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Cluster Computing

, Volume 22, Supplement 2, pp 3453–3462 | Cite as

An improved routing algorithm for advanced metering infrastructure in smart grid

  • Jing GaoEmail author
  • Jia-jia Zhang
  • Xu-liang Guang
Article

Abstract

The advanced metering infrastructure (AMI) is a key component of information transmission networks in smart grid because it connects distribution stations to grid users. However, the ever-increasing amount of information transmitted through smart meters inevitably results in heavy path loads and route congestion in AMI, which can lead to transmission delay and data packet loss. Therefore, in this paper, we proposed a routing algorithm based on load balancing that enabled data packets to select the optimal route between smart meter and data aggregator unit. We defined the routing optimization objective function for estimating the path load by using load factor,and employed an ant colony algorithm combined with simulated annealing (ACA-SA) to solve the optimization problem. Furthermore, with considering the quality of service (QoS) requirements of latency and reliability for AMI applications, two routing methods were proposed: the method with variable initial pheromone for delay-sensitive applications, whereas the method with variable visibility for applications with low packet-loss rate requirements. Analytical and simulation results demonstrated that the proposed algorithm can offer real-time and reliable communication for AMI in smart grid.

Keywords

Smart grid Advanced metering infrastructure Ant colony optimization Quality of service 

Notes

Acknowledgements

This work has been supported by the National Natural Science Foundation of China under Grant No. 61403069 and No. 61473066, Fundamental Research Funds for the Central Universities No. N162304003, Natural Science Foundation of Hebei Province under Grant No. F2014501055, and the Program of Science and Technology Research of Hebei University No. ZD20132003.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringNortheastern UniversityShenyangChina
  2. 2.School of Control EngineeringNortheastern University at QinhuangdaoQinhuangdaoChina

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