Bidirectional Cross-technology Communication (CTC) between WiFi and BLE demonstrates a wide range of applications prospects, such as temperature/humidity monitoring in smart home, LE-Audio relaying by WiFi APs, etc. However, existing CTC techniques between WiFi and BLE only support one-way transmission and are unable to be implemented on Commercial Off-The-Shelf (COTS) devices. This paper proposes BiCross, a bidirectional CTC scheme between WiFi and BLE which requires only software update on COTS devices. In essence, BiCross first presents a channel-specific symbol mapping technique to support all-channel reliable downlink CTC for BLE channel hopping. Then, BiCross leverages the spectrum scan capacity of WiFi Network Interface Cards (NICs) and analyzes the spectral characteristics of BLE frames to achieve uplink CTC. Finally, BiCross solves the problem of discontinuous and uncertain interval of spectrum analysis on commodity WiFi NICs through the design of BLE payload symbols and CRC-based error correction. The evaluation results demonstrate that BiCross achieves high reliability (downlink FRR > 95%, uplink FRR > 90%) and high throughput (downlink 854kbps, uplink 732kbps), and we showcase the application of BiCross on temperature acquisition in smart home scenario.

Bluetooth audio, as a common application in our daily life, faces a major challenge due to its limited transmission range in meeting users' demands. Traditional solutions, such as using high-power Bluetooth transmitters, require hardware upgrades that are neither cost-effective nor energy-efficient. This work introduces WiLE-Audio, a novel approach that extends Low Energy Audio (LE Audio) coverage through physical-layer cross-technology communication (CTC) from WiFi to Bluetooth Low Energy (BLE). We first present a novel symbol mapping technique from WiFi DQPSK to BLE GFSK symbols, which enables all-channel and reliable CTC to support Bluetooth channel hopping. Then, to implement CTC to commodity WiFi Network Interface Card (NIC), we present a real-time reverse scrambling method that dynamically calculates the payload of WiFi packets at the NIC driver. Finally, to align with the strict time window requirements of the BLE receiver, we design a precise timing strategy and a priority scheduling mechanism at the WiFi transmitter, effectively mitigating timing offsets due to Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) and queue management. These systematic innovations allow WiLE-Audio to be easily implemented into existing commercial WiFi and BLE devices with only a simple software upgrade on the WiFi side. Furthermore, using existing WiFi infrastructures, WiLE-Audio enables the relaying of Bluetooth Low Energy Audio (LE-Audio) and the roaming of BLE receiver at any WiFi-covered location. We implement WiLE-Audio on commercial WiFi and BLE devices, and conduct extensive evaluations across various scenarios. Experimental results demonstrate that WiLE-Audio extends the transmission distance of LE Audio by 2× in single-hop mode and at least 2.6× in two-hop roaming mode. This work provides a cost-effective and scalable solution for enhancing Bluetooth audio coverage, providing a promising prospect for whole-house or even whole-building LE Audio listening.

Bluetooth audio faces a major challenge due to its limited transmission range in meeting users' demands. This work introduces WiLE-Audio, a novel approach that extends Low Energy Audio (LE Audio) coverage through cross-technology communication (CTC) from WiFi to BLE. We first present a novel symbol mapping technique to enable all-channel and reliable CTC. Then, we present a real-time reverse scrambling method to implement CTC to commodity WiFi devices. Finally, we design a precise timing strategy and a priority scheduling to align with the strict time window requirements of the BLE receiver. These systematic innovations allow WiLE-Audio to be easily implemented into existing commercial devices with only a simple software upgrade on the WiFi side. Furthermore, we achieve seamless switching between Bluetooth classic audio and WiLE-Audio, thus supporting whole-house audio roaming. We implement our work on commercial WiFi and BLE devices and demonstrate that WiLE-Audio extends the transmission distance of LE Audio more than 2×.

Object detection, a fundamental task in computer vision, is crucial for various intelligent edge computing applications. However, object detection algorithms are usually heavy in computation, hindering their deployments on resource-constrained edge devices. Traditional edge-cloud collaboration schemes, like deep neural network (DNN) partitioning across edge and cloud, are unfit for object detection due to the significant communication costs incurred by the large size of intermediate results. To this end, we propose a Difficult-Case based Small-Big model (DCSB) framework. It employs a difficult-case discriminator on the edge device to control data transfer between the small model on the edge and the large model in the cloud. We also adopt regional sampling to further reduce the bandwidth consumption and create a discriminator zoo to accommodate the varying networking conditions. Additionally, we extend DCSB to video tasks by developing an adaptive sampling rate update algorithm, aiming to minimize computational demands without sacrificing detection accuracy. Extensive experiments show that DCSB can detect 97.26%-97.96% objects while saving 74.37%-82.23% network bandwidth, compared to cloud-only methods. Furthermore, DCSB significantly outperforms the latest DNN partitioning methods, reducing inference time by 92.60%-95.10% given an 8Mbps transmission bandwidth. In video tasks, DCSB matches the detection accuracy of leading video analysis methods while cutting the computational overhead by 40%.

