Block-Sparse Coding-Based Machine Learning Approach for Dependable Device-Free Localization in IoT Environment

Abstract

Device-free localization (DFL) locates targets without equipping with wireless devices or tag under the Internet-of-Things (IoT) architectures. As an emerging technology, DFL has spawned extensive applications in IoT environment, such as intrusion detection, mobile robot localization, and location-based services. Current DFL-related machine learning (ML) algorithms still suffer from low localization accuracy and weak dependability/robustness because the group structure has not been considered in their location estimation, which leads to a undependable process. To overcome these challenges, we propose in this work a dependable block-sparse scheme by particularly considering the group structure of signals. An accurate and robust ML algorithm named block-sparse coding with the proximal operator (BSCPO) is proposed for DFL. In addition, a severe Gaussian noise is added in the original sensing signals for preserving network-related privacy as well as improving the dependability of model. The real-world data-driven experimental results show that the proposed BSCPO achieves robust localization and signal-recovery performance even under severely noisy conditions and outperforms state-of-the-art DFL methods. For single-target localization, BSCPO retains high accuracy when the signal-to-noise ratio exceeds -10 dB. BSCPO is also able to localize accurately under most multitarget localization test cases.

Privacy-Preservation Techniques for IoT Devices: A Systematic Mapping Study

“…Anonymization (31 papers) [34]- [64] Obfuscation (15 papers) [64]- [78] Multi-tier ML (5 papers) [79]- [83] Decentralized ML (10 papers) [78], [84]- [92] Cryptography (154 papers) [48], [51], [59], [62], [63], [76], [83], [85], [86], [92]- [236] Dataflow (51 papers) [61], [82], [83], [89], [101], [165], [216], [221], [223], [237]- [278] Data summarization (6 papers) [279]- [284] Personal data stores (2 papers) [285], [286] ensure privacy (a) blockchain which is used for verifiability and accountability of data collection, storage and access in IoT environments [31]; and (b) privacybased programming languages, which require information flows and privileges to be declared beforehand, so all the data elements are attached to respective policies [32]. 7) Data summarization: it is a process of creating a concise, yet informative, version of data to preserve the privacy of the original data.…”

Applied Machine Learning for IIoT and Smart Production—Methods to Improve Production Quality, Safety and Sustainability

“…An increasing number of studies that analyze the applicability of the so-called device free localization (DFL), which provides user or asset location without any hardware attached, have been published. There are a couple of machine learning techniques utilized in those algorithms, e.g., block-sparse coding with the proximal operator [ 99 ] or Bayesian methods [ 100 ]. A useful summary of this topic and recent state-of-the-art can be found in [ 101 , 102 ].…”

Device-Free Localization Using Enhanced Channel Selection and a Distance-Based Elliptical Model

“…There are five Classical algorithms extensively used in DFL systems, e.g., the fingerprint (FP) [17]- [20], radio tomographic imaging (RTI) [1], [15], [21]- [24], the support vector machine (SVM) [25]- [27], the Bayesian system [16], [28], [29], and compressed sensing (CS) [30]- [32]. Algorithms studied in the literature are presented, as summarized in Table 1.…”

“…The monitoring area was divided into voxels. The Narrowband Relative Ultra-Wideband [10], [11], [12] UWB Relative Localization from RSS [4], [5], [6], [7], [13], [14], [15], [16] RSS Absolute Fingerprint-Matching [3], [17], [18], [19], [20] RSS Absolute Radio Tomographic Imaging [1], [2], [15], [21], [22], [23], [24], [33] RSS Absolute Support Vector Machine [25], [26], [27] RSS Absolute Bayesian system [16], [28], [29] RSS Relative Compressed Sensing [30], [31], [32] RSS Relative weightings of voxels inside one elliptical weight mode were the same, which was not consistent with the actual situation. Consequently, several researchers studied the elliptical weight model for improvement in localization estimation accuracy in RTI.…”