Concerning the low efficiency of data update and low security of mobile terminal, a storage and segmentation encoding technology was proposed. Firstly, the data of mobile terminal was equally segmented and stored in cloud,then each segment of data was marked by coding techniques; secondly, the corresponding data block information was downloaded to update when users update the data; finally, the updated data was re-encoded, and cryptographically uploaded to the cloud in appropriate position to form a complete data to be stored. The experimental results show that compared with traditional mobile security solution including Advanced Encryption Standard (AES) scheme and Regenerating Code (RC) coding technique, the proposed model can save file conversion time when the data is updated frequently in cloud, and significantly reduce the performance overhead of the mobile terminal. The proposed model can significantly improve the efficiency of updating data and resource utilization rate of mobile terminal, and effectively strengthen the confidentiality and integrity of mobile cloud data,it has obvious advantages for the demand of updating mobile terminal data frequently.

For the low tag identification rate problem caused by that reader can not identify multiple tags simultaneously in single-antenna Radio Frequency Identification (RFID) system, combined with multi-antenna technology and the grouping of binary tree slots based on tags ID sequence, an adaptive tree grouping and blind separation anti-collision algorithm for RFID system was proposed. In the presented algorithm, by adjusting query code length of reader according to the number of antennas in RFID system and sending a query signal, the eligible response tags were assigned to the appropriate slots so that the number of tags in each slot was less than or equalled to the number of antennas and met Blind Source Separation (BSS) system conditions which could identify tags, so as to achieve the purpose of identifying tags simultaneously and quickly. Compared with the Blind Separation and Dynamic Bit-slot Grouping (BSDBG) algorithm using the same multi-antenna technology, the simulation results show that the tag identification speed of the proposed algorithm increases from 20% to 69% and the tag identification rate improves from 60% to 88% when the number of antennas is from 4 to 32, while it has low complexity, low hardware overhead and it is relatively simple to implement and conducive to the promotion and use.

Tag collision in Radio Frequency Identification (RFID) system increases the time overhead and energy consumption, reduces the speed of recognition. With the increasing number of tags, the collision is more obvious, thus the system performance decreases sharply. In order to solve the problem of collision among multiple tags in RFID system, an optimized anti-collision algorithm for RFID system based on tag grouping was proposed by analyzing frame slotted ALOHA algorithm. The tags of this algorithm were divided into several groups through the Cyclic Redundancy Check (CRC) code which tags carry, then it recorded tag group number and identified each group according to the grouping sequence, therefore the number of tags which simultaneously responded to the reader's order would be reduced. For the problem of timeslot selective confliction in the identification process, the chaotic system was used to generate uniformly distributed pseudorandom numbers, and it was conducive to select timeslots randomly for the tags within identification state, which made the timeslots selection more uniform in a frame and finally reached the purpose of reducing frequency of tag collision. In the comparative experiments with traditional algorithm, the optimization algorithm needed less orders when the number of tags to be identified was equal, and the order number and tag number showed an approximate linear relationship. The tag identification speed improvement of the optimization algorithm was stable at 50% when the number of tags to be identified was less than 256, and the speed improvement increased to 80% when the number of tags to be identified was more than 256. Theoretical analysis and simulation results indicate that the optimization algorithm has faster tag identification speed, and its performance is more obvious with the increasing of the tags.