Eunkyo Kim

Movement-Aware Vertical Handoff of WLAN and Mobile WiMAX for Seamless Ubiquitous Access

This paper addresses a movement-aware vertical (MAV) handover algorithm between WLAN and Mobile WiMAX for seamless ubiquitous access. An MAV handover algorithm is proposed in this paper to exploit movement pattern for avoiding unnecessary handovers in the integrated WLAN and Mobile WiMAX networks. If a mobile station (MS)s velocity is high and its movement pattern is irregular, unnecessary handovers likely occur more frequently. Therefore, the MS velocity and moving pattern are important factors for the handover decision procedure. To avoid unnecessary handovers, the MAV handover algorithm adjusts the dwell time adaptively and predicts the residual time in the cell of target base station (BS). Consequently, the adaptive dwell timer of MAV handover algorithm allows an MS a better connection as long as possible. Our simulation results show that the reduction of unnecessary handovers by leads to significant throughput improvements.

Effect of localized optimal clustering for reader anti-collision in RFID networks: fairness aspects to the readers

This paper proposes an adaptive and dynamic localized scheme unique to hierarchical clustering in RFID networks, while reducing the overlapping areas of clusters and consequently reducing collisions among RFID readers. Drew on our LLC scheme that adjusts cluster coverage to minimize energy consumption, low-energy localized clustering for RFID networks (LLCR) addresses RFID reader anti-collision problem in this paper. LLCR is a RFID reader anti-collision algorithm that minimizes collisions by minimizing overlapping areas of clusters that each RFID reader covers. LLCR takes into account each RFID readers energy state as well as RFID reader collisions. For the energy state factor, we distinguish homogeneous RFID networks from heterogeneous ones according to computing power of each RFID reader. Therefore, we have designed efficient homo-LLCR and hetero-LLCR schemes for each case. Our simulation-based performance evaluation shows that LLCR minimizes energy consumption and overlapping areas of clusters of RFID readers.

Low-energy localized clustering: an adaptive cluster radius configuration scheme for topology control in wireless sensor networks

This paper addresses an adaptive and dynamic localized scheme unique to hierarchical clustering protocols in wireless sensor networks, while reducing the consumption of residual energy of cluster heads and as a result delivering a prolonged sensor network lifetime. Our proposed scheme, low-energy localized clustering (LLC) aims to minimize energy consumption of cluster heads while the entire sensor network is still being covered. For achieving this goal, LLC dynamically regulates the radius of each cluster. Through a simulation based performance of this algorithm, LLC, we show that our novel cluster radius configuration algorithm achieves the desirable properties.

Optimized transmission power control of interrogators for collision arbitration in UHF RFID systems

The emergence of UHF RFID as one of the dominant technology trends has posed numerous unique challenges to researchers. This letter presents a novel, theoretically-grounded collision arbitration protocol, called TPC-CA, which optimally controls transmission power of RFID interrogators and thereby reducing redundant interrogator collisions

Weighted localized clustering: a coverage-aware reader collision arbitration protocol in RFID networks

This paper addresses a weighted localized scheme and its application to the hierarchical clustering architecture, which results in reduced overlapping areas of clusters. Our previous proposed scheme, Low-Energy Localized Clustering (LLC), dynamically regulates the radius of each cluster for minimizing energy consumption of cluster heads (CHs) while the entire network field is still being covered by each cluster in sensor networks. We present weighted Low-Energy Localized Clustering(w-LLC), which has better efficiency than LLC by assigning weight functions to each CH. Drew on the w-LLC scheme, weighted Localized Clustering for RFID networks(w-LCR) addresses a coverage-aware reader collision arbitration protocol as an application. w-LCR is a protocol that minimizes collisions by minimizing overlapping areas of clusters.

UbiComm: an adaptive vertical Handoff decision scheme for heterogeneous wireless networks

Vertical handoff will be essential for the next generation heterogeneous wireless networks. We propose an Adaptive Vertical Handoff Decision Scheme called UbiComm to avoid unbeneficial handoffs in the integrated WiBro and WLAN networks. If the mobile node (MN)s velocity is high and moving pattern is irregular, more unnecessary handoffs can occur. Therefore, MNs velocity and moving pattern are the important factors of our handoff decision scheme. In order to avoid unbeneficial handoff the UbiComm adjusts the dwell time adaptively, and it also predicts the residence time in the target network. In addition, UbiComms adaptive dwell timer makes a MN receive service of a better network as long as possible. The simulation results show that the reduction of unnecessary handoffs proposed in UbiComm improves the MNs throughput.

ω-LLC: weighted low-energy localized clustering for embedded networked sensors

This paper addresses a weighted dynamic localized clustering unique to a hierarchical sensor network structure, while reducing the energy consumption of cluster heads and as a result prolonging the network lifetime. Low-Energy Localized Clustering, our previous work, dynamically regulates the radii of clusters to minimize energy consumption of cluster heads while the network field is being covered. We present weighted Low-Energy Localized Clustering (w-LLC), which consumes less energy than LLC with weight functions.

On energy-aware dynamic clustering for hierarchical sensor networks

This paper proposes an energy-efficient nonlinear programming based dynamic clustering protocol (NLP-DC) unique to sensor networks to reduce the consumption of energy of cluster heads and to prolong the sensor network lifetime. NLP-DC must cover the entire network, which is another basic functionality of topology control. To achieve these goals, NLP-DC dynamically regulates the radius of each cluster for the purpose of minimizing energy consumption of cluster heads while the entire sensor network field is still being covered by each cluster. We verify both energy-efficiency and guarantee of perfect coverage. Through simulation results, we show that NLP-DC achieves the desired properties.

Dynamic clustering for coverage-time maximization in two-tiered hierarchical sensor network architectures

This paper proposes dynamic clustering for coverage-time maximization (DC-CTM) in sensor networks. The coverage-time is defined as the time until one of cluster heads (CHs) runs out of energy in clustering-based sensor networks. DC-CTM regulates cluster radii for balanced energy consumption among CHs for coverage-time maximization. By using DC-CTM, three advantages can be achieved. The first one is balanced energy consumption among CHs. The second one is minimized energy consumption in each CH. The last one is the consideration of mobility on CHs. The novelty of proposed scheme, DC-CTM scheme, is shown by various simulation-based performance analyses

Resource Adaptation Scheme for QoS Provisioning in Pervasive Computing Environments: A Welfare Economic Approach*This work was supported by Army Research Lab (ARL) number DA/DAKF11-98-9-0359, USA, and by Grant no.R01-2005-000-10267-0 from KOSEF.

Management of applications in the new world of pervasive computing requires new mechanisms to be developed for admission control, QoS negotiation, allocation and scheduling. To solve such resource-allocation and QoS provisioning problems within pervasive and ubiquitous computational environments, distribution and decomposition of the computation are important. In this paper we present a QoS-based welfare economic resource management model that models the actual price-formation process of an economy. We compare our economy-based approach with a mathematical approach we previously proposed. We use the constructs of application benefit functions and resource demand functions to represent the system configuration and to solve the resource allocation problems. Finally empirical studies are conducted to evaluate the performance of our proposed pricing model and to compare it with other approaches such as priority-based scheme and greedy method.