LEO satellite networks are emerging as a global-scale connectivity infrastructure for regions beyond the reach of terrestrial networks. Among them, satellite IoT targets low-power, low-cost IoT applications; however, our real-world measurements reveal that today's commercial satellite IoT still faces substantial challenges in supporting large-volume data transfer for real-world IoT applications. Our results show that a highly-compressed image of only tens of kilobytes typically takes 6–10 hours, extremely exceeding the application time requirements. We find that the bottleneck lies in the direct-to-satellite upload stage, where usable contacts are scarce and underutilized. Moreover, simply adding more nodes does not provide proportional gains, as beacon-triggered upload opportunities remain isolated and exhibit weak correlation. Based on this observation, we propose Co-DtS, a multi-interface upload system that promotes observed beacons into cross-interface coordination signals. Trace-driven evaluation with commercial devices shows that Co-DtS reduces image completion time from 7.62 hours to 0.9 hours.

The Open Radio Access Network (O-RAN) promises unprecedented flexibility through its reconfigurable architecture and AI-driven control. However, this agility exposes a critical fragility: AI models trained on one network configuration suffer significant performance degradation after an upgrade due to dramatic data drift. The standard solution, reactive retraining, is unacceptably slow, leaving the network in a suboptimal state for tens of minutes and undermining the core benefits of O-RAN's dynamism. This paper introduces RANPilot, the first framework to address this challenge through proactive AI adaptation. RANPilot constructs a lightweight "virtual O-RAN" (a trace-driven emulator) to synthesize high-fidelity training data representing the post-reconfiguration state before the physical change occurs, allowing AI models to be adapted in advance. Extensive experiments on a real-world 5G testbed demonstrate that RANPilot achieves near interruption-free AI services upon reconfiguration, reducing AI downtime by 85% to 94% against reactive baselines. By shifting the AI evolution paradigm from reactive redevelopment to proactive preparation, RANPilot explores a digital-leadoff approach to enable robust AI in reconfigurable O-RAN deployments.

LoRa, a leading wireless technology for Low Power Wide Area Networks (LPWAN), is well-known for its long transmission range and low power consumption. The extended range is primarily attributed to the Chirp Spread Spectrum technique. However, the LoRa physical layer (LoRa PHY) contributes only marginally to this advantage, as it employs an inefficient Forward Error Correction (FEC) strategy for error recovery. In this paper, we introduce Sym-FEC, a symbol-level FEC decoder designed to link the received signals' spectrum with the coding correlations inherent in LoRa PHY, thereby enhancing error recovery. The key enabler of Sym-FEC is signal copy retrieval. We begin by facilitating signal copy conversion between two symbols and extend this to the general case, where signal copy conversions can be performed between any symbols in a coding block. Approaches are also introduced to assess the validity of the block-wide decoding results. Extensive hardware evaluations demonstrate that Sym-FEC provides Signal-to-Noise-Ratio (SNR) improvement of 2.3dB to 3dB compared to the traditional decoder in LoRa PHY. Sym-FEC requires no modifications at the transmitter while incurs low storage and computational complexity at the gateway, thus can be easily integrated into gateway nodes.

Physical-layer key generation holds significant potential in establishing cryptographic key pairs for emerging LoRa networks. Nevertheless, current key generation solutions may underperform due to critically impaired channel reciprocity, attributed to the low data rate and long range inherent in LoRa networks. In this study, we presentChirpKey, a novel key generation scheme for LoRa networks. We pinpoint the key hurdles as the coarse-grained channel measurement, inefficient quantization methods, and out-of-range device constraints. To capture fine-grained channel information, we introduce a unique, LoRa-specific channel measurement method that focuses on analyzing chirp-level variations in LoRa packets. We also propose a LoRa channel state estimation algorithm to neutralize asynchronous channel sampling. Instead of the traditional quantization approach, we propose an innovative key delivery method based on perturbed compressed sensing, offering enhanced robustness and security. For LoRa devices beyond each other's communication reach, we integrate relay nodes to ensure reliable key generation. To foster secure group communication, we formulate two protocols that facilitate collaborative key generation across both star and chain configurations. Evaluation across diverse real-world scenarios reveals thatChirpKeyenhances the key matching rate by 11.03–26.58% and increases the key generation rate by 27–49× in comparison to existing leading systems. Our security analysis shows thatChirpKeycan effectively withstand a variety of prevalent attacks. Furthermore, we implement aChirpKeyprototype, demonstrating its capability to operate within 0.2 s.

Low Earth Orbit (LEO) satellites have emerged as a space-based infrastructure to offer networking services anywhere on Earth. Satellite IoTs enable novel Direct-to-Satellite (DtS) connectivity, allowing IoT devices in remote areas to connect to the Internet via LEO satellites using existing terrestrial technologies like LoRa. This paper presents the first-of-its-kind measurement study on satellite IoTs, investigating the practical characteristics of DtS communications and their suitability for IoT applications. We deployed 27 low-cost ground stations across eight locations worldwide to passively measure the network availability of multiple constellations. Our findings reveal a significant gap between the effective durations of DtS connectivity and their theoretical durations, leading to intermittent connections for satellite IoTs. Additionally, we examine the performance of the Tianqi constellation in supporting real-world IoT traffic (agriculture application). We observed longer delays and higher power consumption in satellite IoTs compared to terrestrial IoTs. Our study identifies the bottlenecks and sheds light on potential optimizations for satellite IoTs.

