Population at risk can benefit greatly from remote health monitoring because it allows for early detection and treatment. Because of recent advances in Internet-of-Things (IoT) paradigms, such monitoring systems are now available everywhere. Due to the essential nature of the patients being monitored, these systems demand a high level of quality in aspects such as availability and accuracy. In health applications, where a lot of data are accessible, deep learning algorithms have the potential to perform well. In this paper, we develop a deep learning architecture called the convolutional neural network (CNN), which we examine in this study to see if it can be implemented. The study uses the IoT system with a centralised cloud server, where it is considered as an ideal input data acquisition module. The study uses cloud computing resources by distributing CNN operations to the servers with outsourced fitness functions to be performed at the edge. The results of the simulation show that the proposed method achieves a higher rate of classifying the input instances from the data acquisition tools than other methods. From the results, it is seen that the proposed CNN achieves an average accurate rate of 99.6% on training datasets and 86.3% on testing datasets.
In recent times, large numbers of road accidents occurring all over the world are mainly due to collisions between vehicles. More than 1.2 million peoples were died in road accidents in 2019, according to the World Health Organization (WHO). Human safety features are much needed in the manufacturing of vehicles. The proposed method mainly focuses on reducing the number of accidents in our daily lives by avoiding collision between the vehicles. There are several factors corresponding to such difficult conditions that may results in death or disabilities. The causes are sudden loss of concentration of the driver, braking failure and stability issues. These criteria can be reduced only if there is a possibility for communication between the vehicles and the drivers in order to avoid accidents. There are various vehicular communication system models like Dedicated Short Range Communication and Vehicular Ad-Hoc network operating less than 5.9 GHz. These radio frequency based communication also has some limitations such as interference, congested spectrum and security. These drawbacks can be reduced by implementing the Visible Light Communication (VLC) in vehicles. It provides larger bandwidth, security, interference immunity, and high data rate. High speed data transmission and reception can be achieved using visible light based data communication system. This technology is known as Light Fidelity (Li-Fi). This chapter presents the innovative method to evade collision between two vehicles (rear and front). This communication system is cost effective with high speed data rate capabilities.
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