By utilizing the spatiotemporal correlations of the consecutive frames, video crowd counting methods are usually more accurate and robust than image-based methods. However, they still suffer from two major problems. First, the crowd features are mostly extracted in a single and fixed temporal scale. Second, they often learn the spatial and spatiotemporal features in a serial approach and the interactions between them are mostly neglected. To address the above problems, we present a Dual-branch SpatioTemporal Interaction network (DSTI) for video crowd counting. Specifically, a purely video-based backbone called Meanformer is designed elaborately to establish a long-term, multi-scale and global temporal representation by combining the strengths of 3D convolution and transformer. Considering the lack of pre-trained weights, a spatiotemporal full-connected composition operation is proposed to boost the training of Meanformer with multi-scale features from an image-based backbone. Finally, a channel cross attention module is utilized to further improve the performance of DSTI by achieving cross-modal interaction. Experimental results show that the proposed method achieves advanced counting performance on six public datasets. Compared with the second-best method, the average reduction of MAE and RMSE reaches 5.4% and 5.5%, respectively.

The rapid expansion of mobile communication networks has led to a surge in cellular traffic, highlighting the need for advanced prediction models to improve network performance. Federated learning (FL) offers a promising solution by enabling distributed model training across multiple nodes, aligning well with the decentralized nature of modern networks. However, applying FL to spatiotemporal cellular traffic prediction is challenging due to the substantial communication overhead in distributed learning. To address this, we propose LFedSG, a lightweight FL framework incorporating subgraph partitioning for spatiotemporal traffic prediction. LFedSG supports collaborative training while preserving inter-client dependencies critical for accurate prediction. Communication efficiency is achieved by focusing on essential model parameters, while subgraph partitioning and spatiotemporal graph convolutional networks (STGCN) enhance spatial and temporal correlation modeling. An adaptive transmission weight pruning strategy further reduces communication and computation costs. Extensive experiments on the Telecom Italia and Pems07 datasets demonstrate that LFedSG achieves higher predictive accuracy than traditional methods, with significant reductions in communication overhead and training time, validating its effectiveness and scalability for large-scale mobile network environments.

Passive sensing based on UHF RFID has increasingly proven its efficacy in various applications. However, existing security methods often face challenges in implementation on passive tags or fail to meet the high data read rates required by array sensing operations. To address these constraints, we introduce a novel security framework called PSC-tags, which is in line with the zero-trust security concept and well-suited for parallel deployment within RFID sensing array scenarios. PSC-tags leverages the phase sequence similarities inherent in tag arrays, leading to the development of an optimal tag group selection strategy. Concurrently, we customize a convolutional neural network incorporating an attention mechanism for authentication. This method is applied to XRF55, which is a comprehensive dataset of human indoor activities, as well as a sub–dataset collected in real–world scenarios. Extensive experimental results demonstrate the effectiveness of PSC-tags, with an average accuracy of 98.5% and only 56 milliseconds of authentication time per sample required. Notably, PSC-tags is compatible with commercial off-the-shelf (COTS) devices and does not require any additional data acquisition. The method significantly fortifies the defenses against multiple attacks within RFID array sensing.

In contrast to most of the existing equalization methods, blind equalization (BE) can eliminate the effect of multipath fading without any known sequences. As a result, BE is a promising technique for wireless intelligence and non-cooperative communication. However, the conventional BE methods require long sequences or large-scale training to recover the received signals. In this paper, a deep learning-based neighborhood-assisted symbol-based decision (DL-NA-SBD) method is proposed to tackle this problem. Specifically, we replace the commonly used linear approaches with neural network to optimize the traditional SBD function and the mean square error (MSE) loss function in two stages to generate the equalizer coefficients. To avoid collecting large numbers of signals, we train the network using the online training strategy. Simulation results demonstrate that the proposed method achieves better inter-symbol interference (ISI) elimination and bit error rate (BER) performance compared with the conventional methods while requiring only a short-length signal sequence.

