Wireless Beacon Research Articles

New Wireless Beacon Deployment for Maximum Coverage Efficiency in Indoor Localization

New Wireless Beacon Deployment for Maximum Coverage Efficiency in Indoor Localization

Wireless Beacon Enabled Hybrid Sparse Channel Estimation for RIS-Aided mmWave Communications

The inability of reconfigurable intelligent surfaces (RISs) to signal processing has become a critical bottleneck in terms of the precise capture of cascaded channels. There exists a pair of critical issues: i) the high-dimensional cascaded channel to be estimated, which always entails substantial pilot overhead typically proportional to RIS elements, and ii) the high possibilities of false alarms and unfaithful angle detection during cascaded channel estimates, referred to as the angular distortion, particularly in the case of conventional fully-passive RIS systems. To circumvent these issues, we investigate in this paper a multi-user multiple-input multiple-output (MIMO) system assisted by a wireless beacon (WB) enabled RIS over millimeter wave (mmWave) frequency. Specifically, WB is a functionally independent hardware that attaches parasitically to the RIS using only one radio frequency chain for broadcasting pilots, allowing for the accurate acquisition of partial cascaded channel information, e.g., some of angles of arrival/departure (AoA/AoDs) and complex gains. This efficiently eliminates the angular distortion effect and thus facilitates high precision recovery of sparse cascaded mmWave channels. Then, a hybrid structured sparsity (HSS) model is presented to capture hybrid sparse priors and approximate exact posterior distributions associated with RIS-aided twin-hop channels. We conceive of channel estimation as a compressive sensing problem in contrast to its conventional copy due to the presence of an unknown sensing matrix. To tackle this problem, an expectation-maximization (EM)-based algorithm, i.e., HSS-EM, is developed in order to fully employ the sparse priors encapsulated by the proposed HSS model for a robust and accurate recovery of the cascaded channels. Numerical results validate our analytical claims and demonstrate the significant improvement in terms of the normalized mean square error (NMSE) performance over conventional designs.

Self-Localizing On-Demand Portable Wireless Beacons for Coverage Enhancement of RF Beacon-Based Indoor Localization Systems

Self-Localizing On-Demand Portable Wireless Beacons for Coverage Enhancement of RF Beacon-Based Indoor Localization Systems

From Conception to Retirement: A Lifetime Story of a 3-Year-Old Wireless Beacon System in the Wild

We report a 3-year city-wide study of an operational indoor sensing system based on Bluetooth Low Energy (BLE) called <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">aBeacon</monospace> (short for <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</u> libaba <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Beacon</u> ). <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">aBeacon</monospace> is pilot-studied, A/B tested, deployed, and operated in Shanghai, China to infer the indoor status of Alibaba couriers, e.g., arrival and departure at the merchants participating in the Alibaba Local Services platform. In its full operation stage (2018/01-2020/04), <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">aBeacon</monospace> consists of customized BLE devices at 12,109 merchants, interacting with 109,378 couriers to infer their status to assist the scheduling of 64 million delivery orders for 7.3 million customers with a total amount of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula> 600 million order values. Although in an academic setting, using BLE devices to detect arrival and departure looks straightforward, it is non-trivial to design, build, deploy, and operate <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">aBeacon</monospace> from its conception to its retirement at city scale in a metric-based approach by considering the tradeoffs between various practical factors (e.g., cost and performance) during long-term system evolution. We report our study in two phases, i.e., an 8-month pilot study and a 28-month deployment and operation in the wild. We focus on an in-depth reporting on the five lessons learned and provide their implications in other systems with long-term operation and broad geospatial coverage, e.g., Edge Computing.

Self-Organizing Multilateration of an Unknown Number of Transmitters

Wireless Sensor Networks (WSNs) that classify the source of detected radio signals require mobile transmitters, physical (PHY) and link layer meta data, and packet sniffing capabilities. These signal classifiers are restricted by assumptions that may be difficult to realize in adversarial Signal of Opportunity (SOP) localization settings, and they do not jointly localize transmitters. In this paper, we present a novel framework that self-organizes to classify and jointly localize sets of stationary transmitters emitting SOP. The framework leverages the underlying Gaussian distribution associated with multilateration estimates via the use of Unsupervised Learning (UL) techniques. Inference of spatial multilateration features allows for the joint estimation of classification outcomes with respect to several unknown parameters, including the number of transmitters, source transmitters for each signal, the underlying multilateration distribution, and the transmitter locations. The proposed framework was evaluated in a two-dimensional trilateration experiment. Signals transmitted by vehicular Tire Pressure Monitoring System (TPMS) wireless beacons were observed by a custom-built WSN test bed to produce Received Signal Strength Indicators (RSS) features. We used a trained Convolutional Neural Network (CNN) to make location estimates from the RSS feature data. An Anderson-Darling test showed that these CNN estimates were statistically indistinguishable from those of a normal distribution. The spatial trilateration estimates were clustered to identify six of the eight TPMS transmitters with a 75% cluster detection rate, which was the result of every statistically different spatial and RSS population as determined by a Kruskal-Wallis (KW) test. The source transmitter of every signal was classified with a 76.4% indicator variable accuracy (93.7% when removing statistically identical RSS populations) and the detected source transmitters were localized with an average of 1.72 m variance and 1.19 m bias within a roughly 15 m square whose perimeter is made up of receivers.

