SENSORNETS 2020 Abstracts


Conference

SENSORNETS 2020

Area 1 - Sensors Networks Hardware

Full Papers
Paper Nr: 13
Title:

A Robust Serial FBG Sensor Network with CDM Interrogation Allowing Overlapping Spectra

Authors:

Marek Götten and Steffen Lochmann

Abstract: Massive optical sensor networks gained a lot of attention in recent years. They offer new advances in the fields of smart structures and health monitoring. All serial optical sensor networks rely on multiplexing techniques that provide huge amounts of sensors in a single optical fiber. Wavelength-division multiplex (WDM) which has been established in many applications, is restricted to the spectral width of the used light source that needs to be shared by several non-overlapping fiber-Bragg-grating (FBG) spectra. Time-division multiplex (TDM) uses short impulses and relies on different sensor round trip delays to distinguish each single FBG. These short impulses and long round trip times lead to a low signal-to-noise ratio (SNR). Optical frequency-domain reflectometry (OFDR) offers a high spatial resolution of FBGs but only within a short fiber length. This contribution deals with a code-division multiplex (CDM) interrogation technique that provides numerous sensors in a single optical fiber, a better SNR, and a long range of distributed sensing points. It requires codes with good autocorrelation behavior which is characterized by certain criteria. The detectable criteria are limited which narrows significantly a search for best possible codes for the interrogation system. In this contribution, practical implementation limits such as the trigger timing and the achievable SNR are studied. Based on the introduced SNR definitions for CDM and WDM systems, a direct comparison is possible and it shows the superiority of the proposed CDM scheme. A network with 25 sensors operating at the same wavelength can provide a 2.67 dB improvement compared to WDM

Area 2 - Intelligent Data Analysis and Processing

Full Papers
Paper Nr: 14
Title:

MultiSense: A Highly Reliable Wearable-free Human Fall Detection Systems

Authors:

Avishek Mukherjee and Zhenghao Zhang

Abstract: A reliable fall detection system has tremendous value to the well-being of seniors living alone. We design and implement MultiSense, a novel fall detection system, which has the following desirable features. First, it does not require the human to wear any device, therefore it is convenient to seniors. Second, it has been tested in typical settings including living room and bathroom, and has shown very good accuracy. Third, it is built with inexpensive components, with expected hardware cost around $150 to cover a typical room. Therefore, it has a key advantage over the current commercial fall detection systems which all require the human to wear some device, as well as over academic research prototypes which have various limitations such as lower accuracy. The high accuracy is achieved mainly by combining senses from multiple types of sensors that complement each other, which includes a motion sensor, a heat sensor, and a floor vibration sensor. As the activities that are difficult to classify for some sensors are often not difficult for others, combining the strength of multiple types of sensors brings the performance to a level that can meet the requirements in practice.

Paper Nr: 16
Title:

Deviation Prediction and Correction on Low-Cost Atmospheric Pressure Sensors using a Machine-Learning Algorithm

Authors:

Tiago C. de Araújo

Abstract: Atmospheric pressure sensors are important devices for several applications, including environment monitoring and indoor positioning tracking systems. This paper proposes a method to enhance the quality of data obtained from low-cost atmospheric pressure sensors using a machine learning algorithm to predict the error behaviour. By using the extremely Randomized Trees algorithm, a model was trained with a reference sensor data for temperature and humidity and with all low-cost sensor datasets that were co-located into an artificial climatic chamber that simulated different climatic situations. Fifteen low-cost environmental sensor units, composed by five different models, were considered. They measure – together – temperature, relative humidity and atmospheric pressure. In the evaluation, three categories of output metrics were considered: raw; trained by the independent sensor data; and trained by the low-cost sensor data. The model trained by the reference sensor was able to reduce the Mean Absolute Error (MAE) between atmospheric pressure sensor pairs by up to 67%, while the same ensemble trained with all low-cost data was able to reduce the MAE by up to 98%. These results suggest that low-cost environmental sensors can be a good asset if their data are properly processed.

