Towards Secure and Efficient Data Aggregation in Blockchain‐Driven IoT Environments: A Comprehensive and Systematic Study

A blockchain-enabled Model integrates blockchain technology with Intrusion Detection Systems to enhance the security of Internet of Things (IoT) networks. It ensures data integrity, decentralization, and tamper-proof logging of intrusion detection. The approach improves trust, transparency, and real-time threat detection in distributed IoT environments. The existing blockchain-based IDS approaches, Blockchain Enabled (BCE-IoT), uniquely integrate blockchain consensus with federated-style local training, lightweight cryptography, and Shapley Additive Explanations (SHAP)-based explainability, ensuring both security and interpretability in IoT environments. The proposed work combines Blockchain technology with explainable artificial intelligence solutions to create a new cybersecurity Model that strengthens intrusion detection within IoT networks. The proposed model enhances transparency in tracking cyberattacks by combining blockchain security storage capabilities with SHAP, an explainable AI. This research utilises machine learning and artificial intelligence to detect threats in real-time, countering Distributed Denial of Service (DDoS), Denial of Service (DoS), scanning, Cross-Site Scripting (XSS), injection, password, and backdoor attacks. BCE-IoT delivers more precise security by combining blockchain’s permanent data features and AI anomaly detectors, thereby reducing security alert mistakes. The performance effectiveness of Blockchain-Enabled IoT surpasses that of the Content Integrity Detection System. It combines Blockchain and Software-Defined Networking to enhance security in network environments, utilising blockchain-based mutual confirmation for software-defined networking to detect and block cyber threats. The evaluation establishes BCE-IoT as an effective IoT network security solution that delivers strong cybersecurity features, is adaptable to modern connected environments, and offers interpretable security solutions. The performance evaluations demonstrate that BCE-IoT provides a robust, flexible, and interpretable cybersecurity solution suitable for modern IoT environments.

Aims: Internet of Things (IoT) is the evolution of the Internet designed to sense, collect, analyze and distribute the data via IoT devices that form its core component. An important aspect of pervasive IoT applications is its resource-constrained devices. Most of the real-time Edge-IoT applications generate a huge amount of data, which add to the resource consumption in these devices. To save resources in such applications, efficient node deployment and data aggregation techniques can be used. This paper presents the design and modeling of node deployment and data aggregation in Edge-IoT applications along with the homogeneous and heterogeneous network scenarios for smart agriculture. Objectives: To save resources in such applications, efficient node deployment and data aggregation techniques can be used. This paper presents the design and modeling of node deployment and data aggregation in Edge-IoT applications along with the homogeneous and heterogeneous network scenarios for smart agriculture. Methods: For heterogeneous scenarios, we propose a clustering approach, Superior Aggregator Resource Efficient Clustering (SAREC), to address the resource constraints in pervasive Edge-IoT applications. The comparison of homogeneous and heterogeneous networks is based on LEACH and SAREC protocols, respectively. Results: The results show that SAREC is 25% more efficient in energy utilization and network lifetime than LEACH. The results also show that SAREC is more efficient in terms of storage and processing time as compared to LEACH. Conclusion: Node deployment is an important aspect in determining the architecture, which plays an important role in resource management in pervasive applications of IoT. The IoT nodes are distributed in a selected geographical location and the topology of the network is pre-decided to form an Edge-IoT network. In such an environment, the nodes are deployed to sense, aggregate and analyze the data. This paper presents a pervasive Edge-IoT network along with the mathematical modeling consisting of deployment and aggregation models. An Edge-IoT network for smart agriculture has been deployed and analysis of resource utilization has been performed in homogeneous and heterogeneous scenarios of the network. The resource limitations in pervasive IoT network motivated us to develop a SAREC approach for such Edge-IoT applications that optimizes the use of resources. The comparison of the proposed SAREC protocol is made with respect to LEACH protocol on the basis of energy, network lifetime, number of alive nodes, storage and processing time. The results show that SAREC protocol is 25% more efficient in energy utilization and network lifetime than LEACH. It is also evident from the results that the SAREC is more efficient in terms of storage and processing time as compared to LEACH.

