Quantum Safe Lattice-Based Single Round Online Collaborative Multi-Signature Scheme for Blockchain-Enabled IoT Applications

A roadmap from classical cryptography to post-quantum resistant cryptography for 5G-enabled IoT: Challenges, opportunities and solutions

Due to the growing interconnection and sensitive nature of IoT applications, this article provides a comprehensive overview of cybersecurity in 5G-enabled Internet of Things (IoT) networks. The review highlights the significant potential of 5G technology while also emphasizing the need for ongoing work to mitigate its weaknesses and fully harness its advantages. In this context, prospective research avenues are crucial to enhance the resilience and security of 5G-enabled systems. The future directions outlined in this document are diverse and seek to tackle significant deficiencies in 5G-IoT cybersecurity research. A primary objective is to align theoretical analyses with practical implementations by examining specific assaults in IoT contexts, thus improving the comprehension of real-world vulnerabilities. Furthermore, as the 5G standard progresses through its implementation, continuous research must remain synchronized with improvements, focusing on components and features that are currently in development. Formulating effective protection and mitigation strategies to address sophisticated threats is essential for safeguarding this integrated environment. Furthermore, enhancing standards and regulatory frameworks is essential to ensure uniform security procedures across various businesses and areas, especially considering the global nature of 5G-IoT applications. Ultimately, investigating sophisticated attack scenarios that leverage the interaction between IoT devices and 5G infrastructure would enhance understanding of vulnerabilities and aid in the development of more resilient security measures. These directives underscore the necessity for ongoing, flexible research to guarantee the robustness and safety of 5G-enabled IoT networks.

As more and more applications are deployed using the Internet of Things (IoT) technologies, the fragmentation of general purpose IoT technologies to target particular sectors with different requirements is becoming necessary. In this paper, we summarize the latest developments of application-specific IoTs (ASIoTs) (a term to conceptualize the development of IoTs targeted toward specific domains, communications mediums, and industry sectors) in eight representative studies (Internet of Battlefield Things (IoBT), Internet of Medical Things (IoMT), Internet of Animal Things (IoAT), Internet of Waste Things (IoWT), Internet of Underwater Things (IoUWT), Internet of Underground Things (IoUGT), Internet of Nano Things (IoNT), and Internet of Mobile Things (IoMobT) such as the Internet of Vehicles). The paper gives contributions to ASIoTs from three perspectives: First, we offer a basic classification taxonomy for ASIoTs and discuss various representative studies and applications which can be found in the literature; Second, we discuss a use case for a biometrics-based ASIoT (termed IoBioT) for illustration and experiments of face-based biometric recognition on IoBioT are also performed; and Third, we give discussions and future directions for ASIoTs. An objective of this paper is to spur researchers and facilitate the development of ASIoTs for the different user-defined domains, communication mediums, and technology constrained platforms.

In recent years, the research in generic Internet of Things (IoT) attracts a lot of practical applications including smart home, smart city, smart grid, industrial Internet, connected healthcare, smart retail, smart supply chain and smart farming. The hierarchical IoT network (HIoTN) is a special kind of the generic IoT network, which is composed of the different nodes, such as the gateway node, cluster head nodes, and sensing nodes organized in a hierarchy. In HIoTN, there is a need, where a user can directly access the real-time data from the sensing nodes for a particular application in generic IoT networking environment. This paper emphasizes on the design of a new secure lightweight three-factor remote user authentication scheme for HIoTNs, called the user authenticated key management protocol (UAKMP). The three factors used in UAKMP are the user smart card, password, and personal biometrics. The security of the scheme is thoroughly analyzed under the formal security in the widely accepted real-or-random model, the informal security as well as the formal security verification using the widely accepted automated validation of Internet security protocols and applications tool. UAKMP offers several functionality features including offline sensing node registration, freely password and biometric update facility, user anonymity, and sensing node anonymity compared to other related existing schemes. In addition, UAKMP is also comparable in computation and communication costs as compared to other existing schemes.

