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Aderohunmu, Femi A., Jeremiah D. Deng, and Martin Purvis. "Enhancing Clustering in Wireless Sensor Networks with Energy Heterogeneity." International Journal of Business Data Communications and Networking 7, no. 4 (October 2011): 18–31. http://dx.doi.org/10.4018/jbdcn.2011100102.
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While wireless sensor networks (WSN) are increasingly equipped to handle more complex functions, in-network processing still requires the battery-powered sensors to judiciously use their constrained energy so as to prolong the elective network life time. There are a few protocols using sensor clusters to coordinate the energy consumption in a WSN, but how to deal with energy heterogeneity remains a research question. The authors propose a modified clustering algorithm with a three-tier energy setting, where energy consumption among sensor nodes is adaptive to their energy levels. A theoretical analysis shows that the proposed modifications result in an extended network stability period. Simulation has been conducted to evaluate the new clustering algorithm against some existing algorithms under different energy heterogeneity settings, and favourable results are obtained especially when the energy levels are significantly imbalanced.
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Kneas, Kristi A., J. N. Demas, B. A. DeGraff, and Ammasi Periasamy. "Fluorescence Microscopy Study of Heterogeneity in Polymer-supported Luminescence-based Oxygen Sensors." Microscopy and Microanalysis 6, no. 6 (November 2000): 551–61. http://dx.doi.org/10.1007/s100050010052.
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AbstractDespite the great potential of fluorescence microscopy, its application to date has largely been in the study of biological specimens. It will be shown that conventional fluorescence microscopy provides an invaluable tool with which to study the photophysics of polymer-supported luminescence-based oxygen sensors. The design of the imaging system, the measurement methods, and the data analysis used in the investigation of sensor systems are described. Fluorescence microscopic images of sensor films in which microheterogeneous regions exhibiting enhanced luminescence intensity and poorer oxygen quenching relative to the bulk response are shown. This is the first direct evidence that sensor molecules in various domains of the polymer support can exhibit different oxygen quenching properties. It will be shown that μ- and nano-crystallization of the sensor molecule are the probable source of both the observed heterogeneous microscopic responses and the microscopic and macroscopic nonlinear Stern-Volmer plots. The implications of these results in the rational design of luminescence-based oxygen sensors are discussed.
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Xue, Xingsi, and Junfeng Chen. "A Preference-Based Multi-Objective Evolutionary Algorithm for Semiautomatic Sensor Ontology Matching." International Journal of Swarm Intelligence Research 9, no. 2 (April 2018): 1–14. http://dx.doi.org/10.4018/ijsir.2018040101.
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This article describes how with the advent of sensors for collecting environmental data, many sensor ontologies have been developed. However, the heterogeneity of sensor ontologies blocks semantic interoperability between them and limits their applications. Ontology matching is an effective technique to solve the problem of sensor ontology heterogeneity. To improve the quality of sensor ontology alignment, the authors propose a semiautomatic ontology matching technique based on a preference-based multi-objective evolutionary algorithm (PMOEA), which can utilize the user's knowledge of the solution's quality to direct MOEA to effectively match the heterogeneous sensor ontologies. The authors specifically construct a new multi-objective optimal model for the sensor ontology matching problem, propose a user preference-based t-dominance rule, and design a PMOEA to solve the sensor ontology matching problem. The experimental results show that their approach can significantly improve the sensor ontology alignment's quality under different heterogeneous situations.
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Xue, Xingsi, Chao Jiang, Jie Zhang, Hai Zhu, and Chaofan Yang. "Matching sensor ontologies through siamese neural networks without using reference alignment." PeerJ Computer Science 7 (June 18, 2021): e602. http://dx.doi.org/10.7717/peerj-cs.602.
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Sensors have been growingly used in a variety of applications. The lack of semantic information of obtained sensor data will bring about the heterogeneity problem of sensor data in semantic, schema, and syntax levels. To solve the heterogeneity problem of sensor data, it is necessary to carry out the sensor ontology matching process to determine correspondences among heterogeneous sensor concepts. In this paper, we propose a Siamese Neural Network based Ontology Matching technique (SNN-OM) to align the sensor ontologies, which does not require the utilization of reference alignment to train the network model. In particular, a representative concepts extraction method is presented to enhance the model’s performance and reduce the time of the training process, and an alignment refining method is proposed to enhance the alignments’ quality by removing the logically conflict correspondences. The experimental results show that SNN-OM is capable of efficiently determining high-quality sensor ontology alignments.
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Xue, Xingsi, Jiawei Lu, Chengcai Jiang, and Yikun Huang. "Sensor Ontology Metamatching with Heterogeneity Measures." Wireless Communications and Mobile Computing 2020 (November 25, 2020): 1–10. http://dx.doi.org/10.1155/2020/6666228.
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The heterogeneity problem among different sensor ontologies hinders the interaction of information. Ontology matching is an effective method to address this problem by determining the heterogeneous concept pairs. In the matching process, the similarity measure serves as the kernel technique, which calculates the similarity value of two concepts. Since none of the similarity measures can ensure its effectiveness in any context, usually, several measures are combined together to enhance the result’s confidence. How to find suitable aggregating weights for various similarity measures, i.e., ontology metamatching problem, is an open challenge. This paper proposes a novel ontology metamatching approach to improve the sensor ontology alignment’s quality, which utilizes the heterogeneity features on two ontologies to tune the aggregating weight set. In particular, three ontology heterogeneity measures are firstly proposed to, respectively, evaluate the heterogeneity values in terms of syntax, linguistics, and structure, and then, a semiautomatically learning approach is presented to construct the conversion functions that map any two ontologies’ heterogeneity values to the weights for aggregating the similarity measures. To the best of our knowledge, this is the first time that heterogeneity features are proposed and used to solve the sensor ontology metamatching problem. The effectiveness of the proposal is verified by comparing with using state-of-the-art ontology matching techniques on Ontology Alignment Evaluation Initiative (OAEI)’s testing cases and two pairs of real sensor ontologies.
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Huang, Yikun, Xingsi Xue, and Chao Jiang. "Semantic Integration of Sensor Knowledge on Artificial Internet of Things." Wireless Communications and Mobile Computing 2020 (July 25, 2020): 1–8. http://dx.doi.org/10.1155/2020/8815001.
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Artificial Internet of Things (AIoT) integrates Artificial Intelligence (AI) with the Internet of Things (IoT) to create the sensor network that can communicate and process data. To implement the communications and co-operations among intelligent systems on AIoT, it is necessary to annotate sensor data with the semantic meanings to overcome heterogeneity problem among different sensors, which requires the utilization of sensor ontology. Sensor ontology formally models the knowledge on AIoT by defining the concepts, the properties describing a concept, and the relationships between two concepts. Due to human’s subjectivity, a concept in different sensor ontologies could be defined with different terminologies and contexts, yielding the ontology heterogeneity problem. Thus, before using these ontologies, it is necessary to integrate their knowledge by finding the correspondences between their concepts, i.e., the so-called ontology matching. In this work, a novel sensor ontology matching framework is proposed, which aggregates three kinds of Concept Similarity Measures (CSMs) and an alignment extraction approach to determine the sensor ontology alignment. To ensure the quality of the alignments, we further propose a compact Particle Swarm Optimization algorithm (cPSO) to optimize the aggregating weights for the CSMs and a threshold for filtering the alignment. The experiment utilizes the Ontology Alignment Evaluation Initiative (OAEI)’s conference track and two pairs of real sensor ontologies to test cPSO’s performance. The experimental results show that the quality of the alignments obtained by cPSO statistically outperforms other state-of-the-art sensor ontology matching techniques.