With the development of information technology, a variety of mobile devices with large computing requirements and delay constraints are growing rapidly, such as Connected Autonomous Vehicles (CAVs). In addition to the energy consumption required for vehicle driving, the CAV also needs to support the normal operation of sensors and computing platforms, which brings huge energy consumption and affects the endurance of vehicles. In this paper, a Mobile Edge Computing (MEC) system is considered that includes three types of network nodes: connected Autonomous Vehicles, Roadside Units (RSUs), and a Base Station (BS). Considering the task duration of the CAV, tasks on the CAVs are partitioned into three sections, which are executed at CAVs, RSUs, and BS, respectively. It is challenging to correlate resource-limited CAVs with other types of network nodes with high performance and implement partial computation offloading between them to minimize the total energy consumption of CAVs and RSUs under the delay constraint of CAVs. Based on the classical orthogonal-frequency-division multiple-access (OFDMA), a joint computing and communication cooperation offloading protocol is proposed to minimize the total energy consumption of all CAVs and RSUs under delay constraints and is described as a constrained Mixed-Integer Nonlinear Program (MINLP) problem. To address this problem, a Reference Vector-Guided Evolutionary Algorithm for Multi-Objective Optimization based on Prior Experience (RVEAPE) is designed in this paper. RVEAPE jointly optimizes the task division ratio variables and resource allocation variables (computation resources, communication resources). Simulations demonstrate that RVEAPE significantly outperforms the GA, NSGA-II, and NSGA-II algorithms in terms of the total energy consumption of CAVs and RSUs in the whole system consisting of CAVs, RSUs, and a BS.Note to Practitioners—This paper is motivated by the use of multi-objective optimization algorithms to improve the efficiency and quality of CAVs in edge computing networks. Our goal is to reduce energy consumption by offloading the computation tasks of the CAV to be computed in the RSU and BS. To this end, this paper proposes the use of the RVEAPE algorithm to jointly optimize offloading decisions and resource allocation variables to achieve the lowest energy consumption. The proposed method is theoretically effective in reducing the energy consumption of the CAV under the time constraint. Experimental studies show that the RVEAPE algorithm can effectively offload the computational tasks of the CAV to the BS, and can thus reduce the total energy consumption of the CAV and the RSU. In future research, we will implement computation offloading in large-scale CAV scenarios and consider a binary offloading model.

With the rapid development of the Internet of Things (IoT) related technologies, the application of digital twins (DT) in industry and healthcare becomes possible. Human activity recognition (HAR) is emerging as a hot research area with great potential in healthcare. Activity recognition systems combined with DT will make it easier to monitor human health conditions to improve the quality of life and happiness with individualized healthcare. In this paper, we design an effective HAR system, called HAR-Net, which uses WiFi time series data collected by sensors to train a deep learning network. Deep learning’s great learning ability is utilized to extract features of various human activities for activity recognition. We built the DT system with Unity, which is combined with the HAR system. In the DT system, real-world physical activities are mapped onto human models. The results of activity prediction can be evaluated in real-time in DT, and warnings can be issued quickly when dangerous activities occur. To make our human activity recognition system more adaptive, we propose a one-shot recognition method based on meta-learning. Specifically, we design a Bi-path basic network that extracts features in the time-domain and frequency-domain, and a meta-learning framework with a classification module and a WiFi metric module. Using datasets from different environments, we conducted various experiments on HAR-Net, and the results proved that our presented method was superior to the baseline network.

Abstract Aiming at the issues of low individual identification accuracy of radiation sources under the condition of little samples, this paper proposes a way of individual identification of radiation source based on strengthening global deep convolutional generative adversarial network (SGDCGAN) and deep convolutional neural network (DCNN) to achieve data augmentation. The method first performs IQ map feature splicing processing on the input signal, and then uses DCNN to automatically obtain the deep essential features of the data. Besides, an adaptive improvement is created to the deep convolutional generative adversarial network, and a self‐attention mechanism is introduced into the discriminator and the generator to reinforce the integrity and authenticity of the generated samples. For the common gradient disappearance during model training, the gradient penalty mechanism and spectral normalization are added to make the training process more stable. Through comparison experiments on the collected ADS‐B signals, the experimental results have demonstrated that when the signal‐to‐noise ratio is 0 dB and the number of original samples in each class is 40, the recognition accuracy is improved by 23.6% after doubling the data. Compared with the DCGAN‐DCNN and GAN‐DCNN methods, the recognition accuracy is improved by 3.5% and 4%.