Bitcoin and Ethereum's low transaction per second (TPS) rate has prohibited the adoption of Blockchain in areas such as Internet of Things (IoT) and Internet of Medical Things (IoMT). Despite these constraints, there is considerable research in this area, outlining the benefits of employing Blockchain in IoT applications. Such benefits relate to ensuring that the applications and their data are secure, and integrity is maintained. However, the relatively low TPS rate is still an impediment for the widespread adoption of Blockchain in IoT. This research seeks to explore whether Solana, a new Blockchain technology designed to have a high TPS rate, can overcome this challenge. Can the Solana Blockchain achieve a TPS rate higher than that achieved in other works in the literature? Can a sufficient rate be achieved that could help Blockchain technologies become an enabler in the IoT industry? These are the questions that this research aims to answer. Our experiments conducted on an application coded in Rust on Solana Blockchain demonstrated that it can successfully achieve a 663 TPS rate. Consequently, we conclude that Solana, by overcoming the aforementioned TPS rate issue, could advance the further adoption of Blockchain in IoT context.

Recent emerging technologies are being utilized for the quench of connectivity in real time scenarios. A push is coming to make the information available to humans from the real-time environmental data collected through small sensing devices. Wireless ad-hoc network is a base architecture for Internet of Things (IoT), Unmanned Arial Vehicle (UAV) and drones etc. In this series, IoDTIoDT (Internet of Drone Things) came as the future of drones backend via the Internet of Things, smart vision, cloud computing, enhanced communication, big data, and advanced security approaches. Rapid growth in sensing devices connected to the Internet with intelligence and capabilities also opens the door for attackers because more devices are connected means more chances of security vulnerabilities. Since data authentication is handled only by the central station, which may lead to the chances of device spoofing and false authentication brings less reliability after all. Blockchain (BC) technology is introduced to address such security concerns by eliminating the role of central authority. Blockchain Technology gives decentralized and non-tamperable solutions for the most demanding security service i.e. Authentication. This paper starts with unique characteristics and security challenges in such IOT environment and further covers the authentication process by blockchain with its potential benefits. The multilayer ecosystem is illustrated to fulfill the requirement of the UAV IoT environment, where multiple devices are equally working in a cooperative manner to perform an authorized action. The paper presented a complete study about the integration of blockchain in IoT enabled UAV.

The Internet of Things (IoT) continues to attract attention in the context of computational resource growth. Various disciplines and fields have begun to employ IoT integration technologies in order to enable smart applications. The main difficulty in supporting industrial development in this scenario involves potential risk or malicious activities occurring in the network. However, there are tensions that are difficult to overcome at this stage in the development of IoT technology. In this situation, the future of security architecture development will involve enabling automatic and smart protection systems. Due to the vulnerability of current IoT devices, it is insufficient to ensure system security by implementing only traditional security tools such as encryption and access control. Deep learning and blockchain technology has now become crucial, as it provides distinct and secure approaches to IoT network security. The aim of this survey paper is to elaborate on the application of deep learning and blockchain technology in the IoT to ensure secure utility. We first provide an introduction to the IoT, deep learning, and blockchain technology, as well as a discussion of their respective security features. We then outline the main obstacles and problems of trusted IoT and how blockchain and deep learning may be able to help. Next, we present the future challenges in integrating deep learning and blockchain technology into the IoT. Finally, as a demonstration of the value of blockchain in establishing trust, we provide a comparison between conventional trust management methods and those based on blockchain.

The Internet of Things (IoT) is developing as a novel phenomenon that is applied in the growth of several crucial applications. However, these applications continue to function on a centralized storage structure, which leads to several major problems, such as security, privacy, and a single point of failure. In recent years, blockchain (BC) technology has become a pillar for the progression of IoT-based applications. The BC technique is utilized to resolve the security, privacy, and single point of failure (third-part dependency) issues encountered in IoT applications. Conversely, the distributed denial of service (DDoS) attacks on mining pools revealed the existence of vital fault lines amongst the BC-assisted IoT networks. Therefore, the current study designs a hybrid Harris Hawks with sine cosine and a deep learning-based intrusion detection system (H3SC-DLIDS) for a BC-supported IoT environment. The aim of the presented H3SC-DLIDS approach is to recognize the presence of DDoS attacks in the BC-assisted IoT environment. To enable secure communication in the IoT networks, BC technology is used. The proposed H3SC-DLIDS technique designs a H3SC technique by integrating the concepts of Harris Hawks optimization (HHO) and sine cosine algorithm (SCA) for feature selection. For the intrusion detection process, a long short-term memory auto-encoder (LSTM-AE) model is utilized in this study. Finally, the arithmetic optimization algorithm (AOA) is implemented for hyperparameter tuning of the LSTM-AE technique. The proposed H3SC-DLIDS method was experimentally validated using the BoT-IoT database, and the results indicate the superior performance of the proposed H3SC-DLIDS technique over other existing methods, with a maximum accuracy of 99.05%.

