NDSL: Node Density-Based Subregional Localization in Large Scale Anisotropy Wireless Sensor Networks

Abstract

Localization is emerging as a fundamental component in wireless sensor network and is widely used in the fields of environmental monitoring, national defense and military, transportation, and so on. Current positioning system, however, can only locate an object's position in isotropy wireless sensor network with high accuracy but cannot locate it accurately in anisotropy wireless sensor network. Besides, past proposals only mentioned anisotropy to show that connectivity of network is different in each direction. However, how to quantify the degree of anisotropy is not clearly pointed out. This paper introduces NDSL (node density-based subregional localization), a positioning system that is used in anisotropy wireless sensor network. The network is divided into many subregions where the nodes density is relatively uniform and then corrects the single-hop distance for each beacon node to locate unknown nodes. We also use nodes distribution and signals distribution to build a model to evaluate the degree of anisotropy for anisotropy network. Through the analysis of the degree of anisotropy for different topologies, the results show that the model is consistent with the facts. Results from actual deployments and simulation experiments show that the accuracy of NDSL algorithm obviously improves compared with DV-Hop algorithm.

DFPC: Dynamic Fuzzy-based Primary User Aware clustering for Cognitive Radio Wireless Sensor Networks

“…In [35], the Modified Adaptive Cluster-Based Heuristic Approach (MACHBA) was proposed to solve the optimal spectrum identification problem in various applications. In [36], a localized clustering approach was proposed to promote stability, effective spectrum management, scalability, and reduced communication overhead. Each node computes its weight and communicates it to its one-hop neighbors, and the node with the greatest importance is designated as the CH.…”

“…Subsequently, appropriate actions can be taken to discover the ideal number of clusters within the deployed network. CH forms the connectivity p using the node density parameter, calculated as [36]:…”

“…Thus, the maximum SUs in each cluster are calculated as [36] frequency for each SU in the network is essential to calculate the UB. Leveraging the MCF, the maximum count of SUs that can be accommodated within a single cluster is identified based on the input network's characteristics.…”

“…Identifying the optimal cluster count through UB and MCF directly influences the overall improvement in performance in the proposed protocol. Therefore, the number of clusters is calculated using UB as [36]:…”