High-density low-power and lossy networks (LLNs) for IoT applications (e.g., smart cities) urgently require low latency in emergencies like fires. However, existing hash-based or traffic-aware Time Slotted Channel Hopping (TSCH) scheduling methods fall short in such emergency scenarios. These methods lack both dedicated scheduling mechanisms for emergency packets and effective conflict resolution strategies, thereby resulting in frequent collisions and urgent traffic latency that fails to meet response demands. While reinforcement learning (RL)-based schemes can mitigate these issues through multi-objective optimization, their high computational and energy overheads place stringent demands on the capabilities of edge nodes. To address these challenges, We propose ATSE, an adaptive TSCH scheduling for emergency packets in high-density LLNs. TSE employs subtree-segregated time-slot allocation to assign regular and emergency packets to independent subtree resources for precise scheduling, combined with temporal emergency time-slot adjustment to dynamically reallocate slots for non-real-time emergency packets, ensuring low-latency delivery. For conflict avoidance, we design an Nhash-based conflict avoidance method via selective time-slot segment partitioning, allowing conflicting packets to request resources from sinks in dedicated segments, thereby reducing collisions and control overhead. Additionally, ATSE integrates multi-sink load balancing to evenly distribute dynamic network traffic, improving overall transmission performance and resource utilization. We implement ATSE on Contiki OS and evaluate it in large-scale, high-density scenarios. Experimental results show that ATSE outperforms baseline methods, reducing latency by 18.6%, improving reliability by 10.5%, and lowering energy consumption by 10.6%.

Advances in large language models (LLMs) have opened up new possibilities across various fields, fueling a new wave of interactive AI applications such as DeepSeek and ChatGPT. Inference serving systems play a crucial role in supporting these applications. Recent research indicates that when crossserver parallelization is enabled in inference serving systems, data synchronization overhead can exceed 65% of the total inference delay, making the reduction of communication overhead essential for speeding up inference. While existing systems accelerate cross-server communications by offloading synchronization operations to programmable switches, they often suffer from limited aggregation throughput under bursty traffic conditions, posing challenges for homogeneous network environments. To address these challenges, we propose HeroServe, an innovative inference serving system that leverages heterogeneous networks to accelerate data synchronization in distributed clusters. Our approach enables a fast and scalable inference serving system by employing an offline planner for joint computation allocation and communication scheduling, along with an online scheduler for dynamic traffic management and load balancing. We implement a prototype on a testbed comprising six servers and two programmable switches. Experimental results demonstrate that HeroServe improves scalability by$1.53 \times$while achieving lower latency compared to state-of-the-art solutions.

Monitoring the structural health of bridges, especially sea-crossing bridges, is vital for their longevity and safety. Among the potential risks during the operation of sea-crossing bridges, vortex-induced vibration (VIV) has drawn significant attention due to its potential to cause collapse. To accurately detect VIV, introducing new sensors such as GNSS has become a trend. However, current methods often suffer from high latency and low reliability. Additionally, the integration of GNSS poses significant challenges to power supply. To address these challenges, this paper proposes a novel deep learning-based adaptive GNSSIMU fusion method for low-latency, high-accuracy, and energyefficient detection of bridge VIV. We have employed a lightweight Temporal Convolutional Network (TCN) to replace traditional VIV detection methods, significantly reducing the latency of VIV detection. Additionally, Our method is the first to leverage the unique reflection mechanisms of the ocean surface to model GNSS multipath effects, significantly enhancing the accuracy of adaptive data fusion. Finally, the adaptive fusion mechanism has also substantially decreased the power consumption associated with integrating GNSS. The method is validated using both synthetic data and real vibration testbed data. Experimental results demonstrate that, compared to traditional VIV monitoring methods, our approach reduces detection latency from minutes to seconds while maintaining high detection accuracy. Under the same power supply conditions, the deployment duration is extended by 41.8%. We have also designed an IoT-based bridge Structural Health Monitoring (SHM) station to execute our algorithm.

Bluetooth Low Energy (BLE) and LoRa are two dominant wireless protocols for the Internet of Things (IoT), each built with specific design goals, rendering them non-interoperable. Cross-Technology Communication (CTC) enables heterogeneous communication between BLE and LoRa. Despite these efforts, existing works suffer from low throughput and short communication range. In this paper, we present a novel system named BoRa, which enables bi-directional communication between the COTS BLE and COTS LoRa chips. The key idea of BoRa to emulate the linear frequency changes of the LoRa chirp is continuously tuning its Modulation Offset (MO), which is originally designed to compensate for the Carrier Frequency Offset (CFO) in BLE chips. BoRa can be readily run on COTS chips without any hardware modifications.

