Data Redundancy Elimination Research Articles

Proposed fog computing-enabled conceptual model for semantic interoperability in internet of things

Semantic interoperability has emerged as a key barrier amidst the major developments and challenges brought about by the rapid expansion of internet of things (IoT) applications. Establishing interoperability is essential for IoT systems to function optimally, especially across diverse organizations. Despite extensive research in achieving semantic interoperability, dynamic interoperability, a vital facet, remains inadequately addressed. This paper addresses this gap by presenting a fog-based conceptual model designed to facilitate dynamic semantic interoperability in IoT. The model incorporates a single-tier fog layer, providing the necessary processing capabilities to achieve this goal. The study conducts a comprehensive literature review on semantic interoperability, emphasizing latency, bandwidth, total cost, and energy consumption. Results demonstrate the proposed double skin façade (DSF) model’s remarkable 88% improvement in service delay over IoT-SIM and Open IoT, attributed to its efficient load-offloading mechanism and optimized fog layer, offering a 50% reduction in service delay, power consumption, and 86% reduction in network usage compared to existing approaches through data redundancy elimination via pre-processing at the fog layer.

Energy Efficient Localization Technique Using Multilateration for Reduction of Spatially and Temporally Correlated Data in RFID System

RFID plays a vital role in data communication in multidimensional WSNs as it collects vast amounts of redundant data. The physical phenomena constitute the correlated observations in the space domain and generate spatial correlation. Periodic observations of sensor nodes result in a temporal correlation in the data. Reducing these spatio-temporal correlations in RFID surveillance data is necessary for the smooth functioning of the network. This paper proposes a Voronoi diagram-based spatio-temporal data redundancy elimination approach for RFID systems having multiple readers so only one reader will read every RFID tag depending on the distance between the tag and the center of the Minimum Enclosing Circle of the Voronoi cell to which the reader belongs. This approach eliminates spatial redundancy in the gathered data. Reading the RFID tags at regular time intervals larger than a chosen threshold value minimized temporal redundancy. In contrast to existing methods, the proposed technique is free from any false positive and false negative errors, with no loss of data and every tag being read by only one reader. Simulation of the proposed approach also established its superiority to the existing techniques in terms of these performance parameters.

Compact representations of spatial hierarchical structures with support for topological queries

Among different spatial data models, the topological model for spatial regions explicitly represents common boundaries. This model pursues the efficiency of topology-related queries and the elimination of data redundancy. This paper proposes several space-efficient data structures to support access to the topological representation of two-dimensional regions that are organized in a multi-granular or hierarchical structure, such as the political and administrative partition of a country. In the context of these hierarchies, we focus on queries that search for inclusion, disjointness, and adjacency between regions. The proposed structures build upon compact planar graph embeddings, which show to have a good trade-off between space and time.

Trends in the sample size, statistics, and contributions to the BrainMap database of activation likelihood estimation meta-analyses: An empirical study of 10-year data.

The literature of neuroimaging meta-analysis has been thriving for over a decade. A majority of them were coordinate-based meta-analyses, particularly the activation likelihood estimation (ALE) approach. A meta-evaluation of these meta-analyses was performed to qualitatively evaluate their design and reporting standards. The publications listed from the BrainMap website were screened. Six hundred and three ALE papers published during 2010-2019 were included and analysed. For reporting standards, most of the ALE papers reported their total number of Papers involved and mentioned the inclusion/exclusion criteria on Paper selection. However, most papers did not describe how data redundancy was avoided when multiple related Experiments were reported within one paper. The most prevalent repeated-measures correction methods were voxel-level FDR (54.4%) and cluster-level FWE (33.8%), with the latter quickly replacing the former since 2016. For study characteristics, sample size in terms of number of Papers included per ALE paper and number of Experiments per analysis seemed to be stable over the decade. One-fifth of the surveyed ALE papers failed to meet the recommendation of having >17 Experiments per analysis. For data sharing, most of them did not provide input and output data. In conclusion, the field has matured well in terms of rising dominance of cluster-level FWE correction, and slightly improved reporting on elimination of data redundancy and providing input data. The provision of Data and Code availability statements and flow chart of literature screening process, as well as data submission to BrainMap, should be more encouraged.

