Vittorio Rampa

Wireless Cloud Networks for the Factory of Things: Connectivity Modeling and Layout Design

Large-scale adoption of dense cloud-based wireless network technologies in industrial plants is mandatorily paired with the development of methods and tools for connectivity prediction and deployment validation. Layout design procedures must be able to certify the quality (or reliability) of network information flow in industrial scenarios characterized by harsh propagation environments. In addition, these procedures must account for possibly coexisting heterogeneous radio access technologies as part of the Internet of Things (IoT) paradigm, easily allow post-layout validation steps, and be integrated by industry-standard CAD-based planning systems. The goal of the paper is to set the fundamentals for comprehensive industry-standard methods and procedures supporting plant designer during wireless coverage prediction, virtual network deployment, and post-layout verification. The proposed methods carry out the prediction of radio signal coverage considering typical industrial environments characterized by highly dense building blockage. They also provide a design framework to properly deploy the wireless infrastructure in interference-limited radio access scenarios. In addition, the model can be effectively used to certify the quality of machine-type communication by considering also imperfect descriptions of the network layout. The design procedures are corroborated by experimental measurements in an oil refinery site [modeled by three-dimensional (3-D) CAD] using industry-standard ISA IEC 62734 devices operating at 2.4 GHz. A graph-theoretic approach to node deployment is discussed by focusing on practical case studies, and also by looking at fundamental connectivity properties for random deployments.

Device-Free Radio Vision for Assisted Living: Leveraging wireless channel quality information for human sensing

Wireless propagation is conventionally considered as the enabling tool for transporting information in digital communications. However, recent research has shown that the perturbations of the same electromagnetic (EM) fields that are adopted for data transmission can be used as a powerful sensing tool for device-free radio vision. Applications range from human body motion detection and localization to passive gesture recognition. In line with the current evolution of mobile phone sensing [1], radio terminals are not only ubiquitous communication interfaces, but they also incorporate novel or augmented sensing potential, capable of acquiring an accurate human-scale understanding of space and motion. This article shows how radio-frequency (RF) signals can be employed to provide a device-free environmental vision and investigates the detection and tracking capabilities for potential benefits in daily life.

Physical Modeling and Performance Bounds for Device-free Localization Systems

In this letter, an analytically tractable model based on diffraction theory is proposed to describe the perturbations of the electromagnetic propagation of radio signals caused by the presence of a moving object in the two-dimensional (2-D) area near the transmitting/receiving devices. This novel model is instrumental to the evaluation of non-cooperative device-free localization (DFL) systems as it allows to relate the received signal strength measurements of multiple radio links to the object size, orientation and position. The proposed model is validated experimentally using radio devices and it is used to derive closed-form fundamental limits to DFL accuracy, providing an analytical tool for DFL system design and network 2-D pre-deployment assessment.

Leveraging RF signals for human sensing: Fall detection and localization in human-machine shared workspaces

Safe human-machine interactions promote high flexibility in collaborative workspaces. Fall detection and localization of the operator are major issues in ensuring a safe working environment. However, many proposed solutions are not applicable for deployment in industrial environments due to their performance limitations in practical contexts. In this paper, we propose an integrated framework for both localization and fall detection of operators inside a shared workspace that employs radio-frequency (RF) signal analysis in real-time. Multipath and non-line-of-sight (NLOS) scattering that affect RF signal propagation can be leveraged for human sensing in complex workspaces: the proposed system continuously monitors the fluctuations of the RF field across the space by a dense network of WiFi compliant radio devices operating at 2.4GHz. To increase the accuracy of the localization system, a sensor fusion algorithm using Extended Kalman Filter techniques is employed. The proposed method may be used for integrating measurements from both RF nodes and an additional image-based system. For fall detection, a Hidden Markov Model is applied to discern different postures of the operator and to detect a fall event by tracking the fluctuations of the wireless signal quality. Fall detector performances are validated through experimental measurements. The preliminary results confirm the effectiveness of the proposed approach for different body configurations and pre-impact postures to correctly detect a fall event. Finally, some results about sensor fusion for improved operator localization are presented.

I/Q Compensation of Broadband Direct-Conversion Transmitters

The aim of this paper is to propose a compensation strategy that is able to easily identify the model of the In-phase/Quadrature (I/Q) impairments of Radio-Frequency (RF) direct-conversion devices and to efficiently compensate these unwanted effects. In fact, direct-conversion transmitters that integrate analog and digital components introduce a wideband frequency-dependent I/Q mismatch that strongly reduces the upconverter performances. The wider the signal bandwidth or the higher its spectral efficiency, the more severe the I/Q artifacts on the upconverted signal become. The proposed compensation strategy can mitigate these unwanted effects, eliminating I/Q impairments with a very simple hardware architecture. The compensation model adopted here for a generic upconverter device does not assume any hypotheses about signal modulation and system architecture and can be easily adapted to accommodate specific hardware systems or different wireless standards. To show the capabilities of the proposed compensation scheme, an experimental setup has been arranged to emulate system configurations found in direct-conversion RF Integrated Circuits (RFIC) for 3.5/4G applications.

