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Truthful Online Combinatorial Auction-based Mechanisms for Task Offloading in Mobile Edge Computing

IEEE Transactions on Mobile Computing 2025年第7期24卷 6488-6502页

作者： Wang, Xueyi Wang, Xingwei Wang, Chen Zeng, Rongfei Ma, Lianbo He, Qiang Huang, Min Northeastern University College of Software Liaoning Shenyang110819 China Northeastern University College of Computer Science and Engineering Liaoning Shenyang110819 China Northeastern University College of Medicine and Biological Information Engineering Liaoning Shenyang110819 China Northeastern University College of Information Science and Engineering Liaoning Shenyang110819 China

Mobile edge computation (MEC) is envisioned as a prospective approach for processing the computation-intensive and delay-sensitive tasks of smart mobile devices (SMDs) through offloading them to base stations (BSs) nearby. In fact, efficient task offloading mechanisms are crucial to accomplish an MEC system. The key challenge is to make on-spot decisions upon the arrival of each task and at the same time achieve truthfulness of each SMD. The challenge further escalates, when the unique characteristics of an MEC system, such as locality constraint, delay constraint, etc., are explicitly considered. To solve the challenge, we present a truthful online combinatorial auction-based mechanism (TOCA) for task offloading in an MEC system. Specifically, we first devise the candidate offloading scheme determination algorithm, aiming to determine the candidate offloading schemes of an SMD upon the arrival of its task. Next, we devise the winning offloading scheme selection and pricing algorithm based on the online primal-dual optimization framework, to decide the winning scheme among the SMD's candidate offloading schemes and calculate its payment. By solid theoretical analysis, we verify that TOCA achieves truthfulness, individual rationality and computational efficiency and a smaller competitive ratio. Trace-driven simulation studies validate the effectiveness and efficacy of TOCA. © 2002-2012 IEEE.

关键词： Mobile edge computing

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A Novel Approach to Energy Optimization:Efficient Path Selection in Wireless Sensor Networks with Hybrid ANN

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computers, Materials & Continua 2024年第5期79卷 2945-2970页

作者： Muhammad Salman Qamar Ihsan ulHaq Amil Daraz Atif MAlamri Salman A.AlQahtani Muhammad Fahad Munir Department of Electrical and Computer Engineering International Islamic UniversityIslamabad44000Pakistan School of Information Science and Engineering NingboTech UniversityNingbo315100China Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11495Saudi Arabia Computer Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11495Saudi Arabia

In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing ***,the limited energy resources of Sensor Nodes(SNs)are a big challenge for ensuring their efficient and reliable *** data gathering involves the utilization of a mobile sink(MS)to mitigate the energy consumption problem through periodic network *** mobile sink(MS)strategy minimizes energy consumption and latency by visiting the fewest nodes or predetermined locations called rendezvous points(RPs)instead of all cluster heads(CHs).CHs subsequently transmit packets to neighboring *** unique determination of this study is the shortest path to reach *** the mobile sink(MS)concept has emerged as a promising solution to the energy consumption problem in WSNs,caused by multi-hop data collection with static *** this study,we proposed two novel hybrid algorithms,namely“ Reduced k-means based on Artificial Neural Network”(RkM-ANN)and“Delay Bound Reduced kmeans with ANN”(DBRkM-ANN)for designing a fast,efficient,and most proficient MS path depending upon rendezvous points(RPs).The first algorithm optimizes the MS’s latency,while the second considers the designing of delay-bound paths,also defined as the number of paths with delay over bound for the *** methods use a weight function and k-means clustering to choose RPs in a way that maximizes efficiency and guarantees network-wide *** addition,a method of using MS scheduling for efficient data collection is *** simulations and comparisons to several existing algorithms have shown the effectiveness of the suggested methodologies over a wide range of performance indicators.

关键词： Wireless Sensor Networks(WSNs) mobile sink(MS) rendezvous point(RP) machine learning Artificial Neural Networks(ANNs)

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Joint classification of hyperspectral and lidar using multi-scale spatial adaptive calibration 6

Joint classification of hyperspectral and lidar using multi-...

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6th International Conference on Geoscience and Remote Sensing Mapping, GRSM 2024

作者： Yang, Zhichao Hao, Siyuan Qingdao University of Technology College of information and control engineering Qingdao China Beijing Jiaotong University College of software engineering Beijing China

ISBN: (纸本)9781510688018

In the field of land cover classification, the multimodal fusion of hyperspectral imagery (HSI) and light detection and ranging (LiDAR) data has become a key research direction in remote sensing. Hyperspectral imagery provides rich spectral information, enabling the identification of subtle material differences, while LiDAR data offers high-precision spatial geometric structures. However, the complementarity between these two data modalities faces challenges related to spatial resolution discrepancies and feature inconsistency. To address these challenges, this paper proposes a Multiscale Spatial Adaptive Calibration Network (MSACNet) for joint classification of hyperspectral and LiDAR data. The network extracts multiscale convolutional features and introduces a Spatial Adaptive Calibration Module (SACM) at each scale to adaptively calibrate the fused multimodal features, adjusting for discrepancies between spectral and spatial geometric feature distributions. Experimental results on the Houston2013 dataset demonstrate that the proposed method achieves superior classification performance compared to existing classification models. © 2025 SPIE.

