This paper presents an optimal calibration scheme and a weighted least squares (LS) localization algorithm for received signal strength (RSS) based visible light positioning (VLP) systems, focusing on the often-overlooked impact of light-emitting diode (LED) tilt. By optimally calibrating LED tilt and gain, we significantly enhance VLP localization accuracy. Our algorithm outperforms both machine learning Gaussian processes (GPs) and traditional multilateration techniques. Against GPs, it achieves improvements of 58% and 74% in the 50th and 99th percentiles, respectively. When compared to multilateration, it reduces the 50th percentile error from 7.4 cm to 3.2 cm and the 99th percentile error from 25.7 cm to 11 cm. We introduce a low-complexity estimator for tilt and gain that meets the Cramer-Rao lower bound (CRLB) for the mean squared error (MSE), emphasizing its precision and efficiency. Further, we elaborate on optimal calibration measurement placement and refine the observation model to include residual calibration errors, thereby improving localization performance. The weighted LS algorithm’s effectiveness is validated through simulations and real-world data, consistently outperforming GPs and multilateration, across various training set sizes and reducing outlier errors. Our findings underscore the critical role of LED tilt calibration in advancing VLP system accuracy and contribute to a more precise model for indoor positioning technologies.

Accurate 3D indoor positioning is a sought-after feature in many applications. To achieve this, we perform a comparative study of proposed anchor selection methods applied to a hybrid RF-acoustic indoor positioning system. The positioning accuracy is evaluated for two 3D positioning mechanisms. In the first positioning approach, the proposed anchor selection methods are tested in a classical system which uses the selected noisy range estimates which are converted into a 3D position using a least squares (LS) estimator. This mechanism requires the exact knowledge of the anchor locations, which introduces undesired anchor position calibration, identification and commissioning steps when deploying such a system. In order to overcome this problem, a second positioning mechanism, by means of circular convolutional neural network (CCNN), is proposed. This model does not require the knowledge of the anchor locations, as it is implicitly learned during training. The proposed anchor selection methods are evaluated both for the classical LS estimator and the circular convolutional neural network (CCNN) model in both a shoebox and L-shaped room. The results indicate a significant improvement in accuracy when using anchor selection and/or the machine learning (ML) based models, primarily in rooms where Non-Line-of-Sight (NLoS) situations are encountered. For the classical system, it is more effective to use fewer, but more pertinent anchors, whereas using all anchors is optimal for ML methods. Nevertheless, ML methods outperform traditional positioning algorithms, and anchor selection addresses scalability.

Visible light positioning (VLP) offers a cost-effective and accurate method for indoor localization. Gaussian processes (GPs), a data-driven method widely used in the received signal strength (RSS)-based VLP systems, face difficulties when training data are scarce or when forced to extrapolate. In this work, we propose a novel hybrid model, PhyGP, which integrates physics-based models into GPs through Bayesian active learning to enhance extrapolation capabilities without decreasing the interpolation accuracy of GPs. Experimental results, validated on real-world data, demonstrate significant improvements in extrapolation accuracy compared to GPs and in computational efficiency compared to physics-based models. Our approach achieves an improvement in extrapolation accuracy ranging from 32% to 83%, reducing the $\mathbf{P 5 0}$ error from 72 cm to 12 cm at its best performance. Additionally, the PhyGP model offers a four-order magnitude gain in computational efficiency compared with physics-based models.

Millimeter-wave (mmWave) technology holds the potential to revolutionize head-mounted displays (HMDs) by enabling high-speed wireless communication with nearby processing nodes, where complex video rendering can take place. However, the sparse angular profile of mmWave channels, coupled with the narrow field of view (FoV) of patch-antenna arrays and frequent HMD rotation, can lead to poor performance. We introduce six channel performance metrics to evaluate the performance of an HMD equipped with mmWave arrays. We analyze the metrics using analytical models, discuss their impact for the application, and apply them to 28 GHz channel sounding data, collected in a conference room using eight HMD patch-antenna arrays, offset by 45° from each other in azimuth. Our findings confirm that a single array performs poorly due to the narrow FoV, and featuring multiple arrays along the HMD’s azimuth is required. Namely, the broader FoV stabilizes channel gain during HMD rotation, lessens the attenuation caused by line of sight (LoS) obstruction, and increases the channel’s spatial multiplexing capability. In light of our findings, we conclude that it is imperative to either equip the HMD with multiple arrays or, as an alternative approach, incorporate macroscopic diversity by leveraging distributed access point (AP) infrastructure.

