Recent years have seen the rapid development of long-range wide area network (LoRaWAN) operating in region-specific sub-GHz frequency bands (e.g., 868 MHz in Europe and 915 MHz in North America). To achieve global deployment, LoRaWAN has been extended to operate in the globally available 2.4 GHz unlicensed band. However, this shift exposes LoRaWAN to significant interference from coexisting Wi-Fi networks, which share the same band and typically transmit at much higher power levels. To address this problem, this paper presents InMAC, an interference-aware medium access control (MAC) protocol designed to improve coexistence between LoRaWAN and Wi-Fi networks. To the best of our knowledge, InMAC is the first MAC protocol specifically tailored to mitigate Wi-Fi interference for 2.4 GHz LoRaWAN. InMAC enhances LoRaWAN communication by probabilistically exploiting the silent time in Wi-Fi traffic, leveraging a Wi-Fi traffic profiling mechanism at LoRaWAN gateways and a packet length adaptation strategy at end devices. In addition to mitigating external interference from Wi-Fi, InMAC also tackles internal interference caused by signal collisions among LoRaWAN end devices. It incorporates a novel channel access mechanism based on Channel Activity Detection, a carrier-sensing technique adapted specifically for LoRaWAN. Experimental results demonstrate that InMAC reduces both external Wi-Fi interference and internal LoRaWAN collisions, achieving up to a 111% throughput boost over existing approaches.

Text-to-image person re-identification (TIReID) aims to retrieve the target pedestrians according to specific textual descriptions. Benefiting from abundant annotated training data, current supervised TIReID methods have achieved impressive performance. However, annotating cross-modality data is extremely time-consuming, which limits their application in real-world scenarios. Several methods attempt to generate text descriptions or pseudo-labels but neglect the dependability of image-text matching relationships or identity information. To this end, we propose a Dependability Feature Learning based on Sample Generation (DFLSG) for unsupervised TIReID. First, we introduce a dependable text generation method that leverages multimodal large language models to generate diverse texts and further filtrate dependable texts for establishing image-text matching relationships. Second, we design an Error Sample Filtering Module (ESFM) to eliminate abnormal samples and obtain reliable identity labels. Furthermore, we develop a Multilevel Triplet Joint Learning (MTJL) process, which continuously optimizes the cross-modality dependable feature from center and instance views. Extensive experiments are implemented to assess the proposed DFLSG on four mainstream TIReID databases. Experimental results demonstrate that DFLSG achieves state-of-the-art performance compared with other unsupervised methods. Code will be available at: https://github.com/CLS-2001/DFLSG.

Long-range wide area network (LoRaWAN) has been newly designed to exploit the 2.4 GHz unlicensed band instead of the traditional sub-GHz bands. However, this makes LoRaWAN frequently suffer from wireless communication failures caused by the cross-technology interference (CTI) from coexisting Wi-Fi networks using the same 2.4 GHz band. As a physical-layer solution to this problem, this paper presents CoWiL to combat the CTI from Wi-Fi to LoRa (the physical layer of LoRaWAN) in the 2.4 GHz band for better coexistence between LoRaWAN and Wi-Fi networks. Existing approaches address this problem by sacrificing Wi-Fi transmission performance or assuming that Wi-Fi interferes with only a small portion of LoRa signals. Unlike them, CoWiL does not affect normal Wi-Fi communications and is workable regardless of the degree of the CTI. This is achieved by implementing CoWiL at a LoRa receiver to directly extract LoRa data out of the CTI from Wi-Fi. Specifically, CoWiL exploits the correlation of the signal demodulation results between the preamble and the payload of a LoRa signal. A novel frequency bin mask is generated based on the demodulated preamble and then applied to the following payload for data decoding. Experimental results in various real-world environments show that in comparison with the existing solutions, CoWiL can reduce the packet error rate of LoRa transmissions by up to 96% under the CTI from Wi-Fi.

Most Wi-Fi backscatter systems have been proposed with mechanisms that decode the data from backscatter tags based on XOR operations. These XOR-based mechanisms require two receivers listening on the Wi-Fi and backscatter channels and consider the channel coding process as an obstacle to decode the backscatter packets. We focus on channel coding, which is a technique that all Wi-Fi systems use to detect and correct errors from the wireless channels. In our backscatter system, the tag reflects ambient Wi-Fi signals at the symbol level to transmit tag data. When the receiver demodulates and decodes these reflected signals, certain patterns of errors can be observed because of the symbol and subcarrier structures according to the OFDM scheme in Wi-Fi. We propose a method for detecting bit errors caused by backscatter tags and investigate the possibility of utilizing them in decoding.

Recognizing if two objects are in close physical contact (CPC) is the basis of various Internet-of-Things services such as vehicle proximity alert and radiation exposure reduction. This is achieved traditionally through tailor-made proximity sensors that proactively transmit wireless signals and analyze the reflection from an object. Despite its feasibility, the past few years have witnessed the prosperity of reactive CPC detection techniques that do not need spontaneous signal transmission and merely exploit received wireless signals from a target. Unlike existing approaches entailing additional effort of multiple antennas, dedicated signal emitters, human intervention, or a back-end server, this article presents TONARI, an effortless CPC detection framework that performs in a reactive manner. TONARI is developed for the first time with LoRa, the representative of unlicensed low-power wide area network (LPWAN) technologies, as the wireless signal for CPC detection. At the heart of TONARI lies a novel feature arbitrator that decides whether two devices are in CPC or not by distinguishing different types of LoRa chirp-based additive sample magnitude sequences. Software-defined radio-based experiments are conducted to show that the achievable CPC detection accuracy via TONARI can reach 100% in most practical cases.

