Concurrent OFDM backscatter is promising for enabling efficient connectivity of soaring ultra-low power IoT devices. However, the existing designs rely on frequency-domain division, suffering from short synchronization distance, poor interference robustness and incompatibility with commercial Wi-Fi receivers. We present C2Scatter, the first concurrent OFDM backscatter system that decodes the tags' data in parallel with a single commercial OFDM Wi-Fi receiver. It is enabled by two key techniques. (1) A pulling-driven synchronization scheme on tags that achieves long-distance synchronization by exploiting the injection-pulling capacity rather than amplifying the received signal, thereby resolving the sensitivity-power trade-off in the prior works. (2) A lightweight delay-domain division scheme that deliberately introduces a unique time offset (TO) used as each tag's signal characteristic to separate concurrent tags' signals in the delay domain. The constructed delay-domain characteristics also tolerate frequency-domain distortion, enabling robust decoding with commercial Wi-Fi receivers. We prototype and test C2Scatter. Our design achieves 37dB higher sensitivity with 131.1× lower power consumption than the amplification-driven synchronization schemes, and is compatible with commercial 802.11n Wi-Fi NICs with the BER reduced by 179.1× and 75.6× at no cost of throughput compared with the SOTA NanoScatter and ConcurScatter, respectively.

In this work, we explore energy-efficient covert underwater acoustic backscatter communications (EC-UABCom) within the framework of the Internet of Underwater Things (IoUT). Specifically, a passive buoy node covertly transmits acoustic information passively to a maritime receiver by reflecting incident acoustic carrier signals from an autonomous underwater vehicle (AUV) transmitter. Simultaneously, the system exploits the uncertainty of underwater noise to mask the covert backscatter information, thereby evading detection by a submarine warden. To optimize the covert strategy, we first derive the warden’s optimal power-detection threshold that minimizes the detection error probability, accounting for underwater noise uncertainty. In response to this optimal detection strategy, we propose an energy-efficient covert policy by jointly optimizing the AUV’s transmit power and the buoy’s reflection coefficient to meet the covertness constraint. We then conduct a performance analysis, providing a closed-form expression for the expected detection error probability at the warden and outage probability at the receiver, thereby revealing their inherent trade-off. Numerical simulations validate the effectiveness of our approach compared to the state-of-the-art methods, demonstrating that higher transmit power and reflection coefficient degrade covertness while improving covert rate performance.

Ambient LoRa backscatter enables long-range communication due to its long-period symbol. Most of the existing works struggle to balance range and throughput: systems with symbol-level modulation offers long transmission range at the cost of low data rate, while systems with high modulation efficiency suffer from limited transmission distance due to weak signals. We propose SubLoRa, which significantly improves throughput while maintaining long-range communication. SubLoRa achieves the high-rate modulation by the proposed Subchirp Frequency Offset Modulation (SFOM), which divides a chirp into multiple subchirps each being shifted by frequency. We propose a prewaveform sampling strategy that enables SFOM with low power. For decoding, we propose a Frequency-Difference Recombination of Chirp (FDRC) demodulation based on time-domain correlation, which enables reliable decoding of low-power signals in long-range links. We implement SubLoRa and conduct extensive evaluation. The results show SubLoRa can achieve up to 29.66× throughput gain and 7.54× throughput gain compared with the State-Of-The-Art (SOTA) LoRa backscatter system PLoRa and Pacim, respectively.

We investigate the problem of target object information collection in multi-tagged RFID systems. Different from its single-tagged peers, the multi-tagged RFID scenario introduces three new challenges: 1) Tags on the same object carry the same information, so reusing single-tagged algorithms causes unnecessary redundancy; 2) The transition of objects from being tagged one to multiple tags leads to an upsurge in slot collisions; 3) Gathering information from more tags necessitates more downlink transmission, making it hard to limit broadcast information while ensuring time-efficient information collection. To tackle these technical challenges, we propose an efficient information collection algorithm, called Backtracking Collision Peeling (BCP), featuring three key techniques. First, BCP selects a single time slot (allowing even collision slots) for each target object to convey its information. This approach bypasses the need for a high-latency collision elimination process, thereby significantly reducing time overhead. Second, by exploiting dependencies among the selected slots, BCP recovers object information in complex collision slots using object information from already resolved slots, offering a novel and effective solution for handling signal collisions. Third, to minimize downlink transmission cost, BCP polls tags by transmitting only incremental changes between polling vectors, rather than the complete vectors. We further improve performance with E-BCP by enhancing the utilization of collision slots, thereby reducing the number of required polling rounds. Experiments show BCP and E-BCP outperform existing methods by at least$35\%$in aggregate execution time, while also exhibiting stronger stability and robustness.

