Browsing by Author "Crescini, Damiano"
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Item An Autonomous Multi-Variable Outdoor Air Quality Mapping Wireless Sensors IoT Node for Qatar(Institute of Electrical and Electronics Engineers Inc., 2020-06) Tariq, Hasan; Abdaoui, Abderrazak; Touati, Farid; Al-Hitmi, Mohammed Abdulla E; Crescini, Damiano; Mnaouer, Adel BenIn all outdoor long haul air quality monitoring and mapping applications, sensing accuracy, the sustainability of telemetry, and resilience in node systems and operation are major challenges. The severity of these challenges varies depending on the moderateness and harshness of the climate of observation. In this work, an autonomous environmentally powered, sensors self-diagnostic/calibration, and context-aware IoT-based telemetry for multi-variable sensing node is being proposed. In this node, photo-voltaic and piezoelectric energy harvesters contributed to self-calibration and sustainable measurement of temperature (in °C), humidity (in %), pressure (in bar), geo-position (in NMEA format), volatile organic compounds-VOC (in ppm), particulate matter PM (in ppm), ozone (in Dobson Unit), Carbon mono-oxide (in ppm), Nitrogen dioxide (in ppm), and Sulphur dioxide (in ppm). Results have shown that the proposed system worked autonomously for days and optimized the real-time air quality mapping for the chosen geo-spatial cluster, i.e. Qatar University. © 2020 IEEE.Item Design and Implementation of Cadastral Geo-spatial IoT Network Gateway Analyzer for Urban Scale Infrastructure Health Monitoring(Institute of Electrical and Electronics Engineers Inc., 2020-01) Tariq, Hasan; Abdaoui, Abderrazak; Touati, Farid; Al-Hitmi, Mohammed Abdulla E; Crescini, Damiano; Mnaouer, Adel BenUrban and global scale health monitoring systems have gained significance in digital ecosystems. Every stakeholder is striving for efficiency through performance analysis of digital infrastructure health monitoring (IHM) solutions. In this work, a geospatial network analyzer (GNA) is designed and implemented in python using the synergic strengths of Plotly, NetworkX, Scipy, Numpy, MatplotLib, Network2tikz, Pysocks, and PyPing. The GNA uses as a case study a utility computing model (UCM) to make structural health monitoring (SHM) that is based on analysis of geographical area network (GAN). A geo-distributed SHM deployment is assessed from a network performance perspective and verified from geo-spatial packet processing in GNA. The results have shown that this work can lead to standardizing the future of global-scale IoT networks analytics. © 2020 IEEE.Item Design and implementation of information centered protocol for long haul SHM monitoring(Institute of Electrical and Electronics Engineers Inc., 2019) Tariq, Hasan; Touati, Farid; Al-Hitmi, Mohammed Abdulla E.; Crescini, Damiano; Mnaouer, Adel BenIn structural health monitoring systems (SHM), robust data transmission is the fundamental constraint. In this work, an information centered protocol is being proposed for multi-sensor and multi-variable communication channels in (SHM). The core objective is communication traffic optimization, data streams compression, bottleneck compensation for seamless information system. A novel SHM hierarchical information model has been designed and implemented using addressing taxonomy and domain definitions accumulated with data segments, beacons and flags-handshaking. On both ends of an SHM channel, a SQLite based encoding and decoding preprocessor is implemented, which requires the use of serial protocols such as CANopen, UART, 12C and SPI. Results have shown that the proposed system optimizes traffic monitoring in handling critical situations of dynamic baud rate switching. © 2019 IEEE.Item Design and Implementation of Multi-Protocol Data Networks Interface Detector in Heterogeneous IoTs(Institute of Electrical and Electronics Engineers Inc., 2020-02) Tariq, Hasan; Abdaoui, Abderrazak; Touati, Farid; Al-Hitmi, Mohammed Abdulla E; Crescini, Damiano; Manouer, Adel BenIn Internet of Everything (IoE) or heterogeneous IoTs, there exist a plethora of protocols and inter-connected Internet of Things. For every network protocol, the complexity of communication and wiring at the physical interface becomes more and more challenging. In this work, an automated interface detector (or gateway) is proposed using a novel port, that scans the physical layer parameters, interpolates with Electronic Industry Association/Telecommunication Industry Association (EIA/TIA) standards specification parameters and compares packet format initiates communication. In this approach, line impedance, voltage, current, SNR, power, and network capacity are used as the coefficients of merit for physical