Peregrine: 3-D network localization and navigation

Teague, Bryan; Liu, Zhenyu; Meyer, Florian; Win, Moe Z.

doi:10.1109/latincom.2017.8240193

Cited by 13 publications

(6 citation statements)

References 38 publications

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“…The average number of measurements performed per agent and per time step is 0.13 (M = 4) and 0.19 (M = 6) for TDMA and 0.11 (M = 4) and 0.15 (M = 6) for "Node-Activation". Furthermore, consider a UWB radio that consumes 1.7×10 −4 J per range measurement is used as RMU [16], "Node Activation" can reduce the overall energy consumption of the network for all 1200 time steps by 2.1 J for M = 4 and by 4.2 J for M = 6. Fig.…”

Section: Network Operation Resultsmentioning

confidence: 99%

“…Using Bayes rules together with (14) and (16), the joint posterior PDF of x (0:n) given z (1:n) for n > 0 is obtained as…”

Section: Network Localization For Iotmentioning

confidence: 99%

“…The performance of RMUs such as ultrawide-band (UWB) radios is often limited by the fact that [16], [27], [28] 1) agents often make measurements with nodes with low link quality or poor geometry; or 2) different agents, which simultaneously transmit ranging signals, interfere with each other. To address these issues, node activation strategies to reduce interference and node prioritization strategies to allocate resources to measurements with neighbor nodes can be employed.…”

Section: Efficient Network Operation Efficient Network Operation mentioning

confidence: 99%

“…These error matrices are not accessible in real-world localization systems as they rely on the knowledge of true positions. For this reason, in an implementation of the presented node operations strategies [16] these error matrices are approximated by the corresponding covariance matrices which can be provided by network localization algorithms.…”

Section: Efficient Network Operation Efficient Network Operation mentioning

confidence: 99%

“…In this section, we demonstrate the performance benefits of cooperation among devices and multi-sensor fusion in a large scale IoT network using simulated measurements. Some of the presented algorithms have also been evaluated in the real-time localization system called Peregrine [16]. 6…”

Section: Case Studymentioning

confidence: 99%

See 4 more Smart Citations

Efficient Multisensor Localization for the Internet of Things: Exploring a New Class of Scalable Localization Algorithms

Win

Meyer

Liu

et al. 2018

IEEE Signal Process. Mag.

Self Cite

130

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In the era of the Internet-of-Things (IoT), efficient localization is essential for emerging mass-market services and applications. IoT devices are heterogeneous in signaling, sensing, and mobility as well as their resources for computation and communication are typically limited. Therefore, to enable location-awareness in large-scale IoT networks, there is a need for efficient, scalable, and distributed multi-sensor fusion algorithms. This paper presents a framework to design network localization and navigation (NLN) for IoT. Multi-sensor localization and operation algorithms developed within NLN can exploit spatiotemporal cooperation, are suitable for arbitrary large network sizes, and only rely on an information exchange among neighboring devices. The advantages of NLN are evaluated in a large-scale IoT network with five hundreds agents. In particular, it is demonstrated that due to multi-sensor fusion and cooperation, the presented network localization and operation algorithms can provide attractive localization performance and reduce communication overhead and energy consumption.

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Section: Network Operation Resultsmentioning

confidence: 99%

“…Using Bayes rules together with (14) and (16), the joint posterior PDF of x (0:n) given z (1:n) for n > 0 is obtained as…”

Section: Network Localization For Iotmentioning

confidence: 99%

Section: Efficient Network Operation Efficient Network Operation mentioning

confidence: 99%

Section: Efficient Network Operation Efficient Network Operation mentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

See 3 more Smart Citations

Efficient Multisensor Localization for the Internet of Things: Exploring a New Class of Scalable Localization Algorithms

Win

Meyer

Liu

et al. 2018

IEEE Signal Process. Mag.

Self Cite

130

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Predicting Trends and Research Patterns of Smart Cities: A Semi-Automatic Review Using Latent Dirichlet Allocation (LDA)

Sharma¹,

Batra

Sharma³

et al. 2022

IEEE Access

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Smart cities are a current worldwide topic requiring much scientific investigation. This research instigates the necessity of an organized review to a heedful insight of the research trends and patterns prevailing in this domain. The string is formulated to extract the corpus from Scopus largest database of publications. The corpus of 8320 articles published from 2010 to 2022 is processed using Latent Dirichlet Allocation. Two, five, and ten topics have been extracted to provide the recent trends for IoT in smart cities. There has been an increased recognition that more attention needs to be paid to the area of smart cities so a complete overview of the topic of smart cities research, including the most prominent nations (institutions, sources, and authors) and noteworthy research directions has been presented in this paper. The scientific collaboration across countries (regions), organizations, and authors has also been widely discussed. A detailed and comprehensive overview and visualization of the trends and research patterns used to integrate the Internet of Things in Smart Cities. This data based experimental study signifies a roadmap of the research trends in Smart Cities by implementing topic modeling technique that has never been used in this domain. Based upon the topic modeling using LDA, authors have formulated three research questions and answered those question based on the in-depth research. At the end this study concludes the areas suggested are at the growing phase and need more insight for their growth.

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Device-Free Counting via OFDM Signals of Opportunity

Bartoletti

Conti

Win

2018

2018 IEEE International Conference on Communications Workshops (ICC Workshops)

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Counting targets (people or things) within a monitored area is an important task in emerging wireless applications for smart environments, safety, and security. Counting via passive radars rely on signals of opportunity (i.e., signal already on air for other purposes) to detect and count device-free targets, which is preferable, in terms of privacy and implementation costs, to active radars that rely on dedicated or personal devices. However, conventional radar techniques for multi-target detection require to associate measurements sets to detected targets. Such data association may lead to high dimensionality and complexity even with few targets, despite it is a redundant operation for counting. The need of low dimensionality and complexity calls for the definition of signal features and the development of techniques for their extraction, which enable the association of measured signals directly with the number of targets (namely, crowd-centric algorithms). This paper develops a framework for the design and analysis of crowd-centric algorithms for device-free counting via OFDM signals of opportunity. Preliminary results in simple use cases show the effectiveness of the proposed techniques with respect to individual-centric algorithms.

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Peregrine: 3-D network localization and navigation

Cited by 13 publications

References 38 publications

Efficient Multisensor Localization for the Internet of Things: Exploring a New Class of Scalable Localization Algorithms

Efficient Multisensor Localization for the Internet of Things: Exploring a New Class of Scalable Localization Algorithms

Predicting Trends and Research Patterns of Smart Cities: A Semi-Automatic Review Using Latent Dirichlet Allocation (LDA)

Device-Free Counting via OFDM Signals of Opportunity

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