Traditional solutions for human action recognition usually rely on sensor or video methods. However, these methods have some limitations, such as inconvenient portability, light intensity influence, privacy protection, etc. In this article, an RFID-based nonwearable human action recognition scheme is proposed. In order to reduce the occlusion effect of the human body on the signal and increase the diversity of the reflected signal, a tags array is constructed. The data of phase and RSSI are fused as feature data to enhance the diversity of data. Furthermore, a combined processing method is proposed to eliminate thermal noise generated by the equipment and reduce the interference caused by the environment. Then, an action segmentation algorithm is designed to align the RF signals of human action. Finally, an efficient human action signal classification model is constructed using the spatiotemporal graph convolutional neural network (STGCN). Extensive experiments demonstrate that the overall accuracy rate of the system for human action recognition is 92.8%. Compared with the comparative mainstream recognition algorithms, STGCN shows better classification performance in terms of identification precision. In addition, multimodal RFID data fusion also improves the accuracy of identification.

The rapid development of Internet of Things has led more realization of digital twins (DT), such as healthcare, smart homes, virtual reality, etc., gesture recognition is a fundamental component of DT. Its implementation can provide users with personalized services or improved human-computer interaction, such as smart home control, in-car interaction, etc., most of existing gesture recognition methods are based on vision or wearable device. However, the vision-based methods face the problem of privacy breach, whereas the wearable-based methods may bring inconvenience to users. With the wide deployment of Wi-Fi networks, lots of consumer devices are widely accessible in people’s homes. Motivated by the fact that Wi-Fi signal propagation can be affected by human motion, the opportunity to use Wi-Fi signals for gesture recognition can be further explored. However, the challenge is that the received Wi-Fi signal shows great differences when the same person performs the same gesture in different environments or different person performs the same gesture in the same environment. Therefore, the signal alignment across different domain needs to be solved. In this paper, we propose a gesture recognition system named Phase-Attention-based-Conv-CSI (PAC-CSI), which consists of two modules: data processing and gesture recognition. In the data processing module, we eliminate random phase noise in channel state information (CSI) and perform phase calibration. In the gesture recognition module, we feed the processed phase sequence into a lightweight deep neural network for gesture recognition. PAC-CSI can obtain the gesture category in about 200ms, which can meets the real-time requirements of DT. The gesture recognition accuracy of our proposed system in a single domain is 99.46%, and its performance across new locations, orientations, users, and environments is 98.77%, 98.90%, 97.54%, and 96.47%, respectively.

The development and applications of mobile communication technologies in intelligent autonomous transportation systems have led to an extraordinary rise in the mount of connected and autonomous vehicles (CAVs). Ensuring the security of in-vehicle communication data is the basis for the safety of cooperative transportation systems. An in-vehicle controller area network (CAN) bus is an important issue in in-vehicle security, and some hackers have mastered remote vehicle control methods through the CAN bus network. This paper proposes an improved isolation forest method with data mass (MS-iForest) for data tampering attack detection, in which we use data mass instead of the number of divisions and give an anomaly score ranking to quantify the degree of anomalies. This method is promising to be used as part of the intrusion detection system, like a security component in the onboard gateway, which can effectively avoid the data tampering attacks. We compare the proposed method with other anomaly detection schemes based on the data collected from an in-vehicle simulated dataset and two standard datasets. The experiment results show that the proposed method performs better than the other anomaly detection schemes in terms of the area under the receiver operating curve (AUC).

Infrared and visible data fusion (IVF) aims to generate a fused output that simultaneously highlights salient thermal radiation features and preserves texture information, which can not only grasp the necessary information for traffic movement, but also highlight the invisible objects that need to be dodged in intelligent transportation system (ITS). Therefore, IVF is capable of improving the environmental perception ability for various challenging traffic situations, e.g., foggy scenarios, rainy environments, and low-light illumination. However, current available IVF algorithms cannot offer a theoretical manner to integrate a priori knowledge and the network structure into a unified model. Moreover, they always fail to handle infrared and visible data pairs with different resolutions, which is a common occurrence in real ITS scenarios. To this end, this study develops a novel model-inspired unsupervised network termed IVF-Net. Specifically, an enhanced IVF model (IVFM), which pays more attention on detailed texture information and salient objects, is first established. According to proximal gradient theory, then we map this model into a deep network with learnable feature extraction parameters, aiming to draw on the strengths of the fusion model and deep learning to better describe the IVF task. Finally, a multiple task-driven loss function is designed to train the mapped network. Unlike previous work, our IVF-Net is motivated by IVFM, each layer in which has a semantic interpretability and a clear mission, thereby leading to a significantly enhanced fusion effect. Another advantage is that it is only composed of simple convolution-based structures, which ensures its lightweight and efficiency. Experiments demonstrate that IVF-Net can have a stronger ability to capture the key traffic information and highlight the salient feature of imperceptible objects, which makes it an excellent candidate to improve the reliability of subsequent applications in ITS.