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Borza, Paul Nicolae, Mihai Machedon-Pisu, and Felix Hamza-Lup. "Design of Wireless Sensors for IoT with Energy Storage and Communication Channel Heterogeneity." Sensors 19, no. 15 (July 31, 2019): 3364. http://dx.doi.org/10.3390/s19153364.
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Autonomous Wireless Sensors (AWSs) are at the core of every Wireless Sensor Network (WSN). Current AWS technology allows the development of many IoT-based applications, ranging from military to bioengineering and from industry to education. The energy optimization of AWSs depends mainly on: Structural, functional, and application specifications. The holistic design methodology addresses all the factors mentioned above. In this sense, we propose an original solution based on a novel architecture that duplicates the transceivers and also the power source using a hybrid storage system. By identifying the consumption needs of the transceivers, an appropriate methodology for sizing and controlling the power flow for the power source is proposed. The paper emphasizes the fusion between information, communication, and energy consumption of the AWS in terms of spectrum information through a set of transceiver testing scenarios, identifying the main factors that influence the sensor node design and their inter-dependencies. Optimization of the system considers all these factors obtaining an energy efficient AWS, paving the way towards autonomous sensors by adding an energy harvesting element to them.
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Chand, Satish, Samayveer Singh, and Bijendra Kumar. "3-Level Heterogeneity Model for Wireless Sensor Networks." International Journal of Computer Network and Information Security 5, no. 4 (April 3, 2013): 40–47. http://dx.doi.org/10.5815/ijcnis.2013.04.06.
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Li, Zhenjiangi, Wenwei Chen, Mo Li, and Jingsheng Lei. "Incorporating Energy Heterogeneity into Sensor Network Time Synchronization." IEEE Transactions on Parallel and Distributed Systems 26, no. 1 (January 2015): 163–73. http://dx.doi.org/10.1109/tpds.2014.2307890.
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Zhu, Hai, Xingsi Xue, Chengcai Jiang, and He Ren. "Multiobjective Sensor Ontology Matching Technique with User Preference Metrics." Wireless Communications and Mobile Computing 2021 (March 16, 2021): 1–9. http://dx.doi.org/10.1155/2021/5594553.
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Due to the problem of data heterogeneity in the semantic sensor networks, the communications among different sensor network applications are seriously hampered. Although sensor ontology is regarded as the state-of-the-art knowledge model for exchanging sensor information, there also exists the heterogeneity problem between different sensor ontologies. Ontology matching is an effective method to deal with the sensor ontology heterogeneity problem, whose kernel technique is the similarity measure. How to integrate different similarity measures to determine the alignment of high quality for the users with different preferences is a challenging problem. To face this challenge, in our work, a Multiobjective Evolutionary Algorithm (MOEA) is used in determining different nondominated solutions. In particular, the evaluating metric on sensor ontology alignment’s quality is proposed, which takes into consideration user’s preferences and do not need to use the Reference Alignment (RA) beforehand; an optimization model is constructed to define the sensor ontology matching problem formally, and a selection operator is presented, which can make MOEA uniformly improve the solution’s objectives. In the experiment, the benchmark from the Ontology Alignment Evaluation Initiative (OAEI) and the real ontologies of the sensor domain is used to test the performance of our approach, and the experimental results show the validity of our approach.
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Aslam, Muhammad, Fan Wang, Xiaopeng Hu, Muhammad Asad, and Ehsan Ullah Munir. "Multihopping Multilevel Clustering Heterogeneity-Sensitive Optimized Routing Protocol for Wireless Sensor Networks." Journal of Sensors 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/5378403.
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Effective utilization of energy resources in Wireless Sensor Networks (WSNs) has become challenging under uncertain distributed cluster-formation and single-hop intercluster communication capabilities. So, sensor nodes are forced to operate at expensive full rate transmission power level continuously during whole network operation. These challenging network environments experience unwanted phenomena of drastic energy consumption and packet drop. In this paper, we propose an adaptive immune Multihopping Multilevel Clustering (MHMLC) protocol that executes a Hybrid Clustering Algorithm (HCA) to perform optimal centralized selection of Cluster-Heads (CHs) within radius of centrally located Base Station (BS) and distributed CHs selection in the rest of network area. HCA of MHMLC also produces optimal intermediate CHs for intercluster multihop communications that develop heterogeneity-aware economical links. This hybrid cluster-formation facilitates the sensors to function at short range transmission power level that enhances link quality and avoids packet drop. The simulation environments produce fair comparison among proposed MHMLC and existing state-of-the-art routing protocols. Experimental results give significant evidence of better performance of the proposed model in terms of network lifetime, stability period, and data delivery ratio.
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Skinner, William S., Sunny Zhang, Robert E. Guldberg, and Keat Ghee Ong. "Magnetoelastic Sensor Optimization for Improving Mass Monitoring." Sensors 22, no. 3 (January 22, 2022): 827. http://dx.doi.org/10.3390/s22030827.
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Magnetoelastic sensors, typically made of magnetostrictive and magnetically-soft materials, can be fabricated from commercially available materials into a variety of shapes and sizes for their intended applications. Since these sensors are wirelessly interrogated via magnetic fields, they are good candidates for use in both research and industry, where detection of environmental parameters in closed and controlled systems is necessary. Common applications for these sensors include the investigation of physical, chemical, and biological parameters based on changes in mass loading at the sensor surface which affect the sensor’s behavior at resonance. To improve the performance of these sensors, optimization of sensor geometry, size, and detection conditions are critical to increasing their mass sensitivity and detectible range. This work focuses on investigating how the geometry of the sensor influences its resonance spectrum, including the sensor’s shape, size, and aspect ratio. In addition to these factors, heterogeneity in resonance magnitude was mapped for the sensor surface and the effect of the magnetic bias field strength on the resonance spectrum was investigated. Analysis of the results indicates that the shape of the sensor has a strong influence on the emergent resonant modes. Reducing the size of the sensor decreased the sensor’s magnitude of resonance. The aspect ratio of the sensor, along with the bias field strength, was also observed to affect the magnitude of the signal; over or under biasing and aspect ratio extremes were observed to decrease the magnitude of resonance, indicating that these parameters can be optimized for a given shape and size of magnetoelastic sensor.
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Engouang, Tristan Daladier, Yun Liu, and Zhenjiang Zhang. "TDAL: Thoroughly Data Aggregation of Low Energy Devices in Secure Heterogeneous Wireless Sensor Networks." Journal of Sensors 2014 (2014): 1–15. http://dx.doi.org/10.1155/2014/938480.
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The heterogeneous wireless sensor networks (HWSNs), composed of multiple types of tiny devices (sensor nodes) with wireless communication capability and suffering from computational resources constrains, enable interacting with the physical world, like never before. Innovative applications are developed for security, industrial production, monitoring, and tracking, but theoretical assumptions on these distributed data may not hold in a real scenario. In this paper, the emphasis is on accurate data and sensor nodes privacy preserving while transmitting their sensory information amongst neighbors toward the sink based on parent-child relationship in the wireless sensor network (WSN) environment, while ensuring energy saving. Data aggregation is a known energy efficient technique that is investigated through in-depth analysis of sensor communication through game theory, considering various embodiments of methods like elliptic curve cryptography for secrecy between nodes. This paper endeavors to provide new perspective for secure and energy efficient data aggregation models, where the heterogeneity of a sensor network environment makes it more complex to predict the overall network outputs.
