Privacy-Preserving Information Fusion Technique for Device to Server-Enabled Communication in the Internet of Things:A Hybrid Approach

Due to the overwhelming characteristics of the Internet of Things(IoT)and its adoption in approximately every aspect of our lives,the concept of individual devices’privacy has gained prominent attention from both customers,i.e.,people,and industries as wearable devices collect sensitive information about patients(both admitted and outdoor)in smart healthcare infrastructures.In addition to privacy,outliers or noise are among the crucial issues,which are directly correlated with IoT infrastructures,as most member devices are resource-limited and could generate or transmit false data that is required to be refined before processing,i.e.,transmitting.Therefore,the development of privacy-preserving information fusion techniques is highly encouraged,especially those designed for smart IoT-enabled domains.In this paper,we are going to present an effective hybrid approach that can refine raw data values captured by the respectivemember device before transmission while preserving its privacy through the utilization of the differential privacy technique in IoT infrastructures.Sliding window,i.e.,δi based dynamic programming methodology,is implemented at the device level to ensure precise and accurate detection of outliers or noisy data,and refine it prior to activation of the respective transmission activity.Additionally,an appropriate privacy budget has been selected,which is enough to ensure the privacy of every individualmodule,i.e.,a wearable device such as a smartwatch attached to the patient’s body.In contrast,the end module,i.e.,the server in this case,can extract important information with approximately the maximum level of accuracy.Moreover,refined data has been processed by adding an appropriate nose through the Laplace mechanism to make it useless or meaningless for the adversary modules in the IoT.The proposed hybrid approach is trusted from both the device’s privacy and the integrity of the transmitted information perspectives.Simulation and analytical results have proved that the proposed privacy-preserving information fusion technique for wearable devices is an ideal solution for resource-constrained infrastructures such as IoT and the Internet ofMedical Things,where both device privacy and information integrity are important.Finally,the proposed hybrid approach is proven against well-known intruder attacks,especially those related to the privacy of the respective device in IoT infrastructures.

Dynamics modeling and optimal control for multi-information diffusion in Social Internet of Things

As an ingenious convergence between the Internet of Things and social networks,the Social Internet of Things(SIoT)can provide effective and intelligent information services and has become one of the main platforms for people to spread and share information.Nevertheless,SIoT is characterized by high openness and autonomy,multiple kinds of information can spread rapidly,freely and cooperatively in SIoT,which makes it challenging to accurately reveal the characteristics of the information diffusion process and effectively control its diffusion.To this end,with the aim of exploring multi-information cooperative diffusion processes in SIoT,we first develop a dynamics model for multi-information cooperative diffusion based on the system dynamics theory in this paper.Subsequently,the characteristics and laws of the dynamical evolution process of multi-information cooperative diffusion are theoretically investigated,and the diffusion trend is predicted.On this basis,to further control the multi-information cooperative diffusion process efficiently,we propose two control strategies for information diffusion with control objectives,develop an optimal control system for the multi-information cooperative diffusion process,and propose the corresponding optimal control method.The optimal solution distribution of the control strategy satisfying the control system constraints and the control budget constraints is solved using the optimal control theory.Finally,extensive simulation experiments based on real dataset from Twitter validate the correctness and effectiveness of the proposed model,strategy and method.

A Double-Timescale Reinforcement Learning Based Cloud-Edge Collaborative Framework for Decomposable Intelligent Services in Industrial Internet of Things

With the proportion of intelligent services in the industrial internet of things(IIoT)rising rapidly,its data dependency and decomposability increase the difficulty of scheduling computing resources.In this paper,we propose an intelligent service computing framework.In the framework,we take the long-term rewards of its important participants,edge service providers,as the optimization goal,which is related to service delay and computing cost.Considering the different update frequencies of data deployment and service offloading,double-timescale reinforcement learning is utilized in the framework.In the small-scale strategy,the frequent concurrency of services and the difference in service time lead to the fuzzy relationship between reward and action.To solve the fuzzy reward problem,a reward mapping-based reinforcement learning(RMRL)algorithm is proposed,which enables the agent to learn the relationship between reward and action more clearly.The large time scale strategy adopts the improved Monte Carlo tree search(MCTS)algorithm to improve the learning speed.The simulation results show that the strategy is superior to popular reinforcement learning algorithms such as double Q-learning(DDQN)and dueling Q-learning(dueling-DQN)in learning speed,and the reward is also increased by 14%.