The rapid advancement of UAV swarm networks has enabled their widespread application across various domains, including disaster relief, environmental monitoring, and intelligent transportation. Collaboration among UAVs within a swarm is vital for efficient resource utilization and optimal performance across these diverse applications. To address diverse service demands, deploying service function chains (SFC) in UAV swarm networks facilitates the real-time implementation of services through efficient resource allocation and UAV cooperation, thereby enhancing network reliability and efficiency. However, traditional SFC deployment strategies struggle to achieve reliability and efficiency due to dynamic topology and limited resources. Additionally, Stochastic Network Calculus (SNC) derives end-to-end latency, guaranteeing quality of service (QoS) in UAV swarm networks. To navigate this issue, we propose a cooperative dynamic SFC deployment algorithm that combines hierarchical proximal policy optimization (HPPO) with an edge-enhanced dynamic graph attention network (EDGAT) for real-time network state extraction. The simulation results validate the effectiveness of our proposed algorithm, showcasing improvements in deployment success rate and long-term average revenue.

In this paper, we study uplink covert communication in a space-air system, where an unmanned aerial vehicle (UAV) transmits sensitive data to a Geosynchronous Earth Orbit (GEO) satellite while preventing the transmission action from being discovered by a warden. We derive the optimal decision threshold of the warden. We investigate the 3-dimensional (3D) beamformer and 3D trajectory design for the transmitter UAV against this optimum warden to maximize the covert transmission rate in the presence of imperfect channel state information and uncertain noise. Due to the non-convex structure and dependence between beamforming vectors and locations of the transmitter UAV, we develop a decoupling method that specifies a feasible flight region of the transmitter UAV at each time slot, enabling the decomposition of the original optimization problem into two sub-problems that optimize the trajectory and beamforming vectors individually. We design an iterative algorithm with a new initialization method to solve the sub-problems alternately with the semi-definite relaxation (SDR) and the successive convex approximation (SCA) technique. Numerical results show that the average covert rate of our design approaches the ideal case without the warden and increases by about 102.3% and 19.1% compared with benchmark schemes that do not employ beamforming or design 2D trajectory, respectively.

Physical layer steganography plays a key role in physical layer security. Yet most works are strongly modulation-sensitive and have to modify the modulation at the baseband. However, these methods cannot work with wireless devices whose baseband modulations cannot be software-defined. To overcome these drawbacks, we propose an analog solution that uses a symbiotic hardware component designed, called Pluggable Cloak, connecting to the radio frequency front end (RFFE) to establish a steganographic symbiotic channel (SSC) over constant envelope physical layer (CE-PHY) in 2.4GHz ISM band, such as Bluetooth, ZigBee and 802.11b Wi-Fi, to hide information. The advantage lies in enabling secure transmission of the deployed devices that are not software-defined with this pluggable hardware. Specifically, Pluggable Cloak analogously modulates the amplitude of CE-PHY, so that sensitive information can be securely sent to a customized receiver without being detected by regular CE receivers. To further protect hidden information from the detection of a malicious adversary, we propose methods to randomize the SSC. We develop a lightweight prototype to evaluate symbiosis, undetectability, and throughput. The results show that the symbol error rates (SERs) of the sensitive data received and regular CE data are lower than$10^{-5}$at the customized receiver. In contrast, the SER of the sensitive data is close to 1 in the adversary, confirming the effectiveness of the SSC technique.