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Xiao, Lei, Junhong Feng, Xishuan Niu, and Jian-Hong Wang. "Using Competitive Binary Particle Swarm Optimization Algorithm for Matching Sensor Ontologies." Mobile Information Systems 2022 (February 9, 2022): 1–7. http://dx.doi.org/10.1155/2022/2207252.
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Developing sensor ontologies and using them to annotate the sensor data is a feasible way to address the data heterogeneity issue on Internet of Things (IoT). However, the heterogeneity issue exists between different sensor ontologies hampers their communications. Sensor ontology matching aims at finding all the heterogeneous entities in two ontologies, which is a feasible solution for aggregating heterogeneous sensor ontologies. This work investigates swarm intelligence (SI)-based sensor ontology matching techniques and further proposes a competitive binary particle swarm optimization algorithm (CBPSO)-based sensor ontology matching technique. In particular, a guiding matrix (GM) is proposed to ensure the population’s diversity and a competitive evolutionary framework is presented. The experiment uses ontology alignment evaluation initiative (OAEI)’s benchmark and three real sensor ontologies to test CBPSO’s performance. The experimental results show that the competitive evolutionary framework is able to help CBPSO effectively optimize the alignment’s quality, and it significantly outperforms other SIs at 5% significant level.
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Labouesse, Marie A., Reto B. Cola, and Tommaso Patriarchi. "GPCR-Based Dopamine Sensors—A Detailed Guide to Inform Sensor Choice for In Vivo Imaging." International Journal of Molecular Sciences 21, no. 21 (October 28, 2020): 8048. http://dx.doi.org/10.3390/ijms21218048.
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Understanding how dopamine (DA) encodes behavior depends on technologies that can reliably monitor DA release in freely-behaving animals. Recently, red and green genetically encoded sensors for DA (dLight, GRAB-DA) were developed and now provide the ability to track release dynamics at a subsecond resolution, with submicromolar affinity and high molecular specificity. Combined with rapid developments in in vivo imaging, these sensors have the potential to transform the field of DA sensing and DA-based drug discovery. When implementing these tools in the laboratory, it is important to consider there is not a ‘one-size-fits-all’ sensor. Sensor properties, most importantly their affinity and dynamic range, must be carefully chosen to match local DA levels. Molecular specificity, sensor kinetics, spectral properties, brightness, sensor scaffold and pharmacology can further influence sensor choice depending on the experimental question. In this review, we use DA as an example; we briefly summarize old and new techniques to monitor DA release, including DA biosensors. We then outline a map of DA heterogeneity across the brain and provide a guide for optimal sensor choice and implementation based on local DA levels and other experimental parameters. Altogether this review should act as a tool to guide DA sensor choice for end-users.
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Doležal, F., S. Matula, and J. M. Moreira Barradas. " Percolation in macropores and performance of large time-domain reflectometry sensors." Plant, Soil and Environment 58, No. 11 (October 31, 2012): 503–7. http://dx.doi.org/10.17221/6372-pse.
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The large-diameter time-domain reflectometry soil water sensors placed horizontally in a structured loamy soil are very sensitive to rapid preferential percolation events. Their readings on these occasions rise considerably, often becoming higher than the native soil’s porosity. The effect is caused by gaps between the native soil and the sensors. The geometry of the gaps, even if filled with soil slurry at installation, is not exactly reproducible, which leads to sensor-to-sensor variability of readings. Field calibration in percolation-free periods lead to non-unique trajectories rather than monotonous calibration curves, which can be commented in terms of soil heterogeneity and the dual porosity theory. Data of two typical percolation events are presented. Sensors of this type can be used for detection of preferential flux.
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Kumar Singh, Ritesh, and Kumar Singh. "Lifetime of Sensor Network by Exploiting Heterogeneity- A Survey." International Journal of Modern Education and Computer Science 6, no. 7 (July 8, 2014): 47–54. http://dx.doi.org/10.5815/ijmecs.2014.07.07.
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Damayanthi, A., and Mohammad Riyaz Belgaum. "A Study of Heterogeneity Characteristics over Wireless Sensor Networks." International Journal of Computer Engineering in Research Trends 9, no. 12 (December 29, 2022): 258–62. http://dx.doi.org/10.22362/ijcert/2022/v9/i12/v9i1204.
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Wilson, Daniel Lawrence, Kendra N. Williams, and Ajay Pillarisetti. "An Integrated Sensor Data Logging, Survey, and Analytics Platform for Field Research and Its Application in HAPIN, a Multi-Center Household Energy Intervention Trial." Sustainability 12, no. 5 (February 28, 2020): 1805. http://dx.doi.org/10.3390/su12051805.
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Researchers rely on sensor-derived data to gain insights on numerous human behaviors and environmental characteristics. While commercially available data-logging sensors can be deployed for a range of measurements, there have been limited resources for integrated hardware, software, and analysis platforms targeting field researcher use cases. In this paper, we describe Geocene, an integrated sensor data logging, survey, and analytics platform for field research. We provide an example of Geocene’s ongoing use in the Household Air Pollution Intervention Network (HAPIN). HAPIN is a large, multi-center, randomized controlled trial evaluating the impacts of a clean cooking fuel and stove intervention in Guatemala, India, Peru, and Rwanda. The platform includes Bluetooth-enabled, data-logging temperature sensors; a mobile application to survey participants, provision sensors, download sensor data, and tag sensor missions with metadata; and a cloud-based application for data warehousing, visualization, and analysis. Our experience deploying the Geocene platform within HAPIN suggests that the platform may have broad applicability to facilitate sensor-based monitoring and evaluation efforts and projects. This data platform can unmask heterogeneity in study participant behavior by using sensors that capture both compliance with and utilization of the intervention. Platforms like this could help researchers measure adoption of technology, collect more robust intervention and covariate data, and improve study design and impact assessments.
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Xu, Chunying, Junwei Hu, Jiawang Chen, Yongqiang Ge, and Ruixin Liang. "Sensor Placement with Two-Dimensional Equal Arc Length Non-Uniform Sampling for Underwater Terrain Deformation Monitoring." Journal of Marine Science and Engineering 9, no. 9 (September 1, 2021): 954. http://dx.doi.org/10.3390/jmse9090954.
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Sensor placement plays an important role in terrain deformation monitoring systems and has an essential effect on data collection. The difficulty of sensor placement entails obtaining the most adequate and reliable information with the fewest number of sensors. Most sensor placement schemes are currently based on randomized non-uniform sampling and probability statistics, such as structural modality and optimization methods, which are difficult to directly apply due to the randomness and spatial heterogeneity of terrain deformation. In this study, the placement conditions of two-dimensional non-uniform sampling with equal arc length were deduced for underwater terrain deformation monitoring based on the MEMS accelerometer network. In order to completely reconstruct the underwater terrain, the arc length interval of the sensors should be less than 12Ω (Ω is the maximum frequency of the detected terrain). The maximum MRE and maximum RMSE were both less than seven percent in a terrain deformation monitoring experiment and a water tank test. The research results help technicians apply contact sensor arrays for underwater terrain monitoring.
