Yeon-Mo Yang

On the design of beacon based wireless sensor network for agricultural emergency monitoring systems

In this paper, we proposed new sensor network architecture with autonomous robots based on beacon mode for real time agriculture monitoring system. The proposed scheme also offers a reliable association with parent nodes and dynamically assigns network addresses. For the large scale multi-sensor processing, the proposed system accomplished the intelligent database, which generates alert messages to the handheld terminal by means of the fire and air-based sensor data. Thus farmers can easily check out the current conditions of crops and farms at anytime and anywhere. Moreover, we also developed a robot platform with network based mobility function for mobile surveillance.

Soft Computing Techniques in Vision Science

This Special Edited Volume is a unique approach towards Computational solution for the upcoming field of study called Vision Science. From a scientific firmament Optics, Ophthalmology, and Optical Science has surpassed an Odyssey of optimizing configurations of Optical systems, Surveillance Cameras and other Nano optical devices with the metaphor of Nano Science and Technology. Still these systems are falling short of its computational aspect to achieve the pinnacle of human vision system. In this edited volume much attention has been given to address the coupling issues Computational Science and Vision Studies. It is a comprehensive collection of research works addressing various related areas of Vision Science like Visual Perception and Visual system, Cognitive Psychology, Neuroscience, Psychophysics and Ophthalmology, linguistic relativity, color vision etc. This issue carries some latest developments in the form of research articles and presentations. The volume is rich of contents with technical tools for convenient experimentation in Vision Science. There are 18 research papers having significance in an array of application areas. The volume claims to be an effective compendium of computing developments like Frequent Pattern Mining, Genetic Algorithm, Gabor Filter, Support Vector Machine, Region Based Mask Filter, 4D stereo camera systems, Principal Component Analysis etc. The detailed analysis of the papers can immensely benefit to the researchers of this domain. It can be an Endeavour in the pursuit of adding value in the existing stock of knowledge in Vision Science.

Burst-Polling-Based Dynamic Bandwidth Allocation Using Adaptive Minimum Guaranteed Bandwidth for EPONs

The enhanced burst-polling dynamic bandwidth allocation (EBDBA) method is proposed to support broadband access networks based on quality of service for Ethernet passive optical networks (EPONs). EBDBA adaptively increases or decreases the minimum guaranteed bandwidth of the three traffic clasess¿expedited forwarding (EF), assured forwarding (AF), and best effort (BE) traffic¿according to the requested bandwidth of an optical network unit. Therefore, network resources are efficiently utilized and adaptively allocated to the three traffic classes for unbalanced traffic conditions. Simulation results using OPNET show that EBDBA outperforms conventional bandwidth allocation schemes in terms of the average packet delay (it decreases the maximum performance range to 68%) and the network throughput (it increases the maximum performance range to 20%) at a given offered load of 1.2.

Applying Kalman filter in EEG-Based Brain Computer Interface for Motor Imagery classification

Human imagination and intention can be read by using the Electroencephalography(EEG)-Based Brain Computer Interface(BCI) for Motor Imagery. The systems reads the human brain which consists of left or right imaginary movement and decide the actual movement. It is useful especially for the disable people to help their daily life. But the signals include the unwanted signals and other features together with the actual signal. The challenges are to remove the unwanted signals and extract the accurate feature and classify the signal accurately by using the best classification method. This paper applied the Kalman filter to the BCIs signal processing methods to improve the accuracy and the reliability of the systems. The Common Spatial Pattern (CSP) is used to extract the feature and the Radial Basis Function (RBF) as the classification method. We also compared other classification method which is Fisher Linear Discriminant Analysis (FLDA) to show the RBF gives better result. The simulation result shows improvement by using the proposed method compared to the other method.

A three-dimensional localisation algorithm for underwater acoustic sensor networks

Simultaneous localisation mapping (SLAM) is a scheme for location-aware technology that can be applied in vehicles or autonomous robots in outdoor environments. The SLAM scheme helps in the localisation of AUVs (autonomous underwater vehicles) used in deep ocean exploration. In this paper, we propose a 3D SLAM scheme that utilises an extended Kalman filter (EKF) based on landmarks in underwater sensor networks (UWSNs). We obtained our research results by the following three steps. First, we designed and implemented the AUV model using kinematics. Second, we analysed the EKF for use in SLAM and derived an optimisation model of the EKF using information on a moving path and landmarks in an underwater environment. Finally, we implemented the map through the derived EKF, as applied to SLAM in UWSNs. We have thoroughly compared the value of a pre-defined path to the predicted value and confirmed the average position error in terms of AUV speed, number of landmarks, and the sampling time. Through Matlab simulations, we have shown that the proposed scheme achieves a smaller position error under a change in vehicle speed and number of landmarks. We have also confirmed the minimum performance of the 3D-SLAM technique for use in underwater applications.