Mathematical Modeling of Static Data Attribute Encryption Based on Big Data Technology

Attribute encryption is an effective one to many network communication technologies, which supports flexible access control strategies and is very suitable for fine-grained access control in large-scale information systems. In order to improve the attributes of static data, encryption technology can provide a reliable technical guarantee for network security. This paper presents a mathematical modeling method of static data attribute encryption based on big data technology. The big data redundancy elimination algorithm based on similarity calculation is analyzed. By using static data attribute encryption based on big data technology, the length of encrypted data packets will not increase, and partial redundancy of fragments can be eliminated, which can greatly improve the efficiency of the system. The attribute-based encryption mechanism uses attributes as public keys, and the decryption user is a group; so, the encryption efficiency is very high. It can realize efficient encryption and decryption, as well as flexible access control based on user attributes. This scheme can reflect the importance of attributes; so, it is more practical.

Mathematical Modeling Analysis of Data Attribute Encryption for Robot

Encrypting data based on the data attributes of robots is one of the effective methods to control access users in the data outsourcing environment. Therefore, a mathematical modeling method of robot data attribute encryption based on data redundancy elimination technology is proposed. The encryption algorithm structure is analyzed based on the Bloom filter. The Hamming distance is used to calculate the similarity of big data by the Bloom filter. Finally, a big data ellipse encryption algorithm is designed according to the calculation results. The results show that during the whole experiment, the approximate fluctuation range is 0.08%~0.14%, which is not only high but also has a large fluctuation range. In contrast, the probability of occurrence of redundant data is less than 0.05% under different byte rates of redundant data, which is far lower than the two traditional methods, indicating that the application performance of the proposed method is good.

A Novel Spatio-Temporal Data Redundancy Elimination Approach for RFID Systems with Multiple Readers

In this paper, we propose a Voronoi diagram based spatio-temporal data redundancy elimination approach for RFID systems having multiple readers so that every RFID tag will be read by only one reader depending on the distance between the tag and the center of the Minimum Enclosing Circle (MEC) of the Voronoi cell which the reader belongs to. Thus, the spatial redundancy in the gathered data is completely eliminated. Temporal redundancy is also minimized by reading the RFID tags at regular time intervals larger than an appropriately chosen threshold value. Existing methods of reducing data redundancy in RFID systems are often associated with loss of data, false positive errors and false negative errors. In contrast to this, our proposed technique is free from any false positive and false negative errors with no loss of data and every tag being read by only one single reader. Simulation of our proposed approach also establishes its superiority to the existing techniques in terms of these performance parameters.

Parallel Redundancy Protocol for Railway Wireless Data Communication Network

In order to solve the problem that the railway application services have strict requirements on the reliability, delay, and other indicators of mobile data communication, a parallel redundancy protocol for the railway wireless data communication network is proposed. By introducing a redundancy adaptation layer into the standard TCP/IP model, multi-link parallel data transmission is realized and transparent to the network layer and above. Transmitting multiple copies of the same packet through different paths makes the application systems have redundant communication capability without changing the existing data transmission mode. At the same time, according to the requirements of the receiving entity for redundant data elimination, an improved Bloom filter is proposed. By expanding the bit array and using the countdown mechanism, the elements in the data set can be dynamically inserted, deleted, and retrieved over time. Theoretical analysis and test results show that the proposed protocol can meet the requirements of data transmission between the on-board and trackside equipment and has the characteristics of simple deployment, high reliability, and high stability. Compared to using a single transmission link, the packet loss rate from field test can be reduced by 39.67% at the maximum, and the throughput and end-to-end data transmission delay are also significantly improved. The test in this paper also shows that the additional delay introduced is a maximum of 5.07 μs, which will not significantly affect the data transmission performance.

