Abstract
The majority of the Wireless Sensor Network (WSN) localization methods utilize a large number of nodes to achieve high localization accuracy. However, there are many unnecessary data redundancies that contributes to high computation, communication, and energy cost between these nodes. Therefore, we propose the Intersection and Complement Set (IACS) method to reduce these redundant data by selecting the most significant neighbor nodes for the localization process. Through duplication cleaning and average filtering steps, the proposed IACS selects the normal nodes with unique intersection and complement sets in the first and second hop neighbors to localize the unknown node. If the intersection or complement sets of the normal nodes are duplicated, IACS only selects the node with the shortest distance to the blind node and nodes that have total elements larger than the average of the intersection or complement sets. The proposed IACS is tested in various simulation settings and compared with MSL* and LCC. The performance of all methods is investigated using the default settings and a different number of degree of irregularity, normal node density, maximum velocity of sensor node and number of samples. From the simulation, IACS successfully reduced 25% of computation cost, 25% of communication cost and 6% of energy consumption compared to MSL*, while 15% of computation cost, 13% of communication cost and 3% of energy consumption compared to LCC.
Highlights
A typical robot localization depends on the information from common sensors [1] such as laser range scanner and ultrasonic which has a high cost of remote deployment
Mobile and Static sensor network Localization (MSL)*, Low Communication Cost (LCC), and Intersection and Complement Set (IACS) are simulated in the same environment settings to enable a fair comparison them
The graphicaland representations are based on the maximum velocity mobile in sensor node, degree of irregularity, node densities are studied default parameters stated in Section 3 (Table 1)
Summary
A typical robot localization depends on the information from common sensors [1] such as laser range scanner and ultrasonic which has a high cost of remote deployment. Sensor Network (WSN) localization utilizes wireless communication features to estimate position information. WSN localization is a process to estimate location information of unknown or blind nodes in a wireless network. It can be useful in many areas such as military surveillance, precision agriculture, health monitoring and environmental monitoring. The communication features of WSN localization include hop count, propagation time, received signal phase/angle and received signal strength. These features are forwarded to estimation modules such as the Kalman Filter (KF) and Monte Carlo algorithm. The estimation algorithm is used to accurately estimate the position information based on the data from the features
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