In recent years， a wireless platform for speech recognition using multiple customized and stretchable Radio Frequency Identification （RFID） tags has been proposed， however， it is difficult for the tags to accurately capture large frequency shifts caused by stretching， and multiple tags need to be detected and recalibrated when the tags fall off or wear out naturally. In response to the above problems， a lip language recognition algorithm based on single-tag RFID was proposed， in which a flexible， easily concealable and non-invasive single universal RFID tag was attached to the face， allowing lip language recognition even if the user does not make a sound and relies only on facial micro-actions. Firstly， a model was established to process the Received Signal Strength （RSS） and phase changes of individual tags received by an RFID reader responding over time and frequency. Then the Gaussian function was used to preprocess the noise of the original data by smoothing and denoising， and the Dynamic Time Warping （DTW） algorithm was used to evaluate and analyze the collected signal characteristics to solve the problem of pronunciation length mismatch. Finally， a wireless speech recognition system was created to recognize and distinguish the facial expressions corresponding to the voice， thus achieving the purpose of lip language recognition. Experimental results show that the accuracy of RSS can reach more than 86.5% by the proposed algorithm for identifying 200 groups of digital signal characteristics of different users.

Due to the high dynamics of pervasive computing environment and the constraints of easy interruption and low transmission rate of wireless network, the overhead of context access is very huge. To solve these problems, a framework of context-aware system was proposed in this paper, and then a context caching replacement algorithm based on rules (RCRA) was introduced. The proposed algorithm determined whether to replace a context in the cache based on its access probability, timeliness and access history. When a new context was to get into the context cache, the algorithm was used to ensure that the latest and the most valuable context stayed in the cache. The experimental results show that the RCRA improves the hit rate and effectively reduces the overhead of context access. The RCRA is used in the rules-based context-aware system, and the algorithm has good utility.