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    ADVANCED CHAMFER DISTANCE BASED MULTIDIMENSIONAL SCALING (ACDMDS) ALGORITHM AND DISTRIBUTED 3-D LOCALIZATION IN WIRELESS SENSOR NETWORKS (WSNS)
    (Our Heritage, 2020-01) Nithya A; Kavitha A
    Correct localization in Wireless Sensor Networks (WSNs) is basic toward several applications. In general it becomes very difficult in huge scale 3D WSNs because of irregular topology since it consists of holes in the path, of the network. Node Weight Swallow Swarm Optimization Convex Node Segmentation (NWS2CNS) is introduced recently for solving 3D WSN accurate localization. However in NWS2CNS algorithm is not easy to recreate a global map by means of the relative coordinates subsequent to the network segmentation. So a number of advanced algorithms are needed to recreate the global map establishment. An Advanced Chamfer Distance Based Multidimensional Scaling (ACDMDS) algorithm is proposed in this paper in order to recognize relative localization designed for every approximate convex. The shortest hop count among two arbitrary neighboring nodes in the complete 3D WSN is able to achieve via the Floyd algorithm, and each and every one of these hop count distances is saved in a chamfer distance matrix. Chamfer distance is also additionally calculated to arbitrary neighboring nodes. After that spatial convex node detection algorithm and sub-optimal convex node algorithm have been also carryout individually. Simulation results demonstrate that the proposed ACDMDS technique be able to successfully segment a 3D WSN and considerably increase the localization results when compared to other existing methods. Simulation results are evaluated via the metrics like Localization Error Ratio (LER), Localized Node Proportion (LNP), Bad Node Proportion (BNP) and Network Coverage Ratio (NCR).
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    CONVEX COVERAGE SUPPORT VECTOR MACHINE (CCSVM)CLASSIFIER AND DISTRIBUTED 3-D LOCALIZATION IN WIRELESS SENSOR NETWORKS(WSNS)
    (Journal of Adv Research in Dynamical & Control Systems, 2019-05) Nithya A; Kavitha A
    In wireless sensor networks (WSNs), accurate localization is the fundamental to various applications like geographic routing and position-aware data processing. It is challenging in the large scale 3-D WSNs because of the irregular topology, such as holes in the path, of the network. This work develops a distributed algorithm to acquire 3-D WSN localization. Hence to improve the 3-D WSN localization, the proposed work composed of two steps, segmentation and joint localization. Especially, the complete network is first divided into a number of sub networks by applying the approximate convex partitioning. A spatial convex node recognition method is developed by Convex Coverage Support Vector Machine (CCSVM) to support the network segmentation that relies on the connectivity information. Then, every sub network is accurately localized by with the multidimensional scalingbased algorithm. The proposed localization algorithm uses a new 3-D coordinate transformation algorithm that helps to reduce the errors introduced by coordinate integration between sub networks and improve the localization accurateness. Results using broad simulations, exhibit that the ICCSVM system to segment a complex 3-D sensor network successfully and significantly increases the localization rate when compared with existing methods.
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    NODE WEIGHT SWALLOW SWARM OPTIMIZATION CONVEX NODE SEGMENTATION (NWS2CNS) ALGORITHM FOR DISTRIBUTED 3-D LOCALIZATION IN WIRELESS SENSOR NETWORKS (WSNS)
    (International Journal of Scientific & Technology Research (IJSTR), 2020-02) Nithya A; Kavitha A
    Localization is a significant part in the area of Wireless Sensor Networks (WSNs) with the purpose of has introduced important study significance between academia and research community. The task of establishing substantial manages of sensor nodes in WSNs is identified as localization or positioning and is a key issue in today’s communication systems toward approximation the position of starting point of events. On the other hand, localization is issue in huge scale 3-D WSNs appropriate toward the uneven topology, for example holes in the path, of the network. Recently, spatial convex node detection method is introduced by means of Convex Coverage Support Vector Machine (CCSVM) toward handle this issue. However in CCSVM classifier, Optimization the Number of Convex Pieces becomes extremely hard task. It turns into very hard designed for although minimizing or maximizing a quantity of known criteria or property in the computational geometry. Node Weight Swallow Swarm Optimization Convex Node Segmentation (NWS2CNS) is introduced in this work for handling optimization of number of convex pieces. NWS2CNS is proposed to minimize the number of convex pieces during network segmentation in huge scale 3-D WSNs. NWS2CNS is proposed for increasing higher location accuracy which is obtained by means of using a node inertia weight toward correctly computes the acceleration coefficients. High speed of convergence, solving local extremum, and increased localization accuracy are the advantages of proposed NWS2CNS algorithm. In NWS2CNS algorithm, segmental planes are able to be categorized by means of two steps: inside boundary region and outside boundary region. Once these two steps are optimized subsequently we expand the network segmentation correctly by means of decreased localization error. The proposed localization algorithm moreover is appropriate a new 3-D coordinate transformation algorithm, which helps decreases the errors proposed by means of coordinate integration among subnetworks and increase the localization correctness.