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Zhu, Hai, Jie Zhang, and Xingsi Xue. "Semisupervised Learning-Based Sensor Ontology Matching." Security and Communication Networks 2021 (July 17, 2021): 1–5. http://dx.doi.org/10.1155/2021/2002307.
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Sensor ontology models the sensor information and knowledge in a machine-understandable way, which aims at addressing the data heterogeneity problem on the Internet of Things (IoT). However, the existing sensor ontologies are maintained independently for different requirements, which might define the same concept with different terms or context, yielding the heterogeneity issue. Since the complex semantic relationship between the sensor concepts and the large-scale entities is to be dealt with, finding the identical entity correspondences is an error-prone task. To effectively determine the sensor entity correspondences, this work proposes a semisupervised learning-based sensor ontology matching technique. First, we borrow the idea of “centrality” from the social network to construct the training examples; then, we present an evolutionary algorithm- (EA-) based metamatching technique to train the model of aggregating different similarity measures; finally, we use the trained model to match the rest entities. The experiment uses the benchmark as well as three real sensor ontologies to test our proposal’s performance. The experimental results show that our approach is able to determine high-quality sensor entity correspondences in all matching tasks.
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Santhanavanich, T., S. Schneider, P. Rodrigues, and V. Coors. "INTEGRATION AND VISUALIZATION OF HETEROGENEOUS SENSOR DATA AND GEOSPATIAL INFORMATION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W7 (September 20, 2018): 115–22. http://dx.doi.org/10.5194/isprs-annals-iv-4-w7-115-2018.
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<p><strong>Abstract.</strong> According to the advances in Information & Communication Technology (ICT), nowadays, the use of Internet of Things (IoT) has become a normal part of daily life. It allows interconnections among a wide variety of devices and sensors such as smartphones, smartwatches, automobiles, or any object with a built-in sensor. However, these devices and sensors are developed by numerous different manufacturers which leads to technology lock-in in terms of data formats and protocols. In order of address this heterogeneity, an interoperable sensor protocol is the need of the hour. To address this, we propose a sensor data management system for monitoring <i>pedelec</i> usage and user fitness level. Using a proof-of-concept prototype the study is carried out in downtown of Stuttgart city. The result of the integrated analyzed data is visualized in 3D digital globe CESIUM.</p>
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Jiang, Chenyang, Wenhao Wang, Linlin Du, Guanyu Huang, Caitlin McConaghy, Stanley Fineman, and Yang Liu. "Field Evaluation of an Automated Pollen Sensor." International Journal of Environmental Research and Public Health 19, no. 11 (May 25, 2022): 6444. http://dx.doi.org/10.3390/ijerph19116444.
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Background: Seasonal pollen is a common cause of allergic respiratory disease. In the United States, pollen monitoring occurs via manual counting, a method which is both labor-intensive and has a considerable time delay. In this paper, we report the field-testing results of a new, automated, real-time pollen imaging sensor in Atlanta, GA. Methods: We first compared the pollen concentrations measured by an automated real-time pollen sensor (APS-300, Pollen Sense LLC) collocated with a Rotorod M40 sampler in 2020 at an allergy clinic in northwest Atlanta. An internal consistency assessment was then conducted with two collocated APS-300 sensors in downtown Atlanta during the 2021 pollen season. We also investigated the spatial heterogeneity of pollen concentrations using the APS-300 measurements. Results: Overall, the daily pollen concentrations reported by the APS-300 and the Rotorod M40 sampler with manual counting were strongly correlated (r = 0.85) during the peak pollen season. The APS-300 reported fewer tree pollen taxa, resulting in a slight underestimation of total pollen counts. Both the APS-300 and Rotorod M40 reported Quercus (Oak) and Pinus (Pine) as dominant pollen taxa during the peak tree pollen season. Pollen concentrations reported by APS-300 in the summer and fall were less accurate. The daily total and speciated pollen concentrations reported by two collocated APS-300 sensors were highly correlated (r = 0.93–0.99). Pollen concentrations showed substantial spatial and temporal heterogeneity in terms of peak levels at three locations in Atlanta. Conclusions: The APS-300 sensor was able to provide internally consistent, real-time pollen concentrations that are strongly correlated with the current gold-standard measurements during the peak pollen season. When compared with manual counting approaches, the fully automated sensor has the significant advantage of being mobile with the ability to provide real-time pollen data. However, the sensor’s weed and grass pollen identification algorithms require further improvement.
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Xiang, Y., Y. Tang, and W. Zhu. "Mobile sensor network noise reduction and recalibration using a Bayesian network." Atmospheric Measurement Techniques 9, no. 2 (February 4, 2016): 347–57. http://dx.doi.org/10.5194/amt-9-347-2016.
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Abstract. People are becoming increasingly interested in mobile air quality sensor network applications. By eliminating the inaccuracies caused by spatial and temporal heterogeneity of pollutant distributions, this method shows great potential for atmospheric research. However, systems based on low-cost air quality sensors often suffer from sensor noise and drift. For the sensing systems to operate stably and reliably in real-world applications, those problems must be addressed. In this work, we exploit the correlation of different types of sensors caused by cross sensitivity to help identify and correct the outlier readings. By employing a Bayesian network based system, we are able to recover the erroneous readings and recalibrate the drifted sensors simultaneously. Our method improves upon the state-of-art Bayesian belief network techniques by incorporating the virtual evidence and adjusting the sensor calibration functions recursively.Specifically, we have (1) designed a system based on the Bayesian belief network to detect and recover the abnormal readings, (2) developed methods to update the sensor calibration functions infield without requirement of ground truth, and (3) extended the Bayesian network with virtual evidence for infield sensor recalibration. To validate our technique, we have tested our technique with metal oxide sensors measuring NO2, CO, and O3 in a real-world deployment. Compared with the existing Bayesian belief network techniques, results based on our experiment setup demonstrate that our system can reduce error by 34.1 % and recover 4 times more data on average.
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Al-Medhwahi, Mohammed, Fazirulhisyam Hashim, Borhanuddin Mohd Ali, A. Sali, and Abdulsalam Alkholidi. "Resource allocation in heterogeneous cognitive radio sensor networks." International Journal of Distributed Sensor Networks 15, no. 7 (July 2019): 155014771985194. http://dx.doi.org/10.1177/1550147719851944.
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Cognitive radio sensor networks offer a promising means of meeting rapidly expanding demand for wireless sensor network applications in new monitoring and objects tracking fields. Several challenges, particularly in terms of quality of service provisioning, arise because of the inherited capability-limitation of end-sensor nodes. In this article, an efficient resource allocation scheme, improved Pliable Cognitive Medium Access Protocol, is proposed to tackle multilevel of heterogeneity in cognitive radio sensor networks. The first level is the network’s application heterogeneity, and the second level is the heterogeneity of the radio environment. The proposed scheme addresses scheduling and radio channel allocation issues. Allocation-decision making is centralized, whereas spectrum sensing is distributed, thereby increasing efficiency and limiting interference. Despite the limited capabilities of the sensor’s networks, the effectiveness of the proposed scheme also includes increasing the opportunity to utilize a wider range of the radio spectrum. improved Pliable Cognitive Medium Access protocol is quite appropriate for critical communications that gain attention in the next 5G of wireless networks. Simulation results and the comparison of the proposed protocol with other protocols indicate the robust performance of the proposed scheme. The results reveal the significant effectiveness, with only a slight trade-off in terms of complexity.
