Deep Learning-Based Secure Transmission Strategy with Sensor-Transmission-Computing Linkage for Power Internet of Things

The automatic collection of power grid situation information, along with real-time multimedia interaction between the front and back ends during the accident handling process, has generated a massive amount of power grid data. While wireless communication offers a convenient channel for grid terminal access and data transmission, it is important to note that the bandwidth of wireless communication is limited. Additionally, the broadcast nature of wireless transmission raises concerns about the potential for unauthorized eavesdropping during data transmission. To address these challenges and achieve reliable, secure, and real-time transmission of power grid data, an intelligent security transmission strategy with sensor-transmission-computing linkage is proposed in this paper. The primary objective of this strategy is to maximize the confidentiality capacity of the system. To tackle this, an optimization problem is formulated, taking into consideration interruption probability and interception probability as constraints. To efficiently solve this optimization problem, a low-complexity algorithm rooted in deep reinforcement learning is designed, which aims to derive a suboptimal solution for the problem at hand. Ultimately, through simulation results, the validity of the proposed strategy in guaranteed communication security, stability, and timeliness is substantiated. The results confirm that the proposed intelligent security transmission strategy significantly contributes to the safeguarding of communication integrity, system stability, and timely data delivery.

Multi-source heterogeneous data access management framework and key technologies for electric power Internet of Things

The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.

A Novel Secure Data Transmission Scheme in Industrial Internet of Things

The industrial Internet of Things(IoT)is a trend of factory development and a basic condition of intelligent factory.It is very important to ensure the security of data transmission in industrial IoT.Applying a new chaotic secure communication scheme to address the security problem of data transmission is the main contribution of this paper.The scheme is proposed and studied based on the synchronization of different-structure fractional-order chaotic systems with different order.The Lyapunov stability theory is used to prove the synchronization between the fractional-order drive system and the response system.The encryption and decryption process of the main data signals is implemented by using the n-shift encryption principle.We calculate and analyze the key space of the scheme.Numerical simulations are introduced to show the effectiveness of theoretical approach we proposed.

Federated Learning on Internet of Things:Extensive and Systematic Review

The proliferation of IoT devices requires innovative approaches to gaining insights while preserving privacy and resources amid unprecedented data generation.However,FL development for IoT is still in its infancy and needs to be explored in various areas to understand the key challenges for deployment in real-world scenarios.The paper systematically reviewed the available literature using the PRISMA guiding principle.The study aims to provide a detailed overview of the increasing use of FL in IoT networks,including the architecture and challenges.A systematic review approach is used to collect,categorize and analyze FL-IoT-based articles.Asearch was performed in the IEEE,Elsevier,Arxiv,ACM,and WOS databases and 92 articles were finally examined.Inclusion measures were published in English and with the keywords“FL”and“IoT”.The methodology begins with an overview of recent advances in FL and the IoT,followed by a discussion of how these two technologies can be integrated.To be more specific,we examine and evaluate the capabilities of FL by talking about communication protocols,frameworks and architecture.We then present a comprehensive analysis of the use of FL in a number of key IoT applications,including smart healthcare,smart transportation,smart cities,smart industry,smart finance,and smart agriculture.The key findings from this analysis of FL IoT services and applications are also presented.Finally,we performed a comparative analysis with FL IID(independent and identical data)and non-ID,traditional centralized deep learning(DL)approaches.We concluded that FL has better performance,especially in terms of privacy protection and resource utilization.FL is excellent for preserving privacy becausemodel training takes place on individual devices or edge nodes,eliminating the need for centralized data aggregation,which poses significant privacy risks.To facilitate development in this rapidly evolving field,the insights presented are intended to help practitioners and researchers navigate the complex terrain of FL and IoT.

Machine leaming for internet of things data analysis： a survey

Rapid developments in hardware, software, and communication technologies have facilitated the emergence of Internet-connected sensory devices that provide observations and data measurements from the physical world. By 2020, it is estimated that the total number of Internet-connected devices being used will be between 25 and 50 billion. As these numbers grow and technologies become more mature, the volume of data being published will increase. The technology of Internet-connected devices, referred to as Internet of Things （IoT）, continues to extend the current Internet by providing connectivity and interactions between the physical and cyber worlds. In addition to an increased volume, the IoT generates big data characterized by its velocity in terms of time and location dependency, with a variety of multiple modalities and varying data quality. Intelligent processing and analysis of this big data are the key to developing smart IoT applications. This article assesses the various machine learning methods that deal with the challenges presented by IoT data by considering smart cities as the main use case. The key contribution of this study is the presentation of a taxonomy of machine learning algorithms explaining how different techniques are applied to the data in order to extract higher level information. The potential and challenges of machine learning for IoT data analytics will also be discussed. A use case of applying a Support Vector Machine （SVM） to Aarhus smart city traffic data is presented for a more detailed exploration.

