Coverage and Capacity Analysis of Sigfox, LoRa, GPRS, and NB-IoT

Vejlgaard, Benny; Lauridsen, Mads; Nguyen, Huan Cong; Kovács, István Z.; Mogensen, Preben; Sørensen, Mads Peter

doi:10.1109/vtcspring.2017.8108666

Cited by 208 publications

(123 citation statements)

References 7 publications

Supporting

Mentioning

100

Contrasting

Unclassified

Order By: Relevance

“…Due to the narrow bandwidth, the maximum uplink throughput that can be achieved is only 100 bps. Also, regional regulations impose a limited duty cycle of 1%, i.e., 36 s per hour and six seconds per message [12]. Therefore, the daily limit for a Sigfox device equals 140 uplink messages (twelve bytes each) and four downlink messages (eight bytes each).…”

Section: Sigfoxmentioning

confidence: 99%

Sigfox and LoRaWAN Datasets for Fingerprint Localization in Large Urban and Rural Areas

Aernouts¹,

Berkvens²,

Vlaenderen³

et al. 2018

Preprint

View full text Add to dashboard Cite

Abstract:Because of the increasing relevance of the Internet of Things and location-based services, researchers are evaluating wireless positioning techniques, such as fingerprinting, on Low Power Wide Area Network (LPWAN) communication. In order to evaluate fingerprinting in large outdoor environments, extensive, time-consuming measurement campaigns need to be conducted to create useful datasets. This paper presents three LPWAN datasets which are collected in large-scale urban and rural areas. The goal is to provide the research community with a tool to evaluate fingerprinting algorithms in large outdoor environments. During a period of three months, numerous mobile devices periodically obtained location data via a GPS receiver which was transmitted via a Sigfox or LoRaWAN message. Together with network information, this location data is stored in the appropriate LPWAN dataset. The first results of our basic fingerprinting implementation, which is also clarified in this paper, indicate a mean location estimation error of 214.58 m for the rural Sigfox dataset, 688.97 m for the urban Sigfox dataset and 398.40 m for the urban LoRaWAN dataset. In the future, we will enlarge our current datasets and use them to evaluate and optimize our fingerprinting methods. Also, we intend to collect additional datasets for Sigfox, LoRaWAN and NB-IoT.

show abstract

Section: Sigfoxmentioning

confidence: 99%

Sigfox and LoRaWAN Datasets for Fingerprint Localization in Large Urban and Rural Areas

Aernouts¹,

Berkvens²,

Vlaenderen³

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…SigFox is provided by the SIGFOX company that was founded by the Ludovic Le Moan and Christophe Fourtet in 2009. As a LPWA technology worked on non-licensed spectrum, SigFox has been rapidly commercialized and provides network devices with ultra-narrowband technology [12]. Different from mobile communication technology, it is a protocol that aims to create wireless IoT special networks with low power consumption and low costs.…”

Section: Sigfoxmentioning

confidence: 99%

Cognitive-LPWAN: Towards Intelligent Wireless Services in Hybrid Low Power Wide Area Networks

Chen

Miao

Jian

et al. 2019

IEEE Trans. on Green Commun. Netw.