Area 3 - Sensor Networks Applications

Full Papers
Paper Nr: 19
Title:

Application of Virtual Travel for Alzheimer’s Disease

Authors:

Hamdi Ben Abdessalem, Alexie Byrns, Marc Cuesta, Valeria Manera, Philippe Robert and Marie-Andrée Bruneau

Abstract: Negative emotions such as anxiety, frustration, or apathy can have an impact on the brain capability in terms of memory and cognitive functions. This is particularly visible in Alzheimer’s disease where the participants can have a deterioration of their brain connections which are often the cause of the disorders detected in Alzheimer's participants. It seems important to reduce these symptoms to allow better access to memory and cognitive abilities. Immersion in Virtual Reality is a means of providing the participant with a sense of presence in an environment that isolates them from external factors that can induce negative emotions. The virtual travel is a method that can mobilize the attention of the subject and revive their interest and curiosity. We present here, an experiment in which a participant is immersed in a virtual train using a virtual headset and EEG device to measure the brain signals. To measure the impact of this train on the memory and cognitive functions, some cognitive tasks have been included before and after the travel. Experiments have been done on participants with mild cognitive disorder. Preliminary results show an increase of memory functions and in certain cases of cognitive functions, while negative emotions are reduced.

Area 4 - Energy and Environment

Full Papers
Paper Nr: 30
Title:

Classification of Honeybee Infestation by Varroa Destructor using Gas Sensor Array

Authors:

Andrzej Szczurek, Monika Maciejewska, Beata Bąk and Jakub Wilk

Abstract: Infestation of bee colony with Varroa destructor proceeds exponentially. It is important to detect the disease at its very early stage. However, the distinction of later infestation stages is also practical. We proposed to apply gas sensor array measurements of beehive air as the source of information which may be useful for this kind of assessment. Honeybee infestation was classified into three categories: ‘low’, ‘medium’ and ‘high’, two categories: ‘low’ and ‘medium to high’, and another two categories: ‘high’ and ‘medium to low’. Responses of gas sensor array to beehive air were used as the input data of the classifier, which was trained to distinguish the categories. The results of the analysis demonstrated that category ‘low’ was determined most effectively, with an error rate of about 10%. Category ‘high’ was most difficult to determine. In this case the lowest error rate was about 20%. Based on our analysis, the approach based on binary classification was favoured and SVM outperformed ensemble of classification trees. It was found, that first several minutes of gas sensors exposure to beehive air were sufficient to attain effective classification. The presented method of varroosis determination, based on beehive air sensing with gas sensors is innovative and has high potential of application in beekeeping.

Area 5 - Sensor Networks Applications

Short Papers
Paper Nr: 15
Title:

Validation of a Low-cost Inertial Exercise Tracker

Authors:

Sarvenaz Salehi and Didier Stricker

Abstract: This work validates the application of a low-cost inertial tracking suit, for strength exercise monitoring. The procedure includes an offline processing for body-IMU calibration, online tracking and identification of lower body motion. We proposed an optimal movement pattern of the body-IMU calibration method from our previous work. Here in order to reproduce real extreme situations, we used data from different types of movements with high acceleration intensity. For such movements, an optimal orientation tracking approach is introduced which requires no accelerometer measurements and it thus minimizes error of existing outliers. The online tracking algorithm is based on an extended Kalman filter(EKF), which estimates the position of upper and lower legs with respect to the pelvis along with hip and knee joint angles. This method benefits from the estimated values in calibration process i.e. joint axes and positions, as well as biomechanical constraints of lower body. Therefore it requires no aiding sensors such as magnetometer. The algorithm was evaluated using optical tracker for two types of exercises:squat and abd/adduction which resulted average Root Mean Square Error(RMSE) of 9cm. Additionally, this work presents a personalized exercise identification approach, where an online template matching algorithm is applied and optimised using Zero Velocity Crossing(ZVC) for feature extraction. This results reducing the execution time to 93% and improving the accuracy to 33%.