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.

Data Security Storage Model of the Internet of Things Based on Blockchain

With the development of information technology,the Internet of Things(IoT)has gradually become the third wave of the worldwide information industry revolution after the computer and the Internet.The application of the IoT has brought great convenience to people’s production and life.However,the potential information security problems in various IoT applications are gradually exposed and people pay more attention to them.The traditional centralized data storage and management model of the IoT is easy to cause transmission delay,single point of failure,privacy disclosure and other problems,and eventually leads to unpredictable behavior of the system.Blockchain technology can effectively improve the operation and data security status of the IoT.Referring to the storage model of the Fabric blockchain project,this paper designs a data security storage model suitable for the IoT system.The simulation results show that the model is not only effective and extensible,but also can better protect the data security of the Internet of Things.

Mining Data Correlation from Multi-Faceted Sensor Data in Internet of Things

Sensors are ubiquitous in the Internet of Things for measuring and collecting data. Analyzing these data derived from sensors is an essential task and can reveal useful latent information besides the data. Since the Internet of Things contains many sorts of sensors, the measurement data collected by these sensors are multi-type data, sometimes contai- ning temporal series information. If we separately deal with different sorts of data, we will miss useful information. This paper proposes a method to dis- cover the correlation in multi-faceted data, which contains many types of data with temporal informa- tion, and our method can simultaneously deal with multi-faceted data. We transform high-dimensional multi-faeeted data into lower-dimensional data which is set as multivariate Gaussian Graphical Models, then mine the correlation in multi-faceted data by discover the structure of the multivariate Gausslan Graphical Models. With a real data set, we verifies our method, and the experiment demonstrates that the method we propose can correctly fred out the correlation among multi-faceted meas- urement data.

A Trusted NUMFabric Algorithm for Congestion Price Calculation at the Internet-of-Things Datacenter

The important issues of network TCP congestion control are how to compute the link price according to the link status and regulate the data sending rate based on link congestion pricing feedback information.However,it is difficult to predict the congestion state of the link-end accurately at the source.In this paper,we presented an improved NUMFabric algorithm for calculating the overall congestion price.In the proposed scheme,the whole network structure had been obtained by the central control server in the Software Defined Network,and a kind of dual-hierarchy algorithm for calculating overall network congestion price had been demonstrated.In this scheme,the first hierarchy algorithm was set up in a central control server like Opendaylight and the guiding parameter B is obtained based on the intelligent data of global link state information.Based on the historical data,the congestion state of the network and the guiding parameter B is accurately predicted by the machine learning algorithm.The second hierarchy algorithm was installed in the Openflow link and the link price was calculated based on guiding parameter B given by the first algorithm.We evaluate this evolved NUMFabric algorithm in NS3,which demonstrated that the proposed NUMFabric algorithm could efficiently increase the link bandwidth utilization of cloud computing IoT datacenters.

Smart-City-based Data Fusion Algorithm for Internet of Things

Increasingly,Wireless Sensor Networks(WSNs)are contributing enormous amounts of data.Since the recent deployments of wireless sensor networks in Smart City infrastructures,significant volumes of data have been produced every day in several domains ranging from the environment to the healthcare system to transportation.Using wireless sensor nodes,a Smart City environment may now be shown for the benefit of residents.The Smart City delivers intelligent infrastructure and a stimulating environment to citizens of the Smart Society,including the elderly and others.Weak,Quality of Service(QoS)and poor data performance are common problems in WSNs,caused by the data fusion method,where a small amount of bad data can significantly impact the total fusion outcome.In our proposed research,a WSN multisensor data fusion technique employing fuzzy logic for event detection.Using the new proposed Algorithm,sensor nodes will collect less repeated data,and redundant data will be used to increase the data’s overall reliability.The network’s fusion delay problem is investigated,and a minimum fusion delay approach is provided based on the nodes’fusion waiting time.The proposed algorithm performs well in fusion,according to the results of the experiment.As a result of these discoveries,It is concluded that the algorithm describe here is effective and dependable instrument with a wide range of applications.

