A reward based connectivity-aware IoV neighbor selection for improving reliability in healthcare information exchange

Abstract

Internet of Vehicle (IoV) technology is a promising application of Internet of Things (IoT) employed in smart city environment for smart communication. Traffic handling and non-delay tolerant communication services are expected from the communicating vehicles to improve its application efficiency. With the consideration of IoV in healthcare applications, this manuscript introduces a rapid connection aware neighbor selection (RCNS) for improving the efficiency in handling medical information. The selection process is induced by an instantaneous reward based learning algorithm (RLA) for ease of neighbor selection. The multi-attributes of the neighbors in the information transmitting route are estimated with the knowledge of their position. Distance and reward of the neighbors in the transmission direction are assessed using a beacon-assisted vehicle discovery that minimizes the impact of collision. Different from the other neighbor selection schemes, this method is relies on renewed attribute estimated at the time of transmission. This aids the transmitter to decide transmission and modify transmission attributes. The performance of the proposed neighbor selection method is estimated through extensive simulation using the metrics: delay, delivery ratio, selection overhead and outage.