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.

An Evaluation of the Reliability of Complex Systems Using Shadowed Sets and Fuzzy Lifetime Data

In this paper, we consider the problem of the evaluation of system reliability using statistical data obtained from reliability tests of its elements, in which the lifetimes of elements are described using an exponential distribution. We assume that this lifetime data may be reported imprecisely and that this lack of precision may be described using fuzzy sets. As the direct application of the fuzzy sets methodology leads in this case to very complicated and time consuming calculations, we propose simple approximations of fuzzy numbers using shadowed sets introduced by Pedrycz （1998）. The proposed methodology may be simply extended to the case of general lifetime probability distributions.

Reliability Strategies for Data Transmission in Wireless Mesh Network

Wireless Mesh Network (WMN) is a new-type wireless network. Its core idea is that any of its wireless equipment can act as both an Access Point (AP) and a router. Each node in the network can send and receive signals as well as directly communicate with one or several peer nodes. One important issue to be considered in wireless Mesh networks is how to secure reliable data transmission in multi-hop links. To solve the problem, the 3GPP system architecture proposes two functionalities: ARQ and HARQ. This paper presents two HARQ schemes, namely hop-by-hop and edge-to-edge, and three ARQ schemes: hop-by-hop, edge-to-edge, and last-hop. Moreover, it proposes three solutions for WMNs from the perspective of protocol stock design: layered cooperative mechanism, relay ARQ mechanism and multi-hop mechanism.

Truth finder algorithm based on entity attributes for data conflict solution

The Internet now is a large-scale platform with big data. Finding truth from a huge dataset has attracted extensive attention, which can maintain the quality of data collected by users and provide users with accurate and efficient data. However, current truth finder algorithms are unsatisfying, because of their low accuracy and complication. This paper proposes a truth finder algorithm based on entity attributes (TFAEA). Based on the iterative computation of source reliability and fact accuracy, TFAEA considers the interactive degree among facts and the degree of dependence among sources, to simplify the typical truth finder algorithms. In order to improve the accuracy of them, TFAEA combines the one-way text similarity and the factual conflict to calculate the mutual support degree among facts. Furthermore, TFAEA utilizes the symmetric saturation of data sources to calculate the degree of dependence among sources. The experimental results show that TFAEA is not only more stable, but also more accurate than the typical truth finder algorithms.

Towards Exploring Patterns of Editing Behavior on OpenStreetMap

OpenStreetMap has a large number of volunteers.There is a hypothesis that volunteers with different cultural backgrounds may have different editing behaviors when contributing to OSM.It may be strongly related to data quality and data reliability on OSM.As for the heterogeneity and the reliability of OSM data,previous research usually focuses on the geometric accuracy,spatial location accuracy and semantic integrity of OSM data,while few researchers have analyzed these problems from the perspective of editing behavior.On the grounds of relationship between mapping motivation and editing behavior,the dispersion of editing trajectory and clockwise direction index are proposed in the paper to explore whether the volunteers are sufficiently motivated and knowledgeable.In the experiments,the historical OSM data of four countries suggested that developed countries have lower trajectory dispersion.The lower degree of trajectory dispersion reflects the higher concentration and professionalism of volunteers.A high degree of drawing direction consistency shows volunteers who mapped French data were natives with local knowledge.From this point of view,this paper verifies that volunteer editing behavior is an effective method to analyze data quality heterogeneity and data reliability.

The AMSAA-BISE Model with Gap Intervals

In this paper, the AMSAA-BISE model with missing data is discussed. The ML estimates of model parameters and current MTBF are given, and the chi-squared test and a plot for cumulative number of failures versus cumulative testing time are used to test the goodness of fit for the model. This paper concludes with a numerical example to verify the model.

Autonomous and Mobile Prototype of Curvature Sensor with Remote Reliable Communication of Spectral Curvature

Through the creation and construction of a curvature sensor of accelerometer type, using the spectral curvature concept or curvature energy that measures curvature in Volts/m3, an autonomous and mobile censorship of curvature sensing with reliable data transmission/reception in real time and remote position is designed and constructed considering the spectra of curvature of the measured curvature energy during the advance of the prototype as the normed measure by with β , a constant rationalized parameter according with the required advance of the mobile device in the control scale of their velocity. Likewise, the sensed curvature data are digitalized through wireless interconnectivity using a HC-05 Module with a programmable device that includes logic blocks whose interconnection and functionality can be configured according to the sensor measure in situs. Also an application is planted to the obtaining of an energy plus due to the curvature that could be used in the displacement of a vehicle.

