Recent technological developments, such as the Internet of Things (IoT), artificial intelligence, edge, and cloud computing, have paved the way in transforming traditional healthcare systems into smart healthcare (SHC) systems. SHC escalates healthcare management with increased efficiency, convenience, and personalization, via use of wearable devices and connectivity, to access information with rapid responses. Wearable devices are equipped with multiple sensors to identify a person’s movements. The unlabeled data acquired from these sensors are directly trained in the cloud servers, which require vast memory and high computational costs. To overcome this limitation in SHC, we propose a federated learning-based person movement identification (FL-PMI). The deep reinforcement learning (DRL) framework is leveraged in FL-PMI for auto-labeling the unlabeled data. The data are then trained using federated learning (FL), in which the edge servers allow the parameters alone to pass on the cloud, rather than passing vast amounts of sensor data. Finally, the bidirectional long short-term memory (BiLSTM) in FL-PMI classifies the data for various processes associated with the SHC. The simulation results proved the efficiency of FL-PMI, with 99.67% accuracy scores, minimized memory usage and computational costs, and reduced transmission data by 36.73%.
Vehicle-to-everything (V2X) in networks is a communication technology that allows vehicles to communicate with their surroundings. Traffic congestion and unawareness of the travel of emergency vehicles (EVs) lead to delays in reaching the destination of the EV. In order to overcome this time delay, we propose a jitter-to-highly reliable (J2H) approach of customizing the traffic signals and an alert passer mechanism to alert other vehicles. Once an EV is started, its source, destination and level of emergency will be updated on the network, and based on the traffic density, the fastest route to reach the destination is determined. The V2X system in the J2H approach passes an alert to all the traffic signals on that route. The traffic signals will continuously monitor the position of the vehicle by using the Global Positioning System (GPS). Based on the position of the vehicle, the distance between the vehicle and the traffic signal on that route is periodically updated. Once the vehicle comes within the range of the closest traffic signal, based on constraints such as number of lanes, emergency level, types of roads, traffic density, number of EVs approaching, and time of arrival of the vehicles, the traffic signal will be customized. The V2X then passes the information to all the traffic signals that are available in the route of the EV. The alert passer mechanism warns about the approaching EV to other vehicles on that route. Thus, by adapting the J2H technique, EVs can overcome the time delay to reach the destination. Traffic congestion is overcome by customizing the traffic signals. Path blockage can be cleared by vehicle-to-vehicle (V2V) communication.
Technology evolution in the network security space has been through many dramatic changes recently. Enhancements in the field of telecommunication systems invite fruitful security solutions to address various threats that arise due to the exponential growth in the number of users. It's crucial for upgrading the entire infrastructure to safeguard the system from specific threats. So, there is a huge demand for the learning mechanism to realize the behavior of attacks. Recent upcoming technologies like machine learning and deep learning can support in the process of learning the behavior of all types of attacks irrespective of their deployment criteria. In this chapter, the analysis of various machine learning algorithms with respect to a few scenarios that can be adopted for the benefits of improving the security standard of the network. This chapter briefly discusses various know attacks and their classification and how machine learning algorithms can be involved to overcome the popular attacks. Also, various intrusion detection and prevention schemes were discussed in detail.
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