Towards an automatic deployment model of IoT services in Fog computing using an adaptive differential evolution algorithm

Zhang, Kun; Zhou, Yu; Wang, Chaoyang; Hong, Haizhuang; Chen, Jing; Gao, Qian; Ghobaei-Arani, Mostafa

doi:10.1016/j.iot.2023.100918

Cited by 9 publications

(2 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To enhance spatial resolution and contrast resolution, two parameters, PSL and MLW, are used to construct a fitness function for the sparse array, aiming to minimize both the peak side lobe and main lobe width of the sparse array. The SaDE algorithm [30] is based on the DE algorithm, incorporating adaptive crossover and mutation factors. As the number of iterations increases, the mutation rate gradually decreases, and the mutation factor also decreases accordingly.…”

Section: Sparse Array Optimization Based On Sade Methodsmentioning

confidence: 99%

Multi-layered medium ultrasonic phased array sparse TFM imaging based on self-adaptive differential evolution algorithm

Yao,

Zhao,

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

Multilayer Composite material structures have been widely used in modern engineering fields. However, defects within these materials can adversely affect mechanical properties. Ultrasonic phased array Total Focusing Method (TFM) imaging has advantages of high precision and dynamic focusing over the entire range, achieving significant progress in homogeneous medium detection. However, heavy computational burdens of multilayer structures lead to inefficient imaging. To address this issue, a sparse-TFM imaging algorithm using ultrasonic phased arrays suitable for multilayer media is proposed in this paper. This method constructs a fitness function with constraints such as main lobe width and sidelobe peak. Its objective is to obtain the distribution of sparse array element positions using an Self-adaptive differential evolution algorithm(SaDE). Subsequently, the delay time of each array element in multilayer media sparse TFM is calculated using the Root Mean Square (RMS) principle and combined with amplitude weighting, the method corrects the imaging results. Compared with the Ray-based full-matrix capture and total focusing method method (Ray-based FMC/TFM), the root mean square-based full-matrix capture and total focusing method (RMS-based FMC/TFM), and the phase shift method (PSM), the experimental and simulation results demonstrate that the proposed method significantly reduces the imaging data volume, improves computational efficiency, and maintains quantitative errors within 0.2 mm.

show abstract

Section: Sparse Array Optimization Based On Sade Methodsmentioning

confidence: 99%

Multi-layered medium ultrasonic phased array sparse TFM imaging based on self-adaptive differential evolution algorithm

Yao,

Zhao,

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Reference [21] introduces a hybrid optimization of Particle Swarm and Chemical Reaction to mitigate service delay and enhance Quality of Service. Reference [22] reduces service latency and increases resource utilization by considering the priority of services and the distribution of resource consumption. Reference [23] proposes an online heuristic algorithm.…”

Section: Service Deploymentmentioning

confidence: 99%

MS-GD-P: Priority-Based Service Deployment for Cloud-Edge-End Scenarios

Jin,

Wang,

Luo

2024

Preprint

View full text Add to dashboard Cite

In cloud-edge-end scenarios, how to achieve rational resource allocation, implement effective service deployment, and ensure high service quality has become a hot research topic in academic domains. Service providers usually deploy services by considering the characteristics of different geographical regions, which helps to meet the diverse needs of users in different regions and optimize resource allocation and utilization. However, due to the widespread distribution of users and limited server resources, providing all types of services to users in every geographical region is not feasible. In addition, edge servers are prone to operational failures caused by software anomalies, hardware malfunctions, and malicious attacks, which will decrease service reliability. To address the problems above, this paper proposes a metric for service priorities based on user demands and regional characteristics for different geographical regions. Building upon this foundation, a Multi-Service Geographic region Deployment based on Priority (MS-GD-P) is proposed. This method takes user coverage and service reliability into consideration, which facilitates users' needs for multiple services in different geographical regions. Experimental results on real datasets demonstrate that MS-GD-P outperforms baseline methods in user coverage and service reliability.

show abstract