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.

Traffic Control Based on Integrated Kalman Filtering and Adaptive Quantized Q-Learning Framework for Internet of Vehicles

Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the decision.In this article,these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things data.The framework integrates Kalman filtering and Q-learning.Unlike smoothing Kalman filtering,our data fusion Kalman filter incorporates a process-aware model which makes it superior in terms of the prediction error.Unlike traditional Q-learning,our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from high traffic on the road according to the maximum number of vehicles in the junction roads.For evaluation,the model has been simulated on a single intersection consisting of four roads:east,west,north,and south.A comparison of the developed adaptive quantized Q-learning(AQQL)framework with state-of-the-art and greedy approaches shows the superiority of AQQL with an improvement percentage in terms of the released number of vehicles of AQQL is 5%over the greedy approach and 340%over the state-of-the-art approach.Hence,AQQL provides an effective traffic control that can be applied in today’s intelligent traffic system.

Cold Chain Logistics Equipment Management and Control Based on Internet of Things

The status and supporting policies of cold chain logistics equipment man- agement and control in China were described. The connotation of Internet of Things and its impact on cold chain logistics equipment management and control were ana- lyzed from external form to internal nature. Through introducing the value chain and relevant equipments of cold chain logistics, the correlation between the main technologies in Internet of Things and the common indices for cold chain logistics equipment management and control was analyzed in detail. The application values of Internet of Things technologies in cold chain logistics equipment management and control were illustrated, including the sample analysis on the application of radio-frequency identification （RFID）. After the establishment of BSC performance evaluation index system of cold chain logistics equipment management and control, the optimization measures and suggestions on cold chain logistics equipment management and control under Internet of Things were put forward.

Anomaly Detection for Industrial Internet of Things Cyberattacks

The evolution of the Internet of Things(IoT)has empowered modern industries with the capability to implement large-scale IoT ecosystems,such as the Industrial Internet of Things(IIoT).The IIoT is vulnerable to a diverse range of cyberattacks that can be exploited by intruders and cause substantial reputational andfinancial harm to organizations.To preserve the confidentiality,integrity,and availability of IIoT networks,an anomaly-based intrusion detection system(IDS)can be used to provide secure,reliable,and efficient IIoT ecosystems.In this paper,we propose an anomaly-based IDS for IIoT networks as an effective security solution to efficiently and effectively overcome several IIoT cyberattacks.The proposed anomaly-based IDS is divided into three phases:pre-processing,feature selection,and classification.In the pre-processing phase,data cleaning and nor-malization are performed.In the feature selection phase,the candidates’feature vectors are computed using two feature reduction techniques,minimum redun-dancy maximum relevance and neighborhood components analysis.For thefinal step,the modeling phase,the following classifiers are used to perform the classi-fication:support vector machine,decision tree,k-nearest neighbors,and linear discriminant analysis.The proposed work uses a new data-driven IIoT data set called X-IIoTID.The experimental evaluation demonstrates our proposed model achieved a high accuracy rate of 99.58%,a sensitivity rate of 99.59%,a specificity rate of 99.58%,and a low false positive rate of 0.4%.

A Formal Testing Model for Operating Room Control System Using Internet of Things

Technological advances in recent years have significantly changed the way an operating room works.This work aims to create a platformto solve the problems of operating room occupancy and prepare the rooms with an environment that is favorable for all operations.Using this system,a doctor can control all operation rooms,especially before an operation,and monitor their temperature and humidity to prepare for the operation.Also,in the event of a problem,an alert is sent to the nurse responsible for the room and medical stuff so that the problem can be resolved.The platformis tested using a Raspberry PI card and sensors.The sensors are connected to a cloud layer that collects and analyzes the temperature and humidity values obtained from the environment during an operation.The result of experimentations is visualized through a web application and an Android application.The platform also considers the security aspects such as authorization to access application functionalities for the Web and the mobile applications.We can also test and evaluate the system’s existing problems and vulnerabilities using the IEEE and owasp IoT standards.Finally,the proposed framework is extended with a model based testing technique that may be adopted for validating the security aspects.

