AIU2 – IoT Solutions
Wednesday, 7 June 2023, 16:00-17:30, Room R22-R23
Session Chair: Vittorio Solina (University of Calabria, Italy)
Frequency-Sensitive Soil Moisture Profiling Using WiFi Sensing
Thanh Vinh Nguyen, Junye Li and Deepak Mishra (University of New South Wales (UNSW) Sydney, Australia); Aruna Seneviratne (University of New South Wales, Australia)
In recent times, WiFi signals have been widely used in wireless sensing applications for detecting largescale environmental or physical characteristics, like human count and ambient temperature. Soil moisture detection using WiFi sensing is gaining interest, but the key underlying
challenge is to realise contact-free sensing technology that can characterise the impact of water spreading into the soil on the Channel State Information (CSI). Therefore, we develop a framework for sensing soil water levels using CSI-based sensing. We investigate the WiFi CSI signatures pertinent to the soil water infiltration, enabling applications including soil health monitoring. In our experimental study, we use the 5GHz WiFi spectrum to implement our novel frequencyselective CSI sensing framework for soil moisture profiling using commodity Raspberry Pi devices. The experimental results verified that specific WiFi Orthogonal Frequency Division Multiplexing (OFDM) subcarriers are more sensitive to the changes in soil moisture, leading to a frequencysensitive behaviour on CSI. Our framework exploits the changes in the pressure levels due to the water movement or varying humidity levels in the soil channel between the WiFi transmitter and receiver that leave impressions on the underlying CSI. Furthermore, this paper demonstrates a novel approach by inspecting the CSI amplitude pattern of water entering the soil at a finer level. Lastly, in contrast to the existing works, our low-cost and contact-free method for detecting soil moisture detection has been empirically shown to efficiently utilise the newly-identified frequency-selective CSI signatures for the water infiltration and humidity levels in soil channel for accurate soil moisture prediction.
5G and B5G NEF Exposure Capabilities Towards an Industrial IoT Use Case
George Makropoulos (NCSR Demokritos & National and Kapodistrian University of Athens, Greece); Dimitrios Fragkos (National Centre for Scientific Research Demokritos (NCSRD) & University of Peloponnese, Greece); Harilaos Koumaras (NCSR Demokritos, Greece); Jaka Cijan (Internet Institute Ltd, Slovenia); Luka Korsic (INTERNET INSTITUTE, Ltd, Slovenia); Rudolf Susnik (Internet Institute Ltd, Greece)
Given the large amount of data usage and diverse business models in the current market, 5G and B5G networks are forecasted to manage and support a variety of new business solutions with respect to high-performance needs, while also allowing existing services to be enhanced and optimized. One effective way to accomplish this is by fully utilizing the network’s openness and programmability in terms of both business and technical level coordination. A cornerstone to the above mentioned premise is the implementation of the Network Exposure Function (NEF) interfaces that are required in order for NEF to expose the standardized APIs. In this context, the paper presents the concept of network exposure via a simulation tool that enables application developers to experiment with the northbound APIs under a simulated and configurable environment. Moreover, the required software that interacts with the exposed APIs, namely the Network Application, is introduced and validated on top of the Internet of Things (IoT) and Machine to Machine (M2M) use case, which falls under the scope of the Factory of the Future and Industry 4.0 concept.
An Experimental Comparison of LoRa Versus NB-IoT over Unlicensed Spectrum Using Software Defined Radio
Charbel Lahoud (Technical University Chemnitz, Germany); Shahab Ehsanfar (Technische Universität Chemnitz, Germany); Klaus Mößner (Chemnitz University of Technology, Germany)
In this paper, we present a comprehensive evaluation of two prominent low-power wide-area networks (LPWAN) technologies, low power long range alliance (LoRa) and narrow-band internet-of-things (NB-IoT), which are widely used in the internetof-things (IoT) sector. We investigate their performance under challenging conditions, specifically in a scenario where the signal is subject to non-line-of-sight (NLOS) reception caused by signal diffraction. Additionally, we analyze the potential application challenges and use cases for each technology and provide insight into which technology is more suitable for specific scenarios. Our findings aim to inspire future researchers and manufacturers in the field of LPWAN and IoT.
Human Robot Collaboration: An Assessment and Optimization Methodology Based on Dynamic Data Exchange
Alessio Baratta (University of Calabria, Italy); Antonio Cimino (University of Salento, Italy); Maria Grazia Gnoni (University of salento, Italy); Letizia Nicoletti (Cal-Tek S. r. l., Italy); Vittorio Solina (University of Calabria, Italy)
When talking about Human Robot Collaboration (HRC), Industry 4.0 leverages on the central role of workers that needs to be assessed over multiple dimensions, including ergonomics, costs, productivity and time performance measures. The aim of this research work is to develop a reference methodological approach to enable a holistic HRC assessment and optimization by considering several assessment parameters. Due to the wide complexity of the topic, the authors propose a simulation-based Digital Twin (DT) approach in order to recreate, with satisfactory accuracy, the real workplace in a simulated environment. Regular update of the DT is foreseen both under a static as well as dynamic (by using a set of multiple sensors) way thus enabling DT capability. Ergonomics, costs and performance assessment methods are seamlessly integrated within the simulation environment thus providing a comprehensive setting for sustainable HRC testing based on holistic assessment of different issues. Moreover, the authors present an infrastructure for the implementation of the proposed approach as well as go into the details of the dynamic data exchange between the workplace and the DT by proposing a data acquisition architecture.
Improving Indoor Positioning Accuracy Using RIS-Based RSS Optimization
Somayeh Bazin and Keivan Navaie (Lancaster University, United Kingdom (Great Britain))
In the Received Signal Strength (RSS) based Indoor Positioning Systems (IPS), the position of a receiver is estimated by comparing its RSS values with a fingerprint. The fingerprint is a dataset including the measured RSS values at pre-planned Reference Points (RP) for a set of reference transmitters. For a given RPs’ spatial distribution, the RSS values are however affected by the intrinsic temporal and spatial uncertainties in the indoor wireless channel, hence constraining the positioning accuracy. To address this issue, we propose an algorithm to predesign the RSS values at each RP using Reconfigurable Intelligent Surface (RIS) technology. In the proposed method, the RIS reflection coefficients are obtained to maximize the difference between the RSS values between the RPs. The simulation results confirm that even with a relatively small number of RIS elements, the proposed method significantly improves the IPS efficiency.