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Aslam, M. S., Khan, A., Atif, A., Hassan, S. A., Mahmood, A., Qureshi, H. K. & Gidlund, M. (2020). Exploring Multi-Hop LoRa for Green Smart Cities. IEEE Network, 34(2), 225-231
Open this publication in new window or tab >>Exploring Multi-Hop LoRa for Green Smart Cities
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2020 (English)In: IEEE Network, ISSN 0890-8044, E-ISSN 1558-156X, Vol. 34, no 2, p. 225-231Article in journal (Refereed) Published
Abstract [en]

With the growing popularity of Internet-of-Things (IoT)-based smart city applications, various long-range and low-power wireless connectivity solutions are under rigorous research. LoRa is one such solution that works in the sub-GHz unlicensed spectrum and promises to provide long-range communication with minimal energy consumption. However, the conventional LoRa networks are single-hop, with the end devices connected to a central gateway through a direct link, which may be subject to large path loss and hence render low connectivity and coverage. This article motivates the use of multi-hop LoRa topologies to enable energy-efficient connectivity in smart city applications. We present a case study that experimentally evaluates and compares single-hop and multi-hop LoRa topologies in terms of range extension and energy efficiency by evaluating packet reception ratio (PRR) for various source to destination distances, spreading factors (SFs), and transmission powers. The results highlight that a multi-hop LoRa network configuration can save significant energy and enhance coverage. For instance, it is shown that to achieve a 90% PRR, a two-hop network provides 50% energy savings as compared to a single-hop network while increasing 35% coverage at a particular SF. In the end, we discuss open challenges in multi-hop LoRa deployment and optimization.

Place, publisher, year, edition, pages
IEEE Communications Society, 2020
Keywords
IoT, Green Communications, Smart Cities, LoRa Technology, Multi-hop Networks
National Category
Communication Systems Telecommunications
Identifiers
urn:nbn:se:miun:diva-37129 (URN)10.1109/MNET.001.1900269 (DOI)
Funder
Knowledge Foundation
Available from: 2019-09-06 Created: 2019-09-06 Last updated: 2020-04-03Bibliographically approved
Gidlund, M., Hancke, G. P. J., Eldefrawy, M. & Åkerberg, J. (2020). Guest Editorial: Security, Privacy, and Trust for Industrial Internet of Things. IEEE Transactions on Industrial Informatics, 16(1), 625-628, Article ID 8952830.
Open this publication in new window or tab >>Guest Editorial: Security, Privacy, and Trust for Industrial Internet of Things
2020 (English)In: IEEE Transactions on Industrial Informatics, ISSN 1551-3203, E-ISSN 1941-0050, Vol. 16, no 1, p. 625-628, article id 8952830Article in journal, Editorial material (Refereed) Published
Keywords
IIoT, Security, Privacy
National Category
Communication Systems Computer Engineering
Identifiers
urn:nbn:se:miun:diva-38200 (URN)10.1109/TII.2019.2953241 (DOI)000508428900060 ()2-s2.0-85078292831 (Scopus ID)
Projects
Next Generation Industrial IoT (NIIT)
Funder
Knowledge Foundation
Available from: 2020-01-10 Created: 2020-01-10 Last updated: 2020-02-14Bibliographically approved
Beltramelli, L., Mahmood, A., Österberg, P. & Gidlund, M. (2020). LoRa beyond ALOHA: An Investigation of Alternative Random Access Protocols. IEEE Transactions on Industrial Informatics
Open this publication in new window or tab >>LoRa beyond ALOHA: An Investigation of Alternative Random Access Protocols
2020 (English)In: IEEE Transactions on Industrial Informatics, ISSN 1551-3203, E-ISSN 1941-0050Article in journal (Refereed) Epub ahead of print
Abstract [en]

We present a stochastic geometry-based model to investigate alternative medium access choices for LoRaWAN a widely adopted low-power wide-area networking (LPWAN) technology for the Internet-of-things (IoT). LoRaWAN adoption is driven by its simplified network architecture, air interface, and medium access. The physical layer, known as LoRa, provides quasi-orthogonal virtual channels through spreading factors (SFs) and time-power capture gains. However, the adopted pure ALOHA access mechanism suffers, in terms of scalability, under the same-channel same-SF transmissions from a large number of devices. In this paper, our objective is to explore access mechanisms beyond-ALOHA for LoRaWAN. Using recent results on time- and power-capture effects of LoRa, we develop a unified model for the comparative study of other choices, i.e., slotted ALOHA and carrier-sense multiple access (CSMA). The model includes the necessary design parameters of these access mechanisms, such as guard time and synchronization accuracy for slotted ALOHA, carrier sensing threshold for CSMA. It also accounts for the spatial interaction of devices in annular shaped regions, characteristic of LoRa, for CSMA. The performance derived from the model in terms of coverage probability, throughput, and energy efficiency are validated using Monte-Carlo simulations. Our analysis shows that slotted ALOHA indeed has higher reliability than pure ALOHA but at the cost of lower energy efficiency for low device densities. Whereas, CSMA outperforms slotted ALOHA at smaller SFs in terms of reliability and energy efficiency, with its performance degrading to pure ALOHA at higher SFs.

