Number Of Data Collection Points Research Articles

Swarm Intelligence Internet of Vehicles Approaches for Opportunistic Data Collection and Traffic Engineering in Smart City Waste Management

Recent studies have shown the efficacy of mobile elements in optimizing the energy consumption of sensor nodes. Current data collection approaches for waste management applications focus on exploiting IoT-enabled technologies. However, these techniques are no longer sustainable in the context of smart city (SC) waste management applications due to the emergence of large-scale wireless sensor networks (LS-WSNs) in smart cities with sensor-based big data architectures. This paper proposes an energy-efficient swarm intelligence (SI) Internet of Vehicles (IoV)-based technique for opportunistic data collection and traffic engineering for SC waste management strategies. This is a novel IoV-based architecture exploiting the potential of vehicular networks for SC waste management strategies. The proposed technique involves deploying multiple data collector vehicles (DCVs) traversing the entire network for data gathering via a single-hop transmission. However, employing multiple DCVs comes with additional challenges including costs and network complexity. Thus, this paper proposes analytical-based methods to investigate critical tradeoffs in optimizing energy consumption for big data collection and transmission in an LS-WSN such as (1) finding the optimal number of data collector vehicles (DCVs) required in the network and (2) determining the optimal number of data collection points (DCPs) for the DCVs. These critical issues affect efficient SC waste management and have been overlooked by previous studies exploring waste management strategies. Simulation-based experiments using SI-based routing protocols validate the efficacy of the proposed method in terms of the evaluation metrics.

Identification of subsurface soil layers in Sutami Dam area and its surroundings using magnetic methods

The research was conducted to determine the subsurface layer type in the Sutami Dam area using magnetic methods. This study aims to determine the subsurface soil type in the area around the Sutami Dam. The instrument used in data collection was the Proton Precession Magnetometer (PPM) Scientrex Model G-8. The data collection area covers the Kromengan region (112.494°E; 8.129°S) to the Sukorame area (112.358°E; -8.213°S). The area covers the Sutami Dam area. The research area is 15 km x 15 km or with an area of 225 KM with a space between points 300 m. From the data acquisition, it was found that the number of data collection points was 1372 points. The types of subsurface layers based on the 2,5 D Talwani modeling are soil, pumice, limestone, sandy tuff, sandstone, andesite, and lava.

Deep convolutional neural network with new training method and transfer learning for structural fault classification of vehicle instrument panel structure

Structural defect have been detected by attaching sensors to all possible defect locations. A new method is proposed to enable the identification of structural defect locations with minimal data collection points using a deep convolutional neural network. Transfer learning was used to improve the accuracy of a hard-to-classify task by using a pre-trained model from an easy-to-classify task. To reduce the number of data collection points, it is necessary to learn the spatial information of the structure. To this end, a structure fault classification-deep convolutional neural network (SFC-DCNN) is proposed. It is an end-to-end convolutional neural network. The time-domain input data and convolutional neural network filter have 2 dimensions. With the proposed method, the accuracy of classifying the location of structural defects in a vehicle’s instrument panel structure was verified with a single vibration measurement point where the location is independent of the structural fault location.

Methods for determining the transport detectors’ placement on road network

The article discusses the methods’ development for determining the transport detectors location on the road network. In studying this problem, various approaches to collecting data on traffic flows are analyzed. The basic requirements for determining the number of data collection points and their location are formed. These requirements can be used in traffic monitoring systems. The proposed methods were tested using a model experiment on a test example. The simulation results showed the effectiveness of the considered methods.

Steiner Zone Yaklaşımı İle Bölünmüş Kablosuz Algılayıcı Ağlarda Hareketli Veri Toplama

Wireless sensor networks (WSNs) typically operate in harsh environmental conditions. Hardware constraints and external damage from inhospitable surroundings leave the nodes susceptible to node failures. Depending on the damage scale, the network can be subject to partitioning which segments the network into multiple isolated connected components. A prompt reactive approach to restore network connectivity is to employ a mobile data collector (MDC) that visits and collects data from partitions periodically. Availability of the wireless data communication through multi-hop routing in a partitioned network complicates designating the shortest possible route for data collection. This paper regards the mentioned data collection problem as the Close-enough Traveling Salesman Problem (CETSP) and employs Steiner zone approach to designate respective data collection points for corresponding partitions. We have assessed the proposed approach in terms of the number of points visited for data collection and the total travel distance of the MDC. Obtained results indicate that the proposed approach can reduce the number of data collection points up to 67% and total travel distance up to 42%.

