In contemporary cloud architectures, an increasing number of cloud service providers are adopting programmable switches to deliver cloud network services (e.g., load-balancing gateways) for millions of tenants. The rate limiters are essential for cloud networks to execute network policies such as congestion control and traffic isolation on programmable switches. Most existing rate limiters utilize the token bucket algorithm, which suffers from scalability issues and substantial control overhead, impacting bandwidth utilization. The WF2Q+ (Worst-case FairWeighted Fair Queueing Plus) algorithm, known for its scalability and accuracy, is gaining traction but faces challenges on programmable switches. The hardware limitations hinder key operations of WF2Q+ (sorting and scheduling) in a single switch pipeline. This paper introduces DRAGONKING, a novel rate-limiting system that enables WF2Q+ through a multi-pipeline sorting and scheduling design. DRAGONKING enhances scalability and throughput while maintaining high accuracy. It achieves this by strategically balancing bandwidth across multiple pipelines, allowing for timely packet scheduling. DRAGONKING is implemented and evaluated on Barefoot Tofino switch. Results show that DRAGONKING supports up to two million entries and delivers line-rate throughput, achieving 1.9× improvements compared with token bucket-based limiters. Moreover, it can maintain over 99% accuracy, precisely enforcing rate limits from 10 Gbps to 100 Gbps.

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.

Mobile live-streaming applications with stringent latency and bandwidth requirements have gained tremendous attention in recent years. Encountered with bandwidth insufficiency and congestion instability of the wireless uplinks, multi-access networking provides opportunities to achieve fast and robust connectivity. However, the state-of-the-art multi-path transmission solutions are lack of adaptivity to the heterogeneous and dynamic nature of wireless networks. Meanwhile, the indispensable video coding and transformation bring about extra latency and make the video delivery vulnerable to network throughput fluctuation. This paper presents AggDeliv, a framework that provides efficient and robust multi-path transmission for mobile live video delivery. The key idea is to relate multi-path packet scheduling to congestion control optimization over diverse wireless links and adapt it to the mobile video characteristics. This is achieved by probabilistic packet allocation based on links’ congestion windows, wireless-oriented delay and loss aware congestion control, as well as lightweight video frame coding and network-adaptive frame-packet transformation. Real-world evaluations demonstrate that our framework significantly outperforms the state-of-the-art solutions on aggregate goodput and streaming video bitrate.

Recent years, the rapid development of Internet of Things (IoT) technologies and applications have been witnessed. Three important features are characterized in modern IoT applications: 1) device heterogeneity; 2) long-range communication; and 3) cloud/edge-device integration. Difficulties are raised by the above features toward IoT application developers, e.g., predicting and evaluating the performance of the entire IoT application system. To deal with the above difficulties, we design and implement an IoT simulator, TinySim, which satisfies the requirements of high fidelity, high scalability, and seamless transplantation. TinySim takes advantage of the hardware-independent features of TinyLink programming language. Hence, a similar code can be used for both simulation and execution on real hardware platforms. Many virtual IoT devices can be simulated by TinySim at the PC end. These IoT devices can send or receive messages from the cloud or smartphones, making it possible for the developers to evaluate the entire system without the actual IoT hardware. We connect TinySim with Unity 3-D to provide high interactivity. To reduce the event synchronization overhead between TinySim and Unity 3-D, a dependence graph-based approach is proposed. We design an approximation-based approach to reduce the number of simulation events, greatly speeding up the simulation process. We carefully evaluate TinySim using benchmarks and two concrete case studies. TinySim can simulate representative IoT applications, such as smart flowerspot and shared bikes. We conduct extensive experiments to evaluate the performance of TinySim. Results show that TinySim can achieve high accuracy with an error ratio lower than 9.52% in terms of energy and latency. Further, TinySim can simulate 4000 devices within 11.2 physical-minutes for ten simulation-minutes, which is about$3\times $faster than the state-of-art approach.

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.

