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Towards Radio-Environment Aware IoT Networks: Wireless Coexistence Methods for Low-complexity Devices
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology. (Communication Systems and Networks (CSN))
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
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.

Place, publisher, year, edition, pages
Sundsvall: Mid Sweden University , 2020. , p. 92
Series
Mid Sweden University doctoral thesis, ISSN 1652-893X ; 323
Keywords [en]
IoT, Industrial IoT, Interference, Machine Learning, Wireless Coexistence, Wireless Networks
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:miun:diva-39034ISBN: 978-91-88947-53-6 (print)OAI: oai:DiVA.org:miun-39034DiVA, id: diva2:1430641
Public defence
2020-06-16, Zoom, Holmgatan,10, Sundsvall, 09:00 (English)
Opponent
Supervisors
Funder
Knowledge Foundation
Note

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.

Available from: 2020-05-18 Created: 2020-05-15 Last updated: 2020-05-19Bibliographically approved
List of papers
1. Detecting Communication Blackout in Industrial Wireless Sensor Networks
Open this publication in new window or tab >>Detecting Communication Blackout in Industrial Wireless Sensor Networks
2016 (English)In: IEEE International Workshop on Factory Communication Systems - Proceedings, WFCS, 2016, article id 7496502Conference paper, Published paper (Refereed)
Abstract [en]

Communication blackout is one of the most serious pitfalls of Wireless Sensor Networks (WSN) in industrial automation context. The industrial radio channel exhibits pronounced effects of multipath fading and wireless LAN (WLAN) interference that can potentially lead to temporary communication failures, as well as complete isolation of network devices. The current IWSN standards adopt known countermeasures to cope with the harshness of the radio channel, but they lack solutions specifically oriented to detect blackouts and self-recover the communication fulfilling hard deadline constraints. In this work we focus onthe problem of blackout detection with specific interest for the WirelessHART standard, introducing a Blackout Detection Service (BDS) expressly addressed to multi-hop periodic communicationwith sensors and actuators. The BDS monitors end-to-end acknowledgement messages and builds specific metrics to promptly identify communication outages, enabling three criticality classes. The algorithm is tested in the ns-2 network simulator and results show that the proposed system is able to detect blackout events with reaction delays of the order of 4-5 times the refresh rate of nodes and to discriminate between smalland temporary network issues and serious blackout scenarios, opening the field for recovery strategies.

Keywords
WSN, reliability, real-time, networks
National Category
Computer Engineering
Identifiers
urn:nbn:se:miun:diva-27752 (URN)10.1109/WFCS.2016.7496502 (DOI)000382857300008 ()2-s2.0-84982833586 (Scopus ID)STC (Local ID)978-1-5090-2339-4 (ISBN)STC (Archive number)STC (OAI)
Conference
12th IEEE World Conference on Factory Communication Systems, WFCS 2016; Aveiro; Portugal; 3 May 2016 through 6 May 2016; Category numberCFP16WFC-ART; Code 122676
Projects
ASIS
Funder
Knowledge Foundation
Available from: 2016-05-23 Created: 2016-05-23 Last updated: 2020-05-15Bibliographically approved
2. An SVM-Based Method for Classification of External Interference in Industrial Wireless Sensor and Actuator Networks
Open this publication in new window or tab >>An SVM-Based Method for Classification of External Interference in Industrial Wireless Sensor and Actuator Networks
2017 (English)In: Journal of Sensor and Actuator Network, ISSN 2224-2708, Vol. 6, no 2, article id 9Article in journal (Refereed) Published
Abstract [en]

