Integrated Sensing and Communication (ISAC) is a key enabler for Sixth-Generation (6G) and future wireless networks, which seamlessly combines ambient sensing with data communication. In this paper, we propose a novel ISAC-enabled Non-Orthogonal Multiple Access (NOMA) scheme named ISAC-Space-Time Block Coding (STBC) NOMA. We present a thorough performance comparison of our proposed scheme against three previously studied ISAC-NOMA variants: Conventional ISAC-NOMA, ISAC-Unmanned Aerial Vehicle (UAV) NOMA, and ISAC-Generalized Space Shift Keying (GSSK) NOMA, in a multi-user scenario. The comparison specifically focuses on three critical performance metrics: spectral efficiency, Bit Error Rate (BER), and Successive Interference Cancellation (SIC) decoding complexity. The evaluation results show that ISAC-STBC NOMA consistently outperforms the other schemes across all metrics. Specifically, ISAC-STBC NOMA achieves approximately 24% higher spectral efficiency at 30 dB Signal-to-Noise Ratio (SNR), reduces the BER by approximately 30%, and lowers SIC decoding complexity by up to 25%. These findings position ISAC-STBC NOMA as a strong candidate for next-generation networks, offering a well-balanced solution that enhances communication robustness, spectrum utilization, and computational efficiency.

Multi-enterprise applications in fields like supply chain management, finance, and healthcare require complex collaboration and data exchange among organizations to ensure operational efficiency and build trust. Permissioned blockchains emerged as a promising solution, providing shared, immutable ledgers that enhance transparency, traceability, and trust among authorized parties. However, during asset trading between organizations, they must verify the legitimacy of asset transfers, including asset ownership and quantity, while protecting sensitive asset owner information. To achieve both verifiability and privacy, this paper introduces PAVE, Privacy-preserving Aggregated Verification system for Multi-Enterprises Blockchain, a framework that integrates zero-knowledge proofs to enable secure asset verification without breaking user privacy. To achieve proof efficiency, PAVE introduces a proof aggregation mechanism that consolidates multiple transaction verifications into a single proof, significantly reducing computational overhead for large-scale scenarios. Evaluation results show that, with the proof aggregation mechanism, PAVE achieves low verification latency and resource utilization, making it a scalable solution for privacy-preserving asset verification across multiple enterprises.

Since only a portion of the overall transmit power is reserved in non-orthogonal multiple access (NOMA), cell edge users have slightly reduced spectral density. As a result, the coupled users in NOMA have a limited performance. We tackle this problem after suggesting a totally new scheme called space time block coding with generalized space shift keying (STBC-GSSK) and STBC unnamed aerial vehicle (STBC-UAV) with NOMA compatibility. The proposed scheme’s spectral efficiency and outage performance were evaluated and compared to STBC-NOMA and GSSK-NOMA. The STBC-GSSK NOMA strategy has been seen to surpass all other schemes.

The increasing quantity of consumers has met the requirement to identify a winning plan for various access in the future of wireless communication. It's been observed that while utilizing the available resources, the non-orthogonal multiple access (NOMA) technique performs significantly better than any other techniques. When used widely, sequential interference can-cellation (SIC) can effectively reduce interferences; nonetheless, this is a typical scenario in NOMA. However, heavy use increases the framework's complexity, which lowers energy effectiveness. There is now interest in hybrid NOMA techniques as a way to counteract the negative aspects of the NOMA procedure. Hybrid NOMA schemes have been suggested in several recent articles as a viable substitute to mitigate the drawbacks of NOMA. Since only a portion of the overall transmit power is reserved in non-orthogonal multiple access (NOMA), cell edge users have slightly reduced spectral density. As a result, the coupled users in NOMA have a limited performance. We tackle this problem after suggesting a totally new scheme called space time block coding with generalized space shift keying (STBC-GSSK) and STBC unnamed aerial vehicle (STBC-UAV) with NOMA compatibility. The proposed scheme's spectral efficiency and outage performance were evaluated and compared to STBC-NOMA and GSSK-NOMA. The STBC-GSSK NOMA strategy has been seen to surpass all other schemes.

There have been increasing interests in exploring the sensing capabilities of RFID to enable numerous IoT applications, including object localization, trajectory tracking, and human behavior sensing. However, most existing methods rely on the signal measurement either in a low multipath environment, which is unlikely to exist in many practical situations, or with special devices, which increase the operating cost. This paper investigates the possibility of measuring ‘multi-path-free’ signal information in multipath-prevalent environments simply using a commodity RFID reader. The proposed solution, Clean Physical Information Extraction (CPIX), is universal, accurate, and compatible to standard protocols and devices. CPIX improves RFID sensing quality with near zero cost – it requires no extra device. We implement CPIX and study three major RFID sensing applications: tag localization, device calibration and human behavior sensing. CPIX reduces the localization error by 30% to 50% and achieves the MOST accurate localization by commodity readers compared to existing work. It also significantly improves the quality of device calibration and human behaviour sensing.

