Autonomous Monitoring Research Articles

UAV-Based Air Pollutant Source Localization Using Combined Metaheuristic and Probabilistic Methods

Air pollution is one of the greatest risks for the health of people. In recent years, platforms based on Unmanned Aerial Vehicles (UAVs) for the monitoring of pollution in the air have been studied to deal with this problem, due to several advantages, such as low-costs, security, multitask and ease of deployment. However, due to the limitations in the flying time of the UAVs, these platforms could perform monitoring tasks poorly if the mission is not executed with an adequate strategy and algorithm. Their application can be improved if the UAVs have the ability to perform autonomous monitoring of the areas with a high concentration of the pollutant, or even to locate the pollutant source. This work proposes an algorithm to locate an air pollutant’s source by using a UAV. The algorithm has two components: (i) a metaheuristic technique is used to trace the increasing gradient of the pollutant concentration, and (ii) a probabilistic component complements the method by concentrating the search in the most promising areas in the targeted environment. The metaheuristic technique has been selected from a simulation-based comparative analysis between some classical techniques. The probabilistic component uses the Bayesian methodology to build and update a probability map of the pollutant source location, with each new sensor information available, while the UAV navigates in the environment. The proposed solution was tested experimentally with a real quadrotor navigating in a virtual polluted environment. The results show the effectiveness and robustness of the algorithm.

Visual and passive acoustic observations of blue whale trios from two distinct populations

Visual and passive acoustic observations of blue whale trios from two distinct populations

Intelligent ICU for Autonomous Patient Monitoring Using Pervasive Sensing and Deep Learning

Currently, many critical care indices are not captured automatically at a granular level, rather are repetitively assessed by overburdened nurses. In this pilot study, we examined the feasibility of using pervasive sensing technology and artificial intelligence for autonomous and granular monitoring in the Intensive Care Unit (ICU). As an exemplary prevalent condition, we characterized delirious patients and their environment. We used wearable sensors, light and sound sensors, and a camera to collect data on patients and their environment. We analyzed collected data to detect and recognize patient’s face, their postures, facial action units and expressions, head pose variation, extremity movements, sound pressure levels, light intensity level, and visitation frequency. We found that facial expressions, functional status entailing extremity movement and postures, and environmental factors including the visitation frequency, light and sound pressure levels at night were significantly different between the delirious and non-delirious patients. Our results showed that granular and autonomous monitoring of critically ill patients and their environment is feasible using a noninvasive system, and we demonstrated its potential for characterizing critical care patients and environmental factors.

Autonomous Wireless Sensor Networks in an IPM Spatial Decision Support System

Until recently data acquisition in integrated pest management (IPM) relied on manual collection of both pest and environmental data. Autonomous wireless sensor networks (WSN) are providing a way forward by reducing the need for manual offload and maintenance; however, there is still a significant gap in pest management using WSN with most applications failing to provide a low-cost, autonomous monitoring system that can operate in remote areas. In this study, we investigate the feasibility of implementing a reliable, fully independent, low-power WSN that will provide high-resolution, near-real-time input to a spatial decision support system (SDSS), capturing the small-scale heterogeneity needed for intelligent IPM. The WSN hosts a dual-uplink taking advantage of both satellite and terrestrial communication. A set of tests were conducted to assess metrics such as signal strength, data transmission and bandwidth of the SatCom module as well as mesh configuration, energetic autonomy, point to point communication and data loss of the WSN nodes. Finally, we demonstrate the SDSS output from two vector models forced by WSN data from a field site in Belgium. We believe that this system can be a cost-effective solution for intelligent IPM in remote areas where there is no reliable terrestrial connection.

Cloud based autonomous monitoring and administration of heterogeneous distributed systems using mobile agents

The system administration is comprised of a set of functions intended to a multi-user computing environment to ensure reliable operations, manage efficient processing and enhance overall system performance. Due to the rapid development in wide area networks and powerful computational resources, distributed computing systems have gained widespread applications. As a result, the monitoring and managing of such large-scale heterogeneous distributed systems have become a challenging task. The existing ad hoc as well as the agent-based system monitoring and administration models are not completely suitable and reliable for managing as well as maintaining the large-scale distributed systems. This paper proposes a novel software architectural model for realizing autonomous remote system monitoring and administration in the large-scale heterogeneous distributed systems. The proposed software architecture incorporates cloud computing platform to gain scalability and location transparency. The combinations of mobile agent based approach and script based technologies are employed in the proposed model to achieve scalability, reliability and autonomy. The software architectural model is implemented on the heterogeneous distributed systems testbed and is evaluated with promising results. The heterogeneity of distributed computing systems is considered in the design by employing different operating systems, network connections and system administration domains. A detailed comparative analysis of the proposed design is included in the paper.