Federated Learning (FL, or Collaborative Learning (CL)) has surely gained a reputation for not only building Machine Learning (ML) models that rely on distributed datasets, but also for starting to play a key role in security and privacy solutions to protect sensitive data and information from a variety of ML-related attacks. This made it an ideal choice for emerging networks such as Internet of Things (IoT) systems, especially with its state-of-the-art algorithms that focus on their practical use over IoT networks, despite the presence of resource-constrained devices. However, the heterogeneous nature of the current devices and models in complex IoT networks has seriously hindered the FL training process's ability to perform well. Thus, rendering it almost unsuitable for direct deployment over IoT networks despite ongoing efforts to tackle this issue and overcome this challenging obstacle. As a result, the main characteristics of FL in the IoT from both security and privacy aspects are presented in this study. We broaden our research to investigate and analyze cutting-edge FL algorithms, models, and protocols, with a focus on their efficacy and practical application across IoT networks and systems alike. This is followed by a comparative analysis of the recently available protection solutions for FL that can be based on cryptographic and non-cryptographic solutions over heterogeneous, dynamic IoT networks. Moreover, the proposed work provides a list of suggestions and recommendations that can be applied to enhance the effectiveness of the adoption of FL and to achieve higher robustness against attacks, especially in heterogeneous dynamic IoT networks and in the presence of resource-constrained devices.

The developers and users of Internet of Things (IoT) systems and devices bear a collective obligation to ensure that IoT systems and devices do not make the Internet and other users vulnerable to any potential harm. This chapter proposes a novel network secure framework that can be adapted to the future IoT and finally gives details for the security verification system design. This framework should focus on enhancing the efficacy, dependability, accountability, and regulation in the overall structure that checks the security issues. The chapter discusses the structuring of a security authentication system in the field of IoT. It presents the factors that pose a threat to the IoT security. These include Unprotected Network services, Cloud, mobile and an absence of security configuration. The chapter illustrates the essential components of IoT security and lists the potential challenges in terms of IoT security. It also discusses the concepts representing the risks that increase the probability of intrusions in the IoT devices.

The environmental impacts that are associated with the interconnected sensor devices in the Internet of Things (IoT) network are minimized with the utilization of green communication. This is because, when the IoT devices tend to grow, the IoT system becomes more prone to issues regarding the utilization of resources, sustainability, and energy consumption within the communication system. Tuning the communication protocols, lowering the IoT device’s energy usage rate, and establishing an effective technique for the transmission of data are performed. This is achieved by the green communication technique, which aids in the establishment of significant energy savings in the IoT network. For devices with limited power sources, the green communication technology offers an extended battery life. Despite the enormous potential of IoT technology, a number of obstacles need to be overcome, including those related to load balancing, security, storage, privacy, energy management, and device heterogeneity. In response to the challenges posed by conventional models, an innovative solution for the selection of Cluster Head (CH) using a hybrid approach is devised here. The energy consumption of sensor nodes is influenced by various factors, such asCH load, temperature, distance, residual energy, the number of alive nodes, delay, and so on. To address these challenges in IoT networks, a hybrid approach that combines the Squid Game Optimizer (SGO) and the Artificial Gorilla Troops Optimizer (AGTO) for the optimal selection of CH is suggested. The implemented approach is named Hybrid Squid Game with Artificial Gorilla Troops Optimizer (HSG-AGTO). This approach aims to optimize the above-mentioned factors and overcome energy consumption challenges in IoT networks. The effectiveness of the model is validated, and the results demonstrate the superior performance of the suggested model.

With the rapid growth of digital data and inter-connected devices, the integration of blockchain and Internet of Things technology offers a promising chance to revolutionize data management approaches. With today's global connectivity, the management and securing of ever-increasing amounts the amount of data produced by Internet of Things (IoT) devices is becoming an increasing concern. This study investigates a new solution to address such a concern by integrating blockchain technology into IoT systems. The aim is to develop a strong framework that takes advantage of the decentralized and secure character of blockchain and the wall-to-wall connectivity offered by the IoT. This integration is intended to address important needs such as data integrity, The integration aims to tackle crucial issues like data integrity, privacy, and unauthorized access. With the connectivity and sensing capabilities of the IoT, along with the blockchain technology of decentralized consensus and immutable ledgers, businesses are able to develop secure systems to retrieve, transfer, and store such data. In the experiments conducted on the Ethereum blockchain platform, it has been found that the blockchain and IoT technology solution not only secures data but also facilitates information management. The experiment results demonstrate that the proposed solution is able to protect against tampering, increase the transparency of transactions, and to minimize the chances of unauthorized access. The framework is scalable and highly adaptable, and it is therefore a promising solution for various applications. This framework employs an innovative approach to ensure secure and efficient data management.