LoRa, designed for Low-Power, Wide-Area Networks (LPWANs), is widely used in the Internet of Things (IoT). In contrast, Wireless Personal Area Network (WPAN) technologies like ZigBee struggle to connect directly to LPWANs due to their limited communication range and differing modulation schemes. ZigBee uses Offset Quadrature Phase-Shift Keying (OQPSK) modulation, while LoRa employs Chirp Spread Spectrum (CSS) modulation, complicating cross-technology communication. To address this challenge, we propose a novel approach for seamless physical-layer cross-technology communication between ZigBee and LoRa networks, bridging the gap between short-range and long-range communication technologies. We introduce ZigRa, a communication method that leverages neural networks for efficient modulation translation between ZigBee's IEEE 802.15.4 standard and LoRa's CSS modulation. The core of ZigRa is a deep learning model that adapts and optimizes the transformation of ZigBee signals into ultra-narrowband single-tone sinusoidal signals, which can be reliably detected by LoRaWAN base stations. Our solution enables ZigBee devices to seamlessly connect to LoRa-based LPWANs, overcoming modulation mismatches and providing long-range connectivity. Extensive evaluations with both USRP hardware and commercial devices demonstrate that ZigRa achieves a frame reception rate exceeding 85% at distances up to 500 meters, significantly enhancing the interoperability and coverage of heterogeneous IoT networks.

Wi-Fi is the de facto standard for providing wireless access to the Internet in the 2.4 GHz ISM band. Tens of billions of Wi-Fi devices (e.g., smartphones) have been shipped worldwide with limited types of wireless radios operating only when Wi-Fi connectivity is available, making it challenging to access data in heterogeneous IoT devices. However, the direct connection between Wireless Personal Area Network (WPAN) technologies, such as Bluetooth, and Wi-Fi presents challenges due to the inherent distinct physical layer. In our work, a novel communication method called BlueWi has been introduced, which serves as a cross technology communication method that enables BLE devices to establish connections and engage in communication with Wi-Fi based WPAN networks. We let BLE signals hitchhike on ongoing Wi-Fi signals, enabling Wi-Fi to recognize specific BLE signal waveforms in the frequency domain. By analyzing the decoded Wi-Fi payload, BlueWi can retrieve the BLE data, ensuring this method remains fully compatible with existing commodity Wi-Fi hardware. The direct sequence spread spectrum scheme is appended to handle general BLE frames and can be considered as “COPY” operation, which allows for better correlation and detection of the signal at the receiver. Evaluations conducted using both USRP and commodity devices have demonstrated that BlueWi can achieve concurrent wireless communication from BLE commercial chips to Wi-Fi networks with a frame reception rate exceeding 96%.

LoRa networks operating in a star topology, this configuration creates a single point of failure and may limit scalability and reliability in areas that are large and geographically dispersed. In order to improve the overall transmission capabilities of the network, recent studies show that adding LoRa to the ZigBee devices effectively disseminates network management. By doing so, the strengths of both technologies can be leveraged, with LoRa serving as the long-range transmitter and ZigBee functioning as the mesh network. In this study, we present LoBee, a novel bidirectional communication method between LoRa and ZigBee that relies on Physical-Layer Cross-Technology Communication. Despite the fact that LoRa and ZigBee utilize different modulation techniques, ZigBee devices can detect and recognize LoRa chirps through the process of sampling the received signal strength. For the transmissions from ZigBee to LoRa devices, we carefully select the input chips to generate specific waveforms, where LoBee detects the preamble of a ZigBee frame based on the locations of the repeated peaks. Our evaluation, which was conducted using USRP and commodity devices, demonstrates that LoBee is capable of achieving concurrent bidirectional wireless communications, with a data rate of approximately 639.38 bits per second from LoRa to ZigBee and from ZigBee to LoRa with more than 90% frame reception rate in the 2.4 GHz frequency band.

In-network caching is one of the most important characteristic of Named Data Networking (NDN). However, while replacing producers in responding to interest requests, caching data packets also shields consumers from perceiving the bottleneck bandwidth of the transmission path between the producer and the consumer. Therefore, when the content source switches from the cache node to the producer due to data exhaustion, the consumer can not adjust the requesting rate accordingly, and may lead to the serious bufferbloat or packet loss - we call it as Cache Trap. We found that Cache Trap occurs commonly in streaming services and the state-of-art NDN congestion control schemes cannot achieve efficient and stable quality of service when it happens. To escape Cache Trap, this paper proposes an explicit rate feedback congestion control algorithm, named as RFCC. RFCC leverages NDN routers' ability of encapsulating customized information in data packets to send link state information to consumers. Specifically, when responding to interest packets, RFCC nodes estimate data throughput received from the producer and insert this information into the returned data packets. The consumer perceives the change of content source according to the hopcount tag in data packet, and then adjusts the sending rate of interest packet based on the explicit rate information. We have implemented RFCC in both real-world NDN live video streaming and NDNsim simulation platforms, and compared it with the state-of-arts congestion control algorithms in a variety of scenarios. The experimental results show that when Cache Trap occurs, RFCC maintains a stable QoE in live video streaming, reduces 50% delay jitters compared with DPCCP and achieves$2.4 \times$throughput compared with PCON.