Most of the existing OFDM backscatter systems adopt phase-modulated schemes to embed tag data, suffering from symbol-level modulation limitation, heavy synchronization accuracy reliance, and small tolerability to symbol time offset (STO) / carrier frequency (CFO) offset. We introduce SubScatter, the first subcarrier-level frequency-modulated OFDM backscatter which is able to tolerate bigger synchronization errors, STO, and CFO. The unique feature of SubScatter is our subcarrier shift keying (SSK) modulation. This method pushes the modulation granularity to the subcarrier by encoding and mapping tag data into different subcarrier patterns. We also design a tandem frequency shift (TFS) scheme that enables SSK with low cost and low power. Furthermore, we design SubScatter+ that shows these advantages while providing an even higher throughput without requiring more subcarrier patterns. We prototype and test SubScatter and SubScatter+, and the results show that our systems outperforms prior works in terms of effectiveness and robustness. Specifically, SubScatter has 743 kbps throughput that is 3.1 times and 14.9 times higher than RapidRider and MOXcatter, respectively. It also has a lower BER under noise and interferences which is over 6 times better than RapidRider or MOXcatter. Moreover, our proposed SubScatter+ could increase the throughput of SubScatter by 30%.

We study the problem of target object information collection in multi-tagged COTS RFID systems. Unlike its singletagged peers, the multi-tagged COTS RFID scenario poses new challenges in devising information collection algorithms: 1) Tags attached to the same object carry identical information. Hence, reusing single-tagged information collection algorithms leads to unnecessary redundancy; 2) Multi-tagged RFID systems are often deployed in applications where tags are vulnerable to damage. Such faulty tags may severely degrade the performance of information collection; 3) Most state-of-the-art information collection algorithms rely heavily on the hashing operation that is not seamlessly supported by the C1G2 standard, rendering these solutions inefficient and impractical, especially in largescale RFID systems. To tackle these technical challenges, this paper makes three contributions. First, we develop an efficient and compact tag pseudo-ID design, enabling the reader to select a single tag from each target object to collect information with only one SELECT command. Second, we construct a robust faulthandling mechanism capable of recognizing faulty tags without executing the entire slot. Third, armed with the above two techniques, we develop a novel information collection algorithm by leveraging the functionality offered by C1G2 to optimize the information collection sequence, thus minimizing the overall execution time. Empirical experiments on a COTS RFID system prototype demonstrate that our algorithm outperforms the best existing solution by 35-50% on average.

Ambient OFDM backscatter communication has attracted considerable research efforts. Yet the prior works focus on point-to-point backscatter from a single tag, leaving behind efficient backscatter networking of multiple tags. In this paper, we design and implement ConcurScatter, the first ambient OFDM backscatter system that scales to concurrent transmission of hundreds of tags. Our key innovation is building and using the subcarrier pattern diversity to distinguish concurrent tags. This would yield linear collision states rather than exponential ones in the prior works based on the IQ domain diversity, supporting more concurrent transmission. We concrete this by designing a suit of techniques including midair frequency synthesis that forms a unique subcarrier pattern for each concurrent tag, non-integer cyclic shift that contributes to support more concurrent tags, and subcarrier pattern reconstruction that creates virtual subcarriers to enable single-symbol parallel decoding. The testbed experiment confirms that ConcurScatter supports seven more concurrent tags with similar BER and 8.4× higher throughput than the point-to-point backscatter RapidRider. The large-scale simulation shows that ConcurScatter supports 200 tags which is 40× more than the state-of-the-art concurrent OFDM backscatter FreeCollision.

This paper studies covert MIMO ambient backscatter communication (AmBC). In contrast to the prior covert AmBC works, we equip all devices with multiple antennas, where a backscatter tag works as a beamformer to hide both his transmission behavior and location from a warden. Technically, we derive two key metrics, namely the Kullback-Leibler divergence (KL divergence) measuring the transmission covertness and the covert rate measuring the transmission efficiency. We reveal that they both increase monotonically w.r.t the number of the tag’s antenna. This tradeoff makes the optimum parameter configuration non-trivial. To this end, we optimize the beamformer of the tag to maximize the covert rate subject to the covertness constraint under perfect knowledge of the warden’s channel state information (WCSI). We use the matrix permutation and semi-definite relaxation (SDR) method to make the formulated non-convex problem convex, and obtain the solution with the barrier method. We also study the impact of imperfect WCSI. To address the uncertain estimate error, we use the S-Procedure to reformulate the constraints linearly and the primal-dual interior-point method for a near-optimal solution. We conduct extensive numerical experiments and confirm that our work outperforms the state-of-the-art ones.