Core competencies and perceived training needs of nurses in the background of Internet plus nursing service:a qualitative descriptive study

Background:The Internet plus nursing service program is being piloted in China,which has become a new home nursing service model led by nurses.To enable safe and effective homecare service delivery,nurses working in the program need a high level of competency.However,the content of these core competencies and the training needs of nurses for Internet plus nursing services are still unknown.Aim:To describe nurses’perceptions of core competencies and training needs to work in the Internet plus nursing service program,thereby providing a reference for the development of training programs.Methods:A qualitative descriptive study using semi-structured interviews was conducted on 15 nurses with experience of working in the Internet plus nursing service program.Interviews were audio-recorded and transcribed verbatim,and the data were analyzed using thematic analysis.Results:Core competencies involved comprehensive assessment competence,direct care practice competence,health education and consulting competence,risk estimation and response competence,and communication competence.Training needs involved complex operational items,knowledge of chronic disease management,professional communication,risk identification and response,nursing standards,norms,and procedures,and utilization of information technology.Conclusions:The training system may be developed based on nurses’core competencies and their training needs to promote professional development of the Internet plus nursing service.

Analysis of the Current Situation of“Internet Plus Nursing Service”

In the era of“Internet Plus,”technologies like big data,the Internet of Things,and cloud computing have revitalized nursing services in China,leading to the emergence of the“Internet Plus Nursing Services”model.This model facilitates on-site nursing care for discharged patients and special groups with mobility challenges,further advancing the construction of a Healthy China and enabling more people to access high-quality and continuous nursing services.Governments at all levels should actively improve the systems related to“Internet Plus Nursing Services”and simplify the appointment and operation processes.It is also essential to standardize the medical waste disposal process to ensure the quality of on-site nursing services,protect the legitimate rights and interests of both caregivers and patients and safeguard patient privacy and security.Additionally,building a robust“Internet Plus”platform to promote“online appointment of nurses”services,integrate nursing resources,and foster the healthy development of“Internet Plus Nursing Services”is crucial.

Information freshness optimization of multiple status update streams in Internet of things:Generation rate control and service rate reservation

The Internet of things(IoT)has become a key infrastructure providing up-to-date and fresh information for policy analysis and decision-making of upper-layer applications.However,there are limited sensing and communication resources in IoT devices,which significantly affects the timeliness and freshness of the updated status.This work proposes two schemes,namely,the generation rate control and service rate reservation schemes,to improve the overall information freshness of multiple status update streams at the receiver.Specifically,using the recently proposed Age of Information(AoI)as the metric for evaluating information freshness,we characterized the overall information freshness,i.e.,the overall average AoI at the receiver for both schemes,by considering the urgency difference of status update and streams.Both schemes for status updates and streams,respectively,were formulated as two optimization problems.We proved that both problems are convex and the optimal generation and service rates for different streams are found by the standard convex optimization algorithm.Moreover,we proposed both approximate optimal generation and approximate optimal service rate for fast deployment in heavy and light load cases.Numerical results verify the theoretical findings and accuracy of the proposed approximate solutions,guiding the design and deployment of IoT.

