Yu-Yuan Lin

Bio: Yu-Yuan Lin is an academic researcher from National Cheng Kung University. The author has contributed to research in topics: Packet forwarding & Network simulation. The author has an hindex of 3, co-authored 6 publications receiving 29 citations. Previous affiliations of Yu-Yuan Lin include University of Delaware.

Sharing trajectories of autonomous driving vehicles to achieve time-efficient path navigation

Abstract: Traffic congestion arises to be a very serious problem especially in metropolitan cities nowadays. Drivers need to spend more time to their destinations. In this paper, a dynamic navigation protocol, called STN, is proposed to search for time-efficient paths for autonomous driving vehicles toward their given destinations. The trajectory information of vehicles is maintained in a server to assist the planning of navigation path. With STN, a vehicle sending a request message toward the nearest access point (AP) to acquire the driving path. By comparing the trajectories and time information in the system, the future traffic load can be predicted. The traffic load information enables the server to estimate driving speed within different paths toward the destination and then determines a time-efficient path. In addition, adjustment, update, and replan mechanisms are developed to reduce the deviation of prediction. To evaluate the performance of STN, the real road map of Shalu, Taiwan, including 20 road segments, is used. The simulator Estinet, formerly known as NCTUns (National Chiao Tung University Network Simulation) has been used for the validation of STN. The simulator integrates some traffic simulation capabilities, such as road network construction and vehicles mobility control, in the recent version. The simulation results demonstrate that STN saves around 14% driving time as compared with Vehicle-Assisted Shortest-Time Path Navigation (VAN).

Trajectory-based data Forwarding with future Neighbor Prediction in autonomous driving vehicular environments

Abstract: In VANETs, vehicle-to-vehicle communication is a challenge due to the fast topology changing, frequent disconnections, and highly variable traffic densities. This paper describes a Trajectory-based data Forwarding scheme with future Neighbor Prediction (TFNP) in autonomous driving vehicular environments. The most important characteristic of the environments is that the prediction for future position of vehicles could be sufficiently accurate. With the trajectory information of all autonomous vehicles, the future neighbors of each vehicle can be properly identified. The packet forwarding ability of the future neighbors is evaluated and forwarding sequences are thus constructed. When an autonomous vehicle needs to send data, it will transmit packets based on the constructed forwarding path. The simulation results demonstrate the effectiveness and efficiency of the developed scheme in the autonomous driving vehicular environments.

Efficient Fault-Tolerant Backbone Construction in Tmote Sky Sensor Networks

Abstract: In this study, we have investigated the effectiveness of building “Fault-Tolerant Backbone” for data dissemination in Tmote Sky sensor networks. Tmote Sky sensors provide programmable and adjustable output power for data transmission. Users can control adequate transmission power for each sensor. Based on our measurements of Tmote Sky, there is a steadily transmitted distance for every power level. For certain power level, successfully-transmitted ratio was approximately 100 percent when the distance between sender and receiver was less than the steadily-transmitted distance. In accordance with the character on Tmote Sky, the ideas of fault-tolerant backbone has been made for constructing a fault-tolerant and stable system for Tmote Sky. The fault-tolerant backbone protocol builds up a connected backbone, in which nodes are endowed with a sleep/awake schedule. Practical experimental results reveal the fast fault recovery and high successfully-transmitted ratio can be fulfilled in the realistic system. The following goals in the implementation have been reached, including self-configurable fault-tolerant groups, automatic backbone construction, automatic failure recovery, and route repair.

Reducing Communication Delay by Finding Sink Location in Low-Duty-Cycle Wireless Sensor Networks

Abstract: Low-duty-cycle mechanisms can reduce the energy consumption in wireless sensor networks. Many related researches have been made in recent years. In the low-duty-cycle environment, the latency of sending packets from a sink to each node is much longer than traditional WSNs because nodes stay asleep most of the time. In this paper, the Centralized Cluster-based Location Finding (CCLF) algorithm is proposed to reduce the high latency in low-duty-cycle WSNs by finding a suitable position for the sink. The algorithm mainly consisted of three steps: (1) Cluster construction, (2) The fast look-up table (FLU-table) construction, and (3) Sink location decision. The simulation results show that the performance of the CCLF algorithm approaches the performance of the optimal algorithm. Moreover, the CCLF algorithm requires less operation time compared with the optimal algorithm.

Improving Data Transmission for Collaborative Work Using Forwarding Nodes in Mobile Ad Hoc Environments

Abstract: Mobile users are often involved in team activities and exhibit collaborative mobility in realistic ad hoc network application scenarios. Due to diverse group mobility patterns, the network may remain partitioned for extended durations. This paper describes an approach to reduce the communication disruptions by deploying forwarding nodes. The forwarding nodes explore the field periodically in orderto gather network information for countering the influence from mobility and sparseness of node deployment. By selecting the rendezvous grid pair, the forwarding nodes can move to appropriate locations to recover communication between disconnected groups. The mechanism has been implemented in the network simulator ns-2. The simulation results show that the mechanism can efficiently improve information availability and also achieve the required communication quality.

Reporting road event data and sharing with other vehicles

Abstract: Example systems and methods allow for reporting and sharing of information reports relating to driving conditions within a fleet of autonomous vehicles. One example method includes receiving information reports relating to driving conditions from a plurality of autonomous vehicles within a fleet of autonomous vehicles. The method may also include receiving sensor data from a plurality of autonomous vehicles within the fleet of autonomous vehicles. The method may further include validating some of the information reports based at least in part on the sensor data. The method may additionally include combining validated information reports into a driving information map. The method may also include periodically filtering the driving information map to remove outdated information reports. The method may further include providing portions of the driving information map to autonomous vehicles within the fleet of autonomous vehicles.

Congestion Attacks to Autonomous Cars Using Vehicular Botnets

Abstract: The increasing popularity and acceptance of VANETs will make the deployment of autonomous vehicles easier and faster since the VANET will reduce dependence on expensive sensors. However, these benefits are counterbalanced by possible security attacks. We demonstrate a VANET-based botnet attack in an autonomous vehicle scenario that can cause serious congestion by targeting hot spot road segments. We show via simulation that the attack can increase the trip times of the cars in the targeted area by orders of magnitude. After 5 minutes, the targeted road becomes completely unusable. More importantly, the effect of such an attack is not confined to a specific hotspot; the congestion can spread to multiple roads and significantly affect the entire urban grid. We show that current countermeasures are not effective, and point to new possible defenses.

Method and system of assisting a driver of a vehicle

Abstract: A method of assisting a driver of a vehicle in driving a road, the method includes determining a location of a road boundary relative to the vehicle using a sensor, selecting, via a controller, a parameter to assist a driver of the vehicle at the location of the road boundary, updating the parameter based on a previous operation of the vehicle, determining, via the controller, whether the vehicle is approaching the road boundary based on a vehicle trajectory and the location of a road boundary, and providing a feedback operation to assist the driver in avoiding the road boundary, the feedback operation based on the selected parameter.