In recent years, the adoption of industrial wireless sensor and actuator networks (IWSANs) has greatly increased. However, the time-critical performance of IWSANs is considerably affected by external sources of interference. In particular, when an IEEE 802.11 network is coexisting in the same environment, a significant drop in communication reliability is observed. This, in turn, represents one of the main challenges for a wide-scale adoption of IWSAN. Interference classification through spectrum sensing is a possible step towards interference mitigation, but the long sampling window required by many of the approaches in the literature undermines their run-time applicability in time-slotted channel hopping (TSCH)-based IWSAN. Aiming at minimizing both the sensing time and the memory footprint of the collected samples, a centralized interference classifier based on support vector machines (SVMs) is introduced in this article. The proposed mechanism, tested with sample traces collected in industrial scenarios, enables the classification of interference from IEEE 802.11 networks and microwave ovens, while ensuring high classification accuracy with a sensing duration below 300 ms. In addition, the obtained results show that the fast classification together with a contained sampling frequency ensure the suitability of the method for TSCH-based IWSAN

Keywords
Industrial wireless sensor and actuator networks; support vector machine; interference classification; spectrum sensing; Wireless LAN; microwave owen
National Category
Computer Engineering Communication Systems Telecommunications
Identifiers
urn:nbn:se:miun:diva-30891 (URN)10.3390/jsan6020009 (DOI)000404529000005 ()2-s2.0-85029484316 (Scopus ID)STC (Local ID)STC (Archive number)STC (OAI)
Projects
ASISTIMELINESSSMART (Smarta system och tjänster för ett effektivt och innovativt samhälle)
Funder
Knowledge Foundation
Available from: 2017-06-16 Created: 2017-06-16 Last updated: 2020-05-15Bibliographically approved
3. Real-time Interference Identification via Supervised Learning: Embedding Coexistence Awareness in IoT Devices
Open this publication in new window or tab >>Real-time Interference Identification via Supervised Learning: Embedding Coexistence Awareness in IoT Devices
2019 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 835-850Article in journal (Refereed) Published
Abstract [en]

Energy sampling-based interference detection and identification (IDI) methods collide with the limitations of commercial off-the-shelf (COTS) IoT hardware. Moreover, long sensing times, complexity and inability to track concurrent interference strongly inhibit their applicability in most IoT deployments. Motivated by the increasing need for on-device IDI for wireless coexistence, we develop a lightweight and efficient method targeting interference identification already at the level of single interference bursts. Our method exploits real-time extraction of envelope and model-aided spectral features, specifically designed considering the physical properties of signals captured with COTS hardware. We adopt manifold supervised-learning (SL) classifiers ensuring suitable performance and complexity trade-off for IoT platforms with different computational capabilities. The proposed IDI method is capable of real-time identification of IEEE 802.11b/g/n, 802.15.4, 802.15.1 and Bluetooth Low Energy wireless standards, enabling isolation and extraction of standard-specific traffic statistics even in the case of heavy concurrent interference. We perform an experimental study in real environments with heterogeneous interference scenarios, showing 90%–97% burst identification accuracy. Meanwhile, the lightweight SL methods, running online on wireless sensor networks-COTS hardware, ensure sub-ms identification time and limited performance gap from machine-learning approaches.

Keywords
Bluetooth; interference detection and identification, IoT, machine learning, wireless coexistence, wireless sensor networks, WLAN
National Category
Communication Systems
Identifiers
urn:nbn:se:miun:diva-35184 (URN)10.1109/ACCESS.2018.2885893 (DOI)000455177700001 ()2-s2.0-85058195891 (Scopus ID)
Projects
SMART (Smarta system och tjänster för ett effektivt och innovativt samhälle)
Available from: 2018-12-12 Created: 2018-12-12 Last updated: 2020-05-15Bibliographically approved
4. Understanding the Performance of Bluetooth Mesh: Reliability, Delay and Scalability Analysis
Open this publication in new window or tab >>Understanding the Performance of Bluetooth Mesh: Reliability, Delay and Scalability Analysis
2020 (English)In: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 7, no 3, p. 2089-2101Article in journal (Refereed) Published
Abstract [en]