This paper presents a covert ambient backscatter communication (AmBC) system aided by the intelligent reflecting surface (IRS), where an IRS is used as a beamformer to help Tag for covert AmBC. Technically, we derive the expression of the Kullback-Leibler (KL) divergence to measure the detection performance of the warden. To fight against the warden’s detection, we propose a joint IRS’s beamforming and Tag’s reflection coefficient optimization scheme to maximize the covert AmBC rate subject to a key constraint metric of the KL divergence. To solve the non-convex problem, we formulate it as Fractional Programming (FP) for the linear iterations, and use the Majorization-Minimization (MM) algorithm to obtain the optimal parameters of IRS and Tag. Moreover, we investigate the covert performance with the imperfect channel state information of the warden’s link (WCSI) with Tag and IRS. Numerical results show the covert performance of the system, and illustrate the superiority of the IRS’s assistance to the covert backscatter efficiency. The simulations also show that the channel estimated error of Tag-Willie link has a negative impact on the covert backscatter efficiency, while the channel estimated errors of IRS-Willie link have the positive influence on the contrary.

For mission-critical URLLC applications, timely status updates are essential. This paper investigates the age of information (AoI) of the three HARQ schemes specified in 5G R16, targeting to provide guidelines for future grant-free access design in 5G-Advanced and beyond. Specifically, we analyze two packet management policies: First-come-first-serve (FCFS) and preemption policy where new packets always preempt the buffer. We also study the AoI in a latency-sensitive scenario where expired packets are discarded. We derive exact expressions of AoI and peak AoI for all schemes and their lower bounds, revealing that the number of the maximum consecutive transmissions is critical for information freshness. Simulation results validate the theoretical analysis and show that Proactive HARQ scheme outperforms K-repetition HARQ scheme unconditionally and Reactive HARQ scheme with moderate system load or above. And discarding expired packets enhances system robustness for overload systems but yields larger AoI.

We study the batch writing problem in RFID systems, where the reader seeks the most time-efficient way to write information into a given subset of tags. The problem is analogous to the multicast problem in classical networks, but one-to-many transmission is not supported in COTS RFID systems. Driven by the technical challenge, this paper addresses the problem of designing batch writing algorithms for COTS RFID systems. We make three contributions. Firstly, we establish the minimal execution time for any batch writing algorithm, thus setting the theoretical performance limit. Secondly, we quantitatively compare and gauge the existing propositions applicable to our problem. Thirdly, we develop a novel batch writing algorithm with minimal 25% performance gain over the best state-of-the-art solution. Our key technicalities are designing an encoding scheme allowing the reader to efficiently perform batch writing and optimizing the batch writing sequence to minimize the overall execution time. We also perform extensive experiments to demonstrate the effectiveness of our algorithm.

The lightweight and energy-efficient Frame Slotted Aloha (FSA) protocol has become a promising MAC protocol in large-scale IoT systems. Existing work on minimizing the age of information (AoI) of FSA protocol cannot significantly benefit from frequent packet generations when the packet generation rate$\lambda$exceeds its throughput$e^{-1}$. To fill this gap, this paper proposes two age threshold-based algorithms to reduce the AoI of FSA systems for$\lambda > e^{-1}$, namely TF and TF+. Their core ideas are to only allow the nodes with age gain over the configured thresholds to send their packets so that the FSA systems are slimmed to a stable one with$\lambda < e^{-1}$and a polling system, respectively. Technically, we design the threshold configuration rules for the two algorithms and characterize the normalized average AoI. We also conduct simulation and the results show that TF and TF+ achieve lower AoI than the prior works.

