Adaptive clustering control for energy-harvesting WSNs with non-uniform energy harvesting rate

Maemoto, Daiki; Möri, K.; Sanada, Kosuke

doi:10.1109/icsens.2017.8234063

Cited by 4 publications

(2 citation statements)

References 4 publications

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“…These schemes overcome sensor nodes' energy limitations by balancing energy supply and consumption without relying on an external source of energy. Some hybrid schemes such as Extended Hierarchical Geographical Adaptive Fidelity (EHGAF) [134] combine the benefits of both hardware-based and software-based schemes. In EHGAF, a clustered approach to data aggregation is used in conjunction with energy harvesting based [129] • To reduce the transmission range…”

Section: B Lesson Learnedmentioning

confidence: 99%

QoS-Aware Energy Management and Node Scheduling Schemes for Sensor Network-Based Surveillance Applications

et al. 2021

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Section: B Lesson Learnedmentioning

confidence: 99%

QoS-Aware Energy Management and Node Scheduling Schemes for Sensor Network-Based Surveillance Applications

et al. 2021

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“…Maemoto et al [30] considered the non-uniform harvesting rates for the sensor nodes and proposed a clustering control scheme, which consisted of adaptive cluster size control and adaptive backoff window control. EWMA [2] and its variances are widely used as (solar) energy prediction models.…”

Section: Related Workmentioning

confidence: 99%

A Robust Transmission Scheduling Approach for Internet of Things Sensing Service with Energy Harvesting

Hao

Chen

Wang

et al. 2019

Sensors

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Maximizing the utility under energy constraint is critical in an Internet of Things (IoT) sensing service, in which each sensor harvests energy from THE ambient environment and uses it for sensing and transmitting the measurements to an application server. Such a sensor is required to maximize its utility under THE harvested energy constraint, i.e., perform sensing and transmission at the highest rate allowed by the harvested energy constraint. Most existing works assumed a sophisticated model for harvested energy, but neglected the fact that the harvested energy is random in reality. Considering the randomness of the harvested energy, we focus on the transmission scheduling issue and present a robust transmission scheduling optimization approach that is able to provide robustness against randomness. We firstly formulate the transmission scheduling optimization problem subject to energy constraints with random harvested energy. We then introduce a flexible model to profile the harvested energy so that the constraints with random harvested energy are transformed into linear constraints. Finally, the transmission scheduling optimization problem can be solved traditionally. The experimental results demonstrate that the proposed approach is capable of providing a good trade-off between service flexibility and robustness.

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Modified Clustering Algorithms for Energy Harvesting Wireless Sensor Networks- A Survey

Gurusamy

Abas

2020

2020 21st International Arab Conference on Information Technology (ACIT)

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Adaptive clustering control for energy-harvesting WSNs with non-uniform energy harvesting rate

Cited by 4 publications

References 4 publications

QoS-Aware Energy Management and Node Scheduling Schemes for Sensor Network-Based Surveillance Applications

QoS-Aware Energy Management and Node Scheduling Schemes for Sensor Network-Based Surveillance Applications

A Robust Transmission Scheduling Approach for Internet of Things Sensing Service with Energy Harvesting

Modified Clustering Algorithms for Energy Harvesting Wireless Sensor Networks- A Survey

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