For various wireless sensing and IoT applications, LoRa has emerged as an energy-efficient and long range solution for wireless data transfers. However, real-world deployments face multiple challenges, including packet collisions and variable link qualities, which generally lead to packet corruptions and data loss. Even though LoRa relies on forward error correction to restore corrupted packets, its usage comes with a significant energy overhead and only provides limited capabilities. In our paper, we present CEC-LoRa as an alternative to LoRa’s forward error correction feature. Its key innovation is that CEC-LoRa allows to restore corrupted packets without any error correction codes. Our approach exploits two symbiotic properties in LoRa’s encoding and chirp-based modulation scheme. On the one hand, misinterpreted chirps are often confused with neighboring symbols. On the other hand, neighboring symbol values only differ by one, keeping high similarity between misinterpreted and correct symbols. We leverage these properties by pre-computing a set of all plausible LoRa packets based on the expected payload permutations and compare them with the received packet. This allows us to identify the closest match and thus the most probable candidate packet. While the pre-computations incur a quite substantial energy overhead, CEC-LoRa shifts this from the energy-constrained end devices to the receiver side. The benefits greatly outweigh the energy demand, though: CEC-LoRa reduces the number of corrupted packets by 86.5 % on average without any additional overhead on the end device and while still being fully compliant to the LoRa specification. By including symbol information, CEC-LoRa can reduce the number of corrupt packets even further, by 99.5 % on average. This translates into SNR gains in the range of 0.79 dB to 0.93 dB.

The rising popularity of the Internet of Things and wireless sensing applications calls for energy-efficient and scalable communication protocols. LoRaWAN is one of today’s most prominent choices for this purpose, as it features long communication ranges and high energy-efficiency. However, a major drawback is its limitation to being operated in a star topology. Thus, data originating from nodes outside of the single-hop neighborhood of a gateway cannot be received. We overcome this limitation by presenting Backpack-LoRa, an extension to LoRaWAN which enables multi-hop uplink communication while being compatible with existing deployments. Instead of simply re-transmitting overheard frames entirely, however, Backpack-LoRa nodes extract and buffer only the relevant frame content for later decryption and append it to their own frames, which are transmitted during the next regular transmission slots. To address the limitation of most commodity transceivers – only being able to listen on one out of multiple available channels – we propose a pseudo-random channel allocation algorithm based on time and device address for Backpack-LoRa. We implement our approach in a real-world outdoor deployment consisting of four nodes and gateways. Subsequently, we analyze the improvements in network coverage, packet reception rate, and energy overhead. Our results show that Backpack-LoRa enables nodes to reach gateways which were unreachable for standard LoRaWAN and increase the packet reception rate by up to 54.9 %. As compared to a simple re-transmission approach, Backpack-LoRa can reduce the energy overhead by 28.8 %.

In the world of IoT and wireless sensor networks, LoRaWAN has established itself as a quasi-standard for energy-efficient and long-range wireless communication in recent years. Thanks to its chirp-based modulation, receivers can demodulate packets with negative signal-to-noise ratios (SNRs), even down to −20 dB. As a result of changing ambient conditions, however, transmitted signals can experience increased attenuation, due to which they may be received with SNRs below the demodulation threshold, leading to packet loss. In this paper, we present an alternative demodulation approach that makes it possible to receive packets significantly below LoRa’s standard SNR threshold, which we call PreCo. In contrast to standard LoRa, which demodulates a packet symbol-by-symbol, our approach compares the received signal to a set of pre-computed LoRa packets in order to identify the closest match. We efficiently compute the set of packets against which the comparisons are performed based on the expected data an end device will transmit. This allows us to accumulate the energy of multiple symbols and decrease the SNR thresholds of standard LoRa even further, while neither additional hardware nor modifications on the end device are required. We run an evaluation using commodity hardware and different configurations to validate our approach. Our results show that our demodulation method provides an average SNR gain of 3.1 dB across different transmission settings on commodity hardware and is capable of demodulating packets with SNRs down to −23.1 dB.

WSNs are often deployed outdoors and thus exposed to a variety of different weather conditions. Due to the properties of radio waves, their propagation can be negatively impacted by the prevailing weather conditions. The sender and receiver hardware, including the antennas, can also be affected, potentially leading to variations in signal quality and link degradation. In order to assess the impact of individual weather components on the quality of WSN links, we analyze a large scale LoRaWAN deployment consisting of multiple nodes and gateways over a period of several months. The WSN is used for environmental monitoring, which means that the weather data is directly measured by the nodes and available in a high spatial and temporal resolution. Our results indicate significant dependencies between different weather components like precipitation, temperature, humidity but also time of day and the link quality as measured by RSSI and SNR. We further find that every link is affected to a different degree and has its own individual characteristic. Our analysis also shows significant correlations of the link quality within clusters of nodes that are deployed geographically close to each other. Finally, we evaluate the predictability of the link quality based on the prevailing weather conditions using different supervised learning models. Our results show that the trend of the link quality is indeed predictable, especially periodic patterns and trend changes can be recognized by the models and predicted within the test sets. The best model performance as measured by RMSE ranges from 1.79 dBm to 6.39 dBm for RSSI and 1.15 dB to 6.06 dB for SNR.

In LoRaWANs, the distance between sensor devices and gateways can reach up to several kilometers. Determining whether a particular location within this range is suitable for the reliable operation of a wireless sensing device is, however, not trivial by means of theoretical analysis alone. Numerous factors besides the power at which a signal is transmitted, such as obstacles in the line-of-sight path, govern whether connectivity is given. Real-world deployments of LoRa devices are hence typically preceded by connectivity assessments using field test devices. This process, mostly realized by having a person move around a target zone to find the position that leads to the strongest LoRa signal reception at a gateway, is labor-intensive and needs to be repeated for every device to be newly rolled out. We demonstrate how this process can be automated by pre-populating LoRaWAN coverage maps with the help of bicycle riders, rather than taking measurements on-demand. We accomplish this through determining an optimal set of routes for bicyclists, to ride along all accessible tracks in an area of interest. By equipping cyclists with LoRa transmitters which periodically send out location beacons, receiving gateways can autonomously derive connectivity maps of their surrounding area. We visualize received signal strength values in the form of heatmaps, which can be used to make informed decisions about suitable deployment locations for additional sensors.

Comprehensive health monitoring is highly relevant for the safety of manned spaced missions. Ballistocardiography (BCG) is a method for providing information of the heart physiology by measuring accelerations on the body surface that are caused by forwarded heart and blood movements in the vascular system. In order to provide a sensor system with a high signal quality, this paper presents differential BCG sensing at the system level for digital accelerometers and its integration into a sensing system. The system is part of a running experiment of the Cosmic Kiss mission on the International Space Station (ISS). Compared to single sensor solutions, the noise power scales down to about 50%, the Signal to Noise Ratio (SNR) is increased by a factor of 1.87 and the BCG signal variability especially improves for diastole area. Furthermore, by exploiting differential sensing techniques, both high reliability is achieved and common mode interference is mitigated, which is of high importance within the scope of space applications. Beside the presentation of the entire wireless sensor system including differential sensing approach, pre-processing unit and Ultra Wideband (UWB) communication module, results of the pre-flight tests show the performance of the system as well as the suitability for targeted mission.