Optimal Wireless Charging Inclusive of Intellectual Routing Based on SARSA Learning in Renewable Wireless Sensor Networks

Nelofar Aslam, Kewen Xia, Muhammad Usman Hadi

Research output: Contribution to journalArticlepeer-review

35 Citations (Scopus)

Abstract

The next generation's sensor nodes will be more intelligent, energy conservative, and perpetual lifetime in the set-up of wireless sensor networks (WSNs). These sensors nodes are facing the overwhelming challenge of energy consumption which gradually decreases the lifetime of overall network. The wireless power transfer (WPT) is one of the most emerging technologies of energy harvesting that deploys at the heart of sensor nodes for efficient lifetime solution. A wireless portable charging device (WPCD) is drifting inside the WSN to recharge all the nodes which are questing for the eternal life. In this paper, we aspire to optimize a multi-objective function for charging trail of WPCD, and self-learning algorithm for data routing jointly. We formulated that the objective functions can optimize the fair energy consumption as well as maximize the routing efficiency of WPCD. The fundamental challenge of the problem is, to integrate the novel path for WPCD by applying the Nodal A∗ algorithm. We proposed a novel method of sensor node's training for intellectual data transmission by using of clustering and reinforcement learning (SARSA) defined as clustering SARSA (C-SARSA) along with an optimal solution of objective functions. The whole mechanism outperforms in terms of trade-off between energy consumption and stability (fair energy consumption among all nodes) of the WSN; moreover, it prolongs the lifetime of the WSN. The simulated results demonstrate that our proposed method did better than compared literature in terms of energy consumption, stability, and lifetime of the WSN.

Original languageEnglish
Article number8721665
Pages (from-to)8340-8351
Number of pages12
JournalIEEE Sensors Journal
Volume19
Issue number18
Early online date24 May 2019
DOIs
Publication statusPublished - 15 Sep 2019

Bibliographical note

Funding Information:
Manuscript received March 30, 2019; revised May 13, 2019; accepted May 14, 2019. Date of publication May 24, 2019; date of current version August 15, 2019. This work was supported in part by the Key Supporting Project of Joint Fund of the National Natural Science Foundation of China under Grant U1813222, in part by the Tianjin Natural Science Foundation under Grant 18JCYBJC16500, and in part by the Hebei Province Natural Science Foundation under Grant E2016202341. The associate editor coordinating the review of this paper and approving it for publication was Prof. Giancarlo Fortino. (Corresponding author: Nelofar Aslam.) N. Aslam and K. Xia are with the School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China (e-mail: kwxia@hebut.edu.cn).

Publisher Copyright:
© 2001-2012 IEEE.

Keywords

  • Clustering
  • energy conservation
  • energy harvesting
  • machine learning (SARSA)
  • wireless portable charging device
  • wireless power transfer
  • wireless sensor network

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