The integration of the Internet of Things (IoT) in retail management and supply chain operations has transformed business processes by enabling real-time data collection, automation, and data-driven decision-making. This study conducts a bibliometric analysis using Scopus-indexed literature and VOSviewer to map research contributions, key themes, and emerging trends at the intersection of IoT and retail management. The analysis identifies inventory management, supply chain transparency, automation, and AI-driven analytics as dominant research areas, with growing interest in blockchain-enhanced security, sustainability, and predictive analytics. Citation and co-authorship analyses highlight key influential studies and international collaboration networks, with India, China, and the United States emerging as major research hubs. Despite advancements, challenges remain in IoT security, interoperability, ethical considerations, and regulatory frameworks, necessitating further research on 5G-enabled IoT, edge computing, and resilient supply chains. This study provides a comprehensive overview of IoT-driven retail research, offering valuable insights for academics, industry professionals, and policymakers to drive innovation and efficiency in the digital retail landscape.

To significantly promote Internet of Things (IoT) development, 5G network is enabled for supporting IoT communications without the limitation of distance and location. This paper investigates the channel allocation problem for IoT uplink communications in the 5G network, with the aim of improving the quality of experience (QoE) of smart objects (SOs). To begin with, we define a mean opinion score (MOS) function of transmission delay to measure QoE of each SO. For the sum-MOS maximization problem, we leverage a game-theoretic learning approach to solve it. Specifically, the original optimization problem is equivalently transformed into a tractable form. Then, we formulate the converted problem as a game-theoretical framework and define a potential function which has a near-optimum as the optimization objective. To optimize the potential function, a distributed channel allocation algorithm is proposed to converge to the best Nash equilibrium solution which is the global optimum of maximizing the potential function. Finally, numerical results verify the effectiveness of the proposed scheme.

The Internet of Things (IoT) is emerging as a new trend mainly employed in developing numerous vital applications. These applications endure on a federal storage framework primarily concerned with multiple issues. Blockchain technology (BC) is one of the supportive methods for developing IoT-based applications. It is employed to solve the problems encountered in IoT applications. The attack Distributed Denial of Service (DDoS) is one of the leading security attacks in IoT systems. Attackers can effortlessly develop the exposures of IoT gadgets and restrain them as fragments of botnets to commence DDoS threats. The IoT devices are said to be resource-constrained with computing resources and restricted memory. As a developing technology, BC holds the possibility of resolving security problems in IoT. This paper proposes the Metaheuristic-Optimized Blockchain Framework for Attack Detection using a Deep Learning Model (MOBCF-ADDLM) method. The main intention of the MOBCF-ADDLM method is to deliver an effective method for detecting DDoS threats in an IoT environment using advanced techniques. The BC technology is initially applied to mitigate DDoS attacks by presenting decentralized security solutions. Furthermore, data preprocessing utilizes the min-max scaling method to convert input data into a beneficial format. Additionally, feature selection (FS) is performed using the Aquila optimizer (AO) technique to recognize the most relevant features from input data. The attack classification process employs the deep belief network (DBN) technique. Finally, the red panda optimizer (RPO) model modifies the hyper-parameter values of the DBN model optimally and results in higher classification performance. A wide range of experiments with the MOBCF-ADDLM approach is performed under the BoT-IoT Binary and Multiclass datasets. The performance validation of the MOBCF-ADDLM approach portrayed a superior accuracy value of 99.22% over existing models.

