Data Collection Scheme Research Articles

Space-time smoothing models for subnational measles routine immunization coverage estimation with complex survey data

Despite substantial advances in global measles vaccination, measles disease burden remains high in many low- and middle-income countries. A key public health strategy for controlling measles in such high-burden settings is to conduct supplementary immunization activities (SIAs) in the form of mass vaccination campaigns, in addition to delivering scheduled vaccination through routine immunization (RI) programs. To achieve balanced implementations of RI and SIAs, robust measurement of subnational RI-specific coverage is crucial. In this paper we develop a space–time smoothing model for estimating RI-specific coverage of the first dose of measles-containing-vaccines (MCV1) at subnational level using complex survey data. The application that motivated this work is estimation of the RI-specific MCV1 coverage in Nigeria’s 36 states and the Federal Capital Territory. Data come from four demographic and health surveys, three multiple indicator cluster surveys and two national nutrition and health surveys conducted in Nigeria between 2003 and 2018. Our method incorporates information from the SIA calendar published by the World Health Organization and accounts for the impact of SIAs on the overall MCV1 coverage, as measured by cross-sectional surveys. The model can be used to analyze data from multiple surveys with different data collection schemes and construct coverage estimates with uncertainty that reflects the various sampling designs. Implementation of our method can be done efficiently using integrated nested Laplace approximation (INLA).

Privacy Preserving and Efficient Data Collection Scheme for AMI Networks Using Deep Learning

In advanced metering infrastructure, smart meters (SMs) send fine-grained power consumption readings periodically to the utility for load monitoring and energy management. Change and transmit (CAT) is an efficient approach to collect these readings, where the readings are not transmitted when there is no enough change in consumption. However, this approach causes a privacy problem, that is, by analyzing the transmission pattern of an SM, sensitive information on the house dwellers can be inferred. For instance, since the transmission pattern is distinguishable when dwellers are on travel, attackers may analyze the pattern to launch a presence-privacy attack (PPA) to infer whether the dwellers are absent from home. In this article, we propose a scheme, called “STDL,” for efficient collection of power consumption readings in advanced metering infrastructure (AMI) networks while preserving the consumers’ privacy by sending spoofing transmissions using a deep-learning approach. We first use a clustering technique and real power consumption readings to create a data set for transmission patterns using the CAT approach. Then, we train a deep-learning-based attacker model, and our evaluations indicate that the attacker’s success rate is about 91%. Finally, we train a deep-learning-based defense model to send spoofing transmissions efficiently to thwart the PPA. Extensive evaluations are conducted, and the results indicate that our scheme can reduce the attacker’s success rate to 3.15%, while still achieving high efficiency in terms of the number of readings that should be transmitted. Our measurements indicate that the proposed scheme can increase efficiency by about 41% compared to continuously transmitting readings.

An Adaptive Mode Adjustment for Multimode Household Surveys

Abstract Multimode data collection has emerged as a common approach for conducting household surveys in the United States. A number of different data collection schemes have been investigated, with an emphasis on collecting as many respondents by the Web prior to going to paper data collection to reduce costs. Despite this, little research has been conducted on the approaches to weighting data from multimode surveys. The typical approach assumes that all respondents should be treated the same regardless of mode even though it is well known that the response patterns by mode vary substantially. We examine an adaptive mode adjustment to address these differences and propose an imbalance measure to help determine the adjustment factor using ideas from responsive design. We then compare the effects of the alternative weighting method in two recent sequential mixed-mode surveys and show it appears to reduce bias while only slightly increasing variances of the estimates.

The Application of Wireless Network in the Process of Remote Data Collection

The use of wireless network for remote data collection can provide a fast and reliable wireless data transmission channel for those monitoring points involving a wide area and scattered equipment layout with the help of its large coverage and high communication quality. This article analyzes the characteristics and advantages of wireless networks, and then discusses the networking scheme for remote data collection using wireless networks, and analyzes the reliability of network transmission. Finally, some program fragments on the server side are given.

