Low-power wide-area network (LPWAN) technologies attract great attention within the academic and industry communities, due to the fact they can wirelessly connect great numbers of geographically dispersed devices at low cost. Internet of Things (IoT) deployments in suburban or rural areas could include a lot of different scenarios for various kinds of data gathering. Depending on the use case, data from sensors at remote locations could be transmitted in big- or small-sized payloads, in short or long intervals, and collected by servers with or without latency. The most important requirements for rural IoT deployments are long-range communication and large network coverage. The network should operate in places with complicated terrain where radio communications are difficult and most times even telecommunication providers are not offering coverage. At the same time, IoT networks deployed in rural areas consist of a huge number of end devices (EDs), especially when we are dealing with smart metering. The communication infrastructure should be scalable in order to connect all the EDs without affecting the performance of the whole network in a non-viable way. In addition, the required maximum lifetime for such IoT deployments may vary from 10 to 15 years, or even more. Therefore, the EDs that will be used for communication should consume minimum energy in order to prolong the lifetime of their batteries, while the total cost of each ED in terms of the annual operating cost should be low. Among these requirements, the range of communication, based on the literature, seems to be the most complicated topic. It is worth mentioning that, during the past years, the world record for the maximum distance that a LoRaWAN data packet can be successfully transmitted has been broken many times. One of these records stands at 832 km (517 miles). However, how can this record help the adoption of the protocol from a network designer? It is commonly accepted that the effective allocation of wireless resources, in order to support a big batch of devices within an area, still remains an open challenge [1
]. With LoRa, we have a relatively new technology whose practical limits are not crystal clear yet. In addition, the fact that the theoretical range covers distances of the order of kilometers is creating some difficulties for real-case scenarios, both in laboratory and field tests.
In LoRaWAN, the long range of communication is achieved by transmitting with very low data rates to improve the sensitivity of receivers within the sub-GHz industrial, scientific, and medical (ISM) radio bands. LoRaWAN in the EU offers multiple channel availability with different bandwidths within the range of 868–870 MHz in the ISM band, while the orthogonality of spreading factors (SFs) offers simultaneous transmission in the same frequency channel [2
]. The protocol consists of the physical layer (LoRa) of the Open Systems Interconnection (OSI) model, which is proprietary, and a medium access and network layer, LoRaWAN, which is an open LPWAN standard. LoRaWAN was initially designed for low data-rate sensor networks, where sensors exchange packets only with the network server [3
]. Within the 868 MHz band, LoRaWAN has three common 125 kHz channels by default (868.10, 868.30 and 868.50 MHz). These are the channels that the EDs are using to access the network. The network server can provide extra channels to EDs after the join process. In Europe, which is our case, 10 channels are available and used both for uplinks and downlinks. In addition, apart from LPWANs, a lot of other standardized applications also function on the same spectrum frequencies, such as RFID, alarms, wireless headphones etc. [4
]. The ISM bands are license-free, thus can be utilized by almost any ED as long as it transmits based on the spectrum usage regulations. The regulation for ISM bands within Europe are different from other regions (e.g., in the US the band has no duty cycle restrictions) [5
]. The usage of bandwidth is counted based on the Effective Radiated Power (ERP), while the spectrum access method for the 868 MHz ISM is based on Listen-Before-Talk (LBT) or duty cycling. The maximum ERP on LoRa basic frequencies (868.00–868.60), which are common with SigFox, is limited to 25 mW [4
], although, EDs according to frequency regulation, support transmit power of up to 20 dBm (100 mW) [6
]. Such power allocation is permitted for only one frequency channel (G3 band with a limit of 27 dBm), whilst 14 dBm can be used in any of six channels. The maximum transmit distance calculated with the Friis transmission equation for 14 dBm transmission power results in a theoretical range of more than 300 km [2
]. For LoRa, the duty cycle calculation is based on channels with 1%, 0.1% and 10% access limit.
