Static Sensor Research Articles

The Effect of the Number of Laser Pulses on Some Physical Properties Affecting Gas Sensitivity of CdO Thin Films Prepared by PLD Method

In this research, thin films of cadmium oxide (CdO) were manufactured using pulse laser deposition (PLD) technology on glass substrates under pressure of (0.001 mbar) at room temperature by a laser power effect (800mj/cm) with a number of different laser pulses (N= (150-200-250 and 300)). The optical and synthetic properties of thin cadmium oxide films were studied, as well as their gas sensitivity. The optical properties of thin films were analyzed and studied using absorption spectra (UV-VIS) UV-Visible measurements show that CdO films of different thicknesses have a direct permissible transition with the band gap between (2.51) and (2.22) eV before annealing and after annealing the band gap decreases to become between (2.44) and (2.06). As for synthetic properties, they were analyzed and studied through X-ray diffraction patterns which showed the thin films are multicrystalline the grain size increase with the increase in the number of pulses from (100) pulse to (300) pulse gradually as for the effect of annealing, it also led to an increase in the size of the particles. While gas sensor properties were studied using static gas sensor system. The results of gas sensor showed that the increase in operating temperature improves film sensitivity. the maximum sensitivity of the pure CdO film to gas (NO2) was found to be 46.16% at 200°C and the number of pulses was (150). After annealing, the gas sensitivity decreased to was (34.25%) for (150) pulses.

Sensor Screening Methodology for Virtually Sensing Transmission Input Loads of a Wind Turbine Using Machine Learning Techniques and Drivetrain Simulations

The ongoing trend of building larger wind turbines (WT) to reach greater economies of scale is contributing to the reduction in cost of wind energy, as well as the increase in WT drivetrain input loads into uncharted territories. The resulting intensification of the load situation within the WT gearbox motivates the need to monitor WT transmission input loads. However, due to the high costs of direct measurement solutions, more economical solutions, such as virtual sensing of transmission input loads using stationary sensors mounted on the gearbox housing or other drivetrain locations, are of interest. As the number, type, and location of sensors needed for a virtual sensing solutions can vary considerably in cost, in this investigation, we aimed to identify optimal sensor locations for virtually sensing WT 6-degree of freedom (6-DOF) transmission input loads. Random forest (RF) models were designed and applied to a dataset containing simulated operational data of a Vestas V52 WT multibody simulation model undergoing simulated wind fields. The dataset contained the 6-DOF transmission input loads and signals from potential sensor locations covering deformations, misalignments, and rotational speeds at various drivetrain locations. The RF models were used to identify the sensor locations with the highest impact on accuracy of virtual load sensing following a known statistical test in order to prioritize and reduce the number of needed input signals. The performance of the models was assessed before and after reducing the number of input signals required. By allowing for a screening of sensors prior to real-world tests, the results demonstrate the high promise of the proposed method for optimizing the cost of future virtual WT transmission load sensors.

Experimental flameholding performance of a scramjet cavity with an inclined front wall

This paper presents experimental data examining the flameholding performance of a scramjet cavity with an inclined front wall. Mach 8 flight-equivalent flows were delivered to the axisymmetric, cavity combustor via the T4 reflected shock tunnel. The combustor model was designed to permit interchangeable cavity geometries. Each examined cavity maintained a length-to-depth ratio of 4, with a depth of 8 mm and cavity close-out angle of 22.5 deg. The first examined cavity possessed a 90 deg front wall/step, while the second examined cavity inclined the front wall at 45 deg. Ethylene fuel was injected at a variety of mass flow rates to examine both scram-mode, jet-wake anchored and dual-mode combustion, with data measurements obtained via wall-mounted static pressure sensors. The steady nature of these combustion modes during the shock tunnel test time was demonstrated. Minimal differences in combustion-induced pressure rises were observed between the two cavity geometries across each fuelling condition. This indicates that the low pressure, quasi-stagnant region downstream of a 90 deg step is not essential to attaining robust flame-anchoring in a scramjet combustor. Chemically reacting, Reynolds-averaged Navier-Stokes computations were performed for the three dimensional combustor. Only minor mixing and combustion performance differences were noted between each geometry/fuelling permutation. It was noted that the 45 deg cavity retained more robust vortex structures within the cavity, with the 90 deg cavity seeing complete breakdown of the cavity vortex structures. This work indicates that the cavity front wall could be further modified with minimal impacts on flameholding performance.

