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MOALF-UAV-MEC: Adaptive Multi-Objective Optimization for UAV-Assisted Mobile Edge computing in Dynamic IoT Environments

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IEEE Internet of Things Journal 2025年

作者： Al-Bakhrani, Ali A. Li, Mingchu Obaidat, Mohammad S. Amran, Gehad Abdullah Dalian University of Technology School of Software Dalian116024 China Albaydha University Al Bayda Yemen Jiangxi Normal University School of Computer and Information Engineering Jiangxi China School of Software Technology Dalian University of Technology Nanchang Dalian China University of Science and Technology School of Computer and Communication Engineering Beijing100083 China SRM University School of Computing Department of Computational Intelligence TN Kattankulathur603203 India The Amity University School of Engineering UP Noida201301 India Dalian University of Technology Department of Management Science and Engineering Dalian116024 China

The proliferation of Internet of Things (IoT) devices and computation-intensive applications has led to unprecedented demands on network resources and computing capabilities. This paper presents MOALF-UAV-MEC, a novel Multi-Objective Adaptive Learning Framework for UAV-Assisted Mobile Edge computing (MEC) tailored for dynamic IoT environments. The framework integrates Multi-Objective Reinforcement Learning (MORL), Model Predictive Control (MPC), Adaptive Particle Swarm Optimization (APSO), and Lyapunov Optimization to optimize UAV trajectories, dynamic resource allocation, and system stability. MOALF-UAV-MEC addresses critical challenges in UAV-assisted MEC, including multi-objective optimization, adaptive resource allocation, energy efficiency, scalability, and quality of service guarantees. Our approach employs a unique burst mode feature for UAVs, enabling temporary performance boosts in high-demand situations. Extensive simulations demonstrate the framework's efficiency in enhancing task completion rates, energy efficiency, and long-term system sustainability. Results show a task completion rate of 94.50%, significantly outperforming existing approaches, with an average of 1,890 completed tasks per UAV and a load balancing efficiency of 96%. The framework exhibits robust adaptive behavior, achieving a 38% reduction in UAV route optimization and a 55% increase in task completion during high-load periods. This research contributes to the advancement of edge computing in IoT environments, offering a scalable and adaptive solution for deploying computational resources in areas with limited infrastructure, during temporary events, or in emergency situations. © 2014 IEEE.

关键词： Model predictive control

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A Blockchain-Based Distributed Collaborative Sensing and Spectrum Access Approach for Consumer Electronics

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IEEE Transactions on Consumer Electronics 2025年

作者： Peng, Yuhuai Li, Yuan Guo, Yu Zhang, Dawei Khan, Fazlullah Alturki, Ryan Alshawi, Bandar Northeastern University College of Computer Science and Engineering Shenyang110819 China Zhiyuan Research Institute Xihu District Hangzhou China Zhengzhou University School of Computer and Artificial Intelligence Henan Zhengzhou450001 China University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Zhejiang Ningbo315104 China KKR and KSR Institute of Technology and Sciences Department of Computer Science and Engineering Andhra Pradesh Guntur India Division of Research and Development Lovely Professional University Phagwara India University Centre of Research and Development Chandigarh University Mohali India Umm Al-Qura University Department of Software Engineering College of Computing Makkah Saudi Arabia Umm Al-Qura University College of Computing Department of Computer and Network Engineering Makkah Saudi Arabia

Cognitive radio (CR) provides key technical support for the seamless communication of consumer electronic (CE) devices through distributed cooperative sensing and spectrum access. However, its openness and dynamic nature introduce potential security risks and challenges. Due to the openness of wireless communication, spectrum sensing and access are vulnerable to malicious attacks, and resource-constrained devices cannot effectively implement artificial intelligence (AI)-driven encryption and access control strategies. To address these limitations, this study proposes a blockchain-based distributed collaborative sensing method for CE devices. In the proposed method, after sensing, the smart contracts verify node legitimacy using the ring signature method. The malicious users are rejected by the outlier detection method, which improves the collaborative anti-interference ability. In addition, by using a centralized training and distributed execution architecture, a secure spectrum sensing strategy based on multi-agent reinforcement learning is designed. The proposed strategy ensures that each node makes independent decisions, which enhances the adaptability of the CE ecosystem. The proposed method is verified by extensive simulations, and the results show that it can maintain spectrum awareness of more than 95.72% and an access success rate of 91.19% under malicious attacks. Moreover, communication overhead is significantly reduced by approximately 93.53%. © 1975-2011 IEEE.

