Reliable Data Collection Model and Transmission Framework in Large-Scale Wireless Medical Sensor Networks

Large-scale wireless sensor networks(WSNs)play a critical role in monitoring dangerous scenarios and responding to medical emergencies.However,the inherent instability and error-prone nature of wireless links present significant challenges,necessitating efficient data collection and reliable transmission services.This paper addresses the limitations of existing data transmission and recovery protocols by proposing a systematic end-to-end design tailored for medical event-driven cluster-based large-scale WSNs.The primary goal is to enhance the reliability of data collection and transmission services,ensuring a comprehensive and practical approach.Our approach focuses on refining the hop-count-based routing scheme to achieve fairness in forwarding reliability.Additionally,it emphasizes reliable data collection within clusters and establishes robust data transmission over multiple hops.These systematic improvements are designed to optimize the overall performance of the WSN in real-world scenarios.Simulation results of the proposed protocol validate its exceptional performance compared to other prominent data transmission schemes.The evaluation spans varying sensor densities,wireless channel conditions,and packet transmission rates,showcasing the protocol’s superiority in ensuring reliable and efficient data transfer.Our systematic end-to-end design successfully addresses the challenges posed by the instability of wireless links in large-scaleWSNs.By prioritizing fairness,reliability,and efficiency,the proposed protocol demonstrates its efficacy in enhancing data collection and transmission services,thereby offering a valuable contribution to the field of medical event-drivenWSNs.

Flight Time Minimization of UAV for Cooperative Data Collection in Probabilistic LoS Channel

This paper investigates the data collection in an unmanned aerial vehicle(UAV)-aided Internet of Things(IoT) network, where a UAV is dispatched to collect data from ground sensors in a practical and accurate probabilistic line-of-sight(LoS) channel. Especially, access points(APs) are introduced to collect data from some sensors in the unlicensed band to improve data collection efficiency. We formulate a mixed-integer non-convex optimization problem to minimize the UAV flight time by jointly designing the UAV 3D trajectory and sensors’ scheduling, while ensuring the required amount of data can be collected under the limited UAV energy. To solve this nonconvex problem, we recast the objective problem into a tractable form. Then, the problem is further divided into several sub-problems to solve iteratively, and the successive convex approximation(SCA) scheme is applied to solve each non-convex subproblem. Finally,the bisection search is adopted to speed up the searching for the minimum UAV flight time. Simulation results verify that the UAV flight time can be shortened by the proposed method effectively.

Actor-Critic-Based UAV-Assisted Data Collection in the Wireless Sensor Network

Wireless Sensor Network(WSN)is widely utilized in large-scale distributed unmanned detection scenarios due to its low cost and flexible installation.However,WSN data collection encounters challenges in scenarios lacking communication infrastructure.Unmanned aerial vehicle(UAV)offers a novel solution for WSN data collection,leveraging their high mobility.In this paper,we present an efficient UAV-assisted data collection algorithm aimed at minimizing the overall power consumption of the WSN.Firstly,a two-layer UAV-assisted data collection model is introduced,including the ground and aerial layers.The ground layer senses the environmental data by the cluster members(CMs),and the CMs transmit the data to the cluster heads(CHs),which forward the collected data to the UAVs.The aerial network layer consists of multiple UAVs that collect,store,and forward data from the CHs to the data center for analysis.Secondly,an improved clustering algorithm based on K-Means++is proposed to optimize the number and locations of CHs.Moreover,an Actor-Critic based algorithm is introduced to optimize the UAV deployment and the association with CHs.Finally,simulation results verify the effectiveness of the proposed algorithms.

An adaptive physics-informed deep learning method for pore pressure prediction using seismic data

Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering.Recent deep learning technique has been receiving more interest due to the great potential to deal with pore pressure prediction.However,most of the traditional deep learning models are less efficient to address generalization problems.To fill this technical gap,in this work,we developed a new adaptive physics-informed deep learning model with high generalization capability to predict pore pressure values directly from seismic data.Specifically,the new model,named CGP-NN,consists of a novel parametric features extraction approach(1DCPP),a stacked multilayer gated recurrent model(multilayer GRU),and an adaptive physics-informed loss function.Through machine training,the developed model can automatically select the optimal physical model to constrain the results for each pore pressure prediction.The CGP-NN model has the best generalization when the physicsrelated metricλ=0.5.A hybrid approach combining Eaton and Bowers methods is also proposed to build machine-learnable labels for solving the problem of few labels.To validate the developed model and methodology,a case study on a complex reservoir in Tarim Basin was further performed to demonstrate the high accuracy on the pore pressure prediction of new wells along with the strong generalization ability.The adaptive physics-informed deep learning approach presented here has potential application in the prediction of pore pressures coupled with multiple genesis mechanisms using seismic data.

