Blockchain networks often face isolation and scalability challenges, particularly when interacting across heterogeneous consensus protocols. This paper introduces Proof of Repute Consensus (PoRC), a decentralized framework designed to facilitate secure and trustless cross-chain interoperability without reliance on third-party bridges. PoRC employs a behavior-driven reputation model, where nodes must maintain a minimum Reputation Score to participate in block proposals. A randomly selected committee of auditors continuously evaluates node behavior using verifiable random selection, enabling accountability without economic collateral. The proposed two-tier architecture decouples intra-chain consensus (Tier-1) from inter-chain collaboration (Tier-2), allowing heterogeneous blockchains to coordinate state updates while preserving their native consensus mechanisms. A comprehensive security framework supported by stochastic modeling is employed to analyze resistance against double-spending, Sybil attacks, and collusion. Experimental evaluation demonstrates that the PoRC interoperability layer sustains an aggregate throughput of up to 8,000 transactions per second across parallel clusters, with an average cross-chain finality of approximately 11 seconds under controlled conditions. These results indicate that PoRC provides a viable architectural foundation for scalable and behavior-driven blockchain interoperability.

Connected vehicles rely heavily on mobile vehicular networks and centralized cloud infrastructures for data handling. These networks contain not only environmental information but also sensitive data such as passenger identities, routes, origins, and destinations, making them prone to various cyber threats. Existing authentication mechanisms are predominantly centralized and cloud-based, which introduces significant vulnerabilities, including high latency, single points of failure, and exposure to denial-of-service attacks, man-in-the-middle intrusions, and data breaches. Moreover, the real-time nature of vehicular data sharing exacerbates these risks. Conventional centralized architectures typically depend on a single trusted authority for vehicle authentication and data integrity validation, which further increases susceptibility to unauthorized access, data tampering, and system disruption. To address these limitations, this work proposes a novel distributed blockchain-based authentication mechanism for Internet of Vehicles and autonomous vehicles. The proposed approach leverages decentralized identifiers and verifiable credentials to securely authenticate vehicles within a decentralized network. Extensive experimental evaluations assess the system’s performance across multiple parameters, including latency, trust, and resilience to attacks. Comparative analysis demonstrates a significant improvement in trustworthiness and authenticity, validating the effectiveness of the proposed method.

Vehicular sensor networks (VSNs) are a key part of intelligent transportation systems, supporting real-time communication between vehicles and roadside infrastructure. Ensuring secure and trustworthy communication remains a major challenge, as VSNs are vulnerable to identity spoofing, malicious nodes, and data integrity attacks. Traditional solutions such as public key infrastructure (PKI) and certificateless cryptography suffer from scalability limits, certificate overhead, and reliance on centralized authorities, making them less effective for large-scale deployments. This article presents a blockchain-based key management framework that combines Boneh-Lynn-Shacham (BLS) signatures with a three-layer identity-based key distribution system. The framework decentralizes trust, provides lightweight authentication, and ensures tamper-resistant records. Performance is validated through simulations and real-world testbeds, showing a 20% rise in transaction frequency, a 45% improvement in throughput, and a 50% decrease in block congestion, while maintaining low latency with minimal overhead. The proposed proof of repute consensus (PoRC) establishes a secure, scalable, and efficient communication infrastructure for VSNs.

Blockchain technology offers significant potential for enhancing data integrity, transparency, and patient consent management in clinical trials. However, the security and reliability of these systems, particularly permissioned blockchains relying on threshold cryptography operated by collaborating institutions (nodes), fundamentally depend on the active and honest participation of these nodes. While technical safeguards are crucial, the underlying economic incentives and strategic behaviors governing node participation are often underexplored yet critical for long-term system viability and trustworthiness. This paper addresses this gap by applying game theory to analyze the strategic interactions between participating nodes (shareholders) and potential attackers within a permissioned blockchain framework designed for secure clinical trial data sharing. The strategic interactions are modeled by incorporating rational actors with defined costs (e.g., operational overhead), benefits (e.g., system access, reputation), potential illicit gains (e.g., from data breaches or collusion), and penalties (e.g., regulatory fines, exclusion). Payoff functions explicitly consider the influence of key system parameters: the total number of participating nodes (N) and the security threshold (t) required for critical operations. Using Nash equilibrium analysis, we investigate how variations in $\mathbf{N}$ and $\mathbf{t}$, alongside different cost-benefit-penalty structures relevant to the clinical trial context, impact the rational strategic choices of participants (i.e., choosing honest cooperation versus malicious collusion or attack). Our analysis aims to identify the conditions under which honest participation emerges as the dominant strategy, thereby ensuring the stability and security of the blockchain network from an incentive-compatibility perspective. The findings provide insights for designing more robust and trustworthy blockchain systems for sensitive healthcare applications by aligning participant incentives with overall network security, complementing existing technical and probabilistic security assessments.