Effect of Internet+continuous midwifery service model on psychological mood and pregnancy outcomes for women with highrisk pregnancies

BACKGROUND There are many drawbacks to the traditional midwifery service management model,which can no longer meet the needs of the new era.The Internet+continuous midwifery service management model extends maternal management from prenatal to postpartum,in-hospital to out-of-hospital,and offline to online,thereby improving maternal and infant outcomes.Applying the Internet+continuous midwifery service management model to manage women with highrisk pregnancies(HRP)can improve their psycho-emotional opinion and,in turn,minimize the risk of adverse maternal and/or fetal outcomes.AIM To explore the effectiveness of a midwife-led Internet+continuous midwifery service model for women with HRP.METHODS We retrospectively analyzed the clinical data of 439 women with HRP who underwent prenatal examination and delivered at Shanghai Sixth People's Hospital(affiliated to the Shanghai Jiao Tong University School of Medicine)from April to December 2022.Among them,239 pregnant women underwent routine obstetric management,and 200 pregnant women underwent Internet+continuous midwifery service mode management.We used the State-Trait Anxiety Inventory,Edinburgh Postnatal Depression Scale,and analysis of delivery outcomes to compare psychological mood and the incidence of adverse delivery outcomes between the two groups.RESULTS The data showed that in early pregnancy,the anxiety and depression levels of the two groups were similar;the levels gradually decreased as pregnancy progressed,and the decrease in the continuous group was more significant[31.00(29.00,34.00)vs 34.00(32.00,37.00),8.00(6.00,9.00)vs 12.00(10.00,13.00),P<0.05].The maternal self-efficacy level and strategy for weight gain management were better in the continuous group than in the traditional group,and the effective rate of midwifery service intervention in the continuous group was significantly higher than in the control group[267.50(242.25,284.75)vs 256.00(233.00,278.00),74.00(69.00,78.00)vs 71.00(63.00,78.00),P<0.05].The incidence of adverse delivery outcomes in pregnant women and newborns and fear of maternal childbirth were lower in the continuous group than in the traditional group,and nursing satisfaction was higher[10.50%vs 18.83%,8.50%vs 15.90%,24.00%vs 42.68%,89.50%vs 76.15%,P<0.05].CONCLUSION The Internet+continuous midwifery service model promotes innovation through integration and is of great significance for improving and promoting maternal and child health in HRP.

A Machine Learning-Based Distributed Denial of Service Detection Approach for Early Warning in Internet Exchange Points

The Internet service provider(ISP)is the heart of any country’s Internet infrastructure and plays an important role in connecting to theWorld WideWeb.Internet exchange point(IXP)allows the interconnection of two or more separate network infrastructures.All Internet traffic entering a country should pass through its IXP.Thus,it is an ideal location for performing malicious traffic analysis.Distributed denial of service(DDoS)attacks are becoming a more serious daily threat.Malicious actors in DDoS attacks control numerous infected machines known as botnets.Botnets are used to send numerous fake requests to overwhelm the resources of victims and make them unavailable for some periods.To date,such attacks present a major devastating security threat on the Internet.This paper proposes an effective and efficient machine learning(ML)-based DDoS detection approach for the early warning and protection of the Saudi Arabia Internet exchange point(SAIXP)platform.The effectiveness and efficiency of the proposed approach are verified by selecting an accurate ML method with a small number of input features.A chi-square method is used for feature selection because it is easier to compute than other methods,and it does not require any assumption about feature distribution values.Several ML methods are assessed using holdout and 10-fold tests on a public large-size dataset.The experiments showed that the performance of the decision tree(DT)classifier achieved a high accuracy result(99.98%)with a small number of features(10 features).The experimental results confirmthe applicability of using DT and chi-square for DDoS detection and early warning in SAIXP.

Edge Cloud Selection in Mobile Edge Computing(MEC)-Aided Applications for Industrial Internet of Things(IIoT)Services

In many IIoT architectures,various devices connect to the edge cloud via gateway systems.For data processing,numerous data are delivered to the edge cloud.Delivering data to an appropriate edge cloud is critical to improve IIoT service efficiency.There are two types of costs for this kind of IoT network:a communication cost and a computing cost.For service efficiency,the communication cost of data transmission should be minimized,and the computing cost in the edge cloud should be also minimized.Therefore,in this paper,the communication cost for data transmission is defined as the delay factor,and the computing cost in the edge cloud is defined as the waiting time of the computing intensity.The proposed method selects an edge cloud that minimizes the total cost of the communication and computing costs.That is,a device chooses a routing path to the selected edge cloud based on the costs.The proposed method controls the data flows in a mesh-structured network and appropriately distributes the data processing load.The performance of the proposed method is validated through extensive computer simulation.When the transition probability from good to bad is 0.3 and the transition probability from bad to good is 0.7 in wireless and edge cloud states,the proposed method reduced both the average delay and the service pause counts to about 25%of the existing method.