Unmanned aerial vehicles (UAVs) with integrated computing platforms can be used to provide computing offloading services for ground user equipments (UEs) with limited local computing capabilities, especially in remote areas. In this paper, we focus on the task offloading in an aerial edge network (AEN) assisted by a UAV. We aim at minimizing the sum energy consumption of all UEs by the joint optimization of the task offloading decisions and the UAV position under the constraints of the latency and the total energy of UAV. The formulated optimization problem is a mixed-integer nonconvex problem and involves coupling of many optimization variables. To address this challenge, we first transform the original optimization problem into a linear convex optimization problem via reformulation linearization technology, and then the alternating direction method of multipliers (ADMM) algorithm is proposed to achieve the approximate optimal solution. Numerical results confirm that the proposed ADMM algorithm can effectively reduce the total of energy consumption of UEs and ensure the continuous operation of the UEs.

Interference is a serious problem in Wireless Body Area Networks (WBANs) and heavily weakens system performance. In this paper, we propose an exchange-free resource scheduling scheme to overcome the interference of dynamic coexisting WBANs. For each data transmission period, we design a transmission channel/slot allocation scheme based on a Latin square, where each character denotes a specific combination of a channel and a time slot. For each data retransmission period, we design a retransmission time-slot selection scheme based on a hash function, in which the unique identity information of the collided node is used to calculate the retransmission slot. Compared with existing solutions, our work has two key advantages. First, all nodes can independently allocate and coordinate resources rather than exchange information with each other in traditional methods, and thus guaranteeing strong adaptability to the fast changes of WBANs. Second, the contention-free resource allocation pattern is implemented for both the data transmission period as well as the data retransmission period, and thus guaranteeing no intra-WBAN interference and extremely low probability of inter-WBAN interference. Our simulation results show that interferences can be well addressed based on the metrics of the packet loss rate, throughput, power dissipation, and data delivery delay.

Radio frequency identification technology has been widely used to verify the presence of items in many applications such as warehouse management and supply chain logistics. In these applications, the challenge of how to timely identify the missing tags (namely tag searching or missing tag identification) is a key focus. Existing missing tag identification solutions have not achieved their full potentials because collision slots have not been well explored. In this paper, we propose an approach named collision resolving based missing tag identification (CR-MTI) to break through the performance bottleneck of existing missing tag identification protocols. In CR-MTI, multiple tags are allowed to respond with different binary strings in a collision slot. Then, the reader can verify them together by using the bit tracking technology and particularly designed string, thereby significantly improve the time efficiency. CR-MTI also reduces the number of messages transmitted by the reader using customized coding. We further explore the optimal parameter settings to maximize the performance of our proposed CR-MTI. Extensive simulation results show that our proposed CR-MTI outperforms prior art in terms of time efficiency, total executive time and communication complexity.

How to obtain the information from massive tags is a key focus of RFID applications. The occurrence of collisions leads to problems such as reduced identification efficiency in RFID networks. To tackle such challenges, most tag collision arbitration protocols focus on scheduling tag identification with collision avoidance. However, how to effectively identify tags in collisions to improve identification efficiency has not been well explored. In this paper, we propose a group query allocation method to divide the string space into mutually disjoint subsets which contains several strings. Each string can be viewed as a full ID or partial ID of a tag. When multiple string from a subset are sent simultaneously, the reader can identify all of them in a time slot. Based on the group query allocation method, a segment detection based characteristic group query tree (SD-CGQT) protocol is presented for fast tag identification by significantly reducing the collision slots and transmitted bits. Numerous experimental results verify the superiority of the proposed SD-CGQT, compared to prior arts in system efficiency, total identification time, communication complexity and energy consumption.

In radio-frequency identification (RFID) applications, RFID tags attached to new, misplaced, or counterfeited commodities sometimes may not be timely registered and are unknown for readers. In applications like inventory management and product tracking, these unknown tags pose several challenges for fast tag identification. A simple method to identify unknown tags is to first deactivate the registered known tags, and then collect IDs of the unknown ones. However, this is a nontrivial task. In fact, unknown tags cause interference with the deactivation of known tags. Moreover, the unknown tag collection methods used in existing protocols either suffer severe tag collisions or generate many empty slots, which increases the final execution time. In this article, we propose an efficient unknown tag identification (EUTI) protocol. First, EUTI builds a vector-based filter to exclude the tags that are not expected to reply in each slot, so that EUTI can use both predicted collision slots and singleton slots for unknown tag deactivation and avoid collisions caused by unknown tags. Second, EUTI adopts a reservation mechanism to reduce collision slots and guide each unknown tag to skip empty slots when replying, thus saving execution time. Moreover, we provide a theoretical analysis of EUTI to minimize execution time and extend EUTI to multi-reader scenarios. Numerical results show that EUTI outperforms the state-of-the-art solutions by reducing up to 44.12% in deactivation time, 26.47% in collection time, and 27.75% in total time.

