On energy-balanced backpressure routing mechanisms for stochastic energy harvesting wireless sensor networks
In energy harvesting wireless sensor networks, energy imbalance among sensor nodes is detrimental to network performance and battery life. Particularly, nodes that are closer to a data sink or have less energy replenishment tend to exhaust the energy earlier, leading to some sub-regions of the envir...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi - SAGE Publishing
2016-08-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147716661941 |
| _version_ | 1797706560037715968 |
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| author | Zheng Liu Xinyu Yang Peng Zhao Wei Yu |
| author_facet | Zheng Liu Xinyu Yang Peng Zhao Wei Yu |
| author_sort | Zheng Liu |
| collection | DOAJ |
| description | In energy harvesting wireless sensor networks, energy imbalance among sensor nodes is detrimental to network performance and battery life. Particularly, nodes that are closer to a data sink or have less energy replenishment tend to exhaust the energy earlier, leading to some sub-regions of the environment being left unmonitored. Existing research efforts focus on the energy management based on the assumption that the energy harvesting process is predictable. Unfortunately, such an assumption is not practicable in real-world energy harvesting systems. With the consideration of the unpredictability of the harvestable energy, in this article, we adopt the stochastic Lyapunov optimization framework to jointly manage energy and make routing decision, which could help mitigate the energy imbalance problem. We develop two online policies: (1) Energy-balanced Backpressure Routing Algorithm for lossless networks and (2) Enhanced Energy-balanced Backpressure Routing Algorithm for time varying wireless networks with lossy links. Both Energy-balanced Backpressure Routing Algorithm and Enhanced Energy-balanced Backpressure Routing Algorithm are distributed, queuing stable, and do not require the explicit knowledge of the statistics of the energy harvesting. The simulation data show that our developed algorithms can achieve significantly higher performance in terms of energy balance than existing schemes such as Original Backpressure Algorithm and the Backpressure Collection Protocol. |
| first_indexed | 2024-03-12T05:53:07Z |
| format | Article |
| id | doaj.art-06426f6ef7b84032bee8295d878af1ec |
| institution | Directory Open Access Journal |
| issn | 1550-1477 |
| language | English |
| last_indexed | 2024-03-12T05:53:07Z |
| publishDate | 2016-08-01 |
| publisher | Hindawi - SAGE Publishing |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj.art-06426f6ef7b84032bee8295d878af1ec2023-09-03T04:50:48ZengHindawi - SAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772016-08-011210.1177/1550147716661941On energy-balanced backpressure routing mechanisms for stochastic energy harvesting wireless sensor networksZheng Liu0Xinyu Yang1Peng Zhao2Wei Yu3Department of Computer Science and Technology, School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an, P.R. ChinaDepartment of Computer Science and Technology, School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an, P.R. ChinaDepartment of Computer Science and Technology, School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an, P.R. ChinaDepartment of Computer and Information Sciences, Towson University, Towson, MD, USAIn energy harvesting wireless sensor networks, energy imbalance among sensor nodes is detrimental to network performance and battery life. Particularly, nodes that are closer to a data sink or have less energy replenishment tend to exhaust the energy earlier, leading to some sub-regions of the environment being left unmonitored. Existing research efforts focus on the energy management based on the assumption that the energy harvesting process is predictable. Unfortunately, such an assumption is not practicable in real-world energy harvesting systems. With the consideration of the unpredictability of the harvestable energy, in this article, we adopt the stochastic Lyapunov optimization framework to jointly manage energy and make routing decision, which could help mitigate the energy imbalance problem. We develop two online policies: (1) Energy-balanced Backpressure Routing Algorithm for lossless networks and (2) Enhanced Energy-balanced Backpressure Routing Algorithm for time varying wireless networks with lossy links. Both Energy-balanced Backpressure Routing Algorithm and Enhanced Energy-balanced Backpressure Routing Algorithm are distributed, queuing stable, and do not require the explicit knowledge of the statistics of the energy harvesting. The simulation data show that our developed algorithms can achieve significantly higher performance in terms of energy balance than existing schemes such as Original Backpressure Algorithm and the Backpressure Collection Protocol.https://doi.org/10.1177/1550147716661941 |
| spellingShingle | Zheng Liu Xinyu Yang Peng Zhao Wei Yu On energy-balanced backpressure routing mechanisms for stochastic energy harvesting wireless sensor networks International Journal of Distributed Sensor Networks |
| title | On energy-balanced backpressure routing mechanisms for stochastic energy harvesting wireless sensor networks |
| title_full | On energy-balanced backpressure routing mechanisms for stochastic energy harvesting wireless sensor networks |
| title_fullStr | On energy-balanced backpressure routing mechanisms for stochastic energy harvesting wireless sensor networks |
| title_full_unstemmed | On energy-balanced backpressure routing mechanisms for stochastic energy harvesting wireless sensor networks |
| title_short | On energy-balanced backpressure routing mechanisms for stochastic energy harvesting wireless sensor networks |
| title_sort | on energy balanced backpressure routing mechanisms for stochastic energy harvesting wireless sensor networks |
| url | https://doi.org/10.1177/1550147716661941 |
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