Geolocation estimation of target vehicles using image processing and geometric computation

Estimating vehicles’ locations is one of the key components in intelligent traffic management systems (ITMSs) for increasing traffic scene awareness. Traditionally, stationary sensors have been employed in this regard. The development of advanced sensing and communication technologies on modern vehicles (MVs) makes it feasible to use such vehicles as mobile sensors to estimate the traffic data of observed vehicles. This study aims to explore the capabilities of a monocular camera mounted on an MV in order to estimate the geolocation of the observed vehicle in a global positioning system (GPS) coordinate system. We proposed a new methodology by integrating deep learning, image processing, and geometric computation to address the observed-vehicle localization problem. To evaluate our proposed methodology, we developed new algorithms and tested them using real-world traffic data. The results indicated that our proposed methodology and algorithms could effectively estimate the observed vehicle’s latitude and longitude dynamically.

An energy aware cluster‐based routing and adaptive semi‐synchronized MAC for energy harvesting WSN

AbstractMedium access control (MAC) layer scheduling is an intelligent solution to protract the lifetime of energy harvesting wireless sensor network (WSN) which is challenging issue in recent times. Self‐adaptive MAC protocol was working upon reinforcement learning. However, this decentralized adaptive scheduling introduces collisions and degrades the overall network performance. To fix this problem, this paper proposes novel semi‐synchronized MAC approach for energy harvesting WSN. The major objective of this work is to improve energy efficiency of the network by minimizing energy consumption which is the major issue in WSN. Proposed WSN network is modeled with dual mobile sinks with circular moving path and static sensor nodes to monitor the environment. Our novel MAC approach is initiated with unequal cluster formation by correlation based unequal clustering scheme in which multi‐objective elephant herding optimization (CUC‐MEHO) algorithm is incorporated for cluster head (CH) selection. In clustered network, all sensor nodes follow semi‐synchronized adaptive active period MAC scheduling (S2A2MAC) protocol. In this work, S2A2MAC operates upon hidden Markov model (HMM) which controls the active time period of a node in an adaptive manner. Energy efficient hop‐by‐hop intra‐cluster routing is empowered by Bayes rule enabled perfect matching (BR‐PM) algorithm. Before route selection, all candidate nodes are regarded into perfect match, partially match, and no match nodes. Furthermore, emergency data is identified by CH based on flag value and transmitted without any time delay. For emergency data transmission, extremity aware routing (EAR) algorithm is presented and EAR algorithm is functioning on hopcount and distance criteria. Finally, proposed network is evaluated by extensive simulations in NS‐3.26. Simulation upshots show better performance in percentage of dead nodes, network lifetime, energy consumption, PDR, and delay.

Spatial Analysis of Air Quality Assessment in Two Cities in Nigeria: A Comparison of Perceptions with Instrument-Based Methods

The air quality (AQ) in urban contexts is a major concern, especially in the developing world. The environmental and social challenges created by poor AQ have continued to increase despite improvements in monitoring AQ using earth observation (EO) satellites, static and mobile ground-based sensors and models. However, these types of equipment can be expensive to purchase, operate, and maintain, especially for cities of the developing world, and, as a result, there is growing interest in the elicitation of residents’ perceptions of AQ. However, there is a need to analyse how the results obtained from sensor measurements and models match the AQ as perceived by residents. This study explored AQ in multiple locations in two developing world cities (Abuja and Enugu) in Nigeria by analysing the perceptions of 262 residents and how these compared with findings obtained from ground-based instruments. The results suggest that the perceived AQ of the locations broadly matches those obtained using instruments, although there were statistically significant differences between respondent groups based on the demographic factors of income-education (Abuja) and age (Enugu). This research supports the contention that perceptual AQ assessment provides a valuable source of data for policy and decision-makers when addressing poor AQ and can support action in the absence of instrument-based measurements.