Fuzzy Guided Autonomous Nursing Robot through Wireless Beacon Network.

Robotics is one of the most emerging technologies today, and are used in a variety of applications, ranging from complex rocket technology to monitoring of crops in agriculture. Robots can be exceptionally useful in a smart hospital environment provided that they are equipped with improved vision capabilities for detection and avoidance of obstacles present in their path, thus allowing robots to perform their tasks without any disturbance. In the particular case of Autonomous Nursing Robots, major essential issues are effective robot path planning for the delivery of medicines to patients, measuring the patient body parameters through sensors, interacting with and informing the patient, by means of voice-based modules, about the doctors visiting schedule, his/her body parameter details, etc. This paper presents an approach of a complete Autonomous Nursing Robot which supports all the aforementioned tasks. In this paper, we present a new Autonomous Nursing Robot system capable of operating in a smart hospital environment area. The objective of the system is to identify the patient room, perform robot path planning for the delivery of medicines to a patient, and measure the patient body parameters, through a wireless BLE (Bluetooth Low Energy) beacon receiver and the BLE beacon transmitter at the respective patient rooms. Assuming that a wireless beacon is kept at the patient room, the robot follows the beacon’s signal, identifies the respective room and delivers the needed medicine to the patient. A new fuzzy controller system which consists of three ultrasonic sensors and one camera is developed to detect the optimal robot path and to avoid the robot collision with stable and moving obstacles. The fuzzy controller effectively detects obstacles in the robot’s vicinity and makes proper decisions for avoiding them. The navigation of the robot is implemented on a BLE tag module by using the AOA (Angle of Arrival) method. The robot uses sensors to measure the patient body parameters and updates these data to the hospital patient database system in a private cloud mode. It also makes uses of a Google assistant to interact with the patients. The robotic system was implemented on the Raspberry Pi using Matlab 2018b. The system performance was evaluated on a PC with an Intel Core i5 processor, while the solar power was used to power the system. Several sensors, namely HC-SR04 ultrasonic sensor, Logitech HD 720p image sensor, a temperature sensor and a heart rate sensor are used together with a camera to generate datasets for testing the proposed system. In particular, the system was tested on operations taking place in the context of a private hospital in Tirunelveli, Tamilnadu, India. A detailed comparison is performed, through some performance metrics, such as Correlation, Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), against the related works of Deepu et al., Huh and Seo, Chinmayi et al., Alli et al., Xu, Ran et al., and Lee et al. The experimental system validation showed that the fuzzy controller achieves very high accuracy in obstacle detection and avoidance, with a very low computational time for taking directional decisions. Moreover, the experimental results demonstrated that the robotic system achieves superior accuracy in detecting/avoiding obstacles compared to other systems of similar purposes presented in the related works.

SDR Based Indoor Beacon Localization Using 3D Probabilistic Multipath Exploitation and Deep Learning

Wireless indoor positioning systems (IPS) are ever-growing as traditional global positioning systems (GPS) are ineffective due to non-line-of-sight (NLoS) signal propagation. In this paper, we present a novel approach to learning three-dimensional (3D) multipath channel characteristics in a probabilistic manner for providing high performance indoor localization of wireless beacons. The proposed system employs a single triad dipole vector sensor (TDVS) for polarization diversity, a deep learning model deemed the denoising autoencoder to extract unique fingerprints from 3D multipath channel information, and a probabilistic k-nearest-neighbor (PkNN) to exploit the 3D multipath characteristics. The proposed system is the first to exploit 3D multipath channel characteristics for indoor wireless beacon localization via vector sensing methodologies, a software defined radio (SDR) platform, and multipath channel estimation.

