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design of coverage algorithm for mobile sensor networks based on virtual molecular force

COMPUTER COMMUNICATIONS 2020年第0期150卷 269-277页

作者： Liu, Song Zhang, Runlan Shi, Yongheng Guizhou Normal Univ Key Lab Informat & Comp Sci Guiyang 550003 Peoples R China Guizhou Vocat Technol Inst Informat Technol Coll Guiyang 550023 Peoples R China

General virtual force algorithm, when the density of sensor nodes is large in the monitoring area, there are some shortcomings, such as uneven distribution of nodes, more overlap of coverage area and so on. In view of these shortcomings, based on the basic idea of air molecular theory, a virtual molecular force model of mobile sensor networks is established, and the virtual molecular force algorithm for node deployment and mobile coverage of mobile sensor networks is given. The virtual molecular force algorithm assumes that there is interaction between nodes in mobile sensor networks, and the resultant force of these forces constitutes the resultant force network of sensor nodes in the monitoring area, which drives the sensor nodes to move to the corresponding location to repair the monitoring blind area, so as to maximize the coverage of the network. In order to verify the feasibility and effectiveness of the virtual molecular force algorithm, the virtual molecular force algorithm for mobile sensor network node deployment and mobile coverage is simulated and analysed by using MATLAB simulation tool. The simulation results show that the virtual molecular force algorithm can make the sensor nodes repair the monitoring blind area efficiently and quickly, and maximize the monitoring coverage area of the sensor network. In terms of repairing monitoring blind areas and improving network coverage, the virtual molecular force algorithm is superior to other network coverage algorithms such as general virtual force algorithm.

关键词： Mobile sensor network Network coverage Monitoring blind area algorithm design Simulation

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On the detection of Markov decision processes

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AUTOMATICA 2025年 175卷

作者： Duan, Xiaoming Savas, Yagiz Yan, Rui Xu, Zhe Topcu, Ufuk Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Univ Texas Austin Dept Aerosp Engn & Engn Mech Austin TX 78712 USA Beihang Univ Sch Artificial Intelligence Beijing 100191 Peoples R China Arizona State Univ Sch Engn Matter Transport & Energy Tempe AZ 85287 USA

We study the detection problem fora finite set of Markov decision processes (MDPs) where the MDPs have the same state and action spaces but possibly different probabilistic transition functions. Any one of these MDPs could be the model for some underlying controlled stochastic process, but it is unknown a priori which MDP is the ground truth. We investigate whether it is possible to asymptotically detect the ground truth MDP model perfectly based on a single observed history (state-action sequence). Since the generation of histories depends on the policy adopted to control the MDPs, we discuss the existence and synthesis of policies that allow for perfect detection. We start with the case of two MDPs and establish a necessary and sufficient condition for the existence of policies that lead to perfect detection. Based on this condition, we then develop an algorithm that efficiently (in time polynomial in the size of the MDPs) determines the existence of policies and synthesizes one when they exist. We further extend the results to the more general case where there are more than two MDPs in the candidate set, and we develop a policy synthesis algorithm based on the breadth-first search and recursion. We demonstrate the effectiveness of our algorithms through numerical examples. (c) 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

关键词： Markov decision processes Decision making Asymptotic detection Policy synthesis algorithm design

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Finding the sweet spot: Exploring the optimal communication delay for AI feedback tools

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INFORMATION PROCESSING & MANAGEMENT 2024年第2期61卷

作者： Shi, Yingnan Deng, Bingjie Australian Natl Univ 26c Kingsley St Canberra ACT 2600 Australia Xian Jiaotong Liverpool Univ Suzhou 215123 Jiangsu Peoples R China CSIRO Data61 Bldg 101Clunies Ross St Canberra ACT 2600 Australia