Spreading factor is a customized parameter for each frame that is transmitted and is related to the chirp spread spectrum (CSS) modulation of the physical layer. The choice of SF between 7 to 12, provides a trade-off between range and throughput (data rate). By increasing the SF, the number of chips per symbol are getting increased, which reduces the signal to noise ratio (SNR) that is required for successful demodulation, but simultaneously also increases the time on air (slow transmission). Lower spreading factors increase the data transmission rate, allowing for more data to be transmitted within the ISM band’s duty cycle limits [7
]. Each SF corresponds to a different data rate, which ranges from 0.3 kbps to 27 kbps [8
LoRaWAN GateWays (GW) are multichannel multi-modem transceivers [5
], characterized by high sensitivity due to the usage of very low data rates and sub-GHz ISM radio bands [2
], that are deployed such as base stations (cellular network model) that cover large areas. In most cases LoRaWAN network operators provide connectivity as a service [8
], by offering the GWs infrastructure. Any GW forwards the received messages to the associated network server for processing [9
Typically, each LoRa GW supports demodulation of signals from six quasi-orthogonal logical star networks in parallel (some chips support more [10
]), that corresponds to the available spreading factors [11
], while transmissions are also supported on different frequency channels [5
]. Specifically, the sensitivity of the GWs ranges from −130.0 dBm to −142.5 dBm depending on the spreading factor [12
], while the minimum required RSSI for successful decoding requires 5 to 7 dBm more than the sensitivity for each corresponding spreading factor [9
]. Therefore, when the spreading factor increases, the possibility for successfully receiving a packet on a weak link also increases, which determines a longer communication range.
Due to the message broadcast nature of the protocol, EDs could be clustered below more than one GWs. This happens because as long as a message transmission is heard by any GW within the network, that message is considered to be received [9
]. Once a GW receives a message, encapsulates its data into a UDP packet [10
] and forwards it to the network server of the LoRaWAN network. Next, a JSON string is produced from the network server for forwarding, including the payload, GW information, as well as additional information for physical communication parameters, such as RSSI and SNR [1
LoRaWAN deployment efficiency is mostly controlled by an adaptive data rate (ADR) mechanism which is used to control the transmission of messages between EDs and GWs [13
]. It is argued that LoRaWAN networks cannot truthfully scale without using a dynamic data rate allocation scheme [14
]. The optimal choice of the data rate for transmissions is the one that can ensure energy-efficient operation and increase the capacity of a single GW [14
]. So, in most cases, ADR schemes aim to maximize battery life and throughput by adjusting the data rate. This method offers additional benefits on increasing the overall network capacity, as messages sent with different spreading factors are orthogonal, so they can be simultaneously received and decoded by a GW [7
]. The ADR consists of two independent algorithms, one running on the ED, while the other runs on the Network Server [14
At the moment, studies related to LoRaWAN networks are continuously taking place in order to help understanding and improving the performance of such deployments. For instance, in [9
] it is proposed that by limiting the number of messages sent by each device per day, long power autonomy could be possible. However, this choice limits the range of suitable applications, while at the same time reduces the capacity a LoraWAN network can achieve. Such remarks indicate that an important step before a communication protocol is adopted for deployment in a wireless sensor network (WSN), is the determination of its limits for a specific application under a constrained amount of resources. However, a common problem is that during the dimensioning of a network several questions cannot be clearly answered. For example, what capacity can we achieve with three LoRaWAN GWs in a rural area of 10 square kilometers? Or, how many GWs will we need for such a terrestrial area? In addition, where do we need to place them? Where is it beneficial to place EDs when the locations of GWs are mostly pre-specified due to the ground elevations? How many devices, exchanging payloads with the server every hour or even days following a low data rate scheme, can be successfully deployed? alongway joint problems of GW placement, spreading factor assignment or power allocation are just a few issues that need to be addressed during the planning and deployment of a LoRaWAN network. Some reports [4
] even state that the long-term performance of a LoRaWAN is not only a matter of logical and geographical layout but is also impacted by interference from other networks. A fact is that, as ISM bands become more and more congested, LoRaWAN networks may no longer provide their original performance. LoRa deployments will interfere with each other especially in close proximity scenarios [5
]. Though, LoRaWAN available configuration parameters, including spreading factors, bandwidth, transmit power and duty cycling could alleviate various problems, but only when configured properly, which itself is a definite challenge.
At the moment there are several identified open issues related to LoRaWAN including network management, ADR optimization, high-density LoRaWAN installations and device interoperability [19
]. Additionally, most of the existing studies are trying to tackle challenges by leveraging LoRa features to extend the capacity issue, while others designed new relaying or routing protocols or relied on signal processing techniques to cancel the interference issue [20
]. conclusively, while several studies on LoRaWAN performance, scalability and security exist, the common problem of how to efficiently plan such a network has not yet received the appropriate attention [9
]. At the moment, a lot of interest is taken by researchers on modeling alternative mechanisms for adaptive data rate [1
], which, however, could only partially address the scalability performance of a network.