Dual-UAV Enabled Secure Data Collection With Propulsion Limitation

Unmanned aerial vehicles (UAVs) have been widely utilized to improve the end-to-end performance of wireless communications. However, its line-of-sight makes UAV communication vulnerable to malicious eavesdroppers. In this paper, we propose two cooperative dual-UAV enabled secure data collection schemes to ensure security, with the practical propulsion energy consumption considered. We first maximize the worst-case average secrecy rate with the average propulsion power limitation, where the scheduling, the transmit power, the trajectory and the velocity of the two UAVs are jointly optimized. To solve the non-convex multivariable problem, we propose an iterative algorithm based on block coordinate descent and successive convex approximation. To further save the on-board energy and prolong the flight time, we then maximize the secrecy energy efficiency of UAV data collection, which is a fractional and mixed integer nonlinear programming problem. Based on the Dinkelbach method, we transform the objective function into an integral expression and propose an iterative algorithm to obtain a suboptimal solution to secrecy energy efficiency maximization. Numerical results show that the average secrecy rate is maximized in the first scheme with propulsion limitation, while in the second scheme, the secrecy energy efficiency is maximized with the optimal velocity to save propulsion power and improve secrecy rate simultaneously.

UAV-LEO Integrated Backbone: A Ubiquitous Data Collection Approach for B5G Internet of Remote Things Networks

With the advance of unmanned aerial vehicles (UAVs) and low earth orbit (LEO) satellites, the integration of space, air and ground networks has become a potential solution to the beyond fifth generation (B5G) Internet of remote things (IoRT) networks. However, due to the network heterogeneity and the high mobility of UAVs and LEOs, how to design an efficient UAV-LEO integrated data collection scheme without infrastructure support is very challenging. In this paper, we investigate the resource allocation problem for a two-hop uplink UAV-LEO integrated data collection for the B5G IoRT networks, where numerous UAVs gather data from IoT devices and transmit the IoT data to LEO satellites. In order to maximize the data gathering efficiency in the IoT-UAV data gathering process, we study the bandwidth allocation of IoT devices and the 3-dimensional (3D) trajectory design of UAVs. In the UAV-LEO data transmission process, we jointly optimize the transmit powers of UAVs and the selections of LEO satellites for the total uploaded data amount and the energy consumption of UAVs. Considering the relay role and the cache capacity limitations of UAVs, we merge the optimizations of IoT-UAV data gathering and UAV-LEO data transmission into an integrated optimization problem, which is solved with the aid of the successive convex approximation (SCA) and the block coordinate descent (BCD) techniques. Simulation results demonstrate that the proposed scheme achieves better performance than the benchmark algorithms in terms of both energy consumption and total upload data amount.

UAV-Assisted Time-Efficient Data Collection via Uplink NOMA

Due to the mobility and line-of-sight conditions, unmanned aerial vehicle (UAV) is deemed as a promising solution to sensor data collection. On the other hand, it is vital to guarantee the timeliness of information for UAV-assisted data collection. In this paper, we propose a time-efficient data collection scheme, in which multiple ground devices upload their data to the UAV via uplink non-orthogonal multiple access (NOMA). The total flight time of the UAV is equally divided into <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> time slots. The duration of each time slot is minimized by jointly optimizing the straight-line trajectory, device scheduling, and transmit power. To solve this mixed integer non-convex optimization problem, we decompose it into two steps. In the first step, we study the device scheduling strategy based on the UAV trajectory and the channel gains between the UAV and ground devices, through which the original problem can be greatly simplified. In the second step, the duration of each time slot is minimized by optimizing the transmit power and the UAV trajectory. An iterative algorithm based on alternating optimization is proposed, where each subproblem can be alternatively solved by applying successive convex approximation with the device scheduling updated at the end of each iteration. Numerical results are presented to evaluate the effectiveness of the proposed scheme.

A verifiable privacy-preserving data collection scheme supporting multi-party computation in fog-based smart grid

A verifiable privacy-preserving data collection scheme supporting multi-party computation in fog-based smart grid

A new secure arrangement for privacy-preserving data collection

A big number of users’ healthy data are necessary for the Internet of Things (IoT) healthcare. Therefore, the institutions, which have access to more data can provide better medical services such as more accurate diagnosis. However, privacy is often a bottleneck for IoT healthcare. Users often refuse to provide their health data based on privacy considerations. To balance the requirement of data collection and personal privacy, a lot of privacy-preserving data collection schemes are provided. A very important work of these schemes is to produce a secret position for every user to store her/his data, which is named secure arrangement. A novel secure arrangement method is proposed in this paper, which is based on matrix eigenvalue calculation. Compared with the current secure arrangement methods, the proposed method is more robust and efficient, which drives the proposed scheme to be more suitable for repeated aggregation. Then we use an example to illustrate how to use the proposed arrangement method to construct a privacy data collection protocol. We prove the proposed scheme is secure and efficient in security analysis and efficiency analysis.