关键词： Blockchain

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A graph attention network integrated with gated spiking neural P systems for session-based recommendation

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Expert Systems with Applications 2025年 286卷

作者： Bai, Xinzhu Peng, Hong Huang, Yanping Wang, Jun Yang, Qian Ramírez-De-Arellano, Antonio School of Computer and Software Engineering Xihua University Chengdu610039 China School of Electrical Engineering and Electronic Information Xihua University Chengdu610039 China Department of Computer Science and Artificial Intelligence Research Group of Natural Computing University of Seville Sevilla41012 Spain

With the development of deep learning technology, the recommender system began to use deep neural networks for modeling, which enables the recommendation system to better capture the complex patterns and features in the data. Session-based recommendation has also gradually received attention, especially for scenarios which deal with user behavior sequences and real-time requirements in a session. However, existing session-based recommendation methods tend to ignore users’ long-term interest changes, and there are still challenges for context understanding. In addition, session-based recommendation also has drift problems (i.e., the short-term behavior of users does not necessarily represent their real interests), which will affect the accuracy of recommendations. Therefore, this paper proposes a novel graph attention network, termed GAN-GSNP. This network combines a gated spiking neural P system with an extra balancing effect (EB-GSNP) to alleviate the drift problem in session-based recommendation by processing both global and local embeddings simultaneously. In addition, the GAN-GSNP model also proposes a multi-branch feature extraction strategy, which uses Self-Attention and feedforward networks in parallel to extract the high-order features embedded globally by users, so as to capture the global dependencies within the session or between different items, and make full use of the user's contextual information. Experimental results on two real datasets demonstrate the effectiveness and accuracy of the proposed GAN-GSNP model. © 2025 Elsevier Ltd

关键词： Deep neural networks

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Implementing an NFT by Developing a Smart Contract in OpenSea Marketplace 6th

Implementing an NFT by Developing a Smart Contract in OpenSe...

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6th International Conference on Advanced Informatics for computing Research, ICAICR 2023

作者： Shanmugam, Udhayakumar Krisha, Kotte Mohan Nancy John Mari Vienni, Silviya Nandhini, D. Uma Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidhyapeetham Chennai India Software Analyst Virtusa Corporation Chennai India School of Information and Communications Technology Saskatchewan Polytechnic College SaskatoonSK Canada Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9783031840586

Blockchain-based security applications have tremendously increased in the recent past with a decentralized network being the best way to implement a secure environment. Blockchain is one such implementation methodology that is considered the biggest disruptive technology. Smart contract-based transactions and assets have a high value and work because of a consensus mechanism without centralized trusted authority. Developing a smart contract for non-fungible tokens has various scopes and our proposed work uses various tools and libraries to achieve it. Brownie is used to implement the smart contract and Infura is used for the Ethereum node. Opensea marketplace is used for rendering the transaction. An Interplanetary distributed file system is used to store the NFTs. The work proposes the coordination of multiple tools to create a blockchain that is reliable and efficient. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Smart contract

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Predicting land suitability for wheat and barley crops using machine learning techniques

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Scientific Reports 2025年第1期15卷 1-19页

作者： Ganati, Bikila Abebe Sitote, Tilahun Melak Department of Computer Science Faculty of Computing and Informatics Jimma Institute of Technology Jimma University P.O. Box 378 Jimma Ethiopia Department of Software Engineering College of Engineering Addis Ababa Science and Technology University P.O. Box 16417 Addis Ababa Ethiopia