layer detection by interpolation and averaging methods. Standard packet architectures are the key parameters for communication initiation and network fastening at both ends of the data line. The serial protocols such as CANopen, Ethernet, UART, I2C, and SPI are tested and verified. Results have shown that the proposed system can detect any physical layer interface and initiate a data network regardless of connector pinouts and interface wiring complexities. The implementation results exhibit a fertile resource for redundancy handling in-line parameters of data networks. © 2020 IEEE.Item Design and implementation of programmable multi-parametric 4-degrees of freedom seismic waves ground motion simulation IoT platform(Institute of Electrical and Electronics Engineers Inc., 2019) Tariq, Hasan; Touati, Farid; Al-Hitmi, Mohammed Abdulla E.; Crescini, Damiano; Mnaouer, Adel BenThe early warning and disaster management agencies spend billions of dollars to counter and cater earthquakes but it has always been unique accident. In this work, a programmable four degrees of freedom electromechanical seismic wave events simulation platform design is being proposed to study and experiment seismic waves and earthquakes realization in form of ground motions. The platform can be programmed and interfaced through an IoT cloud-based Web application. The rig has been tested in the range of frequencies of extreme seismic waves from 0.1Hz to 178Hz and terrestrial inclinations from -5.000° to 5.000°, which is key contribution of this work. This would be an enabler for a variety of applications such as training self-balancing and calibrating seismic resistant designs and structures in addition to studying and testing seismic detection devices. Nevertheless, it serves as an adequate training colossus for machine learning algorithms and event management expert systems. © 2019 IEEE.Item Design and simulation of a green bi-variable mono-parametric SHM node and early seismic warning algorithm for wave identification and scattering(Institute of Electrical and Electronics Engineers Inc., 2018) Touati, Farid; Tariq, Hasan; Crescini, Damiano; Mnaouer, Adel BenEarly seismic warning systems are key for safe future scalable infrastructures. In this work, a dual variable i.e. vibration and line of sight (LOS) based structure health monitoring (SHM) node is designed to sense tilt angle for early seismic warning and wave scattering detection. The SHM node, consisting of high-precision five bi-axis tiltmeters and five Blue-Violet laser diodes transmitter/receiver/reflector(LDTRR) assembly, has been designed and simulated in Proteus 7ISIS, MATLAB 7 and drafted in AutoCAD. In AutoCAD, a four LDTRR assembly is oriented at the bottom of building and its four co-planer reflectors have been orthogonally placed at effective radii with respect to the characteristic wavelengths of P, S, and Rayleigh whilst Love seismic waves, and one reflector is placed at the bottom of building. PV umbrella with a Li-ion battery has been used for green ergonomic shape. The time plots from real tiltmeter sensor nodes and data acquired from the proposed SHM node show similar behavior and results. The derived parameters of wavelength S, i.e. seismic parameter F,varied linearly from safe to hazardous seismic conditions. The variation from safe seismic to hazardous seismic transition of randomly simulated environment, also varied network traffic in GPS module as per defined threshold of sensor variables in Proteus ISIS Electronics Design Automation (EDA) engine. As per early warning evaluation functions (EWEF), the proposed design for early seismic warning algorithm (ESWA) can be a cost-effective analytics resource for any scalable SHM solution for observation range within 5km+ radius at low cost and 20km at moderate/high cost. © 2018 IEEE.Item Development of prototype for IoT and IoE scalable infrastructures, architectures and platforms(Springer Verlag, 2018) Touati, Farid; Tariq, Hasan; Crescini, Damiano; Mnaouer, Adel BenIoT is the third wave of economy after the first and second being agriculture and industry, respectively, paving the way for the fourth industrial revolution (4IR). IoT is a combination of all the revolutionary technologies in the last two decades. More than a billion of smart devices have been developed across the world by more than 10 vendors to satisfy billions of needs that are trusted by 98% of economic actors. This study describes design and implementation of IoT architectures stressing on scalability, integration, and interoperability of heterogeneous IoT systems. It gives answers to (i) how systems can be designed to become easily configurable and customizable for a specific IoT infrastructure? And (ii) how Investors, producers