AI writing assistants that are capable of offering rapid, automatic, and corrective feedback are becoming increasingly powerful, providing accurate and automatic feedback to knowledge contributors while they write. This helps people stay engaged and focused on their writing tasks. However, even with improved algorithms, AI still requires communication delays to perform sophisticated language processing and generate accurate responses. Prior research suggests that if the delay is too long, users may lose interest and become frustrated, perceiving the AI agent as ineffective. Conversely, if the AI-generated feedback is too quick, users may push back and perceive the AI as not fully comprehending their input, leading to low levels of trust. The impact of feedback timing on knowledge contribution behavior remains largely unknown. To address this gap, this research employs two sequential studies to explore the effects of feedback timing. The first study is a conjoint analysis, and the second study is a random-assignment-based experiment. Our results show that an optimal range of timing for communication delay is approximately one to three seconds, leading to improved performance. Presentation styles of the AI agent may affect these results but only slightly. Our study is the first to systematically investigate the optimality of communication delay on AI feedback mechanisms. Our findings will contribute to the literature on knowledge management, algorithm design, and anthropomorphism, and also inspire the software designers on the optimal timing of the communication feedback.

关键词： Communication delay AI performance Knowledge management algorithm design Anthropomorphism

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第11期36卷 6462-6476页

作者： Feng, Wenjie Wang, Li Hooi, Bryan Ng, See Kiong Liu, Shenghua Chinese Acad Sci Inst Comp Technol Beijing 100190 Peoples R China Natl Univ Singapore Inst Data Sci Singapore 117602 Singapore Chinese Acad Sci Inst Comp Technol CAS Key Lab AI Secur Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100190 Peoples R China

Given a heterogeneous multilayer network with various connections in pharmacology, how can we detect components with intensive interactions and strong dependencies? Can we accurately capture suspicious groups in a multi-lot transaction network under camouflage? These challenges related to dense subgraph detection have been extensively studied in simple graphs (such as bipartite graph, multi-view network) but remain under-explored on complex networks. Existing methods struggle to effectively handle the intricate dependencies, let alone accurately identify the interrelated dense connected patterns within a series of complex heterogeneous networks. In this paper, we propose InDuen, a novel algorithm designed to detect interrelated densest subgraphs in multilayer networks through joint optimization of coupled factorization and local search for an elaborate-designed joint density measure. It is (a) effective for both large synthetic and real networks, (b) resistant to camouflage for anomaly detection, and (c) linearly scalable. Experimental results demonstrate that InDuen outperforms the state-of-the-art baselines in accurately detecting interrelated densest subgraphs under various settings. Furthermore, InDuen uncovers some intriguing patterns in real-world data, i.e., closely cooperated academic groups and interrelated dependent functional components in biology-net. InDuen achieves more than 35x speedup compared to the SOTA method Destine.

关键词： Nonhomogeneous media Matrix decomposition Diseases Cross layer design Image edge detection Task analysis Optimization Multilayer network dense subgraph detection interrelated pattern mining algorithm design

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Two-stage multi-objective evolutionary algorithm for overlapping community discovery

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PEERJ COMPUTER SCIENCE 2024年 10卷 e2185页

作者： Cai, Lei Zhou, Jincheng Wang, Dan Qiannan Normal Univ Nationalities Sch Comp & Informat Key Lab Complex Syst & Intelligent Optimizat Guizh Duyun Guizhou Peoples R China Guizhou Univ Coll Comp Sci & Technol State Key Lab Publ Big Data Guiyang Peoples R China Qiannan Normal Univ Nationalities Sch Math & Stat Duyun Guizhou Peoples R China

As one of the essential topological structures in complex networks, community structure has signi fi cant theoretical and application value and has attracted the attention of researchers in many fi elds. In a social network, individuals may belong to different communities simultaneously, such as a workgroup and a hobby group. Therefore, overlapping community discovery can help us understand and model the network structure of these multiple relationships more accurately. This article proposes a two-stage multi-objective evolutionary algorithm for overlapping community discovery problem. First, using the initialization method to divide the central node based on node degree, combined with the cross-mutation evolution strategy of the genome matrix, the fi rst stage of non-overlapping community division is completed on the decomposition-based multi-objective optimization framework. Then, based on the result set of the fi rst stage, appropriate nodes are selected from each individual ' s community as the central node of the initial population in the second stage, and the fuzzy threshold is optimized through the fuzzy clustering method based on evolutionary calculation and the feedback model, to fi nd reasonable overlapping nodes. Finally, tests are conducted on synthetic datasets and real datasets. The statistical results demonstrate that compared with other representative algorithms, this algorithm performs optimally on test instances and has better results.

