# Intersection and Complement Set (IACS) Method to Reduce Redundant Node in Mobile WSN Localization
## 1 Introduction  
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. Contrarily, Wireless 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. Therefore, 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 then 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. Apart from the wireless communication features and the estimation module, another unique characteristic of WSN localization is its capability to share information among communication devices. Consequently, nodes with known location can be used to estimate the location of blind nodes. This can be beneficial when information from the selected features are insufficient to estimate the position information. However, the estimation of blind node location from the nodes with known location normally involves a large number of nodes. Involving a large number of nodes can increase the estimation accuracy but at the expense of computation and communication costs. Communication and computation costs are the two main components that affect energy consumption in WSN devices [2]. The communication cost includes the energy consumed during the data transmission and reception, while the computation cost is the energy consumed to execute algorithms in the device or node. A high communication and computation cost will rapidly deplete the energy and shorten the node lifetime. For this reason, a trade-off between accuracy and energy consumption should be taken into consideration in development of WSN localization algorithm. In this paper, a Monte Carlo-based localization algorithm will be studied. Monte Carlo-based localization, also known as Particle Filter (PF) localization is chosen due to its efficiency, lightweight, and adaptability in a dynamic topology environment like mobile WSN environment [3]. This study will be focusing on reducing the computation, communication, and energy costs of the localization process, while maintaining its accuracy. Computation and communication costs are important factors in determining the node lifetime, but limited studies have highlighted the respective issues. Based on our review on recent Monte Carlo-based localization, only Low Communication Cost (LCC) scheme [4] comprehensively addresses the issues. The LCC scheme utilizes adjacency matrix, set theory and intersection set to remove unnecessary redundant data. Their result shows that their method reduces the communication cost, which also reduces the energy consumption while maintaining the accuracy of the localization estimation. Inspired by the LCC, we propose a scheme called Intersection and Complement Set (IACS). Both the LCC and the IACS are based on the Monte Carlo localization algorithm that includes both first hop and second hop data to estimate the blind node location. However, the LCC scheme only considers the intersection set, which only reduces the redundant data in the first hop nodes. Unlike the LCC scheme, the IACS scheme utilizes both intersection and complement sets. Since second hop data is used, the complement set in IACS is expected to find other possible redundant data that can be safely discarded. IACS also introduces two major stages named as duplicate cleaning and average filtering. In the duplicate cleaning stage, neighbor nodes that have the same intersection set or complement set are discarded from the localization estimation.