Internet Of Things Framework Research Articles

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.

Securing Low-Power Blockchain-enabled IoT Devices against Energy Depletion Attack

Blockchain-enabled Internet of Things (IoT) envisions a world with rapid development and implementations to change our everyday lives based on smart devices. These devices are attached to the internet that can communicate with each other without human interference. A well-known wireless network in blockchain-enabled IoT frameworks is the Low Power and Lossy Network (LLN) that uses a novel protocol known as Routing protocol for low power and lossy networks (RPL) to provide effective and energy-efficient routing. LLNs that run on RPL are inherently prone to multiple Denial of Service (DoS) attacks due to the low cost, shared medium, and resource-constrained nature of blockchain-enabled IoT devices. A Spam DODAG Information Solicitation (DIS) attack is one of the novel attacks that drains the energy source of legitimate nodes and ends up causing the legitimate nodes to suffer from DoS. To address this problem, a mitigation scheme named DIS Spam Attack Mitigation (DISAM) is proposed. The proposed scheme effectively mitigates the effects of the Spam DIS attack on the network’s performance. The experimental results show that DISAM detects and mitigates the attack quickly and efficiently.

Correlation Analysis Model of Environment Parameters Using IoT Framework in a Biogas Energy Generation Context

Recently, the significance and demand for biogas energy has dramatically increased. However, biogas operators lack automated and intelligent mechanisms to produce optimization. The Internet of Things (IoT) and Machine Learning (ML) have become key enablers for the real-time monitoring of biogas production environments. This paper aimed to implement an IoT framework to gather environmental parameters for biogas generation. In addition, data analysis was performed to assess the effect of environmental parameters on biogas production. The edge-based computing architecture was designed comprising sensors, microcontrollers, actuators, and data acquired for the cloud Mongo database via MQTT protocol. Data were captured at a home digester on a time-series basis for 30 days. Further, Pearson distribution and multiple linear regression models were explored to evaluate environmental parameter effects on biogas production. The constructed regression model was evaluated using R2 metrics, and this was found to be 73.4% of the variability. From a correlation perspective, the experimental result shows a strong correlation of biogas production with an indoor temperature of 0.78 and a pH of 0.6. On the other hand, outdoor temperature presented a moderated correlation of 0.4. This implies that the model had a relatively good fit and could effectively predict the biogas production process.

Internet of Things Based Horizontal Axis Tracking Solar Panel Performance Evaluation

Renewable energy is undergoing significant advancements through the utilization of new technologies. Among these technologies, fixed solar panels serve as a fundamental type of solar photovoltaic energy generator. Monitoring and optimizing the performance of these panels is achieved through the implementation of controller algorithms. However, there is a growing emphasis on axis tracking systems for solar panels, as that has the feasible to extract more power compared to fixed panels. In this context, the integration of the Internet of Things (IoT), big data analytics, deep learning, and cloud computing presents a contemporary solution for addressing various challenges associated with monitoring renewable solar energy systems. By employing sensors and actuators grouped within an IoT framework, real-time data can be extracted and stored in a cloud server, allowing remote access from anywhere. Furthermore, the use of meta-heuristic optimization algorithms, such as the Whale Optimization Algorithm, has become commonplace in solving engineering issues. In this proposed system, the performance of horizontal axis trackers is analyzed and compared with fixed panels using the WOA-based axis tracking analysis. Through the application of a control algorithm, optimal values can be obtained under typical weather conditions. The system’s simulation and performance are thoroughly assessed and evaluated.

Efficient machine learning classifier to detect and monitor COVID-19 cases based on internet of things framework

<p>In this research work, coronavirus disease (COVID-19) has been considered to help mankind survive the present-day pandemic. This research is helpful to monitor the patients newly infected by the virus, and patients who have already recovered from the disease, and also to study the flow of virus from similar health issues. In this paper, an Internet of things (IoT) framework has been developed for the early detection of suspected cases. This framework is used for collecting and uploading symptoms (data) through sensor devices to the physician, data analytics center, cloud, and isolation/health centers. The symptoms of the first wave, second wave, and omicron are used to identify the suspects. Five machine learning algorithms which are considered to be the best in the existing literature have been used to find the best machine learning classifier in this research work. The proposed framework is used for the rapid detection of COVID-19 cases from real-world COVID-19 symptoms to mitigate the spread in society. This model also monitors the affected patient who has undergone treatment and recovered. It also collects data for analysis to perform further improvements in algorithms based on daily updated information from patients to provide better solutions to mankind.</p>

