David Rojas

Bio: David Rojas is an academic researcher from Cork Institute of Technology. The author has contributed to research in topics: Wireless sensor network & Network packet. The author has an hindex of 4, co-authored 7 publications receiving 107 citations.

FallDeFi: Ubiquitous Fall Detection using Commodity Wi-Fi Devices

Abstract: Falling or tripping among elderly people living on their own is recognized as a major public health worry that can even lead to death. Fall detection systems that alert caregivers, family members or neighbours can potentially save lives. In the past decade, an extensive amount of research has been carried out to develop fall detection systems based on a range of different detection approaches, i.e, wearable and non-wearable sensing and detection technologies. In this paper, we consider an emerging non-wearable fall detection approach based on WiFi Channel State Information (CSI). Previous CSI based fall detection solutions have considered only time domain approaches. Here, we take an altogether different direction, time-frequency analysis as used in radar fall detection. We use the conventional Short-Time Fourier Transform (STFT) to extract time-frequency features and a sequential forward selection algorithm to single out features that are resilient to environment changes while maintaining a higher fall detection rate. When our system is pre-trained, it has a 93% accuracy and compared to RTFall and CARM, this is a 12% and 15% improvement respectively. When the environment changes, our system still has an average accuracy close to 80% which is more than a 20% to 30% and 5% to 15% improvement respectively.

Experimental Analysis of a Wireless Sensor Network in a Multi-Chamber Metal Environment

Abstract: Wireless sensor networks (WSN) are finding increasing use in all-metal marine environments such as ships, oil and gas rigs, freight container terminals, and marine energy platforms However, wireless propagation in an all-metal environment is difficult to model and the use of sealed doors between compartments further complicates wireless network planning This makes it necessary to characterise the physical wireless links performance in real environments to support the design and deployment of the network In this paper, we report on the deployment of a 24 GHz network of 18 nodes distributed in three freight containers, with various obstacles inside and between them Input variables included the placement of the nodes, antenna orientation, transmission power, and door openings while output variables included the key link quality indicators of packet delivery ratio (PDR), RSSI, and LQI for every possible link, as well as the performance of every node We believe that this is the first time that this full range of physical link quality indicators has been measured in this type of application environment We found that, even with apparently fully sealed containers, sufficient propagation occurred through micro-openings to allow an 8065% PDR sink connectivity Providing as little as a 5 cm door opening increased sink connectivity to 9692% Average PDR sink connectivity over all the experiments was 9197%, indicating that a WSN could operate in a multi-chamber metal structure under different conditions, and can be a viable alternative to reduce cost and complexity in these environments

A hardware-software WSN platform for machine and structural monitoring

Abstract: Traditional wired vibration and acoustic sensors used for machine and structural monitoring are currently being replaced by low-cost MEMS-based wireless sensor networks (WSN). However, existing platforms are lacking in computing capabilities and integration, as well as the necessary software features to manage wireless sensing experiments. In this paper, we present a novel hardware-software platform designed to monitor machinery in remote deployments and expedite collection of experimental data, which could also be used for structural monitoring. The hardware module is composed of a single PCB with an IEEE 802.15.4-compatible microcontroller, waterproof Micro-USB connector, battery, battery charger/monitor, humidity/temperature sensor, IMU, and a MEMS microphone. The software developed allows for wireless experiment control and data collection through a gateway node connected to a laptop. Additionally, the user interface supports the placement of the nodes in a 3D view of the environment, as well as visualisation of the collected data. The platform was tested in the laboratory in two different motor setups by measuring vibration and sound in normal operation, showing that the system can facilitate the execution of sensing experiments in rotating machinery and similar equipment.