Efficient and secure group writing plays a crucial role in RFID-enabled multi-task backscatter systems. The prior works lay emphasis on the time efficiency of the group data transmission, but neglect its security. This paper is devoted to providing anonymous group writing. We propose the Overlapped Bloom Filter-based protocol (OBF) and its enhanced version (OBF+). The core is to construct an approximately random sequence as a noise by making transmission data for different tag groups overlap with each other, thus hiding the original information with a low computational complexity. The compact filter can guarantee the time efficiency while improving the security of the group writing. To make tags aware of the correctness of the decoded group data, the OBF+ introduces the complementary code-based check mechanism to eliminate the fault data. We prototype the system with USRP and programmable WISP tags, and conduct extensive simulations to evaluate our approaches in terms of the time efficiency, the accuracy, and the anonymity of the data transmission.

We revisit the problem of missing tag identification in RFID networks by making three contributions. Firstly, we quantitatively compare and gauge the existing propositions spanning over a decade on missing tag identification. We show that the expected execution time of the best solution in the literature is$\Theta \left(N+\frac{(1-\alpha)^2(1-\delta)^2}{ \epsilon^2}\right)$, where$\delta$and$\epsilon$are parameters quantifying the required identification accuracy,$N$denotes the number of tags in the system, among which$\alpha N$tags are missing. Secondly, we analytically establish the expected execution time lower-bound foranymissing tag identification algorithm as$\Theta\left(\frac{N}{\log N}+\frac{(1-\delta)^2(1-\alpha)^2}{\epsilon^2 \log \frac{(1-\delta)(1-\alpha)}{\epsilon}}\right)$, thus setting the theoretical performance limit. Thirdly, we develop two novel missing tag identification algorithms with the expected execution time of$\Theta \left(\frac{\log\log N}{\log N}N+\frac{(1-\alpha)^2(1-\delta)^2}{ \epsilon^2}\right)$, reducing the time overhead by a factor of up to$\log N$over the best algorithm in the literature. The key technicality in our first algorithm is a novel data structure termed as collision-partition tree (CPT), built on a subset of bits in tag pseudo-IDs, leading to a more balanced tree structure and reducing the time complexity in parsing the entire tree. To further improve time efficiency, our second algorithm integrates multiple CPTs to form a collision-partition forest (CPF), reducing both the number of slots and the quantity of information broadcasting.

The virtual live streaming format of Vtubers is gaining immense popularity worldwide, aided by advancements in large language models (LLMs) and 2D/3D avatar simulation technologies. The emergence of pure AI-driven Vtubers represents a promising trend in content creation. However, research has not adequately explored the interactive capabilities between live streaming platforms and Vtubers, nor their implications on parasocial relationships. We developed “Blibug,” an AI-Vtuber on the Chinese Bilibili platform, who can be interacted with through “Danmu” messages (real-time comments overlaid on the video playback, also called a “bullet curtain”). By implementing an interactive AI-Vtuber action and dialogue response system, we translate relatable daily activities into virtual engagements and establish a real-time, one-to-many AI-Human dialogue framework. Our study aims to investigate the entertainment and narrative dimensions of Vtubers and to understand AI’s role as a social agent and its impact on parasocial relationships.

The boom of the Internet of Things has revolutionized people's lives, but it has also resulted in massive resource consumption and environmental pollution. Recently, Green IoT (GIoT) has become a worldwide consensus to address this issue. In this paper, we propose EEWScatter, an energy-efficient WiFi backscatter communication system to pursue the goal of GIoT. Unlike previous backscatter systems that solely focus on tags, our approach offers a comprehensive system-wide view on energy conservation. Specifically, we reuse ambient signals as carriers and utilize an ultra-low-power and battery-free design for tag nodes by backscatter. Further, we design a new CRC-based algorithm that enables the demodulation of both ambient and tag data by only a single receiver while using ambient carriers. Such a design eliminates system reliance on redundant transceivers with high power consumption. Results demonstrate that EEWScatter achieves the lowest overall system power consumption and saves at least half of the energy. What's more, the power consumption of our tag is only 1/1000 of that of active radio.

