Ogst Chen

Mobile Sensor Node Deployment and Asynchronous Power Management for Wireless Sensor Networks

Mobile sensor node deployment and power management are important issues in the wireless sensor network system. This study designs a mobile sensor node platform to achieve a highly accurate localization mechanism by using ultrasonic, dead reckoning, and radio frequency information which is processed through a particle filter algorithm. Mobile sensor node with accurate localization ability is of great interest to basic research works and applications, such as sensor deployment, coverage management, dynamic power management, etc. In this paper, we propose an efficient mobile sensor node deployment method, grid deployment, where the map is divided into multiple individual grids and the weight of each grid is determined by environmental factors such as predeployed nodes, boundaries, and obstacles. The grid with minimum values is the goal of the mobile node. We also design an asynchronous power management strategy in our sensor node to reduce power consumption of the sensor network. Several factors such as probability of event generation, battery status, coverage issues, and communication situations have also been taken into consideration. In network communication, we propose an asynchronous awakening scheme so that each node is free to switch on or off its components according to observed event statistics and make a tradeoff between communication and power consumption. The deepest sleep state period is determined by the residual power. By combining these methods, the power consumption of the sensor node can be reduced.

Wireless and Pyroelectric Sensory Fusion System for Indoor Human/Robot Localization and Monitoring

An indoor localization and monitoring system for robots and people is an important issue in robotics research. Although several monitoring systems are currently under development by previous investigators, these issues remain significant difficulties. For instance, the pyroelectric IR (PIR) system provides less accurate information of human location and is restricted when there are multiple targets. Furthermore, the RF localization system is constrained by its limited accuracy. In this study, we propose an indoor localization and monitoring system based on a wireless and PIR (WPIR) sensory fusion system. We develop a sensor-network-based localization method called the WPIR inference algorithm. This algorithm determines the fused position from both the PIR localization system and RF signal localization system, which utilize the received signal strength propagation model. We have developed and experimentally demonstrated a WPIR sensory fusion system, which can be successfully applied in localizing multiple targets based on two robots and two people in this study. With an accurate localization mechanism for the indoor environment, the provision of appropriate services for people can be realized.

Mobile user localization in wireless sensor network using grey prediction method

Knowing the position of mobile user is an important role for location services in the building. The characteristics of wireless sensor network are low power, low cost and low complexity. With these functions, wireless sensor network have great potential to develop indoor position system. However, radio signal propagation is easily affected by diffraction, reflections, and scattering of radio in the building, the received signal strength need good calibration method to improve the accuracy of position estimation system. In this paper we use grey prediction method in wireless sensor network and employ wireless LAN medium (Zigbee/802.15.4). The grey prediction is used to predict the tendency of RSSI (received signal strength indicator), and we also designed dynamic triangular (DTN) location method. We have done some experiments and compare with other classical location finding methods. The mean distant error of RSSI on mobile user can be within 2.3 m at offline stage. As a result, grey predication with DTN provides more accurate predicted position and carries out mean distance error within 1.3 m at run-time stage.

Multisensor Fusion and Integration: Algorithms, Applications, and Future Research Directions

Multisensor fusion and integration is a rapidly evolving research area and requires interdisciplinary knowledge in control theory, signal processing, artificial intelligence, probability and statistics, etc. The advantages gained through the use of redundant, complementary, or more timely information in a system can provide more reliable and accurate information. This paper provides an overview of current sensor technologies and describes the paradigm of multisensor fusion algorithms and applications of multisensor fusion in localization and tracking, robotics, identification and classification, vehicle sensing, and so on. Finally, future research directions of multisensor fusion technologies including microsensors, smart sensors, and adaptive fusion techniques are presented.

Nodes localization through data fusion in sensor network

The location of nodes in sensor network is an important problem with application in resource allocation, location sensitive browsing, and emergency communications. A key problem in sensor network location is the creation of a method that is robust to measurement quantization and measurement noise and also has a reasonable implementation cost. The RSS and TDoA are the popular distance measurement methods, but can be easy affected by noise and not independent. This paper explores a covariance intersection (CI) that fuses together location estimations obtained from power propagation loss measurements and propagation time to obtain higher accuracy location estimate. The performances of Kalman filter type estimators are severely affected by the ignored cross covariance. CI algorithm provides a mechanism to fuse two or more random variables with unknown correlation such that the computed covariance of the new estimate is consistent; therefore CI method is quite suitable for sensor network application. In simulation result shows that position estimates are correct within small ranging distance with few initial master nodes of the system.

