Skip to Content
You are currently on the new version of our website. Access the old version .
SustainabilitySustainability
  • Article
  • Open Access

17 April 2017

Cooperative Downlink Listening for Low-Power Long-Range Wide-Area Network

and
1
Creative Informatics & Computing Institue, Korea University, Seoul 02841, Korea
2
Department of Embedded Systems Engineering, Incheon National University, Incheon 22012, Korea
*
Author to whom correspondence should be addressed.

Abstract

Recently, the development of the Internet of Things (IoT) applications has become more active with the emergence of low-power wide-area network (LPWAN), which has the advantages of low-power and long communication distance. Among the various LPWAN technologies, long-range wide-area network (LoRaWAN, or LoRa) is considered as the most mature technology. However, since LoRa performs uplink-oriented communication to increase energy efficiency, there is a restriction on the downlink function from the network server to the end devices. In this paper, we propose cooperative downlink listening to solve the fundamental problem of LoRa. In particular, the proposed scheme can be extended to various communication models such as groupcasting and geocasting by combining with the data-centric model. Experiments also show that the proposed technology not only significantly reduces network traffic compared to the LoRa standard, but also guarantees maximum energy efficiency of the LoRa.

1. Introduction

Over the last decades, rapid advances in low-cost low-power wireless communication technologies have accelerated the development of the Internet of Things (IoT) applications and services. These wireless technologies are generally based on short range communication such Bluetooth low energy (BLE), IEEE 802.15.4, and Wi-Fi (IEEE 802.11). However, the range limitation of IoT applications based on these communication technologies has made communication links unreliable and thus it has led to the degradation of communication reliability. Therefore, the demand for wide communication coverage is increased. Finally, these strong demands have led to the advent of low-power, long-range wireless communication systems, called low-power wide-area network (LPWAN). Various research groups are developing new LPWAN systems based on the new standards. These include long-range wide-area network (LoRaWAN) [1] by the LoRa Alliance, Long Term Evolution for machine to machine (LTE-m) [2] and Narrow Band Internet of Things (NB-IoT) [3] by 3GPP, ultra-narrow band communication by Sigfox [4], etc. Among these new technologies, LoRa is widely being accepted as the most promising LPWAN technologies in terms of power efficiency, low-cost chipset, and regional ISM bands. One of the key technologies of LoRa is to use chirp spread spectrum (CSS) for modulation, while the legacy low-power communications are based on frequency shift keying (FSK) to achieve low-power. For decades, the CSS modulation has been used in specific areas such as space communications or the military, since it has advantages of long communication capability and robustness to interference. That is, the LoRaWAN has long-range characteristics while maintaining robustness to interference.
Due to its advantages, interesting applications [5,6,7] are already being introduced based on LPWAN; the LoRaWAN has also attracted many researchers. Adelantado et al. [8] revealed the capabilities and limitations of LoRaWAN. In particular, they emphasized that the following challenges should be addressed: new multiple access mechanism and energy efficient multi-hop solution. Mikhylov et al. [9] presented the results of an analysis of LoRaWAN in terms of uplink throughput and data transmission time. According to the results, LoRaWAN duty-cycle-based Medium Access Control (MAC) scheme may increase packet collision probability and delay. In addition, they also showed that LoRaWAN has critical drawbacks in reliability and delay, especially in terms of downlink traffic. Kim et al. [10] pointed out some problems of LoRa gateway capability, and proposed new data transmission network architecture for long-range sensor networks, which are based on a hybrid approach combining LoRa infrastructure and sensor networks. Taneja et al. [11] proposed the hybrid network architecture of LoRaWAN and 802.11ah, and Schroder et al. [12] also proposed LoRaWAN use for smart grid network instead of RF mesh networks (Wi-SUN). Toussaint et al. [13] proposed a Markov chain model of the OTAA. They analyzed the impact of several parameters on traffic conditions, duty cycles, and channel availability. Petajajarvi et al. [14] studied the indoor performance of LoRaWAN based on health and well-being monitoring equipment. The authors also studied the coverage of LoRaWAN [15]. According to the results, the maximum communication range is over 15 km on ground and close to 30 km on water. Pham et al. [16] designed a low-cost DIY LoRa gateway which has a number of functionalities.
Even though LoRaWAN has several advantages including low-power and long-range capability, it also has some limitations and problems as already discussed in several literatures [5,6,7]. In particular, a key problem arises from the class A for the low-power operation of end device. Therefore, in this paper we first point out the limitations and problems of downlink communication in LoRa class A, and then propose a cooperative downlink listening (CDL) algorithm, which can solve the downlink limitations of class A while maintaining low-power characteristics. In addition, we also propose various data-centric models such as groupcasting and geocasting, based on CDL.
The major contribution of this paper is that we propose a novel algorithm to enhance downlink availability which is considered the most critical drawback of LoRa class A. Nevertheless, the new algorithm and protocol are designed compatible with the LoRa standard. In addition, we also open the new area of a data-centric IoT model for LPWAN, which is introduced in wireless sensor networks.
The remainder of this paper is organized as follows: Section 2 presents the technical overview and limitations of LoRaWAN. In Section 3, to address the problem, a new algorithm, CDL, is presented. In Section 4, IoT data-centric models associated with CDL are introduced. The performance evaluation of the proposed algorithm is presented in Section 5. Finally, this paper concludes with Section 6.

