In LoRa (Long Range), when a collision occurs in the network, each end-device has to retransmit its colliding frame, which reduces the throughput, and increases the energy consumption of the end-devices and the delay of the frames. In this paper, we propose an algorithm to decode colliding synchronized LoRa signals and thus improve the overall performance of the network. Indeed, we use successive transmissions of bitmaps by the end-devices to determine the correct symbols of each colliding frame, instead of retransmitting the whole frames. Simulation results show that our algorithm is able to significantly improve the overall throughput of LoRaWAN, and to decrease the energy consumption and the delay of the transmitters.
In @cite_3 , the author proposed an analysis of packet collision and packet loss probabilities in LoRaWAN, and developed theoretical expressions for both of them. The author showed that his theoretical expressions are more accurate than a Poisson distributed process to describe the collisions.
@cite_3
2766303497 2169146840 2606902731 2750647255
Internet of things (IoT) is considered as the next technological revolution. Therefore, many solutions are developed either in free, i.e. ISM bands or in non free bands with the ultimate aim of affording connectivity over several kilometers. Based on this feature, in urban environment the density of IoT devices will be extremely high. In this paper we propose to analyze the collision and packet loss when LoRaWAN is considered. Based on the LoRaWAN features, we develop closed-form expressions of collision and packet loss probabilities. Simulation results confirm our theoretical developments. We also show that our theoretical expressions are more accurate than the Poisson distributed process to describe the collisions. Current 802.11 networks do not typically achieve the maximum potential throughput despite link adaptation and cross-layer optimization techniques designed to alleviate many causes of packet loss. A primary contributing factor is the difficulty in distinguishing between various causes of packet loss, including collisions caused by high network use, co-channel interference from neighboring networks, and errors due to poor channel conditions. In this paper, we propose a novel method for estimating various collision type probabilities locally at a given node of an 802.11 network. Our approach is based on combining locally observable quantities with information observed and broadcast by the access point (AP) in order to obtain partial spatial information about the network traffic. We provide a systematic assessment and definition of the different types of collision, and show how to approximate each of them using only local and AP information. Additionally, we show how to approximate the sensitivity of these probabilities to key related configuration parameters including carrier sense threshold and packet length. We verify our methods through NS-2 simulations, and characterize estimation accuracy of each of the considered collision types. Internet of Things (IoT) solutions are increasingly being deployed for smart applications. To provide good communication for the increasing number of smart applications, there is a need for low cost and long range Low Power Wide Area Network (LPWAN) technologies. LoRaWAN is an energy efficient and inexpensive LPWAN solution that is rapidly being adopted all around the world. However, LoRaWAN does not guarantee reliable communication in its basic configuration. Transmitted frames can be lost due to the channel effects and mobility of the end-devices. In this study, we perform extensive measurements on a new LoRaWAN network to characterise spatial and temporal properties of the LoRaWAN channel. The empirical outage probability for the farthest measured distance from the closest gateway of 7.5 km in our deployment is as low as 0.004, but the frame loss measured at this distance was up to 70 . Furthermore, we show that burstiness in frame loss can be expected for both mobile and stationary scenarios. Frame loss results in data loss, since in the basic configuration frames are only transmitted once. To reduce data loss in LoRaWAN, we design a novel coding scheme for data recovery called DaRe, which extends frames with redundant information that is calculated from the data from previous frames. DaRe combines techniques from convolutional codes and fountain codes. We develop an implementation for DaRe and show that 99 of the data can be recovered with a code rate of 1 2 for up to 40 frame loss. Compared to repetition coding DaRe provides 21 more data recovery, and can save up to 42 energy consumption on transmission for 10 byte data units. DaRe also provides better resilience to bursty frame loss. This study provides useful results to both LoRaWAN network operators as well as developers of LoRaWAN applications. Network operators can use the characterisation results to identify possible weaknesses in the network, and application developers are offered a tool to prevent possible data loss. In this paper we study the impact of imperfect-orthogonality in LoRa spreading factors (SFs) in simulation and real-world experiments. First, we analyze LoRa modulation numerically and show that collisions between packets of different SFs can indeed cause packet loss if the interference power received is strong enough. Second, we validate such findings using commercial devices, confirming our numerical results. Third, we modified and extended LoRaSim, an open-source LoRa simulator, to measure the impact of inter-SF collisions and fading (which was not taken into account previously in the simulator). Our results show that non-orthogonality of the SFs can deteriorate significantly the performance especially of higher SFs (10 to 12) and that fading has virtually no impact when multiple gateways are available in space diversity.