Paper Nr: 20
Title:

A Sensor Network for Existing Residential Buildings Indoor Environment Quality and Energy Consumption Assessment and Monitoring: Lessons Learnt from a Field Experiment

Authors:

Mathieu Bourdeau and David Werner

Abstract: Enhancing residential buildings energy efficiency has become a critical goal to take up current challenges of human comfort, urbanization growth and the consequent energy consumption increase. In a context of integrated smart infrastructures, sensor networks offer a relevant solution to support building energy consumption monitoring, operation and prediction. The amount of accessible data with such networks also opens new prospects to better consider key parameters such as human behaviour and to lead to more efficient energy retrofit of existing buildings. However, sensor networks planning and implementation in general, and in existing buildings in particular, is a particularly complex task facing many challenges and affecting the performances of such a promising solution. In the present paper, we report on a field experiment of a sensor network deployment involving more than 250 sensors in three collective residential buildings in Paris region for the evaluation of a deep energy retrofit. More specifically, we describe the whole process of the sensor network design and roll-out and highlight the main critical aspects in such complex process. We also provide a feedback after several months of the sensor network operation and preliminary analysis of collected data. Reported results path the way for an efficient and optimized design and deployment of sensor networks for energy and indoor environment quality monitoring in existing buildings.

Area 6 - Intelligent Data Analysis and Processing

Short Papers
Paper Nr: 29
Title:

Anomaly Detection in Beehives using Deep Recurrent Autoencoders

Authors:

Padraig Davidson, Michael Steininger, Florian Lautenschlager and Konstantin Kobs

Abstract: Precision beekeeping allows to monitor bees’ living conditions by equipping beehives with sensors. The data recorded by these hives can be analyzed by machine learning models to learn behavioral patterns of or search for unusual events in bee colonies. One typical target is the early detection of bee swarming as apiarists want to avoid this due to economical reasons. Advanced methods should be able to detect any other unusual or abnormal behavior arising from illness of bees or from technical reasons, e.g. sensor failure. In this position paper we present an autoencoder, a deep learning model, which detects any type of anomaly in data independent of its origin. Our model is able to reveal the same swarms as a simple rule-based swarm detection algorithm but is also triggered by any other anomaly. We evaluated our model on real world data sets that were collected on different hives and with different sensor setups.

Area 7 - Wireless Sensor Networks

Posters
Paper Nr: 2
Title:

Analysing Usage of Harvested Energy in Wireless Sensor Networks: A Geo/Geo/1/K Approach

Authors:

O. P. Angwech

Abstract: A model that considers energy storage and usage in data transmission in Wireless Sensor Network applications is proposed. The system is modelled as a Geo/Geo/1/k system and analysed using standard finite Markov chain model tools. The stationary distribution of the queue length is obtained. In the model, the harvested energy is stored in a buffer and used as required by the packets. In addition to energy usage by the packets, leakage of energy is captured at each state. A situation that involves high and low priority data transmission is also captured in the model. For evaluation, the effects of the system parameters on the performance measures are analysed. The results show that the model accurately captures the energy usage and it can be used for the management of harvested energy in Wireless Sensor Networks.

Paper Nr: 3
Title:

Detecting Tunnels for Border Security based on Fiber Optical Distributed Acoustic Sensor Data using DBSCAN

Authors:

Suleyman A. Aslangul

Abstract: The Border Situational Awareness may consist of many different features. Mainly, these features focus on detecting intrusion activities. New generation security systems are collecting important amount of data obtained from sensors. In general, the alarm confirmation mechanism is visual identification using cameras and Video Management Systems. On the other hand, this approach may not be enough to identify an invisible tunnel digging activity underground for trespassing the border. This paper is suggesting a new method to detect tunnels by using statically filtered alarm data and DBSCAN algorithm. In this particular case MIDAS® Fiber Optic based Distributed Acoustic Sensor (DAS) system is used, which is designed by ASELSAN Inc. The proposed approach is evaluated and positive results are seen on diverse areas of the Turkish borders.