2. Overview of LoRaWAN

Long-range wide-area networks (LoRaWANs) specify a physical (PHY) and medium access control (MAC) layer protocol for an extremely long-range and low-power network, which is standardized by LoRa alliance. The major advantage of LoRa is in the long-range capability. According to LoRa Whitepaper [17], entire cities or hundreds kilometers in radius can be covered by a single LoRa base station or LoRa gateway. Using chirp spread spectrum (CSS), LoRa can significantly increase communication range while maintaining robustness to interference. These characteristics are attractive for many IoT applications, and many regions and countries are trying to accept the LoRa technology on their regional ISM band. Figure 1 shows the LoRa regional spectrum usage.
Figure 1. Long-range (LoRa) regional spectrum usage (Adapted from the specification Ver.1.02 regional Parameters, LoRa Alliance, 2016).
One of the outstanding features of LoRaWAN is based on a star topology connected to the network server in which gateways play a role in relaying messages between end devices and the network server as shown in Figure 2. Gateways have two independent communication links: LoRa communication and IP backhaul. The supported data rates are 0.3–50 kbps. In addition, LoRa provides an adaptive data rate (ADR) function.
Figure 2. Long-range wide-area network (LoRaWAN) architecture (Adapted from LoRa Whitepaper, LoRa Alliance, 2015 [16]).
Figure 3 shows LoRa protocol stack (PHY and MAC). Based on regional ISM band, physical layer includes LoRa modulation, and according to application characteristics, MAC has three different classes: Class A (baseline), Class B and C (optional). Class A is used for the low-energy end devices and allows bi-directional communication between end devices and network server. However, downlink communication from network server to each end device is only allowed shortly after the end of uplink transmission of an end device.
Figure 3. LoRa protocols stack (Adapted from LoRa Specification Ver.1.02, LoRa Alliance, 2016).
As shown in Figure 4, shortly after uplink transmission, the end device allows two RX windows 1 and 2 during RECEIVE_DELAY1 and 2, respectively. For RX1 window, the same channel and data rate as those used in uplink transmission are used, but RX2 window is allowed to use a different channel and data rate from the uplink transmission.
Figure 4. Class A end device slot timing (Adapted from LoRa Specification Ver.1.02, LoRa Alliance, 2016).
LoRaWAN has two different types of join methods: over-the-air activation (OTAA) and activation by personalization (ABP). OTAA is an automatic join process performed during deployment of nodes and ABP is a manual join process.
The DevAddr is composed of a 32-bit identifier in which seven bits are used as the network identifier (NwkID) and 25 bits are used as the network address (NwkAddr) which is assigned to each end device by a network manager.