关键词： Image resolution

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Leanor: A Learning-Based Accelerator for Efficient Approximate Nearest Neighbor Search via Reduced Memory Access 24

Leanor: A Learning-Based Accelerator for Efficient Approxima...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Wang, Yi Liu, Huan Yuan, Jianan Chen, Jiaxian Wang, Tianyu Ma, Chenlin Mao, Rui College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798400706011

Approximate Nearest Neighbor Search (ANNS) is a classical problem in data science. ANNS is both computationally-intensive and memory-intensive. As a typical implementation of ANNS, Inverted File with Product Quantization (IVFPQ) has the properties of high precision and rapid processing. However, the traversal of non-nearest neighbor vectors in IVFPQ leads to redundant memory accesses. This significantly impacts retrieval efficiency. A promising approach involves the utilization of learned indexes, leveraging insights from data distribution to optimize search efficiency. Existing learned indexes are primarily customized for low-dimensional data. How to tackle ANNS in high-dimensional vectors is a challenging *** paper introduces Leanor, a learned-index-based accelerator for the filtering of non-nearest neighbor vectors within the IVFPQ framework. Leanor minimizes redundant memory access, thereby enhancing retrieval efficiency. Leanor incorporates a dimension reduction component, mapping vectors to one-dimensional keys and organizing them in a specific order. Subsequently, the learned index leverages this ordered representation for rapid predictions. To enhance result accuracy, we conduct a thorough analysis of model errors and introduce a specialized index structure named Learned Index Forest (LIF). The experimental results show that, compared to representative approaches, Leanor can effectively filter out non-neighboring vectors within IVFPQ, leading to a substantial enhancement in retrieval efficiency. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Vectors

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SFTCS: Multiform Semantic Fusion Based on Transformer for Code Summarization 14

SFTCS: Multiform Semantic Fusion Based on Transformer for Co...

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14th International Conference on software Technology and engineering, ICSTE 2024

作者： Yang, Jun Ao, Longhao Qi, Rongzhi College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (纸本)9798350378955

Automatic code summarization aims to create co-herent natural language descriptions for code snippets. Recent studies indicate that integrating additional code representation structures improve the quality of generated summaries beyond token sequence-based approaches. Current technical research mainly concentrates on the graph structure information of code snippets. However, using a graph method makes it challenging to extract meaningful semantic information from the code snippets. To address the issue, we investigate how to learn more about code semantics and information from the perspective of code text feature representation. Consequently, we propose a novel model named SFTCS. SFTCS employs a conjoint feature fusion strategy, focusing on two types of information in code snippets: code token sequence features and FGT features based on Abstract Syntax Trees (AST) sequence and graph sequence. An N-adjacency ma-trix based on AST features is constructed, and the corresponding feature for AST block are optimized through a graph-based structure. Subsequently, a FGC decoder processes the two types of features. With the aid of an attention mechanism for feature enhancement, the SFTCS model ultimately predicts the results concretely. Evaluations on two publicly available Java datasets validate the effectiveness of SFTCS, showing its superiority over six advanced code summarization models. Ablation studies further elucidate the contributions of each component within the SFTCS model. © 2024 IEEE.

关键词： Semantics

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SRE-KGC : A Knowledge Graph Completion Model Based on Hidden Graph Structure and Relational Semantic Enhancement

SRE-KGC : A Knowledge Graph Completion Model Based on Hidden...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Lu, Sijun Xu, Guoyan Sun, Shuangyang College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (纸本)9781665410205

Knowledge Graph Completion (KGC) endeavors to use existing knowledge graph data for predicting missing elements in triples. Recently, due to the efficiency of graph neural networks (GNNs) in capturing topological structures and the effectiveness of text descriptions in supplementing semantic information, numerous models integrating graph structures and entity descriptions have emerged. However, these approaches typically focus on aggregating neighboring feature information and overlook mining hidden structural information. Furthermore, they often append textual descriptions to entities independently without considering the semantics within specific relations. Hence, we propose the SRE-KGC model to address these challenges. First, while aggregating neighbors, we analyze and mine the hidden structure in the neighborhood from the perspective of entities and relations;then we introduced a dual-layer attention mechanism to extract the most pertinent textual information towards relations from both the relational semantic level and the neighbor semantic level respectively;finally, the two learned features are fused and sent to the decoder for scoring. Experiments demonstrate that our model delivers superior performance. © 2024 IEEE.

关键词： Knowledge graph

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Study on the Rainfall-flood Association Pattern Database Based on Image Features 7

Study on the Rainfall-flood Association Pattern Database Bas...