This paper concerns the problem of defending against spoofing attacks without a secret key. We address the problem using the Physical-Layer-Authentication (PLA) because of its high security, low overhead, and high compatibility. However, many PLA schemes have the following limitations: quantization errors, local optimums, and performance loss due to the change in the communication environment. In this paper, two phase-noise-based PLA schemes are proposed to address the limitations of the prior schemes. We denote the first scheme as the Multiple Phase Noises PLA (MPP) scheme, which realizes the PLA by using multiple phase noise innovations. Note that since the MPP scheme avoids using any quantization algorithm, it outperforms the prior schemes on the authentication performance. We denote the second scheme as the Enhanced Multiple Phase Noises PLA (EMPP) scheme, which introduces an artificial random phase to the transmitted symbols at the transmitter to further improve the authentication performance. The theoretical analyses of the proposed schemes over fading channels are provided, where the closed-form expressions are derived. Theoretical comparisons between the proposed schemes and prior schemes are provided. The theoretical analyses and simulation results demonstrated the superiority of the proposed schemes. In comparison with the prior schemes, the MPP scheme achieves 13% authentication-performance gain without demodulation-performance loss, while the EMPP scheme achieves 42% authentication-performance gain with merely 16% demodulation-performance loss.

Attributable to the emergence of mobile edge computing (MEC), the hardware-constrained industrial devices have further computational and service capability in industrial Internet of Things (IIoT) systems. Nevertheless, unreliable network environments and unpredictable processing delays are intolerable factors for any service application. Therefore, this article studies the associated constraint problem of how to optimize the offloading decision and resource allocation in collaborative edge computing networks with multiple IIoT devices and MEC servers. In order to attain this purpose, the optimization problem is mathematically derived as a mixed-integer nonlinear programming problem which is a large-scale NP-hard problem. Then, an improved differential evolution algorithm (IDE) is proposed to obtain the optimal solutions in an accessible time complexity. Finally, the performance of the IDE-based resource allocation scheme has been compared with other baseline schemes. Simulation results demonstrate that the IDE-based optimization scheme could significantly reduce the system delay and energy consumption.

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.

In the 6G network, lots of edge devices facilitate the low-latency transmission of video. However, with limited processing and storage capabilities, the edge devices cannot afford to reconstruct the vast amount of video data. On the condition of edge computing in the 6G network, this article fuses a self-similarity-based context feature into Frame Rate Up-Conversion (FRUC) to generate the pseudo-true video sequences at high frame rate, and its core is the extraction of the context layer for each video frame. First, we extract the patch centered at each pixel and use the self-similarity descriptor to generate the correlation surface. Then, the expectation or skewness of the correlation surface in statistics is computed to represent its context feature. By attaching an expectation or a skewness to each pixel, the context layer is constructed and added to the video frame as a new channel. According to the context layer, we predict the motion vector field of the absent frame by using the bidirectional context match and finally produce the interpolated frame. From the experimental results, it can be seen that by deploying the proposed FRUC algorithm on edge devices, the output pseudo-true video sequences have satisfying objective and subjective qualities.

In the last decade, energy harvesting wireless sensor network (EHWSN) has been well developed. By harvesting energy from the surrounding environment, sensors in EHWSN remove the energy constraint and have an unlimited lifetime in theory. The long‐lasting character makes EHWSN suitable for Industry 4.0 applications that usually need sensors to monitor the machine state and detect errors continuously. Most wireless sensor network protocols have become inefficient in EHWSN due to neglecting the energy harvesting property. In this paper, we propose CPEH, which is a clustering protocol specially designed for the EHWSN. CPEH considers the diversity of the energy harvesting ability among sensors in both cluster formation and intercluster communication. It takes the node’s information such as local energy state, local density, and remote degree into account and uses fuzzy logic to conduct the cluster head selection and cluster size allocation. Meanwhile, the Ant Colony Optimization (ACO) as a reinforcement learning strategy is utilized by CPEH to discover a highly efficient intercluster routing between cluster heads and the base station. Furthermore, to avoid cluster dormancy, CPEH introduces the Cluster Head Relay (CHR) strategy to allow the proper cluster member to undertake the cluster head that is energy depletion. We make a detailed simulation of CPEH with some famous clustering protocols under different network scenarios. The result shows that CPEH can effectively improve the network throughput and delivery ratio than others as well as successfully solve the cluster dormancy problem.