4. IoT Query Dissemination Based on CDL

CDL is capable of enabling not only downlink broadcasting by sharing cooperatively a downlink slot among devices, but also data-centric communication utilizing group address field efficiently. In this section, we introduce groupcasting and geocasting in association with CDL.

4.1. DL Groupcasting

Groupcasting, which is a kind of multicasting, is used when data is requested only from the specific types of sensors in the network. For instance, as shown in Figure 9, the application server wants to know the sensors for which the temperature is higher than 30 degrees. In this case, the application server can request a query to the network server and it broadcasts to the nodes the message containing the Addr field, in which the temperature sensor bit is set, and the query as shown in Figure 9. As a result of the query, only the sensors which satisfy the condition (>30 °C) respond to the network server. The network server aggregates data and then the aggregated results might be sent to the application server. This groupcasting capability can reduce entire network traffic as well as excessive data storage in the network server.
Figure 9. Groupcasting based on CDL.

4.2. CDL Geocasting

The Group Addr field already includes functionality capable of configuring a region field as well as sensor type field. Using the region field, the network server can receive data only from the sensors which are in a specific area without assistance of GPS. In particular, since the LoRa can cover more than a few kilometers in radius, it is necessary to divide into subareas the area which is covered by a single gateway. For instance, as shown in Figure 10, if a sensor field can be divided into four subareas (A, B, C, and D), CDL enables gathering sensor data only from sensors which are in a specific area. In this case, a user (application server) requests the recent data in the particular area, then the network server sets the corresponding bitmap in the region field and broadcasts the message into the network. Only the nodes in the interest area (e.g., area A) can respond to the network server. In addition, since the region field is expressed by bitmap, data gathering from multiple areas is also possible. For example, if the region field contains ‘0101’, it means both regions B and D. In addition, more advanced data-centric aggregation is available by utilizing a combination of sensor type field and region field. For example, the query can be as follows: only the nodes of which temperature is below 0 °C in the area A and D.
Figure 10. Geocasting based on CDL.

5. Performance Evaluations

In this section, we test each function of CDL, and conduct performance comparison with LoRa standard. While using basic functions of standard as it is, CDL is implemented by adding additional functionalities at the high level, so that data success ratio and delay analysis are skipped in this performance evaluation. As a result of our experiments, it is shown that the two metrics have almost similar performance to those of the standard, so that they are not arguable in this paper. Instead, we evaluate entire network traffic compared with LoRa standard, and energy consumption, which is the most important feature of LoRa, is analyzed.

5.1. Experimental Environment

In order to implement a LoRa network, we used off-the-shelf LoRa products [18]: Multitech mDot for end device and Conduit for LoRa gateway. Both end device and gateway are configured with Korean frequency regulation specified in the LoRa specification ver. 1.02, and 1 gateway and 5 end devices were used in our experiments. Since mDot and Conduit contains the LoRa standard stack, LoRa standard performance is tested based on the original systems, and on the top of LoRa standard, CDL is implemented. Our experiments are conducted on 5 end devices but based on the results from the real experiments we evaluated the performance in extensive environments as shown in Table 2.
Table 2. Experimental environments.

5.2. Traffic Analysis

One of the critical features of the CDL is that by using a shared downlink slot, the network server is able to process broadcast messages transmitted to each ED. In particular, the time synchronization message of this broadcast message must be used periodically to synchronize all the nodes. Thus, in this experiment, each node is synchronized from the network server once a day, and the total amount of packets (i.e., traffic) generated in the network is measured when the network is maintained for 30 days. For more extended experiments, we measured the number of packets generated by increasing the number of nodes from 1 to 5, and based on this data, we calculated the number of packets when the number of nodes was increased from 10 to 100. In addition to the synchronization message once a day, each node sends its sensing data to the network server once an hour and uses a model that receives ACK for it. Figure 11 shows the total number of packets generated during a month in LoRa and CDL. The results show that when the number of nodes is small, the amount of traffic of LoRa standard and CDL does not show much influence on broadcasting, so there is no great difference in the total amount of traffic, but the amount of traffic is remarkable as the number of nodes increases. This is because the CDL broadcast function has been effective in reducing the traffic. In particular, in LoRa, a large number of nodes are accommodated in a single gateway due to a long communication distance. However, the larger the network, the more the traffic reduction effect becomes.
Figure 11. Network traffic observation.