and consumers can be integrated on the same page of an IoT platform? We have developed a master database and directories from top chart IoT nomenclature, frameworks, vendors, devices, platforms and architectures and integrated data from 27 big online resources commonly used by Forbes, Businessweek and CNBC. Also, datasheets of IoT equipment by vendors (e.g. Intel, IBM, ARM, Microchip, Schneider, and CISCO), used tools (e.g. Labcenter Proteus, AutoCAD and Excel), and platforms (e.g. Visual Studio, Eclipse) are combined to build directories of plethora of data. The main outcome of this work culminates in providing a seamless solution and recommendations for various infrastructures (hardware and software) for effective and integrated resource utilization and management in a new IoT paradigm. © Springer Nature Switzerland AG 2018.Item EAMP-AIDC - energy-aware mac protocol with adaptive individual duty cycle for EH-WSN(Institute of Electrical and Electronics Engineers Inc., 2017) Bouachir, Ons; Mnaouer, Adel Ben; Touati, Farid; Crescini, DamianoNetwork lifetime is the main issue of wireless sensor networks and IoT solutions in real world application. Sensors cannot have an infinite lifetime without battery recharge or replacement. Energy harvesting, from environmental energy sources, is a promising technology to provide sustainable powering for WSN. However, based on harvesting opportunities, nodes power may alternate between two states: a state with sufficient residual power and another with shortage in power. Hence, it is paramount to develop robust networking platforms that are energy-harvesting-aware and that support low-energy consumption and data integrity in a noisy, variable environment. In this paper, we present the EAMP-AIDC protocol, an energy aware MAC protocol for EH-WSN based on individual duty cycle optimization. It takes into consideration nodes' residual energy and application and data requirements in order to define individual dynamic duty cycles (Active and sleep periods) that allow to create a balanced load in term of cooperative data relaying tasks and in terms of energy consumption between the different participating nodes so as to ensure continuous network operation. The proposed protocol was evaluated using the network simulator OMNET++ and was compared to the standard IEEE 802.15.4 MAC. The results showed that EAMP-AIDC protocol outperformed the IEEE 802.15.4 standard in term of better energy consumption, increased survivability in energy savings and in guaranteeing continuous operations. © 2017 IEEE.Item Environmentally powered multiparametric wireless sensor node for air quality diagnostic(M Y U Scientific Publishing Division, 2015) Touati, Farid; Legena, Claudio; Galli, Alessio; Crescini, Damiano; Mnaouer, Adel BenSensor networks dedicated to environmental monitoring have helped in the analysis of primal processes and have also provided vital hazard early warnings. At the same time, environmental energy is now becoming a popular workable energy source dedicated to embedded and wireless computing systems where manual recharging and/or replacement of hundreds or even thousands of batteries on a regular basis is not practical. In this paper, we present a sensor node (SENNO), a multiparametric sensor node that intelligently manages energy transfer for perpetual operation without human intervention during air quality monitoring. The overall system design and experimental results are presented together with energy budget allocation. Preliminary results demonstrate that, after a tailored calibration process, the presented platform could effectively report and trace air quality levels in a type of "set and forget" scenario.Item Feasibility of air quality monitoring systems based on environmental energy harvesting(Institute of Electrical and Electronics Engineers Inc., 2015) Touati, Farid; Galli, Alessio; Crescini, Damiano; Crescini, Paolo; Mnaouer, Adel BenCapillary wireless sensor networks dedicated to air quality monitoring have provided essential information on hazardous air condition, generating early warnings to prevent danger situation for human health. The arising problem connected to capillary networks is the adoption of environmental energy as primary and/or unique energy source instead of the replacement of hundreds or even thousands of batteries on a regular basis that leads to high costs and practical problems of devices management. Aim of this paper is to presents a multiparametric sensor node for air quality monitoring, able to work without battery and human intervention, harvesting energy from the surrounding environment for perpetual operation. A complete autonomy system has been designed, experimental results of the harvest energy section and the