This article evaluates the quality-of-service performance and scalability of the recently released Bluetooth Mesh protocol and provides general guidelines on its use and configuration. Through extensive simulations, we analyzed the impact of the configuration of all the different protocol's parameters on the end-to-end reliability, delay, and scalability. In particular, we focused on the structure of the packet broadcast process, which takes place in time intervals known as \textit{Advertising Events} and \textit{Scanning Events}. Results indicate a high degree of interdependence among all the different timing parameters involved in both the scanning and the advertising processes and show that the correct operation of the protocol greatly depends on the compatibility between their configurations. We also demonstrated that introducing randomization in these timing parameters, as well as varying the duration of the \textit{Advertising Events}, reduces the drawbacks of the flooding propagation mechanism implemented by the protocol. Using data collected from a real office environment, we also studied the behavior of the protocol in the presence of WLAN interference. It was shown that Bluetooth Mesh is vulnerable to external interference, even when implementing the standardized limitation of using only 3 out of the 40 Bluetooth Low Energy frequency channels. We observed that the achievable average delay is relatively low, of around 250~ms for over 10 hops under the worst simulated network conditions. However, results proved that scalability is especially challenging for Bluetooth Mesh since it is prone to broadcast storm, hindering the communication reliability for denser deployments.

Keywords
Bluetooth Low Energy, Bluetooth Mesh, ISM bands, Internet-of-things, performance analysis, interference.
National Category
Communication Systems Telecommunications Computer Engineering
Identifiers
urn:nbn:se:miun:diva-36203 (URN)10.1109/JIOT.2019.2960248 (DOI)000522265900043 ()2-s2.0-85082109779 (Scopus ID)
Funder
Knowledge Foundation
Available from: 2019-05-23 Created: 2019-05-23 Last updated: 2020-05-15Bibliographically approved
5. Onboard Spectral Analysis for Low-complexity IoT Devices
Open this publication in new window or tab >>Onboard Spectral Analysis for Low-complexity IoT Devices
2020 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 8, p. 43027-43045Article in journal (Refereed) 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.

Keywords
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
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:miun:diva-38601 (URN)10.1109/ACCESS.2020.2977842 (DOI)000524706500018 ()2-s2.0-85082017601 (Scopus ID)
Available from: 2020-03-09 Created: 2020-03-09 Last updated: 2020-05-15Bibliographically approved
6. Machine Learning-Aided Classification of LoS/NLoS Radio Links in Industrial IoT
Open this publication in new window or tab >>Machine Learning-Aided Classification of LoS/NLoS Radio Links in Industrial IoT
2020 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Wireless sensors and actuators networks are an essential element to realize industrial IoT (IIoT) systems, yet their diffusion is hampered by the complexity of ensuring reliable communication in industrial environments.A significant problem with that respect is the unpredictable fluctuation of a radio-link between the line-of-sight (LoS) and the non-line-of-sight (NLoS) state due to time-varying environments.The impact of link-state over reception performance, suggests that link-state variations should be monitored at run-time, enabling dynamic adaptation of the transmission scheme on a link-basis to safeguard QoS.Starting from the assumption that accurate channel-sounding is unsuitable for low-complexity IIoT devices, we investigate the feasibility of channel-state identification for platforms with limited sensing capabilities. In this context, we evaluate the performance of different supervised-learning algorithms with variable complexity for the inference of the radio-link state.Our approach provides fast link-diagnostics by performing online classification based on a single received packet. Furthermore, the method takes into account the effects of limited sampling frequency, bit-depth, and moving average filtering, which are typical to hardware-constrained platforms.The results of an experimental campaign in both industrial and office environments show promising classification accuracy of LoS/NLoS radio links. Additional tests indicate that the proposed method retains good performance even with low-resolution RSSI-samples available in low-cost WSN nodes, which facilitates its adoption in real IIoT networks.

Place, publisher, year, edition, pages
IEEE, 2020
National Category
Communication Systems
Identifiers
urn:nbn:se:miun:diva-39031 (URN)
Conference
16th IEEE International Conference on Factory Communication Systems (WFCS 2020), Porto, Portugal / Online, 27-29 April, 2020
Available from: 2020-05-15 Created: 2020-05-15 Last updated: 2020-05-28
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123456 6 of 6
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