Data uploading applications in wireless networks may suffer from the degrade of Quality of Experiences (QoEs), due to the untimely adjustment of congestion window (cwnd) in face of the rapid change of wireless channel. To mitigate this problem, we analyzed the relationship between the NIC driver queue length at the wireless sender and the end-to-end transmission performances, and found a strong correlation between them, since the bottleneck mostly occurs at the wireless link. Based on this observation, we designed QCC, a congestion control algorithm that adjusts cwnd according to the residual queue length after each round of NIC transmission. Since obtaining congestion information locally at the sender leads to a much shorter feedback path than waiting for the end-to-end ACK feedback, QCC can track the time-varying wireless links much faster and more accurately. In addition, QCC also presents adaptive slow start mechanism and MAC layer-assisted fast recovery mechanism, both of which make efficient use of residual queue length to further improve transmission performances. Experiment results on both real-world Wi-Fi and cellular networks reveal that QCC can achieve at least 2.36X lower delay than that of BBR while ensuring 98.5% throughput of BBR.

As one of the basic tasks of computer vision, object detection has been widely used in many intelligent applications. However, object detection algorithms are usually heavyweight in computation, hindering their implementations on resource-constrained edge devices. Current edge-cloud collaboration methods, such as CNN partition over edge-cloud devices, are not suitable for object detection since the large data size of the intermediate results will introduce extravagant communication costs. To address this challenge, we propose a difficult-case based small-big model (DCSB) framework that deploys a difficult-case discriminator on the edge device to control the data transfer between the small model (edge) and the big model (cloud). Upon receiving data, the edge device operates a difficult-case discriminator to classify images into easy cases and difficult cases according to the specific semantics of the images. The difficult cases will be uploaded to the cloud. To reduce bandwidth consumption, we propose a regional sampling method that adaptively down-samples some regions of the difficult case to reduce the amount of transferred data based on the primary results of the lightweight model. Experimental results on VOC, COCO, and HELMET datasets using two object detection algorithms demonstrate that DCSB can detect 93.77%-97.05% objects but save 77.19% -80.55% of network bandwidth compared with the cloud-only method, while the edge-only method can only detect 54.90%-68.28% objects in the same condition. In addition, compared with the state-of-the-art model partition method - CAS, DCSB saves 95.19%-95.80% of the inference time when the transmission bandwidth is 8Mbps.

With the development of wireless communications (e.g., WiFi 6 and 5G), more and more high-bandwidth networks are emerging in our daily life. However, due to the limited speed of the slow start phase in congestion control algorithms, the high-bandwidth links may not be fully utilized, which will degrade the Quality of Service (QoS). The reason is, since the available link capacity is unknown until the link is fully occupied, the sender has to gradually increase the congestion window (cwnd) from a small initial value, causing the link to be underutilized, until a packet is dropped or a congestion signal is detected. Especially, for a short flow, the transmission is often finished before the link capacity is reached, leading to the waste of available bandwidth. To better exploit the high bandwidth links, this paper proposes FBE (Fast Bandwidth Estimation without Filling up the Bottleneck Link), by leveraging the effective ACK's returning rate to estimate the bottleneck link capacity. More specifically, instead of sending out any additional probe packets, FBE uses the ACK rates from the first two RTT rounds to quickly estimate the bandwidth during slow start phase. Since the original ACK rate is significantly lower than the available link bandwidth due to the exhaustion of send window, and the competing flows also have an impact on the ACK rate, FBE elaborates the ACK interval compensation algorithm to refine the ACK intervals to reflect the link rate, and then updates cwnd to a suitable size. To address the challenge of inaccurate bandwidth estimation, especially for rapidly changing wireless link, FBE dynamically adjusts cwnd according to the feedback of driver queue length after the bandwidth estimation. Experiments in real WiFi and LTE networks show that FBE reduces the slow start convergence time by 54.8% and 53.5% compared to CUBIC and BBR with traditional slow start, respectively. And when transferring short flows (512KB in size), FBE reduces the flow completion time by 40.4% and 43.8% compared to CUBIC and BBR, respectively.

Time synchronization is a fundamental requirement for wireless communication systems to work properly. Most of the existing studies focus on time synchronization among homogeneous devices. This work investigates time synchronization with heterogeneous technologies (e.g., WiFi, ZigBee, and Bluetooth) which is important for the rising Internet of Thing (IoT) scenarios where heterogeneous devices coexist. Recent advances in cross-technology communication (CTC) break the wall between heterogeneous wireless devices. In this work, we propose a new time synchronization strategy based on the CTC technique and provide a technique called TimeBee, which takes the advantage of coordination from a WiFi device to assist ZigBee devices for time synchronization. An effective method is employed so that ZigBee nodes are coordinated for time synchronization based on the received timestamps from WiFi devices. The experimental results show that TimeBee achieves global time synchronization with low time errors.