budget allocation of the power consumption of the system are presented. Moreover the paper shows the experimental results of the studies conducted on the sensors section. A tailored calibration process for the sensors and the energy recovery section could effectively lead the system to trace the air quality levels in indoor and outdoor application, in a sort of 'set and forget' scenario. approach could effectively report and trace air quality levels. © 2015 IEEE.Item Geographical area network-structural health monitoring utility computing model(MDPI AG, 2019) Tariq, Hasan; Tahir, Anas; Touati, Farid; Al-Hitmi, Mohammed Abdulla E.; Crescini, Damiano; Mnaouer, Adel BenIn view of intensified disasters and fatalities caused by natural phenomena and geographical expansion, there is a pressing need for a more effective environment logging for a better management and urban planning. This paper proposes a novel utility computing model (UCM) for structural health monitoring (SHM) that would enable dynamic planning of monitoring systems in an efficient and cost-effective manner in form of a SHM geo-informatics system. The proposed UCM consists of networked SHM systems that send geometrical SHM variables to SHM-UCM gateways. Every gateway is routing the data to SHM-UCM servers running a geo-spatial patch health assessment and prediction algorithm. The inputs of the prediction algorithm are geometrical variables, environmental variables, and payloads. The proposed SHM-UCM is unique in terms of its capability to manage heterogeneous SHM resources. This has been tested in a case study on Qatar University (QU) in Doha Qatar, where it looked at where SHM nodes are distributed along with occupancy density in each building. This information was taken from QU routers and zone calculation models and were then compared to ideal SHM system data. Results show the effectiveness of the proposed model in logging and dynamically planning SHM. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.Item IoT-Based Bi-Cluster Forecasting Using Automated ML-Model Optimization for COVID-19(MDPI, 2023-03) Tariq, Hasan; Touati, Farid; Crescini, Damiano; Mnaouer, Adel BenThe current COVID-19 pandemic has raised huge concerns about outdoor air quality due to the expected lung deterioration. These concerns include the challenges associated with an increase of harmful gases like carbon dioxide, the iterative/repetitive inhalation due to mask usage, and harsh environmental temperatures. Even in the presence of air quality sensing devices, these challenges can hinder the prevention and treatment of respiratory diseases, epidemics, and pandemics in severe cases. In this research, a dual time series with a bi-cluster sensor data-stream-based novel optimized regression algorithm was proposed with optimization predictors and responses that use an automated iterative optimization of the model based on the similarity coefficient index. The algorithm was implemented over SeReNoV2 sensor nodes data, i.e., a multi-variate dual time-series sensor, of the environmental and US Environmental Protection Agency standard, which measures variables for the air quality index using air quality sensors with geospatial profiling. The SeReNoV2 systems were placed at four locations that were 3 km apart to monitor the air quality and their data was collected at Ubidots IoT platform over GSM. The results have shown that the proposed technique achieved a root mean square error (RMSE) of 1.0042 with a training time of 469.28 s for the control and an RMSE of 1.646 in a training time of 28.53 s when optimized. The estimated R-Squared error was 0.03, with the Mean-Square Error for temperature being 1.0084 °C, and 293.98 ppm for CO2. Furthermore, the Mean-Absolute Error (MAE) for temperature was 0.66226 °C and 10.252 ppm for the correlated-CO2 at a predicted speed of ~5100 observations/s. In the sample cluster for temperature, 45,000 observations/s for CO2 was achieved due to the iterative optimization of the training time (469.28 s). The correlated temperature and a time of 28.53 s for CO2 were very promising in forecasting COVID-19 countermeasures before time. © 2023 by the authors.Item A new nano-power trigger circuit for battery-less power management electronics in energy harvesting systems(Elsevier B.V., 2017-08-15) Alghisi, Davide; Ferrari, Vittorio; Ferrari, Marco; Touati, Farid Abdelkader; Crescini, Damiano; Mnaouer, Adel BenItem Opportunistic routing and data dissemination protocol for energy harvesting wireless sensor networks(Institute of Electrical and Electronics Engineers Inc., 2016) Bouachir, Ons; Mnaouer, Adel Ben; Touati, Farid; Crescini, DamianoRecent advances in environmental sources harvesting technologies is a promising solution to provide