Symmetric smoothed bootstrap methods for ranked set samples

Ranked set sampling (RSS) is a data collection scheme that usually yields more efficient estimators when a measurement of the variable of interest is difficult or expensive to obtain, but sampling units can be ordered easily without actual quantification. We suggest several natural methods for obtaining smoothed bootstrap samples from each row of RSS data, and show the consistency of these methods for a location parameter. Our results hold even if the ranking is imperfect. Furthermore, we propose an optimal bandwidth that minimises the asymptotic mean integrated squared error of the RSS-based kernel cumulative distribution estimator. Then, we use simulations to verify the accuracy of the percentile confidence interval for the population mean for each bootstrap method. Lastly, we apply the smoothed bootstrap method to the test of symmetry.

A Novel Joint Optimization Method Based on Mobile Data Collection for Wireless Rechargeable Sensor Networks

In wireless sensor networks, energy efficiency is a very critical issue as it impacts the network's lifetime and performance. Mobile data collection and wireless charging are two promising emerging techniques for enhancing energy efficiency. To achieve high charging rates and implement data collection with less energy consumption of sensors, we design a joint data collection and energy charging scheme by taking the strengths of these two techniques. The mobile charger is able to implement the energy charging and data collection simultaneously when it is equipped with the appropriate communication and charging hardware. We provide a two-step method for this joint problem. First, the topology is constructed by a novel clustering algorithm that aims to balance the number of clusters and the energy consumption of inter-cluster communication. Second, two modes of scheduling schemes are developed for facing the scenarios with different delay requirements (i.e., delay-tolerant scenario and delay-aware scenario) with heuristic algorithms. Compared with existing state-of-the-art methods, the simulation results present that our proposed scheduling schemes achieve the outperformance on packet delay and charging efficiency.

Testing of Poisson mean with under-reported counts

For modelling unbounded count data, Poisson distribution is a natural choice. However, count data arising in various fields of scientific research are often under-reported. In such situations, inference carried out on the basis of Poisson model will result in biased parameter estimates and suboptimal tests. A modified Poisson model is developed to accommodate the possible undercount. For model-identifiability a double sampling scheme of data collection has been adopted. The focus of this paper is to develop asymptotically optimal tests for the Poisson mean in presence of undercount. Simulation study is conducted to compare the performance of the tests with respect to level and power and also to investigate the impact of ignoring undercount on each of the tests. The findings are validated using real life data.

An IoT-based deep learning approach to analyse indoor thermal comfort of disabled people

Monitoring the thermal comfort of building occupants is crucial for ensuring sustainable and efficient energy consumption in residential buildings. Existing studies have addressed the monitoring of thermal comfort through questionnaires and activities involving occupants. However, few studies have considered disabled people in the monitoring of thermal comfort, despite the potential for impairments to present thermal requirements that are significantly different from those of an occupant without a disability. Additionally, people with disabilities can experience difficulties in expressing their thermal comfort, which further complicates assessment and monitoring. To overcome this, we propose the development of a new learning model using a deep neural network. Our model can predict the indoor thermal comfort of differently abled people in real time to facilitate remote monitoring. We generated our real dataset using a new Internet of Things (IoT) architecture. Our architecture also includes a data collection scheme to ensure an efficient collection process, enabling the collection of targeted data before transferring them to cloud servers for further data analysis. Experimental results illustrate the reliability of our data collection scheme in gathering useful and targeted data, as well as the efficiency of our deep learning-based model, which achieved an accuracy of 94% and a precision and recall of 98% and 97%, respectively.

Effect of design-based learning on elementary students computational thinking skills in visual programming maker course

ABSTRACT Recently there has been a change in elementary curricula to focus on computational thinking, however, a good curriculum needs to be matched with good learning or teaching strategies. This study explores design-based learning (DBL) in elementary students to examine the development of computational thinking and to support knowledge construction in designing final products. An experimental design was used with 64 students; divided into experimental and control groups. The experimental group used a DBL approach with worksheets aimed at students designing their own final product design, while the control group was taught by the traditional approach with worksheets for redoing, practicing and designing their final product. Pre and posttests were used for data collection and evaluation scheme developed to score the final product. The finding showed no significant difference in the development of computational thinking between the two groups. However, there was a significant difference in designing the final product between the two groups. Therefore, DBL supported elementary students in designing the final product although redesigning curricula by integrating both teaching methods is suggested for the greater achievement of computational thinking.

