Beacon Success Rate versus Gateway Density in Sub-GHz Sensor Networks
Multiple Gateways (GWs) provide network connectivity to Internet of Things (IoT) sensors in a Wide Area Network (WAN). The End Nodes (ENs) can connect to any GW by discovering and acquiring its periodic beacons. This provides GW diversity, improving coverage area. However, simultaneous periodic beac...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/23/9530 |
| _version_ | 1797399608662425600 |
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| author | Başak Can Bora Karaoğlu Srikar Potta Franklin Zhang Artur Balanuta Muhammed Faruk Gencel Uttam Bhat Johnny Huang Pooja Patankar Shruti Makharia Radhakrishnan Suryanarayanan Arvind Kandhalu Vinay Sagar Krishnamurthy Vijaya Shankar |
| author_facet | Başak Can Bora Karaoğlu Srikar Potta Franklin Zhang Artur Balanuta Muhammed Faruk Gencel Uttam Bhat Johnny Huang Pooja Patankar Shruti Makharia Radhakrishnan Suryanarayanan Arvind Kandhalu Vinay Sagar Krishnamurthy Vijaya Shankar |
| author_sort | Başak Can |
| collection | DOAJ |
| description | Multiple Gateways (GWs) provide network connectivity to Internet of Things (IoT) sensors in a Wide Area Network (WAN). The End Nodes (ENs) can connect to any GW by discovering and acquiring its periodic beacons. This provides GW diversity, improving coverage area. However, simultaneous periodic beacon transmissions among nearby GWs lead to interference and collisions. In this study, the impact of such intra-network interference is analyzed to determine the maximum number of GWs that can coexist. The paper presents a new collision model that considers the combined effects of the Medium Access Control (MAC) and Physical (PHY) layers. The model takes into account the partial overlap durations and relative power of all colliding events. It also illustrates the relationship between the collisions and the resulting packet loss rates. A performance evaluation is presented using a combination of analytical and simulation methods, with the former validating the simulation results. The system models are developed from experimental data obtained from field measurements. Numerical results are provided with Gaussian Frequency Shift Keying (GFSK) modulation. This paper provides guidance on selecting GFSK modulation parameters for low bit-rate and narrow-bandwidth IoT applications. The analysis and simulation results show that larger beacon intervals and frequency hopping help in reducing beacon loss rates, at the cost of larger beacon acquisition latency. On the flip side, the gateway discovery latency reduces with increasing GW density, thanks to an abundance of beacons. |
| first_indexed | 2024-03-09T01:42:37Z |
| format | Article |
| id | doaj.art-ffc8f6b2328c453f8608f8b1b996e764 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T01:42:37Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ffc8f6b2328c453f8608f8b1b996e7642023-12-08T15:26:19ZengMDPI AGSensors1424-82202023-11-012323953010.3390/s23239530Beacon Success Rate versus Gateway Density in Sub-GHz Sensor NetworksBaşak Can0Bora Karaoğlu1Srikar Potta2Franklin Zhang3Artur Balanuta4Muhammed Faruk Gencel5Uttam Bhat6Johnny Huang7Pooja Patankar8Shruti Makharia9Radhakrishnan Suryanarayanan10Arvind Kandhalu11Vinay Sagar Krishnamurthy Vijaya Shankar12Amazon Lab126, 1100 Enterprise Way, Sunnyvale, CA 94089, USAAmazon Lab126, 1100 Enterprise Way, Sunnyvale, CA 94089, USAAmazon Lab126, 1100 Enterprise Way, Sunnyvale, CA 94089, USAAmazon Lab126, 1100 Enterprise Way, Sunnyvale, CA 94089, USAAmazon Lab126, 1100 Enterprise Way, Sunnyvale, CA 94089, USAAmazon Lab126, 1100 Enterprise Way, Sunnyvale, CA 94089, USAAmazon Lab126, 1100 Enterprise Way, Sunnyvale, CA 94089, USAAmazon Lab126, 1100 Enterprise Way, Sunnyvale, CA 94089, USAAmazon Lab126, 1100 Enterprise Way, Sunnyvale, CA 94089, USAAmazon Lab126, 1100 Enterprise Way, Sunnyvale, CA 94089, USAAmazon Lab126, 1100 Enterprise Way, Sunnyvale, CA 94089, USAAmazon Lab126, 1100 Enterprise Way, Sunnyvale, CA 94089, USAAmazon Lab126, 1100 Enterprise Way, Sunnyvale, CA 94089, USAMultiple Gateways (GWs) provide network connectivity to Internet of Things (IoT) sensors in a Wide Area Network (WAN). The End Nodes (ENs) can connect to any GW by discovering and acquiring its periodic beacons. This provides GW diversity, improving coverage area. However, simultaneous periodic beacon transmissions among nearby GWs lead to interference and collisions. In this study, the impact of such intra-network interference is analyzed to determine the maximum number of GWs that can coexist. The paper presents a new collision model that considers the combined effects of the Medium Access Control (MAC) and Physical (PHY) layers. The model takes into account the partial overlap durations and relative power of all colliding events. It also illustrates the relationship between the collisions and the resulting packet loss rates. A performance evaluation is presented using a combination of analytical and simulation methods, with the former validating the simulation results. The system models are developed from experimental data obtained from field measurements. Numerical results are provided with Gaussian Frequency Shift Keying (GFSK) modulation. This paper provides guidance on selecting GFSK modulation parameters for low bit-rate and narrow-bandwidth IoT applications. The analysis and simulation results show that larger beacon intervals and frequency hopping help in reducing beacon loss rates, at the cost of larger beacon acquisition latency. On the flip side, the gateway discovery latency reduces with increasing GW density, thanks to an abundance of beacons.https://www.mdpi.com/1424-8220/23/23/9530beacon collisionbeacon intervalCarrier-to-Interference ratiofrequency hoppinggatewayGFSK |
| spellingShingle | Başak Can Bora Karaoğlu Srikar Potta Franklin Zhang Artur Balanuta Muhammed Faruk Gencel Uttam Bhat Johnny Huang Pooja Patankar Shruti Makharia Radhakrishnan Suryanarayanan Arvind Kandhalu Vinay Sagar Krishnamurthy Vijaya Shankar Beacon Success Rate versus Gateway Density in Sub-GHz Sensor Networks Sensors beacon collision beacon interval Carrier-to-Interference ratio frequency hopping gateway GFSK |
| title | Beacon Success Rate versus Gateway Density in Sub-GHz Sensor Networks |
| title_full | Beacon Success Rate versus Gateway Density in Sub-GHz Sensor Networks |
| title_fullStr | Beacon Success Rate versus Gateway Density in Sub-GHz Sensor Networks |
| title_full_unstemmed | Beacon Success Rate versus Gateway Density in Sub-GHz Sensor Networks |
| title_short | Beacon Success Rate versus Gateway Density in Sub-GHz Sensor Networks |
| title_sort | beacon success rate versus gateway density in sub ghz sensor networks |
| topic | beacon collision beacon interval Carrier-to-Interference ratio frequency hopping gateway GFSK |
| url | https://www.mdpi.com/1424-8220/23/23/9530 |
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