RFID-based Human Gesture Recognition (HGR) has gained much attention and become a promising solution for device-free human-computer interaction (HCI) in recent years. However, existing RFID sensing suffers from limited scalability as the system needs to be re-trained whenever unseen gestures are introduced, which causes overheads of data collection and re-training. Meanwhile, cross-domain sensing may also fail as the correlation between gestures and induced variations on wireless signals will change when a different domain (i.e., environment or user) is involved. In this paper, we propose a RFID-based HGR system named RF-HGR, which can recognize unseen classes in new domains with only a few labeled samples. Specifically, RF-HGR employs a lightweight few-shot learning (FSL) framework based on fine-tuning domain adaptation to eliminate model re-training overhead. We establish a real-world prototype using commercial off-the-shelf (COTS) RFID devices and the preliminary evaluation results show that RF-HGR with three-, five-, seven-shot learning can recognize novel classes in unseen domains with an accuracy of 50.6%, 65.7% and 72.8% respectively.

Identifying item-item and user-item interactions is an essential requirement of many ubiquitous computing applications. Recently methods of physically altering RFID tag hardware have been proposed to enable recognizing certain interactions. However, they do not address the problem that when a large number of tags exist in the environment and concurrent interactions may happen, the system may not be able to identify these interactions accurately or efficiently. We propose ChopTags, a low-cost and accurate interaction identification using passive RFID tags, with applications including automatic chess notation, shipment storage tracking, interactive libraries/retail stores/classrooms, and smart conference badges to track the attendees who had conversations. Each ChopTags module contains a passive tag chip and an antenna that are separated and can only be read when the chip is in contact with an antenna (from another pairing ChopTags module). ChopTags costs cheap hardware to scale to many users and items, achieves near 100% accuracy in complex environments, and is easy to use for children, seniors, and others who have difficulty of operating smart devices. We resolve a number of challenges of using ChopTags including improving query throughput/accuracy and identifying concurrent interactions. We build two prototypes based on ChopTags: 1) a chess auto-notation system and 2) a tag array for user interactions. ChopTags allows tracking the moves of 96 tag modules for the chess game with almost 100% accuracy and no prior work can achieve this.

Though widely used in industrial and logistic applications, current passive Radio Frequency Identification (RFID) technology still has a fundamental limitation: Individual users who do not carry any reader find it difficult to interact with tagged items, such as retrieving their digital profiles and requesting certain associations with them. Recent proposals to improve the user–item interaction experience rely on special hardware, such as a smartphone-based RFID scanner. This work presents a promising approach to allowing each user to interact with a tagged item using only one passive tag, which is named the Tag Mutual Identification Interface (TagMii). TagMii requires a user to put one’s user tag in physical proximity with an item tag to express certain interactions between the user and item. The key idea behind TagMii is to utilize two experimental observations: (1) inductive coupling for detecting interaction events, and (2) channel similarity for determining the actual interacting tags. We implement TagMii using commodity off-the-shelf RFID devices and conduct experiments in complex environments with rich multipath, mobility, wireless signals, electrical devices, and magnetic fields. The results show that TagMii provides accurate mutual identification. TagMii is a completely new approach for user–item interactions in pervasive environments and enables many user-friendly Internet of Things applications with low cost and convenience.

The hierarchical mobile edge computing satisfies the stringent latency requirements of data access and processing for emerging edge applications. The data location service is a basic function to provide data storage and retrieval to enable these applications. However, it still lacks research of a scalable and low-latency data location service in the environment. The existing solutions, such as DNS and DHT, fail to meet the requirement of those latency-sensitive applications. Therefore, in this article, we present a low-latency hybrid data-sharing framework, HDS. The HDS divides the data location service into two parts: intra-region and inter-region. More precisely, we design a data sharing protocol called Cuckoo Summary to achieve fast data localization in intra-region. Furthermore, for the inter-region data sharing, we develop a geographic routing based scheme to achieve efficient data localization with only one overlay hop. The advantages of HDS include short response latency, low implementation overhead, and few false positives. We implement the HDS framework based on a P4 prototype. The experimental results show that, compared to the state-of-the-art solutions, our design achieves 50.21% shorter lookup paths and 92.75% fewer false positives.