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Tittebrand, A., and F. H. Berger. "Spatial heterogeneity of satellite derived land surface parameters and energy flux densities for LITFASS-area." Atmospheric Chemistry and Physics Discussions 8, no. 4 (August 26, 2008): 16219–54. http://dx.doi.org/10.5194/acpd-8-16219-2008.
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Abstract. Remote sensing data provide area integrated information of surface properties in different spatial or temporal resolutions according to different sensor features. Landsat ETM+, Terra MODIS and NOAA-AVHRR surface temperature and spectral reflectance were used to infer further surface parameters and radiant- and energy flux densities for LITFASS-area, a 20×20 km2 heterogeneous area in Eastern Germany, mainly characterized by the land use types forest, crop, grass and water. Based on the Penman-Monteith-approach the actual latent heat flux (L.E), as key quantity of the hydrological cycle, is determined for each sensor in the accordant spatial resolution with an improved parametrization. However, using three sensors, significant discrepancies between the inferred parameters can cause flux distinctions resultant from differences of the sensor filter response functions or atmospheric correction methods. The approximation of MODIS- and AVHRR- derived surface parameters to the reference parameters of ETM (via regression lines and histogram stretching, respectively), further the use of accurate land use classifications (CORINE and a new Landsat-classification), and a consistent parametrization for the three sensors were realized to obtain a uniform base for investigations of the spatial variability. For the target area the spatial heterogeneity is analysed investigating frequency distribution functions (PDF) for surface parameters and energy fluxes. PDF is the most promising way to describe subgrid heterogeneity due to the given data in different spatial resolution. Aim of this study is to find typical distribution pattern of parameters (albedo, NDVI) for the determination of L.E determined from the highly resolved ETM data within pixel on coarser scale (MODIS, AVHRR). The analyses for 4 scenes in 2002 and 2003 showed that clear distribution-pattern for forest for NDVI and albedo are found. Grass and crop distributions show higher variability and differ significantly to each other in NDVI but only marginal in albedo. Regarding NDVI-distribution functions NDVI was found to be the key variable for L.E-determination.
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Hajifar, Sahand, Saeb Ragani Lamooki, Lora A. Cavuoto, Fadel M. Megahed, and Hongyue Sun. "Investigation of Heterogeneity Sources for Occupational Task Recognition via Transfer Learning." Sensors 21, no. 19 (October 8, 2021): 6677. http://dx.doi.org/10.3390/s21196677.
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Human activity recognition has been extensively used for the classification of occupational tasks. Existing activity recognition approaches perform well when training and testing data follow an identical distribution. However, in the real world, this condition may be violated due to existing heterogeneities among training and testing data, which results in degradation of classification performance. This study aims to investigate the impact of four heterogeneity sources, cross-sensor, cross-subject, joint cross-sensor and cross-subject, and cross-scenario heterogeneities, on classification performance. To that end, two experiments called separate task scenario and mixed task scenario were conducted to simulate tasks of electrical line workers under various heterogeneity sources. Furthermore, a support vector machine classifier equipped with domain adaptation was used to classify the tasks and benchmarked against a standard support vector machine baseline. Our results demonstrated that the support vector machine equipped with domain adaptation outperformed the baseline for cross-sensor, joint cross-subject and cross-sensor, and cross-subject cases, while the performance of support vector machine equipped with domain adaptation was not better than that of the baseline for cross-scenario case. Therefore, it is of great importance to investigate the impact of heterogeneity sources on classification performance and if needed, leverage domain adaptation methods to improve the performance.
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Murdoch, Olga, Michael J. O'Grady, and Gregory M. P. O'Hare. "A Cyber Sensor Model for Cyber-Physical-Social Systems." International Journal of Agricultural and Environmental Information Systems 12, no. 1 (January 2021): 80–94. http://dx.doi.org/10.4018/ijaeis.20210101.oa6.
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Engineering sustainable cyber-physical-social systems demand a transdisciplinary approach. Within an arbitrary domain, many systems, including those of the physical and cyber categories, may already be in-situ; however, heterogeneity permeates such systems, for example, differing protocols, data formats, among others. Heterogeneity is not a deliberate feature of an arbitrary system; rather, it is the cumulative result of pragmatic decisions that were made during design and is driven by many different factors, some of which may not be technological. Nonetheless, heterogeneity represents a critical obstacle for system designers as they seek to harness and integrate diverse system elements to deliver innovative services. This obstacle is acutely manifested in cyber-physical-social systems when collecting and fusing data for evidence-based decision-making; social and human-derived data exacerbate the problem. This paper proposes a programming model for fusing information sources in cyber-physical-social systems. The efficacy of the model is validated via a usability analysis.
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Baert, Jonathan, Anissa Delepierre, Samuel Telek, Patrick Fickers, Dominique Toye, Anne Delamotte, Alvaro R. Lara, et al. "Microbial population heterogeneity versus bioreactor heterogeneity: Evaluation of Redox Sensor Green as an exogenous metabolic biosensor." Engineering in Life Sciences 16, no. 7 (August 16, 2016): 643–51. http://dx.doi.org/10.1002/elsc.201500149.
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Thangamuthu, A. P. "Security in Wireless Sensor Networks: Issues and Challenges." Shanlax International Journal of Arts, Science and Humanities 8, no. 4 (April 1, 2021): 120–28. http://dx.doi.org/10.34293/sijash.v8i4.3671.
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Wireless sensor networks (WSNs) have made it easier for people to live in various fields: medical engineering, agriculture. With computing power and wireless networking, sensing technology makes it lucrative for its potential abundance of use. Since the many uses of such systems have been used, lightweight, inexpensive, disposable and self-contained computers, known as sensor nodes or “motes,” are created. WSNs are commonly used in applications for monitoring, tracking and control. These include centralized management, system heterogeneity, protocol routing, the versatility of node, the privacy of information and restricted computing capacity. WSN covers a wide geographical area; routing protocols, scalability and security should therefore be addressed. In the traditional networking technique, there are major benefits due to the low cost and cooperative design of wireless networks (WNs). The networks with wireless sensors have more advantages over wired networks. Although wireless networks have various advantages, they are vulnerable to security problems. Due to the broader application, safety has become an important issue for wireless sensor networks.
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Mantri, Dnyaneshwar s., Neeli Rashmi Prasad, and Ramjee Prasad. "Node Heterogeneity for Energy Efficient Synchronization in Wireless Sensor Network." Procedia Computer Science 79 (2016): 885–92. http://dx.doi.org/10.1016/j.procs.2016.03.102.
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Sharma, Deepak, Amritesh Ojha, and Amol P. Bhondekar. "Heterogeneity consideration in wireless sensor networks routing algorithms: a review." Journal of Supercomputing 75, no. 5 (October 5, 2018): 2341–94. http://dx.doi.org/10.1007/s11227-018-2635-8.
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Meyer, Aaron S., Annelien J. M. Zweemer, and Douglas A. Lauffenburger. "The AXL Receptor Is a Sensor of Ligand Spatial Heterogeneity." Cell Systems 1, no. 1 (July 2015): 25–36. http://dx.doi.org/10.1016/j.cels.2015.06.002.