Tales from the C130 Horror Room: A Wireless Sensor Network Story in a Data Center

Abstract: An important aspect of the management and control of modern data centers is cooling and energy optimization. Airflow and temperature measurements are key components for modeling and predicting environmental changes and cooling demands. For this, a wireless sensor network (WSN) can facilitate the sensor deployment and data collection in a changing environment. However, the challenging characteristics of these scenarios, e.g., temperature fluctuations, noise, and large amounts of metal surfaces and wiring, make it difficult to predict network behavior and therefore network planning and deployment. In this paper we report a 17-month long deployment of 30 wireless sensor nodes in a small data center room, where temperature, humidity and airflow were collected, along with RSSI, LQI, and battery voltage. After an initial unreliable period, a connectivity assessment performed on the network revealed a high noise floor in some of the nodes, which together with a default low CCA threshold triggered no packet transmissions, yielding a low PDR for those nodes. Increasing the CCA setting and relocating the sink allowed the network to achieve a reliability of 99.2% for the last eight months of the deployment, therefore complying with the project requirements. This highlights the necessity of using proper tools and dependable protocols, and defining design methodologies for managing and deploying WSNs in real-world environments.

Link quality evaluation of a wireless sensor network in metal marine environments

Abstract: Wireless sensor networks (WSN) are finding increasing use in all-metal marine environments such as ships, oil and gas rigs, freight container terminals, and marine energy platforms. However, wireless propagation in an all-metal environment with ducting and sealed doors between compartments is difficult to model, and the operating machinery further complicates wireless network planning. This makes it necessary to characterize the performance of the physical wireless links in the actual operating environments. However, little has been reported in the literature on methodologies for measuring the full range of physical link quality indicators. In this paper, we present a methodology for doing this that we have verified by the deployment of a 2.4 GHz network of 18 nodes in three different all-metal scenarios: a cluster of freight containers, a full-sized shore-based working ship’s engine room training facility, and an operational ship’s engine room. The output variables included the key link quality indicators of packet delivery ratio (PDR), RSSI, and LQI for every possible link, as well as the performance of every node. We believe that this is the first time that this full range of physical link quality indicators has been measured in this type of application environment. We found that in all three scenarios the network performed with over 90% PDR average. However, as the scenarios become more complex, the communications become more unpredictable, yielding a wider transition zone, indicating that although a WSN could operate in these scenarios under different conditions, a pre-deployment practical study is essential for each new scenario.

WiFi Sensing with Channel State Information: A Survey

Abstract: With the high demand for wireless data traffic, WiFi networks have experienced very rapid growth, because they provide high throughput and are easy to deploy. Recently, Channel State Information (CSI) measured by WiFi networks is widely used for different sensing purposes. To get a better understanding of existing WiFi sensing technologies and future WiFi sensing trends, this survey gives a comprehensive review of the signal processing techniques, algorithms, applications, and performance results of WiFi sensing with CSI. Different WiFi sensing algorithms and signal processing techniques have their own advantages and limitations and are suitable for different WiFi sensing applications. The survey groups CSI-based WiFi sensing applications into three categories, detection, recognition, and estimation, depending on whether the outputs are binary/multi-class classifications or numerical values. With the development and deployment of new WiFi technologies, there will be more WiFi sensing opportunities wherein the targets may go beyond from humans to environments, animals, and objects. The survey highlights three challenges for WiFi sensing: robustness and generalization, privacy and security, and coexistence of WiFi sensing and networking. Finally, the survey presents three future WiFi sensing trends, i.e., integrating cross-layer network information, multi-device cooperation, and fusion of different sensors, for enhancing existing WiFi sensing capabilities and enabling new WiFi sensing opportunities.

Wireless Sensing for Human Activity: A Survey

Abstract: With the advancement of wireless technologies and sensing methodologies, many studies have shown the success of re-using wireless signals (e.g., WiFi) to sense human activities and thereby realize a set of emerging applications, ranging from intrusion detection, daily activity recognition, gesture recognition to vital signs monitoring and user identification involving even finer-grained motion sensing. These applications arguably can brace various domains for smart home and office environments, including safety protection, well-being monitoring/management, smart healthcare and smart-appliance interaction. The movements of the human body impact the wireless signal propagation (e.g., reflection, diffraction and scattering), which provide great opportunities to capture human motions by analyzing the received wireless signals. Researchers take the advantage of the existing wireless links among mobile/smart devices (e.g., laptops, smartphones, smart thermostats, smart refrigerators and virtual assistance systems) by either extracting the ready-to-use signal measurements or adopting frequency modulated signals to detect the frequency shift. Due to the low-cost and non-intrusive sensing nature, wireless-based human activity sensing has drawn considerable attention and become a prominent research field over the past decade. In this paper, we survey the existing wireless sensing systems in terms of their basic principles, techniques and system structures. Particularly, we describe how the wireless signals could be utilized to facilitate an array of applications including intrusion detection, room occupancy monitoring, daily activity recognition, gesture recognition, vital signs monitoring, user identification and indoor localization. The future research directions and limitations of using wireless signals for human activity sensing are also discussed.

