Integrating AI into blockchain consensus, such as Proof-of-Learning and Proof of Useful Work, necessitates AI enablers. However, current consensus protocols cannot ensure AI enabler quality, crucial for AI-powered distributed blockchain and federated learning. Traditional consensus middleware between network and application layers proves inadequate for AI-focused blockchain and federated learning. Thus, an AI-driven application-level consensus with quality-assured enablers is imperative. We propose Proof-of-INtelligence (PIN), an application-level consensus for AI-based blockchain and federated learning, ensuring AI enabler quality. To the best of our knowledge, PIN pioneers the first AI-centric application-level consensus for distributed environments. Employing enablers like accuracy and training quality, PIN is showcased in the federated learning setup “PIN in BlOckchAin-based fedeRateD learning (PIN-BOARD),” the first AI-specific consensus application in blockchain-based federated learning. Both PIN and PIN-BOARD are the highlights of our contributions to the presented work and emphasize the novelty. PIN is the first AI-centric application-level consensus for blockchain and pioneers decentralized AI assurance; PIN addresses the limitations of existing consensus protocols and advances blockchain-based federated learning through the novel framework called PIN-BOARD. Experimental evaluation involves PIN’s accuracy, confirmation time, and a new AI-assurance factor metric. PIN-BOARD’s assessment includes testing accuracy and reward accuracy. A thorough security analysis ensures the strength of PIN and PIN-BOARD. The comparative evaluation highlights PIN’s 20% throughput enhancement and efficient artificial index. PIN-BOARD reduces epochs by 28.5% for peak federated learning accuracy as compared to existing federated models. Thus, PIN emerges as an efficient AI-driven application-level consensus with AI assurance.

Access control is an important security parameter in industrial networks; a mismanaged access control system leads to security breaches. The existing security solutions significantly consider the access control methods in the Industrial Internet of Things (IIoT); however, falsified identity can bypass the secure access control system. Thus, a centralized access control method leads to risks for data security. We are the first to address the risk factors of granted access in an industrial environment and present a risk-adaptive access control framework for IIoTs. Our proposed solution framework uses blockchain to provide secure decentralized access control in the industrial environment with privacy-preserved multi-party data sharing. We name our framework “Adaptive Access controL for Multi-party data cOmputation in iNdustrial Decentralization (AALMOND)”. AALMOND uses lightweight cryptographic operations to reduce the complexity of the execution and loosen up the tight bounds on resource-constrained industrial devices. Further, the risk-adaptive access control in AALMOND provides a better security analysis of the multi-party sharing data. Our framework uses role-based, attribute-based, and organization-based access controls to map the assets for risk calculation. We put all the required policies in a smart contract for the ease of multi-party data sharing to obtain a transparent access control execution more suitably. We also pioneer in the calculation of the risk adaptivity of AALMOND considering the NIST recommendations of operation risk, security risk, and heuristic risk. We measure the performance of AALMOND with state-of-the-art frameworks based on throughput, latency, complexity analysis, and risk adaptivity factors. We find that AALMOND is efficient for IIoTs as it shows 24% reduced latency and 20% better throughput as compared to the other existing models.

The energy conservative extension of the Internet of Things (IoT), green IoT, is a revolutionary approach in connecting people, processes, and things in an energy-efficient way. The existing research works in the domain of green IoT forbid the use of decentralized management (e.g., blockchain) of data due to its intrinsic disadvantages of block mining, transaction incentives, and less throughput. However, the advantages of blockchains urge the development of new decentralized strategies utilizing the architecture of green IoT. The infancy stage of the conjunction between green IoT and blockchains, and the need of decentralization in energy management in green IoT motivate us for the present research. In this article, we address the problems of decentralization, energy conservation, and privacy simultaneously. We introduce the first blockchain-based privacy-preserving framework for green IoT. We name this framework “Blockchain-based energy-efficient and privacy-preserving data management scheme for GREEN-iot (BENIGREEN)” for smart cities. BENIGREEN uses weight metrics for energy-efficient cluster heads (CHs) selection. The use of weight metrics is a novel contribution in the field of green IoT. Furthermore, we integrate a decentralized blockchain framework with an authentication scheme for secure transmission among base station (BS) and sensor nodes by employing registration, certification, and revocation phases. Consequently, BS allocates the collected information from CHs to decentralized blockchain and cloud storage. The BS eliminates all malicious nodes from the network by employing a certificate revocation process. We execute thorough experiments in terms of the operation time, throughput, average energy consumption, and computational latency. The comparative analysis with the state-of-the-art schemes show that BENIGREEN is efficient for IoT paradigm.

