## 4 Underwater Sensor Networks
In order to estimate the location, the system obtains a set of potential locations as candidates for a node's location using the bilateration method and then localizes the sensor node through the weighted mean of all the potential locations with the weights computed based on residuals. It improves the localization accuracy and is more tolerant to errors in the estimation of the distance between projected beacon points and sensor nodes. Simulation results show that LoMoB significantly improves the localization accuracy of other systems, especially in underwater environments that cause irregularities in the radius of the circle formed by the transceiver's beam and the location error of a mobile beacon. Simulations demonstrated that their proposal is more robust in terms of errors in distance estimation than others. There are different sensor network architecture designs that can be used for monitoring underwater pipeline infrastructures. They are underwater wired sensor networks, underwater acoustic wireless sensor networks, Radio Frequency (RF) wireless sensor networks, integrated wired/acoustic wireless sensor networks, and integrated wired/RF wireless sensor networks. In, [22], [PERSON] _et al._ discuss and compare the reliability challenges, characteristics, advantages, and disadvantages and enhancement approaches for these network architectures. The reliability evaluation has been performed in terms of network connectivity, the continuity of power supply for the network, and the physical network security. Authors developed an analytical model to compare the reliability of different architectures for monitoring underwater pipelines. Moreover, they also develop and evaluate a hierarchical sensor network framework for underwater pipeline monitoring. In this architecture, the node batteries are only used if there is a fault in the wires providing power to the nodes, so nodes would have better chance to have power supply in integrated wired/wireless sensor network than in wireless sensor networks. The integrated wired/wireless sensor network can provide good sensing and communication reliability. Moreover, they have less physical security concerns than underwater wired sensor networks because physical attacks on the network will not stop the connectivity of the network in some degree. [PERSON] _et al._ propose in [23] a multimedia distributed monitoring system for UWSNs, exploiting available technologies. It uses acoustic communications between underwater sensors and standard technologies, Zigbee and WiFi, for water surface communications. This integrated application-driven approach is based on a three tier architecture leveraging low cost wireless technologies. The system had a standard Charge-Coupled Device (CCD) camera for recording special underwater events, a standard signal processor and memory to store multimedia information, low cost acoustic modems, and a Zigbee or a WiFi Wireless Network Card Interface to transmit to a gateway in the surface of the water. They selected a suitable Medium Access Control (MAC) layer, T-Lohi MAC protocol, after making a comparison with some common MAC protocols. They simulated the performance of the overall system, taking into account real world parameters and models, as well as features of current standard devices, in terms of Signals Discarding Rate (SDR), signaling delay at the surface gateway as well as the percentage of true detection, pointing out good results. In [24], [PERSON] _et al._ propose an architecture and protocol design for marine vehicles including Autonomous Underwater Vehicles (AUVs), Autonomous Surface Vehicles (ASVs) and Autonomous Underwater Gliders (AUGs), used as a mobile sensor network, for taking samples of a regional ocean area, ocean phenomena observing and ocean feature tracking. Authors analyze the marine vehicle dynamic control, multi vehicles mission assignment and path planning methods, ocean feature tracking and observing techniques. They integrate the control and plan of the sensor platforms with data assimilation to obtain more sample data.