Reliability Analyses of Anomalies of NCEP/NCAR Reanalyzed Wind Speed and Surface Air Temperature in Climate Change Research in China

By means of varied statistical methods, such as normalized root mean square error （RMSE）, correlation analysis, empirical orthogonal function （EOF） decomposition, etc., the reliability of the varied seasonal anomalies of NCEP/NCAR reanalyzed wind speed and surface air temperature （SAT） data frequently used in the climate change research in China is studied. Results show that RMSEs of meteorological variables are smaller in eastern China than in western China, i.e., the reliability of NCEP/NCAR reanalysis in eastern China is better than that in western China. This could be due to effects of the topography in the reanalysis model and the disposition of ＂dense-in-eastern-and-sparse-in-western＂ of meteorological stations in China. The RMSE of anomalies of reanalyzed wind speeds decreases with increasing height, further confirming the possible impact of topography on reliability of reanalysis. Results of correlation analysis inversely correspond to those of RMSE analysis, i.e., if the RMSE is larger, the correlation between reanalyzed and observed data is worse, and vice versa. It is found from comparing the EOF eigenvectors of anomaly of reanalyzed and observed data that if a meteorological variable has smaller RMSE, the spatial patterns of corresponding EOF eigenvectors of anomaly of reanalyzed and observed data are similar and their time coefficients are significantly correlated, and vice versa. Therefore, the similarity of EOF modes and＂ the consistency of their time coefficients can be used to objectively assess the reliability of the reanalysis. On the whole, the reliability of the reanalyzed wind speed is better in spring, summer, and autumn, but worse in winter; and for the reanalyzed SAT, it is the best in winter and the worst in summer.

Reliable Data Storage in Heterogeneous Wireless Sensor Networks by Jointly Optimizing Routing and Storage Node Deployment

In the era of big data,sensor networks have been pervasively deployed,producing a large amount of data for various applications.However,because sensor networks are usually placed in hostile environments,managing the huge volume of data is a very challenging issue.In this study,we mainly focus on the data storage reliability problem in heterogeneous wireless sensor networks where robust storage nodes are deployed in sensor networks and data redundancy is utilized through coding techniques.To minimize data delivery and data storage costs,we design an algorithm to jointly optimize data routing and storage node deployment.The problem can be formulated as a binary nonlinear combinatorial optimization problem,and due to its NP-hardness,designing approximation algorithms is highly nontrivial.By leveraging the Markov approximation framework,we elaborately design an efficient algorithm driven by a continuous-time Markov chain to schedule the deployment of the storage node and corresponding routing strategy.We also perform extensive simulations to verify the efficacy of our algorithm.

SA-RSR:a read-optimal data recovery strategy for XOR-coded distributed storage systems

To ensure the reliability and availability of data,redundancy strategies are always required for distributed storage systems.Erasure coding,one of the representative redundancy strategies,has the advantage of low storage overhead,which facilitates its employment in distributed storage systems.Among the various erasure coding schemes,XOR-based erasure codes are becoming popular due to their high computing speed.When a single-node failure occurs in such coding schemes,a process called data recovery takes place to retrieve the failed node’s lost data from surviving nodes.However,data transmission during the data recovery process usually requires a considerable amount of time.Current research has focused mainly on reducing the amount of data needed for data recovery to reduce the time required for data transmission,but it has encountered problems such as significant complexity and local optima.In this paper,we propose a random search recovery algorithm,named SA-RSR,to speed up single-node failure recovery of XOR-based erasure codes.SA-RSR uses a simulated annealing technique to search for an optimal recovery solution that reads and transmits a minimum amount of data.In addition,this search process can be done in polynomial time.We evaluate SA-RSR with a variety of XOR-based erasure codes in simulations and in a real storage system,Ceph.Experimental results in Ceph show that SA-RSR reduces the amount of data required for recovery by up to 30.0%and improves the performance of data recovery by up to 20.36%compared to the conventional recovery method.

Fuzzy norm method for evaluating random vibration of airborne platform from limited PSD data

For random vibration of airborne platform, the accurate evaluation is a key indicator to ensure normal operation of airborne equipment in flight. However, only limited power spectral density（PSD） data can be obtained at the stage of flight test. Thus, those conventional evaluation methods cannot be employed when the distribution characteristics and priori information are unknown. In this paper, the fuzzy norm method（FNM） is proposed which combines the advantages of fuzzy theory and norm theory. The proposed method can deeply dig system information from limited data, which probability distribution is not taken into account. Firstly, the FNM is employed to evaluate variable interval and expanded uncertainty from limited PSD data, and the performance of FNM is demonstrated by confidence level, reliability and computing accuracy of expanded uncertainty. In addition, the optimal fuzzy parameters are discussed to meet the requirements of aviation standards and metrological practice. Finally, computer simulation is used to prove the adaptability of FNM. Compared with statistical methods, FNM has superiority for evaluating expanded uncertainty from limited data. The results show that the reliability of calculation and evaluation is superior to 95%.

Novel Data Placement Algorithm for Distributed Storage System Based on Fault-Tolerant Domain

The 3-replica redundancy strategy is widely used to solve the problem of data reliability in large-scale distributed storage systems. However, its storage capacity utilization is only 33%. In this paper, a data placement algorithm based on fault-tolerant domain (FTD) is proposed. Owing to the fine-grained design of the FTD, the data reliability of systems using two replicas is comparable to that of current mainstream systems using three replicas, and the capacity utilization is increased to 50%. Moreover, the proposed FTD provides a new concept for the design of distributed storage systems. Distributed storage systems can take FTDs as the units for data placement, data migration, data repair and so on. In addition, fault detection can be performed independently and concurrently within the FTDs.