Edge-Assisted Stream Scheduling Scheme for the Green-Communication-Based IoT

Wang, Licheng; Meng, Yan; Zhu, Haojin; Tang, Minxing; Ota, Kaoru

doi:10.1109/jiot.2019.2915793

Cited by 5 publications

(3 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In 2019, Wang et al 22 have developed an effective strategy for the intra‐superframe case to address “the stream order scheduling (SOS) problem.” The developed model with mobile edge computing was used for reducing the network load by utilizing algorithms like Verifiable Delay Function (VDF) algorithm and Min‐Degree Searching (MDS) algorithm, which has improved the performance of the suggested model. Experimentation was performed on “4800 random original IoT topologies and 19,000 random Hamiltonian edge‐dual topologies” that have shown superior efficiency and attained optimal solutions through a more success probability.…”

Section: Literature Surveymentioning

confidence: 99%

“…More residual energy also exists in the system. By using VDF and MDS algorithm, 22 face recognition training is done with fewer errors, and the optimal solution is better for denser streams. Finding the Hamiltonian path is also easy with the algorithm.…”

Section: Literature Surveymentioning

confidence: 99%

“…MFO algorithm 21 needs consideration of other metrics like link lifetime, network density, and QoS with multi‐objective optimization. By using VDF and MDS algorithm, 22 phase switching increases the power consumption. Jaya‐jDE algorithm 23 has not been extended to analyze the distributed massive MIMO in the model.…”

Section: Literature Surveymentioning

confidence: 99%

See 2 more Smart Citations

A novel hybrid meta‐heuristic concept for green communication in IoT networks: An intelligent clustering model

Akhtar

Ahamad²,

Abdalrahman

et al. 2022

Int J Communication

View full text Add to dashboard Cite

Summary Nowadays, there is an emerging need for applications based on the Internet of Things (IoT). The sensor nodes present in the IoT network produce data constantly, which directly influences the durability of the network. Therefore, two major challenges while designing IoT systems are network lifetime and energy consumption. Although the ability of IoT applications is huge, there are several limitations such as energy optimization, heterogeneity of devices storage, load balancing, privacy, and security that have to be addressed. These constraints have to be optimized for improving the efficiency of the networks. Hence, the main intention of this paper is to develop the intelligent‐based cluster head selection model for accomplishing green communication in IoT. The two famous algorithms like spotted hyena optimization (SHO) and sun flower optimization (SFO) are integrated to form sun flower‐spotted hyena optimization (SF‐SHO) by utilizing the hybrid meta‐heuristic concept for the optimal cluster head selection. The most significant parameters in IoT networks like delay, distance, energy, temperature, and load are considered for deriving a multi‐objective function to offer optimal clustering. The cluster head of the model is optimally tuned based on the hybrid SF‐SHO, to solve the multi‐objective problem, thus showing the enhanced green communication performance. The proposed model is analyzed and evaluated over different approaches in terms of energy‐specific factors, and the attained results confirm the efficiency of the developed method.

show abstract

Section: Literature Surveymentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%