RETRACTED ARTICLE: Redundant data high-efficiency compression based on distributed parallel algorithm

Redundancy occurs when multiple consumers of the network attempt to access the similar content online. Many state-of-the-art studies have been proposed to remove redundancy from the network and to enhance the network efficiency of Internet applications. Still there is a necessity to explore more data redundancy elimination (DRE) techniques, especially for defense-based networks to enhance the data transmission speed and to compress the irrelevant data optimally for reducing the network latency. In order to improve the optimal storage capacity of redundant data over serial hybrid network cascade database, a high-efficiency redundancy elimination method based on the distributed parallel algorithm is proposed in this paper. The distributed storage structure model for handling redundant data of serial mixed network cascade database is designed and tested in simulated environment. The extraction of features of redundant data is performed by using distributed hybrid feature mining technique. The dimensionality reduction of redundant data is also attained by devising the feature transformation method to remove the unwanted features of data. The two benchmarked techniques have been selected for comparative study and to evaluate the performance of the proposed method. The simulation results show that the proposed DRE method can significantly reduce the redundancy from the network traffic. The bandwidth utilization is improved, the duplicate data are also compressed optimally and it is proved that the proposed DRE method is viable for large networks to eliminate the redundant data.

Optimized data manipulation methods for intensive hesitant fuzzy set with applications to decision making

The emergence of large amounts of hesitant fuzzy data brings more opportunities and challenges for optimal decision-making results. The granularity of the hesitant fuzzy set has been significantly improved, but the potential for more inconsistent data also increases. Due to high computational complexity and low efficiency, the existing data aggregation methods cannot handle the intensive hesitant fuzzy data. To solve this problem, we first propose the optimization strategy for the intensive hesitant fuzzy data. Then the data redundancy elimination method and the data aggregation method are proposed. The extensions of hesitant fuzzy set and normal-type hesitant fuzzy set are proposed for intensive hesitant fuzzy data manipulations. The aggregation methods and the data structure estimation methods of the extensions are also discussed. Finally, a practical application to the wine quality determination is provided, and some comparative analyses are conducted to demonstrate the effectiveness of the proposed methods.

A New Adaptive Method for the Extraction of Steel Design Structures from an Integrated Point Cloud.

The continuous and intensive development of measurement technologies for reality modelling with appropriate data processing algorithms is currently being observed. The most popular methods include remote sensing techniques based on reflected-light digital cameras, and on active methods in which the device emits a beam. This research paper presents the process of data integration from terrestrial laser scanning (TLS) and image data from an unmanned aerial vehicle (UAV) that was aimed at the spatial mapping of a complicated steel structure, and a new automatic structure extraction method. We proposed an innovative method to minimize the data size and automatically extract a set of points (in the form of structural elements) that is vital from the perspective of engineering and comparative analyses. The outcome of the research was a complete technology for the acquisition of precise information with regard to complex and high steel structures. The developed technology includes such elements as a data integration method, a redundant data elimination method, integrated photogrammetric data filtration and a new adaptive method of structure edge extraction. In order to extract significant geometric structures, a new automatic and adaptive algorithm for edge extraction from a random point cloud was developed and presented herein. The proposed algorithm was tested using real measurement data. The developed algorithm is able to realistically reduce the amount of redundant data and correctly extract stable edges representing the geometric structures of a studied object without losing important data and information. The new algorithm automatically self-adapts to the received data. It does not require any pre-setting or initial parameters. The detection threshold is also adaptively selected based on the acquired data.

Underwater Wireless Sensor Networks: An Energy-Efficient Clustering Routing Protocol Based on Data Fusion and Genetic Algorithms

Due to the limited battery energy of underwater wireless sensor nodes and the difficulty in replacing or recharging the battery underwater, it is of great significance to improve the energy efficiency of underwater wireless sensor networks (UWSNs). We propose a novel energy-efficient clustering routing protocol based on data fusion and genetic algorithms (GAs) for UWSNs. In the clustering routing protocol, the cluster head node (CHN) gathers the data from cluster member nodes (CMNs), aggregates the data through an improved back propagation neural network (BPNN), and transmits the aggregated data to a sink node (SN) through a multi-hop scheme. The effective multi-hop transmission path between the CHN and the SN is determined through the enhanced GA, thereby improving transmission efficiency and reducing energy consumption. This paper presents the GA based on a specific encoding scheme, a particular crossover operation, and an enhanced mutation operation. Additionally, the BPNN employed for data fusion is improved by adopting an optimized momentum method, which can reduce energy consumption through the elimination of data redundancy and the decrease of the amount of transferred data. Moreover, we introduce an optimized CHN selecting scheme considering residual energy and positions of nodes. The experiments demonstrate that our proposed protocol outperforms its competitors in terms of the energy expenditure, the network lifespan, and the packet loss rate.

