Periodic Data Collection Research Articles

Improving the assessment and documentation of systemic cancer therapy–related toxicity in the ambulatory oncology setting.

221 Background: Accurate documentation of systemic cancer therapy toxicity is crucial for patient safety and effective treatment management. However, achieving consistent and comprehensive documentation remains challenging. While the use of electronic patient-reported outcomes (ePROs) is becoming standard in clinical practice, small community-based practices lack the resources to implement this strategy. This project aims to offer alternative methods for implementing patient-reported outcomes in such practices and utilize this information to enhance the documentation of systemic cancer therapy toxicities using the CTCAE (Common Terminology Criteria for Adverse Events). Methods: We conducted a quality improvement project targeting the adequate assessment and documentation of graded toxicity assessments among healthcare providers. Interventions included implementing a patient-reported outcome survey created using the National Cancer Institute PRO-CTCAE form builder, which patients completed while waiting to be seen by the provider. Additionally, we educated providers on how to use the CTCAE grading tool available in Cerner. Rates of documented graded toxicity assessment using the CTCAE system were evaluated among 71 patients aged 18 years or older receiving systemic cancer therapy and attending follow-up appointments at the Seton Cancer Clinic. Data were collected from electronic medical records both before and after the intervention, with 50 patients assessed pre-intervention and 21 patients assessed post-intervention. Results: The implementation of the patient-reported outcome survey and the utilization of the CTCAE grading tool available in Cerner facilitated a more consistent and comprehensive assessment and documentation process. Post-intervention, the rate of documented graded toxicities increased significantly; the percentage of patients with documented graded toxicities increased from 4% pre-intervention to 80% post-intervention. The intervention also improved the workflow efficiency and the accuracy of toxicity assessments in the oncology clinic. Conclusions: These findings highlight the importance of implementing strategies to improve the assessment and documentation of systemic cancer therapy-related toxicity, especially in small community-based practices where resources may be limited. Continued monitoring and periodic data collection will be essential for sustaining these improvements.

Developing building-specific, occupant-centric thermal comfort models: A methodological approach

This study addresses the research problem of accurately predicting thermal comfort in buildings by developing occupant-centric, building-specific models. Traditional models like PMV and adaptive models often fall short in reflecting individual comfort preferences. This research aims to create more accurate and personalised thermal comfort models by considering human-related factors such as clothing, mood, and activities, along with environmental variables like temperature and humidity. The methodology involved collecting data through two self-reporting campaigns using mobile and smartwatch applications in a university building in The Hague. Regression and classification models were developed, achieving accuracy rates of 72 % and 89 % respectively, which surpass the performance of traditional models. The findings indicate that human factors significantly influence thermal comfort, with mood and clothing being particularly impactful. Seasonal variations were also accounted for, emphasising the need for periodic data collection to capture changes in occupant behaviour and environmental conditions. The key contribution of this research lies in its ability to enhance the accuracy of thermal comfort predictions, leading to more effective and user-centric HVAC system operations. This approach not only has the potential to improve the comfort of building occupants but also has implications for energy efficiency, as HVAC systems can be better tailored to actual comfort needs.

IoT based weather monitoring system for temperature, humidity, rainfall and seismic detection in real time

This paper presents the design and implementation of an IoT-based weather monitoring system enhanced with seismic detection capabilities, employing Node MCU microcontroller and the Blynk mobile application platform. The system incorporates various sensors including a rain sensor, Light light-dependent resistor (LDR), a DHT11 sensor for temperature and humidity measurement, and an accelerometer (MPU6050) for earthquake detection. The hardware components are interconnected with the Node MCU microcontroller, facilitating real-time data acquisition from the sensors. Programming of the Node MCU is conducted using the Arduino IDE, enabling data transmission to the Blynk app via Wi-Fi connectivity. The Blynk app serves as the user interface, allowing seamless visualization and monitoring of weather parameters and seismic activity on smartphones. Key functionalities of the system include periodic data collection from sensors, transmission of sensor data to the Blynk server, and updating of graphical representations in the Blynk app interface. The accelerometer sensor is utilized to detect changes in acceleration, indicative of seismic disturbances. Threshold values are defined to trigger alerts within the Blynk app in the event of potential seismic activity. The implementation undergoes rigorous testing to ensure accuracy, reliability, and responsiveness under diverse environmental conditions. Considerations such as network stability, power efficiency, and data security are addressed to enhance system performance and longevity. The system demonstrates promising capabilities for real-time weather monitoring and seismic event detection, offering valuable insights for disaster preparedness and environmental analysis. IoT-based weather monitoring system integrated with seismic detection provides an effective solution for comprehensive environmental monitoring, leveraging the power of IoT technology and mobile applications for enhanced data accessibility and analysis. Further advancements and optimizations can be explored to extend the functionality and scalability of the system for broader applications in environmental science and disaster management.

