Visible light positioning (VLP) has emerged as a promising solution to address the requirements of indoor industrial localization services, such as the smart healthcare and indoor navigation. Increasing LEDs can boost positioning performance while leading to higher energy consumption, increased costs, and potential signal interference issues. To solve this problem, we propose an LED deployment algorithm that improves VLP performance by optimizing the placement of LEDs, thereby avoiding the introduction of excessive LEDs. The proposed algorithm uses the squared position error bound (SPEB) as the metric for deployment to assess the overall positioning performance. Additionally, we utilize Fisher information (FI) to quantify the information received from different LEDs and derive the fusion rules of information ellipses (IEs) to guide the deployment, aiming to maximize the overall received information in the system. To address the non-convex deployment problem, we introduce the confidence region for convex relaxation of the localization area, achieving deployment by minimizing the upper bound of SPEB within the confidence region. Moreover, we derive the localization error bounds to analyze the impact of various key parameters on positioning performance. Convincing simulation results demonstrate the significant improvement in VLP performance achieved with the proposed algorithm.

Visible light positioning (VLP) has become a popular indoor localization technology due to its low energy consumption and high security. Multi-target VLP systems typically utilize LEDs as anchors for localization, while overlooking the potential collaborative gains among targets. In this paper, we derive the Cramér-Rao lower bound (CRLB) for the received signal strength (RSS)-based collaborative VLP systems. It reveals the long-term performance mechanisms and the gains from both collaboration and prior information. Based on the derived CRLB and Fisher information (FI) fusion rules, we propose a collaborative target selection algorithm to mitigate the computational complexity caused by excessive collaborative targets. By integrating collaborative and prior information, we formulate the collaborative localization (CL) problem as an augmented Lagrangian function and solve it through the alternating direction method of multipliers (ADMM). Simulation and experimental results validate the effectiveness of the proposed algorithm and performance analysis.

In this paper, we aim at exploring the performance limits of passive indoor target localization utilizing multiple light-emitting diode (LED) transmitters embedded into the ceiling and multiple photodiode (PD) sensors anchored on the floor, where the precise locations of the PDs are not predetermined. This work enriches our understanding of error evolution in the time-domain localization cooperation. We propose a novel solution to estimate the target position by identifying the PDs occluded by the target, through measuring the changes in received signal strength (RSS) caused by the shadowing effects induced by this indoor target. Concurrently, these LEDs are used for the positional calibration of the PDs involved in the localization process. Specifically, we derive the Cramér-Rao lower bound (CRLB) on the estimation errors for both target localization and sensor position calibration, and then conduct the convergence and asymptotic performance analyses based on the obtained error bounds. Finally, simulation results corroborate the performance analysis, elucidating how the localization error evolves until it converges, and identifying the factors that influence this intrinsic convergence mechanism. The derived CRLBs and the long-term localization performance from these bounds provide a theoretical basis for optimizing passive positioning algorithms without requiring any auxiliary receiver.

The Internet of Nano Things (IoNT) is a nanonetwork comprised of numerous nano devices capable of data computation, storage, and actuation. IoNT has broad applications, including medicine and environmental protection. Monitoring specific molecular concentrations in the environment is necessary for practical tasks, such as disease monitoring and pollution tracking. However, sensors can only be sparsely arranged due to the limited space and high sensor costs. Consequently, it leads to the loss of monitoring data and seriously affects the performance of the IoNT. Therefore, it is indispensable to address the problem of how to effectively and accurately recover the missing data from the measurements of sparse sensors. To solve this problem, we propose a spatio-temporal constraint tensor completion (SCTC) algorithm based on CANDECOMP/PARAFAC (CP) decomposition. Specifically, we divide the entire environment into uniform grids and model the measurements of all grid positions over a period of time as a tensor. The sparse arrangement of sensors resulted in missing entire columns of data in the tensor, corresponding to the positions without sensors. Our objective is to recover these missing data utilizing the available measurements in the tensor. To effectively and accurately recover the missing data, the spatial and temporal constraint matrices are introduced to leverage the spatio-temporal correlations among the data. Due to the nonconvex optimization problem, CP decomposition is introduced to transform the problem of tensor recovery into solving multiple-factor matrices. The performances of the proposed SCTC algorithm are confirmed via simulation and experiment data.