Construction of energy minimized WSN using GA-SAMP-MWPSO and K-mean clustering algorithm with LDCF deployment strategy

Banerjee, A.; De, Sudip Kumar; Majumder, Koushik; Dash, Dinesh; Chattopadhyay, Samiran

doi:10.1007/s11227-021-04265-7

Cited by 15 publications

(5 citation statements)

References 33 publications

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“…e general power grid project construction has the characteristics of large investment and long construction period; in addition, most of the power grid projects are public utilities involving public electricity consumption; therefore, under the premise of strictly guaranteeing the quality and construction period of the project, the cost of the power grid project should be reasonably controlled. Generally, in the construction and implementation of power grid projects, unexpected emergencies will occur; as a result, the dynamics and uncertainty of the project cost of the power grid project are generated, so a complete set of risk management scheme is required to carry out risk identification, evaluation, management, and control the whole process of power and network project cost, and through the combination strategy management of risk factors, unforeseen costs of grid projects can be reduced [3]. erefore, combined with the characteristics of power grid project engineering cost, it is particularly important to systematically study the risk management theory and management strategy of power grid project engineering cost.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Design of the Project Risk Management System Based on the Fuzzy Clustering Algorithm

Zhong

Yin

2022

Journal of Control Science and Engineering

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In order to effectively control the cost risk of power grid construction projects, the author proposes a cost risk management system for power grid construction projects based on the fuzzy clustering algorithm. The system introduces the fuzzy clustering maximum tree algorithm, and by constructing a mathematical model, combined with the empirical analysis, the key risk factors in the cost risk of power grid construction projects are identified. Through the analysis, it can be concluded that the key risks in the cost risk of power grid construction projects are the planning risks of infrastructure projects, the research risks of infrastructure projects, and the cost risks of infrastructure projects. Experimental results show that combined with expert experience and the actual situation of power grid engineering, the classification result at threshold λ = 0.785 becomes more realistic. At this λ level, 6 risk factors are grouped into 4 categories as follows: class I x 1 , x 2 , x 4 , class II x 3 , class III x 5 , and class IV x 6 . Through research, the identification of key risks can enable project managers to control the cost of power grid construction projects, targeted, so that risks can be minimized and investment returns can be improved.

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Section: Introductionmentioning

confidence: 99%

Analysis and Design of the Project Risk Management System Based on the Fuzzy Clustering Algorithm

Zhong

Yin

2022

Journal of Control Science and Engineering

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“…The system has been deployed to run on the core network of large cellular networks, and the system works well and has been widely praised [4]. Banerjee, A In order to solve the problem that the processing efficiency of frequent subgraph mining decreases when the amount of data increases, a frequent subgraph mining algorithm FSM-Ho FSM-H based on the MapReduce framework is proposed, which is suitable for all the latest FSM algorithms [5]. Through experiments, we verify that the parallel frequent subgraph FSM-H is effective with a large comprehensive dataset.…”

Section: Article Contributionmentioning

confidence: 99%

Research and Application of Big Data Clustering Algorithm based on AI Technology in Cloud Environment

Huang,

Zhang

2024

SCPE

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Traditional big data filling algorithms often give inaccurate results due to vulnerabilities to different data types.To solve this problem, this study presents a new big data clustering algorithm powered by AI technology in a cloud environment. The study proposes an advanced Big Data clustering algorithm that leverages AI technology in a cloud environment. It optimizes clustering based on predicted strength using parallel processing. The research focuses on optimizing the clustering algorithm based on the predicted intensity through parallel processing. Experimental results demonstrate that image clustering stability is achieved when the number of clusters exceeds 4, indicating reduced sensitivity to random factors. Although it was not possible to precisely determine the optimal number of clusters, the use of an optimization algorithm showed that at four clusters the prediction intensity reached its peak, ensuring more accurate cluster identification. Through rigorous testing, the optimal number of clusters was determined to be 4. Clustering results show that visitors characterized by certain attributes show higher interest in most columns. This algorithm makes it easier to cluster incomplete large data, improves clustering speed, and improves the accuracy of filling in missing data. Compared to existing methods, this algorithm leverages AI technology in the cloud environment to optimize clustering based on prediction intensity, providing improved accuracy and efficiency during processing in big data management.

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“…To address this problem, research has been conducted to design a more powerful node processing unit. One of the proposed solutions is the use of DSPs, as reported in [19].…”

Section: A Sensor Node Layersmentioning

confidence: 99%

Energy Saving Multi-Relay Technique for Wireless Sensor Networks Based on Hw/Sw MPSoC System

Loukil

2023

IEEE Access

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Currently, we are witnessing a wide range of Wireless Sensor Networks (WSNs) in various fields of application. In most cases, these networks contain numerous battery-powered sensor nodes. Therefore, the lifetime of WSNs is intrinsically dependent. For this purpose, different studies have been conducted to reduce the power consumption. We propose a new technique for reducing the power consumption of clustered WSNs. We considered clusters in which sensors transmitted data to a Cluster Head (CH) by cooperative communication based on the Bit Error Probability (BEP) criterion for the multi-relay technique. Therefore, the proposed technique uses a multi-relay to reduce the transmission power to communicate between the source and destination which respects the predefined reliability level. The proof of the described technique was performed using a multiprocessor architecture. Second, we used an efficient software/hardware (Sw/Hw) architecture that utilized accelerator modules. The experimental results demonstrate significant gains in terms of energy consumption and workload compared to the SW implementation.

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Construction of energy minimized WSN using GA-SAMP-MWPSO and K-mean clustering algorithm with LDCF deployment strategy

Cited by 15 publications

References 33 publications

Analysis and Design of the Project Risk Management System Based on the Fuzzy Clustering Algorithm

Analysis and Design of the Project Risk Management System Based on the Fuzzy Clustering Algorithm

Research and Application of Big Data Clustering Algorithm based on AI Technology in Cloud Environment

Energy Saving Multi-Relay Technique for Wireless Sensor Networks Based on Hw/Sw MPSoC System

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