Ensuring food security to meet the demands of a growing population remains a key challenge, especially for developing countries like Ethiopia. There are various policies and strategies designed by the government and stakeholders to confront the challenge. One of the strategies is using technology solutions to increase crop productivity. Precision agriculture using advanced technology has been utilized to increase crop yield. Identifying suitable land for a crop is one of the important factors that will affect the crop’s yield. The existing approach to land suitability identification for a crop is time-consuming, expensive, and inaccurate. In this study, land suitability has been predicted for the two widely grown cereal crops in Ethiopia—wheat and barley—using machine learning techniques. The dataset was obtained from the engineering Corporation of Oromia (ECO). To make it suitable for modelling, we have pre-processed it. Features have been selected with univariate feature selection (UFS), recursive feature elimination with cross validation (RFECV), and sequential forward selection (SFS). Then, random forest (RF), gradient boosting (GB), and K-nearest neighbour (KNN) were used to predict the land suitability of the two selected crops. To optimize the performance of the models, hyperparameters were tuned with cross-validated randomized searches. The performance of the models has been evaluated using stratified tenfold cross-validation with performance metrics such as accuracy, precision, recall, and F1-score. GB with the SFS has better performance than the other models, with accuracy of 99.41%, precision of 99.37%, recall of 99.34%, and an F1-score of 99.35%. We believe that predicting land suitability accurately using machine learning techniques for the two commonly cultivated cereal crops in Ethiopia will be helpful in increasing the crops’ productivity. The developed model is very accurate. It can be used to develop a decision support system to identify the land s

关键词： Barley Gradient boosting Land suitability Machine learning Sequential forward selection Wheat

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GIRP: Energy-Efficient QoS-Oriented Microservice Resource Provisioning via Multi-Objective Multi-Task Reinforcement Learning

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IEEE Transactions on Mobile computing 2025年

作者： Yuan, Honggang Wang, Ting Fu, Min Shi, Yuanming East China Normal University MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Laboratory of Trustworthy Computing Shanghai200062 China National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore ShanghaiTech University School of Information Science and Technology Shanghai201210 China

Microservice architecture has revolutionized web service development by facilitating loosely coupled and independently developable components distributed as containers or virtual machines. While existing studies emphasize end-to-end latency, this paper investigates energy-efficient quality-of-service (QoS)-oriented microservice provisioning, focusing on both QoS satisfaction and power consumption (PC) conservation. We propose the Green and Intelligent Resource Provision (GIRP) architecture, integrating a data-driven energy-latency-aware resource allocation and scheduling manager to balance latency and PC. To reconcile the trade-offs involved, a dual-objective optimization problem is formulated to minimize latency and energy use by selecting proper servers, allocating CPU cores, and determining service replicas. To address challenges with discrete variables, dual objectives, and implicit mappings, we leverage a model-free deep deterministic policy gradient-based reinforcement learning algorithm. Specifically, we develop a multi-task agent via the Multi-gate Mixture-of-Experts model to simultaneously make two separate actions regarding CPU core numbers and service replica numbers, followed by a single-task agent to determine service scheduling. Extensive experiments on the DeathStarBenchmark testbed validate GIRP's effectiveness, demonstrating approximately 52% resource savings and a 43% reduction in PC compared to leading methods like Sinan, Firm, and heuristic-based algorithms. These results highlight GIRP's capability to optimize microservice orchestration by balancing end-to-end latency and power efficiency. © 2025 IEEE. All rights reserved.