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7th IEEE International Conference on Big Data and Artificial Intelligence, BDAI 2024

作者： Yu, Yufeng Cai, Difei Wu, Panqi Liu, Weifu Wang, Xuhang Hohai University College of Computer Science and Software Engineering Nanjing China

ISBN: (纸本)9798350352009

Precipitation images can clearly reflect the rainfall spatio-temporal features and play an important role in hydrological analysis and flood forecasting. However, it is challenging to mine the association response relationship between rainfall and flood events. This paper proposed a rainfall-flood association pattern database construction method based on rainfall image features. First, the rainfall pattern database and flood pattern database are constructed respectively through the steps of hydrological process segmentation, feature extraction, similarity measurement and cluster analysis. Additionally, association rule mining algorithm is used to conduct rainfall-flood association pattern mining and consrtuct the rainfall-flood association pattern database. The experimental results in the Jialing River basin show that the method proposed in this paper can effectively mine the rainfall-flood association pattern database and provide technical support for intelligent flood prediction based on historical scene patterns. © 2024 IEEE.

关键词： Rain

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Enhancing Gene Cluster Identification and Classification in Bacterial Genomes Through Synonym Replacement and Deep Learning

Enhancing Gene Cluster Identification and Classification in ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Du, Zhihua Zhong, Ningyu Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9798350386226

Natural products, abundant in biological activities, are synthesized by bacteria during secondary metabolism, yielding a plethora of antibiotics, anticancer drugs, and other potential therapeutics. These compounds are directed by biosynthetic gene clusters (BGCs), which encode the necessary enzymes and regulatory elements for their production. With the rapid advancement of sequencing technology, the number of fully sequenced bacterial genomes has exponentially increased, providing researchers with an unprecedented opportunity to discover more BGCs. However, current identification methods are significantly constrained by their reliance on database quantity and predominantly utilize local features inherent in the BGC sequences. In this study, we introduce Biosynthetic Gene Clusters Catcher: GCN-BERT(BGCCGB), a deep learning model leveraging natural language processing for the identification of BGCs in bacterial genomes and classification of BGCs product types. Our approach represents BGCs as sequences represented by protein family (Pfam) identifiers and performs data augmentation on existing BGC data by introducing a synonym replacement method. Local and global features in BGCs sequences are then integrated by BERT and its self-attention mechanism, in conjunction with Graph Convolutional Networks (GCN). The experiment results indicate that BGCCGB effectively learns the features of BGCs, localizes BGCs in bacterial genomes, and predicts the product types of BGCs. Notably, BGCCGB demonstrates a significant performance improvement over previous methods, with an average precision 6.9% in BGC identification. These enhancements underscore the potential of BGCCGB in advancing the discovery and understanding of natural products synthesized by bacteria. © 2024 IEEE.

关键词： Genome

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MFM: Multimodal Sentiment Analysis Based on Modal Focusing Model

MFM: Multimodal Sentiment Analysis Based on Modal Focusing M...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Sun, Shuangyang Xu, Guoyan Lu, Sijun College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (纸本)9781665410205

Multimodal sentiment analysis integrates various modalities of information to collectively inform decision-making processes. Previous studies often treat different modal features equally or emphasize textual information as the primary consideration. However, when the modalities in the sample contain different sentiment information, these methods may not be able to effectively deal with this situation. To solve this problem, we propose a multimodal sentiment analysis model focusing on each modality (MFM). In this paper, we separately integrate each modality as a primary modality interacting with other secondary modal information so that each modality can play a leading role. In addition, we use shared mask in modal interaction to capture important information in the secondary modality related to the primary modality, and improve the effectiveness of the information interaction process. The model is evaluated against baseline models using the MOSI and MOSEI multimodal sentiment analysis datasets. The experimental results show that the model achieves better performance, thereby validating its effectiveness in multimodal sentiment analysis tasks. © 2024 IEEE.

关键词： Decision making

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Design and Implementation of an Agricultural Logistics control System Based on IoT Techology

Design and Implementation of an Agricultural Logistics Contr...

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2024 International Conference on Advanced control Systems and Automation Technologies, ACSAT 2024

作者： Sun, Pengyu Zhang, Zhiquan Hohai University College of Computer Science and Software Engineering Nanjing China

ISBN: (纸本)9798331541668

In today's society, the agricultural logistics and transportation industry faces numerous challenges in meeting the demands for fresh produce preservation and efficient transport. Insufficient environmental monitoring and information management during transportation lead to increased logistics costs and high produce spoilage rates. A high-quality, efficient logistics control system is essential for the agricultural supply chain. This paper presents a logistics control system based on IoT technology, centered on the ESP32 chip, utilizing ESP-NOW and MQTT protocols for communication, integrating multi-sensor data and RFID technology. A sensor module and host gateway were designed, along with a cloud platform visualization dashboard. Through this system, users can view sensor data, track item dynamics, and effectively monitor and control the agricultural logistics process in real-time, enhancing both the efficiency and quality of agricultural logistics. © 2024 IEEE.

关键词： Spoilage

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