Many intelligent applications based on deep neural networks (DNNs) are increasingly running on Internet of Things (IoT) devices. Unfortunately, the computing resources of these IoT devices are limited, which will seriously hinder the widespread deployment of various smart applications. A popular solution is to offload part of computation tasks from the IoT device to cloud by way of device–cloud collaboration. However, existing collaboration approaches may suffer from long network transmission delay or degraded accuracy due to the large amount of intermediate results, bring enormous challenges to the tasks, such as object detection, that require massive computing resources. In this article, we propose an efficient device–cloud collaborative inference (DCCI) object detection framework, which dynamically adjusts the amount of transferred data according to the content of input images. Specifically, a content-aware hard-case discriminator is proposed to automatically classify the input images as hard-cases or simple-cases, the hard-cases are uploaded to the cloud to be processed by a deployed heavyweight model, and the simple cases are processed by a lightweight model deployed to the IoT device, where the lightweight model is automatically compressed based on reinforcement learning according to the resource constraints of the IoT device. Furthermore, a collaborative scheduler based on the runtime load and network transmission capability of IoT devices is proposed to optimize the collaborative computation between IoT devices and the cloud. Extensive experimental evaluations show that compared to the Device-only approach, DCCI can reduce the memory footprint and compute resources of IoT devices by more than 90.0% and 30.87%, respectively. Compared to Cloud-centric, DCCI can save$2.0\times $of network bandwidth. In addition, compared with the state-of-the-art DNN partitioning method, DCCI can save$1.2\times $of inference latency, and$1.3\times $of IoT device energy consumption with the same accuracy constraint.

Recently, buoyed by advances in the space industry, low Earth orbit (LEO) satellites have become an important part of the Internet of Things (IoT). LEO satellites have entered the era of a big data link with IoT, how to deal with the data from the satellite IoT is a problem worthy of consideration. Conventional object detection method in optical remote sensing simply transmits the raw data to the ground. However, it ignores the properties of the images and the connection with the downstream task. To obtain efficient data transmission and accurate object detection, we propose a task-inspired satellite–terrestrial collaborative object detection framework called STCOD. It detects regions of interest (ROIs) and adopts a block-based adaptive sampling method to compress the background (BG) in optical remote sensing images by introducing satellite edge computing (SEC) on satellites. The STCOD framework also sets the transmission priority of image blocks according to their contributions to the task and uses fountain code to ensure the reliable transmission of important image blocks. We build a whole software simulation framework to validate our method, including the satellite module, the transmission module, and the terrestrial module. Extensive experimental results show that the STCOD framework can reduce the amount of downlink data decreased by 50.04% while losing the detection accuracy by 0.54%. In our simulated satellite–terrestrial link, the STCOD framework can reduce the number of satellite-to-terrestrial transmissions by half. When the packet loss rate is between 5% and 20%, the detection accuracy is lost only 0.05% to 0.5%.

Data upload applications such as streaming of live videos and cloud services bring convenience to our lives. However, the Quality of Experience in wireless networks is often unsatisfactory. One of the reasons is, wireless communication is vulnerable to unpredictable factors such as rapid change of channel and competition of channel resources, leading to hysteresis and inaccuracy when performing a congestion control algorithm. To mitigate this problem, we analyzed the relationship between the real-time length of the NIC driver queue at the sender and the end-to-end transmission performances, and found a strong correlation between them. The reason is, when the wireless link is the first hop of data upload, the bottleneck mostly occurs at this hop, thus causing the accumulation of packets on the NIC driver queue. Based on this observation, we designed QCC, a congestion control algorithm that adjusts the congestion window (cwnd) according to the residual queue length after each round of NIC transmission. Specifically, the cwnd will be quickly reduced when this queue length is large to mitigate congestion, and gradually increased when it is small to increase link utility. By this means, QCC can track the time-varying wireless links quickly and accurately to achieve both high throughput and low latency. We evaluate QCC on both real-world Wi-Fi and cellular network implementations. Our experiment results reveal that QCC can achieve 2.04X lower delays than that of BBR while ensuring the similar link utilization rate as BBR (99% of BBR's throughput).

Radio frequency identification (RFID) has been widely applied in large-scale applications such as logistics, merchandise and transportation. However, it is still a technical challenge to effectively estimate the number of tags in complex mobile environments. Most of existing tag identification protocols assume that readers and tags remain stationary throughout the whole identification process and ideal channel assumptions are typically considered between them. Hence, conventional algorithms may fail in mobile scenarios with unreliable channels. In this paper, we propose a novel RFID anti-collision algorithm for tag identification considering path loss. Based on a probabilistic identification model, we derive the collision, empty and success probabilities in a mobile RFID environment, which will be used to define the cardinality estimation method and the optimal frame length. Both simulation and experimental results of the proposed solution show noticeable performance improvement over the commercial solutions.