关键词： Quality of service

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YOLO-RAW: Advancing UAV Detection With Robustness to Adverse Weather Conditions

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IEEE Transactions on Intelligent Transportation Systems 2025年第6期26卷 7857-7873页

作者： Munir, Adnan Siddiqui, Abdul Jabbar Hossain, M. Shamim El-Maleh, Aiman Computer Engineering Department Dhahran31261 Saudi Arabia KFUPM SDAIA-KFUPM Joint Research Center for Artificial Intelligence IRC for Intelligent Secure Systems and Computer Engineering Department Dhahran31261 Saudi Arabia King Saud University College of Computer and Information Sciences Research Chair of Pervasive and Mobile Computing Department of Software Engineering Riyadh12372 Saudi Arabia KFUPM Computer Engineering Department Information and Computer Science Department IRC for Intelligent Secure Systems Dhahran31261 Saudi Arabia

With the widespread adoption of unmanned aerial vehicles (UAVs) in various applications (e.g., aerial transportation, traffic monitoring), there have been apprehensions regarding the associated risks of employing UAVs in both civilian and military contexts, including concerns about privacy infringement, safety issues, and security threats. Although several methods have been proposed to detect UAVs, a pressing open challenge is posed by varying adverse weather conditions that could degrade the performance of many existing methods. To address these limitations, this work proposes a YOLO-based (You Only Look Once) novel model, YOLO-RAW that exhibits improved performance in adverse weather conditions and considers different scales of UAVs. Furthermore, to facilitate a more comprehensive evaluation of the proposed model’s effectiveness in UAV detection, we have curated a complex background dataset and introduced three distinct test sets affected by adverse weather conditions. These three test sets comprise the Rainy Test Set (RTS), the AWGN (Additive White Gaussian Noise) Test Set (ATS), and the Motion Blurred Test Set (MBTS). The comprehensive experiments demonstrate the effectiveness of the proposed YOLO-RAW model over its counterparts in detecting UAVs under adverse conditions. © 2000-2011 IEEE.

关键词： Drones

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Staging of Different Tumor by Utilizing Laser Guidance (@405 nm) in CT Scan Images Along with Statistical Analysis

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Lasers in engineering 2025年第4-6期59卷 309-325页

作者： Amjad, M. Ullah, H. Siddique, M.A. Majid, M.A. Aleem, A. Akhtar, M. Gilanie, G. Abdullah-Al-Wadud, M. Farhan, M. Akram, A. Batool, Z. Nazir, A. Biophotonics Imaging Techniques Laboratory Institute of Physics The Islamia University of Bahawalpur 63100 Pakistan Department of Computer Science Faculty of Computing The Islamia University of Bahawalpur Bahawalpur Pakistan Dept. of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia School of Earth Sciences and Engineering Hohai University Nanjing China

Early detection of lung cancer, liver cancer (HCC), and pancreatitis is crucial for effective treatment. This study assessed the use of laser guidance CT scans for diagnosis and staging. 26 patients of CT scan which were collected from BVH and civil hospital Bahawalpur, Pakistan. Visible light with a 405 nm wavelength is used. Results showed staging of lung cancer (2 at stage 1, 2 at stage 2, 3 at stage 4) and HCC (stages 1 to 4a). Pancreatic diseases detected included acute pancreatitis (44%), chronic pancreatitis (22%), gallstones (22%), and pancreatic tumors (11%). Alkaline phosphatase, SGOT, and blood urea levels were notably elevated in all three cancers compared to controls. CT scans show promise in identifying cancer stages and diagnosing pancreatitis, aiding in tailored treatment. Statistical analysis shows that alkaline phosphatase emerges as a potential biomarker for these conditions. ©2025 Old City Publishing, Inc.

关键词： Image analysis

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Predictive ISAC Beamforming Using LoRA-Transformer LSTM Integrated with GNN in Ultra-Dense D2D mmWave Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Liu, Haochen Bi, Jia Li, Bohan Xiang, Luping Yang, Xin Zhang, Zhaolin Wang, Ling Northwestern Polytechnical University School of Electronics and Information Xi'an710019 China Rutherford Appleton Laboratory Scientific Computing Department STFC DidcotOX11 0QX United Kingdom Ocean University of China Faculty of Information Science and Engineering Qingdao266100 China Nanjing University State Key Laboratory of Novel Software Technology Nanjing210008 China School of Intelligent Software and Engineering Suzhou China