We identify that to efficiently deploy someone a LoRaWAN network with optimal performance, he/she needs to overcome issues that deform the trade-off triangle of coverage, scalability and energy efficiency. The scope of this study is to contribute to the alleviation of the dimensioning issue.of large LoraWAN networks. To this end, we, at first, critically reviewLoRaWAN’s protocol limitations as they are revealed via the literature, and summarize the factors that affect the performance of such deployments.
Specifically, the main issues during a LoRaWAN network design process that we identify as not properly addressed by the literature concerning the aforementioned performance trade-off could be summarized as follows:
Estimation of the number of GWs we need to setup to cover are required network capacity within a rural area of specific size. Simultaneously, estimation of the number of EDs that can be optimally supported by a network depending on their communication parameters [9
Identification of the performance failures that are due to configurable parameters of LoRaWAN communication [8
Simulation-based performance evaluation depending on several scalability-related metrics such as energy consumption, PDR, throughput, coverage, and latency [19
Through an experimental assessment we will, then, evaluate the correlation of variables such as spreading factors, GW deployment, duty cycle, transmission and interference parameters. With such experiments we attempt not only to analyze the trade-off triangle of coverage, scalability and energy efficacy, but also to dive deeper to the crucial factors that affect these communication performance indices. The result of this study is a set of guidelines towards the optimal configuration of a LoRaWAN network and the clustering of end nodes in the vicinity of a gateway, taking into account distance and transmission settings, that can improve network scalability. Appropriate simulations assess the validity of these guidelines.
Contributions of our work include:
Summarization of the crucial factors, as mentioned in literature, that affect communication performance of LoRaWAN networks.
Experimental assessment of the aforementioned factors, based on insights from the literature, regarding the communication performance alterations they can cause.
Cross-evaluation experiments that take into consideration the causalities of performance observations and the factors that affect them.
Generation of a layered-based configuration logic, in terms of network planning guidelines, that takes into account application constraints and QoS requirements.
The goal of this work is to improve the deployment of large LoRaWAN networks by enhancing arguments for causal relationships among factors that usually are bypassed during the network design stage and contribute further the research on easy-to-use models that are required [24
] to plan and assess the deployment of LoRaWAN networks. This is the challenge we aim to approach in our further studies. The structure of the paper is organized as follows: In Section 2
we review in detail how key mechanisms and concepts of LoRa technology work and how they affect the performance of such a network. Additionally, we identify and analyze the causal relationships between factors that affect performance of the network, we summarize the challenges that a LoRaWAN network needs to overcome in suburban and rural deployments, as well as the necessary metrics for argument on the following findings. Furthermore, we describe the research methodology we are going to follow, including the variables that we will control and observe, and also the experimental setup for making the corresponding measurements. In Section 3
, with our controlled experiments, we contribute some new insights on coverage, scalability and energy consumption of LoRaWAN networks, which are the most important parameters for the protocol adoption. Finally, in Section 4
we discuss our findings and give our guidelines for facilitating network deployment. This proposition aims to make factor-centric observations in relation to communication performance challenges. Our approach bypasses some basic LoRAWAN concepts that network deployers are already aware of, and concentrates directly on key aspects of the technology.
3. Results & Observations
After experimenting with hundreds of different deployments on the simulator we have extracted some cause-to-response observations regarding nearly every factor that can harm a deployment. The experiments have been repeated several times for each setup in order to achieve statistical accuracy. However, it should be noted that, when repeating the same experiments, the results were exactly the same—thus the standard deviation between the results was 0.
