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.

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.

Real-Time Data Transmission with Data Carrier Support Value in Neighbor Strategic Collection in WSN

An efficient trust-aware secure routing and network strategy-based data collection scheme is presented in this paper to enhance the performance and security of wireless sensor networks during data collection.The method first discovers the routes between the data sensors and the sink node.Several factors are considered for each sensor node along the route,including energy,number of neighbours,previous transmissions,and energy depletion ratio.Considering all these variables,the Sink Reachable Support Measure and the Secure Communication Support Measure,the method evaluates two distinct measures.The method calculates the data carrier support value using these two metrics.A single route is chosen to collect data based on the value of data carrier support.It has contributed to the design of Secure Communication Support(SCS)Estimation.This has been measured according to the strategy of each hop of the route.The suggested method improves the security and efficacy of data collection in wireless sensor networks.The second stage uses the two-fish approach to build a trust model for secure data transfer.A sim-ulation exercise was conducted to evaluate the effectiveness of the suggested framework.Metrics,including PDR,end-to-end latency,and average residual energy,were assessed for the proposed model.The efficiency of the suggested route design serves as evidence for the average residual energy for the proposed framework.

Autonomous UAV 3D trajectory optimization and transmission scheduling for sensor data collection on uneven terrains

This paper considers a time-constrained data collection problem from a network of ground sensors located on uneven terrain by an Unmanned Aerial Vehicle(UAV),a typical Unmanned Aerial System(UAS).The ground sensors harvest renewable energy and are equipped with batteries and data buffers.The ground sensor model takes into account sensor data buffer and battery limitations.An asymptotically globally optimal method of joint UAV 3D trajectory optimization and data transmission schedule is developed.The developed method maximizes the amount of data transmitted to the UAV without losses and too long delays and minimizes the propulsion energy of the UAV.The developed algorithm of optimal trajectory optimization and transmission scheduling is based on dynamic programming.Computer simulations demonstrate the effectiveness of the proposed algorithm.

NOMA Empowered Energy Efficient Data Collection and Wireless Power Transfer in Space-Air-Ground Integrated Networks

As the sixth generation network(6G)emerges,the Internet of remote things(IoRT)has become a critical issue.However,conventional terrestrial networks cannot meet the delay-sensitive data collection needs of IoRT networks,and the Space-Air-Ground integrated network(SAGIN)holds promise.We propose a novel setup that integrates non-orthogonal multiple access(NOMA)and wireless power transfer(WPT)to collect latency-sensitive data from IoRT networks.To extend the lifetime of devices,we aim to minimize the maximum energy consumption among all IoRT devices.Due to the coupling between variables,the resulting problem is non-convex.We first decouple the variables and split the original problem into four subproblems.Then,we propose an iterative algorithm to solve the corresponding subproblems based on successive convex approximation(SCA)techniques and slack variables.Finally,simulation results show that the NOMA strategy has a tremendous advantage over the OMA scheme in terms of network lifetime and energy efficiency,providing valuable insights.

AUV-Aided Data Collection Considering Adaptive Ocean Currents for Underwater Wireless Sensor Networks

Autonomous underwater vehicle(AUV)-assisted data collection is an efficient approach to implementing smart ocean.However,the data collection in time-varying ocean currents is plagued by two critical issues:AUV yaw and sensor node movement.We propose an adaptive AUV-assisted data collection strategy for ocean currents to address these issues.First,we consider the energy consumption of an AUV in conjunction with the value of information(VoI)over the sensor nodes and formulate an optimization problem to maximize the VoI-energy ratio.The AUV yaw problem is then solved by deriving the AUV's reachable region in different ocean current environments and the optimal cruising direction to the target nodes.Finally,using the predicted VoI-energy ratio,we sequentially design a distributed path planning algorithm to select the next target node for AUV.The simulation results indicate that the proposed strategy can utilize ocean currents to aid AUV navigation,thereby reducing the AUV's energy consumption and ensuring timely data collection.

Energy-Efficient Data Collection over Underwater MI-Assisted Acoustic Cooperative MIMO WSNs

Underwater magnetic induction(MI)-assisted acoustic cooperative multiple-input-multipleoutput(MIMO) has been recently proposed as a promising technique for underwater wireless sensor networks(UWSNs).For the more,the energy utilization of energy-constrained sensor nodes is one of the key issues in UWSNs,and it relates to the network lifetime.In this paper,we present an energy-efficient data collection for underwater MI-assisted acoustic cooperative MIMO wireless sensor networks(WSNs),including the formation of cooperative MIMO and relay link establishment.Firstly,the cooperative MIMO is formed by considering its expected transmission range and the energy balance of nodes with it.Particularly,from the perspective of the node’s energy consumption,the expected cooperative MIMO size and the selection of master node(MN) are proposed.Sequentially,to improve the coverage of the networks and prolong the network lifetime,relay links are established by relay selection algorithm that using matching theory.Finally,the simulation results show that the proposed data collection improves its efficiency,reduces the energy consumption of the master node,improves the networks’ coverage,and extends the network lifetime.

Joint UAV 3D deployment and sensor power allocation for energy-efficient and secure data collection

Unmanned aerial vehicles(UAVs) are advantageous for data collection in wireless sensor networks(WSNs) due to its low cost of use,flexible deployment,controllable mobility,etc. However,how to cope with the inherent issues of energy limitation and data security in the WSNs is challenging in such an application paradigm. To this end,based on the framework of physical layer security,an optimization problem for maximizing secrecy energy efficiency(EE) of data collection is formulated,which focuses on optimizing the UAV’s positions and the sensors’ transmit power. To overcome the difficulties in solving the optimization problem,the methods of fractional programming and successive convex approximation are then adopted to gradually transform the original problem into a series of tractable subproblems which are solved in an iterative manner. As shown in simulation results,by the joint designs in the spatial domain of UAV and the power domain of sensors,the proposed algorithm achieves a significant improvement of secrecy EE and rate.

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.

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.

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.

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%.

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.

System for Control, Data Collection and Processing in 8 mm Portable Microwave Radiometer-Scatterometer

In this paper we describe a system used to control,collect and process data in an 8 mm portable microwave radiometer scatterometer .We focus on hardware and software design of the system based on a PIC16F874 chip. The system has been successfully used in an 8 mm portable microwave radiometer scatterometer. Compared with other similar systems, the system modularization, miniaturization and intelligentization are improved so as to meet portable instrument requirements.