### Abstract

In this paper, we consider a collaborative sensing scenario where sensing nodes are powered by energy harvested from the environment. In each time slot, an active sensor consumes one unit amount of energy to take an observation and transmit it back to a fusion center (FC). After receiving observations from all of the active sensors in a time slot, the FC aims to extract information from them. We assume that the utility generated by the observations is a function of the number of the active sensing nodes in that slot. Assuming the energy harvesting processes at individual sensors are independent Bernoulli processes, our objective is to develop a sensing scheduling policy so that the expected long-term average utility generated by the sensors is maximized. Under the concavity assumption of the utility function, we first show that the expected time average utility has an upper bound for any feasible scheduling policy satisfying the energy causality constraint. We then propose a myopic policy, which aims to select a fixed number of sensors with the highest energy levels to perform the sensing task in each slot. The myopic policy essentially balances the current energy queue lengths in every time slot. We show that the time average utility generated under the myopic policy converges to the upper bound almost surely as time T approaches infinity, thus the myopic policy is optimal. The corresponding convergence rate is also explicitly characterized.

Original language | English (US) |
---|---|

Title of host publication | 2014 IEEE International Conference on Communications, ICC 2014 |

Publisher | IEEE Computer Society |

Pages | 4077-4082 |

Number of pages | 6 |

ISBN (Print) | 9781479920037 |

DOIs | |

State | Published - Jan 1 2014 |

Event | 2014 1st IEEE International Conference on Communications, ICC 2014 - Sydney, NSW, Australia Duration: Jun 10 2014 → Jun 14 2014 |

### Other

Other | 2014 1st IEEE International Conference on Communications, ICC 2014 |
---|---|

Country | Australia |

City | Sydney, NSW |

Period | 6/10/14 → 6/14/14 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Computer Networks and Communications

### Cite this

*2014 IEEE International Conference on Communications, ICC 2014*(pp. 4077-4082). [6883959] IEEE Computer Society. https://doi.org/10.1109/ICC.2014.6883959

}

*2014 IEEE International Conference on Communications, ICC 2014.*, 6883959, IEEE Computer Society, pp. 4077-4082, 2014 1st IEEE International Conference on Communications, ICC 2014, Sydney, NSW, Australia, 6/10/14. https://doi.org/10.1109/ICC.2014.6883959

**Optimal sensing scheduling in energy harvesting sensor networks.** / Yang, Jing.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Optimal sensing scheduling in energy harvesting sensor networks

AU - Yang, Jing

PY - 2014/1/1

Y1 - 2014/1/1

N2 - In this paper, we consider a collaborative sensing scenario where sensing nodes are powered by energy harvested from the environment. In each time slot, an active sensor consumes one unit amount of energy to take an observation and transmit it back to a fusion center (FC). After receiving observations from all of the active sensors in a time slot, the FC aims to extract information from them. We assume that the utility generated by the observations is a function of the number of the active sensing nodes in that slot. Assuming the energy harvesting processes at individual sensors are independent Bernoulli processes, our objective is to develop a sensing scheduling policy so that the expected long-term average utility generated by the sensors is maximized. Under the concavity assumption of the utility function, we first show that the expected time average utility has an upper bound for any feasible scheduling policy satisfying the energy causality constraint. We then propose a myopic policy, which aims to select a fixed number of sensors with the highest energy levels to perform the sensing task in each slot. The myopic policy essentially balances the current energy queue lengths in every time slot. We show that the time average utility generated under the myopic policy converges to the upper bound almost surely as time T approaches infinity, thus the myopic policy is optimal. The corresponding convergence rate is also explicitly characterized.

AB - In this paper, we consider a collaborative sensing scenario where sensing nodes are powered by energy harvested from the environment. In each time slot, an active sensor consumes one unit amount of energy to take an observation and transmit it back to a fusion center (FC). After receiving observations from all of the active sensors in a time slot, the FC aims to extract information from them. We assume that the utility generated by the observations is a function of the number of the active sensing nodes in that slot. Assuming the energy harvesting processes at individual sensors are independent Bernoulli processes, our objective is to develop a sensing scheduling policy so that the expected long-term average utility generated by the sensors is maximized. Under the concavity assumption of the utility function, we first show that the expected time average utility has an upper bound for any feasible scheduling policy satisfying the energy causality constraint. We then propose a myopic policy, which aims to select a fixed number of sensors with the highest energy levels to perform the sensing task in each slot. The myopic policy essentially balances the current energy queue lengths in every time slot. We show that the time average utility generated under the myopic policy converges to the upper bound almost surely as time T approaches infinity, thus the myopic policy is optimal. The corresponding convergence rate is also explicitly characterized.

UR - http://www.scopus.com/inward/record.url?scp=84906997520&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84906997520&partnerID=8YFLogxK

U2 - 10.1109/ICC.2014.6883959

DO - 10.1109/ICC.2014.6883959

M3 - Conference contribution

AN - SCOPUS:84906997520

SN - 9781479920037

SP - 4077

EP - 4082

BT - 2014 IEEE International Conference on Communications, ICC 2014

PB - IEEE Computer Society

ER -