Driven by the advancements in 5G-enabled Internet of Things (IoT) technologies, the IoT devices have shown an explosive growth trend with massive data generated at the edge of the network. However, IoT systems exhibit inherent vulnerability for diverse attacks, and Advanced Persistent Threat (APT) is one of the most powerful attack models that could lead to a significant privacy leakage of systems. Moreover, recent detection technologies can hardly meet the demands of effective security defense against APTs. To address the above problems, we propose an APT Prediction Method based on Differentially Private Federated Learning (APTPMFL) to predict the probability of subsequent APT attacks occurring in IoT systems. It is the first time to apply a federated learning mechanism for aggregating suspicious activities in the IoT systems, where the APT prediction phase does not need any correlation rules. Moreover, to achieve privacy-preserving property, we further adopt a differentially private data perturbation mechanism to add the Laplacian random noises to the IoT device training data features, so as to achieve the maximum protection of privacy data. We also present a 5G-enabled edge computing-based framework to train and deploy the model, which can alleviate the computing and communication overhead of the typical IoT systems. Our evaluation results show that APTPMFL can efficiently predict subsequent APT behaviors in the IoT system accurately and efficiently.

Ensuring data security and privacy in Internet of Things (IoT) is increasingly critical due to the growing interconnectedness of devices and the sensitivity of the data they handle. This paper presents a novel approach to enhancing data security in IoT through the integration of homomorphic encryption and blockchain technology. We conduct simulations using the Kaggle Smart Home Dataset to evaluate the effectiveness of our proposed methodology on smart home devices and wearable technology. Our approach not only secures data transmission but also guarantees data integrity and privacy through decentralized verification and secure aggregation techniques. Specifically, our evaluation demonstrates an encrypted data transmission rate exceeding 99.5%, a complete absence of unauthorized access instances in the simulated environment, and a verified data integrity rate of over 99.8%. Additionally, our method supports real-time processing and scalability, making it suitable for various IoT applications, including smart contract applications in IoT for privacy and security in supply chain transactions. The study highlights the robustness of combining homomorphic encryption and blockchain to protect sensitive data throughout its lifecycle.

With the continuous increase in the applications of blockchain, the research and academic circles are constantly exploring and providing guidelines for the use of blockchain in new directions. The use of this technology has given new dimension in the Internet of Things (IoT) systems. In this chapter, the authors are providing an outline and structure of blockchain and its various applications in IoT. Blockchain technology helps in building trustless and efficient secure environment in IoT. Therefore, the authors think it is necessary to aim the basic shape of this technology and explain its use and applications in IoT in a concise and comprehensive way. This chapter starts with introduction of blockchain and IoT supported by its working in various applications of IoT. The authors believe that this chapter will provide a basic idea to users in understanding blockchain technology in IoT applications.KeywordsBlockchainBitcoinIoTOrder integrityOwnershipTransaction

This work is an endeavor to give a thought regarding future degrees, potential conceivable outcomes, and issues concerning. The Internet of Things and how it may affect the life of the general public sooner rather than later in terms of developing smart urban regions in India particular and generally in the world. In simple terms, the Internet of Things (IoT) is a system of genuine or virtual articles or things implanted with hardware, sensors, programming projects, and scheduled availability that enables these items to gather and exchange information, thereby providing availability to anything and not just for anyone at anytime, anywhere. As a result of the success of India's 100 Smart Urban Communities initiative, IoT as an idea appears to be quite promising. In India, IoT has a bright future because the government is focusing on, supporting, and experimenting with efforts to improve the infrastructure, as well as organizations concocting innovative products, and businesses are aware of the benefits that the Internet of Things brings to the table. IoT advance could end up being noteworthy on an expansive scale, with further developed research and assets. On the off chance that such advance turns into a reality, the expectations of IoT utilize in India may work out as expected all things considered. The Internet of Things is likely to have a huge impact on our day-to-day lives in cities and will become an integral part of industries such as power, transportation, modern control, retail, utilities administration, medical services, water assets administration, and oil. It has the potential to significantly improve efficiency and our lives. Furthermore, its enormous market potential has piqued the interest of governments, telecom administrators, producers, and industrial clients. The Internet of Things, also known as M2M (which stands for Machines to Mobile, Machine to Machine, Machines to Man, or Man to Machines), cleverly connects people, gadgets, and frameworks. General terms IoT application used to Monitoring and detecting, Plants and Animals, Information providing and utility in the cities. In this chapter discussed various IoT application used to cities build as a smart city.