Jinchul Choi

The Slotted-LBT: A RFID Reader Medium Access Scheme in Dense Reader Environments

RFID technology as a core technology for realizing ubiquitous environment is emerged. Although it was developed rapidly in the past few years, the read rate is still a concern in the RFID technology adoption. At the same time it is also a technical issue which must be resolved. In order to increase the read rate, a number of researches have focused on the air protocol method and the medium access method. In this paper, we propose a distributed Slotted-LBT method to increase the number of readers which can read tags simultaneously, to provide a stable channel access time, and to reduce the interference among readers by using a channel zone. From the results in this research, compared with the existing normal LBT method, the proposed Slotted-LBT method shows that the coefficient of variation for the channel access time is reduced from 96% to 38% and the effect of interference is reduced by 28.48%. Besides, since each channel can be controlled separately, we can use categorized channels according to demanding features of manifold applications.

Energy consumption and lifetime analysis in clustered multi-hop wireless sensor networks using the probabilistic cluster-head selection method

Clustering sensor nodes into groups is an effective way of reducing the transmission of duplicated information in energy-constraint wireless sensor networks (WSNs). The performance of clustering is greatly influenced by the selection of cluster-heads, which are in charge of creating clusters and controlling member nodes. In selecting cluster-heads, a probabilistic method where each sensor node selects itself as a cluster-head with a given probability is often used in large-scale and dense WSNs because it enables all nodes to independently decide their roles while keeping the signaling overhead low. In this method, the probability of being a cluster-head should be optimally chosen to maximize the energy efficiency of the nodes. In this article, we propose a novel energy model to estimate the energy consumed in a multi-hop WSN clustered with probabilistic cluster-head selection. Then, based on our model, we determine optimal probability that maximizes the lifetime of a network. Simulation results achieved by the Monte Carlo method show that our model estimates well in energy consumption from a network and also predicts the optimal clustering probability accurately.

An MILP-Based Cross-Layer Optimization for a Multi-Reader Arbitration in the UHF RFID System

In RFID systems, the performance of each reader such as interrogation range and tag recognition rate may suffer from interferences from other readers. Since the reader interference can be mitigated by output signal power control, spectral and/or temporal separation among readers, the system performance depends on how to adapt the various reader arbitration metrics such as time, frequency, and output power to the system environment. However, complexity and difficulty of the optimization problem increase with respect to the variety of the arbitration metrics. Thus, most proposals in previous study have been suggested to primarily prevent the reader collision with consideration of one or two arbitration metrics. In this paper, we propose a novel cross-layer optimization design based on the concept of combining time division, frequency division, and power control not only to solve the reader interference problem, but also to achieve the multiple objectives such as minimum interrogation delay, maximum reader utilization, and energy efficiency. Based on the priority of the multiple objectives, our cross-layer design optimizes the system sequentially by means of the mixed-integer linear programming. In spite of the multi-stage optimization, the optimization design is formulated as a concise single mathematical form by properly assigning a weight to each objective. Numerical results demonstrate the effectiveness of the proposed optimization design.

Energy Modeling for the Cluster-Based Sensor Networks

The performance of the clustering scheme is influenced by the cluster-head election method and the size or the number of clusters. Thus we should optimize these factors to maximize the energy efficiency of the clustering scheme. In this paper, we propose a new energy consumption model for the LEACH which is a well-known clustering protocol and determine the optimal number of clusters based on our model. Our model has accuracy over 80% compared with the simulation and is considerably superior to the existing model of the LEACH.

An integer linear programming (ILP)-based optimization for finding the optimal independent sets in wireless ad hoc networks

In wireless ad hoc networks, one of the most important issues impacting performance is wireless interference between adjacent nodes. Such interference problem has often been approached to find independent sets in a topological graph. In general, the topological graph have exponentially many independent sets and and thus it is very difficult to find the optimal independent sets for high performance. It is known that this problem belongs to the class of NP-hard problems. To deal with this problem, heuristic methods such as greedy algorithm, local search and genetic algorithm have been usually used. Such heuristics are useful to speed up the process of finding a satisfactory solution, however they do not guarantee the optimality of the solution found. In this paper, we develop a linearization technique to transform the nonlinear equations into linear ones. Then, we propose a novel integer linear programming (ILP)-based optimization design not only to properly find the optimal independent sets, but also to appropriately schedule wireless nodes for high performance. Based on the priority of the multiple objectives, our design is presented as a two-stage problem and optimizes the system sequentially. Numerical results demonstrate the effectiveness of the proposed optimization design.

