The Internet of Medical Things (IoMT) has become a pivotal aspect of IoT applications, playing a crucial role in cutting down healthcare expenses, enhancing access to clinical services, and refining operational efficiency within the healthcare domain. An early detection of neurological brain disorders continues to present a formidable challenge. In response, our research endeavors are directed towards the IoMT-based system used for the real-time diagnosis of neurological disorders. In this paper, IoMT-based real-time diagnosis model is developed for neurological brain disorders using systematic deep learning and electroencephalogram (EEG) signal (RT-NeuroDDSM). We first introduce the time domain, channel and spatial attention network (TCSNet) for feature extraction which extracts the high-level time series, channel and spatial features, respectively. TCSNet aims to learn more valuable features from the input data to achieve good classification results. Furthermore, in order to maximize features, we create the modified normative fish swarm (MNFS) feature selection algorithm. Next, the detection of various neurological brain problems, including neuro-typical, epilepsy, and autism spectrum disorder (ASD), is accomplished by applying the hinging hyperplane neural network (HHNN). To verify the performance, we used the publicly accessible EEG datasets from University of Bonn-Germany, CHB-MIT EEG repository, and King Abdul-Aziz University. The RT-NeuroDDSM has an overall classification accuracy of 99.956%, making it 5.471% more in efficiency compared to the existing state-of-the-art model.

As the acceptance of Internet of Things (IoT) systems quickens, guaranteeing their sustainability and reliability poses an important challenge. Faults in IoT systems can result in resource inefficiency, high energy consumption, reduced security, and operational downtime, obstructing sustainability goals. Thus, blockchain (BC) technology, known for its decentralized and distributed characteristics, can offer significant solutions in IoT networks. BC technology provides several benefits, such as traceability, immutability, confidentiality, tamper proofing, data integrity, and privacy, without utilizing a third party. Recently, several consensus algorithms, including ripple, proof of stake (PoS), proof of work (PoW), and practical Byzantine fault tolerance (PBFT), have been developed to enhance BC efficiency. Combining fault detection algorithms and BC technology can result in a more reliable and secure IoT environment. Thus, this study presents a sustainable BC-Driven Edge Verification with a Consensus Approach-enabled Optimal Deep Learning (BCEVCA-ODL) approach for fault recognition in sustainable IoT environments. The proposed BCEVCA-ODL technique incorporates the merits of the BC, IoT, and DL techniques to enhance IoT networks’ security, trustworthiness, and efficacy. IoT devices have a substantial level of decentralized decision-making capacity in BC technology to achieve a consensus on the accomplishment of intrablock transactions. A stacked sparse autoencoder (SSAE) model is employed to detect faults in IoT networks. Lastly, the Piranha Foraging Optimization Algorithm (PFOA) approach is used for optimum hyperparameter tuning of the SSAE approach, which assists in enhancing the fault recognition rate. A wide range of simulations was accomplished to highlight the efficacy of the BCEVCA-ODL technique. The BCEVCA-ODL technique achieved a superior FDA value of 100% at a fault probability of 0.00, outperforming the other evaluated methods. The proposed work highlights the significance of embedding sustainability into IoT systems, underlining how advanced fault detection can provide environmental and operational benefits. The experimental outcomes pave the way for greener IoT technologies that support global sustainability initiatives.

The development of blockchain technology has facilitated its application in many areas of life due to its ability to enable decentralization in processing and storage. To support blockchain's application in various fields, its algorithms, and consensus protocols should be developed. Nowadays, there are many consensus protocols, each of which can be applied to the appropriate field, considering the cost of each protocol. Users should be motivated to use blockchain and participate in its processes. Considering the processing, communication, and storage costs, effective reward mechanisms are necessary to compensate users and encourage participation. Therefore, this study investigates and reviews a variety of blockchain consensus protocols and corresponding reward strategies. The study analyses twenty-two consensus protocols, explaining how they work, and their advantages and disadvantages. In addition, this paper discusses the types of reward strategies, their sources, limitations, and future. According to the nature of consensus protocols, they are classified into seven categories, which are computational-based, wealth-based, behavioral-based, fairness-based, fault-tolerance-based, leader-based, and transactional-based. The study aims to provide information on the future paths of blockchain consensus mechanisms, promoting a more profound understanding of their capabilities and constraints in many situations.