With the explosive increase in wireless devices, enabling sensing between incompatible radios has become critically beneficial. Integrating diverse IoT devices enhances sensing accuracy by providing richer data, while utilizing the diverse characteristics of heterogeneous signals meets sensing needs in complex environments. However, most existing wireless sensing methods primarily focus on homogeneous signals, while research on sensing with heterogeneous signals is still in its infancy. In this paper, we proposeCrossSense, a novel Cross-Technology Sensing (CTS) framework that enables sensing between incompatible WiFi and LoRa device.CrossSenserecovers the fine-grained trajectory of a WiFi transmitter based on its emulated LoRa signals. To decompose the motion feature components of WiFi transmitter, we develop a chirp difference vector model that utilizes the energy peak within each chirp window for sensing. We model the relationship between sampling frequency offsets and oscillation frequency offsets among heterogeneous devices to guide the extraction of motion features from the emulated signal. We also propose a greedy-based peak enhancement method to calculate the optimized LoRa phases, minimizing the impact of phase discontinuity caused by cyclic prefix (CP) errors. We implement a prototype ofCrossSenseon the USRP platform. The extensive experiments demonstrate thatCrossSensecan achieve an efficient Cross-Technology Sensing with$2.92cm$distance accuracy and$0.26cm/s$speed accuracy over a$120m$sensing range.

LoRa is widely deployed for various applications. Though the knowledge of the channel occupancy is the prerequisite of many aspects of network management, acquiring the channel occupancy for LoRa is challenging due to the large number of possible channels. In this paper, we propose${\sf LoRadar}$, a novel LoRa channel occupancy acquirer based on cross-channel scanning. Our in-depth study finds that Channel Activity Detection (CAD) in a narrow band can indicate the channel activities of wide bands because they have the same slope in the time-frequency domain. Based on this finding, we design a cross-channel scanning mechanism that infers the channel occupancy states of all the overlapping channels by the distribution of CAD results. We elaborately select and adjust the CAD settings to enhance the distribution features and design a pattern correction method to cope with distribution distortions. We also design a CAD scheduler to deal with the low duty-cycle LoRa operations. We implement${\sf LoRadar}$on commercial LoRa platforms and evaluate its performance in the indoor testbed and two outdoor deployed networks. The experimental results show that${\sf LoRadar}$can achieve a detection accuracy of 0.99 and reduce the acquisition overhead by up to 90%, compared to the traversal-based methods.

Unlike existing studies that focus on intra-channel interference, in this paper, we reveal cross-channel interference when collided chirps with different bandwidths have the same slope in the time-frequency domain. Existing methods are inefficient in resolving this type of interference because the demodulation features they use rely on accurate time-domain distributions including the start and end time of all chirps, which is unavailable for uncompleted chirps within the limited receiving bandwidth. We propose SO-LoRa which utilizes the difference in collided chirps' time-domain distributions to identify the target chirp under interference. SO-LoRa adopts self-dechirp operation that maps the chirp's time-domain distribution to recognizable amplitude change of energy peaks that reflect the difference. However, for real received chirps, the amplitude change is unreliable due to the random phase drift and amplified channel noise. So we propose a phase correction method that uses the model between phase difference and the signal energy. We also design time-domain filtering that suppresses noise before self-dechirp. Finally, to avoid extra false energy peaks generated by self-dechirp confusing the demodulation, we separate chirps which cause peak overlapping into different groups and individ-ually perform self-dechirp. The experiments show that SO-LoRa reduces the Symbol Error Rate (SER) by up to 88.6 % compared with state-of-the-art methods.

In this paper, we reveal the existence of partially overlapping interference (POI) when multiple devices concurrently transmit in partially overlapping channels. Existing methods proposed for collisions in the same channel cannot achieve online decoding for target packets under POI due to unpredictable in-window distribution of interfering chirp. We instead propose PrLoRa, a novel method to achieve online LoRa decoding under POI. PrLoRa relies on the insight that only the target chirp is complete in the decoding window. Then PrLoRa adopts a novel operation named phase rotation which converts the difference in chirp's integrity to the amplitude change of energy peak after dechirp and Fast Fourier Transform (FFT). For energy peaks generated by the target chirps, their amplitude change is expected. To use phase rotation in decoding the target chirp, we first establish the theoretical model between amplitude changing and phase rotation, which can be used to infer the expected amplitude change of the target peak. In practice, the peak's amplitude suffers from the influence of channel noise, which causes decoding errors. So, we also propose a noise-aware window setting that can adaptively select the suitable window size for phase rotation according to channel noise. Furthermore, we propose the iterative phase rotation to cope with decoding errors caused by interfering chirps with confusing distributions. The Experimental results show that PrLoRa can reduce the SER by up to 0.92 compared with existing state-of-the-art methods.

Recent work proposes Cross-Technology Communication (CTC) from IEEE 802.11b to LoRa but has a low efficiency due to the extremely asymmetric data rates. In this paper, we propose that emulates LoRa waveform with IEEE 802.11ax. By taking advantage of the OFDMA in 802.11ax, uses only a small Resource Unit (RU) to emulate LoRa chirps and sets other RUs free for high-rate WiFi users. carefully selects the RU and adopts WiFi frame aggregation to emulate the long LoRa frame. We propose a subframe header mapping method to identify and remove invalid symbols caused by irremovable subframe headers in the aggregated frame. We also propose a mode flipping method to solve Cyclic Prefix (CP) errors, based on our finding that different CP modes have different impacts on the LoRa symbol. To cope with channel dynamics, we design an adaptation mechanism to maximize the goodput with a satisfying SER. We further extend to one-to-many transmission scenario by concurrently emulating LoRa chirps in different RUs. We implement a prototype of on the USRP platform and commodity LoRa device. Experiments demonstrate can efficiently transmit complete LoRa frames with the throughput of 40.037kbps and the SER lower than 0.1.

