Bodhibrata Mukhopadhyay

Localization in wireless networks is traditionally performed through centralized architectures that process measurements at a single node. However, such architectures not only lead to a high computational burden but also suffer from limited robustness, as the failure of the central node terminates the localization operation. They also require extremely high communication bandwidth, as all nodes must transmit their measurements to a central server. Moreover, assuming prior knowledge of each target node's transmit power is impractical, as it varies with battery level, hardware imperfections, and antenna orientation. To address these issues, this paper proposes a Distributed Cooperative Localization technique (DCLU), which jointly estimates the location and transmit power of the nodes. The joint maximum-likelihood formulation is non-convex and discontinuous, and cannot be solved using off-the-shelf optimization techniques. We address this challenge by constructing a convex approximation of the ML objective, which enables efficient distributed estimation using a coordinate descent algorithm. We further establish theoretical guarantees for convergence, including the rate of convergence. Compared with centralized approaches, DCLU offers enhanced scalability and robustness. We also derive the Cramer-Rao lower bound (CRLB) as a performance benchmark. Extensive simulations and on-field experiments demonstrate that DCLU achieves higher localization accuracy while significantly reducing computational complexity relative to existing techniques.

Underwater wireless sensor networks (UWSNs) have received significant attention due to their various applications, with underwater target localization playing a vital role in enhancing network performance. Given the challenges and high costs associated with UWSN deployments, Received Signal Strength (RSS)-based localization offers a viable solution due to its minimal hardware requirements and cost-effectiveness. In this paper, we assign distance-based weights to RSS measurements, providing higher reliability to closer anchor nodes. Using the weighted RSS measurements and generalized trust region subproblem (GTRS), we propose the GTRS-based localization technique with Unknown Transmit Power (GUTP), which can be solved by a simple bisection method. Unlike conventional localization methods that require prior knowledge of the target node's transmit power, GUTP jointly estimates both the location and transmit power of the target node, broadening its practical use. Additionally, we derive the Cramer-Rao lower bounds (CRLBs) for RSS-based underwater localization with known and unknown transmit power, respectively. Extensive simulations demonstrate that GUTP achieves enhanced accuracy and significantly lower computational complexity in estimating the target node's location and transmit power compared to existing semidefinite programming (SDP)-based techniques.

This paper introduces a large-scale, non-intrusive person identification framework using footstep-induced structural vibration signals. The increasing adoption of structural vibration analysis for person identification comes from its inherent non-intrusiveness and privacy preserving characteristics. However, existing methodologies are often constrained by the scarcity of extensive datasets, both in terms of the number of subjects and the temporal length of individual recordings, and frequently rely on supervised learning paradigms coupled with manual feature engineering. Consequently, the generalization capabilities of these approaches to broader populations are typically limited. To address these limitations, we have curated a comprehensive dataset of structural vibration signals acquired from 100 individuals. In addition, we have developed an unsupervised event detection method using features based on time, frequency, and wavelet analysis. Furthermore, we have developed DeepStep, a residual attention-based framework specifically designed for efficient feature extraction and classification of structural vibration signals. Experimental evaluation on our curated dataset demonstrates that the proposed approach achieves a Rank-1 accuracy of approximately 92% and a Rank-5 accuracy of approximately 96%.

Localization of source in molecular communication (MC) mimics the finding of cancer cells within the organisms. In this study, our primary aim is to implement a cooperative localization (CL) scheme to pinpoint the positions of multiple transmitters (Txs) within an anomalous diffusion (AD)-based MC (AD-MC) system. We employ maximum likelihood estimation (ML) to formulate the Txs’ locations while also factoring in the practical considerations of inter-symbol interference (ISI) and Brownian noise. Further, we analyze the performance of proposed localization scheme through root mean square error (RMSE) for multiple Tx localization schemes. The coordinates of the Txs are determined by solving the ML using the multi-variable Newton-Raphson method. Furthermore, Monte Carlo simulations are used to evaluate the influence of fluctuating noise levels on the system. Aggregated results from simulations conducted across varied noise values are used for analysis. Our simulations demonstrate the superior performance of our proposed method compared to the existing least squares method employed in diffusion-based MC systems.

