Internet Of Things Framework Research Articles

Maximizing Efficiency in Energy Trading Operations through IoT-Integrated Digital Twins.

The Internet of Things (IoT) has brought about significant transformations in multiple sectors, including healthcare and navigation systems, by offering essential functionalities crucial for their operations. Nevertheless, there is ongoing debate surrounding the unexplored possibilities of the IoT within the energy industry. The requirement to better the performance of distributed energy systems necessitates transitioning from traditional mission-critical electric smart grid systems to digital twin-based IoT frameworks. Energy storage systems (ESSs) used within nano-grids have the potential to enhance energy utilization, fortify resilience, and promote sustainable practices by effectively storing surplus energy. The present study introduces a conceptual framework consisting of two fundamental modules: (1) Power optimization of energy storage systems (ESSs) in peer-to-peer (P2P) energy trading. (2) Task orchestration in IoT-enabled environments using digital twin technology. The optimization of energy storage systems (ESSs) aims to effectively manage surplus ESS energy by employing particle swarm optimization (PSO) techniques. This approach is designed to fulfill the energy needs of the ESS itself as well as meet the specific requirements of participating nano-grids. The primary objective of the IoT task orchestration system, which is based on the concept of digital twins, is to enhance the process of peer-to-peer nano-grid energy trading. This is achieved by integrating virtual control mechanisms through orchestration technology combining task generation, device virtualization, task mapping, task scheduling, and task allocation and deployment. The nano-grid energy trading system's architecture utilizes IoT sensors and Raspberry Pi-based edge technology to enable virtual operation. The evaluation of the proposed study is carried out through the examination of a simulated dataset derived from nano-grid dwellings. This research analyzes the efficacy of optimization approaches in mitigating energy trading costs and optimizing power utilization in energy storage systems (ESSs). The coordination of IoT devices is crucial in improving the system's overall efficiency.

Remote ECG Monitoring System via Internet

Electrocardiography (ECG) is a non-invasive diagnostic tool that records the electrical activity of the heart. It is a valuable tool for diagnosing a variety of heart conditions, including arrhythmias, heart failure, and myocardial infarction. This review paper explores the landscape of Real-time Electrocardiogram (ECG) Monitoring Systems via the Internet, analyzing advancements, challenges, and future prospects in this domain. The study investigates the integration of ECG monitoring systems with internet connectivity, enabling real- time data transmission and analysis for remote healthcare applications. Examining the utilization of IoT frameworks, particularly Raspberry Pi and Arduino, the review elucidates their role in establishing connectivity and data transmission protocols. It assesses the impact of IoT-enabled ECG systems on enhancing patient care, enabling telemedicine, and improving healthcare accessibility. Keywords— Internet of Things (IoT), Raspberry Pi R3, Arduino UNO, Remote Monitoring, Electrocardigraphy (ECG), Internet connected Medical Devices, Remote Healthcare

IoT-Edge Healthcare Solutions Empowered by Machine Learning

Managing the overwhelming volume of data collected by medical sensors presents a challenge in extracting relevant insights. This paper advocates for the development of an algorithm tailored to body sensor networks to identify outliers in collected data. Leveraging machine learning and statistical sampling methodologies, this research aims to optimize real-time response, particularly as computational tasks migrate to backend systems. Addressing the increasing dispersion of computing power across various domains, this study highlights the potential bottleneck posed by computation as Internet-of-Things (IoT) devices proliferate. To mitigate battery drain, a common approach involves offloading processing to background servers. However, the widespread adoption of IoT devices has sparked concerns about privacy and security. Current measures are deemed insufficient in light of escalating cyber threats. Machine learning methods offer promise in identifying vulnerabilities within IoT systems. Edge computing emerges as a solution to enhance network response times, decentralization, and security. By leveraging distributed-edge computing within an IoT framework, this paper investigates the fusion of cloud and edge computing with machine learning. Specifically, it explores how these technologies can be harnessed in the medical field, utilizing sensor-equipped IoT devices to collect extensive data for analysis. The proposed approach involves proactive decision-making at the front end, guided by an IoT server, and employs machine learning algorithms at the backend to identify pertinent data signatures. This paper underscores the significance of combining cloud, edge computing, and machine learning in a distributed-edge-computing-based IoT framework, offering a potential avenue for real-time, efficient solutions in various domains.

