In this paper, we introduce a framework for multi-class novelty detection using structural vibration signals. Structural vibration-based person identification is a promising soft-biometric approach with potential applications in elderly care and access control. However, current research faces two key challenges. The first challenge is the lack of large-scale datasets necessary for thorough evaluation in structural vibration gait recognition. To address this, we created a new dataset with recordings from fifty individuals. The second challenge lies in the limited exploration of deep learning methods for large-scale multi-class novelty detection in structural vibration data. To fill this gap, we propose the energy-shifted contrastive loss function, specifically designed for this task. Our results demonstrate that the proposed framework achieves 96.57% accuracy in multi-class classification. For novelty detection, it achieves an Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) score of 89.15% for single footsteps, which improves to 93.83% with five consecutive footsteps.

Received signal strength (RSS)-based localization techniques rely on the transmit power of nodes, with many existing approaches assuming full knowledge of this parameter. However, transmit power depends on various factors like battery levels, antenna orientation, and component aging. To address this issue, existing techniques typically employ semidefinite programming (SDP), which exhibits significantly high computational complexity. In this paper, we present a cooperative RSS-based localization technique named CR-CA (Convex Relaxation with Centralized Armijo optimization), which jointly estimates nodes’ locations and transmit power. CR-CA transforms the unconstrained maximum likelihood (ML) of the RSS-based localization problem into a constrained optimization problem using a convex approximation of the non-convex and discontinuous objective function. We demonstrate that the relaxed ML objective function possesses a Lipschitz continuous gradient. We solve the relaxed ML problem using the augmented Lagrange multiplier method and provide the theoretical proof of its convergence. Additionally, we derive the Cramer-Rao Lower Bound (CRLB) for RSS-based cooperative localization under scenarios where transmit power is unknown. We conduct extensive simulations and real-world experiments to verify the effectiveness of CR-CA, showcasing its superior accuracy in estimating nodes’ locations and transmit power. Simulations and experiments further validate that CR-CA exhibits linear computational complexity with the number of wireless links, thus making it suitable for large-scale networks.

Structural vibration-based gait recognition has emerged as a promising soft-biometric modality, particularly for privacy-sensitive monitoring and access control. Despite its potential, current research is largely limited to proof-of-concept studies that rely on hand-crafted features, with minimal exploration of deep learning methodologies. This gap reduces the potential for integrating structural vibration-based gait recognition with existing modalities, such as camera-based systems. In this study, we propose a multi-modal gait recognition system that integrates both vision and structural vibration modalities. We address two key challenges: (a) lack of studies exploring outdoor gait recognition using both vision and structural vibration, and (b) absence of a multi-modal training scheme that combines these two modalities. To tackle the first challenge, we curated a dataset comprising five minutes of walking data from ten individuals captured simultaneously by two cameras and a geophone sensor. To address the second challenge, we developed a joint training framework that uses data from both modalities. Our methods achieve an accuracy of 96.03% (±1.12) using structural vibration signals alone, and this improves to 98.27% (±0.06) when both modalities are combined.

Gait recognition based on structural vibration signals is an emerging area in soft biometrics and healthcare. It is valued for its privacy-preserving features, making it ideal for continuous monitoring in healthcare environments. In this work, we propose a method for simultaneous person identification and gait-abnormality detection using structural vibration signals. We have experimented on a dataset of eight people. The system uses shared feature extraction layers with separate branches for identification and abnormality detection. Our framework achieves a person identification accuracy of ~89.00% and ~90.00% accuracy in detecting abnormality in gait patterns.

Received signal strength (RSS)–based cooperative localization has gained significant attention due to its straightforward system architectures and cost-effectiveness. In this paper, we propose Cooperative Localization Techniques (with Unknown Parameters), referred to as CTUP(s), which consider uncertainty in anchor nodes’ locations and assume the transmit power and path loss exponent (PLE) to be unknown. Unlike prior studies, CTUP(s) address unknowns by estimating these parameters, along with the location of target nodes. The non-convex and non-linear nature of the maximum likelihood (ML) estimator of the problem is addressed through relaxation techniques, employing Taylor series expansion, semidefinite relaxation (SDR), and the epigraph method. The resulting problem is solved using semidefinite second-order cone programming (SDP-SOCP), leveraging the precision of SDP and the simplicity of SOCP. We deployed an extensive network comprising 50 BLE nodes covering an area of 640 m$\times 180$m to gather RSS data. The precise location of the nodes is obtained using real-time kinematics global positioning system (RTK-GPS), which is treated as the ground truth. Furthermore, to replicate real-world scenarios, we recorded the positions of the anchor nodes using a standard GPS, thereby introducing uncertainty into the anchor node locations. Extensive simulation and hardware experimentation demonstrate the superior performance of CTUP compared to existing techniques.