OFDM backscatter is crucial in passive IoT. Most of the existing works adopt phase-modulated schemes to embed tag data, which suffer from three drawbacks: symbol-level modulation limitation, heavy synchronization accuracy reliance, and small symbol time offset (STO) / carrier frequency (CFO) offset tolerability. We introduce SubScatter, the first subcarrier-level frequency-modulated OFDM backscatter which is able to tolerate bigger synchronization errors, STO, and CFO. The unique feature that sets SubScatter apart from the other backscatter systems is our subcarrier shift keying (SSK) modulation. This method pushes the modulation granularity to the subcarrier by encoding and mapping tag data into different subcarrier patterns. We also design a tandem frequency shift (TFS) scheme that enables SSK with low cost and low power. For decoding, we propose a correlation-based method that decodes tag data from the correlation between the original and backscatter OFDM symbols. We prototype and test SubScatter under 802.11g OFDM WiFi signals. Comprehensive evaluations show that our SubScatter outstands prior works in terms of effectiveness and robustness. Specifically, SubScatter has 743kbps throughput, 3.1× and 14.9× higher than RapidRider and MOXcatter, respectively. It also has a much lower BER under noise and interferences, which is over 6× better than RapidRider or MOXcatter.

This paper presents TiScatter, a timespan-based WiFi backscatter system that provides high-throughput communication with a single COTS receiver used. It outperforms the prior works that tradeoff between considerable data rate and practical deployment. To improve the data rate, TiScatter introduces a symbol-level times-pan modulation method that encodes tag data into the timespan between two modulated WiFi codewords in two successive WiFi packets. For decoding, TiScatter for the first time employs the injective feature between the checksum and the modulated codeword positions, which enables the demodulation of both the tag and original WiFi data using only one COTS receiver. This makes TiScatter more practical. Furthermore, we design TiScatter+ that shows these advantages while providing an even higher throughput under 802.11b excitations. We prototype our design, and comprehensive evaluations demonstrate that TiScatter shows a throughput over 100× higher than prior single-receiver backscatter systems like FS-Backscatter. It even has a better BER and throughput than the prior double-receiver backscatter systems like MOXcatter. Specifically, TiScatter provides 1) 2× higher peak throughput than MOXcatter and 2) an order of magnitude lower BER than MOXcatter with the presence of substantial interferences. In addition, TiScatter+ can deliver a throughput 3× higher than TiScatter under 802.11b ambient excitations. Our evaluation also confirms that TiScatter is generic and applicable to excitations under diverse WiFi standards (e.g., 802.11b/g/n).

Recently, low-Earth orbit (LEO) satellite Internet of Things (IoT) has received considerable interests due to its global coverage for massive IoT devices distributed over a large area, especially in remote areas, e.g., ocean, desert, and forest. Considering relatively long transmission distance between IoT devices and LEO satellite, we propose a low latency and small overhead sourced grant-free random access (GF-RA) framework, where active devices send their data signals directly without the grant of LEO satellite. In order to detect active device and recover the corresponding data, we design a joint device and data detection algorithm for massive GF-RA in LEO satellite IoT. In particular, the active device maps the data to a codeword of a predetermined and unique codebook, and then sends it to the LEO satellite. By detecting the codeword via maximizing the likelihood function of the received signal, the LEO satellite obtains the active device and recovers the corresponding data. Theoretical analysis shows that the proposed algorithm has a fast convergence behavior and low computational complexity. Finally, we provide extensive simulation results to confirm the effectiveness of the proposed algorithm over baseline ones in LEO satellite IoT.

Frame slotted Aloha (FSA) is the de facto MAC layer standard protocol in many ultra-low-power IoT applications, such as Radio Frequency Identification (RFID) and Machine to Machine (M2M) communications. As the age of information (AoI) is an emerging and critical metric for quantifying the freshness of the status update information collected in time-sensitive IoT applications, systematic analysis of AoI for FSA is called for. However, very limited work has been done on this topic despite its both theoretical and practical implications for the operation and optimization of FSA. To fill this void, this paper delivers a comprehensive analysis of AoI for four versions of FSA, namely synchronous and asynchronous FSA with and without retransmission. The core technique of our analysis is to model the AoI for FSA as Markov chains to derive statistics on the delay and inter-delivery time. Our central results consist of the lower bounds of AoI, the exact AoI expressions in the four FSA protocols and the optimum frame length for the AoI of FSA. Our analysis reveals the impact of the arrival rate and the protocol parameters on AoI, and also shows that the retransmission would improve AoI when the arrival rate is small.