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Ambühl, Lukas, Allister Loder, Nan Zheng, Kay W. Axhausen, and Monica Menendez. "Approximative Network Partitioning for MFDs from Stationary Sensor Data." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 6 (May 2, 2019): 94–103. http://dx.doi.org/10.1177/0361198119843264.
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The macroscopic fundamental diagram (MFD) measures network-level traffic performance of urban road networks. Large-scale networks are normally partitioned into homogeneous regions in relation to road network topology and traffic dynamics. Existing partitioning algorithms rely on unbiased data. Unfortunately, widely available stationary traffic sensors introduce a spatial bias and may fail to identify meaningful regions for MFD estimations. Thus, it is crucial to revisit and develop stationary-sensor-based partitioning algorithm. This paper proposes an alternative two-step partitioning algorithm for MFD estimations based on information collected solely from stationary sensors. In a first step, possible partitioning outcomes are generated in the road networks using random walks. In a second step, the regions’ MFDs are estimated under every possible partitioning outcome. Based on previous work, an indicator is proposed to evaluate the traffic heterogeneity in regions. The proposed partitioning approach is tested with an abstract grid network and empirical data from Zurich. In addition, the results are compared with an algorithm that disregards stationary detectors’ biases. The results demonstrate that the proposed approach performs well for obtaining the quasi-optimal network partitions yielding the lowest heterogeneity among all possible partition outcomes. The presented approach not only complements existing literature, but also offers practice-oriented solutions for transport authorities to estimate the MFDs with their available data.
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Xiang, Y., Y. Tang, and W. Zhu. "Mobile sensor network noise reduction and re-calibration using Bayesian network." Atmospheric Measurement Techniques Discussions 8, no. 8 (August 31, 2015): 8971–9008. http://dx.doi.org/10.5194/amtd-8-8971-2015.
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Abstract. People are becoming increasingly interested in mobile air quality sensor network applications. By eliminating the inaccuracies caused by spatial and temporal heterogeneity of pollutant distributions, this method shows great potentials in atmosphere researches. However, such system usually suffers from the problem of sensor noises and drift. For the sensing systems to operate stably and reliably in the real-world applications, those problems must be addressed. In this work, we exploit the correlation of different types of sensors caused by cross sensitivity to help identify and correct the outlier readings. By employing a Bayesian network based system, we are able to recover the erroneous readings and re-calibrate the drifted sensors simultaneously. Specifically, we have (1) designed a Bayesian belief network based system to detect and recover the abnormal readings; (2) developed methods to update the sensor calibration functions in-field without requirement of ground truth; and (3) deployed a real-world mobile sensor network using the custom-built M-Pods to verify our assumptions and technique. Compared with the existing Bayesian belief network technique, the experiment results on the real-world data demonstrate that our system can reduce error by 34.1 % and recover 4 times more data on average.
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Anees, Junaid, Hao-Chun Zhang, Sobia Baig, Bachirou Guene Lougou, and Thomas Gasim Robert Bona. "Hesitant Fuzzy Entropy-Based Opportunistic Clustering and Data Fusion Algorithm for Heterogeneous Wireless Sensor Networks." Sensors 20, no. 3 (February 8, 2020): 913. http://dx.doi.org/10.3390/s20030913.
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Limited energy resources of sensor nodes in Wireless Sensor Networks (WSNs) make energy consumption the most significant problem in practice. This paper proposes a novel, dynamic, self-organizing Hesitant Fuzzy Entropy-based Opportunistic Clustering and data fusion Scheme (HFECS) in order to overcome the energy consumption and network lifetime bottlenecks. The asynchronous working-sleeping cycle of sensor nodes could be exploited to make an opportunistic connection between sensor nodes in heterogeneous clustering. HFECS incorporates two levels of hierarchy in the network and energy heterogeneity is characterized using three levels of energy in sensor nodes. HFECS gathers local sensory data from sensor nodes and utilizes multi-attribute decision modeling and the entropy weight coefficient method for cluster formation and the cluster head election procedure. After cluster formation, HFECS uses the same techniques for performing data fusion at the first hierarchical level to reduce the redundant information flow from the first-second hierarchical levels, which can lead to an improvement in energy consumption, better utilization of bandwidth and extension of network lifetime. Our simulation results reveal that HFECS outperforms the existing benchmark schemes of heterogeneous clustering for larger network sizes in terms of half-life period, stability period, average residual energy, network lifetime, and packet delivery ratio.
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Lo, Tsz Him, H. C. Pringle, Daran R. Rudnick, Geng Bai, L. Jason Krutz, Drew M. Gholson, and Xin Qiao. "Within-Field Variability in Granular Matrix Sensor Data and its Implications for Irrigation Scheduling." Applied Engineering in Agriculture 36, no. 4 (2020): 437–49. http://dx.doi.org/10.13031/aea.13918.
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Highlights Within-field variability was larger for individual depths than for the profile average across multiple depths. Distributions of the profile average were approximately normal, with increasing variances as the soil was drying. Probability theory was applied to quantify the effect of sensor set number on irrigation scheduling. The benefit of additional sensors sets may decrease for longer irrigation cycles and for more heterogeneous fields. Abstract. Even when located within the same field, multiple units of the same soil moisture sensor rarely report identical values. Such within-field variability in soil moisture sensor data is caused by natural and manmade spatial heterogeneity and by inconsistencies in sensor construction and installation. To better describe this variability, daily soil water tension values from 14 to 23 sets of granular matrix sensors during the middle part of four soybean site-years in the Mississippi Delta were analyzed. The soil water tension data were found to follow approximately normal distributions, to exhibit moderately high temporal rank stability, and to show strong positive correlation between mean and variance. Based on these observations and the existing literature, a probabilistic conceptual framework was proposed for interpreting within-field variability in granular matrix sensor data. This framework was then applied to investigate the impact of sensor set number (i.e., number of replicates) and irrigation triggering threshold on the scheduling of single-day and multi-day irrigation cycles. If a producer’s primary goal of irrigation scheduling is to keep soil water adequate in a particular fraction of land on average, the potential benefit from increasing sensor set number may be smaller than traditionally expected. Improvement, expansion, and validation of this probabilistic framework are welcomed for developing a practical and robust approach to selecting the sensor set number and the irrigation triggering threshold for diverse soil moisture sensor types in diverse contexts. Keywords: Irrigation scheduling, Probability, Sensors, Soil moisture, Soil water tension, Variability, Watermark.
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Guyeux, Christophe, Stéphane Chrétien, Gaby Bou Tayeh, Jacques Demerjian, and Jacques Bahi. "Introducing and Comparing Recent Clustering Methods for Massive Data Management in the Internet of Things." Journal of Sensor and Actuator Networks 8, no. 4 (December 9, 2019): 56. http://dx.doi.org/10.3390/jsan8040056.
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The use of wireless sensor networks, which are the key ingredient in the growing Internet of Things (IoT), has surged over the past few years with a widening range of applications in the industry, healthcare, agriculture, with a special attention to monitoring and tracking, often tied with security issues. In some applications, sensors can be deployed in remote, large unpopulated areas, whereas in others, they serve to monitor confined busy spaces. In either case, clustering the sensor network’s nodes into several clusters is of fundamental benefit for obvious scalability reasons, and also for helping to devise maintenance or usage schedules that might greatly improve the network’s lifetime. In the present paper, we survey and compare popular and advanced clustering schemes and provide a detailed analysis of their performance as a function of scale, type of collected data or their heterogeneity, and noise level. The testing is performed on real sensor data provided by the UCI Machine Learning Repository, using various external validation metrics.