Range-Based Relative Navigation for a Swarm of Centimeter-Scale Femto-Spacecraft

In-orbit relative navigation between a networked swarm of centimeter-scale femto-spacecraft would add considerable value to a range of space mission concepts and applications, such as for multipoint sensing and distributed sparse aperture interferometry. For a swarm of networked femto-spacecraft, relative position determination would be possible by inferring coarse range estimates from the received signal strength indication associated with the communication link between swarm members. This is particularly advantageous for highly resource-constrained devices. In this paper, algorithms for swarm relative positioning using interspacecraft range estimates are presented that can be applied to centralized, decentralized, and distributed network configurations. Relative navigation filters for initial relative orbit determination and state estimation are presented for femto-spacecraft swarm deployment and dispersal scenarios. The algorithms presented could also find use in terrestrial applications, in static and dynamic wireless sensor networks.

Assisted Navigation Algorithm for Wireless Sensor Actuator and Robot Networks

Wireless Sensor, Actuator and Robot Networks (WSARNs) are made of mobile and static sensor nodes that interact in order to collaboratively perform specific tasks, such as supporting assisted navigation for mobile robotic nodes that carry out requested operations in hostile environments, where the human presence is impracticable. In this regard, it is worth noting that assisted navigation algorithms have a highly dynamic nature, and are implemented by sensor nodes that are characterized by limited transmission power and lean autonomy in terms of computing and memory capacity. This paper presents an improved version of the assisted navigation algorithm based on the concept of “credit field”. The main aim of the proposed algorithm is to reduce and balance the energy consumption among the static sensor nodes when running the algorithm to manage the presence of obstacles and adversary areas, thus extending the lifetime of WSARNs. The algorithm has been tested on a hybrid sensor network that employs Mica2 Motes as static sensor nodes and Lego Mindstorms robots integrated with a Stargate board developed by Crossbow as mobile nodes.

Deterministic Static Sensor Node Placement in Wireless Sensor Network based on Territorial Predator Scent Marking Behaviour

An optimum sensor node placement mechanism for Wireless Sensor Network (WSN) is desirable in ensuring the location of sensor nodes offers maximum coverage and connectivity with minimum energy consumption. This paper proposes a sensor node placement algorithm that utilizes a new biologically inspired optimization algorithm that imitates the behaviour of a territorial predator in marking their territories with their odours known as Territorial Predator Scent Marking Algorithm (TPSMA). The main objectives considered in this paper are to achieve maximum coverage and minimum energy consumption with guaranteed connectivity. A simulation study has been carried out to compare the performance of the proposed algorithm implemented in two different single objective approaches with an Integer Linear Programming based algorithm. The proposed single objective approaches of TPSMA studied in this paper are TPSMA with minimum energy and TPSMA with maximum coverage. Simulation results show that the WSN deployed using the proposed TPSMA sensor node placement algorithm is able to arrange the sensor nodes according to the objective required; TPSMA with maximum coverage offers the highest coverage ratio with fewer sensor nodes up to 100% coverage while TPSMA with minimum energy consumption utilized the lowest energy as low as around 4.85 Joules. Full connectivity is provisioned for all TPSMA approaches since the constraint of the optimization problem is to ensure the connectivity from all sensor nodes to the sink node.

SINK MOBILITY MODEL FOR WIRELESS SENSOR NETWORKS USING KOHONEN SELF-ORGANIZING MAP

Wireless sensor networks are expected to operate in an unattended manner for long periods of time. As a result, different mobility models were proposed by many research papers in order to improve the performance and extend the lifetime of the network. In this paper a new sink mobility model that is based on kohonen self-organizing maps is proposed in order to provide a mobile sink node with the ability to collect data from static sensor nodes. Moreover, the performance of the proposed mobility model was studied using NS-2 simulator under different network sizes and movement speeds of the mobile sink. Finally, the performance of the proposed model was evaluated based on different performance metrics namely, end-to-end delay, packet delivery ratio and throughput.

