Data Collection Algorithm Research Articles

Minimum data sampling requirements for accurate detection of terrain-induced gait alterations change with mobile sensor position

Human gait is a key biomarker for health, independence and quality of life. Advances in wearable inertial sensor technologies have paved the way for out-of-the-lab human gait analysis, which is important for the assessment of mobility and balance in natural environments and has applications in multiple fields from healthcare to urban planning. Automatic recognition of the environment where walking takes place is a prerequisite for successful characterisation of terrain-induced gait alterations. A key question which remains unexplored in the field is how minimum data requirements for high terrain classification accuracy change depending on the sensor placement on the body. To address this question, we evaluate the changes in performance of five canonical machine learning classifiers by varying several data sampling parameters including sampling rate, segment length, and sensor configuration. Our analysis on two independent datasets clearly demonstrate that a single inertial measurement unit is sufficient to recognise terrain-induced gait alterations, accuracy and minimum data requirements vary with the device position on the body, and choosing correct data sampling parameters for each position can improve classification accuracy up to 40% or reduce data size by 16 times. Our findings highlight the need for adaptive data collection and processing algorithms for resource-efficient computing on mobile devices.

Optimizing agricultural data security: harnessing IoT and AI with Latency Aware Accuracy Index (LAAI).

The integration of Internet of Things (IoT) and artificial intelligence (AI) technologies into modern agriculture has profound implications on data collection, management, and decision-making processes. However, ensuring the security of agricultural data has consistently posed a significant challenge. This study presents a novel evaluation metric titled Latency Aware Accuracy Index (LAAI) for the purpose of optimizing data security in the agricultural sector. The LAAI uses the combined capacities of the IoT and AI in addition to the latency aspect. The use of IoT tools for data collection and AI algorithms for analysis makes farming operation more productive. The LAAI metric is a more holistic way to determine data accuracy while considering latency limitations. This ensures that farmers and other end-users are fed trustworthy information in a timely manner. This unified measure not only makes the data more secure but gives farmers the information that helps them to make smart decisions and, thus, drives healthier farming and food security.

Integration of Information Technology and Machine Learning to Improve the Efficiency of IoT-Based Logistics Systems

In today's digital era, efficiency in supply chain management and logistics is the main key to maintaining business competitiveness. This article discusses the integration of Information Technology (IT) and Machine Learning (ML) in Internet of Things (IoT)-based logistics systems to improve operational efficiency. By leveraging IoT sensors for real-time data collection and ML algorithms for predictive analysis, the system is able to optimize inventory management, route planning, and preventive maintenance. The case studies discussed in this article show that the use of ML in IoT-based logistics systems can reduce delivery times, lower operational costs, and increase responsiveness to changes in market demand. The results of this study are expected to provide insight for system developers and logistics managers in implementing advanced technologies to address challenges in the modern logistics industry.

Intelligent Decision Support System in Healthcare using Machine Learning Models

Background: The use of intelligent decision support systems (IDSS) is widespread in the healthcare industry, particularly for real-time data, client and family history datasets, and prevalent patient features. Objective: A massive chunk of various kinds of health data sets, including sensor information, medical evidence, and omic statistics, are produced by the modern techniques in this field and eventually transferred to a machine learning (ML) element for extracting data, categorization, as well as mining. Method: In recent times, many patents have been focused on healthcare monitoring; however, they do not adequately incorporate appropriate algorithms for data collection, analysis, and prediction. The data collected is used for predictive modelling, then additionally, machine learning techniques are assisting to compare acquired datasets mathematically for decision-making platforms that may learn to recognise the recent trend and anticipated future problems. Depending on the dataset type, ML-based techniques can assess the circumstances. Training datasets are crucial for correctly anticipating both current and emerging events as well as new challenges. Results: Since the importance of data acquisition determines how well learning models function, any deformed data of the types of dirty data, noisy data, unstructured data, and inadequate information results in inaccurate detection, estimate, and prediction. Conclusion: Additionally, in contrast to other approaches, the experimental findings demonstrate the usefulness of the proposed method as a widespread implementation of machine learning algorithms within healthcare systems.