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Dhage, Manisha R., and Srikanth Vemuru. "Routing Design Issues in Heterogeneous Wireless Sensor Network." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 2 (April 1, 2018): 1028. http://dx.doi.org/10.11591/ijece.v8i2.pp1028-1039.
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<p class="Default">WSN has important applications such as habitat monitoring, structural health monitoring, target tracking in military and many more. This has evolved due to availability of sensors that are cheaper and intelligent but these are having battery support. So, one of the major issues in WSN is maximization of network life. Heterogeneous WSNs have the potential to improve network lifetime and also provide higher quality networking and system services than the homogeneous WSN. Routing is the main concern of energy consumption in WSN. Previous research shows that performance of the network can be improve significantly using protocol of hierarchical HWSN. However, the appropriateness of a particular routing protocol mainly depends on the capabilities of the nodes and on the application requirements. This study presents different aspects of Heterogeneous Wireless Sensor network and design issues for routing in heterogeneous environment. Different perspectives from different authors regarding energy efficiency based on resource heterogeneity for heterogeneous wireless sensor networks have been presented.</p>
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Zong Chen, Dr Joy Iong, and Lu-Tsou Yeh. "Data Forwarding in Wireless Body Area Networks." June 2020 2, no. 2 (June 1, 2020): 80–87. http://dx.doi.org/10.36548/jei.2020.2.002.
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One of the most crucial application of Wireless Body Area Networks in healthcare applications is the process of monitoring human bodies and gather physiological data. Network performance degradation in the form of energy efficiency and latency are caused because of energy depletions which arises due to limited energy resource availability. The heterogeneity of body sensors will lead to variation in the rate of energy consumption. Based on this, a novel Data Forwarding Strategy is presented in this research work to enhance collaborative WBAN operations, improve network lifetime and restrict energy consumption of the sensors. In this paper, we have contributed towards reducing the size of data to be transmitted by compressed sensing and selection of relay sensor based on sampling frequency, energy levels and sensor importance. Using the proposed methodology, it is possible to improve both reliability and energy-efficiency of WBAN data transmission. moreover, it is also possible to adapt to the changing WBAN topologies when the proposed methodology is used, balancing energy efficiency and consumption.
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Phogaat, Sangeeta. "Investigation and Analysis on Energy Efficient Stable Election Protocol (SEP) for WSN." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (June 30, 2023): 3483–89. http://dx.doi.org/10.22214/ijraset.2023.54311.
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Abstract: We study the impact of heterogeneity of nodes, in terms of their energy, in wireless sensor networks that are hierarchically clustered. In these networks some of the nodes become cluster heads, aggregate the data of their cluster members and transmit it to the sink. We assume that a percentage of the population of sensor nodes is equipped with additional energy resources—this is a source of heterogeneity which may result from the initial setting or as the operation of the network evolves. We propose SEP, a heterogeneous-aware protocol to prolong the time interval before the death of the first node (we refer to as stability period), which is crucial for many applications where the feedback from the sensor network must be reliable. SEP is based on weighted election probabilities of each node to become cluster head according to the remaining energy in each node. We show by simulation that SEP always prolongs the stability period compared to (and that the average throughput is greater than) the one obtained using current clustering protocols. We conclude by studying the sensitivity of our SEP protocol to heterogeneity parameters capturing energy imbalance in the network. We found that SEP yields longer stability region for higher values of extra energy brought by more powerful nodes. Sensor nodes usually have limited energy supply and they are impractical to recharge. How to balance traffic load in sensors in order to increase network lifetime is a very challenging research issue. Many clustering algorithms have been proposed recently for wireless sensor networks (WSNs). The use of mobile sinks has been shown to be an effective technique to enhance network performance features such as latency, energy efficiency, network lifetime, etc. In this paper, a modified Stable Election Protocol (SEP), which employs a mobile sink, has been proposed for WSNs with non-uniform node distribution. The decision of selecting cluster heads by the sink is based on the minimization of the associated additional energy and residual energy at each node. Besides, the cluster head selects the shortest path to reach the sink between the direct approach and the indirect approach with the use of the nearest cluster head. Simulation results demonstrate that our algorithm has better performance than traditional routing algorithms, such as LEACH and SEP.
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Tittebrand, A., and F. H. Berger. "Spatial heterogeneity of satellite derived land surface parameters and energy flux densities for LITFASS-area." Atmospheric Chemistry and Physics 9, no. 6 (March 23, 2009): 2075–87. http://dx.doi.org/10.5194/acp-9-2075-2009.
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Abstract. Based on satellite data in different temporal and spatial resolution, the current use of frequency distribution functions (PDF) for surface parameters and energy fluxes is one of the most promising ways to describe subgrid heterogeneity of a landscape. Objective of this study is to find typical distribution patterns of parameters (albedo, NDVI) for the determination of the actual latent heat flux (L.E) determined from highly resolved satellite data within pixel on coarser scale. Landsat ETM+, Terra MODIS and NOAA-AVHRR surface temperature and spectral reflectance were used to infer further surface parameters and radiant- and energy flux densities for LITFASS-area, a 20×20 km2 heterogeneous area in Eastern Germany, mainly characterised by the land use types forest, crop, grass and water. Based on the Penman-Monteith-approach L.E, as key quantity of the hydrological cycle, is determined for each sensor in the accordant spatial resolution with an improved parametrisation. However, using three sensors, significant discrepancies between the inferred parameters can cause flux distinctions resultant from differences of the sensor filter response functions or atmospheric correction methods. The approximation of MODIS- and AVHRR- derived surface parameters to the reference parameters of ETM (via regression lines and histogram stretching, respectively), further the use of accurate land use classifications (CORINE and a new Landsat-classification), and a consistent parametrisation for the three sensors were realized to obtain a uniform base for investigations of the spatial variability. The analyses for 4 scenes in 2002 and 2003 showed that for forest clear distribution-patterns for NDVI and albedo are found. Grass and crop distributions show higher variability and differ significantly to each other in NDVI but only marginal in albedo. Regarding NDVI-distribution functions NDVI was found to be the key variable for L.E-determination.
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Jaumann, Stefan, and Kurt Roth. "Effect of unrepresented model errors on estimated soil hydraulic material properties." Hydrology and Earth System Sciences 21, no. 9 (September 1, 2017): 4301–22. http://dx.doi.org/10.5194/hess-21-4301-2017.
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Abstract. Unrepresented model errors influence the estimation of effective soil hydraulic material properties. As the required model complexity for a consistent description of the measurement data is application dependent and unknown a priori, we implemented a structural error analysis based on the inversion of increasingly complex models. We show that the method can indicate unrepresented model errors and quantify their effects on the resulting material properties. To this end, a complicated 2-D subsurface architecture (ASSESS) was forced with a fluctuating groundwater table while time domain reflectometry (TDR) and hydraulic potential measurement devices monitored the hydraulic state. In this work, we analyze the quantitative effect of unrepresented (i) sensor position uncertainty, (ii) small scale-heterogeneity, and (iii) 2-D flow phenomena on estimated soil hydraulic material properties with a 1-D and a 2-D study. The results of these studies demonstrate three main points: (i) the fewer sensors are available per material, the larger is the effect of unrepresented model errors on the resulting material properties. (ii) The 1-D study yields biased parameters due to unrepresented lateral flow. (iii) Representing and estimating sensor positions as well as small-scale heterogeneity decreased the mean absolute error of the volumetric water content data by more than a factor of 2 to 0. 004.
