Enabling sustainable bulk transfer in environmentally-powered wireless sensor networks
We address the problem of transferring bulk data in environmentally-powered wireless sensor networks where duty cycle compliance is critical for their uninterrupted operation. We propose Pump-and-Nap, a packet train forwarding technique that maximizes throughput while simultaneously enforcing compli...
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Main Authors: | , , |
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Format: | text |
Language: | English |
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Institutional Knowledge at Singapore Management University
2017
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Online Access: | https://ink.library.smu.edu.sg/sis_research/3324 https://ink.library.smu.edu.sg/context/sis_research/article/4326/viewcontent/EnablingSustainableBulkTransfer.pdf |
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Institution: | Singapore Management University |
Language: | English |
Summary: | We address the problem of transferring bulk data in environmentally-powered wireless sensor networks where duty cycle compliance is critical for their uninterrupted operation. We propose Pump-and-Nap, a packet train forwarding technique that maximizes throughput while simultaneously enforcing compliance to dynamic duty cycle limitations. A node using Pump-and-Nap operates by pumping a train of packets followed by a napping period where the node forgoes any transmission. Pump-and-Nap employs an adaptive controller to periodically compute the optimal capacity, that is, the maximum number of packets a node can receive and transmit in a train, given its duty cycle constraint. The controller uses prior input-output observations (capacity allocations and their corresponding duty cycle usage) to continuously tune its performance and adapt to wireless link quality variations. Its use of local information makes the controller easily deployable in a distributed fashion. We implemented Pump-and-Nap in TinyOS and evaluated its performance through experiments and testbed simulations. Results show that Pump-and-Nap provides high transfer throughput while it simultaneously tracks the target duty cycle. More importantly, Pump-and-Nap enables sustainable bulk transfer compared to state-of-the-art techniques that greedily maximize throughput at the expense of downtime due to energy depletion. |
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