To cope with the explosive growth in demands of wireless network, ultradense network (UDN) technology is widely adopted, which could increase the capacity of wireless network, but also bring severe intercell interference (ICI). However, existing solutions cannot work well in such complex scenarios, due to the limits of their mechanisms. To solve the problem, in this article, an interference-oriented radio resource allocation framework is proposed with multiple usages, including supplying precise, stable, and timely performance feedbacks, near perfect offline training, and high compatibility. As the use of the framework is derived from precise interference identification, a practical regression-based interference modeling algorithm is proposed to support the framework. With in-depth analysis of the mechanism of interference, the proposed algorithm could efficiently and accurately model interference between users using only data collected from operating wireless networks. Compared with the baseline algorithm, the proposed algorithm could reach the same accuracy with training time of two orders of magnitude shorter. To further show the advantages of the framework, a high-performance double-deep-$Q$-network-based resource allocation algorithm is also proposed. By integrating into the proposed framework, the proposed algorithm could coordinate ICI better, with 40% to 101% higher energy efficiency compared with baseline algorithms.

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.

Ultra-dense network (UDN) is considered to be the key technology for the fifth generation (5G) networks to provide high capacity. However, intensive deployment of femtocells bring severe inter-cells interference (ICI), which greatly limits the performance of the network and the capacity gain the system can obtain. Therefore, the key to solve this problem is to obtain accurate interference information through accurate interference modeling. In fact, the wireless big data generated during the operation of the wireless network contains rich wireless interference information. Based on this, this paper proposes an uplink interference identification and signal-to-interference-plus-noise ratio (SINR) prediction algorithm based on XGBoost and interference model. The proposed algorithm uses the wireless big data generated during network operation to train the XGBoost algorithm, mining the signal-to-interference ratio (SIR) and signal-to-noise ratio (SNR) information between links in the wireless network without increasing the overhead of wireless resources, and then combining with the proposed interference model to achieve accurate prediction of the SINR. The simulation results show that when the training data of the target user reaches 5000 pieces, the prediction error of its SINR will be reduced to less than 0.5dB, which effectively reduces the requirement of data quantity and computing power, and can meet the practical application requirements.

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%.

Recently, Cross-Technology Communication (CTC), allowing the direct communication among heterogeneous devices with incompatible physical layers, has attracted much research attention. Many efficient CTC protocols have been proposed to demonstrate its promise in IoT applications. However, the applications built upon CTC will be significantly impaired when CTC suffers from malicious attacks such as jamming or sniffing. In this article, we implement a reactive jamming system, JamCloak, that can attack most existing CTC protocols. To this end, we first propose a taxonomy of the existing CTC protocols. Then based on the taxonomy, we extract essential features to train a CTC detection model, and estimate the parameters that can efficiently jam CTC links. Experimental results show that JamCloak consistently achieves 94.7% of classification accuracy on average in both Line-of-Sight and Non-Line-of-Sight scenarios. We also apply JamCloak to attack three existing CTC protocols: WiZig, Esense and EMF. Results show that JamCloak can significantly reduce PDR (packet delivery ratio) by 80.8% on average in practical environments. In the meantime, JamCloak’s jamming gain is more than 1.78× higher than the existing reactive jammer. In addition, we propose a practical countermeasure against reactive jamming attacks over CTC links like JamCloak. Results show that our approach significantly improves the jamming detection accuracy by 91.2% on average than the existing approach, and effectively decreases the reduction in packet delivery ratio to 1.7%.

While numerous wireless applications are emerging to simplify our daily life, updating price tags in large-scale markets is still largely performed manually. Considering the potential adoption of electronic price (e-price) tags, we propose MoRa, a LoRa-based system for timely and energy-efficient price update in Markets. By leveraging the category information in the market, we design a hierarchical address scheme. Based on the scheme, we can perform efficient multicast with nearly zero overhead in group joining and activity scheduling. Furthermore, considering LoRa's characteristics and the asymmetry between the gateway and nodes, we let the gateway make fast and repetitive transmissions before the uncovered nodes send light weight NAKs. Extensive testbed experiments and simulation evaluations are conducted. Results demonstrate that MoRa can efficiently improve the performance in terms of update delay and energy consumption.