3.1. Capacity of Spreading Factors Clusters
The first thing a LoRaWAN deployer has in mind is that different spreading factors are directly related only to the range of the communication. Instead, spreading factor allocation is argued widely as an essential factor for the reduction of interference between node transmissions, due to the demodulation process. In general cases when the number of EDs is increased above 100 the PDR is decreased. Apart from certain observations, we have to mention that when we compared the total simulations where PDR exceed or is equal to 0.95, we notice that for experiments from 300 to 1000 EDs, 16 simulations out of 77 were on that level, while for simulations from 80 to 100 EDs, 30 out of 53 simulations were on that level. Specifically, we have noticed for example that for a deployment with 10 min transmission intervals for 1000 Eds, the PDR due to collisions drops from 0.90 for DR5 to 0.10 for DR0. This happens because when the number of EDs increases the possibility for interference increases as per the ToA which is SF-related. The same is also performed in high SFs for channel availability. So, we getting more arguments for assumption that as SF increases, fewer EDs on the network can be set to this. Thus, schematically, we can tentatively claim that each transmission interval has a corresponding maximum supported number of EDs, different for each DR cluster. In simulations with a setup of 1 h transmission interval, the capacity of EDs when the goal is to achieve more than 0.90 PDR is greatly reduced based on the DR allocation (Figure 4
). DR4 and DR5 clusters showed that they can handle successful transmissions for 5000 or more EDs respectively, DR3 around 3000 EDs, DR2 1500 EDs, DR1 nearly 500 EDs and DR0 around 200 EDs. On the other hand, we have noticed that when the distance is increased the ToA is increased which leads to slight PDR reduction caused by interference.
On the other hand, if we increase the frequency of transmissions, we can see that DR cluster capacity is degraded significantly. We are seeing this impact on simulations with a 10 min transmission interval and 50 byte payloads (Figure 5
). The corresponding DR cluster for this case drops to around 800 for DR5, 600 for DR4, 400 for DR3, 200 for DR2, 100 for DR1 and below 20 for DR0.
3.2. Size of Payload & Transmission Interval
Duty cycle limitations are not as important as we thought prior to our experiments. We perceived these regulations as a great obstacle for the network scalability, but on the other hand a lot of preceding factors affect this aspect of LPWAN deployments. Some experiments with 1000 EDs, distributed in every DR spectrum based on distance, reveal to us the spectrum limits in terms of data acquisition frequency. We have noticed that while 100% of 10 byte payloads are sent with a 200 s transmission interval, 89% were sent with a 100 s interval and 71% with a 60 s interval due to reaching the duty cycle limitation. The fact is that such transmission interval settings are, in one way or another, not viable for large scale deployments as we will argue with the following observations.
For 5000 EDs we can achieve 0.96 PDR when we have small payload (10 bytes), medium transmission interval (1 h), in a range of 2 km with all the devices set to DR5 to avoid long ToA. With the same settings, but using DR3 devices instead, the PDR reduced to 0.88 and the higher chance of successful packet decoding due to the higher SF is not real. We can therefore determine that the importance of the ToA factor is higher than SF ability to decode in noisy environments, when we analyze collisions. It is worth mentioning that on a series of simulations where we have set a very short transmission interval (1 min), the PDR cannot climb higher than 0.11 when more than 2000 EDs exist on the network, and is zero for lower DRs, with even two GWs in some cases. Such low PDR in that case was caused due to GW channel unavailability. Because the distance is some km, the transmission gets wider in time so a huge number of messages arrive at the same time to the GW. Even a random transmission time within the range of 60 s, which is the time between two consecutive payloads, is not enough. The only way to have channel availability in such a small transmission interval is to use DR5 and deploy less than 2000 EDs, but again the collision problem due to ToA is huge (0.54 PDR). From the gateway prespective, based on our experiments, for less than 100 EDs, GW channels are enough in any case, but the GW may be unavailable for receiving packets when the transmission interval is less than 10 minutes or when the transmission retries are getting increased. When we reduce the EDs to 3000, with the previous settings, PDR jumps to 0.98, but reduces if we differentiate the payload size (0.97 for 30 bytes, 0.95 for 50 bytes). When we have a lot of EDs (3000) and high coverage is required, the only way to reduce the collisions with increased ToA (only DR0 and 50 byte payload size) is to increase the transmission interval. In such a way we can achieve 0.95 and 0.97 PDR for 50 bytes and 10 bytes respectively. It needs to be noted that, for such rare in time transmission interval settings (>10 h), the PDR is 1.0 when the DR is 4 or 5—independentently of the payload size—and drops to 0.99 when DR3 is used. Another way to decrease the collisions, when you have a lot of EDs in a great distance that need to be allocated in DR0, is to deploy more gateways. Our observation here is that for a network of 400 EDs with a 10 min interval between the transmissions of 20 byte payloads, the PDR is 0.74 when four GWs are deployed, while with one GW it is 0.56, but the deployment of more EDs along with more GWs is not a choice at all to scale the network. This observation reveals that if we want to deploy a medium-to-small capacity network (<500 EDs), with great coverage and kind of frequent data acquisition the only way is to deploy two GWs instead of one. This situation is not analogous for more EDs. In another experiment with 1000 EDs in DR3 we have noticed that if we send the same amount of data in one payload in a more rare interval it is better than sending every time data are available. The main impact is caused by the overhead each different payload has, which increases the overall ToA. In detail, we have achieved a PDR of 0.96 when sending 50 byte payloads in 50 s interval, 0.93 when sending 20 byte payloads every 20 min and 0.88 by sending 10 byte payloads in 10 min interval. Figure 6
demonstrates the PDR differentiation which is derived from payload size and transmission interval relation. It is worth mentioning that for medium-sized or larger networks, the GW starts to run out of available channels if the transmission interval is set to 1 minute or less.