Paper Nr: 9
Title:

Building an Open Source Access Control System for Fablabs based on odoo and openHAB

Authors:

Fabian Meyer and Michael Schäfer

Abstract: Controlling machine access in Fablabs and makerspaces is a crucial task. Different types of machines require different types of briefings. This is especially important to avoid damage and injury. Controlling access automatically is thereby desirable, as it is otherwise labor-intensive. Currently available software to organize Fablabs and makerspaces have either a rather high price tag or lacking the functionality for automated access control. Self-developed hardware is also quite common but often, due to regulatory constraints, not allowed to operate on mains. Also, there is a wide range of home automation devices that are certified for switching mains voltage. We have developed a prototypical system that makes these devices available for use in the access control of Fablabs and Makerspaces. We have identified openHAB as a useful solution for the abstraction of devices from various manufacturers.

Area 8 - Energy and Environment

Posters
Paper Nr: 10
Title:

Response of a SAW Sensor Array based on Nanoparticles for Measuring Ammonia in the Environment

Authors:

D. Matatagui

Abstract: Four surface acoustic waves (SAW) sensors based on sensitive layers of Fe2O3 nanoparticles, pure and combined with noble metals nanoparticles, composed an array sensor to measure ammonia in the environment. The sensor array was tested with nanostructured sensitive layers, which detected the changes of the elastic properties induced by ammonia interaction. The sensor with pure Fe2O3 nanoparticles exposed to 50 ppm of ammonia showed no significant effect, however the sensors with Fe2O3 nanoparticles combined with Au, Pt and Pd nanoparticles responded to these concentrations of this gas, which is so dangerous for the environment and the health, with a high sensitivity.

Area 9 - Sensor Networks Applications

Posters
Paper Nr: 23
Title:

SALATA: A Web Application for Visualizing Sensor Information in Farm Fields

Authors:

Akayama Nao and Daisaku Arita

Abstract: Semi-automated sensing and visualization of conditions and activities in farm fields have been actively pursued in recent years. There are three types of agricultural information: sensor information, farm work information, and plant biological information. Measuring and visualizing these agricultural information can provide valuable support to farm managers. In this study, we focus on sensor information and farm work information and develop a web application named SALATA (Sharing and AccumuLating Agricultural TAcit knowledge) that collects and shares sensor information and farm work information collected in farm fields and correlates the information in time series. SALATA need to have intuitive operation and quick response in order that people of various ages will use it on a daily basis. Therefore, there are two primary pages: the main page for visualizing simple information quickly and the analytical page for visualizing multiple pieces of information on one page. Usability evaluation experiments are performed, showing that SALATA can be operated intuitively and respond quickly.

Paper Nr: 24
Title:

ICT Technologies, Techniques and Applications to Improve Energy Efficiency in Smart Buildings

Authors:

César Benavente-Peces and Nisrine Ibadah

Abstract: Currently, most of the human activities impact the environment. Worldwide sustainable development is required to preserve a good quality of life. Energy efficiency is one of the most relevant issues that the scientific community and society must face along the next decades. This paper focuses on reviewing and noting the main factors which impact the optimization of electrical energy efficiency in Smart Buildings, including distribution, consumption analysis, strategies and management. Smart grids and smart buildings are playing a key role in the definition of the following generations of cities where the impact of energy consumption on the environment must be reduced as much as possible. Notwithstanding, all the factors impacting the production and distribution must be also taken into consideration by energy production companies and distribution companies as well. Green energies are being introduced in smart cities and buildings, only slower than required, and in general, focusing on the consumption side asking for higher performance monitoring and control techniques, and encouraging to incorporate energy harvesting initiatives to improve the overall efficiency. In this paper, the major target is pointing out all the relevant factors influencing smart building energy efficiency, up to the consumer side and, at the same time, paying attention on distribution and generation issues and, specifically, available communication standards, technologies, techniques, algorithms, which enable high performance systems to optimize energy consumption and occupant comfort.

Paper Nr: 26
Title:

Enhancing Vibroarthrography by using Sensor Fusion

Authors:

Dimitri Kraft

Abstract: Natural and artificial joints of a human body are emitting vibration and sound during the movement. The sound and vibration pattern of a joint is characteristic and changes due to damage, uneven tread wear, injuries, or other influences. Hence, the vibration and sound analysis enables an estimation of the joint condition. This kind of analysis, vibroarthrography (VAG), allows the analysis of diseases like arthritis or osteoporosis and might determine trauma, inflammation, or misalignment. The classification of the vibration and sound data is very challenging and needs a comprehensive annotated data base. Current existing data bases are very limited and insufficient for deep learning or artificial intelligent approaches. In this paper, we describe a new concept of the design of a vibroarthrography system using a sensor network. We discuss the possible improvements and we give an outlook for the future work and application fields of VAG.