Accurate and timely prediction of the Internet traffic flow is important for network performance improvement. Few prediction methods incorporate the topology information of networks. In this paper, we present a novel Internet traffic forecasting algorithm named TTGCN, which applies the graph neural networks for traffic flow prediction on each link of a backbone network. The topology of the network was represented by a novel adjacency matrix, which models the relationship between links. The design makes TTGCN capable of capturing both the temporal and topological information of the traffic flow. TTGCN is validated on UKERNA, a dataset captured from a real backbone network. The experimental results show that the average error of the proposed TTGCN over 90 minutes is 28.249 (Mbit/s), which is a significant improvement of conventional models, while that of ARIMA and GRU and STGCN are 44.955, 40.935, and 36.152, respectively. The proposed TTGCN, by encoding both temporal and topological information in a neural representation, can achieve better prediction performance on network traffic.

Recent advances on Physical-layer Cross-Technology Communication (PHY-CTC) have achieved high throughput direct communication across different wireless technologies. These PHY-CTC works are commonly achieved by emulating the target signal waveform of the receiver. However, signal emulation suffers from inherent unreliability due to imperfect emulation, and it only supports few communication channels. When applied in WiFi to Bluetooth Low Energy (BLE) scenario, it will face two challenges: i) a BLE receiver can not tolerate any bit error in a frame, while emulation errors are easy to appear; and ii) the BLE device performs channel hopping while most BLE channels are unavailable for emulation based CTC.To address these challenges, we present WiBle, a high reliable and all-channel supporting PHY-CTC from WiFi to BLE. The key technical insight of WiBle is symbol transition mapping: When a symbol is transmitted by a WiFi sender and flows into a BLE receiver, it will leave some unique signatures which can be leveraged to extract information. More specifically, it is observed that the phase shifts of BLE received signal can be mapped to the transitions of WiFi symbols. Therefore, by carefully selecting the symbols at the WiFi sender, we can generate the desired phase shifts for correct BLE GFSK demodulation and achieve reliable CTC. Evaluation results on both USRP and commodity chip show that WiBle outperforms state-of-the-art CTCs by higher reliability (> 95% frame reception ratio), wider channel coverage (supporting all 40 BLE channels), and higher throughput (974.3Kbps), under a full range of configurations including indoor/outdoor and LoS/NLoS settings.

Recent research on physical layer cross technology communication (PHY-CTC) brings a timely answer for escalated wireless coexistence and open spectrum movement. PHY-CTC achieves direct communication among heterogeneous wireless technologies (e.g.,WiFi, Bluetooth, and ZigBee) in physical layer and thus brings communication support for coexistence service such as spectrum management and IoT device control. To put PHY-CTC into service, however, there still exists a gap due to its transmission failure and asymmetric link (i.e., one-way PHY-CTC) issues. In this paper, we propose NetCTC – the first networking support design for PHY-CTC to establish feedbacks (e.g., ACKs) and thus meet the upper layer networking requirements in heterogeneous unicast, multicast and broadcast. The core design of NetCTC is a real-time interaction mechanism which achieves reliable, transmission efficient and concurrent interactive communication among heterogeneous devices. We implement and evaluate NetCTC on commodity devices and the USRP-N210 platform. Our extensive evaluation demonstrates that NetCTC achieves reliable bidirectional cross technology communication under a full range of wireless configurations including stationary, mobile and duty-cycled settings.

Today's wireless networks have become increasingly heterogenous, mobile and dense. To satisfy the rising demands of ubiquitous connections, billions of multi-radio gateways have to be deployed, inevitably incurring high deployment cost and extra traffic overhead. Recent advances on Cross-Technology Communication (CTC) have shown its ability to avoid these drawbacks. However, the state-of-the-art CTCs from Bluetooth to WiFi, two of the most popular wireless techniques, still suffer from low data-rate (e.g., 3.1Kbps), which severely restricts their applicability. We present BlueFi, the first physical-layer CTC (PHY-CTC) from Bluetooth Low Energy (BLE) to WiFi, which enables high throughput, bidirectional and parallel transmissions between BLE and WiFi via spectral analysis. The key observation is that commodity WiFi chipsets can operate in the spectral analysis mode, in which WiFi can recognize specific BLE signal waveforms in frequency domain at symbol-level granularity. Leveraging this feature, we manufacture desired waveforms by choosing frame payload at BLE side, and observe spectral patterns at WiFi side. To achieve bidirectional links, we design a PHY-CTC method from WiFi to BLE based on signal emulation. We implement our prototype on USRP (with 802.11g PHY) and commodity BLE devices. Extensive evaluations show that BlueFi can achieve 120Kbps per link from BLE to WiFi with more than 95% frame reception ratio, over 38x faster than state-of-the-art CTCs. Moreover, BlueFi can support 9 wireless links in parallel, leading to the total throughput over 1Mbps.