A Cross-Layer Optimization for a LP-Based Multi-Reader Coordination in RFID Systems

In RFID systems, the system performance such as interrogation range and tag recognition rates may suffer from interferences among readers. The reader interference problem can be alleviated by output signal power control and spectral and/or temporal separation among the readers. However, due to the limited wireless resources, the diverse interference effects, and the constraint of user requirements, it is not easy to derive the optimal solution. In this paper, we propose a cross-layer design to optimally schedule spectral and temporal resources, and to allocate appropriate power to each reader in a dense reader environment. The cross-layer design is as a three-stage optimization problem, where in the first stage the reader interrogation interval is minimized, in the second stage the reader utilization is maximized, and in the last stage output power of each reader is minimized. We merge the three-stage problem into a one-stage problem with more concise mathematical form. Numerical results demonstrate the effectiveness of the proposed optimization problems.

An efficient scheduling and admission control algorithm for IEEE 802.11e WLAN

IEEE 802.11e is being proposed to improve QoS (Quality of Service) by IEEE 802.11 working group. HCCA (HCF Controlled Channel Access), a centralized polling based mechanism of IEEE 802.11e, needs a scheduling algorithm that decides on how the available radio resources are allocated to the polled stations. In IEEE 802.11e a standard Reference Scheduler is presented. The Reference Scheduler polls all stations in a polling list by the same interval which causes ineffectively frequent polling. It increases the overhead and also decreases the TXOP (Transmission Opportunity) utilization. In this paper, we propose a scheduling and admission control algorithm that polls stations depending on the MSI (Maximum Service Interval) of stations in order to solve these shortcomings. In our proposed algorithm a station is polled by an interval close to its MSI, thus polling overhead decreases and TXOP utilization increases in comparison to those of the Reference Scheduler. Simulation results show that our algorithm outperforms the Reference Scheduler. Our algorithm maintains higher aggregated throughput and serves more stations than the Reference Scheduler.

Context-aware agriculture organizer

Ubiquitous computing has become popular and is in demand in many fields of human activity. This kind of computing could significantly enhance applications such as an organizer by making it more agile through sensing the environment. The context-aware agriculture organizer (CAAO), which was pursued by this study, facilitates farmers work by providing relevant contextual information to the users, thus simplifying the selection of plants and resulting to better harvest and more effective resource usage. The idea behind the CAAO is not to control the process of crops growing but rather make it smoother and more productive while simultaneously providing a platform for agricultural research. The study had objectives of creating a technical framework for CAAO with basic functionality and providing ideas for further development. Literature analysis and exploratory software development served as methods of the study. Context-awareness in the CAAO can be achieved by data acquired from sensors, and sensor observation service (SOS) is used for this purpose. The main benefit of SOS is that it connects together different types of sensors. Proposed CAAO framework provides ample of opportunities for further development and makes the system a hub of agricultural services. The mix of web mash-up and ubiquitous computing can enrich the framework and provide an up to date and on demand services. Current version of the CAAO is limited to cotton crop and it does not yet establish physical connection with the sensors. The next steps in the development are to establish connection to sensor networks and to test the CAAO in a real world context.

Environmental sensor network for a pervasive learning space in a Finnish biosphere reserve

Heroes of Koskenniska is a game-based pervasive learning space that combines mobile and sensor technologies to raise environmental and cultural awareness in a Finnish Biosphere Reserve. An environmental sensor network (ESN) is used to gather and process environmental parameters to be used in the game. The developed wireless infrastructure is presented together with several challenges in deployment of an ESN to a Finnish forest. Solutions to these challenges are proposed. Most of the challenges and solutions are generalisable to other outdoor contexts. Results indicate that while low-budget solutions solve majority of challenges there remains work to be done to optimise the system.

Energy Modeling Inside a Cluster for the Multi-hop Clustered Sensor Networks

Wireless Sensor Networks consisting of a number of small sensor nodes with limited battery power are deployed to collect useful information from the field. Since neighboring sensor nodes generally have the data of similar information, duplicate transmissions of similar information are usual. In order to prevent energy wastes by duplicate transmissions, it is advantageous to organize sensor nodes into clusters and have the cluster-head node to aggregate the collected data and transmit them on behalf of its cluster. The performance of the clustering scheme is influenced by a method of cluster-head election and the size or the number of clusters. Thus we should optimize these factors to maximize the energy efficiency of the clustering scheme. In this paper, we model the energy consumed by cluster-member nodes inside a cluster for the multi-hop clustered sensor networks. Simulation shows that our model is more accurate than the existing energy model