Achieving Fine-Grained and Flexible Access Control on Blockchain-Based Data Sharing for the Internet of Things

The traditional centralized data sharing systems have potential risks such as single point of failures and excessive working load on the central node.As a distributed and collaborative alternative,approaches based upon blockchain have been explored recently for Internet of Things(IoTs).However,the access from a legitimate user may be denied without the pre-defined policy and data update on the blockchain could be costly to the owners.In this paper,we first address these issues by incorporating the Accountable Subgroup Multi-Signature(ASM)algorithm into the Attribute-based Access Control(ABAC)method with Policy Smart Contract,to provide a finegrained and flexible solution.Next,we propose a policy-based Chameleon Hash algorithm that allows the data to be updated in a reliable and convenient way by the authorized users.Finally,we evaluate our work by comparing its performance with the benchmarks.The results demonstrate significant improvement on the effectiveness and efficiency.

Semantics Interaction Control for Constructing Intelligent Ecology of Internet of Things and Critical Component Research

Intelligent equipment is a kind of device that is characterized by intelligent sensor interconnections, big data processing, new types of displays, human-machine interaction and so on for the new generation of information technology. For this purpose, in this paper, first, we present a type of novel intelligent deep hybrid neural network algorithm based on a deep bidirectional recurrent neural network integrated with a deep backward propagation neural network. It has realized acoustic analysis, speech recognition and natural language understanding for jointly constituting human-machine voice interactions. Second, we design a voice control motherboard using an embedded chip from the ARM series as the core, and the onboard components include ZigBee, RFID, WIFI, GPRS, a RS232 serial port, USB interfaces and so on. Third, we take advantage of algorithms, software and hardware to make machines “understand” human speech and “think” and “comprehend” human intentions to structure critical components for intelligent vehicles, intelligent offices, intelligent service robots, intelligent industries and so on, which furthers the structure of the intelligent ecology of the Internet of Things. At last, the experimental results denote that the study of the semantics interaction controls based on an embedding has a very good effect, fast speed and high accuracy, consequently realizing the intelligent ecology construction of the Internet of Things.

Internet of Things Enabled Intelligent Energy Management and Control System for Heavy Equipment Industrial Park and Fuzzy Assessment of Its Schemes

In order to solve the problems of poor informationflow,low energy utilization rate and energy consumption data reuse in the heavy equipment industrial park,the Internet of Things(IoT)technology is applied to construct the intelligent energy management and control system(IEMCS).The application architecture and function module planning are analyzed and designed.Furthermore,the IEMCS scheme is not unique due to the fuzziness of customer demand and the understanding deviation of designer to customer demand in the design stage.Scheme assessment is of great significance for the normal subsequent implementation of the system.A fuzzy assessment method for IEMCS scheme alternatives is proposed to achieve scheme selection.Fuzzy group decision using triangular fuzzy number to express the vague assessment of experts is adopted to determine the index value.TOPSIS is modified by replacing Euclidean distance with contact vector distance in IEMCS scheme alternative assessment.An experiment with eight IEMCS scheme alternatives in a heavy equipment industrial park is given for the validation.The experiment result shows that eight IEMCS scheme alternatives can be assessed.Through the comparisons with other methods,the reliability of the results obtained by the proposed method is discussed.

Research on the Application of PLC Control Cabinet in the Construction of Oil Field Internet of Things

PLC control cabinet is mainly used in the construction of oil field Internet of things. PLC is short for “programmer logic controller” in English, and we call it the programmable logic controller. The programmable controller mainly defers to, it which the mature effective black-white control concept and the design concept complete in at present is one kind of new technology, the core electronic device gradual is already invested each domain, became one to have the characteristic series new product that was one can carry on the digital operation and the digital operation computer effectively. So this study control PLC control cabinet in oil , eld application value of construction of the Internet of things, we hope that the content can provide effective reference for the related areas.