ВІДНОВЛЕННЯ ТА РОЗВИТОК БІЗНЕСУ: МАРКЕТИНГОВІ ПІДХОДИ ДО РЕЗИЛЬЄНТНОСТІ СОЦІАЛЬНО-ЕКОНОМІЧНИХ СИСТЕМ

Amidst the persistent socio-economic challenges stemming from hostilities in Ukraine, domestic enterprises must consistently adapt their management decisions and marketing strategies to sustain viability, ensure demand, and enhance competitiveness. The accelerated development of digital technologies and the ongoing digitalization of society have significantly influenced business development in recent years. In response, enterprises need to swiftly identify threats and opportunities, respond instantly to new challenges, and adopt a proactive stance during their marketing activities. This article explores the state of entrepreneurial activity during military operations in Ukraine, revealing that enterprises continue to operate and contribute to the country's economic recovery. The study identifies the main challenges faced by Ukrainian enterprises in wartime conditions, including the loss of customers, declining sales, low solvency of the population, rising prices, a shortage of highly qualified specialists, and the restructuring of marketing activities within enterprises. Key factors contributing to the resilience of socio-economic systems are highlighted, encompassing state support, changes in the economic and social structure, infrastructure development, innovativeness, digitalization of the economy and business, funding opportunities, and international cooperation. The article also identifies essential factors that enable businesses to adapt to the resilience of socio-economic systems, such as the utilization of digital marketing technologies, integration of new platforms, investment in marketing technology and talented marketers, personalized marketing, periodic data collection and utilization, social responsibility, localization and globalization strategies, innovation, creativity, environmental awareness, and resilient marketing. The article proposes specific features for the formation of marketing strategies in the conditions of an economic crisis for the recovery and development of businesses in Ukraine. These features include a comprehensive understanding of the situation, adjustment of the current marketing strategy in accordance with new realities, emphasis on the company's values and mission, fostering innovation and adaptation (such as developing new products or modifying existing ones to meet evolving needs), strengthening online presence, implementing effective budget management, and integrating technology.

Improving Metro Access in India: Evidence from Three Cities

Poor access (last-mile connectivity) to metro rail systems in India has contributed to lower-than-planned ridership, causing the underutilization of over US$25 billion in investments in the sector. There is little understanding of what metro commuters seek from their last-mile commute. This paper draws from a three-city survey of 7,200 metro commuters to understand current metro user demographics and last-mile choices and preferences. Our data shows that Indian metro systems attract young, middle-income commuters. Indian metro commuters are highly sensitive to last-mile wait times and costs. The paper suggests that robust, periodic data collection and analysis are required to plan viable commuter-centric last-mile services.

Approximation algorithms for vehicle-aided periodic data collection from mobile sensors with obstacle avoidance in WSNs

Data collection from the sensors in time is an integral part of many applications of wireless sensor networks (WSNs). Vehicles, referred to as Mobile Sinks (SNKs), may be used to collect data from the sensors by visiting them. Since the sensors have limited memory, the sensed data needs to be collected by the SNKs within a predefined time interval to avoid memory overflow. Periodic data collection by SNKs becomes even more challenging when the sensors are mobile. Moreover, the presence of obstacles in the area makes the path planning for SNKs even more complicated. In this paper, an optimization problem, referred to as Minimum Mobile Sink Aided Periodic Data Collection (MinSnkDC) problem, is formulated, where the objective is to determine the minimum number of SNKs that collect data for every time period from the mobile sensors in the WSN, while avoiding collision with the obstacles in the area. The problem is proved to be NP-complete. Two constant factor approximation algorithms, namely MinSnkDC and Modified MinSnkDC (M-MinSnkDC), are proposed to solve the problem. From the simulation results, it is evident that M-MinSnkDC can produce a better solution compared to the existing obstacle-aware SNK-based data collection algorithms, while using a small number of SNKs.