Internet of Things Technology and Open Data:Application of Indoor Air Control

Home is a place for people to relax and to feel secure.However,there are some external factors,such as temperature and humidity,making living conditions uncomfortable.With the development of Internet of Things(IoT)technology,the research issue of smart home becomes more important.The purpose of this study is to explore the application of IoT technology in indoor air monitoring and control,combined with the analysis of outdoor air quality data.This study develops a prototype system and tests and evaluates the performance of the system through user trial reports.The results show that(1)Air comparison of indoor and outdoor are practical for users,(2)Through the transmission of Bluetooth,restrictions on the practicality should be achieved through the WiFi remote monitoring effect,(3)It can receive multiple sensors at the same time,to achieve multiple indoor space monitoring effects,(4)It can be combined with other home appliances,if the integration of home appliances control will be more practical,(5)The current database is only a record,has not developed other applications,and in the future can develop predictive applications.We hope that through this study,we will provide some suggestions for the application of innovative technology in smart home.

Research on Intelligent Fusion Method of Multisensor Data in Internet of Things

A huge network formed by the combination of information gathering and the Internet aims to realize the connection between things and things,things and people,all goods and networks,and facilitate the identification,management and control of the Internet of Things.In order to solve the inconsistency of various types of sensor communication protocols in the Internet of Things and the problem of data analysis,Association and evaluation of real-time multi-sensor data acquisition for different business applications,it is of great significance to develop multi-sensor protocol integration and information fusion.The method of simulating the sensor data simulates the converted data,and then according to the specific requirements of the measurement,the same type of data obtained by various sensors is converted into different physical quantities they represent.According to the theoretical analysis and ontology relationship of sensor data fusion in the Internet of Things,as well as the general method of sensor ontology intelligent fusion,the application in multi-sensor data fusion in the Internet of Things lays a foundation for intelligent control and decision analysis of the Internet of Things system.

Game Theoretical Secure Wireless Communication for UAV-assisted Vehicular Internet of Things

With the ever-expanding applications of vehicles and the development of wireless communication technology,the burgeoning unmanned aerial vehicle(UAV)assisted vehicular internet of things(UVIoTs)has emerged,where the ground vehicles can experience more efficient wireless services by employing UAVs as a temporary mobile base station.However,due to the diversity of UAVs,there exist UAVs such as jammers to degenerate the performance of wireless communication between the normal UAVs and vehicles.To solve above the problem,in this paper,we propose a game based secure data transmission scheme in UVIoTs.Specifically,we exploit the offensive and defensive game to model the interactions between the normal UAVs and jammers.Here,the strategy of the normal UAV is to determine whether to transmit data,while that of the jammer is whether to interfere.We then formulate two optimization problems,i.e.,maximizing the both utilities of UAVs and jammers.Afterwards,we exploit the backward induction method to analyze the proposed countermeasures and finally solve the optimal solution.Lastly,the simulation results show that the proposed scheme can improve the wireless communication performance under the attacks of jammers compared with conventional schemes.

Research on Intelligent Agricultural Planting System Based on Internet of Things Technology

Agriculture is the basic industry that concerns the national economy and people’s livelihood. In the process of transforming to modern agriculture, the traditional agriculture in our country faces the problems of ensuring the quality of agricultural production, adjusting agricultural industrial structures, improving the low production efficiency and low utilization rate of resources, and environmental pollution, thus it cannot meet the needs of sustainable agricultural development. Therefore, the research on intelligent agriculture technology is imperative. This paper analyzes the key technologies of Internet of things applied in the intelligent agriculture, presents the application of Internet of things technology in agricultural planting system, constructs the intelligent agricultural planting system based on the Internet of things technology, and designs the framework of the management platform.

An Open Architecture for Converged Internet of Things

An open architecture for converged Internet of Things (IoT) is proposed in this paper.By using this model,the various and huge amount of data can be converted into converged data and then encapsulated as service packages.Moreover,a Representational State Transfer (REST) platform has been implemented in the lab environment.The merit of this architecture is the enhancement of data efficiency.Experimental results are provided to show the benefits of the proposed architecture.