Digital Identity Management system is important component of security infrastructure for internet applications. However, existing digital identity management systems encounter various challenges, including difficulties in cross-domain authentication and interoperation, lack of credibility in identity authentication, and vulnerabilities in the security of identity data. Despite the attention blockchain technology has garnered in the field of digital identity management and the development of blockchain-based systems, these systems have not fully resolved the aforementioned problems. To address these issues and establish a secure and trustworthy digital identity management system, this paper proposes an effective model that integrates self-sovereign identity, oracle technology, and blockchain. This model aims to provide solutions and lay the groundwork for overcoming the challenges and ensuring the construction of a secure and reliable digital identity management system.

Internet of Vehicles (IoV) integrates with various heterogeneous nodes, such as connected vehicles, roadside units, etc., which establishes a distributed network. Vehicles are managed nodes providing all the services required during inter-vehicular communication which makes it essential to use a trust management mechanism. Existing trust management systems are insufficient to address the emerging IoV needs of transparency, mobility, immutability, and scalability. Motivated by the need to address the outlined issues, Proposed additive increase and multiplicative decrease (AIMD) trust management model, to improve the quality of communication, as well as vehicle trustworthiness based on multiple inter-vehicular interactions. The proposed layered architecture helps to reduce overhead in terms of latency and average execution time compared to other trust management mechanism and avoid risk of single point of failure. The Proposed AIMD trust management is implemented on permissioned blockchain, where transactions in the IoV network are only managed by minor nodes which accumulate interactions and uses PBFT and AIMD mechanism to derive immutable reputation score securely and transparently. Simulation results show the AIMD trust mechanism using permissioned blockchain can manage the trust of vehicles efficiently by reducing the latency by 0.29 sec, increasing packet delivery ratio from 0.8 to 0.95, and significantly reduce transaction finality time.

Today, blockchain is becoming more popular in academia and industry because it is a distributed, decentralised technology which is changing many industries in terms of security, building trust, etc. A few blockchain applications are banking, insurance, logistics, transportation, etc. Many insurance companies have been thinking about how blockchain could help them be more efficient. There is still a lot of hype about this immutable technology, even though it has not been utilised to its full potential. Insurers have to decide whether or not to use blockchain, just like many other businesses do. This technology keeps a distributed ledger on each blockchain node, making it more secure and transparent. The blockchain network can operate smart contracts and convince others to agree, so criminals cannot make mistakes. On another side, the Internet of Things (IoT) might make a real-time application work faster through its automation. With the integration of blockchain and IoT, there will always be a problem with technology regarding IoT devices and mining the blockchain. This paper gives a real-time view of blockchain—IoT-based applications for Industry 4.0 and Society 5.0. The last few sections discuss essential topics such as open issues, challenges, and research opportunities for future researchers to expand research in blockchain—IoT-based applications.

Image de-hazing processes improve visual clarity and image quality, particularly in areas where sight is limited. This study proposes a novel hybrid methodology with the goal of outperforming the limitations of existing de-hazing strategies. We propose a filtering methodology in this study that combines the advantages of Guided Image Filtering (GIF), Weighted Guided Image Filtering (WGIF), and Globally Guided Image Filtering (WGIF) into a single strategy. The proposed solution is a multi-stage framework in which the three de-hazing techniques are utilized consecutively. GIF is first applied to the foggy image. Following that, WGIF is used, followed by GGIF. Finally, we have included a bilateral filter into the hybrid filter to increase the visual quality of the photos even further. Furthermore, the quality of de-hazed photographs is evaluated using a metric known as Perceptual Fog Density (PFD). For evaluating the performance of the proposed methodology, experiments are done over various hazy images of varying haze levels. When comparing the results obtained from the proposed novel strategy to standard de-hazing procedures, it becomes evident that the proposed approach offers notable advantages in terms of both Perceptual Fog Density (PFD) and visual quality.

