Purpose: The purpose of this paper is to design a human health monitoring system (HHMS) which helps in improving diagnostics at an earlier stage and monitoring after recoup. Design/methodology/approach: The methodology involves a combination of three subsystems which monitors the human parameters such as temperature, heart rate, SpO2, fall and location of the person. Various sensors are used to extract the human parameters, and the data are analysed in a computer subsystem, through Global System for Mobile Communications (GSM) and Internet of Things (IoT) subsystem; the parameters measured are communicated to the caregiver and doctor. Findings: Results have successfully demonstrated monitoring human temperature human temperature, heart rate, SpO2 and fall and location continuously using the HHMS prototype. Reliability of the technique used for monitoring these parameters is assessed by Proteus Professional 8 and LabVIEW simulators. Practical implications: The HHMS enables long-term monitoring without any sort of interference from regular activities and allows daily health monitoring, elderly monitoring and so on. Originality/value: First, the proposed HHMS simultaneously monitors five human parameters. Second, unlike most monitoring systems which uses older communication module, the proposed system is made smart using IoT. The proposed method has been made into a prototype system as detailed in this paper. The proposed HHMS can achieve high detection accuracy. Therefore, this system can be reliably deployed into a consumer product for use as monitoring device with high accuracy.

In intelligent vehicular networks, vehicles should be able to communicate with their surroundings while traveling. This results in more efficient, safer, and comfortable driving experiences, as well as new commercial prospects in a variety of industries. Connected vehicles and autonomous vehicles expect 100% reliable connectivity without any compromise in quality. However, due to challenges such as difficult channel terrains in urban scenarios and dead zones, the reliability of current vehicle-to-infrastructure (V2I) and vehicle-to-vehicle communication systems cannot be guaranteed. The performance of vehicular networks can be considerably enhanced with reconfigurable intelligent surfaces (RIS). Non-orthogonal multiple access (NOMA) allows for massive connectivity with the surroundings. In vehicular networks, the RIS-assisted NOMA can ensure regulated channel gains, better coverage, throughput, and energy efficiency. In this work, a blind RIS-assisted fixed NOMA (FNOMA) system is proposed for a downlink V2I scenario. The closed-form analytical outage probability and throughput expressions are derived by considering RIS as an intelligent reflector and as a roadside unit. It is observed that the analytical and Monte Carlo simulation results are closely related. In simulations, it has been discovered that RIS-assisted FNOMA outperforms the traditional NOMA variants in terms of outage and throughput. Even without precise channel knowledge, blind RIS transmission outperforms traditional NOMA variants due to huge array gain. The increase in the number of reflective elements also results in a significant improvement in signal-to-noise ratio gains.

In recent years there has been an exponential increase in the number of automobiles available to the consumers. Due to high density of vehicles, the potential threats and road accidents are increasing. The vehicles itself designed in such a way to provide lot of services for users in transit such as toll, infotainment and gaming. The vehicle can able to communicate with other vehicle in order to provide updates about different road situations and dangerous conditions, which results in saving time and lives. This new emerging technology called Vehicular Network (VANET) helps in achieving this communication. The main aim of this work is to develop the prototype for the prevention of chain collisions of vehicles on highways, by sending an indication of collision of the collided vehicle to the preceding vehicles. VANET provides the communication between vehicles and road-side units (RSU) with an aim of providing efficient and safe transportation. The ultrasonic sensor interfaced with the on board unit (OBU) has a controller which detects the collision of the target vehicle and sends the information to the OBU of preceding vehicle. This is accomplished by employing NodeMCUs. Both the NodeMCUs uses user datagram protocol (UDP) to communicate with each other. The RSU provide a hotspot for both NodeMCUs to connect. The present format is a prototype model that can adapted with inputs from different sensors to provide more reliable data to the preceding.

A special type of Mobile Ad-hoc Networks (MANETs) which has frequent changes of topology and higher mobility is known as Vehicular Ad-hoc Networks (VANETs). In order to divide the network into groups of mobile vehicles and improve routing, data gathering, clustering is applied in VANETs. A stable clustering scheme based on adaptive multiple metric combining both the features of static and dynamic clustering methods is proposed in this work. Based on a new multiple metric method, a cluster head is selected among the cluster members which is taken from the mobility metrics such as position and time to leave the road segment, relative speed and Quality of Service metrics which includes neighborhood degree, link quality of the RSU and bandwidth. A higher QoS and cluster stability are achieved through the adaptive multiple metric. The results are simulated using NS2 and shows that this technique provides more stable cluster structured with the other methods.