Auditory Attention Decoding (AAD) can help to determine the identity of the attended speaker during an auditory selective attention task, by analyzing and processing measurements of electroencephalography (EEG) data. Most studies on AAD are based on scalp-EEG signals in two-speaker scenarios, which are far from real application. Ear-EEG has recently gained significant attention due to its motion tolerance and invisibility during data acquisition, making it easy to incorporate with other devices for applications. In this work, participants selectively attended to one of the four spatially separated speakers’ speech in an anechoic room. The EEG data were concurrently collected from a scalp-EEG system and an ear-EEG system (cEEGrids). Temporal response functions (TRFs) and stimulus reconstruction (SR) were utilized using ear-EEG data. Results showed that the attended speech TRFs were stronger than each unattended speech and decoding accuracy was 41.3% in the 60s (chance level of 25%). To further investigate the impact of electrode placement and quantity, SR was utilized in both scalp-EEG and ear-EEG, revealing that while the number of electrodes had a minor effect, their positioning had a significant influence on the decoding accuracy. One kind of auditory spatial attention detection (ASAD) method, STAnet, was testified with this ear-EEG database, resulting in 93.1% in 1-second decoding window. The implementation code and database for our work are available on GitHub: https://github.com/zhl486/Ear_EEG_code.git and Zenodo: https://zenodo.org/records/10803261.

The selection of the spreading factor (SF) has important implications on the radio on-time, energy consumption, achievable data rate, and communication range of LoRa devices. In practical applications, LoRa packets can only be exchanged when the SF between transmitter and receiver matches. To ensure that this is the case, current approaches either statically hard-code the SF used to communicate between two devices, or negotiate which SF to use through handshaking mechanisms. Unfortunately, statically assigning the SF may lead to sub-optimal performance, and changing the assignment at runtime through a negotiation process incurs a significant overhead in terms of both latency and energy consumption. In this paper, we propose RSSF, a scheme that allows an off-the-shelf LoRa device to receive and decode a packet without prior knowledge of the SF used by the transmitter. RSSF leverages the observation that the SF with which a packet was sent can be inferred by analyzing the received signal strength (RSS) samples captured while receiving the first preamble symbols, and by characterizing their periodicity. In real-world systems, however, the waveform obtained by sampling the RSS during the reception of the first preamble symbols contains several spikes due to the receiver's DC offset cancellation, which makes it difficult to accurately identify periods. We show that this problem can be solved by letting an off-the-shelf LoRa receiver sample the RSS on a frequency that is shifted by half of the bandwidth from the original frequency at which the packet was transmitted. We then implement a lightweight algorithm that determines the SF by partitioning the RSS samples into sliding windows of different size (corresponding to each possible SF) and by measuring the zero-crossing intervals for each window size. We evaluate RSSF's performance experimentally using both software-defined radios and off-the-shelf LoRa nodes, showing that RSSF can accurately determine the SF within the first 5 preamble symbols.

LoRaWAN is one of the most popular protocols to build low-power wide area networks. Unfortunately, it adopts a star topology, which limits network coverage and may also cause an unnecessary decrease in energy efficiency as well as scalability. In fact, end-devices that are deployed far away from a gateway need to increase their transmission power or spreading factor (SF) to sustain reliable communications, which increases their energy expenditure as well as the size of the collision domain. The only alternative is the deployment of additional gateways or dedicated relay nodes, which results in higher costs and deployment efforts. In this work, we introduce LoRaHop, an extension of LoRaWAN that enriches end-devices with the ability to form a mesh network and to seamlessly relay packets to/from a gateway, thereby providing LoRaWAN networks with multihop support for both uplink and downlink messaging. LoRaHop leverages concurrent transmissions to enable a reliable and efficient data collection or dissemination over the mesh network, as well as to simplify network formation. Furthermore, LoRaHop embeds a mechanism that simplifies rendezvous across devices and that minimizes the impact of mesh operations on existing LoRaWAN transmissions. We implement LoRaHop on off-the-shelf LoRa end-devices (ensuring their interoperability with commercial LoRaWAN gateways and network servers), and evaluate its performance on an outdoor testbed. Our results show that LoRaHop can effectively extend the coverage of an LoRaWAN network while improving reliability by up to 98.33% and reducing energy consumption by up to 48.02%. Our findings further demonstrate that using LoRaHop to create a multihop LoRaWAN network that communicates using low SFs brings significant benefits in terms of energy efficiency and scalability compared to the use of a single-hop LoRaWAN network using high SFs.

This paper presents EMU, a framework that enables the emulation, snipping, and multiplexing of LoRa chirps on commercial IoT devices equipped with low-power sub-GHz transceivers, including those supporting LoRa itself. Chirp snipping consists in artificially removing a sequence of chips and in putting the radio in low-power mode, which allows to reduce energy consumption while still commu-nicating reliably. Chirp multiplexing exploits the gaps introduced by chirp snipping to transmit portions of another chirp on a sep-arate channel, which allows to concurrently transmit two LoRa packets and to increase the throughput. We build EMU as a modu-lar framework and implement support for off-the-shelf LoRa and non-LoRa transceivers. We then evaluate its performance by com-paring the reliability, efficiency, and receiver sensitivity achieved by EMU with that of traditional LoRa for different physical layer settings. We finally showcase EMU's ability to send packets over two channels simultaneously, thereby improving the uplink throughput of LoRaWan, and demonstrate that even non-LoRa transceivers employing EMU can communicate to a LoRaWan gateway, enabling new use cases and expanding the applicability of LoRa technology.

Recently, several datasets shedding light on connectivity aspects in real-world LoRa networks have been provided to the community. However, they typically only involve a limited number of nodes, deal with unidirectional communication only, or focus on very specific physical layer settings. More importantly, existing datasets typically lack fine-grained environmental information such as the temperature in the surroundings of each node, which is known to have a strong impact on communication performance. In this work, we provide the community with a comprehensive dataset that fills all these gaps. We have collected detailed connectivity information in an outdoor LoRa network composed of 21 nodes for more than four months. Our dataset does not only focus on network-level performance (e.g., the average number of correctly-exchanged packets), but sheds light on link-level information such as the received signal strength, signal-to-noise ratio, and the number of available neighbours over time. We further collect environmental information from an online weather site, as well as the on-board temperature of each node in the network, which varies considerably across the deployed locations. We collect all this information while perpetually changing physical layer settings such as the spreading factor and the RF channel. A preliminary analysis of our dataset, which is available in Zenodo1, reveals that temperature has a significant correlation with the link quality and connectivity in the outdoor LoRa network, confirming the findings of earlier studies.

In this study, the authors present a novel channel estimation algorithm for three‐hop multi‐input multi‐output (MIMO) relay systems using the PARATUCK2 tensor model. At the destination, the proposed algorithm exploits a unified formulation of the received signal as a PARATUCK2 model, and jointly estimates all of the channel matrices involved in the communication. Compared with existing algorithms, the proposed algorithm only requires the source to transmit the channel training sequences, does not need relays to perform any task of channel estimation, and yields smaller estimation error. Moreover, the proposed algorithm can be extended to multi‐hop MIMO relay systems with any number of hops. Numerical examples are shown to demonstrate the effectiveness of the PARATUCK2‐based channel estimation algorithm.