Research

Information spread in social networks

1. Information Source Detection in Large Scale Networks 

The problem of finding the information source is to identify the true source of information spread. This is clearly of practical importance, because harmful diffusion can be mitigated or even blocked, e.g., by vaccinating humans or installing security updates. We study intensively on the following approaches.

How can we infer the rumor sources using random snapshots, using side information from querying and protectors? What are the required querying budgets for arbitrary detection efficiency? 


Anomaly detection  (Figure source: Riyaz et al, Real-time big data processing for anomaly detection: A Survey, International Journal of Information Management, 2019)

2. Anomaly Detection over Networks

Due to a rapid development of the Internet, huge information flows over the network. In such a situation, if a virus or malicious information is received in a place such as vehicular network, it can cause a dangerous accident. To avoid this, we study on designing efficient algorithms to detect the abnormal information (or source node) quickly using online learning techniques. 

Reinforcement learning in platooning

3. Deep Reinforcement Learning for Autonomous Driving

The more complete development of autonomous vehicles is one of the key technologies driving the fourth industrial revolution. This is due to the fusion of software technologies such as communication and AI as well as classical dynamics and control. We study on the platooning driving based on Reinforcement Learning (RL), in which various vehicles on the road communicate with each other while learning information about the environment with one agent in relation to autonomous driving.

Adaptive seeding in network

4. Adaptive Seeding Game in Social Networks

Recently, there have been many studies about the spreading phenomena of information/product due to developing of online Social Network Service over social networks such as Facebook and Twitter, etc. Especially, some studies consider two competitive information and/or product diffusions by a game model which called seeding game. In this game, each player chooses a seed set who spreads their information initially in the network that maximizes the diffusion at the end of diffusion process. These are modeled by non-adaptive manner in the sense that the player chooses all possible seed nodes before the diffusion.  Different to this, we are interested in adaptive seeding game for information diffusion that each player can choose their seed nodes after partial observation of the diffusion snapshot to maximizes the number of diffused nodes for their own information. 

Vehicle to Grid Infrastructure (VGI) communication for EVs

5. Information Disclosure and Delivery on VGI Communication for EVs

Recently, millions of Electric Vehicles (EV) are in operation and more vehicles are expected to be replaced by EVs in the future. Under this situation, it is anticipated that the electric power supply will be secured when EV is charging, and the consumer-friendly intelligent charging infrastructure such as the charging amount or the deadline is expected to spread. For this, communication between the EV and the charging infrastructure is essential as shown in the figure. However, in many cases, there is little provision for the communication system that it is easy for anyone to use the communication protocol of different automobile companies or to disclose the information of the vehicle. We study standardization for the disclosure and transmission of information inside and outside of EVs.