Effective beamforming is crucial in ultra-dense device-to-device (D2D) mmWave networks to reduce interference and optimize communication. However, current methods often struggle to balance high data rates with minimal signaling overhead, especially with imperfect channel state information (CSI). This paper introduces a machine learning-assisted beamforming design tailored for integrated sensing and communication (ISAC), combining an efficient Low-Rank Adaptation Blocked LSTM Transformer with Message Passing Graph Neural Network that is called as LoRA_BLT-MPGNN. The LoRA_BLT component provides a key advantage by efficiently capturing both temporal and spatial patterns through fixed-weight updates during training and low-rank approximation for fine-tuning, significantly reducing computational complexity without sacrificing accuracy. Using historical channel data, the method implicitly captures channel characteristics and directly predicts the beamforming matrix for the subsequent time slot, minimizing signaling overhead while maximizing the achievable sum rate. Our research and simulation results show that the proposed predictive beamforming design maintains robust sensing performance and achieves a sum rate closely approaching the upper bound achievable with perfect CSI. Additionally, the incorporation of MPGNNs enables efficient modeling and learning of complex inter-node relationships, making the approach adaptable to networks of varying sizes and densities. Simulations further demonstrate that the LoRA_BLT-MPGNN model can be generalized across different network densities and coverage areas, reducing computational complexity by approximately 32× in terms of Tensor Floating Point Operations (TFLOPs) compared to conventional methods while maintaining high performance. © 2025 IEEE.

关键词： Beamforming

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Computational network analysis of two popular skin cancers provides insights into the molecular mechanisms and reveals common therapeutic targets

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Heliyon 2025年第1期11卷 e41688页

作者： Mahmud, Md Sujan Paul, Bikash Kumar Hasan, Md. Rakibul Islam, K.M. Tanjida Mahmud, Imran Mahmud, Shahin Department of Software Engineering Daffodil International University Bangladesh Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh Tangail 1902 Bangladesh Department of Biotechnology and Genetic Engineering Mawlana Bhashani Science and Technology University Santosh Tangail 1902 Bangladesh Department of Computing and Information System (CIS) Daffodil International University Dhaka Bangladesh

Basal Cell Carcinoma (BCC) and Actinic Keratosis (AK) are prevalent skin conditions with significant health complications. The molecular mechanisms underlying these conditions and their potential shared pathways remain ambiguous despite their prevalence. Therefore, this study aims to elucidate the common molecular pathways and potential therapeutic targets for BCC and AK through comprehensive computational network analysis. Linkage analysis was performed to identify common liable genes between BCC and AK. Protein-protein interactions (PPIs), Topological properties, GO enrichment, pathway enrichment, and gene regulatory network analyses were also performed to reveal potential molecular mechanisms and pathways. Furthermore, we evaluated protein-drug interactions (PDIs) to identify potential therapeutic targets. Our analysis revealed 22 common genes between BCC and AK: TP53, EGFR, CDKN2A, MMP9, PTGS2, VDR, BCL2, MMP2, EZH2, TP63, FOXP3, MSH2, MMP14, FLG, MC1R, CDKN2B, TIMP3, TYR, SOX10, IRF4, KRT17, and NID1. PPI network analysis highlighted TP53 and EGFR as central hubs, validated using RNA-seq data. Co-expression and physical interaction analysis revealed a strong interplay between the common genes at the transcriptional and functional levels. GO analysis identified skin cancer-relevant terms: "skin development", "immune system development", and “response to radiation” as significantly enriched biological processes, while pathway enrichment analysis highlighted several cancer-related pathways enrichment. Gene regulatory network analysis revealed complex interactions between genes, miRNAs, and transcription factors, with TP53, BCL2, and EGFR playing central roles. PDI network analysis identified ibuprofen as a potential therapeutic agent targeting PTGS2 and BCL2, while other proteins VDR, MMP2, MMP9, and TYR showed interactions with multiple drugs. This computational analysis provides valuable insights into the shared molecular mechanisms of BCC and AK, revealing comm

关键词： Actinic keratosis Basal cell carcinoma Bioinformatics Drug repurposing Protein-drug interaction Protein-protein interaction Skin cancers

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