Using 3D Imaging and Machine Learning to Predict Liveweight and Carcass Characteristics of Live Finishing Beef Cattle

Selection of finishing beef cattle for slaughter and evaluation of performance is currently achieved through visual assessment and/or by weighing through a crush. ThusConsequently, large numbers of cattle are not meeting target specification at the abattoir. Video imaging analysis (VIA) is increasingly used in abattoirs to grade carcasses with high accuracy. There is potential for three-dimensional (3D) imaging to be used on farm to predict carcass characteristics of live animals and to optimise slaughter selections. The objectives of this study were to predict liveweight (LW) and carcass characteristics of live animals using 3D imaging technology and machine learning algorithms (artificial neural networks). Three dimensional images and LW’s were passively collected from finishing steer and heifer beef cattle of a variety of breeds and sexes pre slaughter (either on farm or after entry to the abattoir lairage) using an automated camera system. Sixty potential predictor variables were automatically extracted from the live animal 3D images using bespoke algorithms; these variables included lengths, heights, widths, areas, volumes and ratios and were used to develop predictive models for liveweight and carcass characteristics. Cold carcass weights (CCW) for each animal were provided by the abattoir. Saleable meat yield (SMY) and EUROP fat and conformation grades were also determined for each individual by VIA of half of the carcass. From the 3D images, 60 potential predictor variables were automatically extracted using bespoke algorithms; these variables included lengths, heights, widths, areas, volumes and ratios. Performance of prediction models was assessed using R2 and RMSE parameters following regression of predicted and actual variables for LW (R2 = 0.7, RMSE = 42), CCW (R2 = 0.88, RMSE = 14) and SMY (R2 = 0.72, RMSE = 14). The models predicted EUROP fat and conformation grades with 54% and 55% accuracy (R2), respectively. This study demonstrated that 3D imaging coupled with machine learning analytics can be used to predict LW, SMY and traditional carcass characteristics of live animals. This system presents an opportunity to reduce a considerable inefficiency in beef production enterprises through autonomous monitoring of finishing cattle on the farm and marketing of animals at the optimal time.

Autonomous monitoring in healthcare environment: Reward-based energy charging mechanism for IoMT wireless sensing nodes

Abstract The Internet of Medical Things (IoMT) is an essential paradigm for ubiquitous monitoring in healthcare environments. The IoMT system collects data (e.g. temperature, hazardous contamination, light intensity, room and patient status, etc.) from connected medical devices and sensor nodes to a central or distributed computer network. In order for these devices and sensor nodes to continue operating, they must be charged with sufficient energy at all times. In this paper, we propose an IoMT system that employs autonomous mobile chargers, which is equipped with wireless energy transfer technology, to support sensor nodes recharging requests. In this model, the mobile charger must distribute the energy among the sensor nodes so that their operation is not interrupted. This is challenging because the mobile charger carries limited amount of energy that may not be sufficient to satisfy all recharging requests. In this paper, we propose a reward-based energy charging decision mechanism that allows mobile chargers and sensor nodes to coordinate the charging process. The proposed reward-based mechanism utilizes the Analytical Hierarchy Process (AHP) for fair distribution of energy among the nodes. This paper presents the theoretical analysis of the model and the simulation experiments. Our results show that the proposed model can support a larger number of active nodes with less energy compared to conventional first come first served methods. Also the coverage utility of sensor nodes is much higher using our method compared to the on-demand recharging request schemes found in existing studies.

THE EQUIPMENT FOR PRECISE TEMPERATURE MONITORING

The article describes the modern version of the equipment for the autonomous temperature monitoring in boreholes and reservoirs. To characterize the capabilities of the equipment, the results of the long-term measurements of the temperature of Vasyugan swamp (Tomsk region) peatlands and the bottom (water and sediment) of lakes Baikal and Teletskoye are presented.