This paper addresses the interference challenges, aiming to improve spatial reuse and optimize spectrum efficiency in LPWANs. We reveal that existing strategies such as interference cancellation and MIMO are ill-suited to the low-cost low-rate characteristics of LPWANs. Our work introduces a novel framework, HydraNet, which leverages the capture effect of LPWAN radios to enable robust concurrent transmissions. HydraNet exempts from strict clock synchronization or accurate channel estimation as required by conventional spatial reuse strategies for interference nulling. We conduct in-depth studies with LoRa radios to uncover their underlying packet reception mechanisms and for the first time characterize their unique capture effect. Based on the new findings, we devise novel strategies to jointly control the timing and power of concurrent LPWAN transmissions. These strategies ensure sufficient power differences between packets and interference at their intended receivers. We prototype HydraNet and integrate with operational LoRaWANs and comprehensively evaluate its performance. Results show that HydraNet achieves higher spectrum utilization with up to 3.6 × throughput improvements over the state-of-the-art.

LoRaWANs are envisioned to connect billions of IoT devices through thousands of physically overlapping yet logically orthogonal channels (termed logical channels). These logical channels hold significant potential for enabling highly concurrent scalable IoT connectivity. Large-scale deployments however face strong interference between logical channels. This practical issue has been largely overlooked by existing works but becomes increasingly prominent as LoRaWAN scales up. To address this issue, we introduce Canas, an innovative gateway design that is poised to orthogonalize the logical channels by eliminating mutual interference. To this end, Canas develops a series of novel solutions to accurately extract the meta-information of individual ultra-weak LoRa signals from the received overlapping channels. The meta-information is then leveraged to accurately reconstruct and subtract the LoRa signals over thousands of logical channels iteratively. Real-world evaluations demonstrate that Canas can enhance concurrent transmissions across overlapping logical channels by 2.3× compared to the best known related works.

LoRa technology promises to enable Internet of Things applications over large geographical areas. However, its performance is often hampered by poor channel quality in urban environments, where blockage and multipath effects are prevalent. Our study uncovers that a slight shift in the position or attitude of the receiving antenna can substantially improve the received signal quality. This phenomenon can be attributed to the rich multipath characteristics of wireless signal propagation in urban environments, wherein even small antenna movement can alter the dominant signal path or reduce the polarization angular difference between transceivers. Leveraging these key observations, we propose and implement MoLoRa, an intelligent mobile antenna system designed to enhance LoRa packet reception. At its core, MoLoRa represents the position and attitude of an antenna as a state and employs a statistical optimization method to search for states that offer optimal signal quality efficiently. Through extensive evaluation, we demonstrate that MoLoRa achieves a maximum Signal-to-Noise Ratio (SNR) gain of 13 dB in a few attempts, enabling formerly problematic blind spots to reconnect and strengthening links for other nodes.

LoRaWAN offers a compelling solution for delivering cost-effective network access to millions of IoT devices worldwide. However, operators face challenges in scaling their services to meet the growing demands of IoT connections. Moreover, current LoRaWANs foster competition rather than cooperation among coexisting networks, resulting in substantial capacity degradation as network density increases. To identify the root causes limiting LoRaWAN scalability and to enable harmonious coexistence among network operators, this paper conducts an in-depth investigation of operational LoRaWANs. For the first time, our study reveals that the capacity degradation in LoRaWAN is not due to traditionally believed issues (such as wireless contention or interference) but rather a newly-identified decoder contention problem. This problem cannot be resolved using conventional approaches and hinders the scaled deployment of LoRaWANs as a global IoT infrastructure. Based on our new findings, we propose design principles that guide our exploration for effective strategies to address this emerging practical problem. We develop concrete deployable solutions to mitigate contention, optimize spectrum utilization, and promote spectrum sharing among network operators. Extensive evaluations demonstrate that our strategies effectively boost network capacity close to the theoretical bound, and support the coexistence of up to six networks with significant improvement in spectrum efficiency.

LoRaWAN is a prominent technology for Low Power Wide Area Networks (LPWAN). However, the increasing network size has introduced a significant challenge: packet collisions resulting from concurrent transmissions in LoRaWAN. Previous studies either overlooked the issue by examining limited features or tackled it with intricate receivers employing up to eight antennas. To achieve a more favorable balance between implementation cost and system performance, we introduce$\text{Hi}^{2}\text{LoRa}$—a solution utilizing highly dimensional and accurate features for LoRa concurrent decoding, implemented with only two receiving antennas. The feature dimensions are expanded through an exploration of various hardware imperfections and inherent channel state information specific to each transceiver pair. To enhance feature accuracy, low pass filters and BiLSTM networks are applied to capture and learn their temporal patterns. Additionally, an efficient collision suppression strategy is introduced to mitigate feature corruption from concurrently transmitted packets. Extensive real-world testbed evaluations demonstrate that the achievable concurrency in$\text{Hi}^{2}\text{LoRa}$approaches that of state-of-the-art approaches with significantly higher complexity (e.g., utilizing eight antennas) or exceeds prior work by a factor of 2.7 with comparable complexity (e.g., using two antennas).

