## 2 Background  
### Related Studies  
Some researchers have studied range-free algorithms, which do not need to estimate the distance precisely [14; 15; 16]. Instead, sensors can localize themselves by taking advantage of signal connectivity, delay time, hop-distance and angle information. Range-free schemes are always more economic and simpler than the range-based ones, but less accurate. Color-theory based dynamic localization (CDL) [12], FRORF [15] and DV-hop algorithms [14] are range-free methods which do not acquire distance information, but they always have coarser performance. CDL calculates RGB sequences based on DV-hop for both samples and mobile nodes. It converts RGB to Hue, Saturation, Value (HSV) for mobile nodes and samples using the traditional convert algorithm. Color theory indicates that the information of RGB and HSV fuses different red, green and blue data. According to the color theory, only the V (value) of HSV changes in proportion to the distances between anchors and mobile nodes. After the new HSV was obtained, another RGB data of the nodes is acquired by the HSV to RGB method. Then CDL filters the nearest sample by searching for the most similar RGB sequence to the mobile node, and identifies it as the location of the mobile node. Although CDL has better location performance in various terrestrial networks, it's not suitable for localization in UASNs due to the inconsistent distribution of nodes and the weak association between signal hops and actual distances. [PERSON] _et al._ proposed a local sampling and filtering color dynamic localization (LSF-CDL) algorithm [17]. Using the collected signals, LSF-CDL adopts the overlapping signal region of anchors which were able to communicate with the mobile node directly as the new local sampling area. The proportion factor of distance was also used to weight the average hop distance which optimized the calculation of hop distance in CDL. By comparing the RGB difference sequences, samples could be filtered out. The FRORF method represented overlapping rings as fuzzy sets to isolate a region where the node was most likely located [15]. The localization accuracy was improved under different number of anchors and degrees of radio propagation irregularity. In the color-theory based localization schemes, a location database is established in the server for building geographic locations as a function of RGB values. The DV-Hop technology is employed in measuring the distances between mobile nodes and anchors. Based on the RGBtoHSV algorithm [12], the mobile node converts the RGB values received from the anchors into the corresponding HSV values [12; 17]. According to the color theory, the increase of propagation distances only fades the lightness of color, which is the V in HSV of an anchor. Namely, V decreases proportionately to thedistance between the mobile node to the anchor. After the mobile node gets the new HSV values, it adjusts these HSV values into RGB values based on another conversion algorithm called HSVtoRGB [12]. Then the mobile node averages these converted RGB values as its own RGB values corresponding to the anchors. Further, the server can find out the most probable location for the mobile node after receiving its RGB values and searching the location database. Since CDL is centralized and easy to implement, for applications that need centralized user data collection and user activity monitoring, such as community health-care systems and hospital monitoring systems, CDL is an ideal scheme to be used. Compared to terrestrial nodes which can keep their location unchanged after arrangement, underwater nodes are prone to be influenced by tides, ocean currents and other factors leading to their locations being unfixed [1]. Due to the influence of node mobility, multipath fading and shadow, long time delay, the variation of sound velocity and asymmetry factor, there are more challenges for the UABL algorithms [18].