A Hardware and Software Complex for Remote Monitoring of a High-Voltage Line Arrester under Operating Voltage

A hardware and software complex for remote monitoring of high-voltage line arresters (HVLAs) under operating voltage has been developed. The complex is a two-level distributed system with autonomous monitoring points. The main functional units of the complex are a block of current sensors, a registration and communication device, a self-contained power supply, a receiving base station, and an automated workplace (AWP). Apart from the total leakage current and number of operations, the complex registers the amplitude and duration of the HVLA lightning pulse. These parameters may be used for the assessment of the HVLA residual resources and making a decision as to the feasibility of its further operation. An optical sensor on the basis of a bismuth silicate crystal is used as a sensor of an impulse current; its operating principle is based on the Faraday linear magnetooptic effect. The advantage of the bismuth silicate crystal is the feasibility of the noncontact measurement of the pulse magnetic field strength, high temperature stability and high noise immunity. The standard of the wireless LoRa communication was selected for the transmission of information; its advantage is a high noise immunity, larger range of radio signal transmission (up to 10 km), low energy consumption, and high penetrating capacity penetrability. A solar battery with a standby power supply—a rechargeable battery—was selected as the self-contained power supply. Program modules for receiving and transmission of information and AWP were developed. The hardware and software system allow preventing the emergency situations on the high-voltage line, planning more service and repair operations more effectively and efficiently, and starting the operation of HVLAs based on technical conditions.

Detecting Clinical Signs of Anaemia From Digital Images of the Palpebral Conjunctiva

The potential for visually detectable clinical signs of anaemia and their correlation with the severity of the pathology have supported research on non-invasive prevention methods. Physical examination for a suspected diagnosis of anaemia is a practice performed by a specialist to evaluate the pallor of the exposed tissues. The aim of the research presented herein is to quantify and minimize the subjective nature of the examination of the palpebral conjunctiva, suggesting a method of diagnostic support and autonomous monitoring. Here we describe the methodology and system for extracting key data from the digital image of the conjunctiva, which is also based on analysis of the dominant colour classes. Effective features have been used herein to establish the inclusion of each image in a diagnosis probability class for anaemia. The images of the conjunctiva were taken using a new low cost and easy to use device, designed to optimize the properties of independence from ambient light. The performance of the system was tested either by extracting manually the palpebral conjunctiva from images or by extracting them in a semi-automatic way based on the SLIC Superpixel algorithm. Tests were conducted on images obtained from 102 people. The dataset was unbalanced, since many more samples of healthy people were available, as often happens in the medical field. The SMOTE and ROSE algorithms were evaluated to balance the dataset, and some classification algorithms for assessing the anaemic condition were tested, yielding very good results. Taking a photo of the palpebral conjunctiva can aid the decision whether a blood sample is needed or even whether a patient should inform a physician, considerably reducing the number of candidate subjects for blood sampling. It also could highlight the suspected anaemia, allowing screening for anaemia in a large number of people, even in resource-poor settings.

Comparison of Several DPM Field Monitors for Use in Underground Mining Applications

ABSTRACT To improve worker health protection and support engineering applications in underground mines, such as ventilation-on-demand, capabilities are increasingly sought for continuous monitoring of diesel particulate matter (DPM). For near real-time monitoring over periods up to a full workshift, the FLIR Airtec handheld monitor was developed and calibrated to the NIOSH Standard Method 5040 measure of elemental carbon (EC), which is commonly used as an analytical surrogate for DPM. However, needs still exist for autonomous monitoring over longer periods (e.g., weeks to months). To meet those needs, two commercially available instruments are considered here, the Magee Scientific AE33 Aethalometer and the Sunset Laboratory Semi-continuous OC-EC Field Analyzer. Along with a prototyped monitor called the Airwatch, these were tested head-to-head against the Method 5040 EC and the Airtec in a controlled laboratory setting; and against one another in a field study at an underground mine. Key findings include: the OC-EC field analyzer performed well across a wide range of EC concentrations; the AE33 performed well at relatively low concentrations, but modifications or additional data corrections are likely needed at higher concentrations; and the Airwatch showed good potential, though significant improvements will be required if this instrument is to be further developed, including resolution of several mechanical issues and selection of an appropriate filter material and development of robust data corrections. Moreover, the relative advantages and disadvantages associated with each instrument (e.g., in terms of data quality, complexity and maintenance) must be considered in the context of the intended application and sampling environment.

Система мониторинга ограничителей перенапряжений на линиях электропередачи

The hardware and software complex of remote monitoring of the high voltage line arresters (HVLA) under operating voltage is developed. The complex is a two-level distributed system with autonomous monitoring points. The main functional units of the complex are: a block of current sensors, a registration and communication device, an autonomous power supply, a receiving base station and an automated workplace (AWP). In addition to measuring the total leakage current and the number of trips, the system records the amplitude and duration of the lightning current pulses flowing through the HVLA. These parameters can be used for assessment of the HVLA residual resource and making decision on a possibility of its further operation. For information transfer the standard of a wireless communication LoRa which advantages are is chosen: high noise immunity, larger range of transfer of a radio signal (up to 10 km), low energy consumption and high penetration. Program modules are developed for the device of reception and transfer of primary information and an automated workplace. The hardware and software system will allow to prevent emergence of a contingency situation on power lines, quickly and more efficiently to plan service and repair work, to pass to operation of HVLA on technical condition.