Indoor positioning applications are craving for ever higher precision and accuracy across the entire coverage zone. Optimal anchor placement and the deployment of multiple distributed anchor nodes could have a major impact in this regard. This paper examines the influences of these two difficult to approach hypotheses by means of a straightforward ultrasonic 3D indoor positioning system deployed in a real-life scenario via a geometric based simulation framework. To obtain an optimal anchor placement, a particle swarm optimization (PSO) algorithm is introduced and consequently performed for setups ranging from 4 to 10 anchors. In this way, besides the optimal anchor placement layout, the influence of deploying several distributed anchor nodes is investigated. In order to theoretically compare the optimization progress, a system model and Cramér-Rao lower bound (CRLB) are established and the results are quantified based on the simulation data. With limited anchors, the placement is crucial to obtain a high precision high reliability (HPHR) indoor positioning system (IPS), while the addition of anchors, to a lesser extent, gives a supplementary improvement.

In indoor localization, Received Signal Strength (RSS)-based Visible Light Positioning combined with Multi Layer Perceptrons (MLPs) or Gaussian processes (GPs) has attracted much attention due to its high accuracy. However, there is a lack of detailed investigation on the advantages, disadvantages, and applicability of MLPs and GPs in large datasets collected from representative industrial environments. In this paper, we present a comprehensive comparison and analysis of MLPs and GPs from theoretical and experimental perspectives, focusing on model parameters, complexity, and interpretability. Our study demonstrates that while GPs outperform MLPs on small datasets, they exhibit drawbacks such as high computational cost on larger datasets. Furthermore, our investigation reveals that including Batch Normalization (BN) layers in MLPs enhances their generalization and suppresses outliers in prediction. To address the issues of scalability and interpretability, we introduce the Deep Kernel Learning (DKL) model as a solution, supported by both theoretical and experimental findings.

Multiple low power wide area networks (LPWANs) have been rolled out to support the variety of IoT applications. These networks vary largely in terms of quality-of-service, throughput and energy-efficiency. To cover all LPWAN use-cases most optimally, multiple networks can be combined into a multiple radio access technology (multi-RAT) solution. In particular environmental monitoring in both smart city and remote landscapes. We present and share such a multi-RAT platform. To derive an accurate profile of the multi-RAT opportunities in various scenarios, in the-field network parameter are monitored. The platform collects per-packet energy-consumption, packet delivery ratio (PDR) and other parameters of LoRaWAN, NB-IoT and Sigfox. Our preliminary measurements demonstrate the validity of using a multi-RAT solution. For example, we illustrate the potential energy savings when adopting multi-RAT in various scenarios.

Recent works have focused on utilizing LoRa to establish long-range and low-power connections between internet of things (IoT) nodes and gateways. A multi-hop scheme opens opportunities to greatly increase coverage in situations with adverse propagation effects or thin gateway deployment. In this work, we propose a multi-hop LoRa-based protocol tailored to battery-powered IoT devices. The protocol uses a combination of preamble sampling and dynamic payload aggregation to improve energy efficiency. In contrast to prior work, a fine-grained energy model of the devices is considered, impacting the design choices in all protocol layers, i.e., application, medium access control (MAC), and routing layer. The protocol is implemented in C and made public in open source. A first experiment indicates a high packet delivery ratio and showcases the dynamic aggregation feature.

Autonomy of devices is a major challenge in many Internet of Things (IoT) applications, in particular when the nodes are deployed remotely or difficult to assess places. In this paper we present an approach to provide energy to these devices by Unmanned Aerial Vehicles (UAVs). Therefore, the two major challenges, finding and charging the node are presented. We propose a model to give the energy constrained node an unlimited autonomy by taking the Wireless Power Transfer (WPT) link and battery capacity into account. Selecting the most suitable battery technology allows a reduction in battery capacity and waste. Moreover, an upgrade of existing IoT nodes is feasible with a limited impact on the design and form factor.

