A novel energy harvesting aware IEEE 802.11 power saving mechanism

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Abstract

The spread of wirelessly connected computing sensors and devices and hybrid networks are leading to the emergence of an Internet of Things (IoT), where a myriad of multi-scale sensors and devices are seamlessly blended for ubiquitous computing and communication. However, the communication operations of wireless devices are often limited by the size and lifetime of the batteries because of the portability and mobility. To reduce energy consumption during wireless communication, the IEEE 802.11 standard specifies a power management scheme, called Power Saving Mechanism (PSM), for IEEE 802.11 devices. However, the PSM of IEEE 802.11 was originally designed for battery-supported devices in single-hop Wireless Local Area Networks (WLANs), and it does not consider devices that equip with rechargeable batteries and energy harvesting capability. In this paper, we extend the original PSM by incorporating with intermittent energy harvesting in the IEEE 802.11 Medium Access Control (MAC) layer specification, and propose a novel energy harvesting aware power saving mechanism, called EH-PSM. The basic idea of EH-PSM is that a longer contention window is assigned to a device in energy harvesting mode than that of a device in normal mode to make the latter access the wireless medium earlier and quicker. In addition, the device in energy harvesting mode stays active as far as it harvests energy and updates the access point of its harvesting mode to enable itself to be ready for receiving and sending packets, or overhearing any on-going communication. We evaluate the proposed scheme through extensive simulation experiments using OMNeT++ and compare its performance with the original PSM. The simulation results indicate that the proposed scheme can not only improve the packet delivery ratio and throughput but also reduce the packet delivery latency.

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APA

Celik, Y., & Pu, C. (2018). A novel energy harvesting aware IEEE 802.11 power saving mechanism. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10874 LNCS, pp. 14–26). Springer Verlag. https://doi.org/10.1007/978-3-319-94268-1_2

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