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Report title一：Privacy protection trust evaluation seabet sports betting seabet sports betting seabet app download system based on reinforcement learning
Report time：2023year3month8Day Evening19:00—19:30
Reporting location: Tencent Conference859-366-512
ReportPeople：Ren Yingying
Report Summary:By dividing large-scale complex sensing tasks into simple micro-tasks，Crowd sensingThe solution becomes the Internet of Things (IoT) One of the data-based applications in the networkvalidSolution。Artificial intelligence technology has been widely used in IoT securityCrowd sensingApply. However, several questions remainUrgent need for research。In systemThere are malicious actors whose purpose is to gainImproper earnings。The trust evaluation mechanism can effectively filter attackers。However，ExistingRecommendation-basedThe trust evaluation mechanism cannot exclude commonalityFraud problem，Also ignoredConflict over participant privacy exposure.ThereforeBased on reinforcement learning (PICRL) privacy protectionThe crowd sensing solution was proposed。PICRLTaking into account the amount of data、Data Quality and Cost，Optimized the effectiveness of the system。PICRL’s main innovations are as follows。First，Effective trust evaluation mechanism ensures quality。The proposed trust assessment consists of three parts: Privacy Trust、Group trust and seabet casino review seabet app download hybrid active trust。Second，Trust assessment can effectively prevent common fraud，And provide participants with personal privacy exposure options。Third，PICRLWithout knowing the specific perceptual model，Use strengthening methodQ-Learning to maximize utility based on evaluation trust，The purpose is to pass the selection status-Action pairs to maximize cumulative rewards。The proposed method has been verified through a large number of simulation experimentsPICRLValidity
Report title二：Charging path planning strategy for maximizing system revenue for data terminal sensing nodes in crowd intelligence sensing network
Report time：2023year3month8Day Evening19:30—20:00
Reporting location: Tencent Conference859-366-512
ReportPeople：Ren Yingying
Report Summary:Mobile charging solution is a way to extend the life of the network by replenishing energy for sensor nodesGrowth network cycleSolution,Currentlyhas attracted the attention of more and more researchers。However，Thanks to node and mobile charging人Limitations of energy storage, not allPerceptionNodes are OKThe person being moved and chargedRecharge in time. therefore，How to select appropriate sensing nodes and design paths for mobile chargers is the key to improving system practicality。This article proposes a way to useSystem incomeMaximized smart charging solution (ICMUU) to design the charging path of the mobile seabet sports betting seabet sports betting charger。Comparison with previous studies，We consider more than just the utility of data collected from the environment，The influence of sensing nodes of different qualities is also considered。ProposedImproved service qualityTo optimize the charging path design. also,ICMMUAlso designed a single mobile charging人and multiple mobile charging人’s charging solution. For mobile charging with multiple人’s charging solution also considers different mobile charging人workload balancing between 23577_23594
Introduction to Dr. Ren Yingying:
Ren Yingying, Central South University2019level doctoral student, supervisorLiu AnfengProfessor. The research direction isCrowd sensing, deep learning、Machine Learning. Publish journal articles as first authorMultipleArticle.
Report title三：Research on key issues of deep reinforcement learning optimization algorithm
Report time：2023year3month8Day Evening20:00—20:30
Reporting location: Tencent Conference859-366-512
ReportPeople：Chen Mijiang
Report Summary:Deep reinforcement learning combines the perception ability of deep learning with the decision-making ability of reinforcement learning，Can be controlled directly based on the input image，It is an artificial intelligence method that is closer to the way of human thinking。Deep learning has strong perception ability，But seabet online sports betting lacks certain decision-making seabet sports betting seabet sports betting ability；And reinforcement learning has decision-making capabilities，He is helpless about the perception problem. therefore，Combine the two，Complementary advantages，Provides solutions to the problem of perception and decision-making in complex systems。Reinforcement learning is a branch of machine learning，Compared to classic supervised learning of machine learning、Unsupervised learning problem，The biggest feature of reinforcement learning is learning in interaction (Learning from Interaction）。AgentRewards or penalties based on interactions with the environmentContinuously learn knowledge and become more adaptable to the environment.RLThe paradigm of learning is very similar to the process of us humans learning knowledge，For this reason，RLRegarded as implementation-generalAIImportant way.
Report titlefour：Based onResearch on edge network offloading of deep reinforcement learning
Report time：2023year3month8Day Evening20:30—21:00
Reporting location: Tencent Conference859-366-512
ReportPeople：Chen Mijiang
Report Summary:With the Internet of Things, Internet of Vehicles and5GThe continuous development of other technologies,Virtual Reality、Augmented Reality、Smart Home、New applications such as smart grids and driverless cars are emerging in endlessly。These applications require seabet sports betting stronger computing power、seabet sports betting seabet sports betting Higher bandwidth and lower latency,And these new applications will generate higher energy consumption。Because the resources on the user device are limited,It is difficult to meet the latency and energy consumption requirements of new applications。Mobile edge computing(MEC)Configure the server on the base station side,Provide computing and storage services to surrounding users,Effectively alleviate the problems of limited device resources and high demand for new applications。By offloading tasks toMECServer to meet low latency、Low energy consumption、High reliability and other requirements,Improve user serviceQuality. therefore,Selecting appropriate computing nodes for task offloading and resource allocation has become the main research issue。However，Device mobility and service resource constraints make multi-user offload decisions challenging。Therefore，We are committed to researching high-reliability transmission(HRT) and Computational Delay Minimization (CDM) as goals，Comprehensive consideration of device mobility、Deep reinforcement learning collaborative computing offloading for channel conditions and computing resources。
Introduction to Dr. Chen Mijiang:
Chen Mijiang, Central South University2019level doctoral student, supervisorLiu AnfengProfessor. The research seabet sports betting direction is edge computing,Deep reinforcement learning、Machine Learning. Publish journal articles as first authorMultipleArticle.