A Robot–Sensor Network Security Architecture for Monitoring Applications

This paper presents SNSR (Sensor Network Security using Robots), a novel, open, and flexible architecture that improves security in static sensor networks by benefiting from robot-sensor network cooperation. In SNSR, the robot performs sensor node authentication and radio-based localization (enabling centralized topology computation and route establishment) and directly interacts with nodes to send them configurations or receive status and anomaly reports without intermediaries. SNSR operation is divided into stages set in a feedback iterative structure, which enables repeating the execution of stages to adapt to changes, respond to attacks, or detect and correct errors. By exploiting the robot capabilities, SNSR provides high security levels and adaptability without requiring complex mechanisms. This paper presents SNSR, analyzes its security against common attacks, and experimentally validates its performance.

Autonomous Unmanned Heterogeneous Vehicles for Persistent Monitoring

Swarms of unmanned vehicles (air and ground) can increase the efficiency and effectiveness of military and law enforcement operations by enhancing situational awareness and allowing the persistent monitoring of multiple hostile targets. The key focus in the development of the enabling technologies for swarm systems is the minimisation of uncertainties in situational awareness information for surveillance operations supported by ‘system of systems’ composed of static and mobile heterogeneous sensors. The identified critical enabling techniques and technologies for adaptive, informative and reconfigurable operations of unmanned swarm systems are robust static sensor network design, mobile sensor tasking (including re-allocation), sensor fusion and information fusion, including behaviour monitoring. The work presented in this paper describes one of the first attempts to integrate all swarm-related technologies into a prototype, demonstrating the benefits of swarms of heterogeneous vehicles for defence applications used for the persistent monitoring of high-value assets, such as military installations and camps. The key enabling swarm system technologies are analysed here, and novel algorithms are presented that can be implemented in available COTS-based unmanned vehicles. The algorithms have been designed and optimised to require small computational power, be flexible, be reconfigurable and be implemented in a large range of commercially available unmanned vehicles (air and ground).

Supermarket/Hypermarket Opportunistic Screening for Atrial Fibrillation (SHOPS-AF): A Mixed Methods Feasibility Study Protocol.

Aims: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and a key risk factor for ischaemic stroke. Following AF detection, treatment with oral anticoagulation can significantly lower mortality and morbidity rates associated with this risk. The availability of several hand-held devices which can detect AF may enable trained health professionals to adopt AF screening approaches which do not interfere with people’s daily routines. This study aims to investigate the effectiveness of a hand-held device (the MyDiagnostick single-lead Electrocardiogram (ECG) sensor) in screening for AF when embedded into the handles of supermarket trolleys. Methods: A mixed methods two-phase approach will be taken. The quantitative first phase will involve the recruitment of 2000 participants from a convenience sample at four large supermarkets with pharmacies. Prospective participants will be asked to conduct their shopping using a trolley embedded with a MyDiagnostick sensor. If the device identifies a participant with AF, the in-store pharmacist will be dispatched to take a manual pulse measurement and a static control sensor reading and offer a cardiologist consultation referral. When the sensor does not detect AF, a researcher will confirm the reading with a manual pulse measurement. ECGs will be compiled, and the sensitivity, specificity and positive and negative predictive values will be determined. A qualitative second phase will consist of semi-structured interviews carried out with those pharmacists and store managers in-store during the running of the trial period. These will explore the perceptions of staff regarding the merits of embedding sensors in the handles of supermarket trolleys to detect AF. Conclusion: This feasibility study will inform a larger future definitive trial.