关键词： Overlapping community discovery Evolutionary clustering Fuzzy clustering Feedback model algorithm design

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A truthful mechanism for multi-access multi-server multi-task resource allocation in mobile edge computing

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2024年第1期17卷 532-548页

作者： Liu, Xi Liu, Jun Qujing Normal Univ Sch Informat Engn Qujing Peoples R China Qujing Normal Univ Inst Appl Math Qujing Peoples R China

This paper addresses multi-access multi-server multi-task resource allocation in mobile edge computing (MEC). Our aim is to maximize social welfare for heterogeneous MEC servers from multiple access points (APs) providing heterogeneous virtual machine instances to the mobile devices (MDs) within the coverage area. In the system model, each MD has multiple tasks, and tasks can offload to more than one MEC server through different APs within its direct communication range. We formulated the problem with an auction-based model to provide flexible service. However, the MDs are self-interested and can misreport their preferences, leading to inefficient resource allocation. We designed an optimal approximation mechanism to solve this problem. Then, we showed that this mechanism achieves individual rationality and truthfulness, that is, the MDs had no incentive to declare untrue values. In addition, we analyzed the approximation ratio of our truthfulness mechanism. The task allocation problem was also considered, and the proposed approximation algorithm could stop at any step and provide reasonable performance. Experimental results demonstrated that our proposed approximation mechanism provides near-optimal social welfare in a reasonable time and effectively reduces energy consumption.

关键词： Mobile edge computing Truthfulness Resource allocation Energy consumption algorithm design

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To store or not: Online cost optimization for running big data jobs on the cloud

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2024年 156卷 42-52页

作者： Fu, Xiankun Pan, Li Liu, Shijun Shandong Univ Sch Software Jinan Peoples R China

As businesses increasingly rely on cloud-based big data analytics services to drive insights, reducing the cost of storing and analyzing large volumes of data in the cloud has become a major concern. During the execution of big data analysis jobs, some of the generated data can be reused by subsequent jobs. By storing such intermediate data, the cost of running big data jobs can be greatly reduced for businesses using cloud services. An important challenge is how to determine which data should be stored in order to save costs. Existing storing strategies do not differentiate between data with different usage frequencies, resulting in significant storage costs in practical applications. To address the aforementioned challenges, in this paper we propose two online algorithms, one deterministic and the other randomized, which dynamically determine whether to store the data with the aim of saving cost. We show that our proposed deterministic algorithm (resp., randomized) incurs costs within a factor of 2 - alpha' (resp., 2/1+alpha') times the minimum cost obtained by an optimal offline algorithm which is assumed to know the exact future a priori. Finally, through extensive experiments with real-world workload of big data jobs in Alibaba Cloud environment, we demonstrate that our proposed online algorithms can achieve significant cost savings under common cloud pricing schemes.

关键词： Cloud computing Cost optimization Online algorithm Big data analysis Competitive analysis algorithm design

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Soccer Match algorithm for Global Optimization: A Contender Metaheuristic

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IEEE ACCESS 2024年 12卷 93924-93945页

作者： Ben Ammar, Roua Gharbi, Anis Zied Babai, Mohamed Univ Tunis Tunis Business Sch BADEM Lab Tunis 2074 Tunisia King Saud Univ Coll Engn Ind Engn Dept Riyadh 11421 Saudi Arabia Kedge Business Sch F-33405 Talence France

In the quest for enhancing global optimization techniques, this paper introduces the Soccer Match algorithm (SMA), a novel metaheuristic inspired by soccer dynamics. SMA models the strategic elements of a soccer game including tactical roles, compositions, playing styles, and player interactions. Existing metaheuristic algorithms often struggle with the balance between reliability and computational efficiency. Furthermore, many algorithms lack the adaptive mechanisms necessary for dynamic parameter tuning which are based on ongoing performance feedback. The objective of this research is to create a soccer-inspired algorithm that integrates an unprecedented array of soccer concepts and characteristics, alongside an adaptive learning framework, to dynamically boost performance and efficiency. This approach is novel among soccer-inspired algorithms. SMA is designed using simple, soccer-related conceptual frameworks such as player roles and game tactics. It includes mechanisms for dynamic parameter adjustment and tactical shifts during a game. The algorithm's effectiveness was assessed through a series of benchmark unconstrained optimization problems. The experimental analysis reveals that SMA achieves remarkable performance metrics, closely matching those of leading metaheuristics like Harris Hawks Optimization and other soccer-inspired methods such as the Tiki-Taka algorithm. Notably, SMA demonstrates high scalability, reliability, and operational efficiency with minimal computational effort. The obtained results make SMA a promising approach for optimization problems.