Apache IoTDB: A Time Series Database for IoT Applications

A typical industrial scenario encounters thousands of devices with millions of sensors, consistently generating billions of data points. It poses new requirements of time series data management, not well addressed in existing solutions, including (1) device-defined ever-evolving schema, (2) mostly periodical data collection, (3) strongly correlated series, (4) variously delayed data arrival, and (5) highly concurrent data ingestion. In this paper, we present a time series database management system, Apache IoTDB. It consists of (i) a time series native file format, TsFile, with specially designed data encoding, and (ii) an IoTDB engine for efficiently handling delayed data arrivals and processing queries. The system achieves a throughput of 10 million inserted values per second. Queries such as 1-day data selection of 0.1 million points and 3-year data aggregation over 10 million points can be processed in 100 ms. Comparisons with InfluxDB, TimescaleDB, KairosDB, Parquet and ORC over real world data loads demonstrate the superiority of IoTDB and TsFile.

An Efficient Framework for Unsupervised Anomaly Detection over Edge-Assisted Internet of Things

The detection of anomaly status plays a pivotal role in the maintenance of public transportation and facilities in smart cities. Owing to the pervasively deployed sensing devices, one can collect and apply multi-dimensional sensing data to detect and analyze potential anomalies and react promptly. Current efforts concentrate on offline manners and fail to fit the situation in smart cities, where efficient and online solutions are expected. In this paper, a novel framework is designed for anomaly detection over edge-assisted Internet-of-Things (IoTs). The framework allows periodical data collection from sensors and continuous anomaly detection at the edge node. A novel efficient and unsupervised deep learning model is designed to balance the resource consumption and accuracy for anomaly detection, based on the combination of a convolutional autoencoder and adversarial training. Meanwhile, the proposed framework also adopts an adaptive strategy for continuous anomaly detection to reduce the overall resource consumption. According to theoretical analysis and evaluation on several real-world datasets, the proposed framework can discover the potential correlation features among multi-dimensional sensing data, and efficiently detect the abnormality of public transportation and facilities in smart cities.

Identifying social thresholds and measuring social achievement in social-ecological systems: A cross-regional comparison of fisheries in the United States

In marine social-ecological systems (SESs), environmental and human-induced stressors can push ecosystems from a high-functioning state into a new, often undesirable state (i.e., regime shift) with limited delivery of ecological goods and services (e.g., high to low fisheries production). While ecological regime shifts are well studied, social regime shifts within SESs are underexplored. Socioeconomic indicators were used to identify thresholds and trends in fisheries and coastal employment for six marine SESs around the U.S. These study sites represent coastal regions delineated by the National Oceanic and Atmospheric Administration for which periodic monitoring and data collection occur. We first used Generalized Additive Modeling to identify periods of change, then linked them to potential regional and national drivers. Social outcomes were ranked using composite social and environmental indices for each region, constructed using Data Envelopment Analysis. Technological innovation and national regulatory changes (e.g., Magnuson Stevens Act) co-occurred with detected nationwide shifts in fisheries productivity, while engagement in specific fisheries determined local regional shifts. Our study demonstrates the effectiveness of the complementary threshold analysis and outcome ranking methods in identifying regimes and assessing performance. Together, they provide management information and insight into possibilities for preventing unfavorable shifts and to assess society’s ability to adapt to those shifts.

Efforts to Implement Rice Subsidy Distribution for Economically Underprivileged People in Kulon Progo

The distribution of rice subsidies for low-income communities has experienced many problems, so this research attempts to examine the implementation of rice subsidies for low-income communities in Kulon Progo Regency, the obstacles and efforts to overcome them. The methods used include types of sociological juridical research and analytical descriptive research specifications. The results of the study show that the implementation of rice subsidies for people's income still faces many obstacles. includes 5 namely: First, the difficulty of determining the criteria for the level of poverty, in this case, can be attempted by conducting periodic data collection, secondly, the amount of rice and beneficiaries is not the same, this can be attempted by forming special officers in receiving the distribution and weighing of raskin, thirdly the price of rice is expensive, this can be attempted by in accordance with the provisions of the law, the fourth is the delay in distribution, this can be attempted with simultaneous coordination by officials giving, distributing and receiving, fifth the quality of rice is low, this can be attempted by mixing subsidized rice with rice on the market.

