Multi-dimensional data indexing and range query processing via Voronoi diagram for internet of things

Wan, Shaohua; Zhao, Yu; Wang, Tian; Gu, Zonghua; Abbasi, Qammer H.; Choo, Kim‐Kwang Raymond

doi:10.1016/j.future.2018.08.007

Cited by 171 publications

(104 citation statements)

References 37 publications

Supporting

Mentioning

104

Contrasting

Order By: Relevance

“…We observe that the proposed scheme based on a full-adaptive approach causes a suitable impact in real ViSAR dataset and outperforms the other methods. In the recent years, data processing for IoT became an interesting topic of research [27][28][29][30]. In our study, we proposed ALMMSE interpolation algorithm for the remote sensing ViSAR frames captured by imaging radars in an IoT-enabled radar networks of drones and airplanes [31].…”

Section: Resultsmentioning

confidence: 99%

Data compression in ViSAR sensor networks using non-linear adaptive weighting

Khosravi

Samadi

2019

J Wireless Com Network

View full text Add to dashboard Cite

Nowadays, industrial video synthetic aperture radars (ViSARs) are widely used for aerial remote sensing and surveillance systems in smart cities. A main challenge of a group of networked ViSAR sensors in an IoT-based environment is low bandwidth of wireless links for communicating big video data. In this research, we propose a non-linear statistical estimator for adaptive reconstruction of compressed ViSAR data. Our proposed reconstruction filter is based on an adaptively generated non-linear weight mask of spatial observations. It can strongly outperform several conventional and well-known reconstruction filters for three different video samples.

show abstract

Section: Resultsmentioning

confidence: 99%

Data compression in ViSAR sensor networks using non-linear adaptive weighting

Khosravi

Samadi

2019

J Wireless Com Network

View full text Add to dashboard Cite

show abstract

“…Specifically, this scheme uses binary space partitioning, such as KD tree, quad‐tree, k‐means clustering, and Voronoi‐based methods, which can minimizes the average energy consumption and query response time. Compared to the Miao et al's model, the fog layer between the data center and the end devices is not considered by the Wan et al's scheme …”

Section: Related Workmentioning

confidence: 99%

“…Compared to the Miao et al's model, 25 the fog layer between the data center and the end devices is not considered by the Wan et al's scheme. 26 Clustering techniques are data analysis techniques that allow the most similar data to be grouped together in the same cluster and to separate the dissimilar data by separating the groups. These methods can be used as a preprocessing step to separate data, as a knowledge discovery tool, as well as for indexing and compression, etc.…”

Section: Related Workmentioning

confidence: 99%

An efficient indexing for Internet of Things massive data based on cloud‐fog computing

Benrazek

Kouahla

Farou

et al. 2020

Trans Emerging Tel Tech

View full text Add to dashboard Cite

In recent years, the number of sensor and actuator nodes in the Internet of Things (IoT) networks has increased, generating a large amount of data. Most research techniques are based on dividing target data into subsets. On a large scale, this volume increases exponentially, which will affect search algorithms. This problem is caused by the inherent deficiencies of space partitioning. This paper introduces a new and efficient indexing structure to index massive IoT data called BCCF‐tree (Binary tree based on containers at the cloud‐fog computing level). This structure is based on recursive partitioning of space using the k‐means clustering algorithm to effectively separate space into nonoverlapping subspace to improve the quality of search and discovery algorithm results. A good topology should avoid a biased allocation of objects for separable sets and should not influence the structure of the index. BCCF‐tree structure benefits to the emerging cloud‐fog computing system, which represents the most powerful real‐time processing capacity provided by fog computing due to its proximity to sensors and the largest storage capacity provided by cloud computing. The paper also discusses the effectiveness of construction and search algorithms, as well as the quality of the index compared to other recent indexing data structures. The experimental results showed good performance.

show abstract

“…Nowadays, wireless sensor networks (WSNs), seen as a popular technique, are applied into various applications, such as environmental monitoring, smart home, Internet of Things, and military battlefields [1,2]. Many new challenges have emerged with the development of artificial intelligence and big data [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Energy-efficient privacy-preserving data aggregation protocols based on slicing

Liu

Zhang

et al. 2020

J Wireless Com Network

View full text Add to dashboard Cite

Wireless sensor networks (WSNs) have become one of the most vigorous techniques in the network domain. However, the sensor nodes of WSNs tend to become the target of attackers due to the broadcast communication mode and the unattended deployment nature. Although it can prevent the sensitive data from being compromised, Slice-Mix-AggRegaTe (SMART) needs to exchange messages frequently in a network, which put tremendous overhead on the sensor nodes with limited resources. Faced with these issues, this paper proposes an energy-efficient privacy-preserving data aggregation protocol based on slicing (EPPA) where a novel slicing mode is adopted to reduce the numbers of slices, which can significantly prevent the data from being compromised and decrease the communication overhead. Meanwhile, an enhanced scheme based on EPPA, called multi-function privacy-preserving data aggregation protocol (MPPA), is presented and it supports multiple functions in the process of data aggregation, such as max/min, count, and mean. The theoretical analysis and the simulation evaluation show that the proposed aggregation protocols demonstrate a better performance in the privacy preserving and the communication efficiency.

show abstract

Multi-dimensional data indexing and range query processing via Voronoi diagram for internet of things

Cited by 171 publications

References 37 publications

Data compression in ViSAR sensor networks using non-linear adaptive weighting

Data compression in ViSAR sensor networks using non-linear adaptive weighting

An efficient indexing for Internet of Things massive data based on cloud‐fog computing

Energy-efficient privacy-preserving data aggregation protocols based on slicing

Contact Info

Product

Resources

About