Benefícios e desvantagens da implementação do prontuário eletrônico do paciente para o serviço de saúde

Trata-se de uma pesquisa bibliográfica de abordagem qualitativa e caráter descritivo com o objetivo em identificar os benefícios e desvantagens encontradas na implementação dos Prontuários Eletrônico do Paciente no serviço de saúde. Como metodologia. Os dados foram coletados em Base de Dados Virtuais. Para tal utilizou-se a BVS, nas seguintes Bases de informações LILACS, MEDLINE, BDENF e pelo endereço eletrônico scholar.google.com.br, no período de maio de 2019. Como resultados, foram encontradas vantagens como agilidade no acesso de informações relacionadas ao paciente, uso simultâneo dos prontuários, legibilidade, eliminação da redundância de dados, organização sistematizada e ainda desvantagens como grandes investimentos de hardware, software e treinamento, resistência dos profissionais de saúde e lentidão e inoperância dos sistemas. Conclui-se que devem ser considerados, para implementação do PEP, a padronização dos prontuários, o estabelecimento dos fluxogramas de informação da equipe, o treinamento de todos envolvidos e o controle e avaliação dos resultados.

A survey on data aggregation techniques in IoT sensor networks

There is a growing interest in using wireless sensor technologies in various Internet of things scenarios. Considering the huge growth of smart objects and their applications, the need to collect and analyze their product data are becoming one of the main challenges. Sensor nodes are powered by batteries, efficient operations in term of energy are critical. Toward that end, it is desirable for a sensor node to eliminate redundancies in the received data from the neighboring nodes before transferring the final data to the central station. Data aggregation is one of the influential techniques in elimination of data redundancy and improvement of energy efficiency; also it increases the lifespan of Wireless Sensor Networks. In addition, the efficient data aggregation protocol can reduce network traffic. When a specific objective takes place in a specific area, it might be detected by more than one sensor. Considering the main challenges and aspects of data aggregation in wireless sensor networks, a review on different types of data aggregation techniques and protocols are presented in this paper. The ultimate objective of this study is to make the basic foundations to develop new advanced designs based on data integration techniques and clustering that have been proposed so far. Major techniques of data integration in wireless sensor networks covering ground, underground and underwater sensor networks are presented in this paper and the applications, advantages and disadvantages of using each technique are described.

A Strategy for Elimination of Data Redundancy in Internet of Things (IoT) Based Wireless Sensor Network (WSN)

In order to give a complete description of an environment or to make a robust decision, a number of observations must be collected and combined from multiple sensor nodes. In these large collections of data, only some are useful, whereas others are redundant. This redundancy decreases performance in terms of computing overhead, excessive transmission, and covering a large space. The process of selecting and analyzing the useful information from the collection of sensed data is called mining. Mining is used to produce more consistent, accurate, and useful information than that provided by any individual sensor node. Data mining has been widely applied in many areas, such as object recognition, wireless sensor networks (WSNs), image processing, environment mapping, and localization. Nowadays, Internet of Things utilizes WSN as a necessary platform for sensing and communication of the data. For efficiency, mining of spatial and temporal data is performed on the sensed sample collected by sensor nodes. Therefore, in this paper, a redundancy removal strategy is proposed, which performs mining on collected data to select the appropriate information before forwarding to a base station or a cluster head in the WSN. Extensive simulations were conducted, and the related results showed that the proposed scheme had better performance compared to other schemes in our simulated scenarios.

Data fusion-oriented cluster routing protocol for multimedia sensor networks based on the degree of image difference

In recent years, wireless multimedia sensor networks (WMSN) have attracted considerable attentions because of the extensive applications of providing audio-visual information conveniently. However, the traditional routing protocols are not directly applicable to WMSN due to the disadvantages on real-time or energy cost. The routing based on combination of clusters and data fusion provides an effective approach to improve the efficiency of data transmission. Consequently, this paper proposes a novel data fusion-oriented routing protocol, which takes the degree of image difference into consideration when clustering. And meanwhile, the cluster-head is rotated periodically within a cluster, for the purpose of the establishment of data fusion tree which is responsible for the data transmission and redundancy elimination. The simulation results show that the proposal achieves a better performance on energy efficiency, network delay and loading balance, and simultaneously prolongs the network lifetime, compared with other typical protocols.