Indoor localization of unknown acoustic events with MEMS microphone arrays have a huge potential in applications like home assisted living and surveillance. This article presents an Angle of Arrival (AoA) fingerprinting method for use in Wireless Acoustic Sensor Networks (WASNs) with low-profile microphone arrays. In a first research phase, acoustic measurements are performed in an anechoic room to evaluate two computationally efficient time domain delay-based AoA algorithms: one based on dot product calculations and another based on dot products with a PHAse Transform (PHAT). The evaluation of the algorithms is conducted with two sound events: white noise and a female voice. The algorithms are able to calculate the AoA with Root Mean Square Errors (RMSEs) of 3.5° for white noise and 9.8° to 16° for female vocal sounds. In the second research phase, an AoA fingerprinting algorithm is developed for acoustic event localization. The proposed solution is experimentally verified in a room of 4.25 m by 9.20 m with 4 acoustic sensor nodes. Acoustic fingerprints of white noise, recorded along a predefined grid in the room, are used to localize white noise and vocal sounds. The localization errors are evaluated using one node at a time, resulting in mean localization errors between 0.65 m and 0.98 m for white noise and between 1.18 m and 1.52 m for vocal sounds.

Indoor positioning systems are mostly supported by Radio Frequency (RF) technologies, due to the omnipresence of wireless communication infrastructure and handsets. Most of these localization systems rely on a Line Of Sight (LOS) connection to calculate a position based on signal strength, delay or direction. However, indoor environments are characterized by Non Line Of Sight (NLOS) connections and multipath effects such as reflections, scattering, diffraction and refraction. Multipath components are generally considered as undesirable and therefore most techniques focus on mitigation. This research applies an Angle of Arrival (AoA) multipath assistance approach, using ray tracing in a known environment to exploit signal reflections. Because the complete propagation channel is taken into account, a mobile node can already be localized by a single fixed antenna array. In order to assess the performance of the system, LOS and NLOS experiments are performed in three indoor environments in the 2.4 GHz and 5 GHz frequency bands. All measurements are processed by the multipath assisted AoA method, as well as a standard triangulation approach. These tests indicate the superior accuracy of the multipath assisted method, especially in NLOS conditions. Also, the tests demonstrate that the systems performs significantly better at 2.4 GHz than at 5 GHz. Finally, the performance of the presented system is compared to results that can be found in related work.

For large-scale wireless networks, the deployment of new systems can be a costly and tedious task. In this article we show how this effort can be reduced by automating the configuration of new wireless devices, based on their location. This technique is useful in e.g. home automation systems where a light switch needs to be paired with the light fixtures. To show how our system works, we have implemented a demonstrator application based on Bluetooth Low Energy. We assume that each room contains a beacon node that holds the settings for that room. Based on the RSSI in the BLE advertisements of those node, a new node can detect in which room it is deployed and update its own settings accordingly. A node can do this correctly in 96% of the case. To improve the reliability, a node can ask the user to confirm its position, whenever there is doubt. This happens in 12% of the cases, resulting in 100% correct localizations.

Angle of arrival localization systems generally rely on multiple antenna arrays, estimating the position of a mobile node by measuring the line-of-sight directions. However, indoor multipath effects tend to deteriorate system performance. In this paper, an alternative system for in-room positioning is proposed and tested, consisting of a mobile transmitter and a single antenna array for angle of arrival estimation of direct and reflected signals. With the help of a floor plan of the room and a ray tracing algorithm, antenna array outputs can be calculated for every possible position of the mobile node. These simulation outputs are compared to the measurement results to determine the position of the transmitter. The system is evaluated in an extreme multipath environment, being a 2.25 m × 3 m room with metallic walls. Measurements are performed with a 2.45 GHz and a 5.2 GHz 8-element antenna array, followed by an MVDR beamforming algorithm with a variable number of spatial smoothing operations. Two distinct localization algorithms are tested, as well as the influence of various parameters on localization accuracy, resulting in an optimal setup with an average localization error under 1 m.

Abstract This paper describes the experiments that have been conducted to determine the optimal implementation method for AES (Advanced Encryption Standard) data encryption in a ZigBee network in terms of energy consumption. Four possible scenarios have been considered. The first one is a freely available AES‐cryptographic algorithm on the processor which controls the ZigBee nodes. The second also involves the processor but is based on a proprietary, highly optimized algorithm. The other methods are based on hardware implementations. Whereas the third option is based on a cryptographic block, embedded in the ZigBee node controller, the last solution utilizes an AES128‐CBC‐MAC hardware co‐processor embedded on a Spartan 3A FPGA. The ZigBee modules in the network contain an 8‐bit microcontroller which takes care of the ZigBee protocol stack—and the encryption calculations in all but the last case. All approaches are examined and compared. We show that the usage of a microcontroller with an on‐board AES processor is the optimal design choice for a new hardware platform. An optimized software library gives the best results when extending an existing platform. This paper gives developers an idea of the amount of energy they can gain/lose by choosing one of the four solutions. Copyright © 2010 John Wiley & Sons, Ltd.

