Indoor Localization for IoT Using Adaptive Feature Selection: A Cascaded Machine Learning Approach

Abstract

Evolving Internet-of-Things (IoT) applications often require the use of sensor-based indoor tracking and positioning, for which the performance is significantly improved by identifying the type of the surrounding indoor environment. This identification is of high importance since it leads to higher localization accuracy. This paper presents a novel method based on a cascaded two-stage machine learning approach for highly-accurate and robust localization in indoor environments using adaptive selection and combination of RF features. In the proposed method, machine learning is first used to identify the type of the surrounding indoor environment. Then, in the second stage, machine learning is employed to identify the most appropriate selection and combination of RF features that yield the highest localization accuracy. Analysis is based on k-Nearest Neighbor (k-NN) machine learning algorithm applied on a real dataset generated from practical measurements of the RF signal in realistic indoor environments.Received Signal Strength, Channel Transfer Function, and Frequency Coherence Function are the primary RF features being explored and combined. Numerical investigations demonstrate that prediction based on the concatenation of primary RF features enhanced significantly as the localization accuracy improved by at least 50% to more than 70%.

IoT‐based cyber‐physical communication architecture: challenges and research directions

“…Different applications require diverse specification and requirements. For biomedical application, it requires small and wearable antenna whereas the communication industry requires high gain and directivity [46–48]. Therefore, the antenna should design for specific IoT applications, and there is an open challenge to design an universal antenna. Design complexity: When designing the IoT‐based CPSs, the algorithm and physical structure should be low complexity and affordable connectivity to the low‐power devices as shown in Table 1.…”

“…Different applications require diverse specification and requirements. For biomedical application, it requires small and wearable antenna whereas the communication industry requires high gain and directivity [46][47][48]. Therefore, the antenna should design for specific IoT applications, and there is an open challenge to design an universal antenna.…”

Training data augmentation for deep learning radio frequency systems

“…For a given problem within the RFML space, once a reliable training routine and a network of sufficient size have been identified, how well a trained network is able to solve the problem often comes down to the quantity and quality of the data available. 7 Effectively, there are three sources of data that can be used to train networks within the RFML space: simulated/synthetic, 5, captured/collected, 4,6,11,12,18,23,35,37,48,54,[68][69][70][71][72][73][74][75][76][77][78][79][80][81][82][83][84][85] and augmented, 11,47,48,64,71,78 which is a combination of the first two using domain knowledge (focus of this work), or using generative adversarial networks (GAN) as performed in Davaslioglu et al 47 Due to the nature of the RFML data space, simulated data are inexpensive thanks to opensource tool-kits like GNU Radio, where observations can be generated uniquely in parallel, with the only bottleneck being the available computer resources. 30 Comparatively, performing an over-the-air (OTA) collection costs many orders of magnitude greater in terms of time and money due to procurement and configuration of the hardware transceivers and having to generate data in real time rather than in parallel as is done in simulation, yet all the work done in order to simulate the data is still needed when not examining commercial off-the-shelf (COTS) equipment...…”

A Survey of Machine Learning for Indoor Positioning

“…ML has also proven as an effective way to fuse multidimensional data collected from multiple positioning sensors, technologies and methods. For example, both supervised and unsupervised learning have been applied for fusion weight generation in [25]- [27]. However, unsupervised ML fusion technique is superior since it calculates the weights in real-time without offline training [28].…”