Safe human-robot cooperation through sensor-less radio localization

Adaptable workflows in human-robot cooperation (HRC) require a flexible sharing of the same workspace with major impact on human-centered robot motion planning. The standard EN ISO 10218 is fostering the implementation of hybrid production systems characterized by a close relationship among human operators and robots in cooperative tasks. A primary contribution in workers protection is given by real time monitoring of the entire workspace, including tracking of operators trajectories and tentative estimation of motion intentions. Operators localization has the purpose of enabling the Speed and Separation Monitoring (SSM) safety mode, as in draft ISO/TS 15066, and adapting the robot motion to approaching users. The present work discloses some preliminary results about methods of “sensor-less” localization of operators in industrial HRC scenarios, based on wireless sensor networks techniques. The proposed system is composed of a network of small, embedded RF transceivers pervasively distributed in fixed positions inside the robotic cell layout in order to localize the operators, who carry neither wireless active devices (device-free) nor specific tracking sensors (sensor-less sensing). Users positions over time are estimated from the perturbation of the radio field, considering the effect of the concurrently moving robots. Finally, the sensors-robots system is functionally integrated into a safety architecture.

Device-Free RF Human Body Fall Detection and Localization in Industrial Workplaces

Fall detection and localization of human operators inside a workspace are major issues in ensuring a safe working environment. Recent research has shown that the perturbations of the radio-frequency (RF) signals commonly adopted for wireless communications can also be used as sensing tools for device-free human motion detection. Device-free RF-based human sensing applications range from tag-less body localization to detection and monitoring of human well-being (e-Health). In this paper, we propose a real-time system for human body motion sensing with special focus on joint body localization and fall detection. The proposed system continuously monitors and processes the RF signals emitted by industry-compliant radio devices operating in the 2.4 GHz ISM band and supporting machine-to-machine communication functions. Human-induced diffraction and multipath phenomena that affect RF signal propagation are leveraged for body localization while for fall detection a hidden Markov model is applied to discern different postures of the operator and to detect safety-relevant events by tracking the received signal strength indicator footprints. Fall detection performances are corroborated by extensive experimental measurements in different settings. In addition, we propose also a sensor fusion tool that is able to integrate the device-free RF-based sensing system within an industrial image sensors framework. Preliminary results, conducted during field trial measurements, confirm the effectiveness of the proposed approach in terms of localization accuracy, and sensitivity/specificity to correctly detect a fall event from preimpact postures.

Device-free localization of multiple targets

In this paper, we consider the problem of multi-target device-free localization with special focus on modeling and inference. The motion of multiple targets inside the area covered by a wireless network leaves a characteristic footprint on the radio-frequency (RF) field, and in turn affects both the average attenuation and the fluctuation of the received signal strength (RSS). A diffraction-based model is developed to describe the impact of multiple targets on the RSS field, i.e. the multi-body-induced shadowing. As a relevant case study, the model is tailored to predict the effects of two co-located targets on the RF signals. Three novel algorithms are proposed for on-line localization, exploiting both the average and the deviation of the body-induced RSS perturbation. The proposed techniques are compared and some preliminary results, based on experimental data collected in a representative indoor environment, are presented.

Electromagnetic models for device-free localization applications

A human body model for passive device-free localization (DFL) application is proposed. Unlike active localization methods, in DFL applications, the targets do not need to carry any electronic device since their location is estimated by tracking the target-induced attenuation of the electromagnetic (EM) field generated by a network of RF nodes deployed close by. Based on the scalar diffraction theory, the proposed model is able to predict the attenuation caused by a single person by exploiting the received power measurements performed by the RF nodes. The results obtained by the body model have been compared with experimental indoor measurements and validated with full EM simulation results from a commercially available tool.

A dynamic Bayesian network approach for device-free radio vision: Modeling, learning and inference for body motion recognition

In this paper, a time-varying dynamic Bayesian network model is shown to describe human-induced RF fluctuations for the purpose of non-cooperative and device-free radiobased body motion recognition (radio vision). The technology relies on pre-existing wireless communication network infrastructures and processes channel quality information (CQI) for human-scale sensing. Body movements leave a characteristic footprint on the CQI sequences collected during consecutive radio transmissions over multiple co-located links. Body-induced RF footprints are proved to be effectively characterized by temporarily coupled hidden Markov chains: abrupt changes of body postures make CQIs observed over co-located links temporarily coupled while being uncoupled for slow body movements. Learning and classification/inference problems are discussed based on experimental measurements. Device-free radio vision performances are evaluated for arm gesture and fall detection applications.