Research on the Remote Data Collection Based SQL Server

The remote data collection system based on SOL Server database technology was developed by Visual C++and SQL Server database technology together.The Client/Server mode was adopted.The system adopted the database search technologi- cal-ADO to work out the communication procedure of the server.And the old data of corresponding memory units were upgraded by the new data which gathered from PLC through serial port real time in the database.The Client utilizes the network technology and database technology through queries procedure to access the data information in the database.Thus a large number of relevant data that the production line operated were obtained.The goal of understanding operation conditions of product line was achieved through analysis of these data.This system has been debugged by the experiment successfully.

New design for multi-crystal data collection at SSRF

Data collection with microcrystals at synchrotron radiation facilities is challenging because it is difficult to harvest and locate microcrystals. Moreover,microcrystals are sensitive to radiation damage; thus, typically, a complete data set cannot be obtained with a single microcrystal. Herein, we report a new method for data collection with multiple microcrystals having a crystal size ranging from 1 to 30 lm. This method is suitable for not only low-temperature(100 K) data collection but also room-temperature data collection. Thin Kapton membranes were used to capture multiple crystals simultaneously. The microcrystals were visible under an optical microscope and easily located because the membrane was transparent and sufficiently thin. The film was fixed to a bracket that was prepared using a three-dimensional printer. The bracket was fixed on a magnetic base via screwing and employed by the goniometer system for data collection. Multiple data sets of fatty acid-binding protein 4(FABP4) and lysozyme microcrystals were collected using this novel designed device. Finally, the structures of protein FABP4 and lysozyme were obtained from these data via the molecule replacement method. The data statistics reveal that this method provides a comparable result to traditional methods such as a nylon loop.

Evaluation of the Accuracy and Automation of Travel Time and Delay Data Collection Methods

Travel time and delay are among the most important measures for gauging a transportation system’s performance. To address the growing problem of congestion in the US, transportation planning legislation mandated the monitoring and analysis of system performance and produced a renewed interest in travel time and delay studies. The use of traditional sensors installed on major roads (e.g. inductive loops) for collecting data is necessary but not sufficient because of their limited coverage and expensive costs for setting up and maintaining the required infrastructure. The GPS-based techniques employed by the University of Delaware have evolved into an automated system, which provides more realistic experience of a traffic flow throughout the road links. However, human error and the weaknesses of using GPS devices in urban settings still have the potential to create inaccuracies. By simultaneously collecting data using three different techniques, the accuracy of the GPS positioning data and the resulting travel time and delay values could be objectively compared for automation and statistically compared for accuracy. It was found that the new technique provided the greatest automation requiring minimal attention of the data collectors and automatically processing the data sets. The data samples were statistically analyzed by using a combination of parametric and nonparametric statistical tests. This analysis greatly favored the GeoStats GPS method over the rest methods.

A Robust Framework for Multimodal Sentiment Analysis with Noisy Labels Generated from Distributed Data Annotation

Multimodal sentiment analysis utilizes multimodal data such as text,facial expressions and voice to detect people’s attitudes.With the advent of distributed data collection and annotation,we can easily obtain and share such multimodal data.However,due to professional discrepancies among annotators and lax quality control,noisy labels might be introduced.Recent research suggests that deep neural networks(DNNs)will overfit noisy labels,leading to the poor performance of the DNNs.To address this challenging problem,we present a Multimodal Robust Meta Learning framework(MRML)for multimodal sentiment analysis to resist noisy labels and correlate distinct modalities simultaneously.Specifically,we propose a two-layer fusion net to deeply fuse different modalities and improve the quality of the multimodal data features for label correction and network training.Besides,a multiple meta-learner(label corrector)strategy is proposed to enhance the label correction approach and prevent models from overfitting to noisy labels.We conducted experiments on three popular multimodal datasets to verify the superiority of ourmethod by comparing it with four baselines.

Web-based GIS System for Real-time Field Data Collection Using Personal Mobile Phone

Recently, use of mobile communicational devices in field data collection is increasing such as smart phones and cellular phones due to emergence of embedded Global Position System GPS and Wi-Fi Internet access. Accurate timely and handy field data collection is required for disaster management and emergency quick responses. In this article, we introduce web-based GIS system to collect the field data by personal mobile phone through Post Office Protocol POP3 mail server. The main objective of this work is to demonstrate real-time field data collection method to the students using their mobile phone to collect field data by timely and handy manners, either individual or group survey in local or global scale research.