CrossSense: Towards Cross-Site and Large-Scale WiFi Sensing

Abstract: We present CrossSense, a novel system for scaling up WiFi sensing to new environments and larger problems. To reduce the cost of sensing model training data collection, CrossSense employs machine learning to train, off-line, a roaming model that generates from one set of measurements synthetic training samples for each target environment. To scale up to a larger problem size, CrossSense adopts a mixture-of-experts approach where multiple specialized sensing models, or experts, are used to capture the mapping from diverse WiFi inputs to the desired outputs. The experts are trained offline and at runtime the appropriate expert for a given input is automatically chosen. We evaluate CrossSense by applying it to two representative WiFi sensing applications, gait identification and gesture recognition, in controlled single-link environments. We show that CrossSense boosts the accuracy of state-of-the-art WiFi sensing techniques from 20% to over 80% and 90% for gait identification and gesture recognition respectively, delivering consistently good performance - particularly when the problem size is significantly greater than that current approaches can effectively handle.

FarSense: Pushing the Range Limit of WiFi-based Respiration Sensing with CSI Ratio of Two Antennas

Abstract: The past few years have witnessed the great potential of exploiting channel state information retrieved from commodity WiFi devices for respiration monitoring However, existing approaches only work when the target is close to the WiFi transceivers and the performance degrades significantly when the target is far away On the other hand, most home environments only have one WiFi access point and it may not be located in the same room as the target This sensing range constraint greatly limits the application of the proposed approaches in real life This paper presents FarSense--the first real-time system that can reliably monitor human respiration when the target is far away from the WiFi transceiver pair FarSense works well even when one of the transceivers is located in another room, moving a big step towards real-life deployment We propose two novel schemes to achieve this goal: (1) Instead of applying the raw CSI readings of individual antenna for sensing, we employ the ratio of CSI readings from two antennas, whose noise is mostly canceled out by the division operation to significantly increase the sensing range; (2) The division operation further enables us to utilize the phase information which is not usable with one single antenna for sensing The orthogonal amplitude and phase are elaborately combined to address the "blind spots" issue and further increase the sensing range Extensive experiments show that FarSense is able to accurately monitor human respiration even when the target is 8 meters away from the transceiver pair, increasing the sensing range by more than 100% We believe this is the first system to enable through-wall respiration sensing with commodity WiFi devices and the proposed method could also benefit other sensing applications

Elderly Fall Detection Systems: A Literature Survey

Abstract: Falling is among the most damaging event elderly people may experience. With the ever-growing aging population, there is an urgent need for the development of fall detection systems. Thanks to the rapid development of sensor networks and the Internet of Things (IoT), human-computer interaction using sensor fusion has been regarded as an effective method to address the problem of fall detection. In this paper, we provide a literature survey of work conducted on elderly fall detection using sensor networks and IoT. Although there are various existing studies which focus on the fall detection with individual sensors, such as wearable ones and depth cameras, the performance of these systems are still not satisfying as they suffer mostly from high false alarms. Literature shows that fusing the signals of different sensors could result in higher accuracy and lower false alarms, while improving the robustness of such systems. We approach this survey from different perspectives, including data collection, data transmission, sensor fusion, data analysis, security, and privacy. We also review the benchmark data sets available that have been used to quantify the performance of the proposed methods. The survey is meant to provide researchers in the field of elderly fall detection using sensor networks with a summary of progress achieved up to date and to identify areas where further effort would be beneficial.