Wireless Personal Area Network (WPAN) technologies (e.g., Bluetooth, ZigBee) have been widely used in our daily life. However, due to their short transmission distances, the delivery of urgent messages (e.g., emergency alarms) over long distance, suffers from long delay, since the messages have to be transported via multi-hop way. Recent studies show that adding low-power wide-area network (LPWAN) radios such as LoRa onto WPAN devices (e.g., Bluetooth) effectively overcomes the limitation. However, the introduce of heterogeneous communication inevitably incurs extra hardware cost, deployment inconvenience and traffic overhead from gateways. In this paper, we present BLE2LoRa, a novel Bluetooth Low Energy (BLE) to LoRaWAN cross-technology communication (CTC) approach, which leverages the frequency shifting ability of BLE device to emulate LoRa's chirp signal. Such emulation is feasible because a chirp is a signal whose frequency increases or decreases over time, while a BLE device can also construct specific signals with ladder-shaped frequencies which is similar to LoRa chirp by carefully selecting BLE payload bits. Therefore, without any hardware modification at both sender and receiver side, a LoRa device can demodulate BLE frames. Moreover, leveraging the high sensitivity of LoRa base station, a long distant CTC can be achieved. We bulid our BLE2LoRa prototype on USRP B210 (with LoRaWAN PHY) and commodity BLE chips (CC1200). Our evaluation reveals that BLE2LoRa can achieve 4.06kbps throughput from BLE to LoRa with more than 80% frame reception rate, leading to over 600 meters communication distance, which is over 20x range extension over native Bluetooth.

Cross-technology Communication (CTC) is a promising paradigm for efficient coordination and cooperation among heterogeneous wireless technologies. Recent advances in physical-layer CTC (PHY-CTC) approaches the standards' maximum transmission rate by exploring PHY-layer signal features. However, due to the lack of reliable feedback, current PHY-CTC technologies can hardly ensure transmission reliability. This paper presents RAP (Reliable Acknowledged PHY-CTC), a bidirectional CTC design with reliable PHY-CTC feedback. First, we present a novel PHY-CTC technique to efficiently establish a reliable feedback channel (e.g., ACKs or NACKs). Then, based on the feedback, we propose a joint intra-packet coding and inter-packet coding scheme to improve the reliability of CTC. Finally, we present an on-demand data (re)transmission scheme to support unicast, multicast and broadcast more efficiently. We implement and evaluate RAP on USRP N210 with IEEE 802.11g PHY (WiFi) and commodity ZigBee devices. The experiment results show RAP achieves reliable data transmission (>99% packet reception rate (PRR)) and high throughput (over 35kbps) under a wide range of scenarios.

The tag collision avoidance has been viewed as one of the most important research problems in RFID communications and bit tracking technology has been widely embedded in query tree (QT) based algorithms to tackle such challenge. Existing solutions show further opportunity to greatly improve the reading performance because collision queries and empty queries are not fully explored. In this paper, a bit query (BQ) strategy based M-ary query tree protocol (BQMT) is presented, which can not only eliminate idle queries but also separate collided tags into many small subsets and make full use of the collided bits. To further optimize the reading performance, a modified dual prefixes matching (MDPM) mechanism is presented to allow multiple tags to respond in the same slot and thus significantly reduce the number of queries. Theoretical analysis and simulations are supplemented to validate the effectiveness of the proposed BQMT and MDPM, which outperform the existing QT-based algorithms. Also, the BQMT and MDPM can be combined to BQ-MDPM to improve the reading performance in system efficiency, total identification time, communication complexity and average energy cost.

Identification efficiency is a key performance metrics to evaluate the ultra high frequency (UHF) based radio frequency identification (RFID) systems. In order to solve the tag collision problem and improve the identification rate in large scale networks, we propose a collision arbitration strategy termed as group-based binary splitting algorithm (GBSA), which is an integration of an efficient tag cardinality estimation method, an optimal grouping strategy and a modified binary splitting. In GBSA, tags are properly divided into multiple subsets according to the tag cardinality estimation and the optimal grouping strategy. In case that multiple tags fall into a same time slot and form a subset, the modified binary splitting strategy will be applied while the rest tags are waiting in the queue and will be identified in the following slots. To evaluate its performance, we first derive the closed-form expression of system throughput for GBSA. Through the theoretical analysis, the optimal grouping factor is further determined. Extensive simulation results supplemented by prototyping tests indicate that the system throughput of our proposed algorithm can reach as much as 0.4835, outperforming the existing anti-collision algorithms for UHF RFID systems.

Physical-Layer Cross-Technology Communication (PHY-CTC), which achieves direct communication among heterogeneous technologies, brings great opportunities to help diverse IoT devices achieve harmonious coexistence through explicit coordination. The core technique of PHY-CTC is signal emulation which utilizes the signal of one technology (e.g., WiFi) to emulate the signal of another technology (e.g., ZigBee). The signal emulation based approach, however, inevitably introduces emulation errors which further lead to unreliable communication. In this paper, we aim to recover the intrinsic emulation errors and establish reliable PHY-CTC. We propose TwinBee which (i) explores chip-level error patterns and (ii) corrects emulation errors with symbol-level chip-combining coding/decoding and soft mapping. To achieve this, TwinBee dose not require accessing chip information as well as making hardware changes. We implement TwinBee on commodity devices (i.e., Laptops with Atheros AR2425 WiFi card and TelosB motes) and the USRPN210 platform (for physical layer evaluation). Experiment results show that TwinBee significantly improves the Packet Reception Ratio (PRR) of PHY-CTC from 50%-60% to more than 99%. Furthermore, we demonstrate the reliability of TwinBee in a data dissemination application over a network of 20 TelosB nodes, achieving over 42× reduction of data dissemination delay compared to the state-of-the-art.

