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

Simultaneous localization and mapping (SLAM) during communication isemerging. This technology promises to provide information on propagationenvironments and transceivers' location, thus creating several new services andapplications for the Internet of Things and environment-aware communication.Using crowdsourcing data collected by multiple agents appears to be muchpotential for enhancing SLAM performance. However, the measurementuncertainties in practice and biased estimations from multiple agents mayresult in serious errors. This study develops a robust SLAM method withmeasurement plug-and-play and crowdsourcing mechanisms to address the aboveproblems. First, we divide measurements into different categories according totheir unknown biases and realize a measurement plug-and-play mechanism byextending the classic belief propagation (BP)-based SLAM method. The proposedmechanism can obtain the time-varying agent location, radio features, andcorresponding measurement biases (such as clock bias, orientation bias, andreceived signal strength model parameters), with high accuracy and robustnessin challenging scenarios without any prior information on anchors and agents.Next, we establish a probabilistic crowdsourcing-based SLAM mechanism, in whichmultiple agents cooperate to construct and refine the radio map in adecentralized manner. Our study presents the first BP-based crowdsourcing thatresolves the "double count" and "data reliability" problems through theflexible application of probabilistic data association methods. Numericalresults reveal that the crowdsourcing mechanism can further improve theaccuracy of the mapping result, which, in turn, ensures the decimeter-levellocalization accuracy of each agent in a challenging propagation environment.

Enabling Plug-and-Play and Crowdsourcing SLAM in Wireless Communication Systems