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.
Wireless multimedia sensor networks (WMSNs) currently face the problem of rapidly decreasing energy due to the acquisition, processing and transmission of massive multimedia data. This decrease in energy affects the life of the network, resulting in higher overhead costs and a deterioration in quality-of-service. This study presents a new grouping strategy that somewhat reduces energy reduction problems. The objective is to group cameras in the WMSN according to their field of view. The proposed system begins by searching for all polygons created by the intersection of the two cameras' FoV. Based on the generated surfaces, an ascending hierarchical classification is applied to group cameras with strongly overlapping visions fields. The results obtained with 300 randomly positioned cameras show the effectiveness of the proposed method to minimise redundant detection, reduce energy consumption, increase network life, and reduce network overload.
In recent years, the large amount of heterogeneous data generated by the Internet of Things (IoT) sensors and devices made recording and research tasks much more difficult, and most of the state-of-the-art methods have failed to deal with the new IoT requirements. This article proposes a new efficient method that simplifies data indexing and enhances the quality and velocity of the similarity query search in the IoT environment. In this method, the fog layer was divided into two levels. In the clustering fog level, the incremental density-based spatial clustering of applications with noise (DBSCAN) algorithm was used to separate collected data into clusters in order to minimize data overlap during in parallel indexes construction. Parallelism was also used, in the indexing fog level to speed up the similarity-based search process and speed up the similarity-based search process. The data in each cluster were indexed using our proposed structure called B3CF-tree (binary tree based on containers at the cloud-clusters fog computing level). The objects in the leaf nodes of the B3CF-trees are, finally, stored in the cloud. Using this approach for computing multiple datasets, the retrieve time of the similarity search is significantly reduced. The experimental results showed that the combination of DBSCAN clustering and parallel indexing make the B3CF-trees outperform the latest real data indexing methods. For example, in terms of quality, the B3CF-tree has the smallest number of nodes and leaf nodes. In addition, the use of parallelism during kNN search reduced, significantly, the retrieve time of the similarity query search.
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