A remote mobile health monitoring system with mobile phone and web service capabilities is proposed in this paper. It provides an end-to-end solution; specifically, (1) physiologic parameters, including respiration rate and heart rate, are measured by wearable sensors and recorded by a mobile phone which presents the graphical interface for the user to observe his/her health status more easily; (2) it provides doctors and family members with necessary data through a web interface and enables authorized personnel to monitor the patient's condition and to facilitate remote diagnosis; and (3) it supports real-time alarming and positioning services during an urgent situation, such as a tumble or a heart attack, so that unexpected events can be handled in a timely manner. Experimental results show that the proposed system can reliably monitor the physiologic parameters and conveniently report the user's position.

Indoor target localization is an essential and fundamental issue for wireless sensor networks (WSN). However, it is rather difficult for WSN to maintain the localization accuracy in line-of-sight (LOS) and non-line-of-sight (NLOS) mixed environment. NLOS propagation always leads to larger ranging error than LOS does. When the target moves in the rooms and corridors, the signal transmission state will switch frequently between LOS and NLOS. It is a challenging task to deal with this situation because the ranging error characteristics under LOS and NLOS conditions are quite different. In this paper, we propose an interacting multiple model-extended Kalman filter (IMM-EKF) algorithm to improve the localization accuracy for moving target in indoor environment. In the IMM structure, two Kalman filters (KF) are adopted in parallel to accurately smoothen the distance measurement. The proposed algorithm can adapt to the dynamically changing condition between LOS and NLOS due to the two KFs' interaction so that large NLOS ranging errors are further reduced. Once the estimated ranges are obtained, the EKF is employed to estimate the target's location. Empirical measurement results are obtained from typical office environment to verify the effectiveness of the proposed algorithm. Experimental results illustrate that the IMM smoother can efficiently mitigate the NLOS effects on ranging errors and achieve high localization accuracy.

In recent years, collinearity theory is widely used in large-scale sensor network. When the anchor nodes are located at almost a straight line, the collinearity phenomenon will happen and usually cause negative influence on positioning accuracy. From detailed analysis of the relation between DV-Hop localization error and the collinearity, we proposed to select the anchor nodes which can meet the condition of hop count threshold and collinearity to participate in the localization procedure. Since there is uncertain situation that the anchor node's region is hard to be decided for the sensor nodes in one hop area, Voronoi diagram is adopted to divide the sensor network into several regions. Then, we can get the anchor node information in each Voronoi polygon. With this information and the collinearity condition, we can estimate the unknown node's position with relatively higher accuracy. Compared with the traditional DV-Hop and collinearity algorithm, our proposed algorithm can get better positioning accuracy in both homogeneous network and anisotropic network.

In wireless sensor networks (WSN), the geometric distribution of anchor nodes has a significant influence on the positioning accuracy. Geometric dilution of precision (GDOP) can be used to measure the positioning precision of the localization system. In order to select the optimal node combination, traditional algorithms based on GDOP need to spend much time on calculating every possible combination of nodes. This paper proposes GDOP assisted nodes selection (GANS) algorithm to calculate GDOP value of the current geometric distribution. Sensor node's contribution to the overall GDOP value is adopted as the evaluation criteria. The nodes whose contribution value is greater than the threshold will be selected. The anchor nodes subset, which participates in the positioning, will be real-time determined. Simulation results show that the GANS algorithm can effectively reduce the energy consumption of the system, while the positioning accuracy has no obvious loss. Meanwhile, computational complexity is also obviously decreased.