DACODE: Distributed adaptive communication framework for energy efficient industrial IoT-based heterogeneous WSN

Oh, Junsuk; Lee, Dong-Hyun; Lakew, Demeke Shumeye; Cho, Sungrae

doi:10.1016/j.icte.2023.02.009

Cited by 3 publications

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In light of these limitations, there is a compelling need to explore alternative approaches to WSN design and implementation. By embracing the concept of heterogeneity and integrating advanced algorithms and techniques, such as clustering, trust assessment, and energy-efficient routing, this paper aims to overcome the constraints of heterogeneous WSNs and unfasten new possibilities for enhancing the performance, reliability, and scalability of IoT deployments (Oh et al 2023).…”

Section: Introductionmentioning

confidence: 99%

Combined localization and clustering approach for reduced energy presumption in heterogeneous IoT

et al. 2024

Phys. Scr.

View full text Add to dashboard Cite

The field of H-IoT is emerging with enormous potential to empower various technologies. Smart cities and advanced manufacturing are a few of the fields where H-IoT is currently used. The issue with H-IoT is that it uses a lot of energy when transmitting data, which makes it difficult to scale. To overcome such issues, a hybrid approach of CrayFish Optimization (CFO) with FCM and Restricted Boltzmann Machine (RBM) with Soft Sign Activation (SSA) has been proposed. Initially, Node initialization lays the foundation by configuring individual sensor nodes for network participation. After initialization, Fuzzy C Means clustering optimizes data aggregation by categorizing nodes into clusters based on similarity. Gathering Neighbor Node Traffic Data (NNTD) provides insights into communication patterns. Based on the threshold of NNTD node localization is done that enhances network accuracy by pinpointing sensor node locations. Integration of CFO into clustering with localization further improves cluster head selection for optimal data routing. Classification through the RBM with SSA function enhances anomaly detection with data analysis for optimizing energy utilization in heterogeneous IoT environments. The “combined CFO-FCM and SSA-RBM” has been implemented in MATLAB and achieved an accuracy of 94.50 %. As a result, the overall performance of the system is improved.

show abstract

Section: Introductionmentioning

confidence: 99%