Evaluation of bull breeding soundness examination

Records from 2,887 bull breeding soundness examination conducted at Michigan State University from 2007-2017 were analyzed to validate, verify, and evaluate the process. All bulls were examined using Society for Theriogenology Bull Breeding Soundness guidelines. With a conservative α of .001, nominal logistic regression was used to identify influential factors and potential sources of bias, and determine predictive accuracy of the breeding soundness examination itself in classifying reproductive potential. Of 2,887 records evaluated, 82% of bulls were classified as satisfactory potential breeders, 3% unsatisfactory, and 15% deferred. Factors identified as influential in final classification of bulls included season, veterinarian bias, presence of white blood cells, and percent morphologically normal sperm. Season of year and presence of white blood cells in ejaculate significantly affected the outcome. Routine and consistent evaluation of bulls is important for economic success of beef cow-calf operations. Further studies are needed to determine if the seasonal effect is related to ambient temperature or underlying physiology of spermatogenesis in the bull. Some components of the breeding soundness examination may be unnecessary. We concluded that veterinarians and farmers could save time and money by reducing the number of data collection points while increasing reproductive potential of the herd by using only influential factors in the process. Breeding soundness examination has been useful in evaluation of reproductive potential in bulls; however, alternate formulas of reproductive potential may have higher predictive accuracy and validity and may be more cost effective.

Vapor Intrusion Monitoring Method Cost Comparisons: Automated Continuous Analytical Versus Discrete Time-Integrated Passive Approaches

Vapor intrusion characterization efforts are challenging due to complexities associated with indoor background sources, preferential subsurface migration pathways, indoor and shallow subsurface concentration dynamics, and representativeness limitations associated with manual monitoring and characterization methods. For sites experiencing trichloroethylene (TCE) vapor intrusion, the potential for acute risks poses additional challenges, as the need for rapid response to acute toxicity threshold exceedances is critical in order to minimize health risks and associated liabilities. Currently accepted discrete time-integrated vapor intrusion monitoring methods that employ passive diffusion–adsorption and canister samplers often do not result in sufficient temporal or spatial sampling resolution in dynamic settings, have a propensity to yield false negative and false positive results, and are not able to prevent receptors from acute exposure risks, as sample processing times exceed exposure durations of concern. Multiple lines of evidence have been advocated for in an attempt to reduce some of these uncertainties. However, implementation of multiple lines of evidence do not afford rapid response capabilities and typically rely on discrete time-integrated sample collection methods prone to nonrepresentative results due to concentration dynamics. Recent technology innovations have resulted in the deployment of continuous monitoring platforms composed of multiplexed laboratory grade analytical components integrated with quality control features, telemetry, geographical information systems, and interpolation algorithms for automatically generating geospatial time stamped renderings and time-weighted averages through a cloud-based data management platform. Automated alerts and responses can be engaged within 1 minute of a threshold exceedance detection. Superior temporal and spatial resolution also results in optimized remediation design and mitigation system performance confirmation. While continuous monitoring has been acknowledged by the regulatory community as a viable option for providing superior results when addressing spatial and temporal dynamics, until very recently, these approaches have been considered impractical due to cost constraints and instrumentation limitations. Recent instrumentation advancements via automation and multiplexing allow for rapid and continuous assessment and response from multiple locations using a single instrument. These advancements have reduced costs to the point where they are now competitive with discrete time-integrated methods. In order to gain more regulatory and industry support for these viable options, there is an immediate need to perform a realistic cost comparison between currently approved discrete time-integrated methods and newly fielded continuous monitoring platforms. Regulatory support for continuous monitoring platforms will result in more effectively protecting the public, provide property owners with information sufficient to more accurately address potential liabilities, reduce unnecessary remediation costs for situations where risks are minimal, lead to more effective and surgical remediation strategies, and allow practitioners to most effectively evaluate remediation system performance. To address this need, a series of common monitoring scenarios and associated assumptions were derived and cost comparisons performed. Scenarios included variables such as number of monitoring locations, duration, costs to meet quality control requirements, and number of analyses performed within a given monitoring campaign. Results from this effort suggest that for relatively larger sites where five or more locations will be monitored (e.g., large buildings, multistructure industrial complexes, educational facilities, or shallow groundwater plumes with significant spatial footprints under residential neighborhoods), procurement of continuous monitoring services is often less expensive than implementation of discrete time-integrated monitoring services. For instance, for a 1-week monitoring campaign, costs-per-analysis for continuous monitoring ranges from approximately 1 to 3 percent of discrete time-integrated method costs for the scenarios investigated. Over this same one-week duration, for discrete time-integrated options, the number of sample analyses equals the number of data collection points (which ranged from 5 to 30 for this effort). In contrast, the number of analyses per week for the continuous monitoring option equals 672, or four analyses per hour. This investigation also suggests that continuous automated monitoring can be cost-effective for multiple one-week campaigns on a quarterly or semi-annual basis in lieu of discrete time-integrated monitoring options. In addition to cost benefits, automated responses are embedded within the continuous monitoring service and, therefore, provide acute TCE risk-preventative capabilities that are not possible using discrete time-integrated passive sampling methods, as the discrete time-integrated services include analytical efforts that require more time than the exposure duration of concern. ©2016 Wiley Periodicals, Inc.

