Low Power Wide Area Networks (LPWANs) have emerged as a promising technology for facilitating large-scale, cost-effective connections through low-power, long-range communications. Nevertheless, the deployment of existing LPWANs is impeded by severe packet collisions. In this paper, we present OrthoRa, an innovative technology that significantly enhances the concurrency of low-power, long-range LPWAN transmissions. The cornerstone of OrthoRa lies in a groundbreaking design named Orthogonal Scatter Chirp Spreading Spectrum (OSCSS), which facilitates orthogonal packet transmissions while ensuring low signal-to-noise ratio (SNR) communication within LPWANs. Utilizing OrthoRa, different nodes can transmit packets encoded with unique orthogonal scatter chirps, enabling the receiver to decode collided packets from various nodes. We provide a theoretical validation of OrthoRa, demonstrating its capacity for high concurrency in low SNR communication. To surmount practical challenges inherent in real network deployments, we address the detection of multiple packets in collisions, the identification of scatter chirps for each packet’s decoding, and the precise synchronization of packets under Carrier Frequency Offset. We implemented OrthoRa on the HackRF One platform and conducted extensive performance evaluations. The results corroborate that OrthoRa amplifies network throughput and concurrency by a factor of 50 compared to LoRa and significantly outstripping the state-of-the-art in terms of robustness against collision time offset.

LoRa, as a representative Lower Power Wide Area Network (LPWAN) technology, shows great potential in providing low power and long range wireless communication. Real LoRa deployments, however, suffer from severe collisions. Existing collision decoding methods cannot work well for low SNR LoRa signals. Most LoRa collision decoding methods process collisions offline and cannot support real-time collision decoding in practice. To address these problems, we propose Pyramid, a real-time LoRa collision decoding approach. To the best of our knowledge, this is the first real-time multi-packet LoRa collision decoding approach in low SNR. Pyramid exploits the subtle packet offset to separate packets in a collision. The core of Pyramid is to combine signals in multiple windows and transfers variation of chirp length in multiple windows to robust features in the frequency domain that are resistant to noise. We address practical challenges including accurate peak recovery and feature extraction in low SNR signals of collided packets. We theoretically prove that Pyramid incurs a very small SNR loss (< 0.56 dB) to original LoRa transmissions. We implement Pyramid using USRP N210 and evaluate its performance in a 20-nodes network. Evaluation results show that Pyramid achieves real-time collision decoding and improves the throughput by 2.11×.

Low Power Wide Area Networks (LPWANs) have been shown promising in connecting large-scale low-cost devices with low-power long-distance communication. However, existing LPWANs cannot work well for real deployments due to severe packet collisions. We propose OrthoRa, a new technology which significantly improves the concurrency for low-power long-distance LPWAN transmission. The key of OrthoRa is a novel design, Orthogonal Scatter Chirp Spreading Spectrum (OSCSS), which enables orthogonal packet transmissions while providing low SNR communication in LPWANs. Different nodes can send packets encoded with different orthogonal scatter chirps, and the receiver can decode collided packets from different nodes. We theoretically prove that OrthoRa provides very high concurrency for low SNR communication under different scenarios. For real networks, we address practical challenges of multiple-packet detection for collided packets, scatter chirp identification for decoding each packet and accurate packet synchronization with Carrier Frequency Offset. We implement OrthoRa on HackRF One and extensively evaluate its performance. The evaluation results show that OrthoRa improves the network throughput and concurrency by 50× compared with LoRa.

LoRa, as a representative Low-Power Wide Area Network (LPWAN) technology, has emerged as a promising platform for connecting the Internet of Things (IoTs). It enables low-rate communications over upto tens of kilometers with a 10-year battery lifetime. However, practical LoRa deployments suffer from collisions, given the dense deployment of devices and the wide coverage area. We propose CoLoRa, an approach to decompose large numbers of concurrent transmissions from one collision and enable multi-packet reception in LoRa networks. At the heart of CoLoRa, we utilize the packet time offset to disentangle collided packets. CoLoRa incorporates several novel techniques to address practical challenges. (1) We translate time offset, which is difficult to measure, to frequency features that can be reliably measured. (2) We propose a method to extract peak features from low-SNR LoRa signals iteratively. (3) We address frequency shift incurred by carrier frequency offset and time offset for LoRa decoding. We implement CoLoRa on USRP N210 and evaluate its performance in both indoor and outdoor networks. CoLoRa is implemented in software at the base station, and it can work for COTS LoRa nodes. The evaluations show that CoLoRa improves the network throughput by 3.4× compared with Choir and 14× compared with LoRaWAN.