sustainable energy sources for wireless sensor networks (WSN). Renewable energy sources such as solar, thermal and electromagnetic waves constitute viable alternatives to maximize the network lifetime. Therefore, networking software awareness and adaptability to energy availability conditions should translate into innovative supporting features for data gathering, aggregation, routing and dissemination. Thus, it is paramount to develop robust energy harvesting-aware networking platforms that support the above services and guarantee low energy consumption and data integrity in a noisy, constrained environment. In this paper, we present an opportunistic routing and data dissemination protocol for energy harvesting wireless sensor network (EH-WSN) based on cross-layer constructs that allows across the layers synchronization and coordination between the routing protocol and the application layer services. The OMNET++ based extensive simulation of this protocol showed promising results in terms of meeting application requirements of handling urgent traffic and delay tolerant traffic seamlessly and ensuring energy usage efficiency. © 2016 IEEE.Item A real-time early warning seismic event detection algorithm using smart geo-spatial bi-axial inclinometer nodes for Industry 4.0 applications(MDPI AG, 2019) Tariq, Hasan; Touati, Farid; Al-Hitmi, Mohammed Abdulla E.; Crescini, Damiano; Mnaouer, Adel BenEarthquakes are one of the major natural calamities as well as a prime subject of interest for seismologists, state agencies, and ground motion instrumentation scientists. The real-time data analysis of multi-sensor instrumentation is a valuable knowledge repository for real-time early warning and trustworthy seismic events detection. In this work, an early warning in the first 1 micro-second and seismic wave detection in the first 1.7 milliseconds after event initialization is proposed using a seismic wave event detection algorithm (SWEDA). The SWEDA with nine low-computation-cost operations is being proposed for smart geospatial bi-axial inclinometer nodes (SGBINs) also utilized in structural health monitoring systems. SWEDA detects four types of seismic waves, i.e., primary (P) or compression, secondary (S) or shear, Love (L), and Rayleigh (R) waves using time and frequency domain parameters mapped on a 2D mapping interpretation scheme. The SWEDA proved automated heterogeneous surface adaptability, multi-clustered sensing, ubiquitous monitoring with dynamic Savitzky-Golay filtering and detection using nine optimized sequential and structured event characterization techniques. Furthermore, situation-conscious (context-aware) and automated computation of short-time average over long-time average (STA/LTA) triggering parameters by peak-detection and run-time scaling arrays with manual computation support were achieved. © 2019 by the authors.Item A real-time gradient aware multi-variable handheld urban scale air quality mapping IoT system(Institute of Electrical and Electronics Engineers Inc., 2020-06) Tariq, Hasan; Abdaoui, Abderrazak; Touati, Farid; Al-Hitmi, Mohammed Abdulla E; Crescini, Damiano; Manouer, Adel BenIn outdoor urban scale air quality mapping, electrochemical sensors warm-up time, cross-sensitivity, geo-location typography, and energy efficiency are major challenges. These challenges lead to real-time gradient anomalies that effect the accuracy and prolonged lags in air quality mapping campaigns for state and environmental/meteorological agencies. In this work, a gradient aware, multi-variable air quality sensing node is proposed with event-triggered sensing based on position, gas magnitudes, and cross-sensitivity interpolation. In this approach, temperature, humidity, pressure, geo-position, photovoltaic power, volatile organic compounds, particulate matter, ozone, Carbon mono-oxide, Nitrogen dioxide, and Sulphur dioxide are the principle variables. Results have shown that the proposed system optimized the real-time air quality mapping for the chosen geo-spatial cluster, i.e. Qatar University. © 2020 IEEE.Item Real-time Gradient-Aware Indigenous AQI Estimation IoT Platform(ASTES Publishers, 2020-12) Tariq, Hasan; Abdaoui, Abderrazak; Touati, Farid; Al Hitmi, Mohammad Abdullah; Crescini, Damiano; Mnaouer, Adel BenEnvironmental monitoring has gained significant importance in outdoor air quality measurement and assessment for fundamental survival as well as ambient assisted living. In real-time outdoor urban scale, instantaneous air quality index estimation, the electrochemical sensors warm-up time, cross-sensitivity computation-error, geo-location typography, instantaneous capacity or back up time; and energy efficiency are the six major challenges. These challenges lead to real-time