基于5DOF的隧道摄像头清洗装置控制系统设计

针对隧道内摄像头镜面污染严重,影响隧道监控管理的问题,基于高压水射流技术,提出了一种5自由度的机械臂清洗装置。分析系统的功能需求,采用电驱动的方式对机械臂的控制系统进行设计,基于比例积分微分控制(PID)原理设计了各关节的定位功能,并用物联网监控的方式开发了远程APP测控系统。通过室内实验确定了最优的清洗工艺参数,并进行了现场的清洗实验。实验结果表明:其能够实现隧道摄像头镜面的有效清洗,清洗装置各关节的定位相对误差在1.4%以内,为隧道内摄像头或者标识物的清洗提供了安全高效的机械化方式。

A roadmap for security challenges in the Internet of Things

Unquestionably, communicating entities （object, or things） in the Internet of Things （IoT） context are playing an active role in human activities, systems and processes. The high connectivity of intelligent objects and their severe constraints lead to many security challenges, which are not included in the classical formulation of security problems and solutions. The Security Shield for IoT has been identified by DARPA （Defense Advanced Research Projects Agency） as one of the four projects with a potential impact broader than the Internet itself. To help interested researchers contribute to this research area, an overview of the loT security roadmap overview is presented in this paper based on a novel cognitive and systemic approach. The role of each component of the approach is explained, we also study its interactions with the other main components, and their impact on the overall. A case study is presented to highlight the components and interactions of the systemic and cognitive approach. Then, security questions about privacy, trust, identification, and access control are discussed. According to the novel taxonomy of the loT framework, different research challenges are highlighted, important solutions and research activities are revealed, and interesting research directions are proposed. In addition, current stan dardization activities are surveyed and discussed to the ensure the security of loT components and applications.

Cloud control for IIoT in a cloud-edge environment

The industrial Internet of Things(IIoT)is a new indus-trial idea that combines the latest information and communica-tion technologies with the industrial economy.In this paper,a cloud control structure is designed for IIoT in cloud-edge envi-ronment with three modes of 5G.For 5G based IIoT,the time sensitive network(TSN)service is introduced in transmission network.A 5G logical TSN bridge is designed to transport TSN streams over 5G framework to achieve end-to-end configuration.For a transmission control protocol(TCP)model with nonlinear disturbance,time delay and uncertainties,a robust adaptive fuzzy sliding mode controller(AFSMC)is given with control rule parameters.IIoT workflows are made up of a series of subtasks that are linked by the dependencies between sensor datasets and task flows.IIoT workflow scheduling is a non-deterministic polynomial(NP)-hard problem in cloud-edge environment.An adaptive and non-local-convergent particle swarm optimization(ANCPSO)is designed with nonlinear inertia weight to avoid falling into local optimum,which can reduce the makespan and cost dramatically.Simulation and experiments demonstrate that ANCPSO has better performances than other classical algo-rithms.

Anomaly Detection Framework in Fog-to-Things Communication for Industrial Internet of Things

The rapid development of the Internet of Things(IoT)in the industrial domain has led to the new term the Industrial Internet of Things(IIoT).The IIoT includes several devices,applications,and services that connect the physical and virtual space in order to provide smart,cost-effective,and scalable systems.Although the IIoT has been deployed and integrated into a wide range of industrial control systems,preserving security and privacy of such a technology remains a big challenge.An anomaly-based Intrusion Detection System(IDS)can be an effective security solution for maintaining the confidentiality,integrity,and availability of data transmitted in IIoT environments.In this paper,we propose an intelligent anomalybased IDS framework in the context of fog-to-things communications to decentralize the cloud-based security solution into a distributed architecture(fog nodes)near the edge of the data source.The anomaly detection system utilizes minimum redundancy maximum relevance and principal component analysis as the featured engineering methods to select the most important features,reduce the data dimensionality,and improve detection performance.In the classification stage,anomaly-based ensemble learning techniques such as bagging,LPBoost,RUSBoost,and Adaboost models are implemented to determine whether a given flow of traffic is normal or malicious.To validate the effectiveness and robustness of our proposed model,we evaluate our anomaly detection approach on a new driven IIoT dataset called XIIoTID,which includes new IIoT protocols,various cyberattack scenarios,and different attack protocols.The experimental results demonstrated that our proposed anomaly detection method achieved a higher accuracy rate of 99.91%and a reduced false alarm rate of 0.1%compared to other recently proposed techniques.

