Data Collection Scheme Research Articles

Multi-UAVs cooperative task assignment and path planning scheme

The task assignment and path planning problems of multiple unmanned aerial vehicles (multi-UAVs) cooperative assistance roadside units (RSUs) for data collection are optimization problems with the goal of minimizing time and energy consumption. This paper proposes a hierarchical optimization scheme for multi-UAVs collaborative assistance RSUs data collection. This solution solves the problems that the number of UAVs needs to be set in advance, the convergence speed is slow when the number of tasks increases, and it is easy to fall into a local optimal solution, and the convergence accuracy is poor. First, the solution uses the K-means algorithm to allocate tasks and group RSUs to find the right number of UAVs to perform the task. Then, this paper proposes a hybrid optimization algorithm based on bionic learning for path planning. Finally, we set up a reasonable evaluation mechanism and conducted simulation experiments. The algorithm in this paper is compared with genetic algorithm, gray wolf algorithm and whale algorithm, the results show that the total cost of the task obtained by the proposed algorithm is the lowest, the algorithm stability is better, and the convergence accuracy is the highest.

A Conceptual Framework for Incorporating Human Dimensions Into Integrated Ecosystem Assessments

The evolution of fisheries science and management toward an ecosystem perspective necessitates the meaningful incorporation of human dimensions. Whereas great strides have been made over the last several decades at moving toward ecosystem based fisheries management (EBFM), largely through the development of integrated ecosystem assessments (IEAs), the inclusion of human dimensions into these efforts has often been fragmentary and, in juxtaposition to the biophysical dynamics, sometimes even seemingly superficial. This presents a great challenge to the accuracy and applicability of these results, as the lack of appropriate incorporation of humans can be problematic in terms of both social and biophysical consequences. This study systematically documents current social science understanding of the multiple human dimensions that should be incorporated within ecosystem assessments and the overall approach to each of these within IEAs and other EBFM efforts. These dimensions include the multi-faceted nature of human well-being, heterogeneity in human well-being derived from fisheries, adaptive behaviors, and cumulative effects. The systematic inclusion of these dimensions into IEAs is then laid out in a conceptual framework that details how a perturbation reverberates through a fisheries system and the iterative approach that should be undertaken to understand its impacts on human dimensions. This framework is supplemented with a data collection scheme that is intended to facilitate operationalization. The detailed examination of incorporating human dimensions within IEAs presented in this study should further resonate with other ecosystem assessment efforts, providing not just ample evidence of the need for moving beyond simplistic assumptions of human homogeneity but a means of systematically integrating a more realistic and representative perspective.

Deep Learning-Based Ground Vibration Monitoring: Reinforcement Learning and RNN–CNN Approach

This letter studies deep learning-based efficient ground vibration monitoring systems. In this work, artificial intelligence (AI) techniques are adopted to effectively deal with practical issues of data collection and classification. Specifically, we develop a novel energy-efficient data collection scheme by adopting deep Q-network-based reinforcement learning. Also, we propose an enhanced joint recurrent neural network (RNN) and convolutional neural network (CNN) approach for ground vibration classification. The performance of the proposed scheme is evaluated using real-world ground vibration data. The experimental results show that the proposed classification scheme outperforms the best existing scheme with CNN by more than 13% in terms of classification accuracy. It is also shown that the proposed energy management scheme can improve the accuracy of the proposed ground vibration monitoring system by 7.6% over the comparable scheme using equal power allocation.

Perspectives of different stakeholders on data use and management in public health emergencies in sub-Saharan Africa: a meeting report

During public health emergencies (PHEs), data are collected and generated from a variety of activities and sources, including but not limited to national public health programs, research and community-based activities. It is critical that these data are rapidly shared in order to facilitate the public health response, epidemic preparedness, as well as research during and after the epidemic. Nonetheless, collecting and sharing data during PHEs can be challenging, especially where there are limited resources for public health and research-related activities. In a symposium that brought together different stakeholders that were involved in the 2013-2016 Ebola outbreaks in West Africa, meeting attendees shared their perspectives on the values and management of data during PHEs in sub-Saharan Africa. Key factors that could inform and facilitate data management during PHEs in sub-Saharan Africa were discussed, including using data to inform policy decisions and healthcare; a coordinated data collection and management scheme; identifying incentives for data sharing; and equitable data governance mechanism that emphasise principles of reciprocity, transparency and accountability rather that trust between stakeholders or collaborators. Empirical studies are required to explore how these principles could inform best practices for data management and governance during PHE in sub-Saharan Africa.