We propose to study mobile object tracing, which allows a mobile system to report the shape, location, and trajectory of the mobile objects appearing in a video camera and identifies each of them with its cyber-identity (ID), even if the appearances of the objects are not known to the system. Existing tracking methods either cannot match objects with their cyber-IDs or rely on complex vision modules pre-learned from vast and well-annotated datasets including the appearances of the target objects, which may not exist in practice. We design and implement TagAttention, a vision-RFID fusion system that achieves mobile object tracing without the knowledge of the target object appearances and hence can be used in many applications that need to track arbitrary un-registered objects. TagAttention adopts the visual attention mechanism, through which RF signals can direct the visual system to detect and track target objects with unknown appearances. Experiments show TagAttention can actively discover, identify, and track the target objects while matching them with their cyber-IDs by using commercial sensing devices in complex environments with various multipath reflectors. It only requires around one second to detect and localize a new mobile target appearing in the video and keeps tracking it accurately over time.

Edge computing satisfies the stringent latency requirements of data access and processing for applications running on edge devices. The data location service is a key function to provide data storage and retrieval to enable these applications. However, it still lacks research of a scalable and low-latency data location service in mobile edge computing. Meanwhile, the existing solutions, such as DNS and DHT, fail to meet the low latency requirement of mobile edge computing. This paper presents a low-latency hybrid data-sharing framework, HDS, in which the data location service is divided into two parts: intra-region and inter-region. In the intra-region part, we design a data sharing protocol called Cuckoo Summary to achieve fast data localization. In the inter-region part, we develop a geographic routing based scheme to achieve efficient data localization with only one overlay hop. The advantages of HDS include short response latency, low implementation overhead, and few false positives. We implement our HDS framework based on a P4 prototype. The experimental results show that, compared to the state-of-the-art solutions, our design achieves 50.21% shorter lookup paths and 92.75% fewer false positives.

Person identification and tracking (PIT) is an essential research topic in computer vision (CV). A CV-based system typically needs to identify, locate, and track persons appearing in its sight. In this work, we propose RFTrack, an RFID and CV fushion system that enables cameras in public areas (like surveillance cameras) to 'recognize' the physical-identity(ID) of persons in the fields of view and track the persons with specific IDs with no training efforts. By asking the users to perform a simple authentication, the system will be aware of the targets' IDs in its sensing range. Later through comparing the motion trajectories derived from both camera videos and RF signal, we can associate RFID-tagged human objects in videos with their physical IDs. A preliminary study conducted shows that RFTrack can actively identify and track the RFID-tagged target objects using commercial RFID devices and cameras, in complex indoor environments where various multipath reflectors exist.

We provide the first solution to an important question, “how a physical-layer authentication method can defend against signal replay attacks”. It was believed that if an attacker can replay the exact same reply signal of a legitimate authentication object (such as an RFID tag), any physical-layer authentication method will fail. This paper presents Hu-Fu, the first physical layer RFID authentication protocol that is resilient to the major attacks including tag counterfeiting, signal replay, signal compensation, and brute-force feature reply. Hu-Fu is built on two fundamental ideas, namely inductive coupling of two tags and signal randomization. Hu-Fu does not require any hardware or protocol modification on COTS passive tags and can be implemented with COTS devices. We implement a prototype of Hu-Fu and demonstrate that it is accurate and robust to device diversity and environmental changes, including locations, distance, and temperature. Hu-Fu provides a new direction of battery-free/low-power device authentication that enables numerous IoT applications.

Though being widely used in industrial and logistic applications, current passive RFID technology still has a fundamental limitation: Individual users, who do not carry any reader, are difficult to interact with tagged items, such as retrieving their digital profiles and requesting certain association with them. Recent proposals to improve user-item interaction experience rely on special hardware such as smartphone based RID scanner. This work presents a promising approach to allow each user to interact with tagged item using only one passive tag, which is named Tag Mutual Identification Interface (TagMii). TagMii requires a user to put her user tag in a physical proximity with an item tag to express certain interactions between the user and item. The key idea behind TagMii is to utilize two experimental observations: 1) inductive coupling for detecting interaction events, and 2) channel similarity for determining the actual interacting tags. We implement TagMii using commodity off-the-shelf RID devices and conduct experiments in complex environments with rich multipath, mobility, wireless signals, electrical devices, and magnetic fields. The results show that TagMii provides accurate mutual identification. TagMii is a completely new approach for user-item interactions in pervasive environments and enables many user-friendly IoT applications with low cost and convenience.

We propose to study mobile object tracing, which allows a mobile system to report the shape, location, and trajectory of the mobile objects appearing in a video camera and identifies each of them with its cyber-identity (ID), even if the appearances of the objects are not known to the system. Existing tracking methods either cannot match objects with their cyber-IDs or rely on complex vision modules pre-learned from vast and well-annotated datasets including the appearances of the target objects, which may not exist in practice. We design and implement TagAttention, a vision-RFID fusion system that archives mobile object tracing without the knowledge of the target object appearances and hence can be used in many applications that need to track arbitrary un-registered objects. TagAttention adopts the visual attention mechanism, through which RF signals can direct the visual system to detect and track target objects with unknown appearances. Experiments show TagAttention can actively discover, identify, and track the target objects while matching them with their cyber-IDs by using commercial sensing devices, in complex environments with various multipath reflectors. It only requires around one second to detect and localize a new mobile target appearing in the video aWe thank the anonymous reviewers for their suggestions and comments.nd keeps tracking it accurately over time.