LISF: A Security Framework for Internet of Things (IoT) Integrated Distributed Applications

Distributed applications where Internet of Things (IoT) technology integrated are vulnerable to different kinds of attacks. Machine learning algorithms are widely used to detect intrusions in such applications. However, there is need for an effective unsupervised learning approach which can detect known and also unknown attacks. Towards this end, in this paper, we proposed a framework to protect security of IoT integrated architectures that are distributed in nature. Our framework is named Learning based IoT Security Framework (LISF). The framework is designed to have machine learning based security to IoT integrated use cases. Since IoT networks cause network traffic that is to be monitored and protected from external attacks, the proposed system uses deep learning technique for automatic detection of cyber-attacks. Particularly, the system exploits deep autoencoder which comprises of encoder and decoder for automatic detection of different kinds of intrusions. It is based on unsupervised learning which is crucial for distributed environments where network flows cannot have sophisticated training samples. We proposed an algorithm named Deep Autoencoder based Cyber Attack Detection (DAE-CAD). Experiments are made using IoT use case dataset known as UNSW-NB15. Our empirical results revealed that DAE-CAD outperforms existing methods with highest accuracy 91.36%.

AIS for Malware Detection in a Realistic IoT System: Challenges and Opportunities

With the expansion of the digital world, the number of Internet of things (IoT) devices is evolving dramatically. IoT devices have limited computational power and a small memory. Consequently, existing and complex security methods are not suitable to detect unknown malware attacks in IoT networks. This has become a major concern in the advent of increasingly unpredictable and innovative cyberattacks. In this context, artificial immune systems (AISs) have emerged as an effective malware detection mechanism with low requirements for computation and memory. In this research, we first validate the malware detection results of a recent AIS solution using multiple datasets with different types of malware attacks. Next, we examine the potential gains and limitations of promising AIS solutions under realistic implementation scenarios. We design a realistic IoT framework mimicking real-life IoT system architectures. The objective is to evaluate the AIS solutions’ performance with regard to the system constraints. We demonstrate that AIS solutions succeed in detecting unknown malware in the most challenging conditions. Furthermore, the systemic results with different system architectures reveal the AIS solutions’ ability to transfer learning between IoT devices. Transfer learning is a pivotal feature in the presence of highly constrained devices in the network. More importantly, this work highlights that previously published AIS performance results, which were obtained in a simulation environment, cannot be taken at face value. In reality, AIS’s malware detection accuracy for IoT systems is 91% in the most restricted designed system compared to the 99% accuracy rate reported in the simulation experiment.

IoT Applications, Platforms, Systems, And Framework based on Blockchain

The Internet of Things (IoT) has lately evolved as a new technology capable of providing real-time and cutting-edge sensing capabilities to numerous industries such as healthcare, agriculture, smart cities, smart homes, and supply chain. Because of this technology's inherent promise, it has already seen exponential growth in a wide range of use-cases across numerous application domains. As academics around the world continue to examine its capabilities, there is widespread consensus that in order to get the most out of this technology and fully realise its potential, IoT must be built on a flexible network architecture with strong support for security, privacy, and trust. Blockchain (BC) technology, on the other hand, has lately emerged as a breakthrough technology with the promise to give several beneficial qualities such as robustness, support for integrity, anonymity, decentralisation, and autonomous control. Several BC systems are offered, which may be appropriate for various use-cases, including IoT applications. As a result, the integration of IoT with BC technology is seen as a potential solution to some critical concerns. To do this, a good grasp of the requirements of various IoT applications and the viability of a BC platform for a specific application satisfying its underlying requirements is required. This project explains many ways such as the gateway process and sensor device. By addressing the present blockchain concerns, IoT may enable a variety of security services, all of which are described in detail. Various authors present some common facts on the use of blockchain in IoT, which aids in a thorough understanding of the concept. Blockchain improves security and privacy in IoT platforms. In this project, an extra immutable ledger is created using all of the resources and information mentioned in the existing procedure. [1].