Call for Papers——Feature Topic Vol.22,No.2,2025 Efficient Cooperative Transmission over Satellite Internet for 6G

Satellite Internet,as a strategic public information infrastructure,can effectively bridge the limitations of traditional terrestrial network coverage,support global coverage and deep space exploration,and greatly enhance the range of network information services accessible to humans.With the transition of terrestrial mobile communication networks from the 5G era,which provides access to information anywhere,to the 6G era,which seeks to connect everything,the construction of satellite Internet,which promises a"network reaching everywhere and service is ubiquitous",has become the consensus of the industry's development and the focus of global scientific and technological innovation.

A dynamic algorithm for trust inference based on double DQN in the internet of things

The development of the Internet of Things(IoT)has brought great convenience to people.However,some information security problems such as privacy leakage are caused by communicating with risky users.It is a challenge to choose reliable users with which to interact in the IoT.Therefore,trust plays a crucial role in the IoT because trust may avoid some risks.Agents usually choose reliable users with high trust to maximize their own interests based on reinforcement learning.However,trust propagation is time-consuming,and trust changes with the interaction process in social networks.To track the dynamic changes in trust values,a dynamic trust inference algorithm named Dynamic Double DQN Trust(Dy-DDQNTrust)is proposed to predict the indirect trust values of two users without direct contact with each other.The proposed algorithm simulates the interactions among users by double DQN.Firstly,CurrentNet and TargetNet networks are used to select users for interaction.The users with high trust are chosen to interact in future iterations.Secondly,the trust value is updated dynamically until a reliable trust path is found according to the result of the interaction.Finally,the trust value between indirect users is inferred by aggregating the opinions from multiple users through a Modified Collaborative Filtering Averagebased Similarity(SMCFAvg)aggregation strategy.Experiments are carried out on the FilmTrust and the Epinions datasets.Compared with TidalTrust,MoleTrust,DDQNTrust,DyTrust and Dynamic Weighted Heuristic trust path Search algorithm(DWHS),our dynamic trust inference algorithm has higher prediction accuracy and better scalability.

Internet of Things Enabled DDoS Attack Detection Using Pigeon Inspired Optimization Algorithm with Deep Learning Approach

Internet of Things(IoTs)provides better solutions in various fields,namely healthcare,smart transportation,home,etc.Recognizing Denial of Service(DoS)outbreaks in IoT platforms is significant in certifying the accessibility and integrity of IoT systems.Deep learning(DL)models outperform in detecting complex,non-linear relationships,allowing them to effectually severe slight deviations fromnormal IoT activities that may designate a DoS outbreak.The uninterrupted observation and real-time detection actions of DL participate in accurate and rapid detection,permitting proactive reduction events to be executed,hence securing the IoT network’s safety and functionality.Subsequently,this study presents pigeon-inspired optimization with a DL-based attack detection and classification(PIODL-ADC)approach in an IoT environment.The PIODL-ADC approach implements a hyperparameter-tuned DL method for Distributed Denial-of-Service(DDoS)attack detection in an IoT platform.Initially,the PIODL-ADC model utilizes Z-score normalization to scale input data into a uniformformat.For handling the convolutional and adaptive behaviors of IoT,the PIODL-ADCmodel employs the pigeon-inspired optimization(PIO)method for feature selection to detect the related features,considerably enhancing the recognition’s accuracy.Also,the Elman Recurrent Neural Network(ERNN)model is utilized to recognize and classify DDoS attacks.Moreover,reptile search algorithm(RSA)based hyperparameter tuning is employed to improve the precision and robustness of the ERNN method.A series of investigational validations is made to ensure the accomplishment of the PIODL-ADC method.The experimental outcome exhibited that the PIODL-ADC method shows greater accomplishment when related to existing models,with a maximum accuracy of 99.81%.

