Robust distributed cooperative RSS-based localization for directed graphs in mixed LoS/NLoS environments

Carlino, Luca; Jin, Di; Muma, Michael; Zoubir, Abdelhak M.

doi:10.1186/s13638-018-1335-7

Cited by 9 publications

(2 citation statements)

References 41 publications

(80 reference statements)

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“…Wang et al proposed an algorithm based on filtering technology and an optimization method to reduce measurement error [ 2 ]. Furthermore, Carlino et al proposed an RSS-based distributed cooperative localization method with good robustness in a mixed line-of-sight and non-line-of-sight environment [ 14 ].…”

Section: Related Workmentioning

confidence: 99%

SICD: Novel Single-Access-Point Indoor Localization Based on CSI-MIMO with Dimensionality Reduction

Zhang

Wang

et al. 2021

Sensors

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With the rise of location-based services and the rapidly growing requirements related to their applications, indoor localization based on channel state information–multiple-input multiple-output (CSI-MIMO) has become an important research topic. However, indoor localization based on CSI-MIMO has some disadvantages, including noise and high data dimensions. To overcome the above drawbacks, we proposed a novel method of indoor localization based on CSI-MIMO, named SICD. For SICD, a novel localization fingerprint was first designed which can reflect the time–frequency and space–frequency characteristics of CSI-MIMO under a single access point (AP). To reduce the redundancy in the data of CSI-MIMO amplitude, we developed a data dimensionality reduction algorithm. Moreover, by leveraging a log-normal distribution, we calculated the conditional probability of the naive Bayes classifier, which was used to predict the moving object’s location. Compared with other state-of-the-art methods, the results of the experiment confirm that the SICD effectively improves localization accuracy.

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Section: Related Workmentioning

confidence: 99%

SICD: Novel Single-Access-Point Indoor Localization Based on CSI-MIMO with Dimensionality Reduction

Zhang

Wang

et al. 2021

Sensors

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“…Authors in [12] proposed a method for robotic swarms deployed in indoor environments to effectively navigate through narrow passageways by allocating specific roles to robots to ensure localization accuracy. An approach [13] was proposed to provide robustness in localization performance within swarms when nearby agents satisfy both Line-of-Sight and Non-Line-of-Sight conditions. In [14], RSSI signals are leveraged to estimate AoA of signal sources (i.e., transmitters) and humans/robots for target tracking.…”

Section: A Related Literaturementioning

confidence: 99%

Detection and Inference of Randomness-based Behavior for Resilient Multi-vehicle Coordinated Operations

Bonczek

Bezzo

2021

2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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A resilient multi-vehicle system cooperatively performs tasks by exchanging information, detecting and removing cyber-attacks that have the intent of hijacking or diminishing performance of the entire system. In this paper, we propose a framework to: i) detect and isolate misbehaving vehicles in the network, and ii) securely encrypt information among the network to alert and attract nearby vehicles toward points of interest in the environment without explicitly broadcasting safety-critical information. To accomplish these goals, we leverage a decentralized virtual spring-damper mesh physics model for formation control on each vehicle. To discover inconsistent behavior of any vehicle in the network, we consider an approach that monitors the changes in residual sign between the expected and the predicted states of neighbor vehicles, raising an alarm any time the signed behavior does not match with an expectation. Similarly, to disguise important information and trigger vehicles to switch to different behaviors, we leverage side-channel information on the state of the vehicles and characterize a different spring-damper signature model detectable by neighbor vehicles for consistency checking. Our framework is demonstrated in simulation and through experiments on formations of unmanned ground vehicles (UGVs) in the presence of malicious man-in-the-middle (MITM) communication attacks.

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Is Localization of Wireless Sensor Networks in Irregular Fields a Challenge?

Bhat

Santhosh

2020

Wireless Pers Commun

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Wireless sensor networks have been considered as an emerging technology for numerous applications of cyber-physical systems. These applications often require the deployment of sensor nodes in various anisotropic fields. Localization in anisotropic fields is a challenge because of the factors such as non-line of sight communications, irregularities of terrains, and network holes. Traditional localization techniques, when applied to anisotropic or irregular fields, result in colossal location estimation errors. To improve location estimations, this paper presents a comparative analysis of available localization techniques based on taxonomy framework. A detailed discussion on the importance of localization of sensor nodes in irregular fields from the reported real-life applications is presented along with challenges faced by existing localization techniques. Further, taxonomy based on techniques adopted by localization methods to address the effects of irregular fields on location estimations is reported. Finally, using the designed taxonomy framework, a comparative analysis of different localization techniques addressing irregularities and the directions towards the development of an optimal localization technique is addressed.

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Robust distributed cooperative RSS-based localization for directed graphs in mixed LoS/NLoS environments

Cited by 9 publications

References 41 publications

SICD: Novel Single-Access-Point Indoor Localization Based on CSI-MIMO with Dimensionality Reduction

SICD: Novel Single-Access-Point Indoor Localization Based on CSI-MIMO with Dimensionality Reduction

Detection and Inference of Randomness-based Behavior for Resilient Multi-vehicle Coordinated Operations

Is Localization of Wireless Sensor Networks in Irregular Fields a Challenge?

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