Area 10 - Sensors Networks Hardware

Posters
Paper Nr: 28
Title:

papagenoX: Generation of Electronics and Logic for Embedded Systems from Application Software

Authors:

Tobias Scheipel and Marcel Baunach

Abstract: Embedded systems development usually starts with hardware engineering based on specific requirements of the systems. These requirements are mainly derived from the needs of the not yet developed software to be executed on the system. This process is predictive and many iterations are thus needed, as new requirements often arise during the software development period. In the future, the market will demand more and more sophisticated embedded systems with a much reduced time to market. It will thus be inevitable that system prototypes and series products will need to be created as fast as possible. To enable this, we propose a top-down approach termed papagenoX, dealing with the question of “How to generate all layers X of the embedded systems stack including hardware and reconfigurable logic units from application software?”. The present work is a work in progress and deals with the definition of the research questions and several ideas and concepts of how to fundamentally solve them. Hence, it aims at introducing ideas to create a generator for embedded systems electronics, reconfigurable logic and software.

Paper Nr: 31
Title:

Algorithmic State Machine Design for Timely Health Emergency Management in an IoT Environment

Authors:

Fadi T. El-Hassan

Abstract: In emergency cases related to massive accidents, environmental disasters, and war time, health professionals face considerable challenges due to the high number of patients who are in need of emergency treatment. Research works attempt to propose effective in-hospital and pre-hospital smart emergency systems to reduce the mortality rate among the patients who desperately wait to receive appropriate care. This paper presents a model of a timely prehospital emergency management system that can be implemented as an interface to an Internet of Things (IoT) environment. This work presents the necessary stages for prehospital emergency environments, where many factors may make the timely management of emergency systems very difficult. The proposed model is based on an Algorithmic State Machine (ASM) that can be implemented in either hardware or software, providing an embedded system interface for IoT. Moreover, this paper provides a timing analysis for either a single emergency event or multiple simultaneous emergency events. Embedded systems’ developers can use the proposed model to produce an appropriate prehospital smart emergency solution.

Special Session

WSN4PA 2020

Full Papers
Paper Nr: 4
Title:

Digital Villages: A Data-Driven Approach to Precision Agriculture in Small Farms

Authors:

Ram Fishman, Moushumi Ghosh, Amit Mishra, Shmuel Shomrat, Meshi Laks, Roy Mayer and Aakash Jog

Abstract: An approach for system monitoring of smallholder farms. The system will be based on low-cost mobile units (i.e. IoTs, phones) collecting and transmitting data directly from the farms. The IoT information will be merged with available and free access satellite data to form near real-time thematic images to the end-users. It will serve people with low technical literacy who are working with smallholders in developing countries. The novelty of using an integrated interdisciplinary behavioral-technological approach that builds on our respective disciplinary expertise, and the ability to pilot and implement at scale through partnerships, on the ground, allowing gaining new insights into smallholder cultivation and revolutionizing agricultural extension in the developing world. To achieve that goal of Holistic Integrated Precision Agriculture Network (HIPAN) three networks have been established in experimental farms in India: wireless network for “on-the-ground” sensing, virtual network with satellite multispectral imaging-based data and social network collecting the farmers’ inputs. The three networks are fused together and the data is processed using a cloud supported data analysis; the results are visually transferred to the farmers as well as to organizations and companies for their benefit.

Short Papers
Paper Nr: 1
Title:

Reliability Comparison of Routing Protocols for WSNs in Wide Agriculture Scenarios by Means of ηL Index

Authors:

Marco Cagnetti

Abstract: A comparison between the most suitable routing protocols for WSNs applied in wide agriculture scenarios is shown. The protocols, already present in literature, have been conceived to better manage the power budget of the nodes and are particularly suitable to cover the energy issues that wide agriculture scenario can request. This study aims to indicate which of the protocols eligible for this scenario is the most suitable. Comparative simulation test will be shown.

