Internet of Things (IoT) is one of the efflorescing technologies of recent years with splendid real-time applications in the fields of healthcare, agriculture, transportation, industry, and environmental monitoring. In addition to the dominant applications and services of IoT, many challenges exist. As there is a lack of standardization for IoT technologies, the architecture emerged as the foremost challenge. The salient issues in designing an IoT architecture encompass connectivity, data handling, heterogeneity, privacy, scalability, and security. The standard IoT architectures are the ETSI IoT Standard, the ITU-T IoT Reference Model, IoT-A Reference Model, Intel’s IoT Architecture, the Three-Layer Architecture, Middle-Based Architecture, Service-Oriented Architecture, Five-Layer Architecture, and IWF Architecture. In this paper, we have reviewed these architectures and concluded that IWF Architecture is most suitable for the effortless development of IoT applications because of its immediacy and depth of insight in dealing with IoT data. We carried out this review concerning smart healthcare as it is among the major industries that have been leaders and forerunners in IoT technologies. Motivated by this, we designed the novel Smart Healthcare Reference Architecture (SHRA) based on IWF Architecture. Finally, present the significance of smart healthcare during the COVID-19 pandemic. We have synthesized our findings in a systematic way for addressing the research questions on IoT challenges. To the best of our knowledge, our paper is the first to provide an exhaustive investigation on IoT architectural challenges with a use case in a smart healthcare system.

The Internet of Things (IoT) refers to a complex network comprising interconnected devices that transmit their data via the Internet. Due to their open environment, limited computation power, and absence of built-in security, IoT environments are susceptible to various cyberattacks. Denial of service (DDoS) attacks are among the most destructive types of threats. The Multi-vector DDoS attack is a contemporary and formidable form of DDoS wherein the attacker employs a collection of compromised IoT devices as zombies to initiate numerous DDoS attacks against a target server. A Blockchain-based Operational Threat Intelligence framework, OTI-IoT, is proposed in this article to counter multi-vector DDoS attacks in IoT networks. A “Prevent-then-Detect” methodology was utilized to deploy the OTI-IoT framework in two distinct stages. During Phase 1, the consortium Blockchain network validators employ the IPS module, composed of a smart contract for attack prevention and access control, and Proof of Voting consensus, to thwart attacks. Validators are outfitted with deep learning-based IDS instances to detect multi-vector DDoS attacks during Phase 2. Alert messages are generated by the IDS module’s alert generation and propagation smart contract in response to identifying malicious IoT sources. The feedback loop from the IDS module to the IPS module prevents incoming traffic from malicious sources. The proposed OTI framework capabilities are realized as an outcome of combining and storing the outcomes of the IDS and IPS modules on the consortium Blockchain. Each validator maintains a shared ledger containing information regarding threat sources to ensure robust security, transparency, and integrity. The operational execution of OTI-IoT occurs on an individual Ethereum Blockchain. The empirical findings indicate that our proposed framework is most suitable for real-time applications due to its ability to lower attack detection time, decreased block validation time, and higher attack prevention rate.

The Internet of Things (IoT) technology is widely employed in several fields, such as smart cities, intelligent transportation systems, smart power grids, smart homes, and digital healthcare solutions, to enhance the quality of life, promote sustainability, and foster social interactions. The significant challenges related to IoT encompass the legitimacy verification of intelligent devices, handling cryptographic keys for edge devices with limited resources, guaranteeing the confidentiality and integrity of transmitted IoT data, and securely storing that data in the cloud system. The Blockchain system, characterized by its distributed, decentralized, and irreversible ledger, has gained recognition for its efficacy in ensuring security for sensitive smart home applications. This paper presents a comprehensive security solution for smart homes, comprising smart sensor authentication, key management, data confidentiality, data integrity, secure storage, and access control for cloud-based data. A robust and secure end-to-end structure for smart homes is developed in two phases. During the initial stage, a private Blockchain system is established on the fog nodes to facilitate the authentication and access control mechanisms for smart home edge devices. This private Blockchain can distribute and manage keys, enabling it to securely transfer data from intelligent sensors to the cloud environment using fog nodes. A consortium Blockchain is established during the next phase to simulate the cloud service providers. This Blockchain enables the provision of secured storage and data access control within the cloud, enhancing secure cloud storage even in a hostile environment. The novel contribution of this article relies on the integration of private and consortium Blockchain networks with a streamlined collaboration. This amalgamation achieves the efficient and distributed control of multiple Blockchain validators. A secure prototype has been developed to visualize and engage with a smart home environment. This prototype effectively displays data using various graphical and interactive techniques. The performance metrics, including attack resilience, latency and throughput of the network proves the efficiency of proposed framework as compared to state-of-the-art works. In future, we focus on the cross-chain interactions in multi-Blockchain environments and address the existing limitations such as inconsistencies, interoperability issues, and inefficient security measures.

In the day-to-day deployment of the Internet of Things (IoT), IPv6-over-Bluetooth Low Energy (BLE) devices became significant enablers due to their low power, low range, and effortless connectivity. To overcome the latency during BLE device migration, we have proposed a Blockchain-based resilient pairing and bonding of BLE devices using a lightweight authenticated encryption scheme. The bonding information is stored on the local ledger of the intermediate IPv6 over Low power Wireless Personal Area Networks (6LoWPAN) for BLE (6LoWPAN for BLE or 6LoWBLE) over gateway. Whenever the BLE device migrates from one gateway to another, the bonding information is transferred to the local ledger of the current gateway through the global ledger with the Proof-of-Voting consensus algorithm. The resilience of the proposed authentication scheme is analyzed using BAN logic. In addition, we proposed a Man-in-the-Middle (MITM) detection framework using Deep Reinforcement Learning (DRL) due to the escalation of MITM attacks on BLE devices. The DRL-based model is implemented in Python, and its performance is evaluated using the Kitsune Network Attack Dataset. The performance of the overall framework is tested on the Hyperledger Fabric, which results in low latency and a low average pairing time in comparison with existing frameworks.

Distributed Denial of Service (DDoS) attacks in Internet of Things (IoT) networks are the cyber attacks launched by an attacker using a set of compromised IoT devices, called zombies. The infected devices cause the depletion of either bandwidth or resources of the victim server. Detection of DDoS attacks is a crucial defence mechanism to achieve secure IoT applications and services. In this paper, we have developed a lightweight DDoS detection mechanism for IoT networks by employing the feature engineering on network traffic charac-teristics. Entropy of Packet size interval and Expectation of Packet Size are the major parameters for performing the binary classification of incoming IoT traffic into DDoS or legitimate. The experimental results have shown that the proposed mechanism works effectively for different packet rates when compared with state-of-art mechanisms.