Improving maintenance quality in airport baggage handling operations

Purpose: The findings of a recent study at Heathrow are presented. The objective of the study was an operational quality issue with the baggage reclaim process and addressed the problem of unexpected downtime of baggage carousels during operational hours. For airlines and airports, the reclaim carousel is a key element in the process whereby passengers decide about the quality of their journey. Failure that leads to longer waiting times or even the relocation of baggage to another carousel results in passenger dissatisfaction and therefore needs to be avoided. The current regime of ‘time-based’ or ‘preventative’ maintenance can be classified as ‘run-to-break’ or ‘reactive’, causing frequent and costly downtime. A novel condition-based monitoring method to improve the reliability of the time-critical baggage reclaim process is described. Reclaim carousel maintenance quality was improved by the development of innovative condition-based maintenance systems designed to meet the requirements of twenty-first century airport systems and Industry 4.0 in cooperation with Siemens DF (digital factory) and their internet of things (IoT) platform Mindsphere. Methodology: A technical action research approach was undertaken at one of the biggest capital airport baggage handling systems in Europe. From June 2016 a condition monitoring pilot system on one operational carousel was established using engineering cycle theory. A solution was designed, installed, continually monitored and the results discussed with practitioners from the operation and maintenance (O&M) department. Root cause analysis was used to identify reasons for abrasive wear, followed by failure simulations during operation. Technical vibration data was collected so that an adequate condition monitoring system could be developed and optimised that improved the maintenance quality of reclaim carousels and reduced the costs associated with unexpected failure of the baggage handling system. Findings: Run-to-break maintenance is never conducive to quality maintenance and control and results in great uncertainty and unreliability. Meeting the objective of improving quality was difficult because the assets are hidden and baggage carousels are in constant use; however using wireless beacon technology it was possible to identify what problems might occur and when. Using the cloud-based IoT and Airport 4.0 also necessitated many sophisticated checks and measures, which are now in development. This study highlights the value of changing from the antiquated ‘run to break’ maintenance of hidden assets in airport baggage handling carousels to high-quality maintenance through the use of condition monitoring using wireless vibration sensors linked to a cloud-based IoT ecosystem. Originality/Value: With this solution in operation, maintenance quality has been approved and heavy damage can be avoided. The solution addresses problems caused by a variety of hidden equipment needing the highest possible maintenance quality. It can instantly secure the best quality of service for critical assets in an operation that cannot afford any unplanned downtime.

A 24-GHz Wireless Locating System for Human–Robot Interaction

Due to technological advances in various fields and the rising demand for automation, the popularity of service robots is expected to grow rapidly in the upcoming years. One of the core functions of such robots is their interaction with people. One type of interaction, which is studied in this paper, is when the robot leads or follows a person. This presupposes that the position of the person is known relative to the mobile robot. In this paper, we present a 24-GHz wireless locating system to enable this kind of human–robot interaction. The system consists of a radar node attached to the robot and an active portable wireless beacon that is carried by the person. Using a secondary radar approach to measure the distance and angle to the beacon, the 2-D position of the person relative to the mobile robot can be accurately determined by a single radar node on the robot. This is unfeasible with standard multilateration localization approaches. An extended Kalman filter is used to increase the accuracy and reliability of the positioning system. The measurements presented in this paper, performed in a scenario that is both challenging and realistic, demonstrate the effectiveness of the proposed approach.

Easily-Deployable Acoustic Local Positioning System Based on Auto-Calibrated Wireless Beacons.

Self-calibrated Acoustic Local Positioning Systems (ALPS) generally require a high consumption of hardware and software resources to obtain the user’s position at an acceptable update rate. To address this limitation, this work proposes a self-calibrated ALPS based on a software/hardware co-design approach. This working architecture allows for efficient communications, signal processing tasks, and the running of the positioning algorithm on low-cost devices. This fact also enables the real-time system operation. The proposed system is composed of a minimum of four RF-synchronized active acoustic beacons, which emit spread-spectrum modulated signals to position an unlimited number of receiver nodes. Each receiver node estimates the beacons’ position by means of an auto-calibration process and then computes its own position by means of a 3D multilateration algorithm. A set of experimental tests has been carried out where the feasibility of the proposed system is demonstrated. In these experiments, accuracies below 0.1 m are obtained in the determination of the receptor node position with respect to the set of previously-calibrated beacons.

Intelligent Online Learning Strategy for an Autonomous Surface Vehicle in Lake Environments Using Evolutionary Computation

An artificial intelligence strategy for the localization and monitoring of pollution-caused events caused by contamination in a lake, of a system consisting of an Autonomous Surface Vehicle (ASV) and a network of wireless beacons is proposed and evaluated. Particularly the event of algae bloom is considered. For this purpose, the path planning of the ASV is calculated at different phases with the help of an evolutionary algorithm. The main novelty of the proposed strategy is that it follows an intelligent online learning approach. Therefore, the ASV learns from the environment and makes decisions depending on the collected data. The proposed path planning is based on the adaption of the travelling salesman problem with constraints, using the beacons as geo-localization references and information support infrastructure. The approach consists of different phases that balance the exploration of the lake for searching new events and the exploitation of already discovered ones. A suitable configuration of the fitness function allows an efficient balance between exploration and intensification. Simulation results show that the level of coverage achieved are at least 85% for a situation where up to two dynamic algae blooms occurred at different locations in the lake.