Those observations imply that when we want to scale our network and have a capacity of several thousand EDs with medium coverage, devices should be clustered into groups of data acquisition frequency. End devices that have to report data rarely could be set to a lower DR setting while the EDs from those from which we need data more often should be set to a higher DR setting, with the premise of exhausting the 50 byte size limit in each payload. Nevertheless, the impact that short transmission intervals have on PDR and thus on network capacity is so crucial that even in DR5 there are limits. We can see that when transmission is very frequent, even with 10 byte payloads, the capacity for DR5 also drops to around 100 EDs (Figure 7
). It is obvious that for the avoidance of interference, transmission interval is a much more important parameter than higher spreading factor distribution.
In general, we can summarize that if there is a certain amount of data to be sent it is better to send them in one big payload in more rare intervals, than to send data when they are available with shorter payloads. This is not the perfect choice, but one with the least possible drawbacks related to transmission interval context issues (Figure 8
In addition, it is proven that transmission intervals less than 600 s can exponentially harm network performance, when there are more than 100 devices or when a lot of devices use lower data rates. We are claiming that a DR-based distance relative transmission interval distribution method will be vital to scale a network, but this is only applicable if we can break down the EDs, within the application layer, in different classes of data acquisition frequency.
3.3. Acknowledgements, Retransmissions and Gateway Availability
The ACK payload with one retransmission has been shown to work satisfactorily (PDR is 0.95) for deployment of 80 EDs with transmission interval of 10 min and 50 byte payloads. The impact of the ACKs on the gateway side is also active here, independently of the small number of EDs. The gateway is still unable to hear five out of 100 messages, which is the main issue compared to collisions. The aforementioned simulation has been set up in three clusters of nodes, distributed at 1000 m or less, with DR3 to DR5 depending on the distance. We have also tried to scale the above to a 10-times bigger size network that, with the same settings, saw its performance drop to 0.87 PDR. In a parallel simulation we have moved more than half of the devices to DR0 and the PDR has been differentiated a lot and rose to 0.77 and cannot reach more than 0.80 even with one retry. Surely, if we set the transmission interval to 1 h then the PDR is even better than without the DR0 EDs (0.96), which again argues on the importance of rare transmissions for any case. Instead, if we keep the transmission interval the same and reduce the packet size to 10 bytes the PDR increase is not the same (0.84). Based on our analysis, the observation that when we import DR0 EDs on short data acquisition frequency deployments, PDR reduced a lot, occurred in a stepwise fashion. When GW is acknowledging payloads, which are downlinks, in SF12, suddenly a lot more traffic on the same SF occurred, creating a lot more interference. This fact increases the retransmissions on the same SF, which increases the overall traffic that the GW must handle, with the extra drawback that this kind of traffic occupies the channel for a long time. This sequence causes a high transmission failure rate due to the busy condition of the GW. This drawback is always the case when we use ACKs on a medium- or larger-sized network that has DR0 devices in Europe, where SF12 transceiving happens in the same SF as the GW acknowledges the payload receiving success to all nodes. To prove this, we have conducted some targeted simulations with 300 EDs within a 3 km axis, with the goal of getting the most out of the retransmission LoRaWAN feature. We have achieved 0.99 PDR when configuring all EDs to DR5, two retries and a 10 min transmission interval, that falls to 0.97 when all EDs were set to DR3 (due to increased collisions due to ToA). As expected, when we deploy the same network, but with the EDs set to DR0, the PDR rose to 0.58 and even when we increased the interval between transmissions to around 17 min, the PDR was 0.74. In an attempt to scale a network with ACK, we simulated 3000 EDs to transmit every 1 h a short message on DR3 and the results were really satisfying, reaching 0.95 PDR. Achieving as high a PDR as before (0.95) without ACK, with exactly the same settings, can be achieved if we reduce the EDs to 2000.