关键词： Sports Metaheuristics Games Heuristic algorithms Classification algorithms Particle swarm optimization Benchmark testing Globalization algorithm design and analysis Scalability Global optimization soccer-inspired metaheuristic algorithm design unconstrained benchmarking problems efficiency scalability

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An Improved Count-Based Classifier for Categorical Data

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IEEE ACCESS 2024年 12卷 125427-125445页

作者： Singh, Sanskriti Sanjay Kumar Chauhan, Alok Vellore Inst Technol Sch Comp Sci & Engn Chennai 600127 India

The classification of categorical data is a fundamental task in machine learning, with numerous algorithms and techniques available. However, existing approaches often face challenges related to interpretability, scalability, and handling sparse or imbalanced datasets. This study presents an optimized version of the Count-Based Classifier, a novel approach that leverages the simplicity of counting occurrences to perform classification on categorical data. The optimized algorithm addresses the limitations of the original Count-Based Classifier, improving its computational efficiency, robustness, and overall performance. Through a comprehensive evaluation across 21 diverse categorical datasets, the Optimized Count-Based Classifier demonstrates competitive performance, consistently matching or surpassing established classifiers such as Decision Trees, Support Vector Machines, etc. The classifier's inherent interpretability, stemming from its reliance on counting operations, is a valuable asset, particularly in domains where transparency and explainability are crucial. Furthermore, the study explores the classifier's characteristics, including its tendency for overfitting, result consistency, and robustness against label errors. Experimental analyses reveal a low propensity for overfitting, high result consistency, and remarkable resilience to mislabeled data, further solidifying the classifier's practical applicability. The Optimized Count-Based Classifier has been implemented in Python and deployed as a user-friendly package, fostering accessibility and adoption within the machine learning community. By addressing the limitations of traditional approaches and offering a simple yet effective solution, this work contributes to the advancement of count-based classification techniques and their application in real-world scenarios.

关键词： Classification algorithms Training Boosting Support vector machines Prediction algorithms Noise measurement Scalability algorithm design and theory Machine learning Supervised learning algorithm design classification algorithm count-based classifier machine learning supervised learning

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A family of truthful mechanisms for resource allocation with multi-attribute demand in mobile edge computing

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第8期27卷 11595-11610页

作者： Liu, Xi Liu, Jun Wu, Hong Dong, Jing Qujing Normal Univ Sch Informat Engn Key Lab Intelligent Sensor & Syst Design Sanjiang Ave Qujing 655000 Yunnan Peoples R China Yunnan Coll Business Management Sch Educ Kunming 650000 Yunnan Peoples R China

We address the problem of resource allocation with multi-attribute demand considering heterogeneous servers in mobile edge computing (MEC). A mobile device (MD) may be within the coverage area of multiple access points and can get resources from any server that can establish direct communication. However, the configurations of heterogeneous servers are different, so the demand for each MEC server is different. We tackle this problem and consider multi-attribute demand, where each MD is assigned exactly one of multiple demands. We formulate the allocation problem in an auction-based setting to provide server scalability. We propose a family of greedy mechanisms to solve the resource allocation problem. However, each MD is self-interested and may try to manipulate the system to maximize its utility. We show our mechanisms are truthful, and they drive the system into an equilibrium where no MD can declare an untrue value to obtain higher utility. We also analyze the approximation ratios of our greedy mechanisms. Experimental results demonstrate that the greedy mechanisms obtain the near-optimal allocation in a short period while benefiting MDs and edge cloud providers.

关键词： Mobile edge computing Truthfulness Resource allocation algorithm design

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