Handling complex data in uncertain environments has been concerned for a long time. In order to solve the problems of dealing with multidimensional data in fuzzy linguistic environments and mining rules contained between attributes described by linguistic values in different domains， an association rule extraction method based on triadic fuzzy linguistic formal context was proposed. Firstly， a triadic fuzzy linguistic formal context was developed by combining a linguistic term set with triadic concept analysis theory. Subsequently， triadic fuzzy linguistic concepts were defined on the basis of derivation operators， and an incremental construction idea was applied to develop a knowledge discovery algorithm based on triadic fuzzy linguistic formal context， thereby acquiring conceptual knowledge with semantic information under fuzzy triadic relations， as the result， the relationships between concept knowledge were depicted through constructing a triadic fuzzy linguistic diagram. Finally， an association rule extraction method based on triadic fuzzy linguistic concepts was introduced to explore correlations between attributes， resulting in semantic rules with conditional constraints. Experimental results on real datasets of different domains show that the proposed method handles multidimensional data effectively in fuzzy linguistic environments， acquires conceptual knowledge with semantic information， and mines semantic rules with high credibility.

DV-Hop algorithm uses the hop number multiplied by the average distance per hop to estimate the distance between nodes and the trilateral measurement or the maximum likelihood to estimate the node coordinate information, which has defects and then causing too many positioning errors. This paper presented an improved DV-Hop algorithm based on node density regional division (Density Zoning DV-Hop, DZDV-Hop), which used the connectivity of network and the node density to limit the hop number of the estimated node coordinate information and the weighted centroid method to estimate the positioning coordinates. Compared with the traditional DV-Hop algorithm in the same network hardware and topology environment, the result of Matlab simulation test shows that, the communication amount of nodes can be effectively reduced and the positioning error rate can be reduced by 13.6% by using the improved algorithm, which can improve the positioning accuracy.