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

Internet-of-Things (IoT) devices are often used to transmit physical sensordata over digital wireless channels. Traditional Physical Layer Security(PLS)-based cryptography approaches rely on accurate channel estimation andinformation exchange for key generation, which irrevocably ties key qualitywith digital channel estimation quality. Recently, we proposed a new conceptcalled Graph Layer Security (GLS), where digital keys are derived from physicalsensor readings. The sensor readings between legitimate users are correlatedthrough a common background infrastructure environment (e.g., a common waterdistribution network or electric grid). The challenge for GLS has been how toachieve distributed key generation. This paper presents a Federated multi-agentDeep reinforcement learning-assisted Distributed Key generation scheme (FD2K),which fully exploits the common features of physical dynamics to establishsecret key between legitimate users. We present for the first time initialexperimental results of GLS with federated learning, achieving considerablesecurity performance in terms of key agreement rate (KAR), and key randomness.

Securing IoT Communication using Physical Sensor Data -- Graph Layer Security with Federated Multi-Agent Deep Reinforcement Learning