## 3 Underwater Communications
In [15], [PERSON] and [PERSON] show how the underwater localization case can be converted to the traditional range-based terrestrial localization case when the depth information of the nodes is known _a priori_, because underwater medium is assumed to be composed of multiple isogradient sound speed profile (SSP) layers where in each layer the sound speed is linearly related to the depth. They used a ray-tracing approach for high-frequency signal transmission to model the propagation and a set of polynomial root finding equations to distinguish among different possible transmission paths between the nodes, and determine the fastest one. Each sensor node is able to measure its depth and can exchange this information with other nodes. Then, they relate the pair-wise time of flight (ToF) values with nodes positions in order to propose a novel ranging algorithm for an underwater medium. The proposed ranging algorithm considers reflections from the seabed and sea surface and selects the fastest transmission path based on the measured ToF and the nodes' depth measurements. The algorithm estimates iteratively the range of a target by minimizing the difference between the measured ToF and the constructed ToF estimated from the known map. The authors evaluate the performance of the proposed algorithm by running several simulations and comparing successfully the results with other existing algorithms. One of the main drawbacks in underwater communications is the low available data rate due to the use of low frequencies. [PERSON] _et al._ propose the use of EM waves in the 2.4 GHz ISM frequency band in underwater environments when more bandwidth is needed, despite the high attenuation of the water at these frequencies. In [16], they provide an underwater wireless sensor communication performance test using devices compatible with the IEEE 802.11 standard at 2.4 GHz, in fresh water. These tests were performed in a swimming pool by using echo request and echo reply packets in an underwater point-to-point link between two sensor nodes developed by them. They provided the maximum distance between sensors, the number of lost packets and the average round trip time at different frequencies, modulations and data transfer rates. They observed that their underwater communication system had an optimum behavior at 16 cm, working at frequency of 2,432 GHz, with the BPSK and QPSK modulations. These modulations had also good performance at distances of 17 cm, working at 2.422 and 2.427 GHz, with a percentage of lost packets slightly above 30%. They also compared their proposal with the existing systems in order to show that although it can be only used for short distances, it provides high data transfer rates. This system was proposed for precision monitoring, such as ecosystems contaminated by invasive plants, hazardous waste, and for underwater communications at deep sea, such as the neutrino telescope where they are working on. [PERSON] _et al._ investigate the tradeoff between the energy efficiency and the network connectivity through the selection of the transmission range in [17]. They provide an analytical framework to model the relationships between the transmission range, the average energy consumption and the connectivity in an Underwater Wireless Sensor Network scenario. They state that the selection of the transmission range needs to consider the tradeoff between the energy efficiency and the connectivity. Meeting a certain level of network connectivity incurs either cost for additional node deployment or energy due to operational deviation from the optimal transmission range. Their analytical framework provides a design guideline for energy-efficient packet transmission operation given a certain network connectivity requirement for network designers to plan the deployment of an Underwater Wireless Sensor Network.