Efficient 3D-Localization Algorithms in Underwater Acoustic Sensor Network Employing Optimization Methods

Abstract

The Underwater Acoustic Sensor Network (UASN) is a specialized type of wireless sensor network designed for underwater environments. The underwater acoustic sensor network is a fundamental source for ocean exploration. The potential applications of UASN include seismic imaging, disaster prevention, mine reconnaissance, pollution monitoring, exploration of natural resources, military surveillance, etc. To acquire accurate results, implementing all applications of underwater sensor networks requires an adequate network connection and communication technology. The precise placement of underwater sensor nodes must be identified to communicate effectively. The sensor nodes in UASN are intermittently deployed randomly in the three-dimensional scenario. Determining the three-dimensional localization of underwater sensor nodes is one of the most challenging tasks as compared to two dimensional. This motivates us to propose a three-dimensional localization algorithm in USAN. In this thesis, we proposed three range-free localization algorithms. These proposed algorithms are based on the compensation of the stratification effect for the improvement of the performance parameters such as localization accuracy, ranging accuracy, convergence rate, and execution time. Then proposed the fourth algorithm for clustering of localized sensor nodes. The simulation, experimental validation, and analysis are performed employing a Python environment to evaluate the performance of the proposed schemes. Firstly, To fulfill the objective, a localization algorithm I-LASP (Improvement of localization algorithm for compensating stratification effect based on extended improved particle swarm optimization technique) is proposed in three-dimensional UASN, based on compensation of stratification effect for the improvement of the performance parameters such as localization accuracy, ranging accuracy, convergence rate and execution time. To compute the accurate position of target nodes, the EIPSO (Extended improved PSO) technique is applied, and the degree of coplanarity is checked before the calculation of distance among nodes in order to get the accurate location of target nodes (unknown nodes). The Centroid method is used to initialize the position of sensor nodes, and the ray theory method is used to compensate the stratification effect on the layered ocean water. The proposed algorithm is compared to the existing LASP, Std PSO, and GNA-ESSP (Gauss-newton algorithm-extended sound speed profile) localization algorithm. The proposed algorithm provides 34.50%, 38.87%, and 42.66% of high accuracy in terms of localization with low density of target sensor nodes and 37.96%, 29.58%, and 50.77% high accuracy in terms of localization with a high density of target sensor nodes respectively. The proposed algorithm is compared with LASP, GNA-ESSP, and TDOA to obtain 66.84%, 71.14%, and 86.13% of high accuracy in terms of ranging with low density of target sensor nodes and 42.34%, 89.00%, and 95.08% high accuracy in terms of ranging with a high density of target sensor nodes respectively. Experimental results represent that the proposed algorithm obtains better performance in terms of localization accuracy, ranging accuracy, root mean square error, normalized localization error, execution time, and convergence rate. To further enhance the localization accuracy, an effective localization approach for compensating the stratification effect based on a revamped underwater grey wolf optimization method (RLCS-IUGWOM) is presented in the thesis. To determine the precise geographic position of underwater sensor nodes, the nodes in the 3D-UASN are firstly distributed haphazardly, employing an amalgamation of centroid-based localization and the ray theory approach. Subsequently, the coplanarity of the underwater sensor nodes is analyzed. An improved underwater grey wolf optimization method (IUGWOM) is employed subsequently after the estimation of the position of unknown nodes to acquire the precise position and compensate the stratification effect. The mathematical comparative analysis between the I-LASP and the presented algorithm is accomplished. In 3D-UASN for both low and high-density zones, the RLCS-IUGWOM obtains localization accuracy of 72.50% and 78.92%, respectively and ranging accuracy of 72.50% and 78.92%, respectively. The outcomes of the mathematical simulation reveal that the proposed algorithm surpasses the existing algorithm in terms of localization and range accuracy in both low and high-density zones in 3D-UASN. It also exhibits outstanding efficiency regarding RMSE, NLE, computation time, and convergence rate. Next, we have proposed an efficient localization algorithm to compensate for the stratification effect based on an improved underwater salp swarm optimization technique (LAS-IUSSOT). To compute the location of sensor nodes with high accuracy, the nodes are initially randomly deployed in 3D-UASN. After that, the hybridization of centroid-based localization and the ray theory technique is used, and then the degree of coplanarity is analyzed among the underwater sensor nodes. Then, the location of unknown nodes is computed using IUSSOT (Improved underwater salp swarm optimization technique) to obtain the optimized location and compensate the impact of the stratification. The comparison of the simulation results of the existing algorithm and the proposed algorithm is performed. The LAS-IUSSOT achieves 40.46% and 28.00% accuracy in terms of localization of underwater sensor nodes for both the sparse and dense regions in 3D-UASN. The LAS-IUSSOT achieves 49.39% and 62.57% accuracy in terms of ranging of underwater sensor nodes for both the sparse and dense regions in 3D-UASN. Simulation results illustrate that the proposed algorithm outperforms the existing algorithm in terms of localization and ranging accuracy in both sparse and dense regions in 3D-UASN, RMSE, NLE, computation time, and convergence rate. Finally, We analyze from the existing literature that the two most critical requirements for the application’s proper operation are the accurate knowledge of sensor node locations and the efficient transmission of accurate underwater sensor node information to the base station with efficient energy consumption. To accomplish the objective we proposed Energy Efficient Localization Based on the LEACH-Beacon and Reinforced node (EELBL-BR) algorithm which satisfies both the requirements in 3D-UASN.The proposed algorithm considers the deployment and computation of accurate location of sensor nodes in the underwater environment by applying I-LASP(Improvement of localization algorithm for compensating stratification effect based on extended improved particle swarm optimization technique) for 3D environment. It performs clustering of sensor nodes for enhancing network lifetime using three different types of nodes such as beacon, reinforced and member nodes. The proposed clustering LEACH-BR (Low-Energy Adaptive Clustering Hierarchy-Beacon and Reinforced nodes)algorithm is based upon the LEACH algorithm, which provides accurate location of all the sensor nodes and improves energy consumption and reliability in the underwater environment. The result shows that the proposed algorithm EELBL-BR, considering both beacon and reinforced nodes, provides the improvement in the number of alive nodes, reduction in the number of dead nodes, reduction in energy consumption and enhances residual energy in the UASN by 68.90%,51.91%,51.47%and 68.12% respectively with respect to the number of rounds as compared to that of the existing algorithm by authors and thus outperforms the existing algorithm.