Precision Gait Stability Analysis Combining Hardware and Software

Maintaining stable gait and avoiding falls require effective control of the body’s center of mass (CoM) relative to the base of support (BoS), defined by the feet's contact points with the floor. Quadrupeds have an advantage over bipeds but share common spinal neural control mechanisms. Human walking poses stability challenges since the CoM often extends beyond the BoS. This project aims to monitor gait stability by integrating advanced hardware and software solutions. Machine learning algorithms, such as Random Forest, will be employed to predict gait instability by capturing foot positions and sensor data. Inertial measurement sensors will track stability changes, and hardware outputs will be validated against software predictions for accuracy. A mobile app developed using React Native will display predicted parameters and indicate gait instability (yes/no). In case of detected instability, a wearable buzzer will alert the user. This system promises a more accurate solution for monitoring gait stability compared to existing models, by combining real-time data collection and sophisticated algorithms

Learning from user behavior: A survey-assist algorithm for longitudinal mobility data collection

GPS-based travel surveys are widely used in mobility studies to gather crucial qualitative data, like purpose, transportation mode and replaced mode. However, survey response still poses a burden to users, especially in long-term mobility studies, leading to response fatigue. We explore a survey-assist strategy to ease this burden by a novel, user-level modeling approach that leverages past responses from each user to predict responses for new trips, without relying on external data sources like GIS data.We investigate three main algorithms for predicting responses: (i) clustering trips and extrapolating responses for similar trips, (ii) using random forest classification, and (iii) clustering that uses a hybrid algorithm to determine spatial structure, which is then fed as input to a classic random forest classifier. The clustering approach can flexibly predict responses for even complex qualitative survey questions; it achieved F-scores of 65%. The random forest pipeline uses architecture that restricts it to predicting three predetermined survey questions: trip purpose, mode, and replaced mode. However, it achieved F-scores of 78%.While the survey-assist approach has been implemented by several proprietary systems, to our knowledge, this is the first exploration in the academic literature. It follows that this is also the first rigorous evaluation of multiple algorithms that can implement the approach. The evaluation uses a large scale, publicly available, longitudinal dataset consisting of ≈ 92 k trips from 235 users over a period of roughly one and a half years.With this approach, travel surveys can be pre-filled with the predicted responses for each trip, thus streamlining the survey process for users. Combined with an active learning system that requests user input on low-confidence predictions, models can be updated and improved over time to better support the long-term collection of longitudinal qualitative data.

Development of phenotyping algorithms for hypertensive disorders of pregnancy (HDP) and their application in more than 22,000 pregnant women

Recently, many phenotyping algorithms for high-throughput cohort identification have been developed. Prospective genome cohort studies are critical resources for precision medicine, but there are many hurdles in the precise cohort identification. Consequently, it is important to develop phenotyping algorithms for cohort data collection. Hypertensive disorders of pregnancy (HDP) is a leading cause of maternal morbidity and mortality. In this study, we developed, applied, and validated rule-based phenotyping algorithms of HDP. Two phenotyping algorithms, algorithms 1 and 2, were developed according to American and Japanese guidelines, and applied into 22,452 pregnant women in the Birth and Three-Generation Cohort Study of the Tohoku Medical Megabank project. To precise cohort identification, we analyzed both structured data (e.g., laboratory and physiological tests) and unstructured clinical notes. The identified subtypes of HDP were validated against reference standards. Algorithms 1 and 2 identified 7.93% and 8.08% of the subjects as having HDP, respectively, along with their HDP subtypes. Our algorithms were high performing with high positive predictive values (0.96 and 0.90 for algorithms 1 and 2, respectively). Overcoming the hurdle of precise cohort identification from large-scale cohort data collection, we achieved both developed and implemented phenotyping algorithms, and precisely identified HDP patients and their subtypes from large-scale cohort data collection.

