Iterative localization of wireless sensor networks
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The document presents an iterative localization approach for wireless sensor networks that aligns small network elements using a new problem called inflexible body merging. This approach introduces an optimization algorithm that tolerates ranging noise and improves robustness against flip ambiguities, aiming to enhance network localization accuracy. The proposed methods have been validated through theoretical analysis and simulations, demonstrating better performance than traditional techniques.
Iterative localization of wireless sensor networks
- 1. ECRUITMENT SOLUTIONS (0)9751442511, 9750610101 #1, Ist Cross, Ist Main Road, Elango Nagar,Pondicherry-605 011. [email protected] www.ecruitments.com Iterative Localization of Wireless Sensor Networks: An Accurate and Robust Approach ABSTRACT: In wireless sensor networks, an important research problem is to use a few anchor nodes with known locations to derive locations of other nodes deployed in the sensor field. A category of solutions for this problem is the iterative localization, which sequentially merges the elements in a network to finally locate them. Here, a network element is different from its definition in iterative trilateration. It can be either an individual node or a group of nodes. For this approach, we identify a new problem called inflexible body merging, whose objective is to align two small network elements and generate a larger element. It is more generalized than the traditional tools of trilateration and patch stitching and can replace them as a new merging primitive. We solve this problem and make the following contributions. 1) Our primitive can tolerate ranging noise when merging two network elements. It adopts an optimization algorithm based on rigid body dynamics and relaxing springs. 2) Our primitive improves the robustness against flip ambiguities. It uses orthogonal regression to detect the rough collinearity of nodes in the presence of ranging noise, and then enumerate flip ambiguities accordingly. 3) We present a condition to indicate when we can apply this primitive to align two network elements. This condition can unify previous work and thus achieve a higher percentage of localizable nodes. All the declared contributions have been validated by both theoretical analysis and simulation results.
- 2. ECRUITMENT SOLUTIONS (0)9751442511, 9750610101 #1, Ist Cross, Ist Main Road, Elango Nagar,Pondicherry-605 011. [email protected] www.ecruitments.com EXISTING SYSTEM: We identify a new problem called inflexible body merging, whose objective is to align two small network elements and generate a larger element. It is more generalized than the traditional tools of trilateration and patch stitching and can replace them as a new merging primitive. we adopt the fine-grained ranging techniques to obtain the accurate measurements of internode distances. For fine-grained localization, the existing solutions are divided into two categories: whole-topology approach and iterative approach. The former approach analyzes the whole network topology directly, using some numerical optimization algorithms. For example, anchor-free localization (AFL) method models the network topology as a group of nodes interconnected by springs. The spring forces acting on the nodes are used as heuristics that guide the nodes’ movement toward their lowest energy positions. PROPOSED SYSTEM: We have proposed an optimization algorithm that can tolerate ranging noise when aligning two bodies. This algorithm is very generalized and can replace the traditional multilateration and patch stitching as the new primitive for network localization. We also proposed a flip ambiguity Enumeration algorithm to improve the robustness of localization. This algorithm can discover the rough collinearity of constraints due to the interference of ranging noise.
- 3. ECRUITMENT SOLUTIONS (0)9751442511, 9750610101 #1, Ist Cross, Ist Main Road, Elango Nagar,Pondicherry-605 011. [email protected] www.ecruitments.com CONCLUSION: This paper focused on fine-grained iterative localization of wireless sensor networks. We have proposed an optimization algorithm that can tolerate ranging noise when aligning two bodies. This algorithm is very generalized and can replace the traditional multilateration and patch stitching as the new primitive for network localization. We also proposed a flip ambiguity enumeration algorithm to improve the robustness of localization. This algorithm can discover the rough collinearity of constraints due to the interference of ranging noise. contribution is that our body merging condition can achieve higher localization percentage than state-of-the-art SWEEPS and CALL. Our final demonstration shows that our IIBMlocalization algorithm can work well, even in concave networks with non-uniform network density.