Optimal Sampling for Dynamic Complex Networks With Graph-Bandlimited Initialization

Wei, Zhuangkun; Li, Bin; Guo, Weisi

doi:10.1109/access.2019.2946899

Cited by 9 publications

(5 citation statements)

References 37 publications

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“…With the help of the dynamic model, we select the speed deviation as the physical networked signal for GLS encryption. To be specific, each bus (node) i is equipped with an IoT sensor (e.g., the phasor measurement unit PMU), aiming at measuring the speed deviation from time

, by sampling rate

s [ 41 ] (i.e., discrete-time

). As such, the

th element of matrix

in Equation (1) is assigned as the measured

, with a physical measurement noise (not to be confused with communication SNR), which is used to encrypt (for Tx), process (for relays), and decrypt (for Rx) the transmitted information.…”

Section: Simulations and Resultsmentioning

confidence: 99%

Graph Layer Security: Encrypting Information via Common Networked Physics

Wei¹,

Sun²,

Li³

et al. 2022

Sensors

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The proliferation of low-cost Internet of Things (IoT) devices has led to a race between wireless security and channel attacks. Traditional cryptography requires high computational power and is not suitable for low-power IoT scenarios. Whilst recently developed physical layer security (PLS) can exploit common wireless channel state information (CSI), its sensitivity to channel estimation makes them vulnerable to attacks. In this work, we exploit an alternative common physics shared between IoT transceivers: the monitored channel-irrelevant physical networked dynamics (e.g., water/oil/gas/electrical signal-flows). Leveraging this, we propose, for the first time, graph layer security (GLS), by exploiting the dependency in physical dynamics among network nodes for information encryption and decryption. A graph Fourier transform (GFT) operator is used to characterise such dependency into a graph-bandlimited subspace, which allows the generation of channel-irrelevant cipher keys by maximising the secrecy rate. We evaluate our GLS against designed active and passive attackers, using IEEE 39-Bus system. Results demonstrate that GLS is not reliant on wireless CSI, and can combat attackers that have partial networked dynamic knowledge (realistic access to full dynamic and critical nodes remains challenging). We believe this novel GLS has widespread applicability in secure health monitoring and for digital twins in adversarial radio environments.

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, by sampling rate

s [ 41 ] (i.e., discrete-time

). As such, the

th element of matrix

in Equation (1) is assigned as the measured

, with a physical measurement noise (not to be confused with communication SNR), which is used to encrypt (for Tx), process (for relays), and decrypt (for Rx) the transmitted information.…”

Section: Simulations and Resultsmentioning

confidence: 99%

Graph Layer Security: Encrypting Information via Common Networked Physics

Wei¹,

Sun²,

Li³

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

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“…1). This approach was outlined by our recent work [29,44] (Fig. 2(a)), which minimized sensor deployment number at the penalty of relying on the inverse GFT operator to recover the dynamics, only with the full set of sampled data.…”

Section: Contributionmentioning

confidence: 99%

Neural Network Approximation of Graph Fourier Transform for Sparse Sampling of Networked Dynamics

Pagani

Wei

Silva

et al. 2021

ACM Trans. Internet Technol.

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Infrastructure monitoring is critical for safe operations and sustainability. Like many networked systems, water distribution networks (WDNs) exhibit both graph topological structure and complex embedded flow dynamics. The resulting networked cascade dynamics are difficult to predict without extensive sensor data. However, ubiquitous sensor monitoring in underground situations is expensive, and a key challenge is to infer the contaminant dynamics from partial sparse monitoring data. Existing approaches use multi-objective optimization to find the minimum set of essential monitoring points but lack performance guarantees and a theoretical framework. Here, we first develop a novel Graph Fourier Transform (GFT) operator to compress networked contamination dynamics to identify the essential principal data collection points with inference performance guarantees. As such, the GFT approach provides the theoretical sampling bound. We then achieve under-sampling performance by building auto-encoder (AE) neural networks (NN) to generalize the GFT sampling process and under-sample further from the initial sampling set, allowing a very small set of data points to largely reconstruct the contamination dynamics over real and artificial WDNs. Various sources of the contamination are tested, and we obtain high accuracy reconstruction using around 5%–10% of the network nodes for known contaminant sources, and 50%–75% for unknown source cases, which although larger than that of the schemes for contaminant detection and source identifications, is smaller than the current sampling schemes for contaminant data recovery. This general approach of compression and under-sampled recovery via NN can be applied to a wide range of networked infrastructures to enable efficient data sampling for digital twins.

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“…This leads nicely for researchers to consider data informing uncertainty in the parameters and inputs of the system, which enables the quantification of noise [60], optimal sampling theorems on dynamical graphs [61], and the development of stochastic and data-driven control systems [62].…”

Section: ) Conclusion and Limitationsmentioning

confidence: 99%

A Review of Methods to Study Resilience of Complex Engineering and Engineered Systems

Naghshbandi¹,

Varga²,

Purvis

et al. 2020

IEEE Access

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Uncertainty and interconnectedness in complex engineering and engineered systems such as power-grids and telecommunication networks are sources of vulnerability compromising the resilience of these systems. Conditions of uncertainty and interconnectedness change over time and depend on emerging socio-technical contexts, thus conventional methods which can conduct normative, descriptive and prescriptive assessment of complex engineering and engineered systems resilience are limited. This paper brings together contributions of experts in complex engineering and engineered systems who have identified six methods, three each for uncertainty and interconnectedness, which form the foundational methods for knowing complex engineering and engineered systems resilience. The paper has reviewed how these methods contribute to overcoming uncertainty or interconnectedness and how they are implemented using case studies in order to illustrate essential approaches to enhancing resilience. It is hoped that this approach will allow the subject to be quantified and best practice standards to develop.

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Optimal Sampling for Dynamic Complex Networks With Graph-Bandlimited Initialization

Cited by 9 publications

References 37 publications

Graph Layer Security: Encrypting Information via Common Networked Physics

Graph Layer Security: Encrypting Information via Common Networked Physics

Neural Network Approximation of Graph Fourier Transform for Sparse Sampling of Networked Dynamics

A Review of Methods to Study Resilience of Complex Engineering and Engineered Systems

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