EECLA: A Novel Clustering Model for Improvement of Localization and Energy Efficient Routing Protocols in Vehicle Tracking Using Wireless Sensor Networks

Due to increase of usage of wireless sensor networks (WSN) for various purposes leads to a required technology in the present world. Many applications are running with the concepts of WSN now, among that vehicle tracking is one which became prominent in security purposes. In our previous works we proposed an algorithm called EECAL (Energy Efficient Clustering Algorithm and Localization) to improve accuracy and performed well. But are not focused more on continuous tracking of a vehicle in better aspects. In this paper we proposed and refined the same algorithm as per the requirement. Detection and tracking of a vehicle when they are in larges areas is an issue. We mainly focused on proximity graphs and spatial interpolation techniques for getting exact boundaries. Other aspect of our work is to reduce consumption of energy which increases the life time of the network. Performance of system when in active state is another issue can be fixed by setting of peer nodes in communication. We made an attempt to compare our results with the existed works and felt much better our work. For handling localization, we used genetic algorithm which handled good of residual energy, fitness of the network in various aspects. At end we performed a simulation task that proved proposed algorithms performed well and experimental analysis gave us faith by getting less localization error factor

A Novel 3D Indoor Node Localization Technique Using Weighted Least Square Estimation with Oppositional Beetle Swarm Optimization Algorithm

Due to the familiarity of smart devices and the advancements of mobile Internet, there is a significant need to design an effective indoor localization system. Indoor localization is one of the recent technologies of location-based services (LBS), plays a vital role in commercial and civilian industries. It finds useful in public security, disaster management, and positioning navigation. Several research works have concentrated on the design of accurate 2D indoor localization techniques. Since the 3D indoor localization techniques offer numerous benefits, this paper presents a Novel 3D Indoor Node Localization Technique using Oppositional Beetle Swarm Optimization with Weighted Least Square Estimation (OBSO-WLSE) algorithm. The proposed OBSO-WLSE algorithm aims to improvise the localization accuracy with reduced computational time. Here, the OBSO algorithm is employed for estimating the initial locations of the target that results in the elimination of NLOS error. With respect to the initial location by OBSO technique, the WLSE technique performs iterated computations rapidly to determine the precise final location of the target. To improve the efficiency of the OBSO technique, the concept of oppositional based learning (OBL) is integrated into the traditional BSO algorithm. A number of simulations were run to test the model's accuracy, and the results were analyzed using a variety of metrics

A Novel 3D Indoor Node Localization Technique Using Weighted Least Square Estimation with Oppositional Beetle Swarm Optimization Algorithm

Due to the familiarity of smart devices and the advancements of mobile Internet, there is a significant need to design an effective indoor localization system. Indoor localization is one of the recent technologies of location-based services (LBS), plays a vital role in commercial and civilian industries. It finds useful in public security, disaster management, and positioning navigation. Several research works have concentrated on the design of accurate 2D indoor localization techniques. Since the 3D indoor localization techniques offer numerous benefits, this paper presents a Novel 3D Indoor Node Localization Technique using Oppositional Beetle Swarm Optimization with Weighted Least Square Estimation (OBSO-WLSE) algorithm. The proposed OBSO-WLSE algorithm aims to improvise the localization accuracy with reduced computational time. Here, the OBSO algorithm is employed for estimating the initial locations of the target that results in the elimination of NLOS error. With respect to the initial location by OBSO technique, the WLSE technique performs iterated computations rapidly to determine the precise final location of the target. To improve the efficiency of the OBSO technique, the concept of oppositional based learning (OBL) is integrated into the traditional BSO algorithm. A number of simulations were run to test the model's accuracy, and the results were analyzed using a variety of metrics

Development an accurate and stable range-free localization scheme for anisotropic wireless sensor networks

With the high-speed development of wireless radio technology, numerous sensor nodes are integrated into wireless sensor networks, which has promoted plentiful location-based applications that are successfully applied in various fields, such as monitoring natural disasters and post-disaster rescue. Location information is an integral part of wireless sensor networks, without location information, all received data will lose meaning. However, the current localization scheme is based on equipped GPS on every node, which is not cost-efficient and not suitable for large-scale wireless sensor networks and outdoor environments. To address this problem, research scholars have proposed a rangefree localization scheme which only depends on network connectivity. Nevertheless, as the representative range-free localization scheme, Distance Vector-Hop (DV-Hop) localization algorithm demonstrates extremely poor localization accuracy under anisotropic wireless sensor networks. The previous works assumed that the network environment is evenly and uniformly distributed, ignored anisotropic factors in a real setting. Besides, most research academics improved the localization accuracy to a certain degree, but at expense of high communication overhead and computational complexity, which cannot meet the requirements of high-precision applications for anisotropic wireless sensor networks. Hence, finding a fast, accurate, and strong solution to solve the range-free localization problem is still a big challenge. Accordingly, this study aspires to bridge the research gap by exploring a new DV-Hop algorithm to build a fast, costefficient, strong range-free localization scheme. This study developed an optimized variation of the DV-Hop localization algorithm for anisotropic wireless sensor networks. To address the poor localization accuracy problem in irregular C-shaped network topology, it adopts an efficient Grew Wolf Optimizer instead of the least-squares method. The dynamic communication range is introduced to refine hop between anchor nodes, and new parameters are recommended to optimize network protocol to balance energy cost in the initial step. Besides, the weighted coefficient and centroid algorithm is employed to reduce cumulative error by hop count and cut down computational complexity. The developed localization framework is separately validated and evaluated each optimized step under various evaluation criteria, in terms of accuracy, stability, and cost, etc. The results of EGWO-DV-Hop demonstrated superior localization accuracy under both topologies, the average localization error dropped up to 87.79% comparing with basic DV-Hop under C-shaped topology. The developed enhanced DWGWO-DVHop localization algorithm illustrated a favorable result with high accuracy and strong stability. The overall localization error is around 1.5m under C-shaped topology, while the traditional DV-Hop algorithm is large than 20m. Generally, the average localization error went down up to 93.35%, compared with DV-Hop. The localization accuracy and robustness of comparison indicated that the developed DWGWO-DV-Hop algorithm super outperforms the other classical range-free methods. It has the potential significance to be guided and applied in practical location-based applications for anisotropic wireless sensor networks