Wireless sensor’s traditional data storage algorithms exist many problems, such as lack of adaptability and load balancing, great energy consumption, long network cycle lifetime, high delay access rate and so on. This paper proposes an adaptive clustering based on data storage (CBDS) algorithm to deal with these problems. Through analysis of the available data storage methods, we studied how to determine data storage nodes under the premise of limited energy of sensor networks and more consumers exist in the network. We concluded a common storage strategy which combines sensor networks set’s centralized storage, local storage and distributed storage. Finally, we did compared experiments between the CBDS algorithm and other related algorithms. Experiments showed that: CBDS has obvious advantages like self-adaptive, load balancing, low access latency and less energy consumption than traditional algorithm. CBDS is more conducive to data storage
Aiming to solve the problems of high memory access and big storage space and long matching time in the regular expression matching of extended finite automaton (XFA), a new regular expression matching algorithm based on high-efficient finite automaton is presented in this paper. The basic idea of the new algorithm is that some extra judging instruments are added at the starting state in order to reduce any unnecessary transition paths as well as to eliminate any unnecessary state transitions. Consequently, the problems of high memory access consumption and big storage space and long matching time during the regular expression matching process of XFA can be efficiently improved. The simulation results convey that our proposed scheme can lower approximately 40% memory access, save about 45% storage space consumption, and reduce about 12% matching time during the same regular expression matching process compared with XFA, but without degrading the matching quality. Category: Smart and intelligent computing
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