Data Privacy, Conceptual Framework for IoT Based Devices in Healthcare: A Systematic Review

With the rollout of the Fifth-Generation network, more Internet of Things (IoT) devices tend to increase, which increases the amount of data being shared by the devices. It is complex to secure data transmission and device-to-device communication due to the vast number of IoT devices and the complexity of networks. The Internet of Things (IoT) has evolved to enable everyday things and gadgets to connect digitally and communicate with one another, using technologies that send, gather, and analyse data from people using those objects and devices. IoT data privacy risks are widespread use. The primary objective of information technology (IT) security in Web 2.0 was to ensure the privacy, accuracy, and reliability of systems and communications. As a result of IoT devices’ often limited CPU power for longer-lasting activities, these conventional metrics, however, exhibit measurable constraints. IoT security is, therefore, critical in the context of guaranteeing security through the data privacy of IoT users. The author conducted a literature methodological analysis on the data privacy framework that will help to safeguard the 5G IoT-enabled devices on user data, technologies for data privacy in 5 G-based IoT devices, data privacy dangers associated with 5G IoT devices, and data privacy attack surfaces in 5G IoT devices

THE MAINTENANCE SYSTEM FOR A SMART BUILDING IN INDUSTRY 4.0 ENVIRONMENT

Buildings, where we spend the majority of our time, have been quickly altering in recent years. Building systems powering integrate consumer-grade Internet of things (IoT) sensors such as cameras with operational technology controllers and actuators to accommodate the increased complexity of building operations. This study discussed the idea of “smart buildings” and the comparison along with the technologies being developed for these structures and the implications for libraries and specialized smart building technology using various IoT approaches used in smart building categories. The research goal is to improve approaches which active in recent years and the gaps researchers have found in their investigations. As a result, smart buildings provide physical value by generating data intelligently to enhance operations and customer skills. Recent breakthroughs in IoT have piqued experts and engineers across the world. However, improvements are only possible if we consider the numerous flaws and mistakes in the present technology. IoT developers might consider many challenges and hurdles while designing a better model in this survey article. Significant IoT technology sectors in which IoT developers and academics are interested are frequently addressed. In addition to providing services, IoT captures a vast amount of data. Therefore, big data analytics is essential, trustworthy judgments can be made, and a more robust IoT framework can be created. This study compares the previous studies that focused on characteristics that a controlling and advanced approach to making buildings smarter with Maintenance to reduce greenhouse gas emissions and enhance the world's environment at a lower cost.

AI-Enabled IoT Framework for Leakage Detection and Its Consequence Prediction during External Transportation of LPG.

An accident during the transport of liquefied petroleum gas (LPG) via a tanker vehicle leads to the leakage of a flammable substance, causing devastation. In such a situation, the appropriate action with the shortest possible delay can minimize subsequent losses. However, the decision-making mechanism remains unable to detect the occurrence of an accident and evaluate its extent within the critical time. This paper proposes an automatic framework for leakage detection and its consequence prediction during the external transportation of LPG using artificial intelligence (AI) and the internet of things (IoT). An AI model is developed to predict the probable consequences of the accident in terms of the diameter of risk contours. An IoT framework is proposed in which the developed AI model is deployed in the edge device to detect any leakage of gas during transportation, to predict its probable consequences, and to report it to the remotely located disaster management team for initiating appropriate action. A prototype of the proposed model is built and its performance is successfully tested. The proposed solution would significantly help to identify efficient disaster management techniques by allowing for quick leakage detection and the prediction of its probable consequences.

RDAF-IIoT: Reliable Device-Access Framework for the Industrial Internet of Things

The Internet of Things (IoT) has experienced significant growth and is now a fundamental part of the next-generation Internet. Alongside improving daily life, IoT devices generate and collect vast amounts of data that can be leveraged by AI-enabled big data analytics for diverse applications. However, due to the machine-to-machine communication inherent in IoT, ensuring data security and privacy is crucial to mitigate various malicious cyber attacks, including man-in-the-middle, impersonation, and data poisoning attacks. Nevertheless, designing an efficient and adaptable IoT security framework poses challenges due to the limited computational and communication power of IoT devices, as well as their wide-ranging variety. To address these challenges, this paper proposes an Access Key Agreement (AKA) scheme called the “Reliable Device-Access Framework for the Industrial IoT (RDAF-IIoT)”. RDAF-IIoT verifies the user’s authenticity before granting access to real-time information from IIoT devices deployed in an industrial plant. Once authenticated at the gateway node, the user and IIoT device establish a session key for future encrypted communication. The security of the proposed RDAF-IIoT is validated using a random oracle model, while the Scyther tool is employed to assess its resilience against various security attacks. Performance evaluations demonstrate that the proposed scheme requires lower computational and communication costs compared to related security frameworks while providing enhanced security features.

