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

While vehicular sensor networks (VSNs) have earned the stature of a mobilesensing paradigm utilizing sensors built into cars, they have limited sensingscopes since car drivers only opportunistically discover new events.Conversely, social sensing is emerging as a new sensing paradigm wheremeasurements about the physical world are collected from humans. In contrast toVSNs, social sensing is more pervasive, but one of its key limitations lies inits inconsistent reliability stemming from the data contributed by unreliablehuman sensors. In this paper, we present DASC, a road Damage-AwareSocial-media-driven Car sensing framework that exploits the collective power ofsocial sensing and VSNs for reliable disaster response applications. However,integrating VSNs with social sensing introduces a new set of challenges: i) Howto leverage noisy and unreliable social signals to route the vehicles toaccurate regions of interest? ii) How to tackle the inconsistent availability(e.g., churns) caused by car drivers being rational actors? iii) How toefficiently guide the cars to the event locations with little prior knowledgeof the road damage caused by the disaster, while also handling the dynamics ofthe physical world and social media? The DASC framework addresses the abovechallenges by establishing a novel hybrid social-car sensing system thatemploys techniques from game theory, feedback control, and Markov DecisionProcess (MDP). In particular, DASC distills signals emitted from social mediaand discovers the road damages to effectively drive cars to target areas forverifying emergency events. We implement and evaluate DASC in a reputed vehiclesimulator that can emulate real-world disaster response scenarios. The resultsof a real-world application demonstrate the superiority of DASC over currentVSNs-based solutions in detection accuracy and efficiency.

DASC: Towards A Road Damage-Aware Social-Media-Driven Car Sensing Framework for Disaster Response Applications