Blockchain-Enabled EHR Framework for Internet of Medical Things

The Internet of Medical Things(IoMT)offers an infrastructure made of smart medical equipment and software applications for healthcare services.Through the internet,the IoMT is capable of providing remote medical diagnosis and timely health services.The patients can use their smart devices to create,store and share their electronic health records(EHR)with a variety of medical personnel including medical doctors and nurses.However,unless the underlying commination within IoMT is secured,malicious users can intercept,modify and even delete the sensitive EHR data of patients.Patients also lose full control of their EHR since most healthcare services within IoMT are constructed under a centralized platform outsourced in the cloud.Therefore,it is appealing to design a decentralized,auditable and secure EHR system that guarantees absolute access control for the patients while ensuring privacy and security.Using the features of blockchain including decentralization,auditability and immutability,we propose a secure EHR framework which is mainly maintained by the medical centers.In this framework,the patients’EHR data are encrypted and stored in the servers of medical institutions while the corresponding hash values are kept on the blockchain.We make use of security primitives to offer authentication,integrity and confidentiality of EHR data while access control and immutability is guaranteed by the blockchain technology.The security analysis and performance evaluation of the proposed framework confirms its efficiency.

Research on key technologies of UAV cluster cooperative system for Internet of Things applications

The combat tasks faced by UAVs are becoming more and more complex.Traditional single UAVs are limited by the constraints of platform load capacity,lightweight load,and insufficient lowpower equipment,so it is difficult to complete complex tasks independently.Aiming at typical UAV collaborative confrontation application scenarios,this paper constructs a multi-agentoriented cluster collaborative intelligent system architecture and establishes a swarm-oriented intelligent UAV cluster collaborative control algorithm.Moreover,this paper forms a simulation environment for UAV cluster collaborative confrontation and completes the design and implementation of the UAV cluster collaborative confrontation system based on swarm intelligence.In addition,this paper analyses the key technologies of the UAV cluster collaborative system with the support of the Internet of Things technology and verifies the performance of the system after constructing the corresponding system.The experimental results show that the system constructed in this paper is effective.

Ether-IoT:A Realtime Lightweight and Scalable Blockchain-Enabled Cache Algorithm for IoT Access Control

Several unique characteristics of Internet of Things(IoT)devices,such as distributed deployment and limited storage,make it challenging for standard centralized access control systems to enable access control in today’s large-scale IoT ecosystem.To solve these challenges,this study presents an IoT access control system called Ether-IoT based on the Ethereum Blockchain(BC)infrastructure with Attribute-Based Access Control(ABAC).Access Contract(AC),Cache Contract(CC),Device Contract(DC),and Policy Contract(PC)are the four central smart contracts(SCs)that are included in the proposed system.CC offers a way to save user characteristics in a local cache system to avoid delays during transactions between BC and IoT devices.AC is the fundamental program users typically need to run to build an access control technique.DC offers a means for storing the resource data created by devices and a method for querying that data.PC offers administrative settings to handle ABAC policies on users’behalf.Ether-IoT,combined with ABAC and the BC,enables IoT access control management that is decentralized,fine-grained and dynamically scalable.This research gives a real-world case study to illustrate the suggested framework’s implementation.In the end,a simulation experiment is performed to evaluate the system’s performance.To ensure data integrity in dispersed systems,the results show that Ether-IoT can sustain high throughput in contexts with a large number of requests.