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Aasen, Helge. "INFLUENCE OF THE VIEWING GEOMETRY WITHIN HYPERSPECTRAL IMAGES RETRIEVED FROM UAV SNAPSHOT CAMERAS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-7 (June 7, 2016): 257–61. http://dx.doi.org/10.5194/isprs-annals-iii-7-257-2016.
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Hyperspectral data has great potential for vegetation parameter retrieval. However, due to angular effects resulting from different sun-surface-sensor geometries, objects might appear differently depending on the position of an object within the field of view of a sensor. Recently, lightweight snapshot cameras have been introduced, which capture hyperspectral information in two spatial and one spectral dimension and can be mounted on unmanned aerial vehicles. <br><br> This study investigates the influence of the different viewing geometries within an image on the apparent hyperspectral reflection retrieved by these sensors. Additionally, it is evaluated how hyperspectral vegetation indices like the NDVI are effected by the angular effects within a single image and if the viewing geometry influences the apparent heterogeneity with an area of interest. The study is carried out for a barley canopy at booting stage. <br><br> The results show significant influences of the position of the area of interest within the image. The red region of the spectrum is more influenced by the position than the near infrared. The ability of the NDVI to compensate these effects was limited to the capturing positions close to nadir. The apparent heterogeneity of the area of interest is the highest close to a nadir.
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Liu, Qiuming, Li Yu, Zuhao Liu, and Jun Zheng. "Impact of Heterogeneity and Secrecy on theCapacity of Wireless Sensor Networks." Sensors 15, no. 12 (December 10, 2015): 30964–80. http://dx.doi.org/10.3390/s151229844.
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Mantri, Dnyaneshwar S., Neeli Rashmi Prasad, and Ramjee Prasad. "Random Mobility and Heterogeneity-Aware Hybrid Synchronization for Wireless Sensor Network." Wireless Personal Communications 100, no. 2 (November 23, 2017): 321–36. http://dx.doi.org/10.1007/s11277-017-5072-8.
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Méthy, M., J. Fabreguettes, J. L. Salager, and F. Jardon. "A Sensor for Measurement of Spatial Heterogeneity of Photosynthetically Active Radiation." Journal of Agricultural Engineering Research 58, no. 1 (May 1994): 69–72. http://dx.doi.org/10.1006/jaer.1994.1036.
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Batzer, J. C., M. L. Gleason, S. E. Taylor, K. J. Koehler, and J. E. B. A. Monteiro. "Spatial Heterogeneity of Leaf Wetness Duration in Apple Trees and Its Influence on Performance of a Warning System for Sooty Blotch and Flyspeck." Plant Disease 92, no. 1 (January 2008): 164–70. http://dx.doi.org/10.1094/pdis-92-1-0164.
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To determine the effect of sensor placement on the performance of a disease-warning system for sooty blotch and flyspeck (SBFS), we measured leaf wetness duration (LWD) at 12 canopy positions in apple trees, then simulated operation of the disease-warning system using LWD measurements from different parts of the canopy. LWD sensors were placed in four trees within one Iowa orchard during two growing seasons, and in one tree in each of four orchards during a single growing season. The LWD measurements revealed substantial heterogeneity among sensor locations. In all data sets, the upper, eastern portion of the canopy had the longest mean daily LWD, and was the first site to form dew and the last to dry. The lower, western portion of the canopy averaged about 3 h less LWD per day than the top of the canopy, and was the last zone where dew formed and the first to dry off. On about 25% of nights when dew occurred in the top of the canopy, no dew formed in the lower, western canopy. Intracanopy variability of LWD was more pronounced when dew was the sole source of wetness than on days when rainfall occurred. Daily LWD in the upper, eastern portion of the canopy was slightly less than reference measurements made at a 0.7-m height over turfgrass located near the orchard. When LWD measurements from several canopy positions were input to the SBFS warning system, timing of occurrence of a fungicide-spray threshold varied by as much as 30 days among canopy positions. Under Iowa conditions, placement of an LWD sensor at an unobstructed site over turfgrass was a fairly accurate surrogate for the wettest part of the canopy. Therefore, such an extra-canopy LWD sensor might be substituted for a within-canopy sensor to enhance operational reliability of the SBFS warning system.
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Crescini, Damiano, Farid Touati, and Alessio Galli. "Multiparametric Sensor Node for Environmental Monitoring Based on Energy Harvesting." Atmosphere 13, no. 2 (February 15, 2022): 321. http://dx.doi.org/10.3390/atmos13020321.
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The heterogeneity and levels of chemicals released into the environment have dramatically grown in the last few years. Therefore, new low-cost tools are increasingly required to monitor pollution and follow its trends over time. Recent approaches in electronics and wireless communications permit the expansion of low-power, low-cost, and multiparametric sensor nodes that are limited in size and communicate untethered in small distances. For such a monitoring system to be ultimately feasible, a suitable power source for these nodes must be found. The present research falls within the frame of this global effort. The study sits within the context discussed above with the particular aim of developing groundbreaking technology-based solutions by means of efficient environmentally powered wireless smart sensors. This paper presents a multiparametric sensor node for indoor/outdoor air quality monitoring, able to work without battery and human intervention, harvesting energy from the surrounding environment for perpetual operation. The complete system design of the sensor and experimental results are reported. The evaluation of the energy-harvesting blocks with a budget allocation of the power consumption is also discussed.
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Xue, Xingsi, Haolin Wang, and Wenyu Liu. "Matching sensor ontologies with unsupervised neural network with competitive learning." PeerJ Computer Science 7 (November 19, 2021): e763. http://dx.doi.org/10.7717/peerj-cs.763.
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Sensor ontologies formally model the core concepts in the sensor domain and their relationships, which facilitates the trusted communication and collaboration of Artificial Intelligence of Things (AIoT). However, due to the subjectivity of the ontology building process, sensor ontologies might be defined by different terms, leading to the problem of heterogeneity. In order to integrate the knowledge of two heterogeneous sensor ontologies, it is necessary to determine the correspondence between two heterogeneous concepts, which is the so-called ontology matching. Recently, more and more neural networks have been considered as an effective approach to address the ontology heterogeneity problem, but they require a large number of manually labelled training samples to train the network, which poses an open challenge. In order to improve the quality of the sensor ontology alignment, an unsupervised neural network model is proposed in this work. It first models the ontology matching problem as a binary classification problem, and then uses a competitive learning strategy to efficiently cluster the ontologies to be matched, which does not require the labelled training samples. The experiment utilizes the benchmark track provided by the Ontology Alignment Evaluation Initiative (OAEI) and multiple real sensor ontology alignment tasks to test our proposal’s performance. The experimental results show that the proposed approach is able to determine higher quality alignment results compared to other matching strategies under different domain knowledge such as bibliographic and real sensor ontologies.