Low Power Wide Area Networks (LPWAN) have become one of the key techniques to provide long-range, low-power communication for large-scale devices in the Internet of Things. However, LPWAN devices in real deployments (e.g., in buildings and basements) suffer from low-quality links due to signal attenuation, leading to coverage holes and significant deployment overhead. In this work, we propose Ostinato to enable communication for weak links and to enhance the coverage for real deployments of COTS LoRa. The key idea of Ostinato is to transform the original packet to a pseudo packet with repeated symbols and to concentrate the energy of multiple symbols to enhance the signal SNR. To address practical challenges, we reverse engineer the entire coding and modulation process of LoRa and propose a method to generate repeated symbols on COTS LoRa by manipulating input data bits. Thus, Ostinato can be directly used for widely deployed LoRa nodes without hardware modification. We achieve weak packet detection, synchronization, and effective decoding on the receiver side by concentrating energy from multiple symbols with phase offsets. We implement Ostinato on Software Defined Radio (SDR) platform and extensively evaluate its performance. The evaluation results show that Ostinato achieves an 8.5 dB gain on receiving sensitivity and 2.88× gain on the coverage compared with COTS LoRa.

LoRa, as a representative of Low Power Wide Area Network technology, has attracted significant attention from both academia and industry. However, the current understanding of LoRa is far from complete, and implementations have a large performance gap in SNR and packet reception rate. This article presents a comprehensive understanding of LoRa physical layer protocol (PHY) and reveals the fundamental reasons for the performance gap. We present the first full-stack LoRa PHY implementation with a provable performance guarantee. We enhance the demodulation to work under extremely low SNR (-20 dB) and analytically validate the performance, where many existing works require SNR > 0. We derive the order and parameters of decoding operations, including dewhitening, error correction, deinterleaving, and so on, by leveraging LoRa features and packet manipulation. We implement a complete real-time LoRa on the GNU Radio platform and conduct extensive experiments. Our method can achieve (1) a 100% decoding success rate while existing methods can support at most 66.7%, (2) -142 dBm sensitivity, which is the limiting sensitivity of the commodity LoRa, and (3) a 3,600-m communication range in the urban area, even better than commodity LoRa under the same setting.

Low-Power Wide-Area Networks (LPWANs) have been shown as a promising technique to provide long-range low-power communication for large-scale IoT devices. In this paper, however, we show the poor performance of LoRa network due to its link diversity in macro- and micro- scope through one-month measurements in an area of$2.2\ km\times 1.5\ km$. We present FerryLink, which exploits such link diversity and leverages peer nodes to ferry data of weak links, to combat performance degradation. Traditional arts (e.g., building multi-hop networks) are inefficient or too heavyweight for the current star-topology-based LoRa network. FerryLink thus proposes a novel ferry mechanism combining RSSI sampling and Channel Activity detection(CAD) to suit multiple orthogonal transmission parameters of LoRa. To reduce energy overhead, FerryLink leverages convention windows for coarse-grained transmission synchronization between two coupled nodes. Finally, FerryLink utilizes the orthogonality of uplink and downlink signals to avoid data redundancy due to the ferry mechanism, maintaining comparable capacity with original LPWANs. We build FerryLink on top of LoRaWANwith commercial off-the-shelf hardware. The extensive evaluation results show that FerryLink effectively improves the packet delivery rate (PDR) of LoRa nodes (to over 95%), achieves 2x less energy overhead, and increases communication range by 50% compared with the original LoRaWAN.

LoRa, as a representative Lower Power Wide Area Network (LPWAN) technology, shows great potential in providing low power and long range wireless communication. Real LoRa deployments, however, suffer from severe collisions. Existing collision decoding methods cannot work well for low SNR LoRa signals. Most LoRa collision decoding methods process collisions offline and cannot support real-time collision decoding in practice. To address these problems, we propose Pyramid, a real-time LoRa collision decoding approach. To the best of our knowledge, this is the first real-time multi-packet LoRa collision decoding approach in low SNR. Pyramid exploits the subtle packet offset to separate packets in a collision. The core of Pyramid is to combine signals in multiple windows and transfers variation of chirp length in multiple windows to robust features in the frequency domain that are resistant to noise. We address practical challenges including accurate peak recovery and feature extraction in low SNR signals of collided packets. We theoretically prove that Pyramid incurs a very small SNR loss (<; 0.56 dB) to original LoRa transmissions. We implement Pyramid using USRP N210 and evaluate its performance in a 20-nodes network. Evaluation results show that Pyramid achieves real-time collision decoding and improves the throughput by 2.11 ×.