Nursing Care Time for Newborns during Hospitalization in a Mixed Hospital Ward with an Obstetrics Department

Purpose This study aimed to better understand the nursing care time spent with healthy term newborns from birth to discharge, giving insight into neonatal nursing staff management. Method In total, 30 healthy term newborns in a mixed hospital ward with an Obstetrics Department participated in this study. To measure care time, they had a wireless beacon attached to their cots. This measured how much time the nurses stayed in front of the cot from the time of birth until discharge, 24 hours/day. Collected data were tabulated every 24 hours after birth. Results Seventeen newborns had their data analyzed. The average length of hospital stay for the newborns was 8231.3 minutes. The average nursing care time for the newborns was 533.8 minutes. Nurses provided the highest care time during the first 24 hours after birth (157.6 minutes/24hr). After the first 24, the average nursing care time gradually decreased. The average nursing care time during the first 24 hours after birth was longer than the nursing care time for any other 24-hour periods, with a significant difference (p = 0.001 to 0.046). Conclusion The nursing care time for healthy newborns gradually decreased with the passage of time after birth. Healthy newborns should be treated as individuals, and the number of nursing staff should be adjusted according to the number of newborns in the ward to ensure nursing care quality and to prevent life-threatening events during the first 24 hours after birth.

A Smartphone-Based System for Outdoor Data Gathering Using a Wireless Beacon Network and GPS Data: From Cyber Spaces to Senseable Spaces

Information and Communication Technologies (ICTs) and mobile devices are deeply influencing all facets of life, directly affecting the way people experience space and time. ICTs are also tools for supporting urban development, and they have also been adopted as equipment for furnishing public spaces. Hence, ICTs have created a new paradigm of hybrid space that can be defined as Senseable Spaces. Even if there are relevant cases where the adoption of ICT has made the use of public open spaces more “smart”, the interrelation and the recognition of added value need to be further developed. This is one of the motivations for the research presented in this paper. The main goal of the work reported here is the deployment of a system composed of three different connected elements (a real-world infrastructure, a data gathering system, and a data processing and analysis platform) for analysis of human behavior in the open space of Cardeto Park, in Ancona, Italy. For this purpose, and because of the complexity of this task, several actions have been carried out: the deployment of a complete real-world infrastructure in Cardeto Park, the implementation of an ad-hoc smartphone application for the gathering of participants’ data, and the development of a data pre-processing and analysis system for dealing with all the gathered data. A detailed description of these three aspects and the way in which they are connected to create a unique system is the main focus of this paper.

A Novel Algorithm for Determining the Contextual Characteristics of Movement Behaviors by Combining Accelerometer Features and Wireless Beacons: Development and Implementation.

BackgroundUnfortunately, global efforts to promote “how much” physical activity people should be undertaking have been largely unsuccessful. Given the difficulty of achieving a sustained lifestyle behavior change, many scientists are reexamining their approaches. One such approach is to focus on understanding the context of the lifestyle behavior (ie, where, when, and with whom) with a view to identifying promising intervention targets.ObjectiveThe aim of this study was to develop and implement an innovative algorithm to determine “where” physical activity occurs using proximity sensors coupled with a widely used physical activity monitor.MethodsA total of 19 Bluetooth beacons were placed in fixed locations within a multilevel, mixed-use building. In addition, 4 receiver-mode sensors were fitted to the wrists of a roving technician who moved throughout the building. The experiment was divided into 4 trials with different walking speeds and dwelling times. The data were analyzed using an original and innovative algorithm based on graph generation and Bayesian filters.ResultsLinear regression models revealed significant correlations between beacon-derived location and ground-truth tracking time, with intraclass correlations suggesting a high goodness of fit (R2=.9780). The algorithm reliably predicted indoor location, and the robustness of the algorithm improved with a longer dwelling time (>100 s; error <10%, R2=.9775). Increased error was observed for transitions between areas due to the device sampling rate, currently limited to 0.1 Hz by the manufacturer.ConclusionsThis study shows that our algorithm can accurately predict the location of an individual within an indoor environment. This novel implementation of “context sensing” will facilitate a wealth of new research questions on promoting healthy behavior change, the optimization of patient care, and efficient health care planning (eg, patient-clinician flow, patient-clinician interaction).

