LSTM) with reinforcement learning techniques to receive feedback and rewards in real-time. The primary objective is to mitigate security vulnerabilities, enhance processing speed, identify both familiar and unfamiliar attacks, and optimize the functionality of ICPS. Simulations demonstrate the superiority of the proposed approach compared to current blockchain frameworks, showcasing dynamic allocation of microservices and improved security with comprehensive attack detection by 30%.

This paper presents the augmented Internet of Things (AIoT) framework for cooperatively distributed deep blockchain-assisted vehicle networks. AIoT framework splits the vehicle application into various tasks while executing them on different computing nodes. The vehicle application has different constraints, such as security, time, and accuracy, which are considered during processing them on parallel computing nodes (e.g., fog and cloud). We propose a partitioned AIoT scheme, dividing vehicular tasks into local and remote tasks. The objective is to minimize delays and efficiently execute urgent tasks such as vehicle, pedestrian, and traffic signals on local vehicles. The existing blockchain technologies suffer from many security issues, such as anonymous node issues and malware attacks in blockchain blocks. This is why we present the combined deep convolutional neural network (DCNN)-assisted proof-of-trust miner (PoTM) scheme. It safely handles tasks in different blocks. The smart contract is a human-written piece of code in blockchain technologies so that malicious code can be integrated into blockchain blocks during the registration of vehicles among nodes. The main limitation of smart contracts is that they are not changeable and cannot be changed once executed for any block. To avoid this situation, we present an augmented adaptive Trust Management Credibility Score Scheme (TMCSS) scheme that registers the vehicles before starting any services at blockchain miners. These registration certificates are changeable once DCNN detects any malicious activity in the vehicle data. Simulation results show that the proposed schemes improved delays by 35%, reduced the failure ratio of transactions by 39%, and enhanced overall transactions with the minimum failure compared to existing blockchain technologies for road-cooperation services in networks.

Alzheimer's disease (AD) is a memory-related disease that occurs in the human brain where neurons become degenerative. It is an evolved form of dementia that deteriorates over time. Machine learning, an extended version of deep learning, has appeared as an optimistic strategy for AD detection. Regardless, the existing AD detection approaches have yet to acquire the expected accuracy, mainly due to unreasonable data for training and testing. In this paper, we present the Federated Deep Convolutional Neural Network Alzheimer Detection Schemes (FDCNN-AS), specifically designed for varying age groups. FDCNN-AS is an efficient framework that contains architecture, algorithm flow, and implementation. It manages AD data from various laboratories and processes it in additional clinics. Our method mixes training data models from different types of data such as positron emission tomography, summed tomography, magnetic resonance imaging, blood tests, and questionnaires about synaptic degeneration. Further, we look at some restrictions that have yet to be addressed in AD detection. These include seeing AD at different ages, extrapolating the severity of brain damage, comparing treatment and recovery rates, and finding benign and malignant ranges in AD data that has been collected. To ensure secure and privacy-preserving learning, we execute FDCNN-AS within a federated learning environment that concerns considerable laboratories and clinics. Within this setup, we operate the generic deep convolutional neural network. The experimental results indicate that FDCNN-AS performs optimally, reaching a remarkable 99% accuracy in detecting dementia Alzheimer's in the human brain.

