Titlebook: Secure Networked Inference with Unreliable Data Sources; Aditya Vempaty,Bhavya Kailkhura,Pramod K. Varshney Book 2018 Springer Nature Sing

格言

Background,lassification, and tracking. Specific challenges associated with inference in practical sensor networks are discussed next, with emphasis on Byzantines. Finally, a taxonomy of results is presented: the results are divided into fundamental limits of attack strategies from the Byzantine’s perspective

reaching

Distributed Detection with Unreliable Data Sources, ad hoc networks. Following the taxonomy presented earlier, fundamental limits of networked inference in the presence of Byzantine attacks are first presented. Next, design of optimal countermeasures using the insights provided by the fundamental limits is discussed from a network designer’s perspec

sparse

Distributed Estimation and Target Localization, outlined in Chap. . and followed in Chap. ., fundamental limits of distributed estimation with Byzantines are first presented. After characterizing these limits, several countermeasures are discussed, among which some are specific to the estimation framework as they leverage the structure of the pa

BOOR

安定

可触知

Distributed Estimation and Target Localization,hese limits, several countermeasures are discussed, among which some are specific to the estimation framework as they leverage the structure of the parameter space. Specifically, recently developed error-correcting codes-based mitigation scheme is discussed.

amphibian

Distributed Inference with Unreliable Data: Some Unconventional Directions,ed in this chapter. Finally, a framework called Human–Machine Inference Network (HuMaIN) is introduced, which is a network with both sensors and humans working together to solve networked inference problems. This framework is relevant in the age of IoT where all (exponentially increasing number of) devices are connected with humans in daily life.

奖牌

Endometrium

Medicare