Cross-Technology Interference affects the operation of low-power ZigBee networks, especially under severe WiFi interference. Accurate corruption estimation is very important to improve the resilience of ZigBee transmissions. However, there are many limitations in existing approaches such as low accuracy, high overhead, and requirement of hardware modification. In this paper, we propose an accurate corruption estimation approach, AccuEst, which utilizes per-byte SINR (Signal-to-Interference-and-Noise Ratio) to detect corruption. We combine the use of pilot symbols with per-byte SINR to improve corruption detection accuracy, especially in highly noisy environments (i.e., noise and interference are at the same level). We extract pilot symbols by leveraging protocol signatures. In addition, we design an adaptive pilot instrumentation scheme to strike a good balance between accuracy and overhead. We implement AccuEst on the TinyOS 2.1.1/TelosB platform and evaluate its performance through extensive experiments. Results show that AccuEst improves corruption detection accuracy by 79.4 percent on average compared with state-of-the-art approach (i.e., CARE) in highly noisy environments. In addition, AccuEst reduces pilot overhead by 83.7 percent on average compared to the traditional pilot-based approach. We implement AccuEst in a coding-based transmission protocol, and results show that with AccuEst, the packet delivery ratio is improved by 22.1 percent on average.

Most sensor networks employ distributed and dynamic routing protocols. The flexibility that each node can choose the best forwarder from a diverse candidate set could offer excellent routing performance when the network is highly dynamic. However, it sacrifices routing predictability since it is possible that routing loops are frequently formed. Can we increase the network predictability by controlling the network? As a step towards solving this problem, we introduce FlexCut, a flexible approach for cutting off wireless links, which essentially limits the candidate forwarder set of each node. Unlike existing SDN solutions, FlexCut introduces flexible control over existing distributed and dynamic routing protocols. FlexCut can trade arbitrary amounts of routing diversity for better network performance by exposing to network operators a parameter which quantifies the aggressiveness. We propose novel algorithms, both centralized and distributed, to cut off user-defined number of links so that loops can be alleviated while routing flexibility can be preserved to the largest extent. We evaluate FlexCut extensively by both testbed experiments and simulations. Results show that FlexCut improves the performance by 40%~90% compared with a baseline algorithm in terms of our optimization goal. Results also show that FlexCut can improve the network performance of a sensor network by 20%~35%, 30%~50%, 25% respectively, in terms of packet delivery ratio, transmission delay, and radio duty cycle.

Recently, CTC (Cross-Technology Communication), allowing the direct communication among heterogeneous devices with incompatible physical layers, has attracted much research attention. Many efficient CTC protocols have been proposed to demonstrate its promise in IoT applications. However, the applications built upon CTC will be significantly impaired when CTC suffers from malicious attacks such as jamming or sniffing. In this paper, we implement a reactive jamming system, JamCloak, that can attack most existing CTC protocols. To this end, we first propose a taxonomy of the existing CTC protocols. Then based on the taxonomy, we extract essential features to train a CTC detection model, and estimate the parameters that can efficiently jam CTC links. Experimental results show that JamCloak consistently achieves 94.7% of classification accuracy on average in both LoS (Line-of-Sight) and NLoS (Non-Line-of-Sight) scenarios. We also apply JamCloak to attack three existing CTC protocols: WiZig, Esense and EMF. Results show that JamCloak can significantly reduce PDR (packet delivery ratio) by 80.8% on average in practical environments. In the meantime, JamCloak's jamming gain is more than 1.78× higher than the existing reactive jammer. In addition, we propose a practical countermeasure against reactive jamming attack over CTC links like JamCloak. Results show that our approach significantly improves the jamming detection accuracy by 91.2% on average than the existing approach, and effectively decreases the reduction in packet delivery ratio to 1.7%.

With the development of LPWA (Low Power Wide Area)technology, the emerging NB-IoT (Narrowband Internet of Things)has attracted much attention and enabled a wide range of applications. An uplink of NB-IoT is a link from a user equipment (UE)to a base station (BS). Uplink transmission is a key component of NB-IoT, accomplishing the sensor data collection task for many applications. However, the performance of uplink transmission has not been rigorously analyzed in the current literature, while uplink performance degradation like long latency could be harmful to many applications with strict uplink performance requirements. In this work, we show a way of mathematically analyzing the performance of uplink transmission for NB-IoT systems, concerning the transmission latency and transmission reliability. Our model is accurate with consideration of the protocol details and the new features of NB-IoT, including link quality, packet size, channel access contention, and etc. We validate the analytical results through detailed simulations. Results show that our analytical model can achieve 83% accuracy for latency calculation and 96% accuracy for reliability calculation. Moreover, we demonstrate that the analytical results can be used to aid protocol design for performance optimization, e.g., repetition number tuning for reducing the transmission latency.