The quick advancement of Internet of Things (IoT) emphasizes the significance of cryptography and blockchain ensuring the security of sensitive data and connected devices. Blockchain technology and encryption play key roles in ensuring the security of the expansive IoT network. Blockchain offers decentralized trust, immutability, and transparency to IoT networks and transactions, while encryption serves to protect IoT data from unauthorized access. It is a novel approach for integrating cryptography and blockchain into IoT System, cryptography and blockchain stand out as robust technologies that enhance the security of IoT systems. The implementation of an integrated architecture, along with a strategic integration approach, further strengthens the security measures. This methodology proves valuable for managing and validating digital transactions on decentralized, immutable networks. This work also explores the potential significance of integrating cryptography and blockchain into IoT System, this functions and applications in enhancing IoT security. This methodology introduces encryption techniques tailored for resource-constrained IoT devices, which are essential for ensuring end-to-end security.

The rapid growth of Internet of Things (IoT) devices, including smartwatches, home assistants, and connected appliances, has brought significant convenience to daily life, but it has also introduced serious security challenges. These devices often transmit sensitive data, making them vulnerable to theft, misuse, and unauthorized access. Current security measures are insufficient to address the complex and evolving nature of IoT systems, leaving many of them exposed to potential breaches and cyberattacks. This review explores recent developments in IoT security, focusing on how advanced technologies, such as machine learning, can be utilized to enhance the protection of IoT systems. The main objective of this paper is to examine potential solutions to the security problems that arise in IoT environments. It includes a thorough analysis of recent research and technological innovations in the field, with a particular emphasis on how different security methods are applied across IoT systems. By identifying the most common security vulnerabilities and outlining their impact on IoT networks, the review suggests improved methods to safeguard IoT data and ensure privacy. The findings aim to support researchers, developers, and businesses in designing more secure IoT solutions, and contribute to the establishment of stronger data protection policies. Ultimately, the review serves as a resource for those seeking to enhance the security of IoT devices and systems in an increasingly interconnected world.

Internet of Things (IoT) networks are being designed to connect billions of sensors/devices for an application-specific environment to provide services and carry out special tasks. It enables the virtual connection of the real-world physical devices with Internet cyberspace. While, there is an exponentially growing trend in the use of IoT technology for diverse applications such as smart agriculture, smart industries, smart hospitals, smart homes, etc., there are pressing design issues which affect the IoT network performance in the long run, for example, replacement of batteries for increasing the lifetime of devices to maintain the network longevity. To tackle this challenge, various wireless powered-based energy transfer paradigms were proposed over time. Backscatter communication (BackCom) is likewise one of the energy-efficient solutions proposed, which can satisfy the stringent green communication aspects of IoT networks. To achieve massive connectivity in BackCom-enabled IoT network, selecting a suitable multiple access scheme is also a crucial part. Most multiple access schemes proposed for BackCom systems are based on frequency or timeslots sharing techniques. Multiple access techniques proposed for BackCom IoT system can reap the gain of non-orthogonal multiple access (NOMA) principle. In NOMA, multiple end nodes (ENs) of sensors network is served in the same resource block, thereby, increasing the spectrum efficiency of BackCom IoT system. In this chapter, we discuss the prospects of backscatter technique with application of NOMA for green communication in IoT network.

The Internet of Things (IoT) is becoming an indispensable part of the actual Internet and continues to extend deeper into the daily lives of people, offering distributed and critical services. Mobile agents are widely used in the context of IoT and due to the possibility of transmitting their execution status from one device to another in an IoT network, they offer many advantages such as reducing network load, encapsulating protocols, exceeding network latency, etc. Also, Blockchain Technology is growing rapidly allowing for the addition of an approved security layer in many areas. Security issues related to mobile agent migration can be resolved with the use of Blockchain. This paper aims to demonstrate how Blockchain Technology can be used to secure mobile agents in the context of the IoT using Ethereum and a Smart Contract. The transactions within the Blockchain are used to detect the malevolent mobile agents that could infiltrate the IoT systems. The proposed model aims to provide a secure migration of mobile agents to ensure security and protect the IoT applications against malevolent agents. The case of a smart home with multiple applications is applied to verify the proposed solution. The model presented in this paper could be extended to a wider selection of IoT systems outside of the smart home.

