A Pareto-based collaborative multi-objective optimization algorithm for energy-efficient scheduling of distributed permutation flow-shop with limited buffers

2022 ◽  
Vol 74 ◽  
pp. 102277
Chao Lu ◽  
Yuanxiang Huang ◽  
Leilei Meng ◽  
Liang Gao ◽  
Biao Zhang ◽  
2020 ◽  
Vol 93 ◽  
pp. 106343 ◽  
Yuyan Han ◽  
Junqing Li ◽  
Hongyan Sang ◽  
Yiping Liu ◽  
Kaizhou Gao ◽  


The growing requirement for real-time Internet of Things (IoT) applications has ended with Quality of Service (QoS) communication protocols. where heterogeneous IoT data collection and communication processing contains specific requirements in terms of energy, reliability, latency, and priority. Due to energy constraints, a proper estimation model for monitoring and control is accomplished by the objective of sensing and end-to-end communication respectively. moreover, the connectivity requires a QoS routing protocol to finding the route selection for sensor networks. Hence, data routing and prioritization and Satisfying the QoS requirements are the significant challenges in such networks. So for the Multi-objective Optimization for QoS Routing method is used for differentiating the traffics while data communication and gives the requirements to be caring about the network resource. In this paper, the Energy-Efficient Priority-based Multi-Objective QoS routing (PMQoSR) mechanism ensures the energy and Qos in IoT networks. the proposed system regulates the routing performance based on the QoS parameters, using optimization technique for three hybrid algorithms, named as WLFA- Whale Lion Fireworks optimization algorithm with Fitness Function Routing(FFR) mechanisms .the WLFA to prevent congestion and minimizes the localization error using and select the shortest routing path through the network period uses Priority label and time delay patterns when sending data to the destination. We evaluate its performance and existing competing schemes in terms of Energy-Efficient. The results demonstrate that PMQoSR holds out considering network traffic, packets forwarding, error rate, energy, and distance between the nodes and also considers priority-aware routing to improve the traffic load, throughput, end-to-end delay, and packet delivery ratio when compared with the existing systems.

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