Integration of Blockchain Technology and Federated Learning in Vehicular (IoT) Networks: A Comprehensive Survey.

The Internet of Things (IoT) revitalizes the world with tremendous capabilities and potential to be utilized in vehicular networks. The Smart Transport Infrastructure (STI) era depends mainly on the IoT. Advanced machine learning (ML) techniques are being used to strengthen the STI smartness further. However, some decisions are very challenging due to the vast number of STI components and big data generated from STIs. Computation cost, communication overheads, and privacy issues are significant concerns for wide-scale ML adoption within STI. These issues can be addressed using Federated Learning (FL) and blockchain. FL can be used to address the issues of privacy preservation and handling big data generated in STI management and control. Blockchain is a distributed ledger that can store data while providing trust and integrity assurance. Blockchain can be a solution to data integrity and can add more security to the STI. This survey initially explores the vehicular network and STI in detail and sheds light on the blockchain and FL with real-world implementations. Then, FL and blockchain applications in the Vehicular Ad Hoc Network (VANET) environment from security and privacy perspectives are discussed in detail. In the end, the paper focuses on the current research challenges and future research directions related to integrating FL and blockchain for vehicular networks.

Diagnosis of Chronic Ischemic Heart Disease Using Machine Learning Techniques.

Ischemic heart disease (IHD) causes discomfort or irritation in the chest. According to the World Health Organization, coronary heart disease is the major cause of mortality in Pakistan. Accurate model with the highest precision is necessary to avoid fatalities. Previously several models are tried with different attributes to enhance the detection accuracy but failed to do so. In this research study, an artificial approach to categorize the current stage of heart disease is carried out. Our model predicts a precise diagnosis of chronic diseases. The system is trained using a training dataset and then tested using a test dataset. Machine learning methods such as LR, NB, and RF are applied to forecast the development of a disease. Experimental outcomes of this research study have proven that our strategy has excelled other procedures with maximum accuracy of 99 percent for RF, 97 percent for NB, and 98 percent for LR. With such high accuracy, the number of deaths per year of ischemic heart disease will be slightly decreased.

An Intelligent Multi-Floor Navigational System Based on Speech, Facial Recognition and Voice Broadcasting Using Internet of Things.

Modern technologies such as the Internet of Things (IoT) and physical systems used as navigation systems play an important role in locating a specific location in an unfamiliar environment. Due to recent technological developments, users can now incorporate these systems into mobile devices, which has a positive impact on the acceptance of navigational systems and the number of users who use them. The system that is used to find a specific location within a building is known as an indoor navigation system. In this study, we present a novel approach to adaptable and changeable multistory navigation systems that can be implemented in different environments such as libraries, grocery stores, shopping malls, and official buildings using facial and speech recognition with the help of voice broadcasting. We chose a library building for the experiment to help registered users find a specific book on different building floors. In the proposed system, to help the users, robots are placed on each floor of the building, communicating with each other, and with the person who needs navigational help. The proposed system uses an Android platform that consists of two separate applications: one for administration to add or remove settings and data, which in turn builds an environment map, while the second application is deployed on robots that interact with the users. The developed system was tested using two methods, namely system evaluation, and user evaluation. The evaluation of the system is based on the results of voice and face recognition by the user, and the model's performance relies on accuracy values obtained by testing out various values for the neural network parameters. The evaluation method adopted by the proposed system achieved an accuracy of 97.92% and 97.88% for both of the tasks. The user evaluation method using the developed Android applications was tested on multi-story libraries, and the results were obtained by gathering responses from users who interacted with the applications for navigation, such as to find a specific book. Almost all the users find it useful to have robots placed on each floor of the building for giving specific directions with automatic recognition and recall of what a person is searching for. The evaluation results show that the proposed system can be implemented in different environments, which shows its effectiveness.

Monitoring System-Based Flying IoT in Public Health and Sports Using Ant-Enabled Energy-Aware Routing.

In recent decades, the Internet of flying networks has made significant progress. Several aerial vehicles communicate with one another to form flying ad hoc networks. Unmanned aerial vehicles perform a wide range of tasks that make life easier for humans. However, due to the high frequency of mobile flying vehicles, network problems such as packet loss, latency, and perhaps disrupted channel links arise, affecting data delivery. The use of UAV-enabled IoT in sports has changed the dynamics of tracking and working on player safety. WBAN can be merged with aerial vehicles to collect data regarding health and transfer it to a base station. Furthermore, the unbalanced energy usage of flying things will result in earlier mission failure and a rapid decline in network lifespan. This study describes the use of each UAV's residual energy level to ensure a high level of safety using an ant-based routing technique called AntHocNet. In health care, the use of IoT-assisted aerial vehicles would increase operational performance, surveillance, and automation optimization to provide a smart application of flying IoT. Apart from that, aerial vehicles can be used in remote communication for treatment, medical equipment distribution, and telementoring. While comparing routing algorithms, simulation findings indicate that the proposed ant-based routing protocol is optimal.

The mechanism of neurofeedback training for treatment of central neuropathic pain in paraplegia: a pilot study.

BACKGROUND: Central neuropathic pain has a prevalence of 40% in patients with spinal cord injury. Electroencephalography (EEG) studies showed that this type of pain has identifiable signatures, that could potentially be targeted by a neuromodulation therapy. The aim of the study was to investigate the putative mechanism of neurofeedback training on central neuropathic pain and its underlying brain signatures in patients with chronic paraplegia. METHODS: Patients' EEG activity was modulated from the sensory-motor cortex, electrode location C3/Cz/C4/P4 in up to 40 training sessions Results. Six out of seven patients reported immediate reduction of pain during neurofeedback training. Best results were achieved with suppressing Ɵ and higher ß (20-30 Hz) power and reinforcing α power at C4. Four patients reported clinically significant long-term reduction of pain (>30%) which lasted at least a month beyond the therapy. EEG during neurofeedback revealed a wide spread modulation of power in all three frequency bands accompanied with changes in the coherence most notable in the beta band. The standardized low resolution electromagnetic tomography analysis of EEG before and after neurofeedback therapy showed the statistically significant reduction of power in beta frequency band in all tested patients. Areas with reduced power included the Dorsolateral Prefrontal Cortex, the Anterior Cingulate Cortex and the Insular Cortex. CONCLUSIONS: Neurofeedback training produces both immediate and longer term reduction of central neuropathic pain that is accompanied with a measurable short and long term modulation of cortical activity. Controlled trials are required to confirm the efficacy of this neurofeedback protocol on treatment of pain. The study is a registered UKCRN clinical trial Nr 9824.