The convergence of the Internet of Things (IoT) and Artificial Intelligence (AI) is significantly transforming the landscape of future networking. The Internet of Things (IoT) is a technological paradigm that encompasses embedded systems, wireless sensors, and automation, facilitating the integration of various applications ranging from smart homes to wearable devices. In addition, the advent of artificial intelligence (AI) amplifies this influence by providing data-driven analytics, optimising processes, and presenting novel opportunities for growth. Nevertheless, the widespread adoption of devices within Internet of Things (IoT) networks gives rise to apprehensions regarding increased energy consumption. In order to ensure the longevity of network operations, it is imperative to employ energy-efficient protocols for sensor nodes that possess limited power resources. One example of a protocol that demonstrates this concept is the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. This protocol effectively divides networks into clusters and dynamically adjusts the cluster heads to optimise the transmission of data to the base stations. Our study enhances the LEACH protocol by incorporating digital twin simulation, thereby enhancing the efficiency of IoT systems. Virtual network models and AI analytics are employed to assess energy consumption and performance. Cache nodes play a crucial role within this framework as they collect data from cluster heads in order to transmit it to the base station. By leveraging artificial intelligence (AI) and simulation techniques, we are able to improve the energy efficiency and reliability of the Internet of Things (IoT) systems. The findings indicate a significant reduction of 83% in non-functioning nodes and a notable increase of 1.66 times in energy levels of nodes compared to conventional approaches. This study highlights a potential direction for energy-efficient, AI-enhanced Internet of Things (IoT) networking through the utilisation of the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol.

MULTI-BLOCK: A novel ML-based intrusion detection framework for SDN-enabled IoT networks using new pyramidal structure

The rapid expansion of Internet of Things (IoT) networks has brought significant challenges, particularly in optimizing energy consumption, maintaining security, and managing dynamic network conditions. Many existing artificial intelligence (AI)-based solutions, while effective, often demand substantial computational resources, making them unsuitable for edge environments with limited energy and processing capabilities. This study introduces an innovative lightweight AI framework specifically designed to enhance both security and energy efficiency in IoT networks operating at the edge. By combining ensemble learning techniques with attention-based neural networks, the framework enables real-time anomaly detection and optimized resource allocation across dynamic IoT systems. Analytical modeling and simulation results indicate that the proposed approach achieves a 30% reduction in energy usage and a 25% improvement in anomaly detection accuracy compared to existing methods. Additionally, its scalable architecture ensures broad applicability across smart cities, critical infrastructure monitoring, and healthcare IoT systems. By addressing these key gaps, this research contributes to advancing the practical deployment of IoT networks in diverse real-world applications.

The blockchain network architecture is a promising technology for constructing highly secure Internet of Things (IoT) networks. IoT networks typically consist of many sensors and actuators. Blockchain network technology can be applied to secure control robots in smart factories or for reliable drone delivery in smart cities. The distributed ledger and data block validation across blockchain networks guarantee the ultimate data security. However, the current blockchain technology is restricted in terms of its overall deployment across IoT networks. A general permissionless blockchain technology typically targets high-performance network nodes with sufficient computing power and memory space. A blockchain node with less computing power and memory, such as an IoT sensor or actuator, cannot employ blockchain technology as a fully functional node. A lightweight blockchain provides practical blockchain availability to IoT networks. We propose an operational advance to develop a lightweight blockchain for IoT networks. The opportunistic block validation optimizes the block validation process. It measures the network vulnerability based on the node reputation, block validation degree, number of fraudulent messages, and network stability. The reinforcement learning mechanism employed in the opportunistic block validation determines whether block validation is to be performed. Two separately developed reinforcement learning methods can increase the processing performance while maintain the transaction data integrity. In addition, the proposed blockchain technology is easily implementable because it adopts a Hyperledger development environment. Directly embedding the proposed blockchain middleware platform in small computing devices proves the practicability of the proposed block validation mechanism.

Internet of things (IoT) can be described as a platform where physical objects are connected by the Internet. This is the technology that is outside the realm of traditional desktop realm. It is now-a-days used in broader terms and with the enhancement and advancement in technology, the security of IoT becomes the field of ample interest. Confidentiality, authentication, access control and integrity have become the essential features of IoT that need to be fulfilled and taken care of. To secure the data of being transmitted, it requires a system that is secure. Safety has become the fundamental issue in the field of IoT. The IoT is changing into progressively necessary for traffic observance, medical treatment and different industrial applications. With the continual development of the IoT, more and more “things” are going to be ready to access to the IoT. Within the next future, IoT systems are going to be “ubiquitous” and “pervasive”, transportation many edges to grouping and additionally several problems regarding ability, security and privacy. Consequently, security and privacy problems became an excellent challenge within the IoT topic. IoT can drive the expansion of particular cyber threats. Capturing security and privacy necessities within the early stages of system development is crucial for making adequate public confidence to facilitate the adaption of novel systems like the IoT. However, security and privacy necessities are typically not handled properly because of their extensive variety of facts and aspects that create them troublesome to formulate.