English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In the future, sensor nodes or Internet of Things (IoTs) will be tasked withsampling the environment. These nodes/devices are likely to be powered by aHybrid Access Point (HAP) wirelessly, and may be programmed by the HAP with a{\em sampling time} to collect sensory data, carry out computation, andtransmit sensed data to the HAP. A key challenge, however, is random channelgains, which cause sensor nodes to receive varying amounts of Radio Frequency(RF) energy. To this end, we formulate a stochastic program to determine thecharging time of the HAP and sampling time of sensor nodes. Our objective is tominimize the {\em expected} penalty incurred when sensor nodes experience anenergy shortfall. We consider two cases: {\em single} and {\em multi} timeslots. In the former, we determine a suitable HAP charging time and nodessampling time on a slot-by-slot basis whilst the latter considers the bestcharging and sampling time for use in the next $T$ slots. We conductexperiments over channel gains drawn from the Gaussian, Rayleigh or Riciandistribution. Numerical results confirm our stochastic program can be used tocompute good charging and sampling times that incur the minimum penalty overthe said distributions.

On Maximizing Sampling Time of RF-Harvesting Sensor Nodes Over Random Channel Gains