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

This paper considers optimal attack attention allocation on remote stateestimation in multi-systems. Suppose there are $\mathtt{M}$ independentsystems, each of which has a remote sensor monitoring the system and sendingits local estimates to a fusion center over a packet-dropping channel. Anattacker may generate noises to exacerbate the communication channels betweensensors and the fusion center. Due to capacity limitation, at each time theattacker can exacerbate at most $\mathtt{N}$ of the $\mathtt{M}$ channels. Thegoal of the attacker side is to seek an optimal policy maximizing theestimation error at the fusion center. The problem is formulated as a Markovdecision process (MDP) problem, and the existence of an optimal deterministicand stationary policy is proved. We further show that the optimal policy has athreshold structure, by which the computational complexity is reducedsignificantly. Based on the threshold structure, a myopic policy is proposedfor homogeneous models and its optimality is established. To overcome the curseof dimensionality of MDP algorithms for general heterogeneous models, wefurther provide an asymptotically (as $\mathtt{M}$ and $\mathtt{N}$ go toinfinity) optimal solution, which is easy to compute and implement. Numericalexamples are given to illustrate the main results.

Attack Allocation on Remote State Estimation in Multi-Systems: Structural Results and Asymptotic Solution