Chia-Pang Chen

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.

A QoS-guaranteed coverage precedence routing algorithm for wireless sensor networks.

For mission-critical applications of wireless sensor networks (WSNs) involving extensive battlefield surveillance, medical healthcare, etc., it is crucial to have low-power, new protocols, methodologies and structures for transferring data and information in a network with full sensing coverage capability for an extended working period. The upmost mission is to ensure that the network is fully functional providing reliable transmission of the sensed data without the risk of data loss. WSNs have been applied to various types of mission-critical applications. Coverage preservation is one of the most essential functions to guarantee quality of service (QoS) in WSNs. However, a tradeoff exists between sensing coverage and network lifetime due to the limited energy supplies of sensor nodes. In this study, we propose a routing protocol to accommodate both energy-balance and coverage-preservation for sensor nodes in WSNs. The energy consumption for radio transmissions and the residual energy over the network are taken into account when the proposed protocol determines an energy-efficient route for a packet. The simulation results demonstrate that the proposed protocol is able to increase the duration of the on-duty network and provide up to 98.3% and 85.7% of extra service time with 100% sensing coverage ratio comparing with LEACH and the LEACH-Coverage-U protocols, respectively.

High-Precision RSSI-based Indoor Localization Using a Transmission Power Adjustment Strategy for Wireless Sensor Networks

Indoor localization is an important issue in wireless sensor network (WSN) studies. Sensed data may become meaningless, if the locations of sensors are not known. Traditional localization techniques do not meet the requirements of low-cost and energy-conservation while performing localization tasks. Recently, the received signal strength indicator (RSSI)-based range measurement technology is widely used in sensor networks due to its easy implementation. In typical indoor environments, RSSI is affected by dense multipath fading effects because people are moving around, or because furniture and equipment block transmission signals. Therefore, the overall accuracy of RSSI-based localization schemes remains low. In this paper, a new localization scheme which is based on a transmission power adjustment strategy is proposed. Firstly, power decay curves are created in a real indoor environment to accurately estimate the distances between an unknown node and anchor nodes. Secondly, the unknown node selects three nearest anchor nodes by using the minimum transmission power to limit the estimated location to a triangle area. And then, the centroid of the triangle is calculated and serves as the initial estimated point. Finally, based on the estimated distances of corresponding power curves determined by RSSI scores using different transmission power levels, the final estimated location falls in one of the three equally divided areas of the triangle. The experimental results demonstrate that the proposed method can provide a low-cost solution for indoor localization with high precision.

CoCMA: Energy-Efficient Coverage Control in Cluster-Based Wireless Sensor Networks Using a Memetic Algorithm

Deployment of wireless sensor networks (WSNs) has drawn much attention in recent years. Given the limited energy for sensor nodes, it is critical to implement WSNs with energy efficiency designs. Sensing coverage in networks, on the other hand, may degrade gradually over time after WSNs are activated. For mission-critical applications, therefore, energy-efficient coverage control should be taken into consideration to support the quality of service (QoS) of WSNs. Usually, coverage-controlling strategies present some challenging problems: (1) resolving the conflicts while determining which nodes should be turned off to conserve energy; (2) designing an optimal wake-up scheme that avoids awakening more nodes than necessary. In this paper, we implement an energy-efficient coverage control in cluster-based WSNs using a Memetic Algorithm (MA)-based approach, entitled CoCMA, to resolve the challenging problems. The CoCMA contains two optimization strategies: a MA-based schedule for sensor nodes and a wake-up scheme, which are responsible to prolong the network lifetime while maintaining coverage preservation. The MA-based schedule is applied to a given WSN to avoid unnecessary energy consumption caused by the redundant nodes. During the network operation, the wake-up scheme awakens sleeping sensor nodes to recover coverage hole caused by dead nodes. The performance evaluation of the proposed CoCMA was conducted on a cluster-based WSN (CWSN) under either a random or a uniform deployment of sensor nodes. Simulation results show that the performance yielded by the combination of MA and wake-up scheme is better than that in some existing approaches. Furthermore, CoCMA is able to activate fewer sensor nodes to monitor the required sensing area.

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.

Collaborative Localization in Wireless Sensor Networks via Pattern Recognition in Radio Irregularity Using Omnidirectional Antennas

In recent years, various received signal strength (RSS)-based localization estimation approaches for wireless sensor networks (WSNs) have been proposed. RSS-based localization is regarded as a low-cost solution for many location-aware applications in WSNs. In previous studies, the radiation patterns of all sensor nodes are assumed to be spherical, which is an oversimplification of the radio propagation model in practical applications. In this study, we present an RSS-based cooperative localization method that estimates unknown coordinates of sensor nodes in a network. Arrangement of two external low-cost omnidirectional dipole antennas is developed by using the distance-power gradient model. A modified robust regression is also proposed to determine the relative azimuth and distance between a sensor node and a fixed reference node. In addition, a cooperative localization scheme that incorporates estimations from multiple fixed reference nodes is presented to improve the accuracy of the localization. The proposed method is tested via computer-based analysis and field test. Experimental results demonstrate that the proposed low-cost method is a useful solution for localizing sensor nodes in unknown or changing environments.