Energy Aware Data Collection with Route Planning for 6G Enabled UAV Communication

With technological advancements in 6G and Internet of Things(IoT), the incorporation of Unmanned Aerial Vehicles (UAVs) and cellularnetworks has become a hot research topic. At present, the proficient evolution of 6G networks allows the UAVs to offer cost-effective and timelysolutions for real-time applications such as medicine, tracking, surveillance,etc. Energy efficiency, data collection, and route planning are crucial processesto improve the network communication. These processes are highly difficultowing to high mobility, presence of non-stationary links, dynamic topology,and energy-restricted UAVs. With this motivation, the current research paperpresents a novel Energy Aware Data Collection with Routing Planning for6G-enabled UAV communication (EADCRP-6G) technique. The goal of theproposed EADCRP-6G technique is to conduct energy-efficient cluster-baseddata collection and optimal route planning for 6G-enabled UAV networks.EADCRP-6G technique deploys Improved Red Deer Algorithm-based Clustering (IRDAC) technique to elect an optimal set of Cluster Heads (CH) andorganize these clusters. Besides, Artificial Fish Swarm-based Route Planning(AFSRP) technique is applied to choose an optimum set of routes for UAVcommunication in 6G networks. In order to validated whether the proposedEADCRP-6G technique enhances the performance, a series of simulationswas performed and the outcomes were investigated under different dimensions.The experimental results showcase that the proposed model outperformed allother existing models under different evaluation parameters.

A LiDAR Point Clouds Dataset of Ships in a Maritime Environment

For the first time, this article introduces a LiDAR Point Clouds Dataset of Ships composed of both collected and simulated data to address the scarcity of LiDAR data in maritime applications. The collected data are acquired using specialized maritime LiDAR sensors in both inland waterways and wide-open ocean environments. The simulated data is generated by placing a ship in the LiDAR coordinate system and scanning it with a redeveloped Blensor that emulates the operation of a LiDAR sensor equipped with various laser beams. Furthermore,we also render point clouds for foggy and rainy weather conditions. To describe a realistic shipping environment, a dynamic tail wave is modeled by iterating the wave elevation of each point in a time series. Finally, networks serving small objects are migrated to ship applications by feeding our dataset. The positive effect of simulated data is described in object detection experiments, and the negative impact of tail waves as noise is verified in single-object tracking experiments. The Dataset is available at https://github.com/zqy411470859/ship_dataset.

Energy-Efficient Underwater Data Collection:A Q-Learning Based Approach

Underwater data collection is an importance part in the process of network monitoring,network management and intrusion detection.However,the limited-energy of nodes is a major challenge to collect underwater data.The solution of this problem are not only in the hands of network topology but in the hands of path of autonomous underwater vehicle(AUV).With the problem in hand,an energy-efficient data collection scheme is designed for mobile underwater network.Especially,the data collection scheme is divided into two phases,i.e.,routing algorithm design for sensor nodes and path planing for AUV.With consideration of limited-energy and network robustness,Q-learning based dynamic routing algorithm is designed in the first phase to optimize the routing selection of nodes,through which a potential-game based optimal rigid graph method is proposed to balance the trade-off between the energy consumption and the network robustness.With the collected data,Q-learning based path planning strategy is proposed for AUV in the second phase to find the desired path to gather the data from data collector,then a mode-free tracking controller is developed to track the desired path accurately.Finally,the performance analysis and simulation results reveal that the proposed approach can guarantee energy-efficient and improve network stability.

Development of vehicle-recognition method on water surfaces using LiDAR data:SPD^(2)(spherically stratified point projection with diameter and distance)