RSSI-Based Localization Using Wireless Beacon with Three-Element Array

RSSI-Based Localization Using Wireless Beacon with Three-Element Array

A Beacon Transmission Power Control Algorithm Based on Wireless Channel Load Forecasting in VANETs.

In a vehicular ad hoc network (VANET), the periodic exchange of single-hop status information broadcasts (beacon frames) produces channel loading, which causes channel congestion and induces information conflict problems. To guarantee fairness in beacon transmissions from each node and maximum network connectivity, adjustment of the beacon transmission power is an effective method for reducing and preventing channel congestion. In this study, the primary factors that influence wireless channel loading are selected to construct the KF-BCLF, which is a channel load forecasting algorithm based on a recursive Kalman filter and employs multiple regression equation. By pre-adjusting the transmission power based on the forecasted channel load, the channel load was kept within a predefined range; therefore, channel congestion was prevented. Based on this method, the CLF-BTPC, which is a transmission power control algorithm, is proposed. To verify KF-BCLF algorithm, a traffic survey method that involved the collection of floating car data along a major traffic road in Changchun City is employed. By comparing this forecast with the measured channel loads, the proposed KF-BCLF algorithm was proven to be effective. In addition, the CLF-BTPC algorithm is verified by simulating a section of eight-lane highway and a signal-controlled urban intersection. The results of the two verification process indicate that this distributed CLF-BTPC algorithm can effectively control channel load, prevent channel congestion, and enhance the stability and robustness of wireless beacon transmission in a vehicular network.

Secret Key Generation Via Localization and Mobility

We consider secret key generation by a pair of mobile nodes utilizing observations of their relative locations in the presence of a mobile eavesdropper. In our proposed algorithm, the legitimate node pair makes noisy observations of the relative locations of each other. Based on these observations, the nodes generate secret key bits via information reconciliation, data compression, and privacy amplification. We characterize a theoretically achievable secret key bit rate in terms of the observation noise variance at the legitimate nodes and the eavesdropper and show that the performance of our algorithm is comparable to the theoretical bounds. We also test our algorithm in a vehicular setting based on observations made using wireless beacon exchange between the legitimate nodes. To achieve this, we used TelosB wireless radios mounted on the sides of the vehicles on local roads and freeways. Note that our approach relies solely on distance reciprocity, and thus, it is not restricted to the use of wireless radios and can be used with other localization systems (e.g., infrared and ultrasound systems) as well. Overall, this study proves, via both information theoretic and practical analysis, that localization information provides a significant additional resource for secret key generation in mobile networks.

Design of Radio Beacon for Spacecraft-Memory Localization

With the development of space technology and more and more space experiments, it has brought forward higher requests for memory of hard-recovered solid-state recorders. Therefore, Recovery of the memory becomes more important. Aiming at the disadvantages of present ways of recycling, we introduce a real-time GPS positioning wireless beacon can be applied on memory recovery. This paper introduces the principle of the system, circuit, and the function that can realized. Experimental results show that the beacon in a static environment and dynamic environment had a good performance in GPS positioning, and a high positioning accuracy. During Signal transmission, the receivers can received the raw GPS signal without error and can depict the running track of memory. DOI : http://dx.doi.org/10.11591/telkomnika.v12i2.3637

Improving Indoor Positioning Accuracy with Dense, Cooperating Beacons

We improve indoor positioning location accuracy by opportunistically enlisting wireless client devices to temporarily serve as unmanaged beacons (or access points). A two-phase positioning scheme is proposed; in the initial phase, a target node and its neighbors are located using managed APs with known locations. In the second phase, the target node's position is refined by neighbor nodes with uncertain locations serving as short-range wireless beacons themselves. Through simulations and analysis we explore the geographic conditions and noise models under which exploiting the availability of unmanaged beacons can improve location accuracy. We argue that as the spatial density of wireless devices grows, it is increasingly desirable to call on a preferred subset of neighbor nodes with uncorrelated anchor errors to serve as beacons to improve location accuracy.

A New Localization Method for WSNs Based on DOA

This article proposes a new localization method for wireless sensor networks (WSNs) in which four beacon nodes (BNs) are equipped with a directional antenna with special transmission capabilities for sending wireless beacon signals throughout the sensor network. Each beacon node’s directional antenna rotates and broadcasts its angular bearings. Using trigonometry, the proposed method is based on angle-of-arrival estimation technique that does not increase the system cost of the sensor nodes or decrease the positioning accuracy. We present the performance of the proposed method obtained from error analysis and computer simulations.