An Adaptive PMU-based Fault Location Estimation System with a Fault-Tolerance and Load-Balancing Communication Network

Modern fault detection/location technique for an EHV/UHV transmission network usually works based on the data measured by Phaser Measurement Units (PMU). The synchronized voltage and current phasors measured by PMU are transmitted to a monitoring center for analysis. Global Positioning System (GPS) receivers are also equipped with PMU, which is called GPS-PMU, to increase the accuracy of fault detection/location by tagging all of the measured data with time stamps. Once a fault was occurred in the transmission network, the time of those measured data transmitted to the monitoring center is crucial. Therefore, a high-quality communication network is required to reduce the response time of the fault detection/location algorithm. In this study, an evolutionary routing algorithm is developed to handle guarantee the minimal data transmission delay, and also robust against faults in communication system itself. The proposed routing algorithm has been tested through two kinds of experimental simulations, and the result shows that the proposed algorithm can provide minimal transmission delay by balancing the traffic over the communication network, and while the network topology has been changed, the proposed routing algorithm can adapt to the new topology in a very short time without seriously affect the response time of the transmission network fault detection/location algorithm.

Ecological Monitoring Using Wireless Sensor Networks-???Overview, Challenges, and Opportunities

Wireless sensor networks (WSNs) offer a powerful feasible integration of distributed sensing capability, real-time data analysis, and remote surveillance due to the combined result of miniaturization of electronic devices and availability of powerful computational capability, larger information storage, and ubiquitous Internet connection. With these advances, WSNs are starting to be translated into a new ecological knowledge. They are providing a new insight into the observation of the world in new ways of extended spatial and temporal scales. Through WSNs, more unexpected phenomena can be obtained, and new paradigms can be developed. Recently, more and more ecological WSNs have been established, and lagre WSNs are deployed to monitor habitats with different scales. The research in the temporal scale ranges from the evaluation of soil moisture dynamics at several minutes to daily precipitation. Spatial measurements, on the other hand, range from the evaluation of global climate change to those related to the monitoring of forest and riparian environments in the range of a few meters. Although we are seeing more use of ecological WSNs, opportunities and challenges begin to be realized, including newly better design of software and hardware, formulation of new questions, discovery of previously unobservable phenomena, and development of new sensors, etc.

A RSSI-based environmental-adaptive dynamic radiation power management for Wireless Sensor Networks

Wireless sensor network (WSN) is one of the active research topics in the field of computer network since it provides three types of basic services: sensing, processing, and disseminating. Ad-hoc and multi-hop WSNs are commonly used in many applications; therefore, maintaining stability of WSN is an essential issue in present days. However, node failures are not exceptions in WSN. Many factors may cause sensor nodes to fail, such as energy exhaustion, out of communication range, or node module damage. These damages are usual incidents in practical WSN applications. Except node module damages that we are unable to look after, we propose an energy-efficient routing algorithm to prolong the lifetime of nodes in WSN. Another feature of the proposed algorithm is that each node routes network packets independently throughout the entire WSN. It makes that algorithm to provide autonomic management of the links in WSN.

Adaptive Classification of Special Events in Agroecological Monitoring Systems for Pest Management

With powerful feasible integration of distributed sensing capability, real-time data analysis, and remote surveillance, wireless sensor networks (WSNs) provide a new insight into agroecological observation in ways of extending spatial and temporal scales. Through WSNs, some unexpected phenomena can be found, and new paradigms can be developed. Although employing the WSN technology can facilitate agroecological observation, one of the major challenges that need to be overcome is the abnormal readings caused by sensor failure, energy depletion of sensor nodes, low durability of protective cases in a wild environment, unreliable wireless communication, etc. In this study, a WSN-based ecological monitoring system is presented and practically deployed in a field to monitor the number of the oriental fruit fly (Bactrocera dorsalis (Hendel)) and capture long-term and up-to-minute natural environmental fluctuations. Moreover, an adaptive classification approach, built upon self-organizing maps and support vector machines, is incorporated into the monitoring system to automatically identify special events of pest outbreaks and sensor faults. Once the events are detected, farmers and government officials can take precautionary action in time before pest outbreaks cause an extensive loss or schedule maintenance tasks to repair monitoring devices. The proposed classification approach is easily adopted in different monitored farms, and it can automatically identify special events based on machine learning techniques without requiring additional manpower.