UAV-Aided Cooperative Data Collection Scheme for Ocean Monitoring Networks

In this article, we present an unmanned aerial vehicle (UAV)-aided ocean monitoring network for remote oceanic data collection, in which monitoring data are transmitted first from battery-powered underwater sensor nodes (USNs) to sea surface sink nodes (SNs) in a data collection cycle using underwater acoustic communication, and then a UAV hovering in air collects all the data from SNs and relays them to a ground base station via wireless communication links. Aiming at maximizing network lifetime, we model the resource allocation, USN-to-SN access, and SN-to-UAV access issues as a mixed-integer nonconvex optimization problem. To efficiently solve it, we decompose the optimization into two stages. The first stage is to minimize time consumption in an SN-to-UAV nonorthogonal multiple access process and we solve it by designing a UAV deployment scheme, a subchannel matching scheme, and a joint power and time allocation scheme, based on which, the second stage is to maximize the residual energies of USNs in USN-to-SN transmissions under a modified frequency-division multiple access strategy in each collection cycle. The second-stage optimization is further decomposed into some similar subproblems, and each of them is considered as a bipartite graph matching problem between USNs and underwater acoustic channels. For each subproblem, we propose improved weight-based matching and bisection-based searching algorithms. Finally, we design a low-complexity iteration algorithm to approximate the optimal solution of the original problem by solving these subproblems. The simulation results validate the effectiveness of our proposals.

A Secure and Verifiable Continuous Data Collection Algorithm in Wireless Sensor Networks

Data collection is a key operation in wireless sensor networks. In view of the privacy-preserving problem of the existing data collection schemes, this paper proposes a secure and verifiable continuous data collection algorithm (SVCDC) in wireless sensor networks. Taking the temporal correlation of sensory data into consideration, SVCDC reconstructs multiple sensory data in one period, which can effectively decrease data traffic, and then by encrypting the reconstructed data, SVCDC ensures the privacy of the sensory data. In addition, in SVCDC, the “fingerprint” of sensory data is generated, aggregated and transmitted to Sink. Then, Sink extracts the “fingerprint”, which ensures the verification of the integrity and timeliness of each node’s sensory data and the detections of attack such as replaying or discarding data. Since the “fingerprint” is aggregated and much shorter than the original data, the communication cost of “fingerprint” is low. Theoretical analysis and experimental results show that SVCDC has advantages in traffic.

Production Flow Analysis in a Semiconductor Fab Using Machine Learning Techniques

In a semiconductor fab, wafer lots are processed in complex sequences with re-entrants and parallel machines. It is necessary to ensure smooth wafer lot flows by detecting potential disturbances in a real-time fashion to satisfy the wafer lots’ demands. This study aims to identify production factors that significantly affect the system’s throughput level and find the best prediction model. The contributions of this study are as follows: (1) this is the first study that applies machine learning techniques to identify important real-time factors that influence throughput in a semiconductor fab; (2) this study develops a test bed in the Anylogic software environment, based on the Intel minifab layout; and (3) this study proposes a data collection scheme for the production control mechanism. As a result, four models (adaptive boosting, gradient boosting, random forest, decision tree) with the best accuracies are selected, and a scheme to reduce the input data types considered in the models is also proposed. After the reduction, the accuracy of each selected model was more than 97.82%. It was found that data related to the machines’ total idle times, processing steps, and machine E have notable influences on the throughput prediction.

An efficient clustering-based anonymization scheme for privacy-preserving data collection in IoT based healthcare services