Optimal 3D angular sampling with applications to cryo-EM problems

The goal of cryo-EM experiments in the biological sciences is to determine the atomic structure of a molecule and deduce insights into its functions and mechanisms. Despite improvements in instrumentation for data collection and new software algorithms, in most cases, individual atoms are not resolved. Model building of proteins, nucleic acids, or molecules in general, is feasible from the experimentally determined density maps at resolutions up to the range of 3–4 Angstroms. For lower-resolution maps or parts of maps, fitting smaller structures obtained by modelling or experimental techniques with higher resolution is a way to resolve the issue. In practice, we have an atomic structure, generate its density map at a given resolution, and translate/rotate the map within a region of interest in the experimental map, computing a measure-of-fit score with the corresponding areas of the experimental map. This procedure is computationally intensive since we work in 6D space. An optimal ordered list of rotations will reduce the angular error and help to find the best-fitting positions faster for a coarse global search or a local refinement. It can be used for adaptive approaches to stop fitting algorithms earlier once the desired accuracy has been achieved. We demonstrate how the performance of some fitting algorithms can be improved by grouping sets of rotations. We present an approach to generate more efficient 3D angular sampling, and provide the computer code to generate lists of optimal orientations for single and grouped rotations and the lists themselves.

Optimization Algorithm for AoI-Based UAV-Assisted Data Collection

Regarding the issue of information freshness in systems that aid in data collection using unmanned aerial vehicles (UAVs), a data collection algorithm that is based on freshness and UAV assistance is proposed. Under the limitations of wireless sensor node communication distance and UAV parameters, the optimization problem of minimizing the average spatial correlation age of information (SCAoI) of all nodes in the area is set up. This problem is solved by optimizing the number of clusters, UAV flight trajectories, and the order of data collection from cluster member nodes. The maximum communication distance of the nodes is used as the cluster formation radius, and the maximum-minimum distance clustering algorithm is used to cluster the nodes in the region to obtain the minimum number of clusters. After it has been proven that the trajectory optimization problem in this study is NP-hard, the ant colony algorithm is applied to obtain the minimum flight time and the corresponding trajectory. By using the greedy algorithm to determine the member nodes in the sequence of data collection for a cluster, the instantaneous SCAoI of the UAV arriving at the cluster head is solved. Simulation results show that the proposed algorithm in this paper can effectively improve the freshness of data and reduce the average SCAoI of the system compared with the algorithm in the comparative literature, reducing the average SCAoI by about 61%.

Modeling the Geometry of Tree Trunks Using LiDAR Data

The effective development of digital twins of real-world objects requires sophisticated data collection techniques and algorithms for the automated modeling of individual objects. In City Information Modeling (CIM) systems, individual buildings can be modeled automatically at the second Level of Detail or LOD2. Similarly, for Tree Information Modeling (TIM) and building Forest Digital Twins (FDT), automated solutions for the 3D modeling of individual trees at different levels of detail are required. The existing algorithms support the automated modeling of trees by generating models of the canopy and the lower part of the trunk. Our argument for this work is that the structure of tree trunk and branches is as important as canopy shape. As such, the aim of the research is to develop an algorithm for automatically modeling tree trunks based on data from point clouds obtained through laser scanning. Aiming to generate 3D models of tree trunks, the suggested approach starts with extracting the trunk point cloud, which is then segmented into single stems. Subsets of point clouds, representing individual branches, are measured using Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS). Trunks and branches are generated by fitting cylinders to the layered subsets of the point cloud. The individual stems are modeled by a structure of slices. The accuracy of the model is calculated by determining the fitness of cylinders to the point cloud. Despite the huge variation in trunk geometric forms, the proposed modeling approach can gain an accuracy of better than 4 cm in the constructed tree trunk models. As the developed tree models are represented in a matrix format, the solution enables automatic comparisons of tree elements over time, which is necessary for monitoring changes in forest stands. Due to the existence of large variations in tree trunk geometry, the performance of the proposed modeling approach deserves further investigation on its generality to other types of trees in multiple areas.