QKD is performed to secure quantum communication between the ground stations and satellite. To implement the quantum channel, better security and output are required. QKDP is a successful way for satellite quantum communications for secure networks. In this paper, we discuss error correction schemes and their comparison, quantum communication procedures A, D, and M represent encoding, decoding, and performed measurement operations. A/P represents classical bit to quantum conversion. We also present the proposed model designed for a quantum channel to improve the performance of QKD protocol satellite-based communication under problem-environmental noise, adversary attacks, atmospheric turbulence.

The technological revolution over the past decade has transformed several applications in different global sectors such as retail, transportation, automobile, agriculture, medicine, etc. These improvements have been accelerated by the widespread usage and popularity of Internet of Things (IoT) technology. The healthcare industry is a high-priority sector which is majorly responsible for saving human lives, improving a person's lifestyle, and ensuring longevity. Purposes of IoT devices in today's life is "it play a major role in tracking patients, healthcare professionals and providing remote caretakers with vital patient data for patient status monitoring". Machine Learning (ML) is another technological field, revolutionizing the utilities of devices with IoT techniques to provide efficient and cost-effective services to patients and healthcare professionals. In recent years, many studies on IoT and ML for healthcare are gaining traction from several international researchers. About methods, in this research, an attempt on machine learning-based IoT service is made with special concentration on tele-medical and remote healthcare. For results, this work provides a detailed collection of machine learning and IoT-based solutions for telemedicine and patients, living in remote localities. In conclusion, the system proved successful in progressing remote healthcare through IoT and Machine learning technology.

In today's scenario, every organization is dependent on the use of computers for automating business and work. The data generated by the organization is huge and has great significance in analyzing the processes. It can be rightfully called as the lifeblood of any organization. The world of computing requires three technologies: Internet of Things (IoT), machine learning (ML), and blockchain. The confluence of these technologies is inevitable in the coming future due to the benefits which are involved in future applications. There is barely any activity today which is cannot apply the use of these three technologies e.g., healthcare, automation, education, etc. IoT can be defined as interconnection of various autonomous devices which are capable of communicating with each other. IoT requires an intermittent Internet connection and address for every device. The user of these devices can remotely monitor and manage by retrieving the information on a handheld device similar to the cellphone. This way, the devices are connected 24/7 and continuously generating data. Challenges of IoT involve the following: security, connectivity problems, and huge data. The devices can be made capable of taking intelligent decisions by incorporating Artificial Intelligence (AI) and ML technology. ML is a sub-branch of AI. It has got huge potential to detect the patterns and anomalies in the data which is generated by the wireless sensor nodes in IoT. The advanced decision-making process of ML has already influenced our daily routines, for example: banking, healthcare, gaming, transportation, and space exploration. Challenges faced by ML are the following: security, centralized architecture, and resource limitations. To cover the security aspect of these two technologies, blockchain technology is the perfect answer. It is a decentralized peer-to-peer network which stores the records and transactions in blocks which cannot be altered. This technology secures the communication by eliminating the need for any trusted third party. The blocks are stored in such a manner which makes it impossible to hack or tamper the data by taking control of device or capturing the records. This chapter will present a comprehensive and quantitative analysis of the existing research and how these technologies can be a transformative impact for access to information by the users. The convergence of blockchain, ML, and IoT will provide scalable, secure high-level intellectual functioning that will be the new paradigm of digital information. This book chapter presents futuristic potential of convergence of three technologies and elaborate discussion of the past researches.