Simultaneously, we can argue here that deployments with medium capacity that require high data acquisition frequency, wide range and acknowledged transmissions can perform (PDR > 0.75) only if more than one GW has been deployed to the network, but we notice that if we use four instead of two gateways, PDR increased only 2%, so it is a useless choice on its own. On the contrary, if we deploy more GWs on a small-capacity network without ACK we can see this is a useless choice because no difference to the PDR is observed. So, applications that need to achieve high QoS, which depends on messages ACK or/and applications that need a lot of downlinks, need to deploy more gateways. This is a necessity because each ED on a network could have a GW fail when trying to listen on an uplink due to being in TX condition (transceiving). In our experiments we noticed that the messages that were not delivered successfully due to GW TX condition, when only one GW is deployed, are about 5–15% of the total transmissions and their number is based on the network size (100 to 3000 EDs respectively).
3.4. Transmission Power Allocation and Relation to Distance
We have conducted some line-of-sight experiment simulations related to range, DR allocation and TX power. Our observation is that TX and SF are inversely proportional regarding the impact of communication range. For example, an ED link of 4 km distance could be achieved by both DR4 with TX 10 and DR3 with TX 6. Additionally, for TX power levels, our experiments showed that sensitivity fails may occur when a transmission is weak to travel a wide distance. The highlight of TX power centric experiments is the discovery of the abilities of DR5 in terms of range, which could be from around 2 km or less with TX 2, to a maximum of around 4 km with TX 14 (Figure 9
A derivative from that is that DR5 must be allocated to the majority of the EDs on a network, with TX distribution to achieve less power consumption wherever is possible. In a lot of studies, SF7 allocation (DR5) is reported as an option that monopolizes the nodes where ADR is enabled. With our experiments we can conclude that DR5 is the best choice for medium-distance deployments (<4 km) because it achieves the shortest ToA which is directly related to interference, due to less possible channel occupation, and also to power consumption. For these reasons, it should be used in a higher ratio among other DRs and in wider range network deployments. Figure 10
and Figure 11
demonstrate the DR range limits (as per the simulator implementation) for TX = 14 dBm and 2 dBm respectively.
An additional conclusion that has been derived from our distance-based simulations is that as the SF increases the TX should be smaller in order to achieve common maintenance time between the EDs. If this does not happen, EDs with higher SF will need bigger batteries. This can be shown by the fact that, when the spreading factor is increased, the possibility of successfully receiving a packet on a weak link is also increased.
Based on our observations we can argue that collisions (interference between different transmissions within the same network channel) have the most significant negative impact on PDR. We have explored numerous possibilities and we have seen conclusively that ToA is directly related to collisions. Specifically, the bigger the SF, the payload size and the distance, the longer a transmission would last. Further, we can say that the longer a spectrum channel is occupied, the higher the possibility of collisions. This means that we do not care about the ToA of a payload transmission itself but the sum of its repetitions, acknowledgments or retries of all EDs that occurred within the same SF. Thus, we can say that the most crucial factors that affect LoRaWAN performance are those that affect the channel occupancy exponentially. Based on these observations, we determine that the number of EDs that are deployed and the transmission interval are the most important factors. However, the more direct aspect of the problem is not the overall number of EDs but the number of them within each SF, especially as the SF gets higher. It is clear that SF7 can handle a tremendous number of EDs itself but cannot work on its own if we want wider coverage. Finally, we can claim that it is far better to use more GWs to avoid the usage of SF12 completely, which causes a huge number of problems for realistic data acquisition frequency requirements, especially when ACKs are active because in Europe most deployments use SF12 for RX2.