The Modeling and Detection of Attacks in Role-Based Self-Organized Decentralized Wireless Sensor Networks

This article discusses the modeling and detection of attacks in self-organizing decentralized wireless sensor networks (WSNs) that can be applied to various critical scenarios in practice. Security issues in this type of network have previously been studied to a rather poor extent. In particular, existing attack detection approaches and algorithms do not rely on the properties of self-organization and decentralization, which an attacker is able to exploit to compromise the network and its services. We propose, first, a model of a self-organizing decentralized wireless sensor network; second, a model of the attacks on such networks; third, algorithms for data collection and attack detection; and, finally, a technique for their application. The WSN model represents a formal specification of this type of network, defining the conditions and limitations of network self-organization and decentralization. The model is characterized by a proposed underlying role-based operation of network nodes and a set of their functional states. The proposed attack model covers the possible types of attacks that are relevant to a given type of WSN and are based on the exploitation of the self-organization and decentralization of the network. The developed algorithm for collecting data for attack detection presents specific types of data and their sources. The developed combined attack detection algorithm is formed of actions that detect relevant attacks on self-organizing decentralized WSNs using machine learning methods. The distinctive element of this algorithm is a set of highly specific features that are obtained by analyzing the data collected in the WSN and used to detect attacks. The proposed technique combines the constructed models and algorithms for the sake of tuning and deploying the attack detection module and the effective detection of attacks in practice. This technique specifies the main steps for the joint use of the models and algorithms and the assignment of data collection and detection parameters. The results of the experiments confirm the correctness of the constructed models, algorithms and technique due to the high values of the attack detection quality indicators. Therefore, the practical application of the proposed apparatus will facilitate improvements in the security of self-organizing decentralized WSNs. Experimental research has confirmed the practical applicability of our proposed solutions. In particular, it has shown that the proposed algorithms and the detection technique can detect both attacks implemented through the exploitation of the network’s properties of decentralization/self-organization and common variations in these attacks (i.e., without exploiting the decentralization property). In general, the experimental results expose a high quality of detection, with an f1-score equal to 0.99.

A Text Analysis Method for Student Learning Feedback on Network Teaching Platform Based on Natural Language Processing

<p>With the emergence and end of the COVID-19, online learning has become an irreplaceable way of learning. In order to promote the improvement and enhancement of online curriculum resources and increase the learning effect of students, the content of curriculum evaluation is an important reference for the direction of curriculum improvement. Therefore, this article focuses on the student learning feedback of course resources. Firstly, through data collection algorithms, effective evaluation information is crawled, and then based on the collected information, the course evaluation text is annotated and classified, forming a reasonable corpus. Finally, through feature collection and sentiment analysis algorithms, sentiment analysis is performed on the evaluation content, effectively distinguishing between positive and negative evaluations, and guiding teachers to improve the course content.</p> <p> </p>

Data Collection Algorithms for Model Training in Internet of Vehicles

Data Collection Algorithms for Model Training in Internet of Vehicles

АРХІТЕКТУРА ТА МОДЕЛІ НАДІЙНОСТІ ГІБРИДНИХ СЕНСОРНИХ МЕРЕЖ СИСТЕМ ЕКОЛОГІЧНОГО ТА АВАРІЙНОГО МОНІТОРИНГУ

The authors study the aspects of developing and analyzing the hybrid sensor networks’ operability as subsystems of environmental and emergency monitoring systems for critical infrastructure. The proposed architecture of such a system is based on the technology of edge computing (EC) and combines stationary and mobile components, the first of which is implemented by a ground sensor network (GSN), and the second by a swarm of unmanned aerial vehicles that form a flying EC network. The data collection algorithms, scaling problems, and optimization of the operation of the GSN and monitoring systems in general are analyzed. The reliability models of the GSN in the conditions of failure of one and groups of sensors are developed and investigated. Analytical dependencies of reliability indicators on different sizes of sensor failure clusters and their intensity are obtained. Recommendations for the design and implementation of hybrid sensor networks are given. Keywords: hybrid sensor networks, edge computing, reliability models, multiple failures, environmental monitoring systems, emergency monitoring systems.