Pengklasterisasian Data Penyakit Hipertensi dengan Menggunakan Metode K-Means

The increasing number and variety of diseases suffered by the community due to lifestyle changes that are influenced by the progress of the times. Periodic disease data collection will increase the accumulation of data. This is often an error in the data search process so that it takes a long time to search the data. This study focuses on data collection on hypertension and aims to cluster the data. The method used in this study is CRISP-DM with a business understanding process, data understanding, data preparation, modeling, evaluation and deployment. The algorithm used in this clustering is the K-Means algorithm. The results of this study resulted in 2 clusters, namely cluster 0 Normal and cluster 1 Hypertension. The results of this study can provide information about the results of 2 clusters, namely cluster 0 Normal and cluster 1 Hypertension

ANALYSIS OF MOTHER'S KNOWLEDGE ABOUT PENTABIO IMMUNIZATION WITH THE DETERMINATION OF GIVING PENTABIO IMMUNIZATION TO INFANTS IN NAGAN RAYA DISTRICT

The Pentabio vaccine (DTP-Hb-Hib) is a vaccine that functions to prevent diphtheria, tetanus, pertussis, Hepatitis B, and inflammation of the brain epidermis (meningitis) in children under the age of 5 years (infants). The Pentabio vaccine began to be used in 2013 in 4 provinces and in 2014 targeted 33 provinces in Indonesia. The type of research used in this research is descriptive qualitative. This research was conducted at the Nagan Raya District Health Office. The reason for choosing the location was that there was an increased child mortality rate in 2018. The research was conducted from August to September 2022. The results of this study are (1) Standardization of the provision of pentabio immunization is a success factor the course of immunization, (2) there is still a lack of knowledge of parents about immunization, especially Pentabio, (3) to increase parental awareness, proper knowledge about vaccination is needed, (4) incompetent human resources, (5) incomplete information services. Therefore, doctors, pharmacists, nurses and other health service providers can provide correct information to parents about the risks and benefits of vaccines. Nagan Raya, such as carrying out periodic immunizations, socialization, data collection, and appeals.

Condition Monitoring of Rotary Machinery Using Industrial IOT Framework

Modern maintenance strategies, such as predictive and prescriptive maintenance, which derived from the concept of Industry and Maintenance 4.0, involve the application of the Industrial Internet of Things (IIoT) to connect maintenance objects enabling data collection and analysis that can help make better decisions on maintenance activities. Data collection is the initial step and the foundation of any modern Predictive or Prescriptive maintenance strategy because it collects data that can then be analysed to provide useful information about the state of maintenance objects. Condition monitoring of rotary equipment is one of the most popular maintenance methods because it can distinguish machine state between multiple fault types. The topic of this paper is the presentation of an automated system for data collection, processing and interpretation of rotary equipment state that is based on IIoT framework consisting of an IIoT accelerometer, edge and fog devices, web API and database. Additionally, ISO 10816-1 guidance has been followed to develop module for evaluation of vibration severity. The collected data is also visualized in a dashboard in a near-real time and shown to maintenance engineering, which is crucial for pattern monitoring. The developed system was launched in laboratory conditions using rotating equipment failure simulator to test the logic of data collection and processing. A proposed system has shown that it is capable of automated periodic data collection and processing from remote places which is achieved using Node RED programming environment and MQTT communication protocol that enables reliable, lightweight, and secure data transmission.

Periodic data collection from mobile sensors with unpredictable motion along road networks

Periodic data collection from mobile sensors with unpredictable motion along road networks

Testing Polygon for Simulated Vertical Displacement Measurements based on Co-located InSAR Corner Reflector and GNSS Station

With the growing availability of accurate and long-term measurements of displacements of technical infrastructure elements, there is a growing interest in the automated processing of acquired data. Various methods can be used for monitoring; however, radar interferometry and Global Navigation Satellite Systems (GNSS) measurements are among the best for long-term monitoring. GNSS allows for continuous monitoring of individual points, while radar interferometry allows only periodic data collection but with an areal coverage. Radar interferometry can also reach a better precision under certain conditions; therefore, it appears to be more appropriate. Automated systems are being developed that allow not only to process radar data but also to detect anomalies in vertical displacement. It is advisable to have a testing polygon for their verification, enabling the comparison of the automated processing of radar interferometry with an independent GNSS measurement. In autumn 2019, a testing polygon was built at the Department of Geoinformatics, HGF VSB – Technical University of Ostrava, consisting of three corner reflectors. Two are fixed, but one, complemented by a GNSS receiver, has an adjustable height. The article describes its construction and presents the first results of comparing automated radar interferometry processing with GNSS measurements. Keywords: Corner reflector; Corner reflector and GNSS receiver co-location; Displacement monitoring; GNSS; InSAR; PS-InSAR; Sentinel-1; Testing polygon; Vertical displacement.