Wireless network simulation is a fundamental service aiming at providing controlled and repeatable environment for protocol design, performance testing, etc. The existing simulators focus on reproducing the packet behaviors on individual links. However, as observed in some recent works, individual link behaviors alone are not enough to characterize the protocol performance. As a result, while the existing works can mimic the link behaviors very closely, they often fail to simulate protocol level performance. In this paper, we propose a novel performance-aware simulation approach which can preserve not only the link-level behaviors but also the performance-level behaviors. We first devise an accurate performance model by combining link quality and the spatial-temporal link correlation. Based on the performance modeling, we then propose a Performance Aware Hidden Markov Model (PA-HMM), where the protocol performance is directly fed into the Markov state transitions. PA-HMM is able to simulate both link-level behaviors and high-level protocol performance. We conduct extensive testbed and simulation experiments with broadcast and anycast protocols. The results show that compared to the state-of-the-art work, 1) the performance model is able to accurately characterize wireless communication performance and 2) the protocol performance is closely simulated as compared to the empirical results.

Cross-Technology Interference affects the operation of low-power ZigBee networks, especially under severe WiFi interference. Accurate corruption estimation is very important to improve the resilience of ZigBee transmissions. However, there are many limitations in existing approaches such as low accuracy, high overhead, and requiring hardware modification. In this paper, we propose an accurate corruption estimation approach, AccuEst, which utilizes per-byte SINR (Signal-to-Interference-and-Noise Ratio) to detect corruption. We combine the use of pilot symbols with per-byte SINR to improve corruption detection accuracy, especially in highly noisy environments (i.e., noise and interference are at the same level). In addition, we design an adaptive pilot instrumentation scheme to strike a good balance between accuracy and overhead. We implement AccuEst on the TinyOS 2.1.1/TelosB platform and evaluate its performance through extensive experiments. Results show that AccuEst improves corruption detection accuracy by 78.6% on average compared with state-of-the-art approach (i.e., CARE) in highly noisy environments. In addition, AccuEst reduces pilot overhead by 53.7% on average compared to the traditional pilot-based approach. We implement AccuEst in a coding-based transmission protocol, and results show that with AccuEst, the packet delivery ratio is improved by 20.3% on average.

The ZigBee communication can be easily and severely interfered by Wi-Fi traffic. Error recovery, as an important means for ZigBee to survive Wi-Fi interference, has been extensively studied in recent years. The existing works add upfront redundancy to in-packet blocks for recovering a certain number of random corruptions. Therefore, the bursty nature of ZigBee in-packet corruptions under Wi-Fi interference is often considered harmful, since some blocks are full of errors which cannot be recovered and some blocks have no errors but are still requiring redundancy. As a result, they often use interleaving to reshape the bursty errors, before applying complex FEC codes to recover the re-shaped random distributed errors. In this paper, we take a different view that burstiness may be helpful. With burstiness, the in-packet corruptions are often consecutive and the requirement for error recovery is reduced as “recovering any k consecutive errors” instead of “recovering any random k errors”. This lowered requirement allows us to design far more efficient code than the existing FEC codes. Motivated by this implication, we exploit the corruption burstiness to design a simple yet effective error recovery code using XOR operations (called ZiXOR). ZiXOR uses XOR code and the delay is significantly reduced. More, ZiXOR uses RSSI-hinted approach to detect in packet corruptions without CRC, incurring almost no extra transmission overhead. The testbed evaluation results show that ZiXOR outperforms the state-of-the-art works in terms of the throughput (by 47 percent) and latency (by 22 percent).

Path profiling, which aims to trace a program's execution path, has been widely adopted in various areas such as record and replay, program optimizations, performance diagnosis, and etc. Many path profiling approaches have been proposed in the literature, including B.L. algorithm, and PAP. Unfortunately, both approaches suffer from large tracing overhead for representing long execution paths. In this paper, we propose AdapTracer, a path profiling approach based on arithmetic coding. There are two salient features in Adap-Tracer. First, it is space efficient by adopting a path profiling algorithm based on arithmetic coding. Second, it is adaptive by explicitly considering the execution frequency of each edge. We have implemented AdapTracer to profile Android applications. Our experimental evaluation uses modified JGF benchmarks to show AdapTracer's efficiency. Experimental results show that AdapTracer reduces the trace size by 44% on average and incurs execution overhead by 10% at most compared to PAP.