A piezoelectric energy harvester for rotating environment using a linked E-shape multi-beam

This paper analyzes the effects of rotational motion on the performance of an innovative piezoelectric energy harvester (PEH) operating in a very low rotation speed (0.5 to 3 Hz) environment. The rotational energy harvester (REH) consists of two E-shape multiple beams system joined with a rigid beam. The harvester has masses attached and a MFC (Macro Fiber Composite) 2814 P2 piezoelectric sheet mounted on one of the multiple beams. The REH is mounted on a rigid plate that rotates at constant angular speed and has the possibility to adjust the hub distance with respect to the axis of rotation. One of the key points of the design is to have very low natural frequencies and the lowest possible physical volume. For the evaluation of the electro-mechanical response, a nonlinear one-dimensional finite element formulation is used. The proposed numerical approach is validated through Abaqus commercial software models and experimental tests. The performance assessment is done investigating the influence of the hub distance, the rotation speed, the centrifugal force, the softening effect and the electrical resistance over the output voltage and the power generation. Regarding harvester performance, promising results are obtained since the maximum output electric power is 200 μW in a very low frequency interval at R = +0.03 m, for a physical volume of 140 cm3. In this sense, our proposal provides a prototype to power wireless autonomous monitoring systems of wind turbines of 30 kW.

Autonomous main-cable vibration monitoring using wireless smart sensors for large-scale three-pylon suspension bridges: A case study

Suspension bridges are supported by main cables that continue beyond the pillars to deck-level supports and must be anchored at each end of the bridge. The dynamic characteristics of the main cables are key indicators used to assess the structural health status of a bridge. In situ real-time health monitoring is an effective way to assess the dynamic characteristics. This paper presents a case study using vibration-based wireless smart sensors deployed on the main cables of a large-scale three-pylon suspension bridge to obtain its dynamic features. The methods of anti-aliasing filtering, statistical analysis and main cable tension force estimation were proposed and embedded into wireless smart sensors to provide autonomous data processing. According to the analysis of the vibration data from the main cables, the results demonstrate that the main cables have been in a stable state over time, and wireless smart sensors are promising for autonomous main-cable monitoring of large-scale three-pylon suspension bridges.

Hybrid Blockchain and Internet-of-Things Network for Underground Structure Health Monitoring

The Internet-of-things (IoT) and blockchain are growing realities of modern society, and both are rapidly transforming civilization, either separately or in combination. However, the leverage of both technologies for structural health monitoring (SHM) to enable transparent information sharing among involved parties and autonomous decision making has not yet been achieved. Therefore, this study combines IoT with blockchain-based smart contracts for SHM of underground structures to define a novel, efficient, scalable, and secure distributed network for enhancing operational safety. In this blockchain-IoT network, the characteristics of locally centralized and globally decentralized distribution have been activated by dividing them into core and edge networks. This division enhances the efficiency and scalability of the system. The proposed system was effective in simulation for autonomous monitoring and control of structures. After proper design, the decentralized blockchain networks may effectively be deployed for transparent and efficient information sharing, smart contracts-based autonomous decision making, and data security in SHM.

The Exploratory Study for the Citizen-centered Disaster Management Policies: Focus on the City Fire Disaster

도시에서는 급격한 인구증가와 산업화, 도시화, 구도심의 쇠퇴화 등으로 인해 화재 위험성이 지속적으로 증가하고 있다. 관련 법⋅제도 및 정책의 실효성 저하, 소방안전시설의 미흡, 시민에 대한 적극적 교육 및 홍보의 부족 등과 함께 재난취약성은 계속 확대되고 있는 상황이다. 본 연구는 도시재난의 가장 중요한 유형인 화재를 중심으로 사례를 살펴보고, 이에 대한 개선방안을 모색하는 탐색적 연구이다. 분석결과 도시 화재재난 위험에서 시민의 생명과 재산을 지키기 위해서는 시민 중심의 안전관리 대책이 필요하며, 찾아가는 안전위험 요인에 관한 교육 및 홍보의 적극성이 중요하다. 또한, 유관기관 의 협력적인 자율안전 감시 및 점검, 시민 스스로 안전을 지킬 수 있는 생활안전 교육도 강조하고자 한다. 마지막으로 교육은 시나리오 중심의 사례기반으로 되어야 하며, 시민의 내생적 재난학습 정착을 위한 학교교육과의 연계, 시민이 쉽게 접근하고 이해할 수 있는 안전정보 공유체계 마련, 수요자 중심의 재난 특성을 반영한 안전관리 매뉴얼 개발, 신속한 재난 대응을 위한 스마트 관리시스템의 확산 등을 제시하였다.In cities, the risk of fire disasters is increasing due to rapid population growth, industrialization, urbanization, and decline of old towns. The damage caused by the disaster is expended due to the ineffectiveness of related laws and systems, insufficient fire safety facilities, and lack of education and publicity for citizens. This study is an exploratory case study to search for alternatives to urban fire disaster prevention policy. As a result, citizen-centered safety management policy is needed. And citizen education and public relations activities are also required. The government should establish an autonomous monitoring system with relevant institutions and a scenario-oriented disaster education system. In addition, this study suggests that a safety information system that can be utilized by citizens and a smart information system for rapid disaster response should be constructed.