Unlike traditional data collection applications which primarily rely on uplink transmissions, emerging applications (e.g., device actuation, firmware update, packet reception acknowledgment) increasingly demand robust downlink transmission capabilities. Current LoRaWAN systems struggle to support these applications due to the inherent asymmetry between downlink and uplink capabilities. While uplink transmissions can handle multiple packets simultaneously, downlink transmissions are restricted to a single logical channel at a time, significantly limiting the deployment of applications that require substantial downlink capacity. To address this challenge,FDLoRaintroduces an innovative in-band full-duplex LoRa gateway design, featuring novel solutions to mitigate self-interference (i.e., the strong downlink interference to ultra-weak uplink reception). This approach enables full-spectrum in-band downlink transmissions without compromising the reception of weak uplink packets. Building on the capabilities of full-duplex gateways,FDLoRapresents a new downlink framework that supports concurrent downlink transmissions across multiple logical channels of available gateways. Evaluation results show thatFDLoRaenhances downlink capacity by 5.7× compared to LoRaWAN in a three-gateway testbed and achieves 2.58× higher downlink concurrency per gateway than the current leading solutions.

LoRa is a promising technology that provides widespread low-power IoT connectivity. With its capabilities for multi-channel communication, orthogonal transmission, and spectrum sharing, LoRaWAN is poised to connect millions of IoT devices across thousands of logical channels. However, current LoRa gateways rely on hardwired Rx chains that cover less than 1% of these channels, restricting the potential for large-scale LoRa communications. This paper introduces XGate, a groundbreaking gateway design that uses a single Rx chain to simultaneously receive packets from all logical channels, enabling scalable LoRa transmission and flexible network access. Unlike the hardwired Rx chains in existing gateway designs, XGate dynamically allocates resources, including software-controlled Rx chains and demodulators, based on the extracted meta-information of incoming packets. XGate overcomes several challenges to efficiently detect incoming packets without prior knowledge of their parameter configurations. Evaluations demonstrate that XGate enhances LoRa concurrent transmissions by$8.4\times $compared to state-of-the-art solutions.

LoRa has been widely adopted in Internet of Things (IoT) due to its long distance and low cost. With the largescale deployment of LoRa devices, it is not uncommon that multiple nodes transmit concurrently, leading to packet collisions and degraded performance. Previous studies have focused on examining single or multiple features in the received raw signals, named as chirp-level features, to separate collided packets for parallel decoding. However, the accuracy of feature extraction turns out to be vulnerable to interference, which can lead to incorrect packet decoding as network concurrency increases. Our study reveals that, other than the chirp-level features, a standard LoRa packet encoder introduces coding correlations across symbols of the same packet and thus leaves packet-level features to the symbols that can be utilized to disentangle symbols of collided packets in a new dimension. In this work, we introduce DeepDetangle, a Deep-learning-based feature fusion framework that efficiently fuses both packet-level and chirp-level features of symbols to enhance LoRa parallel decoding. DeepDetangle utilizes Complex-CNN and LSTM structures to capture multidimensional chirp-level features, their joint distributions, and temporal patterns. An MLP is employed to aggregate the chirplevel features with the packet-level features, thereby enabling the decoding of symbols on a per-block basis. By integrating features from both levels, erroneous symbols that cannot be recovered with chirp-level features alone can now be effectively corrected with other valid ones in the same block. Therefore, it demonstrates a strong capability to combat interference from other packets. Extensive evaluations have been conducted to assess the performance of DeepDetangle. The results indicate that DeepDetangle achieves$\mathbf{1 8. 1 \%}$to$\mathbf{8 0. 5 \%}$higher network throughput compared to existing works of similar complexity.

LoRa technology has garnered significant attention for its exceptional performance in city-wide applications. LoRa encodes data across multiple samples to enable long-range communication, with the level of redundancy controlled by the Spreading Factor (SF). However, practical limitations restrict how high the SF can be set. To overcome communication challenges at the highest allowable SF settings, we introduce SF-any, a software-based coding paradigm that extends an SFk packet to a quasi-SF(k + m) packet. SF-any encodes a quasi-SF(k + m) symbol with 2mSFk symbols. Hardware imperfections introduce time-varying frequency drifts and phase offsets, resulting in frequency leakage during quasi-SF(k +m) packet decoding. To mitigate this, we strategically insert pilots into the packet for imperfection estimation and compensation. Additionally, to maintain and exploit the coding structure in LoRa PHY, we employ a grouped repetition code at the transmitter and a joint demodulation and decoding scheme at the receiver. Comprehensive evaluations demonstrate that SF-any’s performance seamlessly scales with increasing SF, achieving up to a 14dB improvement over SF12 packets (the highest SF in LoRa PHY), and up to a 12dB improvement compared with state-of-the-art approaches.