Multi-objective particle swarm optimization algorithm for data acquisition and system characteristics research of phase change thermal storage heat pump water heaters

In order to understand the multi-objective particle swarm optimization algorithm for data collection and system characteristics research of heat pump water heaters, the author proposes a multi-objective particle swarm optimization algorithm for data collection and system characteristics research of phase change thermal storage heat pump water heaters. The author first introduced the working principle of the phase change heat storage heat pump water heater, and then conducted a comparative experimental study on the performance of two types of heat pump water heater systems using pure paraffin phase change heat storage and water storage. They compared and analyzed the heat storage process, condensation heat recovery rate of the heat storage box, and comprehensive energy efficiency coefficient of the system under the refrigeration and heat recovery mode of the two types of heat pump water heater systems, on this basis, measures were proposed to improve the performance of pure paraffin phase change thermal storage hot water systems. At long last, the examination of exploratory information shows that the Rx of the water warm capacity framework is around 6% higher than that of the stage change warm capacity framework, and the framework COPt is likewise 20% higher than that of the stage change warm capacity framework, from this, it tends to be seen that the stage change heat capacity box has unfortunate intensity move proficiency, prompting a lessening in the presentation of the intensity siphon water radiator framework. Contrasted and customary water stockpiling heat siphon boiling water frameworks, unadulterated paraffin stage change capacity heat siphon high temp water frameworks lessen the volume of the intensity stockpiling tank, and the framework works somewhat flawlessly during the intensity stockpiling process. However, the system?s overall energy efficiency is impacted by the heat storage tank?s poor heat exchange effect and relatively low condensation heat recovery rate. The intensity move of the intensity stockpiling box can be upgraded by finning the winding curls in the intensity stockpiling box, embedding aluminum foil, adding extended graphite, and different measures to further develop the buildup heat recuperation rate, subsequently working on the general execution of the framework.

Research on the Strategy of Educational Management and Resource Optimization Based on Big Data Technology in the Mode of Industry-Education Integration

Abstract In order to build an education information platform to support “industry-education dialogue” and at the same time serve the teaching and research of industry-education integration. In this paper, we use big data technology to construct a system of education management and resource optimization, supporting education construction in the context of industrial transformation and upgrading. For larger-scale WSNs, an energy-efficient data collection algorithm based on non-uniform clustering is proposed. To deal with large-scale optimization problems, a hybrid algorithm of genetic algorithms and particle optimization hierarchical collaboration is introduced. Then, the algorithm is applied to cluster WSNs, an adaptation function is constructed with the goal of equalizing energy consumption, and a data collection algorithm based on non-uniform clustering is proposed. The Apriori algorithm, which is based on the interest degree model, is used to mine the course evaluation data for the system design in the university industry-teaching fusion model. This system design does both course evaluation and resource optimization. The system’s total evaluation score is high, at 90.84 points. It also improves the efficiency of cooperation between the education industry and schools. It proves that the system’s design is effective and high-level and has a positive effect on teaching management and resource optimization in education.

Intelligent Traffic Data Transmission and Sharing Based on Optimal Gradient Adaptive Optimization Algorithm

This work aims to improve the transmission and sharing efficiency of intelligent transportation data and promote the further development of intelligent transportation and smart city. In this work, an EEMR (Energy Efficient Multi-hop Routing) is designed for the intelligent transportation wireless sensor network. In the EEMR algorithm, the base station runs the IAP clustering algorithm for network clustering after receiving the information of all surviving nodes. A method called ACRR (Adaptive Cluster-head Round Robin) is proposed for local dynamic election of cluster heads. In addition, the deep learning-based stochastic gradient descent algorithm and its evolution algorithm are sorted out, and its application in practical scenarios is analyzed. Due to the disadvantages of the adaptive algorithm in the current data processing process, the gradient optimization algorithm based on deep learning is adopted and the concept of adaptive friction coefficient is applied to the Adam algorithm to obtain a new adaptive algorithm (TAdam). The simulation experiment reveals that the proportion of network surviving nodes of the EEMR algorithm is still as high as 90% in 1,500 rounds of data collection. This shows that the EEMR algorithm achieves the energy balance of the network nodes as much as possible while minimizing the system energy consumption. In application scenario 1, when the network surviving nodes of the four data collection algorithms compares dropped below 40%, the number of surviving nodes in the EEMR algorithm is still as high as 97.6%. On the PTB (Penn Tree Bank) test data set, the TAdam algorithm shows the fastest convergence speed and the best generalization performance. The TAdam algorithm based on deep learning discussed in this work was of great significance for improving the transmission and sharing efficiency of intelligent transportation data.