The healthcare services industry has seen a huge transformation since the prominent rise of the Internet of Things (IoT). IoT in healthcare services includes a large number of unified and interconnected sensors, and medical devices that generate and exchange sensitive information. Thus, an enormous amount of data is transmitted through the network which raises an alarming concern for the privacy of patient information. Therefore, privacy preserving data collection (PPDC) is on-demand to ensure the privacy of patient data. Several pieces of research on PPDC have been proposed recently. However, the research literatures have fallen short in privacy requirements and are prone to various privacy attacks. In this paper, we propose a novel privacy-preserving data collection scheme for IoT based healthcare services systems. A clustering-based anonymity model is utilized to develop an efficient privacy-preserving scheme to meet privacy requirements and to prevent healthcare IoT from various privacy attacks. We formulated the threat model as client-server-to-user to ensure privacy on both ends. On the client-side, a modified clustering-based k-anonymity model with α-deassociation is used to anonymize the data generated from the IoT nodes. The base-level privacy is then ensured through a bottom-up clustering method which generates clusters of records as per the privacy requirements. On the server-side, the cluster-combination method-UPGMA is utilized to reduce communication costs and to achieve a better level of privacy. The proposed scheme is efficient in tackling privacy attacks such as attribute disclosure, identity disclosure, membership disclosure, sensitivity attacks, similarity attacks, and skewness attacks. The effectiveness and efficiency of the proposed scheme are proven through theoretical and experimental analyses.

An Exploration of Stress: Leveraging Online Data from Crowdsourcing Platforms

Background: Early detection of community health risk factors such as stress is of great interest to health policymakers, but representative data collection is often expensive and time-consuming. It is important to investigate the use of alternative means of data collection such as crowdsourcing platforms. Methods: An online sample of Amazon Mechanical Turk (MTurk) workers (N = 500) filled out, for themselves and their child, demographic information and the 10-item Perceived Stress Scale (PSS-10), designed to measure the degree to which situations in one’s life are appraised as stressful. Internal consistency reliability of the PSS-10 was examined via Cronbach’s alpha. Analysis of variance (ANOVA) was utilized to explore trends in the average perceived stress of both adults and their children. Last, Rasch trees were utilized to detect differential item functioning (DIF) in the set of PSS-10 items. Results: The PSS-10 showed adequate internal consistency reliability (Cronbach’s alpha = 0.73). ANOVA results suggested that stress scores significantly differed by education (p = 0.024), employment status (p = 0.0004), and social media usage (p = 0.015). Rasch trees, a recursive partitioning technique based on the Rasch model, indicated that items on the PSS-10 displayed DIF attributable to physical health for adults and social media usage for children. Conclusion: The key conclusion is that this data collection scheme shows promise, allowing public health officials to examine health risk factors such as perceived stress quickly and cost effectively.

Trusted data collection for Internet of Things

SummaryTrusted date collection is the precondition of the security of Internet of Things (IOT). Recently, compressed sensing technology, which can enable the reduction of the energy consumption and time delay of data collection, has been extensively studied in IOT; However, the security of compressed sensing technology in IOT has not been sufficiently considered. Since compressed sensing depends the cooperation between IOT nodes and then sensing data is transparent for all IOT nodes, which is subjected to malicious nodes attack. Therefore, we propose a trusted data collection scheme based on compressed sensing oriented to the IOT. When data collection is tampered with, the scheme can detect malicious nodes. Experiments demonstrate that the proposed scheme can ensure data security with a low energy consumption.

Fast Node Clustering Based on an Improved Birch Algorithm for Data Collection Towards Software-Defined Underwater Acoustic Sensor Networks

In this paper, we take an in-depth study on the data collection scheduling issue in underwater acoustic sensor networks (UASNs). To address this issue, we propose the use of software-defined networking (SDN) technique, based on which the paradigm of software-defined underwater acoustic sensor networks (SD-UASNs) is proposed. With SD-UASNs, we propose an efficient data collection scheme on account of node clustering. We take the data scale into account, propose a fast node clustering approach based on an improved Birch algorithm including node pre-clustering and node re-clustering. In particular, for the node pre-clustering procedure, we improve the cluster feature tree of the Birch algorithm and limit the nodes in each cluster to be within an optimal distance. Based on the proposed pre-clustering approach, we propose a greedy data collection scheme that can heuristically seek optimal solutions when the compressed sampling is taken into account. Evaluation results demonstrate that our scheme performs better in network efficiency than some popular schemes.