LoRa is a promising technology that offers ubiquitous low-power IoT connectivity. With the features of multi-channel communication, orthogonal transmission, and spectrum sharing, LoRaWAN is poised to connect millions of IoT devices across thousands of logical channels. However, current LoRa gateways utilize hardwired Rx chains that cover only a small fraction (<1%) of the logical channels, limiting the potential for massive LoRa communications. This paper presents XGate, a novel gateway design that uses a single Rx chain to concurrently receive packets from all logical channels, fundamentally enabling scalable LoRa transmission and flexible network access. Unlike hardwired Rx chains in the current gateway design, XGate allocates resources including software-controlled Rx chains and demodulators based on the extracted meta information of incoming packets. XGate addresses a series of challenges to efficiently detect incoming packets without prior knowledge of their parameter configurations. Evaluations show that XGate boosts LoRa concurrent transmissions by 8.4× than state-of-the-art.

Unlike traditional data collection applications (e.g., environment monitoring) that are dominated by uplink transmissions, the newly emerging applications (e.g., device actuation, firmware update, packet reception acknowledgement) also pose ever-increasing demands on downlink transmission capabilities. However, current LoRaWAN falls short in supporting such applications primarily due to downlink-uplink asymmetry. While the uplink can concurrently receive multiple packets, downlink transmission is limited to a single logical channel at a time, which fundamentally hinders the deployment of downlink-hungry applications. To tackle this practical challenge, FDLoRa develops the first-of-its-kind in-band full-duplex LoRa gateway design with novel solutions to mitigate the impact of self-interference (i.e., strong downlink interference to ultra-weak uplink reception), which unleashes the full spectrum for in-band downlink transmissions without compromising the reception of weak uplink packets. Built upon the full-duplex gateways, FDLoRa introduces a new downlink framework to support concurrent downlink transmissions over multiple logical channels of available gateways. Evaluation results demonstrate that FDLoRa boosts downlink capacity by 5.7x compared to LoRaWAN on a three-gateway testbed and achieves 2.58x higher downlink concurrency per gateway than the state-of-the-art.

This paper presents our design and implementation of a fast and accurate data aggregation strategy for LoRa networks namedOne-shot. To facilitate data aggregation, One-shot assigns distinctive chirps for different LoRa nodes to encode individual data. One-shot coordinates the nodes to concurrently transmit encoded packets. Receiving concurrent transmissions, One-shot gateway examines the frequencies of superimposed chirp signals and computes application-defined aggregate functions (e.g., sum, max, count, ), which give a quick overview of sensor data in a large monitoring area. One-shot develops techniques to handle a series of practical challenges involved in frequency and time synchronization of concurrent chirps. We evaluate the effectiveness of One-shot with extensive experiments. Results show that One-shot substantially outperforms state-of-the-art data aggregation methods in terms of aggregation accuracy as well as query efficiency.

This paper presents the design and implementation of HyLink which aims to fill the gap between limited link capacity of LoRa and the diverse bandwidth requirements of IoT systems. At the heart of HyLink is a novel technique named parallel Chirp Spread Spectrum modulation, which tunes the number of modulated symbols to adapt bit-rates according to channel conditions. Over strong link connections, HyLink fully exploits the link capability to transmit more symbols and thus transforms good channel SNRs to high link throughput. While for weak links, it conservatively modulates one symbol and concentrates all transmit power onto the symbol to combat poor channels, which can achieve the same performance as legacy LoRa. HyLink addresses a series of technical challenges on encoding and decoding of multiple payloads in a single packet, aiming at amortizing communication overheads in terms of channel access, radio-on power, transmission air-time, etc. We perform extensive experiments to evaluate the effectiveness of HyLink. Evaluations show that HyLink produces up to$10\times $higher bit rates than LoRa when channel SNRs are higher than$\mathrm {5 dB}$. HyLink inter-operates with legacy LoRa devices and can support new emerging traffic-intensive IoT applications.

LoRa Wide Area Network (LoRaWAN) has emerged as a dominant technology for Low Power Wide Area Networks (LPWAN). However, due to the ever-growing network size, packet collisions caused by concurrent transmissions have become a serious challenge in LoRa Wan.Existing studies have either ignored the issue by exploring only a few inaccurate features or addressed it using a complex receiver with up to eight antennas. To strike a better balance between implementation cost and system performance, we propose Hi2LoRa, which leverages highly dimensional and highly accurate features for LoRa concurrent decoding with only two receiving antennas. The feature dimensions are extended by exploring various types of hardware imperfections and channel state information inherent to each transceiver pair. To improve feature accuracy, low pass filters and BiLSTM networks are employed to trace and learn their temporal patterns. Additionally, an effective collision suppression strategy is introduced to combat feature corruption from other concurrent packets. Extensive evaluations on real-world testbeds show that the achievable concurrency in Hi2LoRa is either close to that of state-of-the-art approaches with much higher complexity (e.g., using eight antennas) or 2.7 x of prior work with comparable complexity (e.g., using two antennas).