This paper proposes Combining the Advantages of Radiomic features based Feature Extraction and Hyper Parameters tuned Recalling Enhanced Recurrent Neural Network (RERNN) using Lizard optimization Algorithm (LOA) for Breast cancer Classification. Here, breast cancer images are taken from the real time dataset collected from VPS hospital and then the images are preprocessed using Altered Phase Preserving Dynamic Range Compression (APPDRC) to remove the noises. Then the radiomic features, such as morphologic features, grayscale statistic features and Haralick texture features have been extracted utilizing Entropy Based Local Binary Pattern (ELBP). These extracted features have presented to Recalling Enhanced Recurrent Neural Network (RERNN) classifier. Hence, Lizard optimization Algorithm (LOA) is utilized to optimize the Recalling Enhanced Recurrent Neural Network (RERNN). The proposed approach is executed in MATLAB platform, then the performance is compared with different existing approaches. This approach is applicable in real time applications for screening the abnormalities of the breast cancer at initial stage, thus, determining the proper treatment to be given to the patient for decreasing deaths caused by breast cancer. The novelty or aim of this paper is to diagnose the breast cancer in early stage by extracting the radiomic features and to classify the types (Malignant, Benign, normal) of breast cancer with high accuracy by reducing the computational time and error rate. The simulation outcomes demonstrate that the proposed FE-APPDRC-ELBP-RERNN-LOA attains the accuracy of45.75%, 37.64%, 24.64 %is higher than the existing methods.

Automobiles are required in everyday life to commute from destination to other. But, due to faulty road conditions, heavy traffic and lack of adequate driver skills, crash incidents happen almost every hour of the day. With every vehicle, companies offer insurance which people can claim to get the damaged parts recovered. However, during the recovery, it solely depends on the insurance companies investigating officer to give an adequate claim to the vehicle owner. Due to inadequate visual precipitance, at times, the owner is not given the full insurance claim. The proposed framework offers automated damage detection system by which better and correct claim can be given to such an owner. The proposed framework is based on Machine Learning and IOT approach for transport network scenario. This paper presents the idea of implementing proposed framework to the researchers in future to detect damages and calculate the correct claim to the owner for Insurance claim.

This chapter presents a combined approach to visualize the IoT and cyber-physical system in any wireless communication networks. The brief overview of IoT and its applications in wireless sensor network (WSN) are elaborated in current era. In today's present situation and the challenges faced during COVID-19 pandemic how the things can be monitored through IoT is being focused in this chapter. Also, the threats challenges and monitoring of cyber-physical system for security is presented in analysis form. WSN provides a bridge between the physical and virtual world and finds use in numerous applications like environmental monitoring, civil structural health monitoring and industrial process monitoring. Lately, it has found much use in the latest technology called Internet of Things (IoT). It seems that IoT has a great potential in shaping our future where everything will be connected together all the time. The major challenges faced by WSN include energy-efficiency, heterogeneity, and systematic design. WSN comprise of autonomous sensors which are capable of wirelessly transmitting their sensed parameter-values (also called packets) to remotely 188located sink node. The sink node is a WSN node which has the capability to assemble the data from all the nodes and forward it to farther distances to the remote base station through wired connections. The data transmission from source node to sink node is done by taking multi-hops which means that data is not sent directly to the destination node but through jumping from one intermediate node to another until the sink is reached. The sensor nodes also act as wireless routers which transmit data to other sensor-routers. So, sensor devices are very useful in deployment, collection of data, act as a IoT sensor devices as an application in technology. IoT is a device which can be used as application while a cyber-physical system is a collection of various mechanical components which are controlled and managed by different devices and algorithms. An IFM (Information Flow Monitor) is an application to backtrack queries and find the culprit in an error that has occurred in the cyber system. However, while backtracking and flow monitoring is not always guaranteed in terms of secure and efficient transaction. In some cases, the sender may deny the authority of the data so the information flow monitors may fail to provide a trustful and authentic source of data. Thus, the chapter focuses on both the issues of IoT integration with cyber-physical security (CPS) system. The key challenges and some solutions are proposed for the researchers for future work.