A Tree Based Self-routing Scheme for Mobility Support in Wireless Sensor Networks

Recently, WSNs (Wireless Sensor Networks) with mobile robot is a growing technology that offer efficient communication services for anytime and anywhere applications. However, the tiny sensor node has very limited network resources due to its low battery power, low data rate, node mobility, and channel interference constraint between neighbors. Thus, in this paper, we proposed a tree based self-routing protocol for autonomous mobile robots based on beacon mode and implemented in real test-bed environments. The proposed scheme offers beacon based real-time scheduling for reliable association process between parent and child nodes. In addition, it supports smooth handover procedure by reducing flooding overhead of control packets. Throughout the performance evaluation by using a real test-bed system and simulation, we illustrate that our proposed scheme demonstrates promising performance for wireless sensor networks with mobile robots.

An Efficient Self Routing Scheme by Using Parent-Child Association for WSNs

The IEEE standard 802.15.4 for WPAN (Wireless Personal Ares Networks) shows promise of bringing ubiquitous sensor networking (USN) into reality, at least technically. In this paper, an Association based Self Routing (ASR) scheme of a IEEE 802.15.4 beacon-enabled mode is presented to minimize the control packet overhead and surplus beacon channel power during the routing discovery process in the data transmission for the upstream direction. It also maximizes channel utilization under low duty cycles or traffic congestion. The proposed scheme, ASR routes packets from sensor routers to sink based on the parent-child association relationships established by the IEEE 802.15.4 topology formation procedure. By using the ASR, the network resources for control packets are efficiently utilized and adaptively allocated throughout different duty cycles. Extensive simulation results using TOSSIM (TinyOS mote SIMulator) and experimental results with Mica-Z show that the ASR scheme outperforms well in comparison to the conventional Ad-hoc On-Demand Distance Vector (AODV) routing scheme over the parameters such as: average throughput, number of collisions, number of network commands, and delivery ratio under the upstream data transmission.

QoS provisioning in the EPON systems with traffic-class burst-polling based delta DBA

As an alternative solution to provide the quality of services (QoS) for broadband access over Ethernet Passive Optical Network (EPON), we present the usage of MAC control message for plural class queues and a traffic-class burst-polling based delta dynamic bandwidth allocation (DBA), referred to as TCBP-DDBA, scheme. For better QoS support, the TCBP-DDBA minimizes packet delays and delay variations for expedited forwarding packet and maximizes throughput for assured forwarding and best effort packets. The network resources are efficiently utilized and adaptively allocated to the three traffic classes for the given unbalanced traffic conditions by guaranteeing the requested QoS. Simulation results using OPNET show that the TCBP-DDBA scheme performs well in comparison to the conventional unit-based allocation scheme over the measurement parameters such as: packet delay, packet delay variation, and channel utilization.

TDOA-based distance estimation for mobile beacon-assisted localization in large-scale sensor networks

In large-scale sensor network, self-locating a sensor node is important in order to monitor events occurring in surroundings. A distance to a reference point is key information to estimate location of a sensor node. In this paper, we propose TDOA-based distance estimation to improve accuracy in a distance to a reference point where a mobile beacon broadcast its absolute position. For locating asynchronous sensor nodes, we first model a set of TDOA-based measured distances to a mobile beacon moving along the linear tracks. Based on the model, distance estimation adopts Gauss-Newton method to minimize square errors in measurements. Through simulation, the proposed scheme shows that it can provide high accuracy in distance estimation compared to our previous work [4].

Design and implementation of beacon based Wireless Sensor Network for real-time monitoring systems

Recently, WSNs (wireless sensor networks) with mobile robot is a growing technology that offer efficient communication services for anytime and anywhere applications. However, the tiny sensor node has very limited network resources due to its low battery power, low data rate, node mobility, and channel interference constraint between neighbors. Furthermore, most wireless sensor networks do not support real-time processing for quality of service (QoS) applications. Thus, in this paper, we proposed new sensor network architecture with autonomous robots based on beacon mode and implemented real time monitoring system under real test-bed environments. The proposed scheme offers beacon based real-time scheduling for reliable association process with parent nodes and dynamically and efficiently assigns network address by using NAA (next address assignment) mechanism. For the large scale multi-sensor processing, our real-time monitoring system accomplished the intelligent database processing, which can generate and send the alert messages to the civilians by means of the fire-based sensor data and the air-based sensor data. Moreover, we also developed a mobile robot which has a network based mobility function. Finally, throughout the performance evaluation by using a real test-bed system, we illustrate that our proposed system demonstrates promising performance for emergence monitoring systems.