A Query-Based Greedy Approach for Authentic Influencer Discovery in SIoT

The authors propose an informed search greedy approach that efficiently identifies the influencer nodes in the social Internet of Things with the ability to provide legitimate information.Primarily,the proposed approach minimizes the network size and eliminates undesirable connections.For that,the proposed approach ranks each of the nodes and prioritizes them to identify an authentic influencer.Therefore,the proposed approach discards the nodes having a rank(α)lesser than 0.5 to reduce the network complexity.αis the variable value represents the rank of each node that varies between 0 to 1.Node with the higher value ofαgets the higher priority and vice versa.The threshold valueα=0.5 defined by the authors with respect to their network pruning requirements that can be vary with respect to other research problems.Finally,the algorithm in the proposed approach traverses the trimmed network to identify the authentic node to obtain the desired information.The performance of the proposed method is evaluated in terms of time complexity and accuracy by executing the algorithm on both the original and pruned networks.Experimental results show that the approach identifies authentic influencers on a resultant network in significantly less time than in the original network.Moreover,the accuracy of the proposed approach in identifying the influencer node is significantly higher than that of the original network.Furthermore,the comparison of the proposed approach with the existing approaches demonstrates its efficiency in terms of time consumption and network traversal through the minimum number of hops.

天基物联网:基本概念、体系架构及发展趋势

物联网技术的发展和天基网络的成熟催生了天基物联网的概念。国家新型基础设施建设的提出为天基物联网的建设奠定了政策基础。自然资源调查、时空信息服务、应急救灾服务等为天基物联网提供了广阔的应用空间,天基物联网已经具备了良好的建设基础。介绍了国内外天基物联网的发展现状,结合物联网的定义阐释了天基物联网的基本概念和体系结构,分析了天基物联网技术发展趋势,可为天基物联网的技术研究和建设应用提供参考。

COVID-19 Outbreak Prediction by Using Machine Learning Algorithms

COVID-19 is a contagious disease and its several variants put under stress in all walks of life and economy as well.Early diagnosis of the virus is a crucial task to prevent the spread of the virus as it is a threat to life in the whole world.However,with the advancement of technology,the Internet of Things(IoT)and social IoT(SIoT),the versatile data produced by smart devices helped a lot in overcoming this lethal disease.Data mining is a technique that could be used for extracting useful information from massive data.In this study,we used five supervised ML strategies for creating a model to analyze and forecast the existence of COVID-19 using the Kaggle dataset“COVID-19 Symptoms and Presence.”RapidMiner Studio ML software was used to apply the Decision Tree(DT),Random Forest(RF),K-Nearest Neighbors(K-NNs)and Naive Bayes(NB),Integrated Decision Tree(ID3)algorithms.To develop the model,the performance of each model was tested using 10-fold cross-validation and compared to major accuracy measures,Cohan’s kappa statistics,properly or mistakenly categorized cases and root means square error.The results demonstrate that DT outperforms other methods,with an accuracy of 98.42%and a root mean square error of 0.11.In the future,a devisedmodel will be highly recommendable and supportive for early prediction/diagnosis of disease by providing different data sets.

6G卫星物联网移动性管理:挑战与关键技术

卫星物联网是面向6G的新一代物联网的重要构成部分,是实现"泛在连接"愿景的重要网络形态。移动卫星与终端之间的相对高速运动引起了服务连续性问题。阐述了卫星物联网移动性管理技术,分析了双移动、广域覆盖以及需求非均匀等因素给卫星物联网移动性管理带来的新挑战,从移动性管理架构、预测及增强等方面探讨了面向6G卫星物联网移动性管理的关键技术。