101

View full text Add to dashboard Cite

The relentless development of the Internet of Things (IoT) communication technologies and the gradual maturity of Artificial Intelligence (AI) have led to a powerful cognitive computing ability. Users can now access efficient and convenient smart services in smart-city, green-IoT and heterogeneous networks. AI has been applied in various areas, including the intelligent household, advanced health-care, automatic driving and emotional interactions. This paper focuses on current wireless-communication technologies, including cellular-communication technologies (4G, 5G), low-power wide-area (LPWA) technologies with an unlicensed spectrum (LoRa, SigFox), and other LPWA technologies supported by 3GPP working with an authorized spectrum (EC-GSM, LTE-M, NB-IoT). We put forward a cognitive low-power widearea-network (Cognitive-LPWAN) architecture to safeguard stable and efficient communications in a heterogeneous IoT. To ensure that the user can employ the AI efficiently and conveniently, we realize a variety of LPWA technologies to safeguard the network layer. In addition, to balance the demand for heterogeneous IoT devices with the communication delay and energy consumption, we put forward the AI-enabled LPWA hybrid method, starting from the perspective of traffic control. The AI algorithm provides the smart control of wireless-communication technology, intelligent applications and services for the choice of different wireless-communication technologies. As an example, we consider the AIWAC emotion interaction system, build the Cognitive-LPWAN and test the proposed AI-enabled LPWA hybrid method. The experimental results show that our scheme can meet the demands of communicationdelay applications. Cognitive-LPWAN selects appropriate communication technologies to achieve a better interaction experience.Index Terms-Artificial intelligence, Low-power wide-area network, LoRa, LTE, NB-IoT technologies, e.g., LoRa, EC-GSM, NB-IoT, WiFi, BLE (bluetooth low-power consumption), and LTE-M. Short-distance and high-bandwidth communication technologies, such as WiFi, can cover up to 100 m with a data transmission rate of 100 Mbps. This communication mode is suitable for short-distance and high-bandwidth applications. For short-distance and low-datatransmission-rate communication technologies, e.g., Bluetooth and ZigBee, the highest coverage can reach to 100 m or so while its data transfer rate is 100 kbps. The communication mode is suitable for short-distance, low-bandwidth applications. Longdistance and high-data-transmission-rate communication technologies, such as UMTS and LTE, can cover a maximum range of 10 km, with a data transmission rate of 100 Mbps. This communication method is suitable for long-distance and highbandwidth applications. GSM can provide coverage up to 10 km, and a data-transmission rate of close to 100 kbps. This communication mode is suitable for long-distance and mediumbandwidth applications. Long-distance low-data-transmission-rate communication technologies, such as LoRa, NB-IoT, C-IoT and NB-CIoT, can co...

show abstract

“…From Table 2 we can observe how SIGFOX TM requires 20 times fewer antennas to cover the same urban environment than GSM, also having higher signal penetration into buildings. Regarding signal penetration into buildings, simulations considering a populated area in Denmark returned an indoor building penetration about 40% for General Packet Radio Service (GPRS) and higher than 95% for SIGFOX TM [21].…”

Section: Related Workmentioning

confidence: 99%

Internet of THings Area Coverage Analyzer (ITHACA) for Complex Topographical Scenarios

et al. 2017

View full text Add to dashboard Cite

Abstract:The number of connected devices is increasing worldwide. Not only in contexts like the Smart City, but also in rural areas, to provide advanced features like smart farming or smart logistics. Thus, wireless network technologies to efficiently allocate Internet of Things (IoT) and Machine to Machine (M2M) communications are necessary. Traditional cellular networks like Global System for Mobile communications (GSM) are widely used worldwide for IoT environments. Nevertheless, Low Power Wide Area Networks (LP-WAN) are becoming widespread as infrastructure for present and future IoT and M2M applications. Based also on a subscription service, the LP-WAN technology SIGFOX TM may compete with cellular networks in the M2M and IoT communications market, for instance in those projects where deploying the whole communications infrastructure is too complex or expensive. For decision makers to decide the most suitable technology for each specific application, signal coverage is within the key features. Unfortunately, besides simulated coverage maps, decision-makers do not have real coverage maps for SIGFOX TM , as they can be found for cellular networks. Thereby, we propose Internet of THings Area Coverage Analyzer (ITHACA), a signal analyzer prototype to provide automated signal coverage maps and analytics for LP-WAN. Experiments performed in the Gran Canaria Island, Spain (with both urban and complex topographic rural environments), returned a real SIGFOX TM service availability above 97% and above 11% more coverage with respect to the company-provided simulated maps. We expect that ITHACA may help decision makers to deploy the most suitable technologies for future IoT and M2M projects.

show abstract

Coverage and Capacity Analysis of Sigfox, LoRa, GPRS, and NB-IoT

Cited by 208 publications

References 7 publications

Sigfox and LoRaWAN Datasets for Fingerprint Localization in Large Urban and Rural Areas

Sigfox and LoRaWAN Datasets for Fingerprint Localization in Large Urban and Rural Areas

Cognitive-LPWAN: Towards Intelligent Wireless Services in Hybrid Low Power Wide Area Networks

Internet of THings Area Coverage Analyzer (ITHACA) for Complex Topographical Scenarios

Contact Info

Product

Resources

About