This paper presents a unicast beamforming network for covert ambient backscatter communications (AmBC). Different from the prior covert backscatter works using artificial noise or a power-variable RF source, our work achieves covertness by arming the backscatter tag with multiple antennas. The tag uses a subset of its antennas to modulate covert information on backscattered RF signals while using the other antennas to modulate overt message as a cover to hide the covert transmission from the warden. We first derive the warden’s optimum detection threshold to minimize the detection error probability. To fight against the optimum warden, the tag changes impedance matching on each antenna to vary the reflected power and make the reflected overt and covert signals beamforming at space. We derive the optimum beamforming vectors by solving the problems of maximizing the covert rate at the receiver and the detection error probability at the warden, respectively. To address the non-convex constraints, we use semi-definite relaxation (SDR) and obtain near optimal parameters by designing binarySearch algorithms. Numerical results confirm the superiority of our work to the state-of-the-art one, and the existence of tradeoffs between the covert rate and the detection error probability.

We investigate multiset membership lookup prob-lem, a pivotal functionality in many computing and networking paradigms. We devise compact data structures and lookup algorithms that are amendable for hardware implementation, while guaranteeing high lookup accuracy and supporting interactive query processing. We first propose multi-hash color table, a variant of Bloom filter, to encode subset IDs compactly and map the ID of an item to its subset ID. We further construct a more balanced data structure called balanced multi-hash color table to improve the compactness by integrating load balancing.

We revisit the problem of missing tag identification in RFID networks by making three contributions. Firstly, we quantitatively compare and gauge the existing propositions spanning over a decade on missing tag identification. We show that the expected execution time of the best solution in the literature is $\Theta \left( {N + \frac{{{{(1 - \alpha )}^2}{{(1 - \delta )}^2}}}{{{\varepsilon ^2}}}} \right)$, where δ and ϵ are parameters quantifying the required identification accuracy, N denotes the number of tags in the system, among which αN tags are missing. Secondly, we analytically establish the expected execution time lower-bound for any missing tag identification algorithm as $\Theta \left( {\frac{N}{{\log N}} + \frac{{{{(1 - \delta )}^2}{{(1 - \alpha )}^2}}}{{{\varepsilon ^2}\log \frac{{(1 - \delta )(1 - \alpha )}}{\varepsilon }}}} \right)$, thus giving the theoretical performance limit. Thirdly, we develop a novel missing tag identification algorithm by leveraging a tree structure with the expected execution time of $\Theta \left( {\frac{{\log \log N}}{{\log N}}N + \frac{{{{(1 - \alpha )}^2}{{(1 - \delta )}^2}}}{{{\varepsilon ^2}}}} \right)$, reducing the time overhead by a factor of up to log N over the best algorithm in the literature. The key technicality in our design is a novel data structure termed as collision-partition tree (CPT), built on a subset of bits in tag pseudo-IDs, leading to more balanced tree structure and reducing the time complexity in parsing the entire tree.

Incorporating domain adaptation is a promising solution to mitigate the domain shift problem of WiFi-based human activity recognition (HAR). The state-of-the-art solutions, however, do not fully exploit all the data, only focusing either on unlabeled samples or labeled samples in the target WiFi environment. Moreover, they largely fail to carefully consider the discrepancy between the source and target WiFi environments, making the adaptation of models to the target environment with few samples become much less effective. To cope with those issues, we propose a Target-Oriented Semi-Supervised (TOSS) domain adaptation method for WiFi-based HAR that can effectively leverage both labeled and unlabeled target samples. We further design a dynamic pseudo label strategy and an uncertainty-based selection method to learn the knowledge from both source and target environments. We implement TOSS with a typical meta learning model and conduct extensive evaluations. The results show that TOSS greatly outperforms state-of-the-art methods under comprehensive 1 on 1 and multi-source one-shot domain adaptation experiments across multiple real-world scenarios.

