Aiming at the drawbacks that Sparrow Search Algorithm （SSA） has relatively low search accuracy and is easy to fall into the local optimum， an Enhanced Sparrow Search Algorithm based on Multiple Improvement strategies （EMISSA） was proposed. Firstly， in order to balance the global search and local search abilities of the algorithm， fuzzy logic was introduced to adjust the scale of sparrow finders dynamically. Secondly， the mixed differential mutation operation was performed on sparrow followers to generate mutation subgroups， thereby enhancing the ability of EMISSA to jump out of the local optimum. Finally， Topological Opposition-Based Learning （TOBL） was used to obtain topological opposition solutions of sparrow finders， thereby fully mining high-quality position information in the search space. EMISSA， standard SSA and Chaotic Sparrow Search Optimization Algorithm （CSSOA） were evaluated by 12 test functions in 2013 Congress on Evolutionary Computation （CEC2013）. Experimental results show that EMISSA achieves 11 first places on 12 test functions in the 30-dimensional case； in the 80-dimensional case， the proposed algorithm has the optimal results on all the test functions. In the Friedman test， EMISSA ranks first on all the test functions. Experimental results of applying EMISSA to the Wireless Sensor Network （WSN） node deployment in obstacle environment show that compared with other algorithms， EMISSA achieves the highest wireless node coverage with more uniform node distribution and less coverage redundancy.