Blockchain-based distributed storage systems offer enhanced security, transparency, and lower costs compared to traditional centralized storage, making them ideal for peer-to-peer collaboration. However, with the trend towards the Web of Things (WoT), lower transaction speeds and higher computational requirements limit their access to high-density data such as IoT. To address this, we propose TimeChain, an efficient off-chain blockchain storage system for IoT time series data. TimeChain batches discrete time series data, storing only the hash value of each batch on-chain while keeping the complete data off-chain. This significantly reduces storage overhead on the blockchain and storage latency by 37.4 times. TimeChain adopts an adaptive packaging mechanism to reduce the additional latency in range queries by converting the batch processing problem into a graph partitioning problem. To reduce the overhead of node selection, TimeChain integrates a node selection mechanism based on consensus protocol, combining node selection and consensus processes together. TimeChain also proposes a Locality-Sensitive Hashing tree-based data integrity verification mechanism to reduce transmission size. Our evaluation shows a reduction in query latency by 64.6% and storage latency by 35.3% compared to existing systems.

Bluetooth Low Energy (BLE) technology supports various Internet-of-Things (IoT) applications. However, because of their limited transmission power and channel interference, their performance is deficient over weak links. Extending physical layer symbols or using error correction code to the link layer is effective somehow. Introducing excessive BLE bits to both respectively can also decrease the network throughput. To optimize the BLE technology performance, we proposeCPL, a combining PHY and link layer optimization technology that adaptively allocates BLE bits to both the physical layer and link layer. Then we propose theCross-Layer BLE Bits Dynamic Allocation Modelthat unifies the gain of BLE bits in different layers. Finally, we propose aInterference-Aware Controlled CFO Fine-Tuning Methodthat calibrates the model according to different interference patterns. We implementCPLon Commercial-Off-The-Shelf (COTS) BLE chips and SDR. The experiment results show that under various interference conditions,CPLachieves 50× and 32.16% throughput improvement than RSBLE and Symphony.CPLreduces energy consumption by 60.42% to 97.95% compared to RSBLE, and 11.04% to 25.15% compared to Symphony.

The fifth-generation (5G) has empowerd various applications. Effective bandwidth and delay prediction in 5G cellular networks are essential for many applications, such as virtual reality and holographic video streaming. However, accurate bandwidth and delay prediction in 5G networks remains a challenging task due to the short-distance coverage and frequent handover properties of 5G base stations. In this paper, we propose HYPER, a hybrid bandwidth and delay prediction approach that uses an Auto Regressive Moving Average (ARMA) time series predictive model for intra-cell prediction and a Random Forest (RF) regression model for cross-cell prediction. Our ARMA model takes prior information as its input, while the RF model further uses related network and physical features to predict future performance. We conduct a measurement study in commercial 5G networks to analyze the relationship between these features and bandwidth/delay. Moreover, we also propose a handover window adaptation algorithm to automatically adjust the handover window size and determine which model to use during handover for accurate bandwidth and delay prediction. We use commercial 5G smartphones for data collection and conducted extensive experiments in diverse urban environments. Experimental results show that HYPER can reduce the prediction error by more than 13% compared to state-of-the-art prediction approaches.

In recent years, Bluetooth Low Energy (BLE) has become one of the most wildly used wireless protocols and it is common that users carry one or more BLE devices. With the extensive deployment of BLE devices, there is a significant privacy risk if these BLE devices can be tracked. However, the common wisdom suggests that the risk of BLE location tracking is negligible. The reason is that researchers believe there are no stable BLE fingerprints that are stable across different scenarios (e.g., temperatures) for different BLE devices with the same model. In this paper, we introduce a novel physical-layer fingerprint named Transient Dynamic Fingerprint (TDF), which originated from the negative feedback control process of the frequency synthesizer. Because of the hardware imperfection, the dynamic features of the frequency synthesizer are different, making TDF unique among different devices, even with the same model. Furthermore, TDF keeps stable under different thermal conditions. Based on TDF, we propose BTrack, a practical BLE device tracking system and evaluate its tracking performance in different environments. The results show BTrack works well once BLE beacons are effectively received. The identification accuracy is 35.38%-57.41% higher than the existing method, and stable over temperatures, distances, and locations.

