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

Recent research on reconfigurable intelligent surfaces (RIS) suggests thatthe RIS panel, containing passive elements, enhances channel performance forthe internet of things (IoT) systems by reflecting transmitted signals to thereceiving nodes. This paper investigates RIS panel assisted-wireless network toinstigate minimal base station (BS) transmit power in the form of energyharvesting for batteryless IoT sensors to maximize bits transmission in thesignificant multi-path environment, such as urban areas. Batteryless IoTsensors harvest energy through the RIS panel from external sources, such asfrom nearby BS radio frequency (RF) signal in the first optimal time frame, fora given time frame. The bits transmission among IoT sensors, followed by adevice-to-device (D2D) communications protocol, is maximized using harvestedenergy in the final optimal time frame. The bits transmission is at least equalto the number of bits sampled by the IoT sensor. We formulate a non-convexmixed-integer non-linear problem to maximize the number of communicating bitssubject to energy harvesting from BS RF signals, RIS panel energy consumption,and required time. We propose a robust solution by presenting an iterativealgorithm. We perform extensive simulation results based on the 3GPP UrbanMicro channel model to validate our model.

Enhanced IoT Batteryless D2D Communications Using Reconfigurable Intelligent Surfaces