Near‐field atmospheric inversions for the localization and quantification of controlled methane releases using stationary and mobile measurements

AbstractThis study evaluates two local‐scale atmospheric inversion approaches for the monitoring of methane (CH4) emissions from industrial sites based on in situ atmospheric CH4 mole fraction measurements from stationary or mobile sensors. We participated in a two‐week campaign of CH4 controlled‐release experiments at TotalEnergies Anomaly Detection Initiatives (TADI) in Lacq, France in October 2019. We analyzed releases from various points within a 40 m × 50 m area with constant rates of 0.16 to 30 g CH4 s−1 over 25 to 75 mins, using fixed‐point and mobile measurements, and testing different inversion configurations with a Gaussian dispersion model. An inlet switching system, combining a limited number (6–7) of high‐precision gas analyzers with a higher number (16) of sampling lines, ensured that a sufficient number of fixed measurement points sampled the plume downwind of the sources and the background mole fractions for any wind direction. The inversions using these fixed‐point measurements provide release rate estimates with approximately 23%–30% average errors and estimates of the location of the releases with approximately 8–10 m average errors. The inversions using the mobile measurements provide estimates with approximately 20%–30% average errors for the release rates and approximately 30 m average errors for the release locations. The precision of the release rate estimates from both inversion frameworks corresponds to the best estimation precision documented on site‐scale CH4 inversions. However, the use of continuous measurements from fixed stations provides much more robust estimates of the source locations than that of the mobile measurements.

Partial field visualization using an advanced circular aperture, continuous-scan acoustic array

ATA Engineering, Inc. designed, fabricated, and tested a novel circular aperture acoustic array for continuous-scan beamforming and acoustical holography applications. The array comprises an outer perimeter of stationary reference sensors coupled to a multi-arm inner rotating array that samples the entire aperture plane. The unique architecture allows the user to perform both fixed-receiver and continuous-scan beamforming with a single array with the latter method also enabling high-spatial-resolution partial field visualization. The presentation will show results from far- and near-field partial field visualizations over the rotating array circular aperture taken at different orientations. The presentation will also describe the use of the array to measure and model isolated sources, and experimentally verify their boundary element method-predicted sound fields in the presence of a scattering body.

Efficient Data Uploading for Mobile Crowdsensing via Team Collaborating and Matching

With the proliferation of mobile devices and crowdsensing applications, mobile crowdsensing (MCS) has been an appealing sensing paradigm as an alternative to the traditional sensor networks. Instead of deploying static and expensive sensors in sensing areas, MCS leverages sensors embedded in mobile devices and intelligence of mobile users to sense their surroundings, which utilizes the existing communication infrastructure. In a typical sensing cycle in MCS, recruited mobile users as workers collect data according to specified requirements and upload them to an MCS platform. A challenging problem of MCS is data uploading, which requires workers to upload their collected data in a cost-effective manner. A promising solution is to integrate edge computing and exploit the redundant resources of various edge nodes to facilitate data uploading. In this paper, we investigate such a data uploading problem in MCS, which incorporates collaborations among multiple edge nodes and properly matches a team of edge nodes with a sensing worker according to various constraints. Notably, we ensure that the demand of a worker for data uploading is fully satisfied even if served by multiple edge nodes. As we prove that the problem is NP-hard, we propose an efficient solution based on Lagrangian relaxation. Extensive numerical results show that our approach achieves a high approximation ratio and performs stably in various experiment settings.

Air pollution exposure monitoring using portable low-cost air quality sensors

Urban environments with a high degree of industrialization are infested with hazardous chemicals and airborne pollutants. These pollutants can have devastating effects on human health, causing both acute and chronic diseases such as respiratory infections, lung cancer, and heart disease. Air pollution monitoring is vital not only to citizens, warning them on the health risks of air pollutants, but also to policy-makers, assisting them on drafting regulations and laws that aim at minimizing those health risks. Currently, air pollution monitoring predominantly relies on expensive high-end static sensor stations. These stations produce only aggregated information about air pollutants, and are unable to capture variations in individual's air pollution exposure. As an alternative, this article develops a citizen-based air pollution monitoring system that captures individual exposure levels to air pollutants during daily indoor and outdoor activities. We present a low-cost portable sensor and carry out a measurement campaign using the sensors to demonstrate the validity and benefits of citizen-based pollution measurements. Specifically, we (i) successfully classify the data into indoor and outdoor, and (ii) validate the consistency and accuracy of our outdoor-classified data to the measurements of a high-end reference monitoring station. Our experimental results further prove the effectiveness of our campaign by (i) providing fine-grained air pollution insights over a wide geographical area, (ii) identifying probable causes of air pollution dependent on the area, and (iii) providing citizens with personalized insights about air pollutants in their daily commute.