Swarm robot systems are an important application of autonomous unmanned surface vehicles on water surfaces.For monitoring natural environments and conducting security activities within a certain range using a surface vehicle,the swarm robot system is more efficient than the operation of a single object as the former can reduce cost and save time.It is necessary to detect adjacent surface obstacles robustly to operate a cluster of unmanned surface vehicles.For this purpose,a LiDAR(light detection and ranging)sensor is used as it can simultaneously obtain 3D information for all directions,relatively robustly and accurately,irrespective of the surrounding environmental conditions.Although the GPS(global-positioning-system)error range exists,obtaining measurements of the surface-vessel position can still ensure stability during platoon maneuvering.In this study,a three-layer convolutional neural network is applied to classify types of surface vehicles.The aim of this approach is to redefine the sparse 3D point cloud data as 2D image data with a connotative meaning and subsequently utilize this transformed data for object classification purposes.Hence,we have proposed a descriptor that converts the 3D point cloud data into 2D image data.To use this descriptor effectively,it is necessary to perform a clustering operation that separates the point clouds for each object.We developed voxel-based clustering for the point cloud clustering.Furthermore,using the descriptor,3D point cloud data can be converted into a 2D feature image,and the converted 2D image is provided as an input value to the network.We intend to verify the validity of the proposed 3D point cloud feature descriptor by using experimental data in the simulator.Furthermore,we explore the feasibility of real-time object classification within this framework.

Artificial Intelligence-Enabled Cooperative Cluster-Based Data Collection for Unmanned Aerial Vehicles

In recent times,sixth generation(6G)communication technologies have become a hot research topic because of maximum throughput and low delay services for mobile users.It encompasses several heterogeneous resource and communication standard in ensuring incessant availability of service.At the same time,the development of 6G enables the Unmanned Aerial Vehicles(UAVs)in offering cost and time-efficient solution to several applications like healthcare,surveillance,disaster management,etc.In UAV networks,energy efficiency and data collection are considered the major process for high quality network communication.But these procedures are found to be challenging because of maximum mobility,unstable links,dynamic topology,and energy restricted UAVs.These issues are solved by the use of artificial intelligence(AI)and energy efficient clustering techniques for UAVs in the 6G environment.With this inspiration,this work designs an artificial intelligence enabled cooperative cluster-based data collection technique for unmanned aerial vehicles(AECCDC-UAV)in 6G environment.The proposed AECCDC-UAV technique purposes for dividing the UAV network as to different clusters and allocate a cluster head(CH)to each cluster in such a way that the energy consumption(ECM)gets minimized.The presented AECCDC-UAV technique involves a quasi-oppositional shuffled shepherd optimization(QOSSO)algorithm for selecting the CHs and construct clusters.The QOSSO algorithm derives a fitness function involving three input parameters residual energy of UAVs,distance to neighboring UAVs,and degree of UAVs.The performance of the AECCDC-UAV technique is validated in many aspects and the obtained experimental values demonstration promising results over the recent state of art methods.

UAV trajectory planning algorithmfor data collection in wireless sensor networks

In order to maximize the value of information(VoI)of collected data in unmanned aerial vehicle(UAV)-aided wireless sensor networks(WSNs),a UAV trajectory planning algorithm named maximum VoI first and successive convex approximation(MVF-SCA)is proposed.First,the Rician channel model is adopted in the system and sensor nodes(SNs)are divided into key nodes and common nodes.Secondly,the data collection problem is formulated as a mixed integer non-linear program(MINLP)problem.The problem is divided into two sub-problems according to the different types of SNs to seek a sub-optimal solution with a low complexity.Finally,the MVF-SCA algorithm for UAV trajectory planning is proposed,which can not only be used for daily data collection in the target area,but also collect time-sensitive abnormal data in time when the exception occurs.Simulation results show that,compared with the existing classic traveling salesman problem(TSP)algorithm and greedy path planning algorithm,the VoI collected by the proposed algorithm can be improved by about 15%to 30%.

Optimized air-ground data fusion method for mine slope modeling

Refined 3D modeling of mine slopes is pivotal for precise prediction of geological hazards.Aiming at the inadequacy of existing single modeling methods in comprehensively representing the overall and localized characteristics of mining slopes,this study introduces a new method that fuses model data from Unmanned aerial vehicles(UAV)tilt photogrammetry and 3D laser scanning through a data alignment algorithm based on control points.First,the mini batch K-Medoids algorithm is utilized to cluster the point cloud data from ground 3D laser scanning.Then,the elbow rule is applied to determine the optimal cluster number(K0),and the feature points are extracted.Next,the nearest neighbor point algorithm is employed to match the feature points obtained from UAV tilt photogrammetry,and the internal point coordinates are adjusted through the distanceweighted average to construct a 3D model.Finally,by integrating an engineering case study,the K0 value is determined to be 8,with a matching accuracy between the two model datasets ranging from 0.0669 to 1.0373 mm.Therefore,compared with the modeling method utilizing K-medoids clustering algorithm,the new modeling method significantly enhances the computational efficiency,the accuracy of selecting the optimal number of feature points in 3D laser scanning,and the precision of the 3D model derived from UAV tilt photogrammetry.This method provides a research foundation for constructing mine slope model.