In this paper, we study a Visible Light Positioning (VLP) system using Code Division Multiple Access (CDMA). In order to facilitate implementation, the LEDs are transmitting data in a non-synchronized way to the receiver requiring no backbone network. The positioning algorithm uses the received optical power calculated from the auto- correlation peak value. Because of the asynchronous system Multiple Access Interference (MAI), random interference with the auto- correlation peak, will occur and cause position errors. Practical results show that a CDMA VLP system can have position errors smaller then 40 cm and not be influenced by synchronization problems due to MAI. The positioning error and synchronization aren't only determined by the MAI but also the receiver Field Of View (FOV) is an important parameter. Depending on the CDMA code, the LEDs still have 97% of the illumination functionality compared when there is no communication.

Multipath propagation is generally considered as an inconvenience in indoor radio frequency positioning systems, causing a degradation of localization accuracy. However, it is possible to exploit spatial information of reflected signals in a multipath assisted indoor positioning system, based on angle of arrival estimation. In order to assess the accuracy of the estimated angles and optimize system parameters, a simulation tool was developed. The tool simulates indoor line-of-sight signals and specular reflections of a 2.4 GHz signal, impinging on an antenna array. With this simulator, the performance of the MVDR, Beamscan, root MUSIC and ESPRIT algorithms for angle of arrival estimation is compared. Other investigated parameters include the position of the array in a square or rectangular room, the number of antennas, the number of signal decorrelation operations and the number of angles of arrival to be estimated. For each configuration, the spatial distribution of angular errors was evaluated, facilitating the design and dimensioning of an accurate indoor angle of arrival system.

In current healthcare environments, a trend toward mobile and personalized interactions between people and nurse call systems is strongly noticeable. Therefore, it should be possible to locate patients at all times and in all places throughout the care facility. This paper aims at describing a method by which a mobile node can locate itself indoors, based on signal strength measurements and a minimal amount of yes/no decisions. The algorithm has been developed specifically for use in a healthcare environment. With extensive testing and statistical support, we prove that our algorithm can be used in a healthcare setting with an envisioned level of localization accuracy up to room revel (or region level in a corridor), while avoiding heavy investments since the hardware of an existing nurse call network can be reused. The approach opted for leads to very high scalability, since thousands of mobile nodes can locate themselves. Network timing issues and localization update delays are avoided, which ensures that a patient can receive the needed care in a time and resources efficient way.

This paper proposes a novel indoor localization system, specifically designed for use in healthcare environments. The challenge for this indoor localization project is to decide in which room or in which area of a corridor the blindfolded node is situated, and this with a 100% guarantee that the determined location complies with the actual location of the blindfolded node. We propose a decentralized localization algorithm which uses Received Signal Strength information between the beacons and blindfolded nodes, combined with a smart beacon addressing scheme which is used by the localization algorithm. These beacons are existing access points to a backbone network equipped with a wireless interface. By using a proximity-based approach, we are able to decide where a blindfolded node is situated on a low resource and decentralized basis.

In the European Esprit project VIVA (Visual Identity Verification Auditor) a real-time digital watermarking system for broadcast monitoring has been investigated and implemented. On top of the usual requirements for watermarks, the VIVA watermarking system has to satisfy an additional number of constraints. One of the most important constraints in a broadcast environment is the robustness of the watermark against scaling. This paper describes how robustness against scaling is achieved in the VIVA project. Furthermore, a real-time implementation of the algorithms is discussed. Experimental results prove the effectiveness of the algorithms.

The main objective of the VIVA project (Visual Identity Verification Auditor) is to investigate and demonstrate a professional broadcast surveillance system. Broadcast material is pre-encoded with an invisible and unique watermark identifier. By automatically monitoring television broadcasts and registering which assets have been pre-encoded, a mechanism for IPR protection is provided. The applications of such a system include copyright protection and proof of ownership, verification of commercial transmissions, assessment of sponsorship effectiveness, protection against illegal transmission, statistical data collection and analysis of broadcast content. The watermarking technology is optimised for the high picture quality needed in a broadcast environment. At the same time, the watermark survives signal processing operations which routinely occur in broadcasting systems such as digital to analogue conversion, editing and compression. The monitoring system detects a 36-bit payload every second, which guarantees an operation time of more than 13 years, without recourse to repeat identifiers. The detection algorithm has reasonably low complexity, enabling real time watermark detection for many broadcast channels simultaneously. We report on the first results of a field trial, using a satellite link between Sweden and Belgium, proving the feasibility of the system.