Development of a modification of the method for constructing energy-efficient sensor networks using static and dynamic sensors

Due to the widespread use of sensors and sensor networks in the tasks of territory coverage, the relevant criteria are maximizing coverage and minimizing energy consumption. At the same time, the compliance of the network with these criteria is an urgent problem in the modern technological world. A modification of the method for constructing energy-efficient sensor networks is proposed by introducing an additional criterion for minimizing the number of sensors and limiting the number of sensors used, which allows reducing the energy consumption of sensor networks by 19 %. In the resulting optimization problem, the optimality criteria are the functions of minimizing the area of uncovered territory, the value of energy consumption, and the number of sensors. The optimum solution is formed by pairs of values of the coverage radius and the level of intersection of the coverage areas, which provide maximum coverage while minimizing energy consumption and the number of sensors used. To solve the problem, the parameter convolution method and the genetic algorithm were used. In the case of dynamic sensors, the problem is to find such a trajectory of the sensor that provides the maximum flyby of the territory with a minimum length. A grid algorithm is proposed to find the necessary trajectory. The presented algorithm consists in dividing the territory into nodes and estimating the value of the covered territory by the sensor in this node. After the formation of estimates, the search for a Hamiltonian path was used. The case of a multiply connected territory with the possibility of turning it into a simply connected one is considered. A scheme for finding the parameters of energy-efficient coverage of the territory using static and dynamic sensors is proposed.

Applying AES algorithm for secure data transmission between Sensor node and LoRa Gateway to Web Server

Lora Gateway is an intermediary device that can connect devices in the IoT system. IoT is the Internet of Things, consisting of a system of interrelated digital and mechanical devices and machines, capable of transmitting data over a network without requiring human-computer interaction. Lora is a long distance wireless communication technology that enables communication over a wide range between devices. Through this device, the Sensor nodes in the IoT system can transmit and receive data by LoRa waves to the Gateway and by Wifi/3G to the web server via the Internet. Data communicates in the internet environment, so important information needs to be protected by data encryption. This paper presents research and application of 128-bit AES symmetric encryption algorithm in LoRa wide area sensor network to secure data transmission between IoT devices and web server through LoRa Gateway device. The research team has designed and built models for testing sensor stations with integrated humidity and temperature sensors, LoRa Gateway integrating Lora module and wifi/3G module, developing the interface on web server with decoding, monitoring, and data storage features, and proposing a solution with AES encryption algorithm and architecture applied in the development of embedded software for LoRa module. The research results are tested on the model to test the encryption, data transmission, and decryption functions in applications for IoT LoRa Gateway systems. With this initial research, it is possible to apply the AES algorithm to secure data transmission in IoT Gateway systems.

Unmanned Aerial System-Based Data Ferrying over a Sensor Node Station Network in Maize.

Agriculture is considered a hotspot for wireless sensor network (WSN) facilities as they could potentially contribute towards improving on-farm management and food crop yields. This study proposes six designs of unmanned aerial system (UAS)-enabled data ferries with the intent of communicating with stationary sensor node stations in maize. Based on selection criteria and constraints, a proposed UAS data ferrying design was shortlisted from which a field experiment was conducted for two growing seasons to investigate the adoptability of the selected design along with an established WSN system. A data ferry platform comprised of a transceiver radio, a mini-laptop, and a battery was constructed and mounted on the UAS. Real-time monitoring of soil and temperature parameters was enabled through the node stations with data retrieved by the UAS data ferrying. The design was validated by establishing communication at different heights (31 m, 61 m, and 122 m) and lateral distances (0 m, 38 m, and 76 m) from the node stations. The communication success rate (CSR) was higher at a height of 31 m and within a lateral distance of 38 m from the node station. Lower communication was accredited to potential interference from the maize canopy and water losses from the maize canopy.