The Intelligent Transport System (ITS) is an emerging paradigm that offers numerous services at the infrastructure level for vehicle applications. Vehicle-to-infrastructure (V2I) is an advanced form of ITS where diverse vehicle services are deployed on the roadside unit. V2I consists of distributed computing nodes where transport applications are parallel processed. Many research challenges exist in the presented V2I paradigms regarding security, cyber-attacks, and application processing among heterogeneous nodes. These cyber-attacks, Sybil attacks, and their attempts cause a lack of security and degrade the V2I performance in the presented paradigms. This paper presents a new secure blockchain framework that handles cyber-attacks, as mentioned earlier. This paper formulates this complex problem as a combinatorial problem, encompassing concave and convex problems. The convex function minimizes the given constraints, such as time and security risk, and the concave function improves performance and accuracy. Therefore, numerous constraints, such as time, energy, malware detection accuracy, and application deadlines, require optimization for the considered problem. Combining the jointly non-dominated sorting genetic algorithm (NSGA-II) and long short-term memory (LSTM) schemes is the best way to meet the problem’s limitations. In this study, the paper designed a malware dataset with known and unknown malware. The different kinds of malware lists (e.g., cyber-attacks) are considered in the form of known and unknown malware lists with the characteristics, size of code, where malware comes from, attack on which data, and current status of the workload after being attacked by the malware. Our main idea is to present blockchain, NSGA-II, and LSTM schemes that handle phishing, routing, Sybil, and 51% of cyber-attacks without compromising application performance. Simulation results show that the study reduces delay and energy, improves accuracy, and minimizes security risks for vehicular applications.

These days, the usage of cloud computing services for different applications has been growing progressively. The applications, including business, commerce, healthcare, and others, require additional computation capabilities for their executions. To fulfil their expanding computational demands, cloud computing offers a pay-as-you-go billing model to run these applications cost-effectively. However, due to the complex requirements of these applications, more than one cloud system is required because single-cloud solutions are often limited by resource constraints, such as inadequate storage and computing power, as well as single-point failures that can compromise the integrity of the entire application. Consequently, multi-cloud strategies, which provide more scalable storage and computing resources, are becoming increasingly popular. However, the multi-cloud landscape consists of many cloud providers, and effectively managing workflow scheduling presents a significant hurdle in this dynamic environment. This paper focuses on scheduling Spark workflow tasks in multi-cloud networks. It addresses the challenges posed by different pricing models, dynamic resource provisioning, inter- and intra-transmission time, and the instability of resource performance. To solve these challenges, we propose a novel heuristic-based approach that considers different constraints such as VM instances heterogeneity, priority constraints, transmission times, and the impact of performance uncertainty. The goal is to schedule all tasks on virtual machines (VMs) with rental costs as low as possible while meeting workflow deadlines. The simulation results show that the proposed method effectively schedules Spark workflow tasks in multi-cloud networks, improving the scheduling performance by 50% compared to existing approaches.

These days, the usage of machine-learning-enabled dynamic Internet of Medical Things (IoMT) systems with multiple technologies for digital healthcare applications has been growing progressively in practice. Machine learning plays a vital role in the IoMT system to balance the load between delay and energy. However, the traditional learning models fraud on the data in the distributed IoMT system for healthcare applications are still a critical research problem in practice. The study devises a federated learning-based blockchain-enabled task scheduling (FL-BETS) framework with different dynamic heuristics. The study considers the different healthcare applications that have both hard constraint (e.g., deadline) and resource energy consumption (e.g., soft constraint) during execution on the distributed fog and cloud nodes. The goal of FL-BETS is to identify and ensure the privacy preservation and fraud of data at various levels, such as local fog nodes and remote clouds, with minimum energy consumption and delay, and to satisfy the deadlines of healthcare workloads. The study introduces the mathematical model. In the performance evaluation, FL-BETS outperforms all existing machine learning and blockchain mechanisms in fraud analysis, data validation, energy and delay constraints for healthcare applications.

The Internet of things (IoT) is a network of technologies that support a wide variety of healthcare workflow applications to facilitate users' obtaining real-time healthcare services. Many patients and doctors' hospitals use different healthcare services to monitor their healthcare and save their records on the servers. Healthcare sensors are widely linked to the outside world for different disease classifications and questions. These applications are extraordinarily dynamic and use mobile devices to roam several locales. However, healthcare apps confront two significant challenges: data privacy and the cost of application execution services. This work presents the mobility-aware security dynamic service composition (MSDSC) algorithmic framework for workflow healthcare based on serverless, serverless, and restricted Boltzmann machine mechanisms. The study suggests the stochastic deep neural network trains probabilistic models at each phase of the process, including service composition, task sequencing, security, and scheduling. The experimental setup and findings revealed that the developed system-based methods outperform traditional methods by 25% in terms of safety and 35% in application cost.