A Comprehensive Survey on Internet of Things for Smart Cities : Applications, Communication Protocols, Network Types and Requirements

Rapid population growth in recent decades has made it imperative to find innovative answers to pressing problems in areas like transportation, healthcare, energy, and public works. One of the most interesting technology enablers for meeting these difficulties is the Internet of Things (IoT), which involves the global interconnection of a vast quantity of items equipped with electronics, software, sensors, and network connections. The IoT is a cutting-edge contemporary technology that has the potential to link a wide variety of digital objects to the web, enabling a wide variety of new services within the context of a "smart city". These gadgets come equipped with a wide range of sensing, acting, and processing options. Attractive IoT technology and big data analytics are making smart city plans a reality all around the world. By improving infrastructure and transportation networks, decreasing traffic congestion, managing trash, and enhancing the quality of human life, these services are revolutionizing urban areas. When fully implemented, the IoT will allow for the seamless integration of a wide range of disparate end systems and the open sharing of data for the development of an abundance of digital services. The focus of this essay is on an urban IoT framework, a still-broad category that is yet to be characterized by its particular application domain. Actually, urban IoTs are made to support the Smart municipal concept, which seeks to apply current communication technology to supply extra services to municipal administration and people. This article, therefore, offers a comprehensive analysis of the relevant technologies, protocols, and architecture.

Security Framework for IoT Implementing Random Forest Classifier

The Internet of Things will become commonplace by making global connections possible at any moment. The necessity of well-thought-out, carefully implemented, and strictly enforced security standards over the entire lifecycle of IoT devices cannot be overstated.The Internet of Things (IoT) is a relatively new phenomenon that connects disparate computing infrastructures and infrastructure components. Given that the vast majority of the data collected will be shared with an unknowable audience, security is of paramount importance when connecting multiple independent IoT units across the Internet. This article provides a comprehensive review of the state of security in the Internet of Things.The essay emphasizes the necessity to provide security in the device itself alongside conventional security solutions to offer a method employing machine learning exible for preventing, detecting, diagnosing, isolating, and counteracting successful breaches.The bulk of IoT end hosts are low-end devices, This means that many common security practices cannot be used to protect IoTdevices., leaving IoT services and the wider Internet vulnerable to attacks and exploits.To solve this problem, this article presents a unified IoT framework that employs machine learning to implementthe proposed GNRS&NC architecture. This framework's primary goal is to ensure the safety of IoT devices.The framework makes use of random forest classifier. The suggested architecture allows for the seamless incorporation of regional IoT infrastructures into global frameworks without compromising on usability, interoperability, or security.

An improved energy-efficient cloud-optimized load-balancing for IoT frameworks

As wireless communication grows, so does the need for smart, simple, affordable solutions. The need prompted academics to develop appropriate network solutions ranging from wireless sensor networks (WSNs) to the Internet of Things (IoT). With the innovations of researchers, the necessity for enhancements in existing researchers has increased. Initially, network protocols were the focus of study and development. Regardless, IoT devices are already being employed in different industries and collecting massive amounts of data through complicated applications. This necessitates IoT load-balancing research. Several studies tried to address the communication overheads produced by significant IoT network traffic. These studies intended to control network loads by evenly spreading them across IoT nodes. Eventually, the practitioners decided to migrate the IoT node data and the apps processing it to the cloud. So, the difficulty is to design a cloud-based load balancer algorithm that meets the criteria of IoT network protocols. Defined as a unique method for controlling loads on cloud-integrated IoT networks. The suggested method analyses actual and virtual host machine needs in cloud computing environments. The purpose of the proposed model is to design a load balancer that improves network response time while reducing energy consumption. The proposed load balancer algorithm may be easily integrated with peer-existing IoT frameworks. Handling the load for cloud-based IoT architectures with the above-described methods. Significantly boosts response time for the IoT network by 60 %. The proposed scheme has less energy consumption (31 %), less execution time (24\\%), decreased node shutdown time (45 %), and less infrastructure cost (48\\%) in comparison to existing frameworks. Based on the simulation results, it is concluded that the proposed framework offers an improved solution for IoT-based cloud load-balancing issues.

IoT Programming Toolkits and Frameworks: Building a Foundation for Smart Connectivity at Home

The Internet of Things (IoT) has ushered in a new era of smart connectivity, where devices and systems interact seamlessly to enhance our daily lives. However, the complexity of IoT development necessitates robust toolkits and frameworks to empower developers in building efficient and secure IoT applications. An overview of IoT programming toolkits and frameworks, highlighting their significance in simplifying IoT development is provided. IoT toolkits, such as Arduino and Raspberry Pi, offer a rich ecosystem of hardware and software resources. PlatformIO extends further, providing a unified development environment for various microcontroller platforms, enhancing productivity and collaboration. On the other hand, IoT programming frameworks like Node-RED, AWS IoT Core, Google Cloud IoT, and Microsoft Azure IoT focus on simplifying the creation, deployment, and management of IoT applications. These IoT programming toolkits and frameworks empower developers with the resources and abstractions necessary to navigate the intricacies of IoT development. The choice of toolkit or framework depends on specific project requirements, hardware compatibility, and the desired level of abstraction. Ultimately, the advent of these invaluable resources paves the way for innovative and impactful IoT applications, propelling us further into the era of interconnected devices and smart environments.