Keywords
Interference, Multiaccess communication, Logic gates, Modulation, Analytical models, Scalability, Synchronization, Energy efficiency, Internet-of-things, LoRa, Low-power wide-area networks, Medium access
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:miun:diva-38600 (URN)10.1109/TII.2020.2977046 (DOI)
Available from: 2020-03-09 Created: 2020-03-09 Last updated: 2020-05-12Bibliographically approved
Bombino, A., Grimaldi, S., Mahmood, A. & Gidlund, M. (2020). Machine Learning-Aided Classification of LoS/NLoS Radio Links in Industrial IoT. In: : . Paper presented at 16th IEEE International Conference on Factory Communication Systems (WFCS 2020), Porto, Portugal / Online, 27-29 April, 2020. IEEE
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
Grimaldi, S., Martenvormfelde, L., Mahmood, A. & Gidlund, M. (2020). Onboard Spectral Analysis for Low-complexity IoT Devices. IEEE Access, 8, 43027-43045
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
Rondón, R., Mahmood, A., Grimaldi, S. & Gidlund, M. (2020). Understanding the Performance of Bluetooth Mesh: Reliability, Delay and Scalability Analysis. IEEE Internet of Things Journal, 7(3), 2089-2101
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
Aydogan, E., Yilmaz, S., Sen, S., Butun, I., Forsström, S. & Gidlund, M. (2019). A Central Intrusion Detection System for RPL-Based Industrial Internet of Things. In: 2019 15th IEEE International Workshop on Factory Communication Systems (WFCS): . Paper presented at 15th IEEE International Workshop on Factory Communication Systems (WFCS'19), Sundsvall, Sweden, May 27-29, 2019.. IEEE, Article ID 8758024.
Open this publication in new window or tab >>A Central Intrusion Detection System for RPL-Based Industrial Internet of Things
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2019 (English)In: 2019 15th IEEE International Workshop on Factory Communication Systems (WFCS), IEEE, 2019, article id 8758024Conference paper, Published paper (Refereed)
Abstract [en]

Although Internet-of-Things (IoT) is revolutionizing the IT sector, it is not mature yet as several technologies are  still being offered to be candidates for supporting the backbone of this system. IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) is one of those promising candidate technologies to be adopted by IoT and Industrial IoT (IIoT). Attacks against RPL have shown to be possible, as the attackers utilize the unauthorized parent selection system of the RLP protocol. In this work, we are proposing a methodology and architecture to detect intrusions against IIoT. Especially, we are targeting to detect attacks against RPL by using genetic programming. Our results indicate that the developed framework can successfully (with high accuracy, along with high true positive and low false positive rates) detect routing attacks in RPL-based Industrial IoT networks.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
Industrial IoT (IIoT), Security, Intusion Detection, RPL Networks
National Category
Communication Systems Computer Engineering
Identifiers
urn:nbn:se:miun:diva-36736 (URN)10.1109/WFCS.2019.8758024 (DOI)000490866300023 ()2-s2.0-85070092698 (Scopus ID)978-1-7281-1268-8 (ISBN)
Conference
15th IEEE International Workshop on Factory Communication Systems (WFCS'19), Sundsvall, Sweden, May 27-29, 2019.
Projects
SMART (Smarta system och tjänster för ett effektivt och innovativt samhälle)TIMELINESS
Funder
European Regional Development Fund (ERDF)Knowledge Foundation
Available from: 2019-07-15 Created: 2019-07-15 Last updated: 2019-11-13Bibliographically approved
Zhang, W., Yang, D., Wang, H., Zhang, J. & Gidlund, M. (2019). AESGRU: An Attention-based Temporal Correlation Approach for End-to-End Machine Health Perception. IEEE Access, 7, 141487-141497
Open this publication in new window or tab >>AESGRU: An Attention-based Temporal Correlation Approach for End-to-End Machine Health Perception
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2019 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 141487-141497Article in journal (Refereed) Published
Abstract [en]

Accurate and real-time perception of the operating status of rolling bearings, which constitute a key component of rotating machinery, is of vital significance. However, most existing solutions not only require substantial expertise to conduct feature engineering, but also seldom consider the temporal correlation of sensor sequences, ultimately leading to complex modeling processes. Therefore, we present a novel model, named Attention-based Equitable Segmentation Gated Recurrent Unit Networks (AESGRU), to improve diagnostic accuracy and model-building efficiency. Specifically, our proposed AESGRU consists of two modules, an equitable segmentation approach and an improved deep model. We first transform the original dataset into time-series segments with temporal correlation, so that the model enables end-to-end learning from the strongly correlated data. Then, we deploy a single-layer bidirectional GRU network, which is enhanced by attention mechanism, to capture the long-term dependency of sensor segments and focus limited attention resources on those informative sampling points. Finally, our experimental results show that the proposed approach outperforms previous approaches in terms of the accuracy.