Internet-of-Thing (IoT) faces various security attacks. Different solutions exist to mitigate the intrusion problems. However, the existing solutions lack behind in dealing with heterogeneity of attack sources and features. The future anticipated demand of devices’ connections also urge the need of new solutions addressing the concerns of time consumption and complexity. In this article, we show a novel solution for the intrusion detection in IoT framework. We configure the intrusion detection in the edge computing layer so that the effect of the attack is not propagated to the clouds. Our solution uses cerebellar model articulation controller with kernel map. This combination is very new in the direction of intrusion detection; hence, it emphasizes the novelty of our proposed intrusion detection solution. We name our solution asCerebellar Model Articulation Controller based Intrusion Detection System (CMACIDS). Additionally, we use spline fitting to the kernel mapping for the model fit; this adds on another novel contribution to CMACIDS. The results obtained with our detection system are compared with the state-of-the-art solutions in terms of complexity, false alarms, and precision of detection. The analysis of the comparative study proves the efficiency of the solution and makes CMACIDS suitable for IoT paradigm.

The decentralization and transparency have provided wide acceptance of blockchain technology in various sectors through numerous applications. The claimed security services by blockchain have been proved using various cryptographic techniques, mainly public key infrastructure and digital signatures. However, the use of generic cryptographic primitives using large prime numbers or elliptic curves with logarithms is going to be an issue with quantum computers as those techniques are vulnerable in post-quantum era. Therefore, the paradigm shift from pre-quantum to the post-quantum era has necessitated new cryptographic developments which are robust against quantum attacks and applicable in blockchain for post-quantum decentralization. Therefore, we have presented a solution for post-quantum decentralization in the blockchain. It uses lattices with polynomials for identity-based encryption (IBE) and aggregate signatures for the consensus to ensure efficiency and suitability in post-quantum blockchain applications. We experiment the proposed approach based on delay, throughput, energy consumption and complexity. The comparative results prove that the presented work is efficient.

The pandemic/epidemic of COVID-19 has affected people worldwide. A huge number of lives succumbed to death due to the sudden outbreak of this corona virus infection. The specified symptoms of COVID-19 detection are very common like normal flu; asymptomatic version of COVID-19 has become a critical issue. Therefore, as a precautionary measurement, the oxygen level needs to be monitored by every individual if no other critical condition is found. It is not the only parameter for COVID-19 detection but, as per the suggestions by different medical organizations such as the World Health Organization, it is better to use oximeter to monitor the oxygen level in probable patients as a precaution. People are using the oximeters personally; however, not having any clue or guidance regarding the measurements obtained. Therefore, in this article, we have shown a framework of oxygen level monitoring and severity calculation and probabilistic decision of being a COVID-19 patient. This framework is also able to maintain the privacy of patient information and uses probabilistic classification to measure the severity. Results are measured based on latency of blockchain creation and overall response, throughput, detection, and severity accuracy. The analysis finds the solution efficient and significant in the Internet of Things framework for the present health hazard in our world.

The integration between blockchains, Internet-of-Thing (IoT), and smart contracts is an emerging and promising technology. The advantages of this technology have raised the importance of Industrial Internet-of-Thing (IIoT) and have paved the pathway for “Industry 4.0.” Surprisingly, access control has received less attention in IIoTs. Though there are some solutions coming forward to use blockchains for IIoT to enable secure and resilient access control management, the challenge is to satisfy the low-latency requirements of IIoTs for validating and adding the blocks to the chain. Besides, role-based and rule-based access controls in the existing systems can be forged without organizational access controls and compliance. Therefore, we address these problems in this article. In the present work, we proposeDHACS, aDecentralized Hybrid Access Control for Smart contract, for IIoTs. DHACS aims to provide transparency, reliability, and robustness to the existing access control mechanism in IIoTs. The framework is based on blockchain feasibilities that contribute to an interconnected hybrid access control through smart contract provision. It is a novel idea in the domain of IIoTs. We use three access control strategies, role-based, rule-based, and organization-based, to develop a hybrid approach for smart contract in DHACS. The operational transactions along with their access controls are accounted and blocks are made by the transaction pooler and block creator. We use a private blockchain environment; however, it can be extended to a public blockchain or consortium blockchain for geographical distributed dependency. We compare DHACS with three existing approaches in recent time. We measure the performance in terms of computational costs, storage complexity, and energy consumption. DHACS outperforms the others approaches and is considered to be efficient for IIoT applications with more than 30% better efficiency in access control management. To the best of our knowledge, DHACS is the first attempt to use decentralized blockchains with smart contract for hybrid access control in IIoTs.