ADVICE: Towards adaptive scheduling for data collection and DDoS detection in SDN

Distributed Denial of Service (DDoS) is one of the most rampant attacks which threaten network security. To overcome DDoS in Software-Defined Networking (SDN), many DDoS detection methods have been presented, among which periodic detection approaches with a specific interval are widely utilized. However, periodic data collection and DDoS detection may result in high network load occupancy between SDN controller and switches, high overhead of SDN controller, and long response time to DDoS attacks. Hence, in order to address those issues above, an ADaptiVe schedulIng for data Collection and DDoS dEtection (ADVICE) mechanism is proposed in this work, to flexibly adjust the data collection and detection interval and decrease the workload of the SDN controller. Instead of detects all flow entries at one period, ADVICE collects the flow statistic information with dynamic intervals and finely-grained initiate DDoS detection for each flow entry. Based on the survival time and credence degree of each flow entry, ADVICE can reduce the network load occupancy and assure the rapid detection of DDoS. Experimental results indicate that ADVICE can effectively minimize the controller's workload and optimize the usage of the limited switch-controller connection bandwidth, shorten the response time of DDoS attacks compared with state-of-the-art methods, and thus protect the network from various DDoS attacks.

Data delivery performance on MongoDB generated by WSN over high data load

Wireless sensor networks (WSN) Environment is a collection of nodes that sensing and sending data to other nodes in a large number of nodes. One of the WSN concepts is continuous or periodic data collection, then the data obtained can be further processed. From this process, it is possible to produce large data, so the process of sending and storing data becomes a high load. Data obtained from WSN nodes vary greatly, which affects on sending and storing of data. Therefore, a study is needed to analyze WSN's ability to send and store large amounts of data. Message query telemetry protocol (MQTT) is used because it could save resources on communication and MongoDB used because it does not have the concept of tables and rows, this is very suitable for variations in data generated by WSN. In this study, it can be concluded that the performance of MongoDB on high data amount is acceptable, so MongoDB is highly recommended for WSN implementation.

NACCHO announces funding and TA for SSPs

Via federal grants, the harm‐reduction workforce may take off for the first time (see story, p. 1), with the help of funding for staffing, technical assistance (TA), and more. Most recently, the Centers for Disease Control and Prevention and the University of Washington have partnered with the National Association of County and City Health Officials (NACCHO) to fund monitoring and evaluation of syringe services programs (SSPs), and to support the use of data for SSP program improvement. The funding will go to local health departments and community‐based SSPs. SSPs, originally created to help prevent HIV transmission via needle sharing, have become part of the harm‐reduction landscape with the opioid overdose epidemic, as a place for drug users to turn instead of the street for help. Monitoring and evaluations will include routine (e.g., intake or encounter‐level) and periodic (e.g., surveys) data collection, as well as management, analysis, evaluation and reporting. Up to $98,750 can be requested per application. Applications are due May 21. For more information, go to the Request for Applications (RFA) at https://www.naccho.org/uploads/body‐images/NACCHO‐Harm‐Reduction‐Monitoring‐and‐Evaluation‐RFA‐FINAL.pdf. The RFA includes an appendix that characterizes common software used by SSPs. NACCHO and UW will hold “office hours” Tuesdays from 1:00 to 2:00 p.m. ET from April 27 to May 18. Applicants are encouraged to sign up for a specific time slot. Go to the Zoom link (https://washington.zoom.us/meeting/register/tJwqceiupzgiHtUY8mVISv2eopBnk9PHwmLD) and also go to the sign‐up link (https://airtable.com/shrDeGHAbUO5K4mq8). If you have questions, submit them in advance via form or by emailing SSP Measurement and Evaluation Technical Assistance Program Manager Kelly Knudtson (kellyknu@uw.edu). Kat Kelley, senior program analyst at NACCHO for HIV, STI, and viral hepatitis, will need all of the documents for the grant, titled “Building Capacity for Harm Reduction Monitoring and Evaluation.” State health departments are eligible to apply for the funding. SSPs that are currently operating are eligible as well. In states that don't allow SSPs, there may be no opportunity for funding. The grant money cannot be used for lobbying. However, if SSPs are legally allowed to operate in a certain jurisdiction but face restrictions such as exchange only with no secondary syringes allowed, the grant could be used for data collection and analysis to demonstrate the benefits of changing policy and practice. Funds cannot be used for syringes, naloxone or fentanyl test strips. Funding can be used for staff time to conduct monitoring and evaluation. Approaches such as vending machines, marketing promotion of SSPs and other issues may be funded as well, providing the applicant describes how these activities would enhance SSP monitoring and evaluation, or the use of SSP monitoring and evaluation data. Funds could not be used to purchase a vending machine. The number of awards depends on the funding amounts requested by the organizations with the highest scores in their applications. The smallest number of programs that would be funded is 16, but the amount could be higher if applicants do not request the maximum funding amount ($98,750). “Equity” is important as well; grantees who find that they primarily serve cisgender men and women, for example, may use the funds to conduct data‐collection activities with trans, nonbinary or gender‐nonconforming people to see how the SSP could better meet their needs.