MalCon: A blockchain-based malware containment framework for Internet of Things

IoT devices have become a primary medium for malware (e.g., botnets) to launch Distributed Denial of Service (DDoS) attacks. Such malware exploit low-security measures in IoT devices to spread in networks and recruit new victims. Thus, there is a need for malware countermeasures that consider both the security and operability of the network. Indeed, some IoT devices might run critical processes that do not tolerate interruptions.This paper proposes MalCon, a blockchain-based malware containment framework for IoT. It aims to stop malware from spreading in a network by a set of containment strategies encoded into smart contracts to be executed by the infected devices. Moreover, MalCon provides a monitoring service that ensures trustworthy behavior in the network and reports to the system administrator any fraudulent activity of the monitored devices. MalCon was tested extensively with real-life malware and use cases. It quickly and drastically reduces the number of infected devices in a network, even in an extreme case of a fully connected network.

Scalable Incident Reporting Framework: A Sensor and IoT Research

The Internet of Things (IoT) is one of the most rapidly emerging technologies. It is observed that while many devices/machines get connected in an application, it is a challenge for the IoT application designer to keep the application scalable. Scalability is the ability of a device/application to adapt to the changes in the environment and meet the changing needs in the future. The paper presents a layered IoT architecture and discusses issues related to the scalability of each layer. The best open-source technologies are explored. A novel system architecture of a scalable IoT framework is conceptualized in this paper. An application covering vehicle accident reporting is designed with the proposed framework. The application is tested in real-time using the standalone hardware and its ability to report the incidents is confirmed. The scalability metrics of the proposed framework are evaluated and the results are reported.

Mobility Assisted Adaptive Clustering Hierarchy for IoT Based Sensor Networks in 5G and Beyond

One of the massive machine type communication (mMTC) applications for monitoring and sensing in 5G cellular network is the Internet of Things (IoT) based wireless sensor network (WSN). Non uniform battery usage by the nodes in these networks often results in creating energy holes or voids in the network making the network disconnected. An effective solution is to deploy multiple mobile nodes throughout the network, however finding optimal path for these mobile nodes is reported to be an NP hard problem. This paper proposes MAACH (Mobility Assisted Adaptive Clustering Hierarchy), an efficient mobility assisted clustering and routing framework for IoT based sensor network in 5G and beyond. Also, an elaborate method to calculate the exact path optimally for multiple mobile nodes is presented to alleviate non uniform energy dissipation of the sensing nodes. Simulation results show that our algorithm effectively finds the optimal trajectory of multiple mobile nodes in a distribute manner and also improves network stability period by 60-70% and the network lifetime by 70-90% across multiple network deployments.

Experimental Evaluation of an IoT-Based Platform for Maritime Transport Services

In recent years, the adoption of innovative technologies in maritime transport and logistics systems has become a key aspect towards their development and growth, especially due to the complex and heterogeneous nature of the maritime environment. On the other hand, Internet of Things (IoT) solutions are gaining importance in the shipping industry thanks to the huge number of distributed cameras and sensors in modern ships, cargoes and sea ports, which can be exploited to improve safety, costs and productivity. This paper presents an experimental evaluation of a maritime platform, which enables a wide range of 5G-based services in the context of logistics and maritime transportation. Its core is a Narrow Band (NB)-IoT framework used to run massive IoT services on top of a hybrid terrestrial–satellite network and feed a OneM2M platform with significant data on maritime transport to develop high-level and value-added logistic applications on top. Among the many different services that could be provided by the maritime platform, we focus on the cargo-ship container tracking use case through the Global Tracking System, which allows for continuous container monitoring all over the seas in a port-to-port service scenario. The results of the experimental tests illustrate the capacity of the platform in managing the high number of messages transmitted by the container tracking devices (i.e., more than 3000) and its efficiency in limiting the average maximum latency and packet loss below 5.5 s and 0.9%, respectively.

Sazgar IoT: A Device-Centric IoT Framework and Approximation Technique for Efficient and Scalable IoT Data Processing.

