Seung Il Moon

Spectrum handoff model based on Hidden Markov model in Cognitive Radio Networks

Cognitive Radio Network (CRN) is one of technologies to enhance the spectrum utilization by allowing unlicensed users to exploit the spectrum in an opportunistic manner. In CRN, the spectrum handoff function is a necessary component to provide a resilient service for the unlicensed users. This function is used to discover spectrum holes in a licensed network and avoid interference between unlicensed users and licensed users. Due to the randomness of the appearance of Primary users, disruptions to communications of Secondary users are often difficult to prevent and lead to low throughput of CRN. In our paper, we analyze the status of channels and propose the spectrum handoff model based on Hidden Markov model (HMM) to optimize the spectrum handoff scheme for CRN. Moreover, we compare our method with the random channel selection in the simulation.

Intelligent M2M network using healthcare sensors

Machine to Machine (M2M), communication between the machines without or with the least-human involvement, is going to be an important part of life. Healthcare is one of the areas on which M2M is going to play major roles. At present time, healthcare sensors monitor patient information and notify remote doctors. However, if we can integrate more intelligence in healthcare sensors, then these can sense patients emergency condition by themselves and can notify doctor before severe condition. In this context, we have developed intelligent mobile sensor agents in a healthcare scenario in a M2M context. This sensor agent can sense blood pressure of a patient and can notify remote doctor, with the help of an intelligent adapter and a manager in a M2M healthcare scenario.

Network economics approach to data offloading and resource partitioning in two-tier LTE HetNets

In two-tier LTE heterogeneous networks (HetNets), picocells can be offered radio resource in order to mitigate interference to picocell users in downlink transmission from high-power macrocell base station (MBS). This becomes important in order to maintain efficient operation of the network and generate benefit tradeoff between macrocell and picocells. In this paper, we propose a game based approach for joint resource partitioning and data offloading scheme to determine the amount of radio resource a MBS should offer to picocells and to determine how much traffic each picocell access point (AP) should admit from MBS. In our proposal, a two-stage Stackelberg game theory is applied to optimize the strategies of both MBS and APs in order to maximize both of their utilities and this scheme is implemented using the notion of Almost Blank Subframes (ABS) proposed in the LTE standard.

Network-assisted congestion control for information centric networking

Internet has grown very rapidly in the last couple of decades and still growing because of the expansion and utilization of various services and applications. Consequently, demand of delay and throughput sensitive services, like audio/video is also increasing. Information Centric Networking (ICN) is proposed as an architecture for the future Internet to meet the modern users and application requirements. In ICN users send requests (Interest Packets) for the Data they need. Interest packet is assigned a lifetime, which greatly affects the Quality of Experience (QoE) because user needs to resend the Interest, when the lifetime expires. Interest lifetime may be expired because of congestion, or Interest lifetime is shorter than the network delay, etc. Waiting for the expiration of an Interest lifetime to resend it is merely appropriate for best effort traffic, rather than services which require high throughput and are delay sensitive. In this paper, we propose Network-Assisted Congestion Control mechanism in ICN, which detects the congestion before it happens, and provides notification to downstream node. On reception of the notification, downstream node continuously reduces the traffic rate. However, when the downstream node fails to adjust the sending rate, the same procedure continues, until the sending node reduces the traffic rate through adjusting its congestion window. We have intensively evaluated our proposal by comparing it with similar proposal using ndnSIM. The experimental results show that our proposal achieves up to 59 percent performance improvement over other proposal in the literature.

Finding realistic shortest path in road networks with lane changing and turn restriction

Most existing work on path computation has been focused on the shortest-path problem, which is to find the optimal path between an origin and destination pair. To get an optimal route, they usually consider travel time moving forward on the road and distance from a source to a target as crucial factors to value and select a path. However, it is not sufficient for real life traffic. Firstly, when we drive on the road, the road driving time includes the duration of moving forward on the road section, going through an intersection and performing lane changes. Secondly, it is not possible, in practice, to go on any road section. Some road sections have restricted rules. Turn restrictions are commonly restricted in a real network to reduce disruption to traffic. Therefore, in our work, we direct to find not only the shortest path but also realistic or feasible path. Both lane changes and turn restrictions are considered in our work. Simulation results show that two these additional constraints are necessarily considered to achieve a realistic shortest path. In addition, our proposal is not only giving a practical path but also a satisfied path for each individual car.

An efficient and reliable Green Light Optimal Speed Advisory system for autonomous cars

An autonomous car is a self-driving car that is to keep the human being out of the car and to relieve them from the task of driving. An autonomous car can make more convenient, safer, and less energy intensive. In addition, Green Light Optimal Speed Advisory (GLOSA) systems reduce the travel time and CO2 emission. In this paper, we propose a novel GLOSA, called R-GLOSA system to support to autonomous cars. We assume that an autonomous car can access to all traffic light schedules that it will encounter on its route. The route is divided into each segment according to traffic lights. In each segment, an autonomous car can communicate to Road Side Units (RSUs) distributed among the road. An autonomous car collects the road information transmitted by an RSU and then, it optimizes speed in order to arrive at the intersection when the light is green. The R-GLOSA system provides an autonomous car with speed advisory for each RSUs coverage. The simulation results show that an autonomous car using R-GLOSA system outperforms in terms of travel time and waiting time compared to using single-segment and multi-segment GLOSA system.

A load balancing algorithm with QoS support over heterogeneous wireless networks

Coexistence of different wireless networks is a common phenomenon in todays smart communication infrastructure. Now, the big issue is to explore benefits from the heterogeneous nature of communication technology. Load balancing among the heterogeneous wireless networks is the primary goal of this paper. Load balancing without considering Quality of Service (QoS) merely inadequate in convergence of resource utilization and grade of service. So, this paper proposed a load balancing algorithm with QoS provisioning. This paper is based on a semi-distributed load balancing architecture. Firstly, IP-flow dividing ratio based soft load balancing approach is discussed for high speed features of next generation wireless networks. Secondly, an admission control function of QoS requirements is developed. Thirdly, a joint optimization function is derived and a load balancing algorithm is proposed by using the cost function. Finally, simulation results are presented for performance appraisal.

A general and practical consolidation framework in CloudNFV

In cloud computing environment, power consumption is a critical factor for data centers. Effective power consumption control can improve significantly benefit for providers. In this paper, we derive from CloudNFV architecture, an open platform for implementing Network Functions Virtualization based on cloud computing and Software Defined Networking, to obtain an optimal power control strategy toward reducing CO2 emissions and maximize total utility of active resources. We propose a consolidation function for determining the On/Off strategy of each server and use live migration method to rearrange virtual machines in active resource components. The efficiency of our proposed model is demonstrated on the simulation environment.

Cooperative caching for adaptive bit rate streaming in content delivery networks

This work proposes a cooperative caching model which supports adaptive bit-rate streaming in content delivery networks. A linear program (LP) problem is applied to maximize the total user satisfaction. The optimal content placement and content fetching strategies are the solutions to the LP. The LP is a large-scale optimization problem because of the large amount of contents in the network. We also introduce a technique to decompose the large-scale LP into many subproblems which can be solved in parallel. Each subproblem is associated with one content.