A New Preventive Routing Method Based on Clustering and Location Prediction in the Mobile Internet of Things

Sadrishojaei, Mahyar; Navimipour, Nima Jafari; Reshadi, Midia; Hosseinzadeh, Mehdi

doi:10.1109/jiot.2021.3049631

Cited by 49 publications

(32 citation statements)

References 53 publications

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“…Multi-access Edge Computing, also known as mobile edge computing, provides resources through the Radio Access Network (RAN) infrastructure. This approach is projected to gain greatly from the 5G platform, which is now in development and will make edge computing available to a wide variety of mobile devices with minimal latency [56].…”

Section: Resultsmentioning

confidence: 99%

Replica selection and placement techniques on the IoT and edge computing: a deep study

Shao

Huang

2021

Wireless Netw

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Internet of Things (IoT) has lately been presented as a new technological transformation in which things are connected via the Internet. Several sensors and devices create data and send vital signals constantly over sophisticated networks that allow machine-to-machine interactions and monitor and manage key smart-world infrastructures. Since huge amounts of data are generated, reducing the data access costs is a critical issue. Edge computing has been developed as a novel paradigm for solving IoT demands to reduce the rise in resource congestion. One of the most significant data management challenges in the IoT is selecting suitable replication things that minimize reaction time and cost. Therefore, our goal is to examine replica selection and placement techniques in IoT and edge computing. The findings revealed that the edge computing environment might significantly enhance system performance regarding access response time, prediction accuracy, effective network, and increased data availability. Furthermore, the results illustrate that data provenance is necessary to raise the accuracy of the data by. Also, the results showed that the most important challenge in data replication and placement techniques in IoT and edge computing was the availability of data and access response time.

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Section: Resultsmentioning

confidence: 99%

Replica selection and placement techniques on the IoT and edge computing: a deep study

Shao

Huang

2021

Wireless Netw

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“…Given that the values of the pixels are arranged in energy order and not in color order, however, the repetition of cluster‐heads is normally inevitable. To prevent this duplication, during the K‐means process each time a cluster‐head is selected, the selected cluster‐head is compared to the values of the previous cluster‐heads 48 . If repeated, the next pixel is selected as the cluster‐head, and this operation is repeated until there is no duplicate cluster‐head.…”

Section: The Proposed Methodsmentioning

confidence: 99%

A hybrid method for reduction of size and number of hues in the color images used in a carpet map

Cui

Shi

2021

Color Research & Application

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The carpet industry is no longer a small business done in the villages on a small scale; instead, it has carved an altogether different space, identity, and appreciation for itself in the cosmopolitan world. As computers are is becoming more and more ubiquitous, most industries, including the carpet industry, use computers for quality improvement, accuracy enhancement, speed development, and cost reduction purposes. Unlike traditional carpet maps, many modern maps include images of human faces for hand‐woven carpet tableau. These digital images comprise millions of colors and thousands of pixels, making it practically impossible to construct and weave the carpet in the same dimensions. Many weavers currently use manual and experience‐based methods for reducing the size and number of hues for making a hand‐woven carpet tableau map. Therefore, the outcomes are not the optimal results and can be improved. Also, many color reduction methods do not focus on the hand‐woven carpet tableau map. To overcome these problems and gaps, this research focuses on proposing a new automatic method for reducing the size of color images without compromising facial nuances, lessening the number of colors used while protecting the important areas of the images, and transforming those images into carpet tableau maps. The proposed approach inputs the original color image. It continuously detecting the face and specifying important areas, and finally, outputs carpet tableau map that is proportional to the given dimensions and color count. To evaluate the proposed method, MATLAB, as a powerful simulation tool, was employed. Final results are compared to the existing approaches in terms of face detection, size reduction, and color quantization. The obtained results have shown that the approach improves speed by 39% in face detection and increases the precision of size reduction and color quantization phases. The results have also confirmed that when images of human faces are reduced by a proposed method to form an appropriate image for tableau maps, they are nearly always perceived as more attractive than the reduced faces via traditional methods.

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“…Clustering is a technique to group some related objects using similarity or location information (Sadrishojaei et al, 2021). Partitioning, hierarchical (Solihah, Azhari & Musdholifah, 2020), grid, density (Mehdi Cherrat, Alaoui & Bouzahir, 2020, graph, and model-based algorithms are different clustering methods.…”

Section: Overview and Backgroundmentioning

confidence: 99%

A technique for parallel query optimization using MapReduce framework and a semantic-based clustering method

Azhir

Navimipour

Hosseinzadeh

et al. 2021

PeerJ Computer Science

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Query optimization is the process of identifying the best Query Execution Plan (QEP). The query optimizer produces a close to optimal QEP for the given queries based on the minimum resource usage. The problem is that for a given query, there are plenty of different equivalent execution plans, each with a corresponding execution cost. To produce an effective query plan thus requires examining a large number of alternative plans. Access plan recommendation is an alternative technique to database query optimization, which reuses the previously-generated QEPs to execute new queries. In this technique, the query optimizer uses clustering methods to identify groups of similar queries. However, clustering such large datasets is challenging for traditional clustering algorithms due to huge processing time. Numerous cloud-based platforms have been introduced that offer low-cost solutions for the processing of distributed queries such as Hadoop, Hive, Pig, etc. This paper has applied and tested a model for clustering variant sizes of large query datasets parallelly using MapReduce. The results demonstrate the effectiveness of the parallel implementation of query workloads clustering to achieve good scalability.

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A New Preventive Routing Method Based on Clustering and Location Prediction in the Mobile Internet of Things

Cited by 49 publications

References 53 publications

Replica selection and placement techniques on the IoT and edge computing: a deep study

Replica selection and placement techniques on the IoT and edge computing: a deep study

A hybrid method for reduction of size and number of hues in the color images used in a carpet map

A technique for parallel query optimization using MapReduce framework and a semantic-based clustering method

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