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

We develop a resilient binary hypothesis testing framework for decisionmaking in adversarial multi-robot crowdsensing tasks. This framework exploitsstochastic trust observations between robots to arrive at tractable, resilientdecision making at a centralized Fusion Center (FC) even when i) there existmalicious robots in the network and their number may be larger than the numberof legitimate robots, and ii) the FC uses one-shot noisy measurements from allrobots. We derive two algorithms to achieve this. The first is the Two StageApproach (2SA) that estimates the legitimacy of robots based on received trustobservations, and provably minimizes the probability of detection error in theworst-case malicious attack. Here, the proportion of malicious robots is knownbut arbitrary. For the case of an unknown proportion of malicious robots, wedevelop the Adversarial Generalized Likelihood Ratio Test (A-GLRT) that usesboth the reported robot measurements and trust observations to estimate thetrustworthiness of robots, their reporting strategy, and the correct hypothesissimultaneously. We exploit special problem structure to show that this approachremains computationally tractable despite several unknown problem parameters.We deploy both algorithms in a hardware experiment where a group of robotsconducts crowdsensing of traffic conditions on a mock-up road network similarin spirit to Google Maps, subject to a Sybil attack. We extract the trustobservations for each robot from actual communication signals which providestatistical information on the uniqueness of the sender. We show that even whenthe malicious robots are in the majority, the FC can reduce the probability ofdetection error to 30.5% and 29% for the 2SA and the A-GLRT respectively.

Exploiting Trust for Resilient Hypothesis Testing with Malicious Robots (evolved version)