Paper Nr: 2
Title:

A Wireless Sensor Network based on Laser-annealed ZnO Nanostructures for Advance Monitoring in Precise Agriculture

Authors:

Davide Polese, Francesco Maita, Ivano Lucarini, Antonio Ferraro and Antonio De Luca

Abstract: Plants own a complex way to communicate with each other based on the exchange of chemical and electrical signals. Indeed, plants are capable of creating extensive communication networks thus warning each other of the presence of pests. In response, plants trigger natural strategy against the infestation. The main tool used by plants for exchanging information is the emission and detection of specific volatile organic compounds in air. To this end, monitoring these compounds can be crucial to reveal the state of health of a cultivation far before visual symptoms arise. In this work, we present a wireless sensor network where each node is based on highly sensitive zinc oxide nanostructures enabling the detection and the discrimination of several chemical gases such as CO, CO2, NO, NO2, CH4, etc. The response of each sensor is tuned by using excimer laser annealing procedure, a technique that changes the electrical and morphological properties of the sensing material. This wireless sensor network can be an appealing solution to capture signals coming from the plants without the usage of bulky and expensive equipment.

Paper Nr: 3
Title:

Internet of Trees: A Vision for Advanced Monitoring of Crops

Authors:

Alessandro Checco and Davide Polese

Abstract: Ecosystem preservation and production maximisation are competing objectives in agriculture. Reducing the need of undifferentiated or late interventions on the crops would reduce the number of disease treatments needed, as well as the consumption of water and fertiliser. This objective is only attainable through crop monitoring systems able to reach a single plant. Precision agriculture employ continuous and pervasive monitoring of crops, that in turn allows fast and targeted interventions. The aim of this paper is to highlight the problems that can be found in designing a wireless sensor network (WSN) able to measure environmental parameters such as relative humidity, irradiance and volatile pollutant concentration and introduces a possible solution that we named the Internet of Trees.

Open Communications

OCSENSORNETS 2020

Posters
Paper Nr: 1
Title:

Gallium Nitride based Hall-effect Sensors for Automotive, Aerospace, and Embedded Power Systems

Authors:

Hannah S. Alpert, Caitlin A. Chapin, Karen M. Dowling, Savannah R. Benbrook and Helmut Köck

Abstract: Hall-effect sensors are widely used in the automotive industry, in power electronics, and within inertial measurement units (IMUs) for navigation and position sensing. There is a growing need for Hall-effect sensors that can operate under extreme conditions, specifically in high temperature environments such as deep underground (e.g., well-logging) and in outer space. Electronic components, including Hall-effect sensors, are typically made of silicon, but these components begin to breakdown at temperatures beyond 200°C, and thus external cooling is often required. However, implementing external cooling processes necessitates additional power and contributes further bulk and complexity to the system, leading to increased size, weight, and overall costs of the system. Thus, components that can operate at extreme temperatures without additional cooling are necessary for achieving higher efficiency, higher reliability, and lower cost. Wide bandgap semiconductors such as gallium nitride (GaN) and aluminum nitride (AlN) have been shown to operate up to 1000°C in vacuum and thus are a prime candidate for electronics in harsh environmental conditions. We have developed GaN-based Hall-effect sensors with high sensitivity (similar to commercial silicon devices) and extremely low offsets. We characterized their voltage- and current-scaled magnetic sensitivities between room temperature and 600°C. Both devices showed decreasing voltage-scaled sensitivity at high temperature, corresponding to the decreasing electron mobility due to scattering effects at elevated temperatures. Alternatively, current-scaled sensitivities remained stable over the temperature range, only varying by ~10% from their mean, due to a minimal dependence of sheet carrier concentration on temperature. Both devices showed consistency in their voltage- and current-scaled sensitivity over multiple temperature cycles as well as nearly full recovery when returned to room temperature after thermal cycling. Additionally, a sample held at 576°C for 12 hours also showed nearly full recovery at room temperature, further suggesting that GaN-based Hall-effect sensors are a good candidate for use in high temperature applications.