Zoning-based localization in indoor sensor networks using belief functions theory

Abstract

Localization is an essential issue in wireless sensor networks to process the information retrieved by sensor nodes. This paper presents an indoor zoning-based localization technique that works efficiently in real environments. The targeted area is composed of several zones, the objective being to find the zone where the mobile node is instantly located. The proposed approach collects first strengths of received WiFi signals from neighboring access points and builds a fingerprints database. It then uses belief functions theory to combine all measured data and define an evidence framework, to be used afterwards for estimating the most probable node's zone. Real experiments demonstrate the effectiveness of this approach and its competence compared to state-of-the-art methods.

Decentralized Sensor Localization by Decision Fusion of RSSI and Mobility in Indoor Environments

“…An estimation is said to be correct if the algorithm assigns the highest confidence to the zone where the sensor actually resides. Table I shows the performance of the proposed method and compares it to a belief functions centralized approach (BCA) presented in [14], and to a hierarchical centralized approach (HCA) presented in [19]. The BCA uses the BFT to fuse all evidence using one calculator, while the HCA creates a hierarchy of clusters from the original zones using divergence similarity.…”

“…Localization is an essential aspect in WSNs, since the knowledge of the sensor's location is critical to process the information originating from this sensor. To tackle the localization problem in indoor environments, researchers use various types of signals, such as ultra-wideband, WiFi, and Bluetooth [12], [13], [14]. One of the advantages of WiFi signals is that one can use the Access Points (APs) already installed in the building, with no additional hardware.…”

A Hidden Markov Model for Indoor Trajectory Tracking of Elderly People

“…One advantage of WiFi signals over the others is that one can rely only on the Access Points (APs) present inside the building, with no additional hardware. The localization process consists then in finding the sensor's location according to the WiFi signals collected from APs [11], [12].…”

Localization of Sensors in Indoor Wireless Networks: An Observation Model Using WiFi RSS

“…In our previous work [10], we proposed a basic observation model that works well in case of small number of zones. When the number of zones increases, the basic model fails to achieve a high accuracy due to difficulty in discriminating between overlapping distributions representing the different zones.…”