Xiang-Yao Zheng

A Distributed RSS-Based Localization Using a Dynamic Circle Expanding Mechanism

This paper focuses on localization that serves as a smart service. Among the primary services provided by Internet of Things (IoT), localization offers automatically discoverable services. Knowledge relating to an objects position, especially when combined with other information collected from sensors and shared with other smart objects, allows us to develop intelligent systems to fast respond to changes in an environment. Today, wireless sensor networks (WSNs) have become a critical technology for various kinds of smart environments through which different kinds of devices can connect with each other coinciding with the principles of IoT. Among various WSN techniques designed for positioning an unknown node, the trilateration approach based on the received signal strength is the most suitable for localization due to its implementation simplicity and low hardware requirement. However, its performance is susceptible to external factors, such as the number of people present in a room, the shape and dimension of an environment, and the positions of objects and devices. To improve the localization accuracy of trilateration, we develop a novel distributed localization algorithm with a dynamic-circle-expanding mechanism capable of more accurately establishing the geometric relationship between an unknown node and reference nodes. The results of real world experiments and computer simulation show that the average error of position estimation is 0.67 and 0.225 m in the best cases, respectively. This suggests that the proposed localization algorithm outperforms other existing methods.

Application of a web-based remote agro-ecological monitoring system for observing spatial distribution and dynamics of Bactrocera dorsalis in fruit orchards

Improving fruit farm profitability through integrated pest management (IPM) programs is always an important issue to modern agriculture systems. In order to enhance IPM programs against Bactrocera dorsalis, an automatic infield monitoring system is required to efficiently capture long-term and up-to-the-minute environmental fluctuations in a fruit farm. In this study, a remote agro-ecological monitoring system built upon wireless sensor networks has been developed to provide precision agriculture (PA) services with large-scale, long-distance, long-term, scalable, and real-time infield data collection capabilities. Historical data with spatial information is available through a web-based decision support program built upon a database. Pest population forecast results are also provided so that farmers and government officials would be able to accurately respond to infield variations. Compared with the previous version of the system, various useful functions have been added into the system, and its accuracy has been improved when measuring different parameters in the field. The system could provide a valuable framework for farmers and pest control officials to analyze the relations between population dynamics of the fruit fly and meteorological events. Based on the analysis, a better insect pest risk assessment and accurate decision-making strategy can be made as an aid to PA against B. dorsalis.

A self sustainable air quality monitoring system using WSN

In recent years, urban air quality monitoring is increasingly important. To monitor air quality in urban areas, wireless sensor networks (WSNs) might be a great tool, because they can automatically collect air-quality data. WSNs are able to provide data with spatiotemporal continuity, so researchers can analyze the data in detail. This paper proposes a self-sustainable air quality monitoring system based on the WSN technology to collect air quality parameters in an urban area. The proposed system uses carbon monoxide (CO) and particulate matter (PM) sensors to monitor CO and PM, and the readings from the two sensors are viewed as air quality indices. The proposed system combines a solar cell and a lead-acid battery as its power supply devices, so the system has the long-tern monitoring capability.

On Dispatching Line Ampacities of Power Grids Using Weather-Based Conductor Temperature Forecasts

Finding ways to improve the line ampacity supplied by existing power grids is an inevitable problem that electricity dispatch operators are now facing. Generally, the overhead power grid can operate at a proper rating when the weather dependant dynamic thermal rating (DTR) of lines is provided. A comprehensive understanding of future variation of line temperature is necessary and useful for line operators to decide a proper dispatch measure. However, line operators usually lack real-time DTR information. Thus, this paper proposes a principal component regression (PCR)-based method, which can predict the DTR of lines by only using the weather data forecasted by meteorological stations. The possible thermal bottlenecks at different locations along the line and the margin of line ampacity can be real-time determined by the proposed PCR-based prediction method. A case study of the 161 kV transmission grid of TaiPower in Taiwan is utilized to examine the performance of the proposed forecasting model. The experimental results show that the proposed method is useful to enhance line ampacity of the power grids without installing any costly sensing instruments.

A data retransmitting mechanism for ecological monitoring system

For ecological monitoring systems, spatiotemporal continuity of sensed data is an important issue. Wireless sensor networks (WSNs), composed of many tiny sensing devices, give a great way to collect continuously temporal and spatial data. Deployed in a wild field, harsh weather may lead to breakdown of these electronic devices or transmission loss between sensor devices. The data delivery rate may decrease because of these reasons. This article proposes a data retransmitting mechanism (DRM) for WSNs to solve the data discontinuity problems. The proposed mechanism allows sensor networks to retransmit sensed data, so the data continuity can be maintained.

Design of a Seismic Hazard Risk Assessment Model for EHV Transmission Grid

The Chi-Chi earthquake is one of the biggest earthquake occurred in Taiwan and caused a huge damage to the power system, especially the extra- high voltage (EHV) towers. Therefore, seismic hazards for EHV transmission towers should not be underestimated. In particular, earthquakes are especially a significant threat to EHV transmission towers in Taiwan. Thus, this study establishes a quantitative risk assessment model for the seismic hazard analysis on the EHV transmission tower. Fragility curves of EHV towers were established by nonlinear dynamic analysis to describe the probability of structures at different damage levels caused by earthquakes. The damage level of an EHV tower after an earthquake can be accurately estimated by the proposed model, and emergency repair operations can be arranged. In addition, before an earthquake occurs, the proposed model can be used as a tool for estimate the damage potential of EHV towers.

A Novel Transmission Line Safety Monitoring System for Smart Grid

A smart grid is defined as novel electric power grid infrastructure that improves the efficiency, reliability and safety of the grid, by integrating renewable and alternative energy sources through automated control and novel communication technologies. The increasing demand for more effective electrical power system control has led to the rapid development of smart grids. In this study, a novel transmission line safety monitoring system for smart grid is proposed. The proposed system consists of transmission line sensor modules and wireless communication gateways. To verify the proposed system, a number of experiments are conducted in real extra high-voltage laboratory environment.

A seismic assessment algorithm for smart grid safety monitoring

Among natural disasters, earthquakes are a major factor that damages transmission lines and power towers, but the impacts of earthquakes on transmission towers are less discussed. For many countries, earthquakes often cause power outages. Traditionally, the safety of extra-high voltage (EHV) transmission systems is inspected by the staff of power companies. Due to high labor costs and time consuming patrols, system anomalies are difficult to be identified immediately. Therefore, this study develops a seismic assessment algorithm for electrical grid safety monitoring with the wireless sensing technology to assess seismic hazard for the EHV transmission system. In the monitoring system, the environment sensing data can be transmitted in real-time by a wireless network. And the intensity data can be analyzed by the algorithm immediately after an earthquake occurs. Specifically, peak ground acceleration (PGA) is adopted as the seismic intensity parameter, and effective peak acceleration (EPA) is a reference parameter used to evaluate towers seismic hazard. Based on the simulation and verification results, the accuracy of the proposed seismic intensity algorithm is high, and the calculation speed is fast. Thus, the purpose of early warning can be achieved by using the proposed algorithm.