Radio-frequency identification (RFID) is a major enabler of Internet of Things (IoT), and has been widely applied in tag-intensive environments. Tag collision arbitration is considered as a crucial issue of such RFID system. To enhance the reading performance of RFID, numerous anti-collision algorithms have been presented in previous literatures. However, most of them suffer from the slot efficiency bottleneck of 0.368. In this paper, we revisit the performance of tag identification in Aloha-based RFID anti-collision approaches from the perspective of time efficiency. Based on comprehensive reviews and analysis of the existing algorithms, a novel partitioning approach is proposed to maximize identification performance in framed slotted Aloha based UHF RFID systems. In the proposed approach, the tag set is divided into many groups which only contains a few tags, and then each group is identified in sequence. Benefiting from the optimal partition, the proposed algorithm can achieve a significant performance improvement. Simulation results supplemented by prototyping tests show that the proposed solution achieves an asymptotical slot efficiency up to 0.4348, outperforming the existing UHF RFID solutions.

Radio frequency identification (RFID) is widely applied in massive items tagged domains. Existing medium access control (MAC) solutions primarily focus on improving slot efficiency or reducing the total number of slots. However, with pervasive applications of RFID, the time and energy consumption are increasingly important and should be considered in the new design. In this paper, we re-exam the problem of tag identification in UHF RFID system from the perspective of time and energy consumption. The presented work comprehensively reviews and analyzes the prior tag reading protocols. Based on prior art, we further discuss a novel design of tag reading algorithm to improve both time and energy efficiency of EPC C1 Gen2 UHF RFID standard. By exploring the effectiveness of embedding slot-by-slot mechanism in a sub-frame observation phase and combine the sub-frame and slot-by-slot observation in the proposed algorithm, which can achieve more fine-grained frame size adjustment with time and energy-efficiency. Moreover, the cardinality estimation function of the algorithm is implemented by the look-up tables, which allows dramatically reduction in computational complexity and energy consumption. Both simulation results and experiments show clear performance improvement over the commercial solutions.

Low-Power Wide-Area Network (LPWAN) is an emerging platform for Internet-of-Thing (IoT) devices to access the base station far away. However, two of the most popular IoT techniques, Bluetooth and ZigBee, can not be connected to LPWAN directly due to their very short communication distance (e.g., 30 meters). Our work, named as Symphony, implements an universal LPWAN on existing heterogeneous wireless devices by overcoming two challenges. First, Symphony achieves a long-range communication from both Bluetooth Low Energy (BLE) and ZigBee to LoRaWAN, enabling these ubiquitously deployed low-power devices to access a base station from faraway. It is achieved by exploiting Narrow-Band Communication, where the BLE/ZigBee devices generate ultra narrow-band signals (i.e., single-tone sinusoidal signals) through payload manipulation, while the LoRaWAN base station detects these signals via its demodulator, which has a high receiver sensitivity for long range communication. Second, Symphony enables concurrent transmissions from heterogeneous radios (i.e., BLE, ZigBee and LoRa) at a LoRaWAN base station. This is achieved by Cross-Technology Parallel Decoding, which is able to disentangle and decode the interfering transmissions. Our evaluations on USRP and commodity devices reveal that Symphony achieves a concurrent wireless communication from BLE, ZigBee and LoRa commercial chips to a LoRaWAN base station over 500 meters, $16 \times$ range extension over native BLE/ZigBee.

Recent advances on physical-layer Cross-Technology Communication (PHY-CTC) achieve high throughput direct communication across different wireless technologies, by emulating the standard waveform of the receiver. However, this signal emulation method faces the challenges of inherent unreliability due to the imperfect emulation. Therefore, it's not suitable to achieve PHY-CTC from WiFi to BLE, since a BLE receiver can not tolerate any bit error in preamble checking when receiving a BLE frame. We present NBee, the first WiFi to BLE physical-level CTC. The key insight lies in Narrow-Band Decoding, i.e., 22MHz bandwidth WiFi (802.11b) signal can be correctly decoded at the BLE RF front-end with only 1MHz bandwidth, if the WiFi payload bits are selected by a specific pattern. More specifically, NBee leverages the unique signatures in the WiFi signal distorted by 1MHz Low Pass Filter (LPF) at BLE to extract information. Evaluation results on commodity BLE chips show NBee can achieve 1Mbps CTC with 95% packet reception rate (PRR), 3400x faster than the state-of-art CTC from WiFi to BLE.

Cross-Technology Communication (CTC) is an enabling technology for efficient coexistence and effective cooperation among heterogeneous wireless devices by exchanging data frames directly without gateways. Recent advances in the physical-layer CTC achieve thousands of times faster speed than that of previous packet-level CTC techniques. However, physical-layer CTC still faces the challenge of inherent unreliability due to the imperfection of physical-layer signal emulation. Our work, named TwinBee, aims to recover the intrinsic errors of physical-layer CTC, by exploring chip-level error patterns. This is achieved interestingly without even observing the chip information and without any hardware modification. System evaluation shows that our key idea, namely symbol-level chip-combining decoding with soft mapping, significantly improves the Packet Reception Ratio (PRR) of the physical-layer CTC from 50%-60% to more than 99%. We also demonstrate the reliability of TwinBee in a data dissemination application over a network of 20 TelosB nodes, achieving over 40x reduction of data dissemination delay compared to Deluge.

