Nonlinear event-based state estimation using particle filter under packet loss

Gasmi, Elhadi; Sid, Mohamed Amine; Hachana, Oussama

doi:10.1016/j.isatra.2023.10.012

Cited by 3 publications

(1 citation statement)

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“…During network transmission, DoS attacks may result in signal interruptions, followed by deteriorating network conditions, potentially leading to packet loss [24,25]. To address this issue, predictors in both the encoder and decoder infer missing signals based on previously received data.…”

Section: System Descriptionmentioning

confidence: 99%

Resilient Integrated Control for AIOT Systems under DoS Attacks and Packet Loss

Cao,

Wang,

Chen

et al. 2024

Electronics

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This paper addresses bandwidth limitations resulting from Denial-of-Service (DoS) attacks on Artificial Intelligence of Things (AIOT) systems, with a specific focus on adverse network conditions. First, to mitigate the impact of DoS attacks on system bandwidth, a novel model predictive control combined with a dynamic time-varying quantization interval adjustment technique is designed for the encoder–decoder architecture of AIOT systems. Second, the network state is modeled to represent a Markov chain under suboptimal network conditions. Furthermore, to guarantee the stability of AIOT systems under random packet loss, a Kalman filter algorithm is applied to precisely estimate the system state. By leveraging the Lyapunov stability theory, the maximum tolerable probability of random packet loss is determined, thereby enhancing the system’s resilient operation. Simulation results validate the effectiveness of the proposed method in dealing with DoS attacks and adverse network conditions.

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Section: System Descriptionmentioning

confidence: 99%