This paper presents our design and implementation of a fast and accurate data aggregation strategy for LoRa networks named One-shot. To facilitate data aggregation, One-shot assigns distinctive chirps for different LoRa nodes to encode individual data. One-shot coordinates the nodes to concurrently transmit encoded packets. Receiving concurrent transmissions, One-shot gateway examines the frequencies of superimposed chirp signals and computes application-defined aggregate functions (e.g., sum, max, count, etc.), which give a quick overview of sensor data in a large monitoring area. One-shot develops techniques to handle a series of practical challenges involved in frequency and time synchronization of concurrent chirps. We evaluate the effectiveness of One-shot with extensive experiments. Results show that One-shot substantially outperforms state-of-the-art data aggregation methods in terms of aggregation accuracy as well as query efficiency.

Physical-layer key generation is promising in establishing a pair of cryptographic keys for emerging LoRa networks. However, existing key generation systems may perform poorly since the channel reciprocity is critically impaired due to low data rate and long range in LoRa networks. To bridge this gap, this paper proposes a novel key generation system for LoRa networks, named ChirpKey. We reveal that the underlying limitations are coarse-grained channel measurement and inefficient quantization process. To enable fine-grained channel information, we propose a novel LoRa-specific channel measurement method that essentially analyzes the chirp-level changes in LoRa packets. Additionally, we propose a LoRa channel state estimation algorithm to eliminate the effect of asynchronous channel sampling. Instead of using quantization process, we propose a novel perturbed compressed sensing based key delivery method to achieve a high level of robustness and security. Evaluation in different real-world environments shows that ChirpKey improves the key matching rate by 11.03–26.58% and key generation rate by 27–49× compared with the state-of-the-arts. Security analysis demonstrates that ChirpKey is secure against several common attacks. Moreover, we implement a ChirpKey prototype and demonstrate that it can be executed in 0.2 s.

LoRaWAN suffers dramatic performance degradation over a long communication range due to signal attenuation and blockages. To ensure reliable data transfer, LoRaWAN adopts retransmission mechanism where an unacknowledged packet is retransmitted multiple times in the hope of successfully delivering the packet at least once over harsh wireless channels. This retransmission mechanism is ill-suited for LoRa: 1) unsuccessful retransmissions lead to high power consumption for battery-powered LoRa nodes, and 2) a retransmission at another time does not necessarily improve the signal strength over harsh wireless channels.

This paper describes our design and implementation of a covert channel over LoRa physical layer (PHY). LoRa adopts a unique modulation scheme (chirp spread spectrum (CSS)) to enable long range communication at low-power consumption. CSS uses the initial frequencies of LoRa chirps to differentiate LoRa symbols, while simply ignoring other RF parameters (e.g., amplitude and phase). Our study reveals that the LoRa physical layer leaves sufficient room to build a covert channel by embedding covert information with a modulation scheme orthogonal to CSS. To demonstrate the feasibility of building a covert channel, we implementCloakLoRa.CloakLoRaembeds covert information into a regular LoRa packet by modulating the amplitudes of LoRa chirps while keeping the frequency intact. As amplitude modulation is orthogonal to CSS, a regular LoRa node receives the LoRa packet as if no secret information is embedded into the packet. Such an embedding method is transparent to all security mechanisms at upper layers in current LoRaWAN. As such, an attacker can create an amplitude modulated covert channel over LoRa without being detected by current LoRaWAN security mechanism. We conduct comprehensive evaluations with COTS LoRa nodes and receive-only software defined radios and experiment results show thatCloakLoRacan send covert information over 250 m.

LoRa technology promises to connect billions of battery-powered devices over a long range for years. However, recent studies and industrial deployment find that LoRa suffers severe signal attenuation because of signal blockage in smart cities and long communication ranges in smart agriculture applications. As a result, weak LoRa packets cannot be correctly demodulated or even be detected in practice. To address this problem, this paper presents the design and implementation of MALoRa: a new LoRa reception scheme which aims to improve LoRa reception performance with antenna diversities. At a high level, MALoRa improves signal strength by reliably detecting and coherently combining weak signals received by multiple antennas of a gateway. MALoRa addresses a series of practical challenges, including reliable packet detection, symbol edge extraction, and phase-aligned constructive combining of weak signals. Moreover, MALoRa can also be applied to mobile devices. Experiment results show that MALoRa can effectively expand communication range, increase battery life of LoRa devices, and improve packet detection and demodulation performance especially in ultra-low SNR scenarios.

