## 2 Related Work  
In general, factoring in the SSP variability in underwater environments yields better estimates of acoustic propagation paths and eventually improves localization accuracy [9]. Since acoustic propagation in an underwater environment tends to refract due to SSP variations, ray tracing models such as Bellhop are employed to find the actual path of underwater acoustic signals [7]. On the other hand, underwater range estimation using time of flight (ToF) is most commonly used in underwater networks, where the range is obtained by correlating the measured ToF and the angle of arrival [10]. However, short distances are assumed among nodes and possible refractions of acoustic signal are ignored. Moreover, we have modeled the acoustic trajectory of convex links using a second order polynomial and proven that for a convex link, a parabola could be used to approximate the actual signal propagation path for ranges less than 3 km [11]. Underwater acoustic waves travel in an inhomogeneous environment with varying speeds [12]. [PERSON] et al. provide a comparison between six different SS estimation algorithms and provide the ranges of temperature, salinity and depths at which each algorithm is applicable [13]. On the other hand, [PERSON] et al. have proposed a unified equation to estimate the SS across all oceans by introducing the latitude [14]. Due to the varying SS, a different propagation behavior is observed in underwater environments when compared to free space cases. The varying SS has a significant impact on the spreading pattern of an acoustic signal that eventually translates to losses. Taking such fact into consideration, we proposed an efficient method to use broad beams to establish communication links and then proposed narrowing these beams to conserve energy by estimating the transmission and reception angles [8]. However, the method starts by using a broad beam width for neighbor discovery that demands higher power. Moreover, no means are presented to aid nodes in avoiding shadow and overlapping zones by leveraging the capability of the used transducer. Finally, the method cannot suggest new angles when nodes drift or when mobile nodes are used. In the context of localization, [PERSON] et al. have studied the effect of increasing the node count on the localization error when a received signal strength indicator (RSSI) is used as a ranging method [15]. The authors attempt to overcome the problems of multipath and fading by increasing the node density. A complete survey for underwater localization schemes is provided in [16], where the tradeoff between various localization methods is highlighted. [PERSON] and [PERSON] have proposed a surface-based reflection scheme to perform localization using both line-of-sight and non-line-of-sight links [17]. The authors use an average SS, i.e., a static medium model, to distinguish the different received signals, namely reflected and direct paths. Moreover, [PERSON] et al. exploit the multipath signals between an anchor node and a neighbor to perform ranging by leveraging Time Difference of Arrival (TDoA) among different paths [12]. Since the medium is inhomogeneous and due to the fact that the acoustic wave speed varies along its flight, the authors suggest using an effective SS value derived from the TDoA and the distance that each signal has travelled. However, a rectilinear wave trajectory is assumed, where a single beam follows a straight-line trajectory. Clearly such approximation is not valid for long range communication where acoustic rays tend to bend due to refraction exerted by the medium inhomogeneity. Moreover, a static, pre-defined, model cannot be used to categorize actual propagation paths of acoustic waves that tend to refract with SSP changes. [PERSON] et al. attempt to use a hybrid localization method where the TDoA and Frequency Difference of Arrival (FDoA) are used to complement each other to predict a more accurate range [18].