Tag identification in a fast‐moving environment is an emerging challenge for future RFID systems. However, existing literatures on the tag reading protocol design primarily apply to stationary scenarios, which fail to cope with mobile environments with unreliable channel condition. In this paper, we first review various types of prior reading protocols and then discuss a new direction of mobile tag reading by proposing a novel partitioning strategy. This analysis and experimental results show its superiority in achieving reading performance for the UHF RFID system under a mobile environment.

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.

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.

Distance bounding (DB) protocols are crucial solutions against “distance attacks.” Traditional one-hop DB protocols can estimate an upper-bound of the physical distance between two nodes within their communication range, but they fail when this distance exceeds their coverage. Recently, a two-hop DB protocol to verify the proximity of nodes beyond the first hop was proposed. Nonetheless, this approach is still lacking for meeting security requirements of multihop communication scenarios like ad-hoc wireless network. Motivated by this situation and considering security problems of a relay communication scenario, in this paper, we propose a multihop DB protocol, where a node can detect multihop neighbors and verify legality beyond its communication range. First, we present a description of traditional one-hop DB protocols and provide a general model for easily extending them to multihop capabilities. Then, we analyze one-hop and multihop models security giving theoretical and simulated attack success probabilities for different cases, and showing that the proposed solution can better prevent distance attacks.

Efficient and effective objects identification using radio frequency identification (RFID) is always a challenge in large-scale industrial and commercial applications. Among existing solutions, the tree-based splitting scheme has attracted increasing attention because of its high extendibility and feasibility. However, the conventional tree splitting algorithms can only solve tag collision with counter value equals to zero and usually result in performance degradation when the number of tags is large. To overcome such drawbacks, we propose a novel tree-based method called fast splitting algorithm based on consecutive slot status detection (FSA-CSS), which includes a fast splitting (FS) mechanism and a shrink mechanism. Specifically, the FS mechanism is used to reduce collisions by increasing commands when the number of consecutive collision is above a threshold, whereas the shrink mechanism is used to reduce extra idle slots introduced by the FS. Simulation results supplemented by prototyping tests show that the proposed FSA-CSS achieves a system throughput of 0.41, outperforming the existing ultra high frequency RFID solutions.

Distance bounding(DB) protocol is a critical solution to preventing distance attacks. Since the traditional DB protocols can only estimate the upper-bound within its communication range, it fails to work once the distance exceeds its coverage. Currently, the DB protocol is difficult to apply to wireless sensor networks(WSN) to prevent relay attacks, such as wormhole attack which is one of the most serious threats in WSN's routing. In this paper, a multi-hop DB protocol is proposed to estimate the upper-bound with multi-hop nodes, which can help a Central Node(CN) or Cluster Head(CH) node estimate the communication distance upper-bound of all the nodes in a sub-group, even if the nodes don't lie in its direct communication coverage. The proposed scheme not only ensures the safety of the communication, but also optimizes the routing and networking in WSN.

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.

In this paper, we propose an efficient identification algorithm for RFID systems based on EPC C1 Gen2 RFID standard1. Specifically, the proposed anti-collision algorithm is based on the observation of sub-frame during an identification process, and makes effective use of idle and collision statistics to accurately estimate the tag backlog and determine the proper frame size for the next inventory round. Simulation results are supplemented to demonstrate the advantages of the proposed algorithm in achieving time and computation efficiency.

Tag collision severely affects the performance of radio-frequency identification (RFID) systems. Most anti-collision algorithms focus on preventing or reducing collisions but waste lots of idle slots. In this letter, we propose a time efficient anti-collision algorithm based on a query tree scheme. Specifically, the dual prefixes matching method is implemented based on the traditional query tree identification model when the reader detects the consecutive collision bits, which can significantly remove idle slots. Moreover, the proposed method can also make extensive use of collision slots to improve the identification efficiency. Both theoretical and simulation results indicate that the proposed algorithm can achieve better performance than existing tree-based algorithms.