LoRa backscatter is a promising technology to achieve low-power and long-distance communication for connecting millions of devices in the Internet of Things. We present P2LoRa, the first ambient LoRa backscatter system with parallel decoding and long-range communication. The high level idea of P2LoRa is to modulate data by shifting ambient LoRa packets with a small frequency. To achieve long distance communication, we enhance the SNR of the backscatter signal by concentrating leaked energy in both the frequency domain and time domain. We propose a method to accurately reconstruct and cancel the in-band excitation signal, which is orders of magnitude higher than the backscatter signal. For parallel decoding, we propose a method to cancel inter-tag interference with very low overhead and address the signal misalignment problem due to different time of flight. We prototype the P2LoRa tag with customized low-cost hardware and implement the P2LoRa gateway on USRP. Through extensive evaluations, we show that P2LoRa achieves a long communication distance of 2.2 km with ambient LoRa, and supports 101 parallel tag transmissions.

LoRa, more generically Low-Power Wide Area Network (LPWAN), is a promising platform to connect Internet of Things. It enables low-cost low-power communication at a few kbps over upto tens of kilometers with a 10-year battery lifetime. However, practical LPWAN deployments suffer from collisions, given the dense deployment of devices and wide coverage area. We propose CoLoRa, a protocol to decompose large numbers of concurrent transmissions from one collision in LoRa networks. At the heart of CoLoRa, we utilize packet time offset to disentangle collided packets. CoLoRa incorporates several novel techniques to address practical challenges. (1) We translate time offset, which is difficult to measure, to frequency features that can be reliably measured. (2) We propose a method to cancel inter-packet interference and extract accurate feature from low SNR LoRa signal. (3) We address frequency shift incurred by CFO and time offset for LoRa decoding. We implement CoLoRa on USRP N210 and evaluate its performance in both indoor and outdoor networks. CoLoRa is implemented in software at the base station and it can work for COTS LoRa nodes. The evaluation results show that CoLoRa improves the network throughput by 3.4× compared with Choir and by 14× compared with LoRaWAN.

LoRa is recently a rising star in Low Power Wide Area Network (LPWAN) family to provide low power and long range communication for large number of devices in Internet of Things. LoRa is based on Chirp Spread Spectrum (CSS) and uses chirp frequency shift to encode data. It has been shown that collision significantly degrades LoRa performance in practice. We propose FlipLoRa, a new mechanism to disentangle LoRa collisions, which allows concurrent transmission of multiple packets. The key idea of FlipLoRa is to utilize the quasi-orthogonality between upchirp and downchirp. FlipLoRa encodes packets with interleaved upchirps and downchirps instead of only using upchirps as in LoRa. We then propose a novel method to disentangle chirps and decode multiple collided packets. To evaluate the performance, we formally prove the quasi-orthogonality and analyze its applicable conditions. We validate the performance improvement by theoretical analysis. Further, we implement FlipLoRa on software-defined radio and extensively evaluate its performance for real LoRa networks. The evaluation results show that FlipLoRa can improve the throughput by 3.84x over LoRa physical layer.

LoRa has been shown as a promising technology for connecting millions for devices in the era of the Internet of Things. LoRa can provide long-distance (up to several kilometers) and low power communication with a modest data rate. However, developing LoRa protocols and testing LoRa systems face practical challenges compared with traditional networks such as Wi-Fi. For example, it requires network deployment in a wide area and even frequently updating the node program in the network for testing. To address those challenges, this paper presents Dandelion, an online testbed for LoRa development. Dandelion provides an easy-to-use interface for developing and testing LoRa applications. Dandelion consists of multiple LoRa nodes, gateways, central controller and web user interfaces. In Dandelion, LoRa nodes are deployed in different environments, each attached to a Raspberry Pi based edge node with out-of-band communication capability to collect system information. Dandelion also provides web based user interface to conveniently program each node, view the status of each node, update the program, etc. This significantly reduces the overhead for LoRa network development. We believe Dandelion can significantly reduce the costs of deployment, maintenance and evaluation of LoRa network. We show two examples of using Dandelion for LoRa network deployment. First, we measure packet reception rate of the LoRa packet with different parameters. Second, we do an RSSI-distance measurement to test and verify existing RSSI-distance LoRa model based on Dandelion.

The wireless information-theoretic security from inter-session interference has attracted considerable attention recently. A prerequisite for available works is the precise distinction between legitimate nodes and eavesdroppers. However, the authentication error always exists in the node authentication process in Two-Hop wireless networks. This paper presents an eavesdropper model with authentication error and two eavesdropping ways. Then, the number of eavesdroppers can be tolerated is analyzed while the desired secrecy is achieved with high probability in the limit of a large number of relay nodes. Final, we draw two conclusions for authentication error: 1) the impersonate nodes are chosen as relay is the dominant factor of the transmitted message leakage, and the impersonation attack does seriously decrease the number of eavesdroppers can be tolerated. 2) The error authentication to legitimate nodes is almost no effect on the number of eavesdroppers can be tolerated.