5.3. Energy Consumption and Battery Lifetime

One of the most important features of LoRa is its low-power communication capability. This low-power capability is very attractive for a variety of IOT applications. This experiment evaluates energy consumption of LoRa standard and CDL. LoRa gateways are not considered because they are supplied with AC power. The current consumption at each state is shown in Table 2. (They include MCU current consumption). As shown in Figure 12, to measure the power consumption, we divide into the following three states (TX state, RX state, SLEEP state), integrate the consumption current according to each state into each state time, The power consumption was calculated by multiplying the power consumption by the power consumed during one hour. Figure 13 shows the results of increasing the number of data transfers per node from 1 to 10 for one cycle (1 h). In fact, CDL is an extended version of the standard LoRa, yet it accommodates all of the low-power features in LoRa, so the energy consumption of each node is not significantly different. This does not take into account the effects of broadcasts, and if time synchronization is taken into consideration, energy consumption may be slightly less than that of the standard LoRa.
Figure 12. Power state in terms of communications.
Figure 13. Energy consumption.
To further understand the more intuitive energy consumption, we also calculated the battery life of a node based on two AA batteries. Figure 14 shows the battery life when transferring data from 1 to 10 times per hour. As shown in the figure, when the data is sent once or twice in one hour, the CDL tends to be somewhat shorter than the standard LoRa due to the additional energy consumption for maintaining the shared downlink slot. However, as the number of data increases, it converges to the consumed amount. Also, considering the data transmission once per hour, CDL can guarantee a lifetime of more than 10 years as well as LoRa, which is also suitable for long life applications such as remote meter reading.
Figure 14. Battery life.

6. Conclusions

In this paper, we have dealt with LoRaWAN, a representative technology of LPWAN. In particular, we addressed a fundamental problem in LoRaWAN, the DOWNLINK constraint, by cooperative downlink listening, which is based on a shared downlink slot triggered periodically by the DL initiator. In addition, energy balancing has been achieved through the selection of a dynamic DL initiator to avoid intensive energy consumption of the DL initiator. In addition, it shows that various data-centric models, groupcasting and geocasting, are possible in association with CDL. The CDL was implemented with minimal modifications to the existing LoRa standard and maintained compatibility with the LoRa standard. Experiments have shown that the CDL significantly reduces traffic over the LoRa standard, especially when the number of nodes in a network increases significantly. In addition, CDL showed almost similar energy consumption compared to LoRa, and it was shown that it can guarantee battery life of more than 10 years considering one hour communication. Based on the results of this paper, it is expected that it can be applied to various application fields based on LoRaWAN.

Acknowledgments

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Grant No. 2015R1D1A1A01059317).