Optimization of the iot network protocol choozing for monitoring audio information

The subject of research is the choice of a protocol in the Internet of Things (IoT) network for monitoring and analyzing audio information. The purpose of the article is to optimize the process of choosing a protocol for monitoring audio information based on IoT and evaluate the results. The authors have developed an IoT framework for monitoring and analyzing audio and voice information. The IoT network includes a sound sensor (microphone), a unit for analyzing the information received from it and a decision-making module. The description of protocols in the IoT network for assessing the sound level is given: the Wireless USB standard, the narrowband standard of the NB-IoT.The hierarchy analysis method was used to select the best protocol. The methodology and its main formulas for calculating the best option are given. A table of the four selected protocols for the IB network is given. As characteristics of the protocols, the following are considered: downlink speed, latency, device bandwidth, device transmission power. A matrix of paired comparisons of protocol parameter indicators based on expert assessments has been formed. Four matrices of paired comparisons of transmission standards variants in relation to the indicator of speed, delay, bandwidth of devices, transmission power of devices are constructed. Calculations of local vectors and priority vectors of quality indicators were performed for these four matrices. Then the matrix of vectors of global priorities is calculated by calculating the sum of the products of local vectors of matrices of paired comparisons of transmission standards variants on the corresponding priority vectors of quality indicators of the matrix of paired comparisons of protocol parameters. From the results obtained, the best protocol option for the network and sound monitoring was determined by the maximum value.

Design and Implementation of Bit-Coupled Recursive Decoding for Turbo Codes for IoT Networks

The internet of things framework has seen a rapid change in terms of the applications and users worldwide. However, the need for trustworthiness to satisfactory quality of service is of utmost importance keeping in mind the nature of data transfer in wireless media. The advent of high compute power processors with miniature sizes and low power consumption, implementing relatively complex algorithms has become possible which is necessity for internet of things applications. This research paper focusses on the design and implementation of the code blocks of turbo codes based on the BCJR algorithm so as to couple the bits in the code blocks in the composite transport block. The information and parity bits are to be coupled so as to have more information sharing within the transport block and hence reduce the error rate steeply in section of the error waterfall. The proposed technique attains lower bit error rate performance compared to the conventional un-coded and hard coded counterparts. A comparative analysis with respect to the error rate has been done so as to evaluate the quality of service of the proposed work. The lower error rate of the proposed work ensures the high quality of service and trustworthiness of the IoT system. Keywords: Internet of things, Turbo Codes, trustworthiness, error rate, bit sharing.

Clinical Prediction on ML based Internet of Things for E-Health Care System

Machine learning (ML) is a powerful method for uncovering hidden patterns in data from the Internet of Things (IoT). These hybrid solutions intelligently improve decision-making in a variety of fields, including education, security, business, and healthcare. IoT uses machine learning to uncover hidden patterns in bulk data, allowing for better forecasting and referral systems. IoT and machine learning have been embraced in healthcare so that automated computers may generate medical records, anticipate diagnoses, and, most critically, monitor patients in real time. On different databases, different ML algorithms work differently. The overall outcomes may be influenced by the variance in anticipated results. In the clinical decision-making process, there is a lot of variation in prognostic results. As a result, it's critical to comprehend the various machine learning methods utilised to handle IoT data in the healthcare industry. Machine learning of adaptive neuro fuzzy inference system (ANFIS) algorithms is being used to monitor human health in this suggested effort. The UCI database is used for initial training and validation of machine learning systems. Using the IoT system, the test phase collects the person's heart rate, blood pressure, and temperature. The test stage assesses if the sensor data obtained by the IoT framework can predict any irregularities in the health state. To evaluate the accuracy of the forecast %, statistical analysis is performed on cloud data acquired from the IoT. Other routines are derived from K-neighbour results.