This paper proposes a heterogeneous carrier-sense multiple access (CSMA) protocol named LoHEC as the first research attempt to improve energy fairness when applying CSMA to long-range wide area network (LoRaWAN). LoHEC is enabled by Channel Activity Detection (CAD), a recently introduced carrier-sensing technique to detect LoRaWAN signals even below the noise floor. The design of LoHEC is inspired by the fact that existing CAD-based CSMA proposals are in a homogeneous manner. In other words, they require LoRaWAN end devices to perform identical CAD regardless of the differences of their used network parameter – spreading factor (SF). This causes energy consumption imbalance among end devices since the consumed energy during CAD is significantly affected by SF. By considering the heterogeneity of LoRaWAN in terms of SF, LoHEC requires end devices to perform different numbers of CAD operations with different CAD intervals during channel access. Particularly, the number of needed CADs and CAD interval are determined based on the CAD energy consumption under different SFs. We conduct extensive experiments regarding LoHEC with a practical LoRaWAN testbed including 60 commercial off-the-shelf end devices. Experimental results show that in comparison with the existing solutions, LoHEC can achieve up to$0.85\times $improvement of the energy fairness on average.

The last decade has witnessed the ever-increasing deployment of Wi-Fi networks and the explosion of Bluetooth-based applications. As a result, the coexistence of Bluetooth piconets with highly-dense Wi-Fi networks is a common phenomenon currently. Unlike Wi-Fi that conducts carrier sensing before channel access, Bluetooth adopts frequency hopping based on a predefined hop sequence, which inevitably incurs considerable cross-technology interference to Wi-Fi. While the Adaptive Frequency Hopping technique is standardized for interference reduction, it does not perform well in current practice where densely-deployed Wi-Fi networks commonly cover the whole 2.4 GHz unlicensed spectrum. In this context, this article presents BuSAR, a novel approach to account for the coexistence problem between Bluetooth piconets and dense Wi-Fi networks. BuSAR embodies the first work to aim at mitigating the cross-technology interference between Bluetooth and highly-dense Wi-Fi networks in a distributed manner. At the heart of BuSAR lies a subtle technique called Bluetooth slot availability randomization, which exploits the redundancy of erroneous Bluetooth packets for better Bluetooth/Wi-Fi coexistence. With BuSAR adopted, multiple Bluetooth piconets are guaranteed to operate independently and only a lightweight algorithm is needed to be implemented at each Bluetooth device. Both theoretical analysis and experimental results validate the feasibility and superiority of BuSAR.

The prosperity of IEEE 802.11-based Wi-Fi networks aggravates cross-technology interference to IEEE 802.15.4-enabled ZigBee networks widely deployed to enable various Internet-of-Things applications. To make ZigBee communication reliable and robust even in a dense Wi-Fi environment, taming Wi-Fi interference in ZigBee networks especially from the perspective of physical layer is of paramount importance. In this context, this work takes aim to design a novel Wi-Fi interference-resilient ZigBee decoder called PolarScout, which separates collided ZigBee signal samples out of Wi-Fi interference to bootstrap ZigBee data decoding. Unlike several existing solutions which need clear signal preamble, tremendous signal strength difference between ZigBee and Wi-Fi, and Wi-Fi interference recognition in prior to ZigBee decoding, PolarScout aims at direct ZigBee decoding in a more generic and challenging case where Wi-Fi interference features a wide range of power levels and arises within a ZigBee packet at an arbitrary position. At the heart of PolarScout lies a subtle shell-shaping technique which harnesses a customized sample sequence to smooth the shell of corrupted signal samples. PolarScout then refers to the resulting shell to recover each contaminated ZigBee sample. Experimental results validate the superiority of PolarScout and its resilience to a wide range of Wi-Fi interference types.

Radio frequency identification (RFID) technology provides a promising solution to the problem of missing object identification in large-scale systems, such as warehouses and bookstores by employing RFID readers to communicate with numerous RFID tags, each of which is attached to a monitored object. To achieve prompt identification of missing tags/objects, extensive research is carried out while the transmission of expatiatory bits from each tag and the occurrence of substantial tag collisions critically degrade the time efficiency. Therefore, this motivates us to propose ProTaR, a probabilistic tag retardation-based protocol, which addresses the missing tag identification problem in a more time-efficient way than the prior work. Based on an improvement of conventional frame-slotted ALOHA algorithm, ProTaR aims at alleviating the tag collision problem and achieving compact tag transmissions. The novelty of ProTaR is manifested mainly in two aspects. ProTaR leverages a mask at a reader to distill partial bits from 96-bit identifier (ID) of each tag for the characterization of tag uniqueness. In this context, ProTaR averts the transmission of redundant bits from both the tags and reader. A bit vector is constructed by the reader to inform each tag of the transmissions of others. This idea successfully eliminates the tag collisions, and hence makes full utilization of tag responses. Experimental results validate that ProTaR achieves 100% identification accuracy regardless of missing tag ratio. Furthermore, extensive simulations present that ProTaR enables the time efficiency improvement of up to 88% compared with benchmarks while merely degrading the optimum by 15%.