Розроблення шляхів підвищення надійності інтегрованого комплексу супутникових та інерційних навігаційних систем

Мета: аналіз методів та моделей забезпечення функцій контролю цілісності СНС та функціонального діагностування ІНС в умовах польоту, а також розробці алгоритму моніторингу цілісності СНС та контролю правильності її функціонування. Методи дослідження: методи теорії оптимального оцінювання, метод теорії вірогідності та теорії випадкових процесів. Результати: Проведено аналіз факторів, які впливають на якість роботи автономного моніторингу цілісності супутникової радіонавігаційної системи та аналіз можливих шляхів підвищення надійності та цілісності. Представлено алгоритм моніторингу цілісності СНС та контролю правильності функціонування ІНС. Обговорення: На основі підсумків дослідження пропонується розбити прикладний математичний комплекс, що дозволяє проводити повний цикл моделювання роботи алгоритму на заданому етапі польоту повітряного судна при використанні існуючих угрупувань космічних апаратів, а також експериментальних навігаційних даних СНС та ІНС, зареєстрованих в ході польотів.

An adaptive model for the autonomous monitoring and management of water end use

Most pattern classification systems are usually developed based on the training of historical data, and as a result, the performance of these models relies heavily on the amount of collected information. However, in many cases, such data collection process is relatively costly, which eventually limits the efficiency as well as the widespread implementation of the final developed model. In this context, the paper focuses on presenting an advanced universal water management system, which could interface with both water consumers and utilities via smart phone and web application. Originally, Autoflow©, a prototype tool that is used to disaggregate total water consumption into each end-use category was developed, which achieved an accuracy ranging from 74 to 94%. However, a drawback of this model was that it was trained with data collected from only Australia; therefore, accuracy reductions would likely be observed when this system is implemented in different countries having very different water using appliances and behaviour patterns. To avoid the costly data collection process for model calibration when operating in new regions, this research study introduces an enhanced model, namely AutoflowU (i.e. U stands for Universal). This new tool can be applied in residential properties globally to autonomously disaggregate water consumption into the seven main water end-use categories, namely: shower, toilet, tap, clothes washer, dishwasher, evaporative air cooler and irrigation, without the need for collecting new regional end-use data for model calibration. In order to develop this new tool, Decision Trees, Dynamic Time Warping (DTW), Self Organising Map (SOM) and Hidden Markov Model (HMM) techniques were utilised. The test results obtained from 230 properties in both Australia and the US showed that the AutoflowU achieved 72–93% accuracy.

Healthcare Monitoring: Materials and Wearable Devices for Autonomous Monitoring of Physiological Markers (Adv. Mater. 41/2018)

Healthcare Monitoring: Materials and Wearable Devices for Autonomous Monitoring of Physiological Markers (Adv. Mater. 41/2018)

Robust, real‐time and autonomous monitoring of ecosystems with an open, low‐cost, networked device

Abstract Automated methods of monitoring ecosystems provide a cost‐effective way to track changes in natural system's dynamics across temporal and spatial scales. However, methods of recording and storing data captured from the field still require significant manual effort. Here, we introduce an open source, inexpensive, fully autonomous ecosystem monitoring unit for capturing and remotely transmitting continuous data streams from field sites over long time‐periods. We provide a modular software framework for deploying various sensors, together with implementations to demonstrate proof of concept for continuous audio monitoring and time‐lapse photography. We show how our system can outperform comparable technologies for fractions of the cost, provided a local mobile network link is available. The system is robust to unreliable network signals and has been shown to function in extreme environmental conditions, such as in the tropical rainforests of Sabah, Borneo. We provide full details on how to assemble the hardware, and the open‐source software. Paired with appropriate automated analysis techniques, this system could provide spatially dense, near real‐time, continuous insights into ecosystem and biodiversity dynamics at a low cost.