Using Delayed Observations for Long-Term Vehicle Tracking in Large Environments

The tracking of vehicles over large areas with limited position observations is of significant importance in many industrial applications. This paper presents algorithms for long-term vehicle motion estimation based on a vehicle motion model that incorporates the properties of the working environment and information collected by other mobile agents and fixed infrastructure collection points. The prediction algorithm provides long-term estimates of vehicle positions using speed and timing profiles built for a particular environment and considering the probability of a vehicle stopping. A limited number of data collection points distributed around the field are used to update the estimates, with negative information (no communication) also used to improve the prediction. This paper introduces the concept of observation harvesting, a process in which peer-to-peer communication between vehicles allows egocentric position updates to be relayed among vehicles and finally conveyed to the collection point for an improved position estimate. Positive and negative communication information is incorporated into the fusion stage, and a particle filter is used to incorporate the delayed observations harvested from vehicles in the field to improve the position estimates. The contributions of this work enable the optimization of fleet scheduling using discrete observations. Experimental results from a typical large-scale mining operation are presented to validate the algorithms.

Single Receiver Emitter Geolocation Based on Signal Periodicity With Oscillator Instability

We propose a method for localizing a stationary emitter, not necessarily cooperative, using a single moving receiver based on time of arrival (TOA) measurements. The sensor intercepts the incoming signal at different locations along its trajectory, thus, effectively creating an array of spatially separated sensors. This manuscript presents the method and its performance in the presence of noise and oscillator instability. The proposed method provides a few advantages over the traditional multi-sensor TOA method. These include the economic aspect of using a single receiver instead of several ones, synchronization between stations is not required since only a single sensor is used, on line optimization of the emitter-sensor geometry in order to improve performance (path design, number of data collection points, etc.), and finally, there is no requirement to transmit data between sensors. The method is applicable to any signal as long as it includes a portion that appears periodically such as a synchronization sequence, or a signal whose transmission time intervals are known, even if they are not identical. We derive the Cramer-Rao lower bound, perform small error analysis for the algorithm, and corroborate the results with simulations. We show that reasonable localization results can be obtained under realistic conditions.

Probabilistic Long-Term Vehicle Motion Prediction and Tracking in Large Environments

Vehicle position tracking and prediction over large areas is of significant importance in many industrial applications, such as mining operations. In a small area, this can easily be achieved by providing vehicles with a constant communication link to a control center and having the vehicles broadcast their position. The problem dramatically changes when vehicles operate within a large environment of potentially hundreds of square kilometers and in difficult terrain. This paper presents algorithms for long-term vehicle motion prediction and tracking based on a multiple-model approach. It incorporates a probabilistic vehicle model that includes the structure of the environment. The prediction algorithm evaluates the vehicle position using acceleration, speed, and timing profiles built for the particular environment and considers the probability that the vehicle will stop. A limited number of data collection points distributed around the field are used to update the vehicle position estimate when in communication range, and prediction is used at points in between. A particle filter is used to estimate the vehicle position using both positive and negative information (whether communication is possible) in the fusion stage. The algorithms presented are validated with experimental results using data collected from a large-scale mining operation.

Locating Defects Using Dynamic Strain Analysis and Artificial Neural Networks

An inverse artificial neural network (ANN) assessment for locating defects in bars with or without notches is presented in the paper. Postulated void defects of 1mm x 1mm were introduced into bars that were impacted with an impulse step load; the resultant elastic waves propagate impinging on the defects. The resultant transient strain field was analyzed using the finite element method. Transient strain data was collected at nodal points or sensors locations on the boundary of the bars and used to train and assess ANNs. The paper demonstrates quantitatively, the effects of features such as the design of ANN, sensing parameters such as number of data collection points, and the effect of geometric features such as notches in the bars.