Precise and energy-efficient localization is a critical requirement in many Internet of Things (IoT) applications, particularly in large-scale deployments such as asset tagging, agriculture, and smart cities, where long battery life and cost-effectiveness are crucial. The Semtech SX1280 longRange (LoRa) transceiver presents a promising solution for IoT localization. It combines low cost, low power, and precise ranging capability over distances of up to 1 km. However, the ranging process requires two devices to be simultaneously active, one initiating the ranging request and the other responding to it, which can lead to significant energy expenditure if not properly managed. Despite the transceiver’s excellent performance, no existing system-level framework effectively manages sleep–wake coordination and role assignment needed for energy-efficient operation. This article presents a coordination framework that significantly reduces power consumption while maintaining the inherent precise ranging capability of the chip. The framework schedules short, synchronized wake-up windows between the initiator and the responder, allowing devices to remain in deep sleep for most of their duty cycle. This scheduling strategy minimizes reliance on precise continuous timing and mitigates drift in low-cost oscillators. To validate the framework, we designed and developed custom nodes that are compliant with the framework’s protocol. Experimental results show that the proposed approach allows a node to stay in ultralow-power mode and wake periodically to check for instructions. The node can remain in standby mode for up to nine months on a single coin cell battery and can perform ranging operations on demand in near real time, all while maintaining a localization accuracy within 5 m.

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.

Precise and energy-efficient localization is a critical requirement in many Internet of Things (IoT) applications, particularly in large-scale deployments such as asset tagging, agriculture, and smart cities, where long battery life and cost-effectiveness are crucial. The Semtech SX1280 LoRa transceiver presents a promising solution for IoT localization. It combines low cost, low power, and precise ranging capability over distances of up to 1 km. However, the ranging process requires two devices to be simultaneously active, one initiating the ranging request and the other responding to it, which can lead to significant energy expenditure if not properly managed. Despite the transceiver's excellent performance, no existing system-level framework effectively manages sleep-wake coordination and role assignment needed for energy-efficient operation. This paper presents a coordination framework that significantly reduces power consumption while maintaining the inherent precise ranging capability of the chip. The framework schedules short, synchronized wake-up windows between the initiator and the responder, allowing devices to remain in deep sleep for most of their duty cycle. This scheduling strategy minimizes reliance on precise continuous timing and mitigates drift in low-cost oscillators. To validate the framework, we designed and developed custom nodes that are compliant with the framework's protocol. Experimental results show that the proposed approach allows a node to stay in ultra-low power mode and wake periodically to check for instructions. The node can remain in standby mode for up to nine months on a single coin cell battery and can perform ranging operations on demand in near real-time, all while maintaining a localization accuracy within five meters.

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 can be highly effective when the target node’s transmit power is known. However, in practical scenarios, factors including antenna orientation and battery level may introduce variability, making accurate power estimation a critical challenge. This paper addresses localization with unknown transmit power using RSS difference (RSSD) measurements. A framework is proposed to first derive an initial location estimate via linear least squares (LLS) and then refine it by solving a weighted least squares (WLS) problem using semidefinite programming (SDP). Unlike existing SDP-based techniques, our technique meets a rank-one constraint to prevent accuracy loss. A compact expression for the Cramér-Rao lower bound (CRLB) in RSSD-based localization is derived. To validate our approach, extensive experiments using 13 nodes deployed in a 164×77 m2 outdoor arena are conducted. Experimental results and numerical simulations demonstrate that the proposed technique outperforms existing methods with respect to localization accuracy and computational efficiency.

Signal strength-based cooperative localization has gained significant interest due to its low complexity and cost-effectiveness. The conventional techniques often use Taylor expansion to address the non-convex, non-linear, and discontinuous characteristics of the maximum likelihood (ML) objective function. However, this results in a considerable residual error that exponentially grows with noise. To address this issue, we propose a cooperative (and non-cooperative) localization technique using received signal strength (RSS) measurements, named C-UA (and NC-UA), which employs a relative error-based estimator. We then use weighted non-linear least squares (NLS) to formulate a semidefinite programming (SDP) problem. C-UA and NC-CA can jointly estimate the location and transmit power of target nodes while considering the uncertainty in anchor nodes' locations. We also derive the Cramer-Rao lower bound (CRLB) involving unknown transmit power and anchor location uncertainty. We perform extensive outdoor real-world tests in an open field (640 m × 180 m), using 50 Bluetooth transceivers to collect RSS measurements. We record the location of each node using two devices: a real-time kinematic (RTK)-GPS system, providing centimeter-level accuracy, and a standard GPS device, offering meter-level accuracy. The RTK-GPS measurements are considered the true positions, while the standard GPS measurements, with lower precision, are used to represent the uncertainty in the locations. Through extensive simulations and experimentation, we demonstrate the superior performance of C-UA and NC-CA over existing localization techniques.