Monitoring water quality metrics of ponds with IoT sensors and machine learning to predict fish species survival

Aquaculture involves cultivating various marine and freshwater aquatic creatures within regulated environments. Monitoring the aquatic environmental conditions in real-time is crucial for successful fish farming. The Internet of Things (IoT) offers significant potential for real-time monitoring, and this paper introduces an IoT framework designed for efficient monitoring and effective control of various water-related aquatic environmental parameters. The proposed system is implemented as an embedded system utilizing sensors and an Arduino microcontroller. In cultivating pond water, diverse sensors such as pH, temperature, and turbidity sensors are deployed, with each sensor connected to an Arduino Uno-based microcontroller board. These sensors collect data from the water, which is then stored as a CSV file in an IoT cloud platform called ThingSpeak through the Arduino microcontroller. To gather data for analysis, we conducted measurements across five ponds, varying in size and environmental conditions. After getting the real-time data, we compared our experimental results with the standard reference values. As a result, we could take the decision of whether a pond is suitable for cultivating fish or not. After that, we labeled the data with 11 fish categories: Katla, sing, prawn, shrimp, rui, tilapia, pangas, karpio, magur, silver carp, and koi. The data was analyzed using 10 machine learning (ML) algorithms, including J48, Random Forest, K Nearest Neighbors (K-NN), K*, Logistic Model Tree (LMT), Reduced Error Pruning Tree (REPTree), Jumping Rule Inference with Pruned Search (JRIP), Partial Decision Trees (PART), Decision Table, and Logit boost. After experimental analyses, it was discovered that only three of the five ponds were ideal for fish farming, and those three ponds only met the required standards for pH, Temperature, Turbidity, and Conductivity. Among the state-of-art machine learning algorithms, Random Forest achieved the highest score of performance metrics as accuracy 94.42%, kappa statistics 93.5%, and Avg. TP Rate 94.4%. In addition, we calculated the Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), and Dissolved Oxygen (DO) for one scenario. This study includes prototype hardware details of the proposed IoT system.

Blockchain-based data integrity framework for Internet of Things

Blockchain-based data integrity framework for Internet of Things

Integration of IoT and blockchain for decentralized management and ownership in the metaverse

SummaryThis manuscript provides an in‐depth exploration of metaverses, charting their historical development, technological foundations, and potential multifarious applications. It critically assesses the prevailing challenges and explores potential pathways for the evolution of these expansive, virtual environments. In shedding light on the wide‐ranging implications of metaverses, it navigates societal, cultural, regulatory, and economic landscapes. It further elucidates the symbiosis between the Internet of Things (IoT) and metaverses, presenting empirical evidence derived from purpose‐built IoT frameworks for metaverse applications. A series of experiments were conducted to affirm the hypothesis that the integration of IoT into metaverse utilities, including advanced concepts such as smart buildings, the intricate task of power grid management, and the precision‐centric domain of agriculture, can drive significant progress. Quantitative findings provide compelling evidence of marked improvements in energy conservation, cost‐efficiency, and operational effectiveness arising from the incorporation of IoT into metaverse use cases. With observed mean improvements of 25% in energy conservation, 17% in cost reduction, and an impressive 22% increase in operational efficiency, specifically in the realms of smart building applications and power grid management, this study underscores the pressing need for sustained research and development initiatives in this emerging.

A Security Framework for Increasing Data and Device Integrity in Internet of Things Systems.

The trustworthiness of a system is not just about proving the identity or integrity of the hardware but also extends to the data, control, and management planes of communication between devices and the software they are running. This trust in data and device integrity is desirable for Internet of Things (IoT) systems, especially in critical environments. In this study, we developed a security framework, IoTAttest, for building IoT systems that leverage the Trusted Platform Module 2.0 and remote attestation technologies to enable the establishment of IoT devices' collected data and control plan traffic integrity. After presenting the features and reference architecture of IoTAttest, we evaluated the privacy preservation and validity through the implementation of two proof-of-concept IoT applications that were designed by two teams of university students based on the reference architecture. After the development, the developers answered open questions regarding their experience and perceptions of the framework's usability, limitations, scalability, extensibility, potential, and security. The results indicate that IoTAttest can be used to develop IoT systems with effective attestation to achieve device and data integrity. The proof-of-concept solutions' outcomes illustrate the functionalities and performance of the IoT framework. The feedback from the proof-of-concept developers affirms that they perceived the framework as usable, scalable, extensible, and secure.

