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Onboard Spectral Analysis for Low-complexity IoT Devices
Mittuniversitetet, Fakulteten för naturvetenskap, teknik och medier, Institutionen för informationssystem och –teknologi.
Ostwestfalen-Lippe University of Applied Sciences, 326 57 Lemgo, Germany.
Mittuniversitetet, Fakulteten för naturvetenskap, teknik och medier, Institutionen för informationssystem och –teknologi.ORCID-id: 0000-0003-3717-7793
Mittuniversitetet, Fakulteten för naturvetenskap, teknik och medier, Institutionen för informationssystem och –teknologi.ORCID-id: 0000-0003-0873-7827
2020 (engelsk)Inngår i: IEEE Access, E-ISSN 2169-3536, Vol. 8, s. 43027-43045Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

The lack of coordinated spectrum access for IoT wireless technologies in unlicensed bands creates inefficient spectrum usage and poses growing concerns in several IoT applications. Spectrum awareness becomes then crucial, especially in the presence of strict quality-of-service (QoS) requirements and mission-critical communication. In this work, we propose a lightweight spectral analysis framework designed for strongly resource-constrained devices, which are the norm in IoT deployments. The proposed solution enables model-based reconstruction of the spectrum of single radio-bursts entirely onboard without DFT processing. The spectrum sampling exploits pattern-based frequency sweeping, which enables the spectral analysis of short radio-bursts while minimizing the sampling error induced by non-ideal sensing hardware. We carry out an analysis of the properties of such sweeping patterns, derive useful theoretical error bounds, and explain how to design optimal patterns for radio front-ends with different characteristics. The experimental campaign shows that the proposed solution enables the estimation of central frequency, bandwidth, and spectral shape of signals at runtime by using a strongly hardware-limited radio platform. Finally, we test the potential of the proposed solution in combination with a proactive blacklisting scheme, allowing a substantial improvement in real-time QoS of a radio link under interference.

sted, utgiver, år, opplag, sider
2020. Vol. 8, s. 43027-43045
Emneord [en]
Spectral analysis, Radio frequency, Interference, Band-pass filters, Quality of service, Microwave filters, Hardware, Central frequency estimation, cognitive radio, dynamic spectrum access, interference, Internet-of-things, jamming, spectral analysis, spectrum sensing, unlicensed bands, wireless coexistence
HSV kategori
Identifikatorer
URN: urn:nbn:se:miun:diva-38601DOI: 10.1109/ACCESS.2020.2977842ISI: 000524706500018Scopus ID: 2-s2.0-85082017601OAI: oai:DiVA.org:miun-38601DiVA, id: diva2:1412949
Tilgjengelig fra: 2020-03-09 Laget: 2020-03-09 Sist oppdatert: 2020-05-15bibliografisk kontrollert
Inngår i avhandling
1. Towards Radio-Environment Aware IoT Networks: Wireless Coexistence Methods for Low-complexity Devices
Åpne denne publikasjonen i ny fane eller vindu >>Towards Radio-Environment Aware IoT Networks: Wireless Coexistence Methods for Low-complexity Devices
2020 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Wireless technologies for short-range communication play a central role in the massive diffusion of the Internet of Things (IoT) paradigm. Such communication solutions rely extensively on the availability of unlicensed spectrum in the form of bands for industrial, scientific, and medical (ISM) applications. While ISM bands greatly simplify network deployments by avoiding operator-related costs and facilitating worldwide applicability, they present the shortcoming of non-cooperative spectrum usage, which manifests in the form of radio interference. Interference and time-varying environments generate complex and dynamic scenarios for wireless network deployments, endangering communication performance. The problem becomes especially critical when the timeliness and reliability of the communication are subject to stringent requirements, which is the case for several industrial IoT (IIoT) applications.

This work aims to enhance the reliability and performance of wireless communication in IoT networks by enriching the existing methods for radio-environment analysis. The central idea of this research is that a run-time analysis of the radio channel properties is a crucial element to ensure performance stability in unpredictable radio environments with potentially disruptive interference.An added challenge of this work comes from the hypothesis that such an analysis can be performed even with strongly resource-constrained platforms without hindering routine network functionalities. The employed methodology is heavily reliant on experimental validation, encompassing implementation on IoT radio devices and measurement campaigns. This thesis makes two principal scientific contributions.

The first contribution is the design of a comprehensive collection of methods for the analysis of the radio environment, designed to operate entirely onboard on IoT radio platforms.The approaches encompass interference detection, classification, spectrum analysis, link-state analysis, and detection of outages in end-to-end communication. The methods are designed to overcome the gap that exists in the related literature between the elaborate signal analysis operated with dedicated hardware and the lightweight, but sub-optimal, analysis methods developed for legacy wireless sensor networks.

The second contribution of this work is made by showing potential uses of the developed analysis methods to: i) safeguard the performance of wireless communication under interference and ii) enhance the coexistence of co-located wireless networks. To this end, firstly, a proactive method for dynamic blacklisting is designed that exploits real-time signal analysis and significantly improves the communication reliability of an IIoT radio link under heavy radio interference. Secondly, a method for autonomous radio environment mapping (REM) in IoT networks is proposed that employs onboard interference identification and tracks the sources of wireless interference in space, time, and frequency. The approach ensures a dynamic level of REM detail and provides a powerful tool for predicting the IoT network performance and adapting the network parameters at run-time.

sted, utgiver, år, opplag, sider
Sundsvall: Mid Sweden University, 2020. s. 92
Serie
Mid Sweden University doctoral thesis, ISSN 1652-893X ; 323
Emneord
IoT, Industrial IoT, Interference, Machine Learning, Wireless Coexistence, Wireless Networks
HSV kategori
Identifikatorer
urn:nbn:se:miun:diva-39034 (URN)978-91-88947-53-6 (ISBN)
Disputas
2020-06-16, Zoom, Holmgatan,10, Sundsvall, 09:00 (engelsk)
Opponent
Veileder
Forskningsfinansiär
Knowledge Foundation
Merknad

Vid tidpunkten för disputationen var följande delarbete opublicerat: delarbete 7 inskickat.

At the time of the doctoral defence the following paper was unpublished: paper 7 submitted.

Tilgjengelig fra: 2020-05-18 Laget: 2020-05-15 Sist oppdatert: 2020-05-19bibliografisk kontrollert

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