Collaborative State Estimation and Actuator Scheduling for Cyber-Physical Systems Under Random Multiple Events

Mo, Lei; Kritikakou, Angeliki; Cao, Xianghui

doi:10.1007/978-3-030-00247-3_24

Cited by 1 publication

(2 citation statements)

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“…Since the collected received signal strength (RSS) from the nodes is used to determine the communication radius, the RSS-to-distance model needs to be determined. The most popular model for RSS-to-distance is the log-normal shadow model, 49 which is described as follows:…”

Section: Impact Of Anchor Node Numbersmentioning

confidence: 99%

“…Since the collected received signal strength (RSS) from the nodes is used to determine the communication radius, the RSS‐to‐distance model needs to be determined. The most popular model for RSS‐to‐distance is the log‐normal shadow model, 49 which is described as follows:

P_{r} false(d false) = P_{0} false(d_{0} false) - 10 \cdot η \cdot l o g_{10} ((), \frac{d}{d_{0}}) + X_{σ},

where

P r false(d false)

denotes the received power at distance

d

and

P_{0} false(d_{0} false)

means the average received power at some reference distance

d_{0}

(usually

d_{0} = 1 m

η

means the path‐loss exponent that indicates the transmitted signal power decays with

d

on average, and

X_{σ}

denotes a log‐normal random variable with variance

σ^{2}

that represents shadowing effects.…”

Section: Simulations and Analysismentioning

confidence: 99%

See 1 more Smart Citation

A weighted range‐free localization algorithm for irregular multihop networks

Yan

Zhou

Huang

et al. 2022

Int J Communication

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Summary Range‐free localization is a widely concerned technique that supports many essential network applications like coverage and clustering. Most existing localization technologies assume that nodes are uniformly distributed in a regular network with no obstacles and that propagation paths between pairwise nodes are close to straight lines. However, in real‐world scenarios, network topology is usually irregular, resulting in estimated distances that differ significantly from the corresponding physical distances and poor anchor deployment. These will lead to erroneous distance measurement and inaccurate localization results. This paper proposes a new algorithm, called the weighted range‐free localization algorithm (WRL for short), to improve the localization performance in irregular networks. Specifically, we first present a feasible scheme that can fulfill the information exchange and relative distance estimation for each node simultaneously. We then employ an optimal weight function to suppress the effect of cumulative errors and introduce the weighted bounding‐box algorithm to mitigate outlier location estimation induced by probably collinear anchor nodes. Simulation results show that, compared to state‐of‐the‐art methods, WRL improves localization accuracy while incurring lower computation and communication overheads in various irregular network topologies.

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Section: Impact Of Anchor Node Numbersmentioning

confidence: 99%