The optimization techniques usually work with the maximization or minimization of the problem to obtain the local loci or cumulative global loci. Two-dimensional bio-inspired optimization techniques face convexing problems towards a global solution and use an increased number of iterations. Besides, the duality principle considers the dual optimization aspects of the problems leading to a large duality gap of uncertain deviations and optimization errors between any prime solution and its dual solution. Moreover, several problems exist where one objective function requires maximization and another objective function requires minimization using the same set of parameters and some chaining of the feedback process. In such cases, we generally use two different optimization problems as per the best suit to the problem environment and obtain the different sub-solutions of the individual problems. This increases the complexity of the system and often deviates from the original optimal solution. We address these problems of dual optimization in our present work. In this paper, we introduce the first optimization duo model for computing services. To be specific, our proposed model is the first optimization model that works in a dual combined mode with maximization and minimization simultaneously to obtain a global optimum value or loci. We call our model GREen PHotosynthesis and Respiratory-based Optimization (GREPHRO). GREPHRO is primarily motivated by the observation of plants’ photosynthesis and respiratory processes, which work on the same set of environmental variables and have a chaining process. Further, our proposed nature-inspired GREPHRO can value the global optima or infimum point considering a single objective function serving as maximization and minimization combined. GREPHRO uses the Lagrange dual principle and non-linear parameters to obtain a linear solution for the infimum optima. We use a set of experiments on the GREPHRO model in the domain of Wireless Sensor Network (WSN)-based Internet of Thing (IoT) to derive the use case of our proposed work. The experimental results and the comparative analysis with the two of our previous works show that GREPHRO takes fewer iterations with more stability of the optimum solution. Moreover, the computational and memory complexities are also less. Therefore, GREPHRO is efficient and suitable for two-dimensional optimization problems in resource-constrained environments for IoTs.

Access control (AC) is an important security parameter in industrial networks; a mismanaged AC system leads to security breaches. The existing security solutions significantly consider the AC methods in the Industrial Internet of Things (IIoT); however, falsified identity can bypass the secure AC system. Thus, a centralized AC 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 AC framework for IIoT. Our proposed solution framework uses blockchain to provide secure decentralized AC in the industrial environment with privacy-preserved multiparty data sharing. We name our framework 'adaptive AC for multiparty 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 AC in AALMOND provides a better security analysis of the multiparty sharing data. Our framework uses role-based, attribute-based, and organization-based ACs to map the assets for risk calculation. We put all the required policies in a smart contract for the ease of multiparty data sharing to obtain a transparent AC execution more suitably. We also pioneer in the calculation of the risk adaptivity of AALMOND considering the National Institute of Standards and Technology 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 IIoT 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.

The progress of sensor devices, digital communication, and computing techniques enhances the functionalities of e-healthcare, where the medical facilities are manageable based on the Internet. Now a days, e-healthcare not only helps in disease diagnosis, or tele-medicine, but also implies to general medical scope and medical resource management. Thus, Internet-of-Medical Thing (IoMT) becomes an umbrella term to connect e-healthcare, tele-medicine, and medical supply chain management. In the recent past, the outbreak of COVID-19 pandemic has shown the loopholes in the existing medical systems such as the mismanagement of medical resources and the unavailability of the basic requirements for patients. The medical infrastructure urges for the new frameworks to handle accountability and transparent governance in a medical emergency situation. This motivates us to address medical resource accountability in e-healthcare. In the present work, we introduce the first resource accountability framework to balance the demand–supply of medical facilities in an e-healthcare and IoMT ecosystem. Our solution is based on blockchain and we call it bloCkchained framework for resOUrce accouNTability (COUNT). We use a customized Proof of Vote (PoV) for consensus in COUNT. We call this consensus COUNT-PoV, which is another direction of novelty in our solution. The blocks in the proposed COUNT contain the resource requirements and their availability–production–supply status. Multiple stakeholders are involved in the process based on providing pre-attained credentials; thus, COUNT supports consortium blockchain. Existing literature shows a number of studies for blockchain-based frameworks for e-healthcare; however, those works do not address the accountability and transparency issues of medical e-governance and balancing the demand–supply of the medical facilities/resources. Therefore, our proposed COUNT is beneficial for e-healthcare and IoMTs. We use signcryption process to reduce the complexity of the cryptographic processes, which is an add-on to the contribution. We evaluate our proposed framework based on the Hyperledger Caliper benchmark test with latency, throughput, and resource consumption. Additionally, we also analyse the cost of the implementation. The comparative analysis of our consensus with other stake-based consensus protocols on the COUNT framework shows that our consensus, COUNT-PoV is efficient and suitable for the use of e-healthcare and IoMTs. Moreover, being a generic framework, COUNT is helpful for the e-governance of medical facilities, the vaccination process, and COVID passports.

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 as Cerebellar 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 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 propose DHACS, a Decentralized 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.