Redundancy Elimination Techniques in Wireless Multimedia SensorNetworks: Survey

The evolution of Wireless Multimedia Sensor Networks(WMSNs) has been enhanced by the existence of cheap hardware such as microphones and (CMOS) cameras that can ubiquitously hold multimedia content from the entire environment. The spatial proximity of the different cameras (the overlapping Field of Views (FoVs)) causes a transmission of redundant data. Redundancy is the existence of data that is additional to the actual data and permits. A redundancy causes transmission of same data repeatedly. In WMSN, the redundant data elimination is a critical task to reduce the communication cost in terms of unnecessary energy wastage, bandwidth used and CPU processing. In this context, this paper proposed a brief discussion of the following techniques: i) Event monitoring based actuation algorithms which depends on activating the minimum number of cameras sensors while still maintaining the necessary coverage of the event of interest to reduce the possibility of multimedia data redundancy. ii) Cover set construction strategies for enabling efficient scheduling of nodes in mission-critical surveillance applications. iii) Clustering algorithms for WMSNs based on the overlapped field of views (FoVs) coverage areas.

Integration of Models of Building Interiors with Cadastral Data

Abstract Demands for applications which use models of building interiors is growing and highly diversified. Those models are applied at the stage of designing and construction of a building, in applications which support real estate management, in navigation and marketing systems and, finally, in crisis management and security systems. They are created on the basis of different data: architectural and construction plans, both, in the analogue form, as well as CAD files, BIM data files, by means of laser scanning (TLS) and conventional surveys. In this context the issue of searching solutions which would integrate the existing models and lead to elimination of data redundancy is becoming more important. The authors analysed the possible input- of cadastral data (legal extent of premises) at the stage of the creation and updating different models of building’s interiors. The paper focuses on one issue - the way of describing the geometry of premises basing on the most popular source data, i.e. architectural and construction plans. However, the described rules may be considered as universal and also may be applied in practice concerned may be used during the process of creation and updating indoor models based on BIM dataset or laser scanning clouds

Resemblance and mergence based indexing for high performance data deduplication

A new de-duplication scheme based on two-level index structure and dynamic bloom filter.A fast resemblance approach to index duplicate data based on segments.A novel resemblance mergence strategy that groups segments into bins.A new frequency based cleanup method to avoid storing low-frequent fingerprints.A thorough evaluation of our approach to demonstrate its effectiveness. Data deduplication, a data redundancy elimination technique, has been widely employed in many application environments to reduce data storage space. However, it is challenging to provide a fast and scalable key-value fingerprint index particularly for large datasets, while the index performance is critical to the overall deduplication performance. This paper proposes RMD, a resemblance and mergence based deduplication scheme, which aims to provide quick responses to fingerprint queries. The key idea of RMD is to leverage a bloom filter array and a data resemblance algorithm to dramatically reduce the query range. At data ingesting time, RMD uses a resemblance algorithm to detect resemble data segments and put resemblance segments in the same bin. As a result, at querying time, it only needs to search in the corresponding bin to detect duplicate content, which significantly speeds up the query process. Moreover, RMD uses a mergence strategy to accumulate resemblance segments to relevant bins, and exploits frequency-based fingerprint retention policy to cap the bin capacity to improve query throughput and data deduplication ratio. Extensive experimental results with real-world datasets have shown that RMD is able to achieve high query performance and outperforms several well-known deduplication schemes.

Maximising energy efficiency using data aggregation in sensor networks

Wireless sensor networks (WSNs) is a class of wireless ad hoc networks in which sensor nodes sense, process, and communicate data acquired from the physical environment to an external-base-station (BS). But the fundamental challenge in the design of wireless sensor networks (WSNs) is to maximise their lifetimes especially when they have a limited and non-replenish able energy supply. Energy-efficient communication is necessary to increase the lifetime of the sensor network. In this work solutions are for the data gathering and routing problem with in network aggregation in WSNs. The aim of our work is to focus on data gathering and redundancy elimination with in-network aggregation in WSNs. The objective is to maximise the network lifetime by utilising data aggregation with redundancy removal by increasing accuracy thereby improving packet delivery ratio and reducing energy consumption. This work focuses on the maximum lifetime data gathering and routing to avoid packet loss in wireless sensor networks.