Proposal of Vehicle-to-Vehicle Based Delay Tolerant Networks with Adaptive Aray Antenna Control Systems

Abstract

The recent technological growth of autonomous moving nodes including vehicles or drones expect us to the various usages such as for M2M or V2V communications as well as the disaster information systems in the near future. However, it is difficult to keep continuous data transmissions during the nodes are moving. Therefore, this paper proposes the Delay Tolerant Networks (DTN) with Adaptive Array Antenna (AAA) Control systems for the V2V based disaster information networks. In the proposed system, the automobiles or the drones equip the AAA control units with IEEE802.11a/b/g, and the controls based Kalman filter algorithm adjusts the directional antenna for the prediction of the target nodes. The simulation is held for the effectiveness of the proposed systems with the map of Takizawa city in Japan, and the results are discussed for the future studies.

Vehicular Delay-Tolerant Networks with Image Recognition-Based Adaptive Array Antenna for Winter Road Surveillance in Local Areas

“…In the system, first of all, the road conditions are estimated by the QEF and temperature sensors on vehicles, and the estimated data are stored in each vehicle. Then, when there are vehicles which can receive transmissions or wireless devices on the utility poles along the roads, the data are communicated to others via DTN routing using the data triage methods [10,11].…”

“…One is the time update phase, and it updates the current time and the predictions of the observed values. Another process is the measurement update phase, and it updates the observed values and the adjustments [8,11]. In the proposed system, as mentioned in the previous paper [11], the calculated angle and distance from the captured image continuously used for the observed values in the Kalman filter process in OpenCV's API (Application Programming Interface) [15], and the output adjustment values were used for the angle controls of the beam-forming.…”

“…Another process is the measurement update phase, and it updates the observed values and the adjustments [8,11]. In the proposed system, as mentioned in the previous paper [11], the calculated angle and distance from the captured image continuously used for the observed values in the Kalman filter process in OpenCV's API (Application Programming Interface) [15], and the output adjustment values were used for the angle controls of the beam-forming. Therefore, even if the target vehicles are out of sight in the system because of the obstacles, it is considered that the adjustments of the beam-forming proceed in order to keep the stable connectivity in the system.…”