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.

Indoor monitoring system for robot and human is an important issue in robotics research. In this study, we propose an indoor monitoring system based on wireless and pyroelectric infrared (WPIR) sensory fusion system. Pyroelectric infrared (PIR) system alone for localization provides less accurate information of human location and suffers from multiple targets localization. Furthermore, radio frequency (RF) localization system alone is also constrained by its limited accuracy. We develop a sensor network based localization method called WPIR inference algorithm. This algorithm determine the fused position from both PIR localization system and radio frequency signal localization system which utilizes received signal strength (RSS) model. We have developed and experimentally demonstrated a WPIR sensory fusion system which can be applied in localizing targets, such as human and robot. With the proper localization mechanism in the indoor environment, the provision of appropriate services for people can be realized with less expensive cost.

Indoor human monitoring is important for providing various services for individuals. In this research, we propose an indoor monitoring system based on wireless and pyroelectric infrared (WPIR) sensory fusion system which can be embedded and integrated with the traditional fire/smoke detector where they are usually installed on the ceiling by the use of pyroelectric motion sensor and low power wireless communication device. Basically, each of radio frequency (RF) localization system and pyroelectric infrared (PIR) monitoring system provides the coarse information of individuals' location respectively. We develop a sensor network based localization method called WPIR intersection algorithm to determine the fused position from PIR and radio frequency signal localization system which utilizes received signal strength (RSS) model. We have developed and experimentally demonstrated a wireless pyroelectric sensory fusion system and WPIR intersection algorithm which can be applied in multi target tracking issues against pure PIR sensing system. Furthermore, WPIR intersection algorithm provides higher accuracy than PIR or RF localization respectively. With the proper localization and tracking in the indoor environment, the provisions of appropriate services for people can be realized.

Within an intelligent building, it is expected to offer various intelligent services by recognizing residents along with their lifestyle and needs. One of the key issues for realizing the intelligent building is how to detect the locations of residents, so that it can provide the interactive services based on the identified need and requests. In this work, we develop a wireless pyroelectric sensory system embedded with traditional fire detector, which can be implanted on the ceiling. Both of wireless transmission model and pyroelectric sensor monitoring system can provide the rough information of residents' location respectively. These data can be further improved by reducing the sensory uncertainty through covariance intersection (CI) data fusion method. Pyroelectric localization system suffers from multi target tracking and wireless pyroelectric sensor system works this issue. With the location obtained from wireless pyroelectric sensor system, intelligent building can offer suitable services.

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.

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.

We design a "two-layered" dynamic power management strategy in our sensor node to reduce the power consumption. There are many factors such as the probability of event generation, the battery status, the coverage problem, and the communication situations in our consideration. In network communication, we propose an asynchronous awakening scheme so that each node is free to power on or off its components based on the observed event statistics, and make a trade-off between communication and power consumption. The deepest sleep state period is determined by taking account of the residual power. Combining with these methods, the lifetime of the sensor node can be extended as longer as possible without influencing the system performance.