A secure blockchain-oriented data delivery and collection scheme for 5G-enabled IoD environment

−There are innumerable ways the Internet of Drones (IoD) technology can impact our society. With the deployment of an airborne network, the IoD can support real-time low-cost delivery of services ranging from military surveillance to a myriad of civilian applications. Nevertheless, the drones employ insecure wireless communication channels to communicate with other entities in the system, inhibiting its induction in sensitive installations if insecure or inefficient Authenticated Key Agreement (AKA) schemes are employed. The blockchain, an open distributed ledger-based technology, is increasingly being adopted to address the security concern as discussed. Recently, Bera et al. presented an efficient blockchain-enabled AKA scheme for data management among various entities in IoD network. However, their scheme does not support anonymity and untraceability for the drones; also, it does not provide resistance to Ground station server impersonation attack, while the protocol has a few redundancies. Later, we proposed an enhanced blockchain-enabled AKA scheme BOD5-IOD to authenticate drones in the system. The BOD5-IOD, other than supporting a robust access control mechanism between drones and GSS, also ensures safe transactions among all entities in the IoD environment. The formal analysis and performance evaluation endorse that our scheme supports security requirements with computational and communication efficiency of 34.4% and 23.3%, respectively.

Analysis and Design of an Online Data Collection Scheme Based on Campus Public Opinion Monitoring System

The network data collection module is critical to the monitoring system of the public opinion on campus. To optimize the effect of network data collection, this study carried out comparative analysis on several different network data collection schemes, and formulated a data collection and filtering scheme based on the Libnids library. To improve the data collection performance, a multi-process pseudo-distributed processing scheme was prepared under multi-core CPU and the Linux framework. Finally, the proposed scheme was proved feasible and valuable in application based on campus public opinion monitoring system.

Maximum lifetime convergecast tree in wireless sensor networks

We study the problem of building a maximum lifetime data collection tree for periodic convergecast applications in wireless sensor networks. We experimentally observe that if two nodes transmit the same number of data packets, the amount of energy consumption of the nodes is approximately the same even if the payload lengths of the transmitted packets are different. This is because the major energy consumption during a packet transmission arises from radio start-up and medium access control overhead. Our formulated lifetime maximization problem captures the energy expenditure due to message transmissions/ receptions in terms of the number of data packets transmitted/ received, in contrast to prior works, which consider the number of data units (amount of sensor data generated by a node) transmitted/ received. Variable transmission power levels of the radio and accounting for the sensor energy consumption are other factors that make our problem formulation different from those in prior work. We prove that this problem is NP-complete by reducing the set cover problem to it and propose an algorithm to solve it. The performance of the proposed algorithm is experimentally evaluated using Jain’s fairness index as a metric by implementing it on an actual testbed consisting of 20 sensor nodes and compared with those of the widely used shortest path tree and random data collection tree algorithms. The energy consumption of different nodes under the proposed algorithm are shown to be more balanced than under the shortest path tree and random data collection tree algorithms. Also, the performance of the proposed algorithm in large networks is studied through simulations and is compared with those of the state-of-the-art RaSMaLai algorithm, directed acyclic graph algorithm and clustering based OPTIC algorithm, and the shortest path tree, minimum spanning tree, and random tree based data collection schemes. Our simulations show that the proposed algorithm provides a significantly higher network lifetime compared to all the other considered data collection approaches.