Impact of Integrated Agronomic Practices on Soil Fertility and Respiration on the Indo-Gangetic Plain of North India

Global agricultural production is accountable for the emission of ~30% of greenhouse gases. Therefore, the wide-scale adoptions of low-input, soil-friendly, and resource-conserving agronomic practices are imperative for the ‘planet healthy food production’ and also for reducing the carbon emissions from agricultural soil. In this context, the present study aimed to analyze the impacts of integrated agronomic interventions i.e., the application of arbuscular mycorrhizal fungi (AMF) + reduced tillage (RT), biochar + RT, and AMF + biochar + RT, on spatiotemporal variations in soil-quality and soil-sustainability indicators, including microbial and soil respiration, in the Indo-Gangetic Plain (IGP) of North India. For this, field experiments on the above-mentioned agronomic interventions were employed using three different staple crops (Zea mays, Vigna mungo, and Brassica juncea) growing in three different agro-climatic zones of IGP (Varanasi, Sultanpur, and Gorakhpur) in a randomized block design. Periodic data collection was done to analyze the changes in physiochemical, biological, and biochemical properties of the soil, and statistical analyses were done accordingly. Irrespective of the sites, the experimental results proved that the integrated application of AMF + biochar + RT in V. mungo resulted in the highest soil organic carbon (i.e., 135% increment over the control) and microbial biomass carbon (24%), whereas the same application (i.e., AMF + biochar + RT) in Z. mays had the maximum reduction in microbial (32%) and soil (44%) respiration. On the other hand, enhanced occurrence of glomalin activity (98%) was noted in Z. mays cropping for all the sites. Significant negative correlation between soil respiration and glomalin activity under AMF + biochar + RT (−0.85), AMF + RT (−0.82), and biochar + RT (−0.62) was an indication of glomalin’s role in the reduced rate of soil respiration. The research results proved that the combined application of AMF + biochar + RT was the best practice for enhancing soil quality while reducing respiration. Therefore, the development of suitable packages of integrated agronomic practices is essential for agricultural sustainability.

A Novel UAV-Enabled Data Collection Scheme for Intelligent Transportation System Through UAV Speed Control

The rapid and convenient travel of people and the timely transportation of goods depend on the correct decision of the Intelligent Transportation Systems (ITS). Due to the decision-making of ITS requires a large amount of data to support, UAV-enabled periodic data collection is an effective method. However, due to the limited resources of UAV, UAV cannot directly collect data from all storage devices, resulting in unfair data collection. Therefore, we propose a UAV Speed Control based Fairness Data Collection (USCFDC) scheme. First, since the fairness of data collection will affect the decision-making of ITS, a framework for controlling the flight speed of the UAV is proposed to improve the fairness of data collection. The flight speed of UAV will slow down in areas with a large number of nodes, thereby improving the fairness of data collection. Second, a novel method is proposed to maximize the amount of data collected by UAV from each node. With this method, the value of the amount of data will be used as the dichotomous value in the dichotomy algorithm, and the UAV must collect a certain amount of data from each node. The upper and lower limits of the dichotomy algorithm are adjusted according to the time duration for UAV to collect data. Compared with previous schemes, the fairness of data collection can be improved by a maximum of 15.89% under the same flight time of UAV. Besides, the energy consumption is reduced by 49.31%-52.55% and the flight time of the UAV is reduced by 48%-62.38% when the amount of collected data is the same.