Research on Digital Practical Training Teaching Under the Background of“Internet+”:Taking the“Front Office Service”Course as an Example

This article aims to explore the application of digitalization in practical training teaching under the background of“Internet+”by taking the teaching of“Front Office Service”in the hotel management and digital operation major as an example.By using digital teaching methods,tools,and practical training methods,this study analyzes the impact of digital practical training teaching on students’mastery of front office service skills as well as explores the ways to implement digital practical training teaching.The research results show that digital practical training teaching can effectively improve students’learning effects and comprehensive quality,and simultaneously has positive significance for cultivating comprehensive hotel management talents that adapt to the needs of the“Internet+”era.

Age-Driven Joint Sampling and Non-Slot Based Scheduling for Industrial Internet of Things

Effective control of time-sensitive industrial applications depends on the real-time transmission of data from underlying sensors.Quantifying the data freshness through age of information(AoI),in this paper,we jointly design sampling and non-slot based scheduling policies to minimize the maximum time-average age of information(MAoI)among sensors with the constraints of average energy cost and finite queue stability.To overcome the intractability involving high couplings of such a complex stochastic process,we first focus on the single-sensor time-average AoI optimization problem and convert the constrained Markov decision process(CMDP)into an unconstrained Markov decision process(MDP)by the Lagrangian method.With the infinite-time average energy and AoI expression expended as the Bellman equation,the singlesensor time-average AoI optimization problem can be approached through the steady-state distribution probability.Further,we propose a low-complexity sub-optimal sampling and semi-distributed scheduling scheme for the multi-sensor scenario.The simulation results show that the proposed scheme reduces the MAoI significantly while achieving a balance between the sampling rate and service rate for multiple sensors.

基于WebService的Internet GIS集成与应用

InternetGIS是GIS发展的重要方向,InternetGIS要想真正达到实用化,必须解决网络环境下多源异构空间数据集成、网络环境下的空间元数据服务等关键技术。WebService是分布式计算领域出现的最新成果,它是实现异构系统互操作的理想计算模型。本文研究了基于WebService的InternetGIS软件的体系结构,网络环境下多源异构空间数据的互操作实现,基于WebService的空间元数据服务,提出了一个基于WebService的InternetGIS应用系统集成方案,以期为InternetGIS应用集成提供一个新的思路,并以南京“数字房产”项目为例介绍了其应用。

Internet based environmental monitoring information system and its application in Yili Prefecture

With the development of Internet technologies, Internet Based Information System (IBIS) arises with more advantages: Firstly, it is convenient for users to use owing to the introduction of Internet technologies. Secondly, it benefits greatly the data dissemination and data sharing based on Internet. The most important but not the last is that we can establish the information service network supported by its technologies and the facilities of information highway. So, it would be significant to design IBIS to realize the data collection, sharing and dissemination on Internet. There are many factors affecting regional environment. So it is not easy to realize the integrated environmental monitoring. Given this, we designed the Internet based environmental monitoring information system. By the virtue of the Internet based environmental monitoring information system, the management and storage of environmental monitoring data would be easier, which lays the foundation to actualize the environmental monitoring efficiently.

Unweighted Voting Method to Detect Sinkhole Attack in RPL-Based Internet of Things Networks

The Internet of Things(IoT)consists of interconnected smart devices communicating and collecting data.The Routing Protocol for Low-Power and Lossy Networks(RPL)is the standard protocol for Internet Protocol Version 6(IPv6)in the IoT.However,RPL is vulnerable to various attacks,including the sinkhole attack,which disrupts the network by manipulating routing information.This paper proposes the Unweighted Voting Method(UVM)for sinkhole node identification,utilizing three key behavioral indicators:DODAG Information Object(DIO)Transaction Frequency,Rank Harmony,and Power Consumption.These indicators have been carefully selected based on their contribution to sinkhole attack detection and other relevant features used in previous research.The UVM method employs an unweighted voting mechanism,where each voter or rule holds equal weight in detecting the presence of a sinkhole attack based on the proposed indicators.The effectiveness of the UVM method is evaluated using the COOJA simulator and compared with existing approaches.Notably,the proposed approach fulfills power consumption requirements for constrained nodes without increasing consumption due to the deployment design.In terms of detection accuracy,simulation results demonstrate a high detection rate ranging from 90%to 100%,with a low false-positive rate of 0%to 0.2%.Consequently,the proposed approach surpasses Ensemble Learning Intrusion Detection Systems by leveraging three indicators and three supporting rules.

