Traditional vision-based or sensor-based falling detection systems possess certain inherent shortcomings such as hardware dependence and coverage limitation, hence Fallsense, a passive falling detection method based on wireless Channel State Information (CSI) was proposed. The method was based on low-cost, pervasive and commercial WiFi devices. Firstly, the wireless CSI data was collected and preprocessed. Then a model of motion-signal analysis was built, where a lightweight dynamic template matching algorithm was designed to detect relevant fragments of real falling events from the time-series channel data in real time. Experiments in a large number of actual environments show that Fallsense can achieve high accuracy and low false positive rate, with an accuracy of 95% and a false positive rate of 2.44%. Compared with the classic WiFall system, Fallsense reduces the time complexity from O( mN log N) to O( N) ( N is the sample number, m is the feature number), and increases the accuracy by 2.69%, decreases the false positive rate by 4.66%. The experimental results confirm that this passive falling detection method is fast and efficient.

This article focused on the mobile sink scheduling problem in Wireless Sensor Networks (WSN). A mobile single-sink scheduling algorithm in wireless sensor networks was proposed based on Linear Programming (LP). Firstly, the problem was mathematically modeled and formulated in time domain, and the problem was re-formulated from time to space domain to reduce the complexity. Then a polynomial-time optimal algorithm was proposed based on linear programming. The simulations confirm the efficiency of the algorithm and the results show that the algorithm can significantly improve the network lifetime of wireless sensor networks.