In wireless sensor networks,RSSI is considered as an appealing modality for localization in WSN as RSSI information can be obtained at almost no additional cost.To effectively utilize RSSI for localization,two directions have been investigated:RSSI fitting and RSSI profiling.Many state-of-art localization algorithms—falling in these two categories,however,work poorly in real environments because of imprecise mapping relationship between RSSI and the physical distance due to the impact from multi-path,environment noisy,et al. This paper proposes an Omni-Fitting RSSI Map Based Self-Localization Algorithm(ORM).In ORM,the study samples limited RSSI values in several different directions and distances in advance.Based on this information,the study can endue the global radio strength distribution map of the positioning area.According to this map,the unknown nodes can conduct localization to acquire their coordinates.ORM considers the anisotropic characteristics of radio transmission model carefully which makes it own the advantages of both good scalability and acceptable precision.In order to demonstrate the performance of the approach,the study builds up a testbed with 14 MICAz motes to run ORM.The results show that this method can outperform W-Centroid algorithm by about 26% in indoor environment and as much as 42% under outdoor circumstance.