Long Range-Frequency Hopping Spread Spectrum (LR-FHSS) is a novel wireless communication technology to improve the coverage of Low-Power Wide-Area Network (LP-WAN). But our measurement finds that given the same set of sub-channels, different Frequency Hopping Sequence (FHS) can result in a reliability difference of up to 52.6 % in terms of Packet Reception Rate (PRR). The key observation indicates that the reliability of LR-FHSS is significantly influenced by the FHS besides the link quality. Hence, in this paper, we propose DFH that takes both link quality and FHS into consideration to improve the reliability of LR-FHSS. We first propose using the hop Signal-to-noise Ratio (SNR), a new indicator to reflect the quality of the sub-channels and establish the PRR prediction model according to hop SNR and FHS. Based on the model, we design an interleaving-based search algorithm to decide the optimal FHS. We implement and evaluate DFH on the commercial transceivers and SDR-based gateway. The results of experiments in real environments show that DFH can improve the PRR by up to 2.76x, compared to the standard LR-FHSS.

Existing LoRa only supports the concurrency of orthogonal channels but ignores the large number of non-orthogonal channel concurrency opportunities. In this paper, we propose Mc-LoRa that enables LoRa concurrency for non-orthogonal overlapping channels by solving cross-channel collision that happens when chirps with different bandwidths have the same slope in time-frequency domain. Existing single-channel concurrency methods fail to resolve this new collision because the deterministic symbol offset is invalid anymore due to the asymmetric symbol duration. But we find that when wiping a part of collided signals, the amplitude change of target chirp that aligns with the decoding window is predictable, while the collided chirps experience different changes. We accordingly regard the amplitude change ratio before and after wiping as a new decoding feature. We propose a wiper selection method based on our theoretical model to obtain robust features. We also design noise-aware wiper searching and grouping mechanisms to balance the feature accuracy and computing overhead. The experiments show that Mc-LoRa efficiently decodes packets in non-orthogonal overlapping channels and improves the network throughput by up to 3.4× under cross-channel collision, compared with the state-of-the-art single-channel concurrency methods.

Cross-Technology Communication (CTC) is an emerging technique that enables direct interconnection among incompatible wireless technologies. Recent work proposes CTC from IEEE 802.11b to LoRa but has a low efficiency due to their extremely asymmetric data rates. In this paper, we propose WiRa that emulates LoRa waveforms with IEEE 802.11ax to achieve an efficient CTC from WiFi to LoRa. By taking advantage of the OFDMA in 802.11ax, WiRa can use only a small Resource Unit (RU) to emulate LoRa chirps and set other RUs free for high-rate WiFi users. WiRa carefully selects the RU to avoid emulation failures and adopts WiFi frame aggregation to emulate the long LoRa frame. We propose a subframe header mapping method to identify and remove invalid symbols caused by irremovable subframe headers in the aggregated frame. We also propose a mode flipping method to solve Cyclic Prefix errors, based on our finding that different CP modes have different and even opposite impacts on the emulation of a specific LoRa symbol. We implement a prototype of WiRa on the USRP platform and commodity LoRa device. The extensive experiments demonstrate WiRa can efficiently transmit complete LoRa frames with the throughput of 40.037kbps and the symbol error rate (SER) lower than 0.1.

LoRa is widely deployed for various applications. Though the knowledge of the channel occupancy is the prerequisite of all aspects of network management, acquiring the channel occupancy for LoRa is challenging due to the large number of channels to be detected. In this paper, we propose LoRadar, a novel LoRa channel occupancy acquirer based on cross-channel scanning. Our in-depth study finds that Channel Activity Detection (CAD) in a narrow band can indicate the channel activities of wide bands because they have the same slope in the time-frequency domain. Based on our finding, we design the cross-channel scanning mechanism that infers the channel occupancy states of all the overlapping channels by the distribution of CAD results. We elaborately select and adjust the CAD settings to enhance the distribution features. We also design the pattern correction method to cope with distribution distortions. We implement LoRadar on commodity LoRa platforms and evaluate its performance on the indoor testbed and the outdoor deployed network. The experimental results show that LoRadar can achieve a detection accuracy of 0.99 and reduce the acquisition overhead by up to 0.90, compared to existing traversal-based methods.

Cross-Technology Communication (CTC) is an emerging technique that enables the direct communication across incompatible wireless technologies. Without modifying any hardware, CTC establishes mutually sensible side channel by manipulating the packet transmissions and encodes information by constructing transmission patterns in terms of signal strength, packet interval, and etc. However, the transmission patterns are prone to the coexisting interference, leading to the unreliability of CTC. Most of the existing methods deal with the reliability problem by reactive retransmission of the corrupted packets, which incurs large delay. In this paper, we propose RCTC, a rateless-coding based CTC that proactively copes with the unreliability. Since the computation ability of low-power ZigBee nodes is limited, we carefully design the coding combination with proper degree distribution to balance the trade-off between reliability and decoding latency. We also propose a coding adaptation algorithm to adapt to the channel dynamics. We implement a prototype on commercial WiFi and ZigBee platforms. The experiment results show that RCTC can reduce the BER by up to 92.6%, compared to existing CTC methods.