LoRaWAN forms a one-hop star topology where LoRa nodes send data via one-hop uplink transmission to a LoRa gateway. If the LoRa gateway can be jammed by attackers, it may not be able to receive any data from any nodes in the network. Our empirical study shows that although the LoRa physical layer (PHY) is robust and resilient by design, it is still vulnerable to synchronized jamming chirps. Potential protection solutions (e.g., collision recovery, parallel decoding) may fail to extract LoRa packets if an attacker transmits synchronized jamming chirps at higher power. To protect the LoRa PHY from such attacks, we propose a new protection method that can separate LoRa chirps from jamming chirps by leveraging their difference in power domain. We note that the new protection solution is orthogonal to existing solutions that leverage the chirp misalignment in the time domain or the frequency disparity in the frequency domain. We conduct experiments with COTS LoRa nodes and software-defined radios with varied experiment settings such as different spreading factors, bandwidths, and code rates. Results show that synchronized jamming chirps at high power can jam all previous solutions, whereas our protection solution can effectively protect LoRa gateways from the jamming attacks.

LoRa technology promises to connect billions of battery-powered devices over a long range for years. However, recent studies and industrial deployment find that LoRa suffers severe signal attenuation because of signal blockage in smart cities and long communication ranges in smart agriculture applications. As a result, weak LoRa packets cannot be correctly demodulated or even be detected in practice. To address this problem, this paper presents the design and implementation of MALoRa: a new LoRa reception scheme which aims to improve LoRa reception performance with antenna diversities. At a high level, MALoRa improves signal strength by reliably detecting and coherently combining weak signals received by multiple antennas of a gateway. MALoRa addresses a series of practical challenges, including reliable packet detection, symbol edge extraction, and phase-aligned constructive combining of weak signals. Experiment results show that MALoRa can effectively expand communication range, increase battery life of LoRa devices, and improve packet detection and demodulation performance especially in ultra-low SNR scenarios.

This paper presents the design and implementation of HyLink which aims to fill the gap between limited link capacity of LoRa and the diverse bandwidth requirements of IoT systems. At the heart of HyLink is a novel technique named parallel Chirp Spread Spectrum modulation, which tunes the number of modulated symbols to adapt bit-rates according to channel conditions. Over strong link connections, HyLink fully exploits the link capability to transmit more symbols and thus transforms good channel SNRs to high link throughput. While for weak links, it conservatively modulates one symbol and concentrates all transmit power onto the symbol to combat poor channels, which can achieve the same performance as legacy LoRa. HyLink addresses a series of technical challenges on encoding and decoding of multiple payloads in a single packet, aiming at amortizing communication overheads in terms of channel access, radio-on power, transmission air-time, etc. We perform extensive experiments to evaluate the effectiveness of HyLink. Evaluations show that HyLink produces up to 10× higher bit rates than LoRa when channel SNRs are higher than 5 dB. HyLink inter-operates with legacy LoRa devices and can support new emerging traffic-intensive IoT applications.

This article presents the design and implementation of PCube, a phase-based parallel packet decoder for concurrent transmissions of LoRa nodes. The key enabling technology behind PCube is a novel air-channel phase measurement technique that is able to extract phase differences of air-channels between LoRa nodes and multiple antennas of a gateway. PCube leverages the reception diversities of multiple receiving antennas of a gateway and scales the concurrent transmissions of a large number of LoRa nodes, even exceeding the number of receiving antennas at a gateway. As a phase-based parallel decoder, PCube provides a new dimension to resolve collisions and supports more concurrent transmissions by complementing time and frequency-based parallel decoders. PCube is implemented and evaluated with synchronized software defined radios and off-the-shelf LoRa nodes in both indoors and outdoors. Results demonstrate that PCube can substantially outperform state-of-the-art works in terms of aggregated throughput by 4.9× and the number of concurrent nodes by up to 5×. More importantly, PCube scales well with the number of receiving antennas of a gateway, which is promising to break the barrier of concurrent transmissions.

LoRaWAN forms a one-hop star topology where LoRa nodes send data via one-hop up-link transmission to a LoRa gateway. If the LoRa gateway can be jammed by attackers, the LoRa gateway may not be able to receive any data from any nodes in the network. Our empirical study shows that although LoRa physical layer (PHY) is robust and resilient by design, it is still vulnerable to synchronized jamming chirps. Potential protection solutions (e.g., collision recovery, parallel decoding) may fail to extract LoRa packets if an attacker transmits synchronized jamming chirps at high power. To protect the LoRa PHY from such attacks, we propose a new protection method that can separate LoRa chirps from jamming chirps by leveraging their difference in the received signal strength in power domain. We note that the new protection solution is orthogonal to existing solutions which leverage the chirp misalignment in time domain or the frequency disparity in frequency domain. We conduct experiments with COTS LoRa nodes and software defined radios. The results show that synchronized jamming chirps at high power can jam all previous solutions, while our protection solution can effectively protect LoRa gateways from the jamming attacks.

This paper presents the design and implementation of PCube, a phase-based parallel packet decoder for concurrent transmissions of LoRa nodes. The key enabling technology behind PCube is a novel air-channel phase measurement technique which is able to extract phase differences of air-channels between LoRa nodes and multiple antennas of a gateway. PCube leverages the reception diversities of multiple receiving antennas of a gateway and scales the concurrent transmissions of a large number of LoRa nodes, even exceeding the number of receiving antennas at a gateway. As a phase-based parallel decoder, PCube provides a new dimension to resolve collisions and supports more concurrent transmissions by complementing time and frequency based parallel decoders. PCube is implemented and evaluated with synchronized software defined radios and off-the-shelf LoRa nodes in both indoors and outdoors. Results demonstrate that PCube can substantially outperform state-of-the-art works in terms of aggregated throughput by 4.9× and the number of concurrent nodes by up to 5×. More importantly, PCube scales well with the number of receiving antennas of a gateway, which is promising to break the barrier of concurrent transmissions.