Received signal strength (RSS)-based localization has been popular as it can achieve reasonable accuracy without the need for additional hardware. In this work, we consider RSS-based cooperative and non-cooperative localization when the transmit power of the nodes is unknown. The maximum likelihood (ML) formulation of the RSS-based localization is non-linear, non-convex, and discontinuous and cannot be solved using conventional techniques. Firstly, we linearize the relation between pairwise distance between nodes and received power using a least squares-based linearization technique. Next, we propose two RSS-based localization techniques, referred to as SDP-URSS and SDP-RSS, that convert the ML into a constrained convex optimization problem using the proposed linearization technique and semidefinite relaxation. SDP-URSS assumes the transmit powers are unknown and estimates them along with the location of the nodes, whereas SDP-RSS uses the transmit power information to improve the location estimates. Extensive performance evaluation of the proposed technique considering various performance metrics under non-cooperative and cooperative scenarios demonstrates the superiority of the proposed localization techniques over the existing methods.

Received signal strength (RSS)-based localization techniques have attracted a lot of interest as they are easy to implement and do not require any localization-specific hardware. However, maximum likelihood (ML) formulation of RSS-based localization problem is non-convex, non-linear, and discontinuous, and cannot be solved using standard optimization techniques. We propose techniques that converts the ML objective function into an invex (invariant convex) function and solve them using gradient descent. We also employ coordinate descent to solve the invex problem in a completely distributed manner without any synchronization requirements. The coordinate descent-based technique can be implemented on the sensor nodes as it has low computational complexity and scales very well to large networks. We prove the convergence theoretically, derive the convergence rate, and provide a detailed computational complexity and communication overhead analysis of the techniques. We perform extensive performance analysis and compare our techniques with centralized and distributed localization methods, and demonstrate the superior performance of the proposed techniques in terms of convergence rate, localization accuracy, and execution time.

In this article, we present a person identification system for Internet-of-Things-based smart home applications that utilizes footstep induced structural vibrations as biometric modality. Footfall events are generated by the rhythmic contact of the heel and toe on the floor while walking. In the case of such biometric systems, there is no disturbance of the natural movement of the individuals and, thus, they provide an advantage over the existing systems that deal with human intervention. Another key advantage is that footfall signals are not subjected to spoofing attacks as they are nearly impossible to mimic unlike other biometric traits (fingerprint, face, and voice). We propose a 3-layer computing architecture, for the decentralized implementation of the biometric system. We also propose a basis pursuit-based data compression technique (DS8BP) to reduce power and bandwidth for the wireless transmission of footfall events. DS8BP achieves a compression ratio of 108 and increases the scalability of the system. We performed extensive experimentation to evaluate the proposed biometric system using indigenous databases containing 100,000+ footfall events of eight individuals collected in three types of surfaces (concrete tile floor, carpet floor, and wooden floor). The proposed system achieves a prediction accuracy of 93%, 98%, and 96% in three surface types when features from seven footsteps are considered.

Geotagging of sensor data in a wireless sensor network is important in many applications. It may not always be possible to record locations of the sensor nodes during deployment. Localization techniques provide the node location information, however most of these techniques rely on the neighboring nodes for localization. Thus it is essential that the information from neighbors be trustworthy and/or the localization techniques be robust to some of the nodes turning malicious. In this paper, we address the scenario where the malicious node(s) attempt to disrupt the localization process of a target node in an uncoordinated manner. We propose a secure localization technique, called the weighted least square (WLS) localization, in a network with one or more compromised anchor nodes (nodes with known positions are referred to as anchor nodes). The WLS technique assigns larger weights to the anchor nodes that are closer to the target node and is shown to offer significant advantages over existing techniques. The Cramer-Rao lower bound (CRLB) on the root mean square error (RMSE) of the position estimate for the uncoordinated attack is also derived. The proposed technique is shown to provide better localization accuracy than existing algorithms.

Economical, low-power, and easy to deploy indoor localization schemes have always been a challenge. We propose a technique that utilizes the analog output of pyroelectric (or passive) infrared (PIR) sensors for indoor localization. We propose two models for distance estimation based on the characterization of the analog sensor output in terms of the peak to peak value and its relationship with distance from the sensor. The proposed distance estimation models are based on bijective functions: Mbhusing a hyperbolic function and Mplusing a piecewise linear function. Once distances of the subject from the PIR sensors (or anchors) are estimated, multilateration or support vector regression (SVR) based techniques are used for computing the location coordinates of the subject. Using four PIR sensors, we demonstrate the localization of a human subject in a 7 m × 7.5 m area with the regression based technique outperforming the other techniques in terms of accuracy and achieving an RMS localization error of 0.65 m using the Mplmodel for distance estimation. We also compare the computational complexity and memory or storage requirements of the proposed techniques which are an important consideration for distributed implementation on resource constrained devices such as sensor nodes.