Exploiting Sensed Radio Strength and Precipitation for Improved Distance Estimation

Indoor navigation and location-based services increasingly show promising marketing prospects. Indoor positioning based on Wi-Fi radio signal has been studied for more than a decade because Wi-Fi, a signal of opportunity without extra cost, is extensively deployed for internet connections. Bayesian fingerprinting positioning, a classical Wi-Fi-based indoor positioning method, consists of two phases: radio map learning and position inference. Thus far, the application of Bayesian fingerprinting positioning is limited due to its poor usability; radio map learning requires an adequate number of received signal strength indication (RSSI) observables at each reference point, long-term fieldwork, and high development and maintenance costs. In this paper, based on a statistical analysis of actual RSSI observables, a Weibull–Bayesian density model is proposed to represent the probability density of Wi-Fi RSSI observables. The Weibull model, which is parameterized with three parameters that can be calculated with fewer samples, can calculate the probability density with a higher accuracy than the traditional histogram method. Furthermore, the parameterized Weibull model can simplify the radio map by storing only three parameters that can restore the whole probability density, i.e., it is not necessary to store the probability distribution based on traditionally separated RSSI bins. Bayesian positioning inference is performed in the positioning phase using probability density rather than the traditional probability distribution of predefined RSSI bins. The proposed method was implemented on an Android smartphone, and the performance was evaluated in different indoor environments. Results revealed that the proposed method enhanced the usability of Wi-Fi Bayesian fingerprinting positioning by requiring fewer RSSI observables and improved the positioning accuracy by 19–32% in different building environments compared with the classic histogram-based method, even when more samples were used.

Section: Introductionmentioning

confidence: 99%

A Bayesian Density Model Based Radio Signal Fingerprinting Positioning Method for Enhanced Usability

Zheng

Liu

Yang

et al. 2018

“…The hybrid TOA/RSS maximum likelihood estimator is obtained by maximizing the joint pdf of the RSS and TOA observations. The enhanced RSS-based distance estimator [15,16] uses an iterative form of Newton’s method for maximum likelihood-based distance estimation due to the rain attenuation effect, achieving up to 90% error reduction rate. The proposed mechanism in Ref.…”

Section: State Of the Artmentioning

confidence: 99%

Photography Trilateration Indoor Localization with Image Sensor Communication

Jang

2019

Localization has become an important aspect in a wide range of mobile services with the integration of the Internet of things and service on demand. Numerous mechanisms have been proposed for localization, most of which are based on the estimation of distances. Depending on the channel modeling, each mechanism has its advantages and limitations on deployment, exhibiting different performances in terms of error rates and implementation. With the development of technology, these limitations are rapidly overcome with hybrid systems and enhancement schemes. The successful approach depends on the achievement of a low error rate and its controllability by the integration of deployed products. In this study, we propose and analyze a new distance estimation technique employing photography and image sensor communications, also named optical camera communications (OCC). It represents one of the most important steps in the implemented trilateration localization scheme with real architectures and conditions of deployment which is the second our contribution for this article. With the advantages of the image sensor hardware integration in smart mobile devices, this technology has great potential in localization-based optical wireless communication

“…To minimize their temporal variation and fluctuation, during the 2017 IPIN competition explained in [50], the UMinho Team merged the fingerprints collected in the same position to generate a less noisy fingerprint and potentially improve the localization accuracy. In this paper, we exploit multiple RSSI measurements like the authors in [51], expecting to remove the noise and improve the localization accuracy. CNN are used taking into account the correlation between different RSSI measurements.…”

Section: Introductionmentioning

confidence: 99%

Deep CNN for Indoor Localization in IoT-Sensor Systems

Njima

Ahriz

Zayani

et al. 2019

Currently, indoor localization is among the most challenging issues related to the Internet of Things (IoT). Most of the state-of-the-art indoor localization solutions require a high computational complexity to achieve a satisfying localization accuracy and do not meet the memory limitations of IoT devices. In this paper, we develop a localization framework that shifts the online prediction complexity to an offline preprocessing step, based on Convolutional Neural Networks (CNN). Motivated by the outstanding performance of such networks in the image classification field, the indoor localization problem is formulated as 3D radio image-based region recognition. It aims to localize a sensor node accurately by determining its location region. 3D radio images are constructed based on Received Signal Strength Indicator (RSSI) fingerprints. The simulation results justify the choice of the different parameters, optimization algorithms, and model architectures used. Considering the trade-off between localization accuracy and computational complexity, our proposed method outperforms other popular approaches.