There have been increasing interests in exploring the sensing capabilities of RFID to enable numerous IoT applications, including object localization, trajectory tracking, and human behavior sensing. However, most existing methods rely on the signal measurement either in a low multipath environment, which is unlikely to exist in many practical situations, or with special devices, which increase the operating cost. This paper investigates the possibility of measuring `multipath-free' signal information in multipath-prevalent environments simply using a commodity RFID reader. The proposed solution, Clean Physical Information Extraction (CPIX), is universal, accurate, and compatible to standard protocols and devices. CPIX improves RFID sensing quality with near zero cost - it requires no extra device. We implement CPIX and evaluate its effectiveness on improving the performance on tag localization. The results show that CPIX reduces the localization error by 30% to 50% and achieves the MOST accurate localization by commodity readers compared to existing work.

We provide the first solution to an important question, "how a physical-layer authentication method can defend against signal replay attacks''. It was believed that if an attacker can replay the exact same reply signal of a legitimate authentication object (such as an RFID tag), any physical-layer authentication method will fail. This paper presents Hu-Fu, the first physical layer RFID authentication protocol that is resilient to the major attacks including tag counterfeiting, signal replay, signal compensation, and brute-force feature reply. Hu-Fu is built on two fundamental ideas, namely inductive coupling of two tags and signal randomization. Hu-Fu does not require any hardware or protocol modification on COTS passive tags and can be implemented with COTS devices. We implement a prototype of Hu-Fu and demonstrate that it is accurate and robust to device diversity and environmental changes, including locations, distance, and temperature. Hu-Fu provides a new direction of battery-free/low-power device authentication that enables numerous IoT applications.

There have been increasing interests in exploring the sensing capabilities of RFID beyond its basic identification task, to reveal more information of tagged objects and the physical world. Phase is a crucial physical feature for many RFID sensing applications, such as tag localization. Most existing methods rely on a low multipath environment for accurate phase measurement. Unfortunately, practical environments are highly likely to be multipath-revalent, especially for indoors. This paper presents the first work to extract "clean" phase measurement of RFID tags in multipath-prevalent environments. We propose CPEX (Clean Phase EXtraction) based on theoretical modeling, which is also validated via experimental results of our prototype implementation. We studied the performance of CPEX on tag localization. CPEX can achieve median errors of 4.3-6.4cm (in different setups), which is the most accurate 3D tag localization result in multipath-prevalent environments.

Dynamic spectrum access (DAS) is an efficient way to redistribute spare channels among users. Conventionally, dynamic spectrum access is conducted through (double) spectrum auction, where a third-party auctioneer collects bids from buyers and sellers, and determines the spectrum allocation. Rather than placing bids only on individual channels, combinatorial spectrum auction allows buyers to express their valuations for different combinations of channels. However, auction mechanisms are generally vulnerable to the collusion between the auctioneer and buyers or sellers. Furthermore, to find the optimal allocation in combinatorial auction is usually NP-hard. In this paper, we propose to leverage a many-to-many matching framework to realize combinatorial spectrum trading. Unlike traditional many-to-many matching problem, spectrum matching is more challenging, because spectrum allocation is interference-limited rather than quota-limited. To deal with this problem, we propose a novel matching algorithm, which takes buyers' interference relationship into consideration. We theoretically prove that the matching result is individual rational, strong pairwise stable and is a subgame-perfect Nash equilibrium of the corresponding spectrum bargaining game. Simulation results show that the proposed algorithm can converge to a stable matching within a few iterations.

Dynamic spectrum access (DSA) redistributes spectrum from service providers with spare channels to those in need for them. Existing works on such spectrum exchange mainly focus on double auctions, where an auctioneer centrally enforces a certain spectrum allocation policy. In this paper, we take a different and new perspective, proposing to use matching as an alternative tool to realize DSA in a distributed way for a free market, which consists of only buyers and sellers, but no trustworthy third-party authority. Compared with conventional many-to-one matching problems, the spectrum matching problem is distinctively challenging due to the interference bound between buyers: the same channel can be reused by an unlimited number of non-interfering buyers, but must be exclusively occupied by only one of interfering buyers. In this paper, we firstly formulate the spectrum matching problem as a many-to-one matching with peer effects, i.e., a buyer's utility is affected by other buyers who are matched to the same seller. We then present a two-stage distributed algorithm that converges to an interference-free and Nash-stable matching result. Simulations show that the proposed distributed matching algorithm can achieve 90% of the social welfare from the optimal matching result.