The tag collision problem is considered as one of the critical issues in RFID system. Recently, bit tracking technology has been proposed for query tree (QT) based protocols to resolve tag collision efficiently. However, the performance of these protocols remain to be improved due to unused collided bits and idle slots. In this paper, a query method Bit query is presented, which requires the tag to respond a mapped bit string instead of its ID sequence. Compared with traditional ID query, it not only can eliminate idle queries, but also can separate collided tags into many small subsets and make full use of the collided bits as well. Based on this method, a novel query tree protocol Bit Query based M-ary Tree (BMQT) protocol is proposed, which recursively resolves collisions by forming a M-ary tree, and optimally switches from Bit query mode to ID query mode for quickly identifying the tags when tag is readable. Theoretical analysis and simulation results show that the system efficiency of BMQT is closed to 0.89, which outperforms the other existing QT-based and hybrid algorithms.

Energy-efficient and reliable beacon dissemination is essential for many protocols and applications in wireless sensor networks (WSNs). In low-duty-cycle sensor networks, a node may miss incoming beacons due to unsynchronized active/sleep modes as a result of clock errors. Therefore it is critical to guarantee beacons arrive at the receiver in the right time. In this paper, we analyze this problem of unsuccessful beacon reception, and present Guard Beacon, an optimal beacon scheduling strategy for energy efficient and reliable beacon transmission. By jointly investigations on the beacon reception probability and power consumption on beacon sending and receiving, we find an optimal iterative solution as well as a sub-optimal analytical solution, of how many beacons should be sent, and when to send. The strategy is implemented in a real-world testbed. The evaluation results show that the proposed Guard Beacon can not only reduce the power consumption on beacon dissemination compared with other strategies, but also ensure reliable beacon reception, in low-duty-cycle wireless sensor networks.

Delivering sensed data to the sink reliably in wireless sensor networks (WSN) calls for a Delivering sensed data to the sink reliably in wireless sensor networks (WSN) calls for a scalable, energy-efficient, and error-resilient routing solution. In this paper, a distributed energy-efficient and reliable routing protocol is proposed using cross-layer design techniques, by jointly considering the routing algorithm in network layer and the power control policy in physical layer. Based on the analysis of how to construct a minimum-power route with the end-to-end reliability constraint in WSN, a distributed cross-layer routing protocol with adaptive transmission power control (DRPC) is proposed. Evaluation results show that our strategy performs better than other routing algorithms in terms of energy efficiency as well as reliability.

This paper demonstrates the possibility of increasing energy performance in Wireless Sensor Networks by the adaptation of spreading code length. Through the experimental approach, it is shown that a WSN device can achieve higher data rate with adaptive spreading code length, which can increase the throughput and reduce energy usage. Based on our findings from experiment, we propose to actively adjust the transmission power to enable higher transmission rate, which will significantly decrease the device active time. As a result, the overall energy efficiency can be increased. Such scheme has been validated using simulation, which shows that the network life time can be increased by 36%.

We present a novel cross-layer approach in minimizing energy consumption for multi-hop cooperative sensor networks, under the end-to-end reliability QoS requirement. Taking into consideration the power consumed in receiving and processing circuitry, we jointly determine the optimized routing, relay selection and power allocation strategies for direct transmission, single-relay cooperation and multi-relay cooperation scenarios. We demonstrate through analysis and simulations that an energy efficiency trade-off exists for cooperation, and the tradeoff depends on several parameters such as the average inter-node distance, the required QoS, the number of relays, the receiving power and so on. Results also show the proposed cross-layer strategy achieves significant energy savings compared to other recently proposed algorithms.

A cross-layer forward error control (FEC) scheme for wireless multimedia sensor networks (WMSNs) is proposed in this paper. Employing a novel fountain code so called iterative joint source-channel fountain code (ISCFC), this cross-layer FEC scheme joint exploits the physical layer, transport layer and the application layer FEC approaches, and is capable of offering strong bit error correction capability as well as overcoming the packet loss in the multi-hop wireless communication.

We propose a trellis-based soft-input-soft-output (SISO) a posteriori probability (APP) decoding technique for variable-length coded correlated sources. This technique allows not only the (ML) sequence estimation but also the computation of bit-based reliability values. The notable feature of the proposed technique is that its calculation complexity and memory space requirement are both low. Moreover, we bring the technique of scaling extrinsic information to the field of iterative joint source-channel decoding (ISCD), and obtain fixed scaling factors (SFs) based on the extrinsic information transfer (EXIT) charts analysis. This technique obviously improves the ISCD performance with increasing neglectable complexity. Simulation results show that the proposed ISCD scheme provides a significant improvement on error protection capability for the variable-length coded correlated sources.