Minimization of Localization Error of Amorphous Algorithms for Wireless Sensor Networks

Abstract

The exploration of wireless sensor network localization is a growing field of study. Achieving the precise location of sensor nodes is crucial for enhancing network longevity, expanding coverage, implementing geographical routing, and maintaining a congestion-free network. In this thesis, we introduced four range-free localization strategies aimed at minimizing the localization error associated with the conventional Amorphous algorithm. We conducted simulations and experiments to assess the effectiveness of these proposed schemes. A wireless sensor network (WSN) is made up of numerous tiny sensor nodes that are inexpensive, low-power, and need little computing. The purpose of these nodes is to collect essential environmental data in a uniform or random manner. To provide effective communication between known (beacon, anchor, reference) and unknown (dumb node), it is essential to determine the precise and accurate position of the sensor node. Rescue, traffic management and surveillance, underwater farming, target tracking, and many other uses for localization exist. In addition, people utilize it in their daily lives as a GPS to direct them while traveling. Therefore, the sensor’s precise location is required in order to use localization services accurately. An Ensemble approach consisting of a weighted Amorphous and DV-Hop algorithm is proposed in the first proposed work to reduce the localization error of traditional Amorphous algorithm. The distance between the unknown node and the beacon node is determined by two different distance measurements that consider hop value and hop size. To obtain the actual distance, probabilistic distance estimation is applied to the distances that were obtained. Lastly, the proposed Ensemble approach is compared with three different improved Amorphous algorithms and the conventional Amorphous algorithm. It is observed that the proposed approach provides higher accuracy in terms of MAE (Mean Absolute Error), MSE (Mean Square Error), and RMSE (Root Mean Square Error). Among all localization algorithms, Amorphous localization is highly suggested for usage in many application domains due to its simplicity, viability, low cost, and no additional hardware requirements. Position estimation of the dumb node in the Amorphous algorithm considers three different practical scenarios, such as the position of dumb nodes falling within the range of anchor nodes, in the opposite direction of the anchor node, and not within the range of the anchor node. However, the localization error for identifying the location of dumb sensor nodes by the Amorphous algorithm is high. To further address the limitations of the Amorphous algorithm, we have proposed a PSO-based Amorphous algorithm in our second work. The proposed work reduces the average hop size of anchor nodes and the localization error of the Amorphous algorithm. The simulation results demonstrate that, in comparison to other existing Amorphous algorithms, the proposed PSO-based Amorphous localization algorithm has a superior performance in terms of MAE, MSE, and RMSE. In the third work, the position error of the Amorphous algorithm is minimized by optimizing the hop size. For optimization of the hop size of the Amorphous algorithm, two different optimization algorithms, such as ALO and GWO, are considered. It is observed that the position errors of Amorphous-ALO and Amorphous-GWO are nearly the same. In order to determine the suitable optimization algorithm for Amorphous, the parameters such as minimum, average, and maximum execution times of Amorphous-ALO and Amorphous-GWO are considered for evaluation of the performance of the proposed algorithms. Whichever algorithm has lesser execution time is considered to be the suitable method for the localization of Amorphous. From the experimental outcome it is observed that the Amorphous-GWO takes less execution time than Amorphous-ALO; therefore, GWO is more suitable for optimization in Amorphous algorithm. In the fourth proposed work, a hybrid localization algorithm named the Weighted Centroid Amorphous algorithm is proposed to reduce the position error in WSN. Instead of the conventional centroid algorithm, a weighted centroid algorithm is used. The weight in this work is considered as a function of the hop value and hop size estimated by the Amorphous algorithm. To determine the coordinate of a single unknown node ten nearest beacon nodes are considered. The hop value and hop size of that unknown node are calculated from ten nearest beacon nodes and by using hop value and hop size, the weight is calculated. After estimation of weight the weighted sum is calculated. Using weighted sum, the coordinate of unknown node is determined. The simulation results demonstrate that, in comparison to other existing and proposed Amorphous algorithms, the proposed Weighted Centroid Amorphous localization algorithm has a superior performance in terms of MSE, and RMSE.