Soyi Jung

This paper proposes a cloud-assisted joint charging scheduling and energy management framework for unmanned aerial vehicle (UAV) networks. For charging the UAVs those are extremely power hungry, charging towers are considered for plug-and-play charging during run-time operations. The charging towers should be cost-effective, thus it is equipped with photovoltaic power generation and energy storage systems functionalities. Furthermore, the towers should be cooperative for more cost-effectiveness

The fifth-generation (5G) new radio standard defines various functions for forwarding treatments under differentiated quality of services requirements. In particular, a gNB base station helps combat overbuffering due to massive packet flows from associated mobile devices. Several active queue management (AQM) schemes have been proposed previously, with controlled delay (CoDel) proving to be an efficient method that can properly handle excessive buffering, that is called bufferbloat. This article

This paper proposes on-driving experience sharing algorithms at junctions in infrastructure-assisted vehicles-to-everything networks. For the purpose, a millimeter-wave (mmWave) technology is used because it provides multi-Gbps data rates which is helpful for handling users' short stay times at junctions and spatial reuse due to high beam directionality which is helpful for interference-avoidance among densely deployed vehicles at junctions. To realize on-driving experience sharing, the proposed

It seems as though progressively more people are in the race to upload content, data, and information online; and hospitals haven't neglected this trend either. Hospitals are now at the forefront for multi-site medical data sharing to provide ground-breaking advancements in the way health records are shared and patients are diagnosed. Sharing of medical data is essential in modern medical research. Yet, as with all data sharing technology, the challenge is to balance improved treatment with prot

In modern surveillance systems, the use of unmanned aerial vehicles (UAVs) has been actively discussed in order to extend target monitoring areas, even for an extreme circumstances. This paper proposes an energy-efficient UAV-based surveillance system that operates from two different sequential methods. First, the proposed algorithm pursues energy-efficient operations by deactivating selected surveillance cameras on the UAVs located in overlapping areas. For this objective, a message-passing bas

Cellular-vehicle to everything (Cellular-V2X) has been designed to support Long Term Evolution (LTE) communication standard, whereas direct short range communication (DSRC) is based on IEEE 802.11p. In this paper, we focus on cellular-V2X with Mode 4, where vehicles schedule their resources in a distributed way employing sensing based semi persistent scheduling (SPS) to avoid collision of cooperative awareness message (CAM). We analyze the effect of Mode 4 configuration and some parameters on th

This paper proposes a novel coordinated multi-agent deep reinforcement learning (MADRL) algorithm for energy sharing among multiple unmanned aerial vehicles (UAVs) in order to conduct big-data processing in a distributed manner. For realizing UAV-assisted aerial surveillance or flexible mobile cellular services, robust wireless charging mechanisms are essential for delivering energy sources from charging towers (i.e., charging infrastructure) to their associated UAVs for seamless operations of a

This paper proposes a cooperative multi-agent deep reinforcement learning (MADRL) algorithm for energy trading among multiple unmanned aerial vehicles (UAVs) in order to perform big-data processing in a distributed manner. In order to realize UAV-based aerial surveillance or mobile cellular services, seamless and robust wireless charging mechanisms are required for delivering energy sources from charging infrastructure (i.e., charging towers) to UAVs for the consistent operations of the UAVs in

Recently, a low earth orbit (LEO) satellite network is one of major systems to provide seamless access for terrestrial network systems. In order to provide robust access, efficient handover mechanisms are essential. However, conventional mechanisms may introduce frequent handovers due to the rapid movement of satellites. To deal with this problem, this paper proposes a learning-based auction handover under the consideration of received signal strength and service time between terrestrial users a

The Internet of Things (IoT) devices with enhanced machine type communication (eMTC) technology require random access (RA) to transmit data. The success rate of data delivery in the eMTC depends on the probability of failure in the RA. Access class barring (ACB) can decrease the probability of failure in the RA procedure. However, it is hard to precisely predict the success rate of the RA with the ACB. In this paper, we aim to control the failure rate of the RA to the desired probability by desi