# Research on Localization Algorithms Based on Acoustic Communication for Underwater Sensor Networks
## 3 Localization Algorithm
### Computation Algorithm
#### 3.1.2 Distributed Technique  
In this subsection, we discuss the distributed techniques in two aspects, namely, the estimation-based and prediction-based schemes. Similarly, we introduce the main algorithms of these types of techniques. Firstly, we consider the estimation-based schemes. In [6], the authors present the experimental results obtained with two techniques for navigating an AUV within an acoustic sensors network by using a mixed USBL/LBL system. The vehicle employs the USBL measurements first to localize the fixed nodes and then to perform autonomous navigation in the first technique. The second technique can estimate both the navigation of the vehicle and the topology of the network by using the acoustic measurements. Furthermore, the authors called this framework Acoustic based SLAM (A-SLAM). The vehicle, called Typhoon, is able to cooperate in swarms to perform navigation, exploration, and surveillance of underwater archaeological sites. As shown in Figure 2, three different underwater vehicles (vision explorer, acoustic explorer, and team coordinator) cooperate as a single team, and each vehicle of the team can be customized for different mission profiles; moreover, the team composition can be altered. The algorithms have been tested on experimental data collected during the CommsNet13 campaign, led by the NATO CME in September 2013, with the Typhoon vehicle. The results show that the algorithm can achieve the error bounded within values suitable for most of the applications even with a low-cost, low-accuracy INS and acoustic positioning information coming at irregularly spaced. In [26], the authors take isolated unknown nodes into account. Considering the mobile UWSNs, the nodes are mobile with the water currents. Hence, the nodes may be dispersed and cannot communicate with enough beacons. The authors propose a Multi-hop Fitting Localization Approach (MFLA) that sets the intermediate nodes between beacons and unknown nodes as routers to construct paths (shown in Figure 3) via a greedy method, then fitting the multi-hop path into straight lines and estimating the position of the node through trilateration. When hidden nodes cannot find three neighboring beacons, MFLA will be executed. Then, some intermediate nodes are employed to form a polygonal line from the hidden nodes to the nearest beacons subsequently. This polygonal line is fitted for trilateration localization. Figure 2: Typhoon AUVs of the swarm [6]. In [27], a Localization with Directional Beacons (LDB) for use in 3D UWSNs is proposed. The AUV acts as the beacon sender to estimate the locations of the sensor node. Hence, the AUV broadcasts beacons towards the sensor nodes at a constant interval. The nodes silently listen to the beacons. After two or more beacons are received, the nodes can estimate the positioning information. Those nodes hearing the beacon and the beam form different circles of different \(h\). The circle center is \((x,y,h)\). Hence, the circle radius can be expressed as follows:  
\[r=\tan(\alpha/2)\times\Delta h, \tag{1}\]  
where \(\alpha\) is the angle of the conical beacon and \(\Delta h=|h_{A}-h|\). Through the receiving beacons, the rough position \((x,y,h)\) can be obtained. In the LDB scheme, each node uses received beacons from the AUV to obtain its positioning information independently. In [28], a Localization with a Mobile Beacon (LoMoB) is proposed. The LoMoB is an improvement of LDB. The mobile beacon that knows its location broadcasts a beacon containing its positioning information with constant distance intervals. The sensor nodes within communication range can receive the beacon. Similarly, the LoMoB has a beacon point selection process. As for the prediction-based scheme, the authors in [29] propose an Efficient Mobility Based Localization (EMBL) scheme. In this scheme, the prior known positioning information of the node is used to predict its future mobility pattern and obtain its position at the next time. The kinematic mobility model is employed in this scheme to represent the movement in UWSNs.