The Internet of Things (IoT) plays a fundamental role in monitoring applications; however, existing approaches relying on cloud and edge-based IoT data analysis encounter issues such as network delays and high costs, which can adversely impact time-sensitive applications. To address these challenges, this paper proposes an IoT framework called Sazgar IoT. Unlike existing solutions, Sazgar IoT leverages only IoT devices and IoT data analysis approximation techniques to meet the time-bounds of time-sensitive IoT applications. In this framework, the computing resources onboard the IoT devices are utilised to process the data analysis tasks of each time-sensitive IoT application. This eliminates the network delays associated with transferring large volumes of high-velocity IoT data to cloud or edge computers. To ensure that each task meets its application-specific time-bound and accuracy requirements, we employ approximation techniques for the data analysis tasks of time-sensitive IoT applications. These techniques take into account the available computing resources and optimise the processing accordingly. To evaluate the effectiveness of Sazgar IoT, experimental validation has been conducted. The results demonstrate that the framework successfully meets the time-bound and accuracy requirements of the COVID-19 citizen compliance monitoring application by effectively utilising the available IoT devices. The experimental validation further confirms that Sazgar IoT is an efficient and scalable solution for IoT data processing, addressing existing network delay issues for time-sensitive applications and significantly reducing the cost related to cloud and edge computing devices procurement, deployment, and maintenance.

Improved chimp optimization algorithm (ICOA) feature selection and deep neural network framework for internet of things (IOT) based android malware detection

Internet of Things (IoT) is extensively implemented using Android applications thus detecting malicious Android apps is necessary. Malicious has been multiplying fast as a result of the growing usage of smartphones. The Android platform is often left vulnerable to new and undiscovered malware because to the increasing quantity and variety of Android malware that has considerably undermined the efficiency of the traditional defence systems. Several data-driven malware detection techniques were being out. Furthermore, these techniques face two major obstacles: How to acquire useful feature representations from raw data; How to lessen feature learning's reliance on past knowledge or individual workers. This research work proposed a system for detecting malware that begins by teaching rich characteristics an improved chimp optimization algorithm (ICOA) based feature selection and Deep Neural Network Framework (DNNF) for accurate detecting of Android malware. Deep learning methods' widespread use in the industry of feature representation learning served as an inspiration for this framework. Raw feature preprocessing, feature representation learning, and malware detection are the three key components of the proposed DNNF. The second phase is to learn high-level discriminative features to fuel malware identification using the ICOA. Finally, a DNNF based on Long Short-Term Memory (LSTM) is created for efficient malware detection in Android applications. Based on the findings of the simulations, it seems that the DNNF model that was suggested offers high recognition accuracy in comparison to other approaches that are considered to be state of the art.

Blockchain-Empowered Security Enhancement IoT Framework in Building Management System

Centralized architectures, like the cloud model, have their advantages, but they also come with drawbacks, such as higher upfront costs, longer deployment times, and a higher probability of catastrophic failure. Building Management Systems (BMS) is an application that can adopt Internet of Things (IoT) designs and services. However, implementing IoT in a highly modular environment with various moving parts and interdependencies between stakeholders can create security issues. Therefore, this paper proposes a system design using Blockchain technology as a means to protect and control the system, which includes the integration of IoT and BMS technologies. This paper has also included broad discussion on current Blockchain based IoT solution and its IoT limitations in Building Management Systems.

IoT and AI-based Intelligent Agriculture Framework for Crop Prediction

Background: Currently, Artificial Intelligence (AI) and the Internet of Things (IoT) have transformed the field of agriculture with the innovative idea of automation and intelligence. The agriculture field completely relies on the uncertainty parameter of soil, atmosphere, and water. Technological advancement in IoT and AI assist in resolving this uncertainty factor and recommend the best crops to the farmers so that they can also enhance the productivity of the crops and meet the world's large food demand smartly. Objective: In this paper, we have suggested an IoT and AI-based model which trained with 2200 records of the dataset and seven attributes in Python. The model suggests 22 different crops to farmers after collecting samples through different sensor data. We used soil, temperature, humidity, pH, and rainfall sensors. Soil sensors were used to measure the amount of N, P, and K in soil. Methods: Various supervised machine learning algorithms such as KNN, Decision Tree, Naïve Bayes and Logistic Regression classifiers have applied to build the proposed model. The model is continuously monitoring the field via various sensor data as a sample data for the prediction of best crops to be grown for farmers. Results: In this research, we investigated the contribution of supervised machine learning classifiers like KNN, Decision Tree, Naïve Bayes and Logistic Regression classifiers. The maximum accuracy has been observed as 99.39% of the Naïve Bayes classifier. Conclusion: In this paper an AI and IoT based model is used to recommend/predict the best crop based on environmental factors. The proposed model will collect the real time sensor data to predict the crops and plants smartly.