Keywords
Health Perception, Temporal Correlation, Gated Recurrent Unit Networks, Long-term Dependency, Attention Mechanism
National Category
Communication Systems Computer Engineering
Identifiers
urn:nbn:se:miun:diva-37398 (URN)10.1109/ACCESS.2019.2943381 (DOI)000497156000110 ()2-s2.0-85077674239 (Scopus ID)
Projects
NIIT
Funder
Knowledge Foundation
Available from: 2019-09-27 Created: 2019-09-27 Last updated: 2020-01-20Bibliographically approved
Anjum, M., Khan, M. A., Hassan, S. A., Mahmood, A. & Gidlund, M. (2019). Analysis of RSSI Fingerprinting in LoRa Networks. In: 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC): . Paper presented at 15th International Wireless Communications & Mobile Computing Conference, 24-28 June, 2019, Tangier, Morocco (pp. 1178-1183). IEEE
Open this publication in new window or tab >>Analysis of RSSI Fingerprinting in LoRa Networks
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2019 (English)In: 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), IEEE, 2019, p. 1178-1183Conference paper, Published paper (Refereed)
Abstract [en]

Localization has gained great attention in recent years, where different technologies have been utilized to achieve high positioning accuracy. Fingerprinting is a common technique for indoor positioning using short-range radio frequency (RF) technologies such as Bluetooth Low Energy (BLE). In this paper, we investigate the suitability of LoRa (Long Range) technology to implement a positioning system using received signal strength indicator (RSSI) fingerprinting. We test in real line-of-sight (LOS) and non-LOS (NLOS) environments to determine appropriate LoRa packet specifications for an accurate RSSI-to-distance mapping function. To further improve the positioning accuracy, we consider the environmental context. Extensive experiments are conducted to examine the performance of LoRa at different spreading factors. We analyze the path loss exponent and the standard deviation of shadowing in each environment

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
Curve fitting, LoRa, path loss, positioning, RSSI fingerprinting, spreading factor
National Category
Communication Systems Telecommunications
Identifiers
urn:nbn:se:miun:diva-37131 (URN)10.1109/IWCMC.2019.8766468 (DOI)000492150100200 ()2-s2.0-85073899834 (Scopus ID)978-1-5386-7747-6 (ISBN)
Conference
15th International Wireless Communications & Mobile Computing Conference, 24-28 June, 2019, Tangier, Morocco
Funder
Knowledge Foundation
Available from: 2019-09-06 Created: 2019-09-06 Last updated: 2019-12-20Bibliographically approved
Zhang, W., Yang, D., Wang, H., Huang, X. & Gidlund, M. (2019). CarNet: A Dual Correlation Method for Health Perception of Rotating Machinery. IEEE Sensors Journal, 19(16), 7095-7106, Article ID 8695784.
Open this publication in new window or tab >>CarNet: A Dual Correlation Method for Health Perception of Rotating Machinery
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2019 (English)In: IEEE Sensors Journal, ISSN 1530-437X, E-ISSN 1558-1748, Vol. 19, no 16, p. 7095-7106, article id 8695784Article in journal (Refereed) Published
Abstract [en]

As a key component of rotating machinery, the health perception of hearings is essential to ensure the safe and reliable operation of industrial equipment. In recent years, research on equipment health perception based on data-driven methods has received extensive attention. Overall, most studies focus on several public datasets to verify the effectiveness of their algorithms. However, the scale of these datasets cannot completely satisfy the representation learning of deep models. Therefore, this paper proposes a novel method, called CarNet, to obtain a more robust model and ensure that the model is sufficiently trained on a limited dataset. Specifically, it is composed of a data augmentation method named equitable sliding stride segmentation (ESSS) and a hybrid-stacked deep model (HSDM). The ESSS not only amplifies the scale of the original dataset but also enables newly generated data with both spatial and temporal correlations. The HSDM can, therefore, extract shallow spatial features and deep temporal information from the strongly correlated 2-dimensional (2-D) sensor array using a CNN and a bi-GRU, respectively. Moreover, the integrated attention mechanism contributes to focusing limited resources on informative areas. The effectiveness of CarNet is evaluated on the CWRU dataset, and an optimal diagnostic accuracy of 99.92% is achieved.

Keywords
Health perception, convolutional neural network, gated recurrent unit, attention mechanism, temporal and spatial correlation
Identifiers
urn:nbn:se:miun:diva-36821 (URN)10.1109/JSEN.2019.2912934 (DOI)000476795500059 ()2-s2.0-85069780589 (Scopus ID)
Projects
SMART (Smarta system och tjänster för ett effektivt och innovativt samhälle)
Available from: 2019-08-12 Created: 2019-08-12 Last updated: 2019-09-19Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-0873-7827

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