A Triangular Selection Path Planning Method with Dead Reckoning System for Wireless Mobile Sensor Mote

Leveraging numerous integrated sensing devices placed close to the actual monitoring environments, the information that such networks can provide is more accurate and richer than the information provided with a system of few, expensive sensing devices. The general design of such sensor node is motionless, but the mobile sensor is useful in some aspects. Nodes can be distributed at any corner easily, move to a specific emergent area, or replace the power exhausted nodes. Mobile sensor nodes can increase the interaction between user and system. Moreover, the ability of auto-recharging for mobile sensor nodes can solve the powerlessness problem for sensor network. We had designed a mobile sensor node with compass to correct the direction of movement; six IR sensors let mobile nodes with the ability of obstacle avoidance. We propose an algorithm called triangular selection path planning (TSPP) method, and along with the dead-reckoning (DR) algorithm to merge heading readings from compass and two encoders mounted on the wheels to compute the dead-reckoned location estimation. With these technologies, our mobile sensor node can know their own position accurately and achieve the destination efficiently.

Indoor robot/human localization using dynamic triangulation and wireless Pyroelectric Infrared sensory fusion approaches

Indoor localization and monitoring system of robots and people are essential issues in robotics research. Several monitoring systems are currently under development by different investigators but they do encounter significant difficulties. For instance, a Pyroelectric Infrared (PIR) system provides less accurate information of human location and is restricted when there are multiple targets. Furthermore, a Radio Frequency (RF) localization system is constrained by its limited accuracy. In this study, we develop a system which combines PIR and RF localization system as wireless pyroelectric infrared sensory fusion system to monitor the location information of robots and people. We will reduce the error of RF localization information through tile proposed dynamic triangulation (DTN) method. We also develop a sensory fusion algorithm called the WPIR inference algorithm. This algorithm determines the fused position from both the PIR localization system and radio frequency signal localization system which utilize tile received signal strength (RSS) propagation model. We have developed and experimentally demonstrated a WPIR sensory fusion system which can be successfully applied in locating targets such as people and robot. With an accurate localization mechanism for tile indoor environment, tile provision of appropriate services to people can be realized.

Robotics human tracking system through wireless pyroelectric sensor system

This paper present a human tracking system using pyroelectric sensor and radio frequency signal to detect the motion of people in front of robot. A prototype system contains 4 pyroelectric sensor elements that are able to detect the angular displacement of the moving human body. And we also use a tag which contains ZigBee radio frequency (RF) transceiver as body sensor network element. We can use this tag to identify the user and locate the user through radio frequency localization technology. With the pyroelectric angular and RF distance message, we can apply two signals into Kalman tracking scheme to trace the trajectory of user accurately (improved displacement error 21% from 1.01 m reduce to 0.79 m). We have developed a prototype wireless pyroelectric sensing system on mobile robot. After process the pyroelectric signals, we can successfully convert those signals into angular and distance displacement with respect to pyroelectric sensor and radio frequency localization method.

A Novel Indoor Heat Source Distribution Surveillance System through Sensor Network

A novel method is advanced in this approach; using cheap sensor module and combining a characteristic of sensor networks to attend to an overall monitoring, low-cost, and simplify. However, several systems use different kinds of sensor (i.e. radar, ultrasonic, and laser-scanner). They might be expensive, complexity and the monitoring area are limited. And in our research, a concept of image processing is recommended. Our sensor-board is produced by Dr Robot, Inc., pyroelectric human motion sensor module, the sensor can detect a motion and a presence alarm of target. The goal of our system is to save energy by controlling light and air condition. Users can get the heat source distribution; and control the air condition through our system effectively.

MEMS based thin film pressure/temperature sensor for on-line monitoring injection molding

Various sensors such as traditional thermocouples and pressure sensors have been used in process monitoring and control. These traditional sensors have several limitations. The authors propose a new approach for measuring information of molds core and cavity. They also propose a fabrication process of a thin-film pressure/temperature sensor using the information from micro sensor into core and cavity to realize online monitoring.