Inflammatory bowel disease: An evaluation of health information on the internet

To evaluate the quality and accuracy of websites written to the public on inflammatory bowel disease (IBD) (Crohn’s disease and ulcerative colitis) and assess their readability level.METHODSGoogle™, Bing™, and Yahoo™ search engines were searched independently by three researchers in December 2014. Only English-language websites were selected on the basis of predetermined inclusion and exclusion criteria. Researchers independently evaluated the quality of each website by using the DISCERN and the HONcode instruments. The readability levels were calculated using two formulas; the Flesch-Kincaid Grade Level Index, and the Coleman-Liau Readability Index. The agreement between the evaluators was calculated using Cohen kappa coefficient.RESULTSEighty-four websites were finally identified. Scores varied from a minimum DISCERN score of 18 to a maximum of 68 [mean ± SD, 42.2 ± 10.7; median = 41.5, interquartile range, interquartile range (IQR) = 15.8] and a minimum score of HONcode of 0.14 and a maximum of 0.95 (mean ± SD, 0.16 ± 0.19; median = 0.45, IQR = 0.29). Most of these websites were reviewed in 2014 and 2015 (n = 51). The creators of these websites were: universities and research centers (n = 25, 30%), foundations and associations (n = 15, 18%), commercial and pharmaceutical companies (n = 25, 30%), charities and volunteer work (n = 9, 10%), and non-university educational bodies (n = 10, 12%). The Flesch-Kincaid Grade Level readability score (mean ± SD) was 11.9 ± 2.4 and the Coleman-Liau Readability Index score was 12.6 ± 1.5. Significant correlation was found between the two readability scores (R2 = 0.509, P = 0.001). The overall agreement between evaluators measured by Cohen kappa coefficient was in the range of 0.804-0.876; rated as 'Good'.CONCLUSIONThe DISCERN and the HONcode scores of websites varied and the readability levels of most websites were above the public readability level. The study highlights the areas that need further improvement and development in patient education online materials about IBD.

Strategic Thinking of the Internet of Things and M2M Services

Editor＇s Desk：The Internet of Things is an important part of the emerging high-tech industry and has been recognized as one of the important technologies for addressing global financial crisis and revitalizing the economy. Therefore, at the beginning of the new year, we are honored to have invited Mr. Samuel Qi （Qi Qingzhong）, a famous Chinese communication technology expert and GM of Shanghai Symphony Telecommunications Co., Ltd., to give his views on the development strategy of the Internet of Things and Machine to Machine （M2M） Communications.

Length matters:Scalable fast encrypted internet traffic service classification based on multiple protocol data unit length sequence with composite deep learning

As an essential function of encrypted Internet traffic analysis,encrypted traffic service classification can support both coarse-grained network service traffic management and security supervision.However,the traditional plaintext-based Deep Packet Inspection(DPI)method cannot be applied to such a classification.Moreover,machine learning-based existing methods encounter two problems during feature selection:complex feature overcost processing and Transport Layer Security(TLS)version discrepancy.In this paper,we consider differences between encryption network protocol stacks and propose a composite deep learning-based method in multiprotocol environments using a sliding multiple Protocol Data Unit(multiPDU)length sequence as features by fully utilizing the Markov property in a multiPDU length sequence and maintaining suitability with a TLS-1.3 environment.Control experiments show that both Length-Sensitive(LS)composite deep learning model using a capsule neural network and LS-long short time memory achieve satisfactory effectiveness in F1-score and performance.Owing to faster feature extraction,our method is suitable for actual network environments and superior to state-of-the-art methods.