This paper presents LiteNap which improves the energy efficiency of LoRa by enabling LoRa nodes to operate in a downclocked ‘light sleep’ mode for packet reception. A fundamental limit that prevents radio downclocking is the Nyquist sampling theorem which demands the clock-rate being at least twice the bandwidth of LoRa chirps. Our study reveals under-sampled LoRa chirps suffer frequency aliasing and cause ambiguity in symbol demodulation. LiteNap addresses the problem by leveraging an empirical observation that the hardware of LoRa radio can cause phase jitters on modulated chirps, which result in frequency leakage in the time domain. The timing information of phase jitters and frequency leakages can serve as physical fingerprints to uniquely identify modulated chirps. We propose a scheme to reliably extract the fingerprints from under-sampled chirps and resolve ambiguities in symbol demodulation. We update the reception pipeline of LoRa radio to enable reliable packet detection and decoding when operating in downclocked mode. We implement LiteNap on a software defined radio platform and conduct trace-driven evaluation to validate the proposed strategies. Experiment results show that LiteNap can downclock LoRa receiver to sub-Nyquist rates for energy savings (e.g., 1/8 of Nyquist rate), without substantially affecting packet reception performance (e.g., >95% packet reception rate).

This paper presents LiteNap which improves the energy efficiency of LoRa by enabling LoRa nodes to operate in a downclocked `light sleep' mode for packet reception. A fundamental limit that prevents radio downclocking is the Nyquist sampling theorem which demands the clock-rate being at least twice the bandwidth of LoRa chirps. Our study reveals under-sampled LoRa chirps suffer frequency aliasing and cause ambiguity in symbol demodulation. LiteNap addresses the problem by leveraging an empirical observation that the hardware of LoRa radio can cause phase jitters on modulated chirps, which result in frequency leakage in the time domain. The timing information of phase jitters and frequency leakages can serve as physical fingerprints to uniquely identify modulated chirps. We propose a scheme to reliably extract the fingerprints from under-sampled chirps and resolve ambiguities in symbol demodulation. We implement LiteNap on a software defined radio platform and conduct trace-driven evaluation. Experiment results show that LiteNap can downclock LoRa nodes to sub-Nyquist rates for energy savings (e.g., 1/8 of Nyquist rate), without substantially affecting packet reception performance (e.g., >95% packet reception rate).

LoRa has emerged as a promising Low-Power Wide Area Network (LP-WAN) technology to connect a huge number of Internet-of-Things (IoT) devices. The dense deployment and an increasing number of IoT devices lead to intense collisions due to uncoordinated transmissions. However, the current MAC/PHY design of LoRaWAN fails to recover collisions, resulting in degraded performance as the system scales. This article presents FTrack, a novel communication paradigm that enables demodulation of collided LoRa transmissions. FTrack resolves LoRa collisions at the physical layer and thereby supports parallel decoding for LoRa transmissions. We propose a novel technique to separate collided transmissions by jointly considering both the time domain and the frequency domain features. The proposed technique is motivated from two key observations: (1) the symbol edges of the same frame exhibit periodic patterns, while the symbol edges of different frames are usually misaligned in time; (2) the frequency of LoRa signal increases continuously in between the edges of symbol, yet exhibits sudden changes at the symbol edges. We detect the continuity of signal frequency to remove interference and further exploit the time-domain information of symbol edges to recover symbols of all collided frames. We substantially optimize computation-intensive tasks and meet the real-time requirements of parallel LoRa decoding. We implement FTrack on a low-cost software defined radio. Our testbed evaluations show that FTrack demodulates collided LoRa frames with low symbol error rates in diverse SNR conditions. It increases the throughput of LoRaWAN in real usage scenarios by up to 3 times.

LoRa has emerged as a promising Low-Power Wide Area Network (LP-WAN) technology to connect a huge number of Internet-of-Things (IoT) devices. The dense deployment and an increasing number of IoT devices lead to intense collisions due to uncoordinated transmissions. However, the current MAC/PHY design of LoRaWAN fails to recover collisions, resulting in degraded performance as the system scales. This paper presents FTrack, a novel communication paradigm that enables demodulation of collided LoRa transmissions. FTrack resolves LoRa collisions at the physical layer and thereby supports parallel decoding for LoRa transmissions. We propose a novel technique to separate collided transmissions by jointly considering both the time domain and the frequency domain features. The proposed technique is motivated from two key observations: (1) the symbol edges of the same frame exhibit periodic patterns, while the symbol edges of different frames are usually misaligned in time; (2) the frequency of LoRa signal increases continuously in between the edges of symbol, yet exhibits sudden changes at the symbol edges. We detect the continuity of signal frequency to remove interference and further exploit the time-domain information of symbol edges to recover symbols of all collided frames. We implement FTrack on a low-cost software defined radio. Our testbed evaluations show that FTrack demodulates collided LoRa frames with low symbol error rates in diverse SNR conditions. It increases the throughput of LoRaWAN in real usage scenarios by up to 3 times.