Delirium screening for patients in the intensive care unit: A prospective validation study of the iCAM-ICU by nurse researchers and bedside nurses in routine practice

BackgroundDaily delirium assessment using the Confusion Assessment Method for the Intensive Care Unit was recommended for patients in the intensive care unit, yet implementation may be difficult because of lack of simple and standardized data collection schemes which may result in low sensitivities when used by bedside nurses. ObjectiveThis study was to validate the diagnostic accuracy of the intelligent Confusion Assessment Method for the Intensive Care Unit (iCAM-ICU) used by both nurse investigators and bedside nurses in Chinese patients in the intensive care unit. DesignProspective cohort study. SettingA university affiliated tertiary hospital in China. ParticipantsA total of 373 hospitalized patients (181 in phase I and 192 in phase II) in the intensive care units met the inclusion criteria and participated in the study. There were two nursing researchers (phase I) and 24 bedside nurses (phase II) used the iCAM-ICU to assess delirium among patients. MethodsTwo prospective cohort studies were conducted to validate the diagnostic accuracy of iCAM-ICU on delirium screening when it was used by nurse investigators and bedside nurses in the intensive care unit. Using the Diagnostic and Statistical Manual of Mental Disorders as the gold standard, the sensitivity, specificity, predictive values along with the likelihood ratios were determined to estimate the performance of the iCAM-ICU in patients in the intensive care setting. The Kappa consistency test was examined to determine the inter-rater consistency. Subgroup analysis in terms of different age, level of education, severity of illness and cognitive status were also conducted to evaluate potential variations of the iCAM-ICU performance in different patient groups. ResultsA total of 373 patients were included in the validation studies. In comparing with the gold standard, the sensitivities of the iCAM-ICU demonstrated by the two nurse investigators were 95.2 % and 93.7%, while the specificities of the iCAM-ICU were 93.3% and 93.2%. The Kappa consistency between two nurse investigators was 0.96. The sensitivity and specificity of the iCAM-ICU demonstrated by bedside nurses in intensive care patients were 86.7% and 97.7%, respectively. Subgroup analysis also revealed that the sensitivities and specificities in those different subgroups were acceptable, with all statistics being above 80%. ConclusionsThe iCAM-ICU, an information technology enabled delirium screening tool, showed highly acceptable accuracy in detecting delirium in the intensive care units. It can assist bedside nurses to detect delirium reliably and identify potential patients with delirium accurately. Registration numberChiCTR-OCH-13003050.

Perspectives of different stakeholders on data use and management in public health emergencies in sub-Saharan Africa: a meeting report

During public health emergencies (PHEs), data are collected and generated from a variety of activities and sources, including but not limited to national public health programs, research and community-based activities. It is critical that these data are rapidly shared in order to facilitate the public health response, epidemic preparedness, as well research during and after the epidemic. Nonetheless, collecting and sharing data during PHEs can be challenging, especially where there are limited resources for public health and research-related activities during a PHE. In a symposium that brought together different stakeholders that were involved in the 2013-2016 Ebola outbreaks in West Africa, meeting attendees shared their perspectives on the values and management of data during PHEs in sub-Saharan Africa. Key factors that could inform and facilitate data management during PHEs in sub-Saharan Africa were discussed, including using data to inform policy decisions and healthcare; a coordinated data collection and management scheme; identifying incentives for data sharing; and equitable data governance mechanism that emphasise principles of reciprocity, transparency and accountability rather that trust between stakeholders or collaborators. Empirical studies are required to explore how these principles could inform best practices for data management and governance during PHE in sub-Saharan Africa.

Distributed Data Collection in Age-Aware Vehicular Participatory Sensing Networks

The advent of vehicle-to-everything communication facilitates the emergence of vehicular sensing networks, where vehicles equipped with advanced sensors continuously sample informative status updates of its surroundings and forward the sampled data to roadside infrastructure based on a certain routing strategy. The collected data is analyzed to obtain real-time situational awareness to impose certain behaviors on the vehicles. In such networked control systems, the timeliness of collected data is of critical importance to system performance, which can be quantified by the concept of Age of Information. Note that to obtain timely perception of its surroundings, each vehicle tends to sample status updates at the maximum frequency, which may congest the network due to limited communication resource. Moreover, the highly dynamic nature of vehicular network poses a great challenge in finding a reliable route for timely data forwarding. Therefore, the data collection scheme should be carefully designed to balance the timeliness of collected information and network stability. In this article, we study an age optimization problem by jointly considering the data sampling at source vehicles and the data forwarding process for multiple information flows across the network. We employ the Lyapunov optimization technique to develop a distributed age-aware data collection scheme consists of a threshold-based sampling strategy at source vehicles and a learning-based data forwarding strategy. Simulation results show that our proposed scheme outperforms existing strategies in collecting status updates in a timely manner.

