Robust, Cost-Effective and Scalable Localization in Large Indoor Areas

Abstract

Indoor location information plays a fundamental role in supporting various interesting location-aware indoor applications. Widely deployed WiFi networks make it feasible to perform indoor localization by first establishing a received signal strength (RSS) map covering the whole area based on a signal propagation model, then determining a location from an online RSS measurement given the RSS map. However, challenges remain in practical deployments, due to inaccurately estimated RSS values in the RSS map and insufficient number of access points (APs) in large indoor areas. To address these challenges, we develop a robust, cost-effective and scalable localization system (REAL). Our approach takes the error from the indoor radio signal propagation model into consideration. It also exploits information of unobserved APs at a given location and an optimal clustering method in the location searching phase. Our realworld experimental results demonstrate that REAL achieves considerable localization accuracy at a very low training cost even for a large indoor area. In addition, the results show that our scheme can also be effectively applied to Bluetooth networks with sparse signal coverage.

Efficient Indoor Localization via Switched-Beam Antennas

“…Like all fingerprint-based schemes, our approach requires the offline creation of fingerprint maps at deployment time and whenever there is a major change in the environment [46]. The cost associated with the labor to perform such on-site surveys should be considered when accessing the total cost of fingerprint-based localization and there is a large body of work on reducing the cost of this offline phase, see [47], [48], [49], [50] for recent ideas. Although it is beyond the scope of this work to propose ways to further reduce this cost, we do conduct experiments to show that environmental changes pose at most the same level of overhead on our approach as it does on the traditional core fingerprint method.…”

RoC: Robust and Low-Complexity Wireless Indoor Positioning Systems for Multifloor Buildings Using Location Fingerprinting Techniques

“…Lee and Park [19] presented a technique to estimate the robot position in each floor by using gyroscopes to recognize the robot motion status on the stairs. e authors of [20][21][22][23] presented robust and reliability positioning techniques for the IPSs based on Scene Analysis. ey focused on handling the problems of different mobile devices and reducing the impact of environmental dynamics on the accuracy performance.…”

“…eir algorithm was based on the location fingerprinting technique that used the ensemble learning approaches to handle the problems of Wi-Fi positioning caused by unstable and uncontrollable infrastructure such as the movement of people. Guan et al [23] presented a localization algorithm that reduces the influence of symmetry by considering extrarestrictions from redundant APs. ey used a novel clustering method to robustly estimate the target location from the fingerprint location candidates.…”

“…According to the literature reviewed on the robust localization process, although some existing works have studied the robust and reliable location problem [18][19][20][21][22][23], they focused on robustness issues, including robust tracking of floor changes when the robots moved in the staircase, improving the robustness of dynamic changes to the indoor environment which lead to the instability of fingerprint information, and reducing the influence of different mobile Single floor and multifloor Yes devices. In particular, they did not consider the robustness in terms of faulty RNs during the online estimation phase.…”

Indoor Smartphone Localization Based on LOS and NLOS Identification

“…Many studies have strived to improve the accuracy of RSSI-based indoor localization. RSSI-based indoor localization is classified into fingerprinting and trilateration [ 15 ]. Many works have proposed fingerprinting-based localization, because it is hard to apply a propagation model in trilateration-based localization due to the fading effect.…”