The advancement in transport applications increases at the everyday progress in technologies. Therefore, intelligent transport systems (ITS) gain a lot of progress at the different vehicle levels and in the vehicular area network. However, privacy and security at the network level are critical issues for ITS applications in the existing mechanism. In this paper, the study devises the cost-efficient and secure Serverless Blockchain Enable Task Scheduling (SBETS) ITS system and algorithm framework. The main goal is to reduce processing and security blockchian costs for ITS applications in the system. The processing cost minimizes based on the new proposed function-based price model and secures the data by a suggested deep graph convolutional neural network scheme in the network. The simulation results show that SBETS outperformed all existing ITS systems and minimized processing costs by 10% and fraud detection issues by 50% for transport applications.

The Internet of Things (IoT) paradigm has applications in many domains and is growing these days progressively. The applications are e-business, e-healthcare, and e-transportation. Recently, the container microservices-based Mobile Cloud Computing (MCC) has gained popularity, a lightweight framework compared to the monolithic virtual machine-based system. MCC combines fog nodes or cloud nodes with a base station to run the applications. However, storing the sensitive data of IoT applications on the untrusted nodes and failure of services are critical challenges in the existing architecture. This study proposes a novel microservices-based by combining fog and cloud services with efficient schemes. The first scheme is the Latency Aware Task Assignment Algorithm, which determines the optimal assignment of tasks to minimize the makespan of all applications. The second scheme is Fully Homomorphism Encryption, which ensures data security before an offload to any external assistance for execution. The final one is the Failure Aware, which handles any transient failure during application execution in the architecture. The experimental results show that the recommended architecture improved resource utilization, and the proposed schemes satisfied the security demand while reducing the makespan of applications.

Sensor-aware distributed workflow applications are becoming increasingly popular underwater. The apps are marine operations that generate data and process it based on its characteristics. Mobile-fog-cloud paradigms, as well as computing such as sensor nodes, have emerged. As previously stated, the nodes can be combined into a single system to achieve several goals. Many factors are considered, including network contents, workload fluctuation, variable execution durations, deadlines, and bandwidth. As a result, scheduling mobile workflow systems with multiple parameters might be challenging. The study suggests a novel content-efficient decision-aware task scheduling (CATSA) method for defining and adapting to complicated environmental changes. The CATSA consists of several components that work together to perform various benchmarks in the system, including a decision planner, sequencing, and scheduling. As evidenced by test findings during evaluation, the suggested architecture outperforms current studies regarding workflow execution quality of services and improved the makespan 30% and deadline meeting 40% in the study.

The development of vehicular Internet of Things (IoT) applications, such as E-Transport, Augmented Reality, and Virtual Reality are growing progressively. The mobility aware services and network-based security are fundamental requirements of these applications. However, multi-side offloading enabling blockchain and cost-efficient scheduling in heterogeneous vehicular fog cloud nodes network become a challenging task. The study formulates this problem as a convex optimization problem, where all constraints are the convex set. The goal of the study is to minimize communication cost and computation cost of applications under mobility, security, deadline, and resource constraints. Initially, we propose a novel vehicular fog cloud network (VFCN) which consists of different components and heterogeneous computing nodes. The ensure mobility privacy, the study devises Mobility Aware Blockchain-Enabled offloading scheme (MABOS). It extends blockchain enable multi-side offloading (e.g., offline offloading and online offloading) with proof of work (PoW), proof of creditability (PoC) and fault-tolerant techniques. The purpose is to offload all tasks under the secure network without any violation. Furthermore, to ensure Quality of Service (QoS) of applications, this work suggests linear search based task scheduling (LSBTS) method, which maps all tasks onto appropriate computing nodes. The experimental results show that devise schemes outperform all existing baseline approaches to the considered problem.