ATTDC: An Active and Traceable Trust Data Collection Scheme for Industrial Security in Smart Cities

With billions of sensing devices are deployed in smart cities to monitor regions of interests and collect large sensing data, the Internet-of-Things (IoT) applications are being widely used in various fields and empower the intelligent smart cities. Due to the smart decision made by IoT applications depends on the reliability of data collection, it is pivotal to collect data from the trust sensing devices. However, how to identify the credibility of sensor nodes to ensure the credibility of data collection is a challenge issue. In this article, an active and traceable trust-based data collection (ATTDC) scheme is proposed to collect trust data in Internet of Thing. The main contribution of this article are as follows: 1) an active trust framework is proposed to quickly obtain the trustworthiness of sensor nodes by using unmanned aerial vehicles (UAVs) with a piggybacking method; 2) in order to accurately obtain the trust degree of the sensor nodes, a traceable trust method of obtaining is proposed in which nodes in the network send data packets by digital signature, Tracing suspicious nodes according to data routing paths to obtain active trust, therefore, the acquisition cost of the network can be effectively reduced; and 3) In order to reduce the acquisition cost of UAV, an ant colony algorithm-based flight path algorithm is designed to reduce the flight path of UAV, and obtain the credibility evaluation of as many nodes as possible. The experimental results show that the ATTDC scheme proposed in this article can identify the trust of the sensing nodes faster and more accurately, ensuring the credibility of data collection.

Secure Healthcare Data Aggregation and Transmission in IoT—A Survey

Internet of medical things (IoMT) is getting researchers' attention due to its wide applicability in healthcare. Smart healthcare sensors and IoT enabled medical devices exchange data and collaborate with other smart devices without human interaction to securely transmit collected sensitive healthcare data towards the server nodes. Alongside data communications, security and privacy is also quite challenging to securely aggregate and transmit healthcare data towards Fog and cloud servers. We explored the existing surveys to identify a gap in literature that a survey of fog-assisted secure healthcare data collection schemes is yet contributed in literature. This paper presents a survey of different data collection and secure transmission schemes where Fog computing based architectures are considered. A taxonomy is presented to categorize the schemes. Fog assisted smart city, smart vehicle and smart grids are also considered that achieve secure, efficient and reliable data collection with low computational cost and compression ratio. We present a summary of these scheme along with analytical discussion. Finally, a number of open research challenges are identified. Moreover, the schemes are explored to identify the challenges that are addressed in each scheme.

Energy-Efficient and Fast Data Collection in UAV-Aided Wireless Sensor Networks for Hilly Terrains

Energy-constrained sensor nodes are often deployed in remote, hilly, and hard-to-reach areas for civilian and military purposes. In such wireless sensor networks (WSNs), an unmanned aerial vehicle (UAV) can be used to collect data from the sensor nodes. Low-altitude UAVs can be utilized to reduce the energy consumption of WSNs by optimizing the data collection position. In this study, we designed an energy-efficient and fast data collection (EFDC) scheme in UAV-aided WSNs for hilly areas with the help of a UAV as a data mule. First, we proposed a central bias hybrid energy-efficient distributed clustering algorithm for grouping the sensors. Then, we applied a modified tabu search algorithm to optimize the UAV position for collecting data from a cluster. To achieve fast data collection, we developed the traveling salesman problem with the derived data collection positions and solved it by applying a modified genetic algorithm. Based on our simulation results, the proposed EFDC scheme outperforms the conventional ones in terms of energy consumption, scalability, control overhead, delay, and load balancing.