Optimization of the Rapid Design System for Arts and Crafts Based on Big Data and 3D Technology

In this paper, to solve the problem of slow design of arts and crafts and to improve design efficiency and aesthetics, the existing big data and 3D technology are used to conduct an in-depth analysis of the optimization of the rapid design system of arts and crafts machine salt baking. In the system requirement analysis, the functional modules of this system are identified as nine functional modules such as design terminology management system and external information import function according to the actual usage requirements. In the system design, the overall structure design, database design, and functional module design of the system are comprehensively elaborated, and the key issues such as 3D display and home layout generation algorithm based on reinforcement learning are analyzed and designed. In the implementation part of the system, the overall construction of the system and the composition of functional modules are introduced in detail and the main functional modules of the system are presented with interface diagrams. In the system implementation part, the overall system construction and functional module composition are introduced in detail, the main functional modules of the system are shown with interface diagrams, codes, and algorithms, and the specific implementation process of 3D display and soft layout algorithms are also explained in detail. The process of Surface Mount Technology (SMT) big data processing and analysis is designed, and the design of SMT production line data collection scheme and real-time data processing architecture is completed. Based on the characteristics of SMT production line data, the K-means algorithm is used to detect data outliers and verify the accuracy of the method; also, the Spark-based association rule printing parameter recommendation model is designed, and the efficiency of the Apriori algorithm is significantly improved by parallelization.

Low-Complexity Data Collection Scheme for UAV Sink Nodes in Cellular IoT Networks

Owing to the nature of battery-operated sensors, the residual energy of their battery should be considered when a sink node collects sensory data from them. If a sink attempts to collect data from a sensor that does not have sufficient residual energy to transmit data, the utilization of wireless communication resources may be degraded. However, requiring a periodic residual energy report of the sensors considerably reduces the energy efficiency of them. Consequently, the sink node must be able to estimate the residual energy of the sensors without additional information exchange before attempting to collect data. To tackle this problem, we propose a data collection scheme for mobile sinks in cellular Internet-of-things (IoT) networks. The scheme consists of two phases. In the first phase, the mobile sink estimates the residual energy of the surrounding sensors. To reduce the complexity, we design a state diagram composed of three states based on the Markov chain of the sensor. The sink node calculates the communicable likelihood of each sensor based on the state diagram and collects data from the sensor with the highest likelihood. In the second phase, a cellular base station (BS) selects the appropriate mobile sink to improve the signal-to-interference-plus-noise ratio (SINR) of the signal from a cellular user. Each sink node delivers the bidding price to the BS based on the likelihoods of the sensors. The BS determines an appropriate sink node to allocate resources based on the received bidding prices. Moreover, we show the performance of the proposed method in terms of SINR and IoT network utilization through simulation.

Probability maps of anthropogenic impacts affecting ecological status in European rivers

Understanding how anthropogenic pressures affect river ecological status is pivotal to designing effective management strategies. Knowledge on river aquatic habitats status in Europe has increased tremendously since the introduction of the European Union Water Framework Directive, yet heterogeneities in mandatory monitoring and reporting still limit identification of patterns at continental scale. Concurrently, several model and data-based indicators of anthropogenic pressures to freshwater that cover the continent consistently have been developed. The objective of this work was to create European maps of the probability of occurrence of river conditions, namely failure to achieve good ecological status, or to be affected by specific pervasive impacts. To this end, we applied logistic regression methods to model the river conditions as functions of continental-scale water pressure indicators. The prediction capacity of the models varied with river condition: the probability to fail achieving good ecological status, and occurrence of nutrient and organic pollution were rather well predicted; conversely, chemical (other than nutrient and organic) pollution and alteration of habitats due to hydrological or morphological changes were poorly predicted. The most important indicators explaining river conditions were the shares of agricultural and artificial land, mean annual net abstractions, share of pollution loads from point sources, and the share of upstream river length uninterrupted by barriers. The probability of failing to achieve good ecological status was estimated to be high (>60%) for 36% of the considered river network of about 1.6 M km. Occurrence of impact of nutrient pollution was estimated high (>60%) in 26% of river length and that of organic pollution 20%. The maps are built upon information reported at country level pursuant EU legal obligations, as well as indicators generated from European scale models and data: both sources are affected by epistemic uncertainty. In particular, reported information depend on data collection scoping and schemes, as well as national knowledge and interpretation of river system pressures. In turn, water pressure indicators are affected by heterogeneous biases due to incomplete or incorrect inputs and uncertainty of models adopted. Lack of effective reach- and site-scale indicators may hamper detection of locally relevant impacts, for example in explaining alteration of habitats due to morphological changes. The probability maps provide a continental snapshot of current river conditions, and offer an alternative source of information on river aquatic habitats, which may help filling in knowledge gaps. Foremost, the analysis demonstrates the need for developing more effective continental-scale indicators for hydromorphological alterations and chemical pollution.