Application of 9-component S-wave 3D seismic data to study sedimentary facies and reservoirs in a biogasbearing area:A case study on the Pleistocene Qigequan Formation in Taidong area,Sanhu Depression,Qaidam Basin,NW China

To solve the problems in restoring sedimentary facies and predicting reservoirs in loose gas-bearing sediment,based on seismic sedimentologic analysis of the first 9-component S-wave 3D seismic dataset of China,a fourth-order isochronous stratigraphic framework was set up and then sedimentary facies and reservoirs in the Pleistocene Qigequan Formation in Taidong area of Qaidam Basin were studied by seismic geomorphology and seismic lithology.The study method and thought are as following.Firstly,techniques of phase rotation,frequency decomposition and fusion,and stratal slicing were applied to the 9-component S-wave seismic data to restore sedimentary facies of major marker beds based on sedimentary models reflected by satellite images.Then,techniques of seismic attribute extraction,principal component analysis,and random fitting were applied to calculate the reservoir thickness and physical parameters of a key sandbody,and the results are satisfactory and confirmed by blind testing wells.Study results reveal that the dominant sedimentary facies in the Qigequan Formation within the study area are delta front and shallow lake.The RGB fused slices indicate that there are two cycles with three sets of underwater distributary channel systems in one period.Among them,sandstones in the distributary channels of middle-low Qigequan Formation are thick and broad with superior physical properties,which are favorable reservoirs.The reservoir permeability is also affected by diagenesis.Distributary channel sandstone reservoirs extend further to the west of Sebei-1 gas field,which provides a basis to expand exploration to the western peripheral area.

Large-scale spatial data visualization method based on augmented reality

Background A task assigned to space exploration satellites involves detecting the physical environment within a certain space.However,space detection data are complex and abstract.These data are not conducive for researchers'visual perceptions of the evolution and interaction of events in the space environment.Methods A time-series dynamic data sampling method for large-scale space was proposed for sample detection data in space and time,and the corresponding relationships between data location features and other attribute features were established.A tone-mapping method based on statistical histogram equalization was proposed and applied to the final attribute feature data.The visualization process is optimized for rendering by merging materials,reducing the number of patches,and performing other operations.Results The results of sampling,feature extraction,and uniform visualization of the detection data of complex types,long duration spans,and uneven spatial distributions were obtained.The real-time visualization of large-scale spatial structures using augmented reality devices,particularly low-performance devices,was also investigated.Conclusions The proposed visualization system can reconstruct the three-dimensional structure of a large-scale space,express the structure and changes in the spatial environment using augmented reality,and assist in intuitively discovering spatial environmental events and evolutionary rules.

Runout prediction of potential landslides based on the multi-source data collaboration analysis on historical cases

Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance,high mobility and strong destructive power.Numerical methods have been widely used to predict the landslide runout but a fundamental problem remained is how to determine the reliable numerical parameters.This study proposes a framework to predict the runout of potential landslides through multi-source data collaboration and numerical analysis of historical landslide events.Specifically,for the historical landslide cases,the landslide-induced seismic signal,geophysical surveys,and possible in-situ drone/phone videos(multi-source data collaboration)can validate the numerical results in terms of landslide dynamics and deposit features and help calibrate the numerical(rheological)parameters.Subsequently,the calibrated numerical parameters can be used to numerically predict the runout of potential landslides in the region with a similar geological setting to the recorded events.Application of the runout prediction approach to the 2020 Jiashanying landslide in Guizhou,China gives reasonable results in comparison to the field observations.The numerical parameters are determined from the multi-source data collaboration analysis of a historical case in the region(2019 Shuicheng landslide).The proposed framework for landslide runout prediction can be of great utility for landslide risk assessment and disaster reduction in mountainous regions worldwide.

Design and implementation of railway green performance basic data collection system

This study addressed the issues related to the collection and management of basic data for railway green performance. A railway green performance basic database has been constructed based on metadata and data exchange schemas. A data classification system has been established from the perspectives of businesses, processes,and entities. A BIM(Building Information Modelling) model data extraction scheme is proposed based on field similarity matching and a document content extraction scheme is proposed based on image recognition. A railway green performance basic data collection system has been developed, achieving efficient collection and integrated management of railway green performance basic data. This system can provide data support for applications such as railway carbon emissions accounting, green cost-benefit analysis, and evaluation of green design solutions.