Virtualized radio access networks (vRAN) enable network operators to run RAN functions on commodity servers instead of proprietary hardware. It has garnered significant interest due to its ability to reduce costs, provide deployment flexibility, and offer other benefits, particularly for operators of 5G private networks. However, the non-deterministic computing platforms pose difficulties to effective quality of service (QoS) provision, especially in the case of hybrid deployment of time-critical and throughput-demanding applications. Existing approaches including network slicing and other resource management schemes fail to provide fine-grained and effective QoS support at the User Equipments level. In this paper, we propose UQ-vRAN, a UE-level QoS provision framework. UQ-vRAN presents the first comprehensive analysis of the complicated impacts among key network parameters, e.g., network function splitting, resource block allocation, and modulation/coding scheme selection and builds an accurate and comprehensive network model. UQ-vRAN also provides a fast network configurator which gives feasible configurations in seconds, making it possible to be practical in actual 5G vRAN. We implement UQ-vRAN on OpenAirInterface and use simulation and testbed-base experiments to evaluate it. Results show that compared with existing works, UQ-vRAN reduces the delay violation rate by 12%–41% under various network settings, while minimizing the total energy consumption.

Bluetooth Low Energy (BLE) is one of the most popular wireless protocols for building IoT applications because of its low energy, low cost, and wide compatibility nature. However, BLE communication performance can be easily affected by interference and blockages because of its low transmission power. This paper presents BLEW, a technique to improve the BLE communication performance over weak links by exploiting adaptive symbol extension and DNN-based demodulator to combat channel interference and maximize network throughput. First, we propose a phase peak clustering-based preamble detection method that coherently adds up the phase difference of preambles to combat the interference. We then propose a multi-domain DNN-based demodulator to fully extracts the temporal and spectrum features of the signal and enhance the demodulation performance. Finally, we model the throughput of Commercial Off-The-Shelf (COTS) BLE chips transmitting extended packets, which can be used to optimize the symbol length in an adaptive manner. We implement BLEW with USRP B210 and COTS nRF52840 platform. Experiments show that BLEW can increase throughput by up to 157.39 Kb/s compared with native BLE over typical weak links. Compared with existing approaches, BLEW has up to 25.70% higher preamble detection rate and up to 3.37 dB demodulation gain.

The high energy consumption of 5G New Radio (NR) poses a major challenge to user experience. A major source of energy consumption in User Equipments (UE) is the radio tail, during which the UE remains in a high-power state to release the radio sources. Existing energy optimization approaches cut radio tails by forcing the UE to enter a low-power state. However, these approaches introduce extra promotion delays and energy consumption with soon-coming data transmissions. In this paper, we first conduct an empirical study to reveal that the 5G radio tail introduces significant energy waste on UEs. Then we propose 5GSaver, a two-phase energy-saving approach that utilizes the inactive state of New Radio to better eliminate the tail in 5G cellular networks. 5GSaver identifies the end of App communication events in the first phase and predicts the next packet arrival time in the second phase. With the learning results, 5GSaver can automatically help the UE determine which radio resource control state to enter for saving energy. We evaluate 5GSaver using 15 mobile Apps on commercial smartphones. Evaluation results show that 5GSaver can reduce radio energy consumption by 9.5% and communication delay by 12.4% on average compared to the state-of-the-art approach.

Recent years have witnessed the rapid growth of IoT (Internet of Things). Bluetooth Low Energy (BLE) is one of the most popular wireless protocols to implement IoT applications because of its energy efficiency and low-cost properties. However, the development of BLE applications is time-consuming and exhausting. Users are required to write programs for both sides of a BLE connection using complicated low-level APIs. Moreover, it needs much expertise for developers to set appropriate parameters in accordance to different application requirements, especially when there exist multiple concurrent BLE connections. To address these problems, we propose BLEdge , an edge-centric programming approach for BLE applications with multi-connection optimization. First, we propose a wireless bus abstraction for BLE programming. With this, users can write BLE applications in an edge-centric way, as if the BLE-connected peripherals are physically attached to the edge node. Second, we advocate an optimization approach for BLE connection parameters. This optimization approach considers the time slot collision problem under a multi-connection scenario. We conduct extensive experiments with the nRF52840DK platform. Experiment results show that BLEdge can reduce 62.50% to 90.55% LOC (Lines of Code) when developing BLE applications. Furthermore, our parameter optimization approach can reduce up to 42.23% energy consumption.

