A Sensor-Service Collaboration Approach for Target Tracking in Wireless Camera Networks

Mobile target tracking is a necessary function of some emerging application domains, such as virtual reality, smart home and intelligent healthcare. However, existing portable devices for target tracking are resource intensive and high-cost. Camera tracking is an effective location tracking way for those emerging applications which can reuse the existing ubiquitous video monitoring system. This paper proposes a dynamic community-based camera collaboration(D3C) framework for target location and tracking. The contributions of D3C mainly include that(1) nonlinear perspective projection model is selected as the camera sensing model and sequential Monte Carlo is employed to predict the target location;(2) a dynamic collaboration scheme is proposed, it is based on the local community-detection theory deriving from social network analysis. The performance of proposed approach is validated by both synthetic datasets and real-world application. The experiment results show that D3C meets the versatility, real-time and fault tolerance requirements of target tracking applications.

Privacy-Preserving Healthcare and Medical Data Collaboration Service System Based on Blockchain and Federated Learning

As the volume of healthcare and medical data increases from diverse sources,real-world scenarios involving data sharing and collaboration have certain challenges,including the risk of privacy leakage,difficulty in data fusion,low reliability of data storage,low effectiveness of data sharing,etc.To guarantee the service quality of data collaboration,this paper presents a privacy-preserving Healthcare and Medical Data Collaboration Service System combining Blockchain with Federated Learning,termed FL-HMChain.This system is composed of three layers:Data extraction and storage,data management,and data application.Focusing on healthcare and medical data,a healthcare and medical blockchain is constructed to realize data storage,transfer,processing,and access with security,real-time,reliability,and integrity.An improved master node selection consensus mechanism is presented to detect and prevent dishonest behavior,ensuring the overall reliability and trustworthiness of the collaborative model training process.Furthermore,healthcare and medical data collaboration services in real-world scenarios have been discussed and developed.To further validate the performance of FL-HMChain,a Convolutional Neural Network-based Federated Learning(FL-CNN-HMChain)model is investigated for medical image identification.This model achieves better performance compared to the baseline Convolutional Neural Network(CNN),having an average improvement of 4.7%on Area Under Curve(AUC)and 7%on Accuracy(ACC),respectively.Furthermore,the probability of privacy leakage can be effectively reduced by the blockchain-based parameter transfer mechanism in federated learning between local and global models.

Construction and Implementation of Multi Collaborative Service Platform Based on Industrial Competitive Intelligence

With the economic globalization and the increasingly fierce industrial competition at home and abroad, the importance of industrial competitive intelligence service is becoming increasingly prominent. Under the policy background of cooperation and sharing, pluralistic coordination has become a new trend in regional economic development. The multi collaborative online service platform of industrial competitive intelligence is jointly constructed by all service subjects. The platform is guided and promoted by the government. Colleges and universities provide support for industrial competitive intelligence theory and professionals, scientific research institutes provide talent and advanced technology support, industry associations are responsible for dynamic monitoring of industrial development, and profit-making institutions are responsible for supplementing industrial competitive intelligence achievements. All service subjects integrate and explore existing intelligence resources and services through the unified online industrial competitive intelligence sharing platform, so as to realize benign cooperation, collaborative management, resource integration, user integration and service integration among subjects, so as to realize multiple collaborative services of industrial competitive intelligence.

Multiple Collaborative Service Model and System Construction Based on Industrial Competitive Intelligence

This paper constructs a multiple collaborative service model of industrial competition intelligence with the main purpose of promoting the development of regional industries. The multiple service subjects include enterprises, governments, colleges and universities, scientific research institutes, industry associations and for-profit institutions. This article starts from the overall development of regional industrial economy, weighs the mutual relationship between the elements of the service model, and promotes multiple service subjects such as enterprises, governments, universities, research institutes, industry associations, and profit-making organizations to realize the collaborative service of resource intelligence, demand intelligence and data intelligence provides linkage intelligence service for the development and innovation of regional industries. This service model can improve the efficiency of industrial competitive intelligence services and the overall competitiveness of regional industries.

面向工业互联网平台的二维制造服务协作优化

工业互联网平台被公认为智能制造的必要推动者,使物理制造资源得以虚拟化,并允许资源以服务的形式进行协作。作为平台的核心功能,制造服务协作优化致力于为制造任务提供高质量的服务协作解决方案。这种优化与任务的功能和数量要求密不可分,在编排服务时必须满足这些要求。然而,现有的制造服务协作优化方法主要关注服务之间针对功能需求的横向协作,很少考虑纵向协作来覆盖所需的数量。为了解决这一差距,本文提出了一种结合功能和数量协作的二维服务协作方法。首先,提出了一种描述服务的多粒度制造服务建模方法。在此基础上,建立了二维制造服务协同优化模型。在垂直维度上,多个功能等效的服务组成一个服务集群来完成一个子任务;在水平维度上,互补服务集群协作完成整个任务。服务的选择和所选服务的金额分配是模型中的关键问题。为了解决这个问题,设计了一种具有多个局部搜索算子的多目标模因算法。将该算法嵌入竞争机制来动态调整本地搜索算子的选择概率。实验结果表明,与常用算法相比,该算法在收敛性、解质量和综合度量方面具有优势。