4.1. Safe Pathways to Deploy a LoRaWAN Network
After experimentation with different variables based on the influence of crucial factors, we are proposing some optimal deployments aiming to include as many EDs as possible (scalability), cover a wide range (coverage) and use as little power as possible (power efficiency). In simple words, the parameters for the protocol adoption is the trade-off between scalability, coverage and power consumption and a parallel goal is to offer LoRaWAN-wide solutions depending on the application requirements, in order to avoid the withdrawal to other LPWAN alternatives (NB-IoT, Sigfox) without reason [30
For small-scale networks (<100 EDs), which is the easiest situation, it is safe to deploy EDs to only DR5 when the required coverage within a rural area is under 3–4 km. In this case the data acquisition frequency could be even less than a minute. When we want to reach medium coverage on such a deployment we can additionally deploy EDs on DR4, DR3, DR2, DR1, but the transmission interval needs to be increased in most cases. For reaching the maximum coverage on small networks we can deploy DR0 EDs but only for transmitting in intervals more than 1 h. Once the capacity increases (<500 EDS) and the required range is medium (<6 Km) the network can work flawlessly when data acquisition has been set to 10 min or more and the data rates used are distributed to DR3 or greater. Once the network gets bigger the safe option is to also decrease the data acquisition frequency. It is proved that you can have the highest data acquisition assurance once on a medium-to-large network (<1000 EDS) the data acquisition frequency drops to 1 h, while only DR4 and DR5 EDs are deployed for short-to-medium (<4 km) coverage deployments, that involve DR2 and DR3 along with ACKs for medium-to-large (<6 km) coverage deployments. Data acquisition frequency of 1 h can certainly support even large-capacity deployments (<5000 EDs). For such dimensioned networks only DR5 should be used for short-to-medium (<4 km) communication range requirements, or DR2 to DR5 with ACKs for medium coverage ones (<6 km). When large capacity and wide range are both requirements for a network then the way to have the highest possible acquisition assurance is to set the transmission interval to 12 h, deploy EDs in all DRs (DR0 for less than 20% of EDs) and activate ACKs with one retransmission.
It is worth mentioning here that The Things Industries proposed and facilitated some great concepts to overcome a lot of the issues we have also identified. A fair access policy and the ability to configure RX2 to SF9 are by far the most advanced methods to maintain great capacity common infrastructure LoRaWAN networks while respecting the shared ISM spectrum.
4.2. Proposed Approach for LoRaWAN Network Deployment
When we can configure a LoRaWAN network of thousands of devices over a range of several km with the least power consumption, we can define some different application-oriented demands—namely data acquisition assurance, data acquisition frequency, and data size—which all are different aspects of Quality of service (QoS), as long as a hybrid solution for serving applications based on data-level requirements such as the distinction of data and so in EDs level QoS. The level of data acquisition assurance is not directly connected with ACK and retransmissions, as it could be, but to the PDR cut-off which is acceptable for our needs. For example, this cut-off could be 0.50 if our application is not precise centric and we do not care if we lose one out of two messages.
By following the below application-wide clustering approach method, we have gathered some great insights. We have achieved greater than 0.90 PDR on a large network (6000 EDs) on a wide coverage (10 km) with a data acquisition frequency range from 10 min to 12 h and medium to very high data acquisition assurance.
The proposed clustering approach is as follows:
Count the overall EDs that are needed;
Deploy as many GW as are needed to avoid as much as possible the DR0 allocation that could be necessary for distantly deployed nodes. Do not exceed 900 nodes per GW channel [31
Separate the EDs into several clusters based on the distance from the closest gateway. The clusters should be in donut shape with the inner cycle being the shortest distance away and the outer being the furthest;
If there is not an application-wide QoS level, further distribute the EDs in each distance cluster to several new clusters based on QoS aspects (data acquisition assurance, data acquisition frequency and data size);
Distribute the EDs for each of the previous clusters to DR-TX groups based on the following criteria: (a) The larger the distance and data acquisition frequency, the larger the DR that should be allocated, while being proportional to the acknowledgment and amount of retransmissions, (b) The higher the DR, the higher the TX power needed to simultaneously achieve the best trade-off between coverage and power consumption, (c) The more EDs in the same cluster, the higher the DR allocation should be, (d) Exhaust the distance limits of each DR cluster in order to use as many DRs as possible, while the DR5 cluster should be configured to around 64% of the total EDs [32
Limit the distance-based clusters to equalize as much as possible the maintenance intervals, or distribute the final clusters according to power needs to determine which power capacity configuration should be used in each case (if this is a possible capability for the deployment).