This paper identifies two energy saving opportunities of Wi-Fi interface emerged during smartphone's screen-off periods. Exploiting the opportunities, we propose a new power saving strategy, BackPSM, for screen-off Wi-Fi communications. BackPSM regulates client to send and receive packets in batches and coordinates multiple clients to communicate at different slots (i.e., beacon interval). The core problem in BackPSM is how to coordinate client without incurring extra traffic overheads. To handle the problem, we propose a novel paradigm, Out-of-Band Communication (OBC), for client-to-client direct communications. OBC exploits the Traffic Indication Map (TIM) field of Wi-Fi Beacon to create a free side-channel between clients. It is based upon the observation that a client may control 1 → 0 appearing on TIM bit by locally regulating packet receiving operations. We adopt this 1 → 0 as the basic signal, and leverage the time length in between two signals to encode information. We demonstrate that OBC can be used to convey coordination information with close to 100% accuracy. We have implemented and evaluated BackPSM on a testbed. The results show that BackPSM can decode the traffic pattern of peers reliably using OBC, and establish collision-free schedules fast to achieve out-ofband coordination of client communications. BackPSM reduces screen-off energy by up to 60% and outperforms the state-ofthe-art strategies by 16%-42%.

This paper identifies two energy saving opportunities of Wi-Fi interface emerged during smartphone's screen-off periods. Exploiting the opportunities, we propose a new power saving strategy, BackPSM, for screen-off Wi-Fi communications. BackPSM regulates client to send and receive packets in batches and coordinates multiple clients to communicate at different slots (i.e., beacon interval). The core problem in BackPSM is how to coordinate client without incurring extra traffic overheads. To handle the problem, we propose a novel paradigm, Out-of-Band Communication (OBC), for client-to-client direct communications. OBC exploits the TIM (Traffic Indication Map) field of Wi-Fi Beacon to create a free side-channel between clients. It is based upon the observation that a client may control 1 → 0 appearing on TIM bit by locally regulating packet receiving operations. We adopt this 1 → 0 as the basic signal, and leverage the time length in between two signals to encode information. We demonstrate that OBC can be used to convey coordination information with close to 100% accuracy. We have implemented and evaluated BackPSM on a testbed. The results show that BackPSM reduces screen-off energy by up to 60%, and outperforms state-of-the-art strategies by 16%-42%.

This paper presents ScaPSM (i.e., Scalable Power-Saving Mode Scheduler), a design that enables scalable competing background traffic scheduling in crowd event 802.11 deployments with Power-Saving Mode (PSM) radio operation. ScaPSM prevents the packet delay proliferation of previous study, if applied in the crowd events scenario, by introducing a new strategy of adequate competition among multiple PSM clients to optimize overall energy saving without degrading packet delay performance. The key novelty behind ScaPSM is that it exploits delay-aware load balance to control judiciously the qualification and the number of competing PSM clients before every beacon frame’s transmission, which helps to mitigate congestion at the peak period with increasing the number of PSM clients. With ScaPSM, the average packet delay is bounded and fairness among PSM clients is simultaneously achieved. ScaPSM is incrementally deployable due to only AP-side changes and does not require any modification to the 802.11 protocol or the clients. We theoretically analyze the performance of ScaPSM. Our experimental results show that the proposed design is practical, effective, and featuring with significantly improved scalability for crowd events.

Energy balance is a critical issue in wireless sensor networks. Several mixed data transmission (MDT) schemes have been proposed to achieve energy balance. However, most existing works are lack of theoretical study, especially understanding the relationship between network-wide energy balancing and lifetime optimization. In this paper, we conduct comprehensive theoretical analysis to the two-level based MDT scheme when applying to network-wide energy balancing, and eventually to maximize the network lifetime. We propose a novel network model, named energy balance area (EBA), and formally analyze its characteristics under the two-level based MDT scheme. To maximize the network lifetime, we convert the transmission probability allocation problem in the MDT scheme into an EBA partitioning (EBA-PT) problem, which is shown to be NP-hard. We then propose a heuristic approximation algorithm to determine the optimal configuration of EBAs, which is proven in this paper to be the key for maximizing the network lifetime. In this way, we obtain a near-optimal result. Our experimental studies show that network lifetime can be further improved as compared the hop-by-hop and the two-level based MDT schemes.

This paper presents ToneSense, a new paradigm for communication between devices using different wireless technologies. ToneSense encodes information into the transmission power levels of regular frames. Receivers sense the power strength and decode as meaningful information bits. Evaluations show that ToneSense achieves ≤ 4% symbol error rate. Our work sheds light on wireless coexistence problems and cross-technology communications.