Author Contributions

The authors listed all contributed to the development of the idea and experiments contained within this paper. More specifically, the first version of this paper was written by the corresponding author, Kwang-il Hwang, and structure and revisions of this paper were led by the 1st author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. LoRa Alliance, Inc. LoRaWAN™ Specification; LoRa Alliance, Inc.: San Ramon, CA, USA, 2015. [Google Scholar]
  2. LTE-M Optimizing LTE for the Internet of Things. White Paper. Available online: https://novotech.com/docs/default-source/default-document-library/lte-m-optimizing-lte-for-the-internet-of-things.pdf?sfvrsn=0 (accessed on 3 February 2016).
  3. Cellular System Support for Ultra-Low Complexity and Low Throughput Internet of Things. Available online: https://portal.3gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx?specificationId=2719 (accessed on 16 August 2016).
  4. Sigfox Homepage. Available online: http://www.sigfox.com (accessed on 13 January 2016).
  5. Fernández-Garcia, R.; Gil, I. An alternative wearable tracking system based on a low-power wide-area network. Sensors 2017, 17, 592. [Google Scholar] [CrossRef] [PubMed]
  6. Trasviña-Moreno, C.A.; Blasco, R.; Marco, Á.; Casas, R.; Trasviña-Castro, A. Unmanned aerial vehicle based wireless sensor network for marine-coastal environment monitoring. Sensors 2017, 17, 460. [Google Scholar] [CrossRef] [PubMed]
  7. Aquino-Santos, R.; González-Potes, A.; Edwards-Block, A.; Virgen-Ortiz, R.A. Developing a new wireless sensor network platform and its application in precision agriculture. Sensors 2011, 11, 1192–1211. [Google Scholar] [CrossRef] [PubMed]
  8. Adelantado, F.; Vilajosana, X.; Tuset-Peiro, P.; Martinez, B.; Melia, J. Understanding the limits of LoRaWAN. Available online: https://arxiv.org/pdf/1607.08011.pdf (accessed on 27 July 2016).
  9. Mikhaylov, K.; Petäjäjärvi, J.; Haenninen, T. Analysis of capacity and scalability of the LoRa low power wide area network technology. In Proceedings of the European Wireless 2016, 22th European Wireless Conference, Oulu, Finland, 18–20 May 2016; pp. 119–125. [Google Scholar]
  10. Kim, D.Y.; Jung, M. Data transmission and network architecture in long range low power sensor networks for iot. Wirel. Pers. Commun. 2017. [Google Scholar] [CrossRef]
  11. Taneja, M. 802.11 ah-LPWA interworking. In Proceedings of the 2016 IEEE NetSoft Conference and Workshops (NetSoft), Seoul, Korea, 6–10 June 2016. [Google Scholar]
  12. Schroder Filho, H.G.; Pissolato Filho, J.; Moreli., V.L. The adequacy of LoRaWAN on smart grids: A comparison with RF mesh technology. Proceeding of the 2016 IEEE International Smart Cities Conference (ISC2), Trento, Italy, 12–15 September 2016. [Google Scholar]
  13. Toussaint, J.; EI Rachkidy, N.; Guitton, A. Performance analysis of the on-the-air activation in LoRaWAN. Proceeding of the 2016 IEEE 7th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver, BC, Canada, 13–15 October 2016. [Google Scholar]
  14. Petäjäjärvi, J.; Mikhaylov, K.; Hämäläinen, M.; Iinatti, J. Evaluation of LoRa LPWAN technology for remote health and wellbeing monitoring. Proceeding of the 10th International Symposium on Medical Information and Communication Technology (ISMICT), Worcester, MA, USA, 20–23 March 2016. [Google Scholar]
  15. Petäjäjärvi, J.; Mikhaylov, K.; Roivainen, A.; Hanninen, T.; Pettissalo, M. On the coverage of LPWANs: Range evaluation and channel attenuation model for LoRa technology. Proceeding of the 2015 14th International Conference on ITS, Copenhagen, Denmark, 2–4 December 2015. [Google Scholar]
  16. Pham, C. Building Low-Cost Gateways and Devices for Open LoRa IoT Test-Beds. In Testbeds and Research Infrastructures for the Development of Networks and Communities, Proceeding of the 11th International Conference TRIDENTCOM, Hangzhou, China, 14–15 June 2016; Springer: Cham, Switzerland, 2016; pp. 70–80. [Google Scholar]
  17. LoRa Alliance. A Technical Overview of LoRa and LoRaWAN. White Paper. Available online: https://www.lora-alliance.org/portals/0/documents/whitepapers/LoRaWAN101.pdf (accessed on 1 November 2015).
  18. Multitech Product. Available online: https://www.multitech.net (accessed on 1 January 2017).

Article Metrics

Citations

Article Access Statistics

Multiple requests from the same IP address are counted as one view.