LoRa Backscatter Network Efficient Data Transmission Using RF Source Range Control

Networks based on backscatter communication provide wireless data transmission in the absence of a power source.A backscatter device receives a radio frequency(RF)source and creates a backscattered signal that delivers data;this enables new services in battery-less domains with massive Internet-of-Things(IoT)devices.Connectivity is highly energy-efficient in the context of massive IoT applications.Outdoors,long-range(LoRa)backscattering facilitates large IoT services.A backscatter network guarantees timeslot-and contention-based transmission.Timeslot-based transmission ensures data transmission,but is not scalable to different numbers of transmission devices.If contention-based transmission is used,collisions are unavoidable.To reduce collisions and increase transmission efficiency,the number of devices transmitting data must be controlled.To control device activation,the RF source range can be modulated by adjusting the RF source power during LoRa backscatter.This reduces the number of transmitting devices,and thus collisions and retransmission,thereby improving transmission efficiency.We performed extensive simulations to evaluate the performance of our method.

Intelligent Home Using Fuzzy Control Based on AIoT

The Internet of Things has grown rapidly in recent years,and the technologies related to it have been widely used in various fields.The idea of this paper is to build a set of Internet of Things systems in a smart home wireless network environment,with the purpose of providing people with a more comfortable,convenient,and safe life.In the sensing layer of the Internet of Things,we discuss the uses of common sensing technologies on the Internet and combine these with Arduino microprocessors to integrate temperature sensing modules,humidity sensing modules,gas sensing modules,and particulate matter 2.5(PM2.5)sensing modules.In the network layer,we discuss using the Wi-Fi wireless networking function composed of a home router and a wireless Wi-Fi chip Espressif system 8266(ESP8266)to transmit the collected home-sensing data to the ThingSpeak cloud database.Finally,in the application layer part,the system uses a mobile device with fuzzy calculation optimization software.The system is also connected remotely for home environment monitoring,so the home environment can be optimized anytime,anywhere.

Enhanced Water Quality Control Based on Predictive Optimization for Smart Fish Farming

The requirement for high-quality seafood is a global challenge in today’s world due to climate change and natural resource limitations.Internet of Things(IoT)based Modern fish farming systems can significantly optimize seafood production by minimizing resource utilization and improving healthy fish production.This objective requires intensive monitoring,prediction,and control by optimizing leading factors that impact fish growth,including temperature,the potential of hydrogen(pH),water level,and feeding rate.This paper proposes the IoT based predictive optimization approach for efficient control and energy utilization in smart fish farming.The proposed fish farm control mechanism has a predictive optimization to deal with water quality control and efficient energy consumption problems.Fish farm indoor and outdoor values are applied to predict the water quality parameters,whereas a novel objective function is proposed to achieve an optimal fish growth environment based on predicted parameters.Fuzzy logic control is utilized to calculate control parameters for IoT actuators based on predictive optimal water quality parameters by minimizing energy consumption.To evaluate the efficiency of the proposed system,the overall approach has been deployed to the fish tank as a case study,and a number of experiments have been carried out.The results show that the predictive optimization module allowed the water quality parameters to be maintained at the optimal level with nearly 30%of energy efficiency at the maximum actuator control rate compared with other control levels.

Real-Time Memory Data Optimization Mechanism of Edge IoT Agent

With the full development of disk-resident databases(DRDB)in recent years,it is widely used in business and transactional applications.In long-term use,some problems of disk databases are gradually exposed.For applications with high real-time requirements,the performance of using disk database is not satisfactory.In the context of the booming development of the Internet of things,domestic real-time databases have also gradually developed.Still,most of them only support the storage,processing,and analysis of data values with fewer data types,which can not fully meet the current industrial process control system data types,complex sources,fast update speed,and other needs.Facing the business needs of efficient data collection and storage of the Internet of things,this paper optimizes the transaction processing efficiency and data storage performance of the memory database,constructs a lightweight real-time memory database transaction processing and data storage model,realizes a lightweight real-time memory database transaction processing and data storage model,and improves the reliability and efficiency of the database.Through simulation,we proved that the cache hit rate of the cache replacement algorithm proposed in this paper is higher than the traditional LRU(Least Recently Used)algorithm.Using the cache replacement algorithm proposed in this paper can improve the performance of the system cache.