Energy-Efcient Transmission Range Optimization Model for WSN-Based Internet of Thing

With the explosive advancements in wireless communications and digital electronics,some tiny devices,sensors,became a part of our daily life in numerous elds.Wireless sensor networks(WSNs)is composed of tiny sensor devices.WSNs have emerged as a key technology enabling the realization of the Internet of Things(IoT).In particular,the sensor-based revolution of WSN-based IoT has led to considerable technological growth in nearly all circles of our life such as smart cities,smart homes,smart healthcare,security applications,environmental monitoring,etc.However,the limitations of energy,communication range,and computational resources are bottlenecks to the widespread applications of this technology.In order to tackle these issues,in this paper,we propose an Energy-efcient Transmission Range Optimized Model for IoT(ETROMI),which can optimize the transmission range of the sensor nodes to curb the hot-spot problem occurring in multi-hop communication.In particular,we maximize the transmission range by employing linear programming to alleviate the sensor nodes’energy consumption and considerably enhance the network longevity compared to that achievable using state-of-the-art algorithms.Through extensive simulation results,we demonstrate the superiority of the proposed model.ETROMI is expected to be extensively used for various smart city,smart home,and smart healthcare applications in which the transmission range of the sensor nodes is a key concern.

Time to forge ahead:The Internet of Things for healthcare

Situated at the intersection of technology and medicine,the Internet of Things(IoT)holds the promise of addressing some of healthcare's most pressing challenges,from medical error,to chronic drug shortages,to overburdened hospital systems,to dealing with the COVID-19 pandemic.However,despite considerable recent technological advances,the pace of successful implementation of promising IoT healthcare initiatives has been slow.To inspire more productive collaboration,we present here a simple—but surprisingly underrated—problemoriented approach to developing healthcare technologies.To further assist in this effort,we reviewed the various commercial,regulatory,social/cultural,and technological factors in the development of the IoT.We propose that fog computing—a technological paradigm wherein the burden of computing is shifted from a centralized cloud server closer to the data source—offers the greatest promise for building a robust and scalable healthcare IoT ecosystem.To this end,we explore the key enabling technologies that underpin the fog architecture,from the sensing layer all the way up to the cloud.It is our hope that ongoing advances in sensing,communications,cryptography,storage,machine learning,and artificial intelligence will be leveraged in meaningful ways to generate unprecedented medical intelligence and thus drive improvements in the health of many people.

Research of Intelligent Transportation System Based on the Internet of Things Frame

According to city public transit problem characteristic, the main body of a paper has been submitted and has worked out one kind of based on the Internet of things frame Intelligent transportation system. That system collects data by vehicle terminal and uploads data to the server through the network and makes data visible to the consumer passing an algorithm in the server. One aspect, the consumer may inquire about public transit vehicle information by Web. On another aspect, the consumer can know public transit vehicle information by station terminal. The experiments have tested that the Intelligent transportation system can offer public transit vehicle information to many consumers with convenient way thereby this system can solve the city mass transit problem.

Barcelona Smart City: The Heaven on Earth(Internet of Things: Technological God)

Cities are the most preferable dwelling places, having with better employment opportunities, educational hubs, medical services, recreational facilities, theme parks, and shopping malls etc. Cities are the driving forces for any national economy too. Unfortunately now a days, these cities are producing circa 70% of pollutants, even though they only oeeupy 2% of surface of the Earth. Pub- lic utility services cannot meet the demands of unexpected growth. The filthiness in cities causing decreasing of Quality of Life. In this light our research paper is giving more concentration on necessity of ＂ Smart Cities＂, which are the basis for civic centric services. This article is throwing light on Smart Cities and its important roles. The beauty of this manuscript is scribbling ＂Smart Cities＂ concepts in pictorially. Moreover this explains on ＂Barcelona Smart City＂ using lnternet of Things Technologies. It is a good example in urban paradigm shift. Braeelona is like the heaven on the earth with by providing Quality of Life to all urban citizens. The GOD is Interenet of Things.

A Dynamic Management Scheme for Internet of Things(IoT)Environments:Simulation and Performance Evaluation

In recent years,the Internet of Things(IoT)technology has been considered one of the most attractive fields for researchers due to its aspirations and implications for society and life as a whole.The IoT environment contains vast numbers of devices,equipment,and heterogeneous users who generate massive amounts of data.Furthermore,things’entry into and exit fromIoT systems occur dynamically,changing the topology and content of IoT networks very quickly.Therefore,managing IoT environments is among the most pressing challenges.This paper proposes an adaptive and dynamic scheme for managing IoT environments is proposed.This management scheme depends on the use of previous management methodologies,considering two main factors.The first factor is network status,which is determined in real-time.The second factor is a management method’s suitability according to its desired administration.To test the proposed management scheme,a simulation environment is created using NS3.The metrics used to measure the management scheme performance are bandwidth consumption,energy consumption,packet loss,throughput,delay,usage rate of individualmanagement techniques,and transformation.The simulation results prove that the proposed management scheme outperformed the individual 6LowPANSNMP,CoAP,and LWM2M management schemes.