SNOT-WiFi: Sensor network-optimized training for wireless fingerprinting

Balzano, Walter; Murano, Aniello; Vitale, Fabio

doi:10.3233/jhs-170582

Cited by 22 publications

(6 citation statements)

References 19 publications

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“…Among these, it remains to define the strategies for balancing and scheduling the tasks between peripheral devices and centralized servers with the associated coordination protocols, the development of machine learning applications capable of processing streaming of data collected in real-time by the sensors, or the issues related to the delivery of a federated global identity management framework for provisioning secure ICT services, or the development of mixed precision algorithms to improve the ratio between performance and energy consumption. Some of these aspects will be the subject of further works still in progress that can benefit from methodologies developed for similar scenarios with distributed structure and high-performance demand even in wireless networks [ 39 , 40 , 41 ].…”

Section: Discussionmentioning

confidence: 99%

Clustering Algorithms on Low-Power and High-Performance Devices for Edge Computing Environments

Lapegna

Balzano

Meyer

et al. 2021

Sensors

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The synergy between Artificial Intelligence and the Edge Computing paradigm promises to transfer decision-making processes to the periphery of sensor networks without the involvement of central data servers. For this reason, we recently witnessed an impetuous development of devices that integrate sensors and computing resources in a single board to process data directly on the collection place. Due to the particular context where they are used, the main feature of these boards is the reduced energy consumption, even if they do not exhibit absolute computing powers comparable to modern high-end CPUs. Among the most popular Artificial Intelligence techniques, clustering algorithms are practical tools for discovering correlations or affinities within data collected in large datasets, but a parallel implementation is an essential requirement because of their high computational cost. Therefore, in the present work, we investigate how to implement clustering algorithms on parallel and low-energy devices for edge computing environments. In particular, we present the experiments related to two devices with different features: the quad-core UDOO X86 Advanced+ board and the GPU-based NVIDIA Jetson Nano board, evaluating them from the performance and the energy consumption points of view. The experiments show that they realize a more favorable trade-off between these two requirements than other high-end computing devices.

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Section: Discussionmentioning

confidence: 99%

Clustering Algorithms on Low-Power and High-Performance Devices for Edge Computing Environments

Lapegna

Balzano

Meyer

et al. 2021

Sensors

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“…Positioning systems and related services, in particular based on V2V and/or various localization systems are having a lot of attention from literature in the latest years [16]. With regards to positioning systems, many proposals were presented in the latest years, either based on satellites (like GPS or GLONASS), wireless networks (Wireless Positioning Systems or WPS) [8,9,10] or Inertial Navigation Systems (INS, based on sensors like accelerometers and gyroscopes) [7].…”

Section: Related Workmentioning

confidence: 99%

RADS: a smart Road Anomalies Detection System using Vehicle-2-Vehicle network and cluster features (S)

Balzano¹,

Vitale²

2018

International Conferences on Distributed Multimedia Systems

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Vehicle-2-Vehicle is an emerging and interesting field of research area due to the several possible application in IoT and self-driving vehicles. It allows communication between vehicles, allowing them to share information about traffic and road conditions. Road accidents are nowadays one of the major causes of casualties worldwide, and therefore increasing road safety is very important. In this paper we present RADS: a smart Road Anomalies Detection System using Vehicle-2-Vehicle network and cluster features, a methodology which uses V2V and car distances in order to warn users of incoming dangers on the road, such as road blocks or existing accidents.

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“…To resolve these problems, many studies have been invested in infrastructure-free indoor localization. For example, to reduce human effort, Balzano et al [ 29 , 30 ] proposed a framework to automatically and continuously update fingerprints. Based on an opportune deployment of a WSN, every sensor detects wireless data and sends to the server for updating RadioMap.…”

Section: Related Workmentioning

confidence: 99%

Using Acoustic Signal and Image to Achieve Accurate Indoor Localization

Liu

Hou

et al. 2018

Sensors

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Location information plays a key role in pervasive computing and application, especially indoor location-based service, even though a mass of systems have been proposed, an accurate and practical indoor localization system remains unsettled. To tackle this issue, in this paper, we present a new localization scheme, SITE, combining acoustic Signals and Images to achieve accurate and robust indoor locaTion sErvice. Relying on a pre-deployed platform of acoustic sources with different frequencies, using proactively generated Doppler effect signals, SITE could track relative directions between the phone and the sources. Given m (m≥5) relative directions, SITE can use the angle differences to compute a set of locations corresponding to different subsets of sources. Then, based on a key observation—while the simultaneously estimated locations using different sets of acoustic anchors are within a small circle, the results converge to a point near the true location—SITE proposes a decision scheme that confirms whether these locations satisfy the demand of localization accuracy and can be used to search the user’s location. If not, SITE utilizes VSFM(Visual Structure from Motion) technique to achieve a set of relative locations using some images captured by the phone’s camera. By exploiting the synergy between the set of relative locations and the set of initial locations computed by relative directions, an optimal transformation relationship is obtained and applied to refine the initial calculated results. The refined result will be regarded as the user’s location. In the evaluation, we implemented a prototype and deployed a real platform of acoustic sources in different scenarios. Experimental results show that SITE has excellent performance of localization accuracy, robustness and feasibility in practical application.

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SNOT-WiFi: Sensor network-optimized training for wireless fingerprinting

Cited by 22 publications

References 19 publications

Clustering Algorithms on Low-Power and High-Performance Devices for Edge Computing Environments

Clustering Algorithms on Low-Power and High-Performance Devices for Edge Computing Environments

RADS: a smart Road Anomalies Detection System using Vehicle-2-Vehicle network and cluster features (S)

Using Acoustic Signal and Image to Achieve Accurate Indoor Localization

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