ТЕХНОЛОГІЯ ОПРАЦЮВАННЯ ІНФОРМАЦІЇ В СИСТЕМІ ПІДГОТОВКИ ФАХІВЦІВ ІЗ ВИЩОЮ ОСВІТОЮ

The article analyzes the organization of collection and processing of information from higher education institutions (HEIs) in the software and technological system for the formation and distribution of public procurement in the automated system "Legal entities" (AS "Legal entities"). This system ensures the submission of information to the Unified register of state property objects and is used for the recording of state real property transferred to the operational administration of HEIs. The material and technical support of educational institutions is a determining factor in the proper performance of their functions in providing quality educational services, organizing and implementing scientific projects. The analysis of research of domestic scientists on certain issues showed that the technology of information collection and processing in budgetary institutions requires continuous improvement and practical application of information and communication technologies. The article describes similar and distinguishing features of the way information is transmitted and received in the above-mentioned system, a generalized technological scheme of data collection, processing and aggregation is constructed. In accordance with the developed Methodical recommendations on working with AS "Legal entities" the procedure of the users of this system on entering information and forming reports on state property was established. A technological framework was developed for the processing of information received from reporting educational institutions. In the higher education system, distribution of state orders for master’s degrees based on bachelor’s, and bachelor’s degrees based on educational qualification level of junior specialists among performers is carried out by the Ministry of education and science of Ukraine competition commission on the basis of the competitive process. The information for the criteria is generated by the HEIs using the developed software and transmitted for processing to the system administrator using telecommu­nications. The tabulation procedure involves controlling the input of information and minimizing the operator’s work by automatically introducing some indicators.

Multi-Timeslots Data Collection With Low Rank and Modified Second-Order Horizontal Total Variation for Wireless Sensor Networks

Decreasing the number of data gathered is the most highly effective way to decrease the power consumption for wireless sensor networks. Compressed Data Gathering, as it known to all, is a data collection method in wireless sensor networks, but it cannot achieve sparse sensing as all data need to be sensed and then transmitted in most practical applications. At the same time, it has been shown the effectiveness of the total variation and low rank constraints in data restoration. In order to enhance the accuracy of data recovery and decrease energy cost in wireless sensor networks, we propose a Multi-Timeslots Data Collection scheme, which includes two aspects: Structure Random Sparse Sampling method and data restoration algorithm with Low Rank and Modified Second-Order Horizontal Total Variation Constraints. By adopting the proposed sampling method, the number of data sensing and transmission is greatly reduced, thereby prolong the network lifetime. We fully exploit temporal stability and low rank characteristics of wireless sensor networks data, and build a temporal-stability based nuclear norm regularization minimization model. Meanwhile, we apply the alternating direction method to solve the problem. The simulation results present that the proposed sampling method has a corresponding enhancement effect on the matrix-completion based data restoration algorithms. In terms of recovery precision, the proposed scheme outperforms the state-of-the-art methods for different types of data in the network. Moreover, with the compression ratio increasing, the proposed scheme can still exactly recover the lost data and the advantages become increasingly obvious.

An Efficient Data Aggregation Scheme Based on Differentiated Threshold Configuring Joint Optimal Relay Selection in WSNs

In Wireless Sensor Networks (WSNs), when multiple data packets meet during routing to the sink, redundant data can be removed through data fusion, thereby reducing the amount of data transmitted, and increasing the life of the network. However, how to increase the data fusion rate as much as possible and ensure that the delay is lower than the deadline is a challenge issue. To solve this problem, a Differentiated Threshold Configuring joint Optimal Relay Selection based Data Aggregation (DTC-ORS-DA) scheme is proposed, which can significantly reduce redundant data routing and guarantee the delay for WSN. The main innovation is as follow: (1) In DTC-ORS-DA scheme, there are two thresholds: data volume threshold N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</sub> and time threshold T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</sub> . Data routing can only be performed when N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</sub> or T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</sub> of the node meet the threshold requirements, so that the data can be fully integrated to minimize the amount of data to be transmitted. More importantly, DTC-ORS-DA adopts differentiated threshold settings based on the characteristics of unbalanced energy consumption in WSNs, and sets a smaller threshold in the far sink area with sufficient energy, so that data packets can be routed quickly. And the near sink area where the energy is tight uses a larger threshold to maximize data fusion, so that the combination can make the data fusion high, the energy is effectively used, and the delay is small. (2) We propose a priority-based relay selection algorithm, which enables child nodes to dynamically select the parent node with the highest priority based on the number of data packets, waiting time, and remaining energy. In the process of routing, the probability of nodes with many data packets or long waiting time being selected as transmission relay is high, which can either increase the data fusion rate or reduce the delay. Finally, the performance comparison with Common data collection Scheme (CS) proves that, the DTC-ORS-DA scheme reduces the average delay by 10.74%-19.91%, increases the life cycle by 9.81% at most, and the energy utilization rate is increased by 6.67%-9.48%.