Webcams are an integral part of our daily life now. Through webcams, we are able to remotely monitor the surrounding. Webcams are able to capture images which need to be extracted with meaningful information. This paper presents a method to use programming to extract the information. Python’s super flexible and easy to use nature is what makes it amongst the popular programming language, especially for Data Scientists. A major point is that how simple it is to work with datasets of large size. Today, every technology company is building up some strategy regarding data. They’ve all realized that having the right kind of data: which provide great insight and should be clean and, as much of it as possible, gives them a key advantage over their competitors. Data, if used carefully and effectively, can offer deep insights that cannot be discovered anywhere else.Using image processing capabilities of python, we are able to develop a webcam motion detector. Code for this is written on Pycharm framework and OpenCV is the library used for purpose of image processing.

In today's scenario, every organization is dependent on the use of computers for automating business and work. The data generated by the organization is huge and has great significance in analyzing the processes. It can be rightfully described as the lifeblood of any organization. The world of computing requires three technologies: IoT, machine learning and blockchain. The confluence of these technologies is inevitable in the future due to the benefits which are involved in future applications. There is barely any activity today which cannot apply the use of these three technologies; e.g. healthcare, automation, education, and the Internet of Things (IoT) can be defined as interconnection of various autonomous devices which are capable of communicating with each other. IoT requires an intermittent Internet connection and address for every device. The user of these devices can remotely monitor and manage by retrieving the information on a handheld device such as a cell phone. This way, the devices are connected 24/7 and continuously generating data. Challenges of IoT involve security, connectivity problems, and huge amounts of data. The devices can be made capable of making intelligent decisions by incorporating artificial intelligence (AI) machine learning technology. Machine learning is a sub-branch of AI. It has huge potential to detect the patterns and anomalies in the data which is generated by the wireless sensor nodes in IoT. The advanced decision-making process of machine learning has already influenced our daily routines in, for example, healthcare, the banking sector, interactive gaming, transportation, and missions for exploration of space. Challenges faced by ML include data security, centralization, and limitations in resources. To cover the security aspect of these two technologies, blockchain technology is the perfect answer. It is a decentralized peer-to-peer network which stores the records and transactions in blocks which cannot be altered. This technology secures the communication by eliminating the need for any trusted third party. The blocks are stored in such a manner that it is impossible to hack or tamper with the data by taking control of device or capturing the records. This chapter will present a comprehensive and quantitative analysis of the existing research in these fields and how these technologies can be used for a transformative impact in accessing information by the users. The convergence of blockchain, machine learning, and IoT will provide scalability and high-level intellectual functioning which will be secure to bring a new paradigm of digital transformation.

Ensemble methods is a strategy based algorithm. It is the combination of best suited machine learning algorithms to get more reliable, accruable result, ensemble methods came in to picture. Troupes (ensemble) are sets of learning machines that consolidate their choices or their learning calculations, or different perspectives on information, or other specific attributes to acquire increasingly solid and progressively precise expectations in regulated and also learning issues. In this paper the previous study related to Ensemble methods is discussed and gives a future aspect of Ensemble methods. Here also discussed about the categorization of Ensemble methods, As well as the comparison between single machine learning model and combination of multiple models is given. These days outfit strategies speak to one of the fundamental momentum research lines in Artificial Intelligence. The main purpose of this study is to spread out the importance of Ensemble Methods and prediction of future aspect of it.

The Internet of things, also known as IoT, is a connected system consisting of interrelated devices used for computing purposes, mechanical machines, digital machines, various objects, components, animals or human beings, where all of them are provided with a certain unique identifying identity called UID. This provides the users with the ability to transfer data over a large and connected network without the requirement of human-to-human kind of communication or human-to-computer interaction.Internet of Things is a virtual ecosystem consisting of physical objects connected over internet and is accessible through an internet connection. The embedded technology used in the IoT connected objects helps the objects to interact with their internal states or with the external environment, which in turn is used to come up to a decision. IoT is a transformational technology that helps many companies to improve their performances through IoT analytic techniques. Moreover, IoT security also helps in delivering better results to the companies. abstract should summarize the contents of the paper and should contain at least 70 and at most 150 words. It should be set in 9point font size and should be inset 1.0 cm from the right and left margins. There should be two blank (10-point) lines before and after the abstract. This document is in the required format.