Bluetooth Low Energy (BLE) has gained large popularity as an important infrastructure of Internet of Things (IoT). Recently, researchers have integrated the TCP/IP stack with the BLE stack for interoperability, supporting more upper-layer protocols and applications. However, these works gain extremely low TCP goodput due to connection event inefficiency when TCP cooperates with BLE. How to improve the performance is an urgent problem. This paper proposes TCPle, a performant TCP-over-BLE stack that presents a novel adaption layer design bridging the gap between TCP and BLE without violating their specifications. TCPle improves the efficiency of connection events significantly by two fundamental mechanisms: connection event length adaption and connection event maintenance which correspond to the two root causes of low goodput. The connection event length adaption mechanism predicts the data size to send based on an online learning method and updates the connection event capacity adaptively. This mechanism avoids the long waiting time of ACK to back to the TCP sender. The connection event maintenance mechanism prefetches data packets to maintain the connection event. This mechanism avoids the long waiting time of data packets when out of the sender after the ACK is back. We implemented TCPle on nRF52840 DK with RIOT OS based on lwIP stack and NimBLE stack and conducted extensive experiments to evaluate its performance. Results show that TCPle 1) is lightweight and well-suited for resource-constrained IoT devices; 2) improves TCP goodput by up to 101.6% compared with other existing TCP-over-BLE stacks.

Interoperability among a vast number of heterogeneous IoT nodes is a key issue. However, the communication among IoT nodes does not fully interoperate to date. The underlying reason is the lack of a lightweight and unified network architecture for IoT nodes having different radio technologies. In this paper, we design and implement TinyNet, a lightweight, modular, and unified network architecture for representative low-power radio technologies including 802.15.4, BLE, and LoRa. The modular architecture of TinyNet allows us to simplify the creation of new protocols by selecting specific modules in TinyNet. We implement TinyNet on realistic IoT nodes including TI CC2650 and Heltec IoT LoRa nodes. We perform extensive evaluations. Results show that TinyNet (1) allows interoperability at or above the network layer; (2) allows code reuse for multi-protocol co-existence and simplifies new protocols design by module composition; (3) has a small code size and memory footprint.

Long communication range and low energy consumption are the two most important design goals of Low-power Wide-area Networks (LPWANs); however, many prior works have revealed that the performance of LPWAN in practical scenarios is not satisfactory. Although there are PHY-layer and link-layer approaches proposed to improve the performance of LPWAN, they either rely heavily on the hardware modifications or suffer from low data recovery capability, especially with bursty packet loss patterns. In this article, we propose a practical system, eLoRa, for COTS devices. eLoRa utilizes rateless codes and joint decoding with multiple gateways to extend the communication range and lifetime of LoRaWAN. To further improve the performance of LoRaWAN, eLoRa optimizes parameters of the PHY-layer (e.g., spreading factor) and the link layer (e.g, block length). We implement eLoRa on COTS LoRa devices and conduct extensive experiments on an outdoor testbed to evaluate the effectiveness of eLoRa. Results show that eLoRa can effectively improve the communication range of DaRe and LoRaWAN by 43.2% and 55.7% with a packet reception ratio higher than 60%, and increase the expected lifetime of DaRe and LoRaWAN by 18.3% and 46.6%.

Low-Power Wide Area Networks (LPWANs) emerges as attractive communication technologies to connect the Internet-of-Things. A new LoRa chip has been proposed to pro-vide long range and low power support on 2.4GHz. Comparing with previous LoRa radios operating on sub-gigahertz, the new one can transmit LoRa packets faster without strict channel duty cycle limitations and have attracted many attentions. Prior studies have shown that LoRa packets may suffer from severe corruptions with WiFi interference. However, there are many limitations in existing approaches such as too much signal processing overhead on weak devices or low detection accuracy. In this paper, we propose a novel weak signal detection approach, LoFi, to enable the coexistence of LoRa and WiFi. LoFi utilizes a typical physical phenomenon Stochastic Resonance (SR) to boost weak signals with a specific frequency by adding appropriate white noise. Based on the detected spectrum occupancy of LoRa signals, LoFi reserves the spectrum for LoRa transmissions. We implement LoFi on USRP N210 and conduct extensive experiments to evaluate its performance. Results show that LoFi can enable the coexistence of LoRa and WiFi in 2.4GHz. The packet reception ratio of LoRa achieves 98% over an occupied 20MHz WiFi channel, and the WiFi throughput loss is reduced by up to 13%.