Navigating Routing Challenges in Internet of Things (IoT): A Focus on Routing Protocols and Wireless Sensor Networks

This paper states the routing issues in the Internet of Things (IoT). The main point of focus is especially on routing protocols in IoT and wireless sensor networks (WSN). “Everything which is connected to the internet is alive”, is going to be the new rule for the future. The future is the Internet of Things (IoT), the world is changing every day with new technologies and inventions. The Internet of Things (IoT) has known as a future scenario of technology in human life. IoT expands the concept of the Internet from a network of rather homogenous devices such as computers to a network of heterogeneous devices such as home appliances and the conventional network is mostly replaced with IoT. Devices are the main users of the IoT framework an important part is IEEE 802.15.4, a standard that operates at the PHY layer and MAC layer of IoT devices. The IoT Ecosystem includes a large number of integrated heterogeneous low-power, low-cost devices, and secure connections are needed between them [15]. They communicate with each other to gather, share and forward the information in a multi-hop manner, this requires a complex routing between them. WSN, new technology in the IoT system comprised of a large collection of sensors, a mesh network can be used to individually gather data and send data through a router to the internet in an IoT system [6]. an enormous number of sensors are used and its growth is also fast, they are collecting inexperienced data and transforming it into meaningful and useful information. considering the full usability of these devices and a huge number of them, produce routing challenges [16]. Routing in IoT is the most critical part because we are working with devices that have restricted device resources. the optimum goal is to have enhanced routing protocols and efficient performance with such limited resource devices [15, 18].

Three-tier Storage Framework Based on TBchain and IPFS for Protecting IoT Security and Privacy

Recently, most of the Internet of things (IoT) infrastructures are highly centralized with single points of failure, which results in serious security and privacy issues of IoT data. Fortunately, blockchain technique can provide a decentralized and secure IoT framework to deal with security issues based on the characteristics of decentralization, non-tampering, openness, transparency, and traceability. However, the blockchain consensus protocol guarantees the safety and reliability of data, but it also brings problems such as scalability limitations and poor storage extensibility, resulting in the inability to directly integrate blockchain and the IoT in existing conditions. In this article, a private three-tier local blockchain, Three-tier architecture Blockchain (TBchain), is proposed to solve the problem by splitting part of the transactions in the public blockchain and locking them in a higher-level blockchain TBchain. Additionally, the private blockchain TBchain is connected to the public blockchain to build a hierarchical blockchain network to provide privacy protection for the IoT data stored on the blockchain. Finally, we implement an IoT framework based on TBchain and the InterPlanetary File System (IPFS) to realize the decentralized IoT, which guarantees the user’s access control right to personal data. Experimental results show that the IoT framework based on TBchain and IPFS realizes the user’s access control right to personal data by verifying in advance to ensure the confidentiality and security of shared data, and improves the security and privacy of IoT data and transactions. Moreover, we prove that the scalability and storage extensibility of the blockchain is positively correlated with the number of data blocks in TBchain.

S-BDS: An Effective Blockchain-based Data Storage Scheme in Zero-Trust IoT

With the development of the Internet of Things (IoT) , a large-scale, heterogeneous, and dynamic distributed network has been formed among IoT devices. There is an extreme need to establish a trust mechanism between devices, and blockchain can provide a zero-trust security framework for IoT. However, the efficiency of the blockchain is far from meeting the application requirements of the IoT, which has become the biggest resistance to the application of the blockchain in the IoT. Therefore, this paper combines sharding to build an effective Blockchain-based IoT data storage scheme (S-BDS) . Sharding can solve the problem of blockchain capacity and scalability. While the blockchain provides data immutability and traceability for the IoT, it also brings huge demands for data credibility verification. The communication delay in the IoT system seriously affects the security of the system, while the Merkle proof of traditional blockchain occupies a lot of communication resources. This paper constructs Insertable Vector Commitment (IVC) in the bilinear group and replaces the Merkle tree with IVC to store IoT data in the blockchain. The construct has small-sized proof. It also has the ability to record the number of updates, which can prevent replay-attacks. Experiments show that each block processes 1,000 transactions, the proof size of a single data piece is 30% of the original scheme, and proofs from different shards can be aggregated. IVC can effectively reduce communication congestion and improve the stability and security of the IoT system.