gradient anomalies that effect the accuracy and pro-longed lags in air quality index mapping campaigns for state and environmental/meteorological agencies. In this work, a gradient-aware, multi-variable air quality sensing node is proposed with event-triggered sensing based on position, gas magnitudes, and cross-sensitivity interpolation. In this approach, temperature, humidity, pressure, geo-position, photovoltaic power, volatile organic compounds, particulate matter (2.5), ozone, Carbon mono-oxide, Nitrogen dioxide, and Sulphur dioxide are the principle variables. Results have shown that the proposed system optimized the real-time air quality monitoring for the chosen geo-spatial cluster (Qatar University). © 2020 ASTES Publishers. All rights reserved.Item Renewable energy-harvested sensor systems for air quality monitoring(Institute of Electrical and Electronics Engineers Inc., 2014) Touati, Farid; Legena, Claudio; Galli, Alessio; Crescini, Damiano; Crescini, Paolo; Mnaouer, Adel BenWireless sensor networks (WSNs) devoted to environmental monitoring has preponderantly assumed the adoption of a portable and limited energy source, (e.g. lithium, alkaline, NiMH batteries), to support the sensor functionalities. The usage of environmental resources as energy booster is now rising up as a workable energy source dedicated to embedded and wireless computing systems where manual replacement of hundreds or even thousands of batteries on a regular basis is not practical. Consequently, substantial research efforts have been spent on designing energy-efficient smart sensor nodes and networks to maximize the lifetime of WSNs. However, in air quality monitoring systems sensors are required to operate for much longer durations (like years or even decades) after they are deployed. Following the above approach this paper presents SENNO (SENsor NOde), a renewable energy-harvested sensor node that intelligently manages energy transfer for continuous operation without human intervention during air quality monitoring. This paper discusses the challenges of designing an autonomous system powered by ambient energy harvesting. Preliminary results show that, the presented approach could effectively report and trace air quality levels. © 2014 IEEE.Item Structural health monitoring installation scheme using utility computing model(Institute of Electrical and Electronics Engineers Inc., 2018) Tariq, Hasan; Al-Hitmi, Mohammed Abdulla E.; Tahir, Anas; Crescini, Damiano; Touati, Farid; Mnaouer, Adel BenIn view of intensified disasters and fatalities caused by natural phenomena, there is a pressing need for an efficient environment logging that provides structural information to administrators for a better management and urban planning. This paper proposes a novel utility model for structural health monitoring that would enable early detection of risk factors and mitigation of loss. The proposed utility computing model takes the input data in terms of 'number of occupants' in a building (i.e. MAC, International Mobile Equipment Identifier addresses and biometric attendance system installed) and SHM system data (i.e. sensors readings). It give visual representation of all the data for utility managers and experts to decide better location of SHM and number of SHM needed per zone depending on high disturbances created due to higher occupancy and number of structures per zone. Denser area with higher structures and higher population will require more precise and accurate SHM systems compared to rural areas. It also analyzes the data from SHM system and using simple machine learning algorithm give experts' suggestions for type of SHM needed at an area. It make it possible for the data of each and every device of SHM systems over several zones to be accessible by specific authorities that can be used to predict as well as forecast any natural disaster. The Structural Health Monitoring utility model is unique in terms of its heterogeneity of resource management in realizing the utility processes. Finally, a case study of Qatar University is looked at where nodes are distributed in zones along with occupant measuring is used over each building. The data was taken over simulated occupation models and mathematical models from literature for occupation and zone calculation using ideal SHM system data. It can be inferred from the data that real-time analysis data will act similar to simulated and proposed Utility Computing System will give visual data and analyze the zones as can be seen in the results. Therefore, SHM Utility Computing model is efficient and most effective system that save cost as well as prepare authorities for maintenance of a structure or crisis management due to external surroundings. © 2018 IEEE.