Internet of Things (IoT), which connects a large number of devices with wireless connectivity, has come into the spotlight. Various wireless radio technologies and application protocols are proposed. Due to scarce channel resources, different network traffic may do interact in negative ways. This paper argues that there should be an isolation layer in IoT network communication stacks making each traffic’s perception of the wireless channel independent of what other traffic is running.We present Isolayer, an isolation layer design providing fine-grained and flexible channel isolation services in the heterogeneous IoT networks. By a shared collision avoidance module, Isolayer can provide effective isolation even between different wireless technologies (e.g., BLE and 802.15.4). Isolayer provides four levels of isolation services for users, i.e., protocol level, packet-type level and source-/destination-address level. Considering the various isolation requirements in practice, we design a domain-specific language for users to specify the key logic of their requirements. Taking the codes as input, Isolayer generates the control packets automatically and lets related nodes that receive the control packets update their isolation services correspondingly.We implement Isolayer on realistic IoT nodes, i.e., TI CC2650, Heltec LoRa node 151, and perform extensive evaluations. The results show that: (1) Isolayer incurs acceptable overhead in terms of delay and memory usage; (2) Isolayer provides effective isolation service in the heterogeneous IoT network. (3) Isolayer achieves about 18.6% reduction of the end-to-end delay of isolated packets in the IoT network with heavy traffic load.

Long communication range and low energy consumption are two most important design goals of Low-Power Wide-Area Networks (LPWAN), however, many prior works have revealed that the performance of LPWAN in practical scenarios is not satisfactory. Although there are PHY-layer and link layer approaches proposed to improve the performance of LPWAN, they either rely heavily on the hardware modifications or suffer from low data recovery capability especially with bursty packet loss pattern. In this paper, we propose a practical system, eLoRa, for COTS devices. eLoRa utilizes rateless codes and jointly decoding with multiple gateways to extend the communication range and lifetime of LoRaWAN. To further improve the performance of LoRaWAN, eLoRa optimizes parameters of the PHY-layer (e.g., spreading factor) and the link layer (e.g, block length). We implement eLoRa on COTS LoRa devices, and conduct extensive experiments on outdoor testbed to evaluate the effectiveness of eLoRa. Results show that eLoRa can effectively improve the communication range of DaRe and LoRaWAN by 43.2% and 55.7% with packet reception ratio higher than 60%, and increase the expected lifetime of DaRe and LoRaWAN by 18.3% and 46.6%.

With the continued proliferation of smart mobile devices, Quick Response (QR) code has played an important role in daily life. They may be distorted and partially invisible due to bright spots, folding and stains When they are printed on soft materials such as plastic bags. Existing scanners may fail in detecting and decoding QR codes due to distortion. In this paper, we propose a simple but effective approach to decoding distorted and partial QR codes. First, we improve an existing QR code detection algorithm to extract QR codes. Then based on the structural features of QR codes that white and black modules are staggered, we propose a novel distortion correction mechanism that uses an adaptive window to match each module. In order to tackle the problem of invisibility, we print multiple QR codes and capture them in an image. Considering confidence of each module in separate, we reconstruct a relatively complete QR code. Extensive experiments have been conducted to evaluate the performance of our approach. The results show that our approach improves the decoding rate by 50% – 60% compared to the other two baselines.

The openness of indoor WiFi networks makes it prone to spoofing attacks and password leakage/sharing, where undesired users may be able to access the network resources. The fundamental reason is that the actual communication range is much larger than the valid authentication range, leaving undesired users the same chance to access the network from invalid authentication ranges. The existing works exploit various features of wireless signals to identify legitimate/undesired users. Either frequent measurement/learning of signal features or hardware modifications are required. In this paper, we identify visible light (VL) as a good indicator to distinguish the positions of the accessing users due to its frequency diversity. Based on this observation, we propose VL-Auth, a novel Authentication framework based on VL for indoor WiFi networks, which uses the VL features as credentials to authenticate WiFi users. VL-Auth can be easily installed on commercial-off-the-shelf (COTS) devices and do not require frequent measurement/learning of the VL features. We implement VL-Auth with OpenWrt and Android devices. The evaluation results show that compared to the existing works, VL-Auth can accurately identify legitimate/undesired users and the connection delay is also reduced.

For network operators, knowing the problems in their networks before users complain can help improve their Quality-of-Experience (QoE)and bring great commercial value. However, accurate measurement of the internal performance of a network generally requires the deployment of additional dedicated instrumentation in the network. Another possible approach is to use network tomography technique to infer these internal states from measurements obtained from the edge of the network. However, network tomography usually requires that the network topology and the path of each probe are known. In this paper, using a sparse crowdsourced measurement dataset from mobile users to varies types of network resources, we build a BRAS (broadband remote access server)-level QoE model to help the network operator locate network problems quickly. By reasonably mining the intrinsic correlation among the measurement data, the proposed scheme is able to model the user QoE accurately and achieve an accuracy of 97 %.