The impact of scale on extracting urban mobility patterns using texture analysis

The development of high-precision location tracking devices and advancements in data collection, storage, transmission technologies, and data mining algorithms have led to the availability of large datasets with high spatiotemporal resolution. These geospatial big data can be used to identify human movement patterns in urban areas. However, identifying human movement patterns may yield different results depending on the scale size used. In this paper, we employed first and second order texture analysis algorithms to identify spatial patterns of human movement for various scale sizes based on taxi trajectory data from Nanjing, China. The results demonstrated that texture analysis can quantify changes in human movement patterns for different scale sizes in an urban area. Furthermore, the results may differ based on the location of the study area. This study contributed both methodologically and empirically. Methodologically, we used texture analysis to examine the impact of different scale sizes on the extraction of aggregated human travel patterns. Empirically, we quantified the effects of different scale sizes on extracting aggregated travel patterns of an urban area. Overall, the findings of this study can have significant implications for urban planning and policy-making, as understanding human movement patterns at different scales can provide valuable insights for optimizing transportation systems and enhancing overall urban mobility.

A new mobile data collection and mobile charging (MDCMC) algorithm based on reinforcement learning in rechargeable wireless sensor network

Recent research emphasized the utilization of rechargeable wireless sensor networks (RWSNs) in a variety of cutting-edge fields like drones, unmanned aerial vehicle (UAV), healthcare, and defense. Previous studies have shown mobile data collection and mobile charging should be separately. In our paper, we created an novel algorithm for mobile data collection and mobile charging (MDCMC) that can collect data as well as achieves higher charging efficiency rate based upon reinforcement learning in RWSN. In first phase of algorithm, reinforcement learning technique used to create clusters among sensor nodes, whereas, in second phase of algorithm, mobile van is used to visit cluster heads to collect data along with mobile charging. The path of mobile van is based upon the request received from cluster heads. Lastly, we made the comparison of our proposed new MDCMC algorithm with the well-known existing algorithms RLLO [32] & RL-CRC [33]. Finally, we found that, the proposed algorithm (MDCMC) is effectively better collecting data as well as charging cluster heads.

МЕТОДИКА ОБНАРУЖЕНИЯ АТАК В САМООРГАНИЗУЮЩИХСЯ ДЕЦЕНТРАЛИЗОВАННЫХ БЕСПРОВОДНЫХ СЕНСОРНЫХ СЕТЯХ

The work is devoted to the development of an attack model and a technique for detecting attacks in self-organizing decentralized wireless sensor networks. The proposed model describes possible types of attacks and their characteristics, taking into account the properties of self-organization and decentralization. The methodology is focused on the protection of wireless sensor networks deployed on the ground, used for emergency response, and describes the stages of the process of building and configuring an attack detection mechanism based on data collection algorithms in wireless sensor networks and the use of machine learning methods. The analysis of possible types of data that need to be collected at the nodes of wireless sensor networks to detect attacks is carried out. The distinctive features of the proposed technique include the sets of features used that characterize specific types of attacking influences and allow detecting attacks with high values of the detection quality indicator. On the fragment of the hardware-software prototype of wireless sensor networks used in the work with an attack detection mechanism built into it, an experiment was conducted to check the quality of attack detection, confirming the correctness of the proposed technique.