Missing item event detection is one of the most important radio-frequency identification (RFID)-enabled functions. Yet it is largely unaddressed how to fast and reliably detect missing item event in multitagged RFID systems where multiple tags are tagged on one item. The canonical methods can only solve tag-level detection problem where each item is associated with one tag, and applying them to detect the missing multitagged items would falsely alarm and is time inefficient. To bridge the gap, this article formulates and analyzes the missing multitagged item detection problem. Our key idea is to search the proper seeds so that the reader only needs to probe a subset of the tags each being selected from different items instead of the entire tag set for the missing item detection. By employing the computation-communication tradeoffs, we design two protocols named M2ID and M2ID+ that classifies the tags before the segmentation compared to the former to improve time efficiency. With the derived optimum parameters, our protocols can achieve up to$4\times$performance gain in terms of time efficiency compared with the state-of-the-art solution.

We investigate a novel downlink multicarrier direct-sequence code division multiple access (MC-DS-CDMA) resource allocation scheme in the context of low earth orbit (LEO) satellite-ground integrated networks (SGINs). In contrast to the existing MC-DS-CDMA works which mainly focus on delay-tolerant services in terrestrial networks, we consider the heterogeneous delay traffic and take the unique characteristics of LEO satellite systems into account. Specifically, we exploit the channel information, the delay requirement, the buffer state, and the visible time of users to construct a utility function and formulate the resource allocation optimization problem in presence of co-channel interferences (CCI) in LEO satellite-ground heterogeneous systems. We transform the original nonconvex optimization problem into two convex ones and use the Lagrange dual decomposition method to derive the solution. We also design an efficient algorithm dynamically scheduling subcarriers, codes, and transmission power of MC-DS-CDMA. The simulation results confirm the superiority of our work in terms of lower average delay and higher overall throughput.

The satellite–air–ground networks (SAGNs) are experiencing unprecedented deployment for the advantages of global coverage and flexibility. However, the open light-of-sight communication links from satellites to the unmanned aerial vehicles (S2U) in SAGNs are vulnerable to malicious jamming and inadvertent interferences from other communication satellites, challenging the reliability of SAGNs. Despite the great importance, the anti-jamming S2U communication problem in SAGNs has been largely overlooked. To address this problem, we propose a two-stage anti-jamming scheme that embraces the cooperation among the unmanned aerial vehicles (UAVs) within the same group and jointly designs the hovering altitude of UAVs and the beamformers of satellites and cooperative UAVs to combat the malicious jamming and the cross-tier interferences. Specifically, following the proposed scheme, we formulate the problem of maximizing the minimum rate of the UAVs with the presence of the malicious jamming and inadvertent interferences into a nonconvex optimization problem. We then transform the problem into two tractable convex subproblems with feasible point pursuit successive convex approximation (FPP-SCA) method and provide the solutions to configure the hovering altitudes and the beamformers. We also conduct extensive simulations and the results show that the proposed anti-jamming scheme is efficient in terms of the UAVs’ capacity and the anti-jamming ability compared with the ones without cooperative UAVs and with fixed cooperators (FixCo).

In this work, we study the covert communications in ambient backscatter systems (ABS) with uncontrollable RF source on AWGN channels. In contrast to the prior works impractically assuming the existence of acontrollableRF excitation source, our work arms the receiver of covert information with the full-duplex ability. The covert receiver can emit artificial noise (AN) with variable power to cover up the modulation action of a tag on the excitation signals while receiving the backscattered information. Specifically, we first derive the warden’s optimum power-detection threshold that minimizes the detection error probability. To against the optimal warden, we design the covert backscatter communication policy that determines the feasible region of the AN power at the covert receiver depending on the transmission power of the RF source and the reflection coefficient of the tag and guarantees the covertness constraint. We analyze the performance of the policy and provide the closed-form maximum covert rate and maximum detection error probability at the warden, revealing their tradeoff. The numerical analysis shows the increasing transmission power of the RF source and tag’s reflection coefficient would degrade the covertness of ABS when the covert receiver has to increase the AN power.

Given a dataset$\mathcal S$composed of$g$subsets with each data item belonging to one of them,multiset membership lookuptakes an item$e$as input and outputs a binary answer whether$e\in {\mathcal S}$and, in case of yes, the ID of the subset to which$e$belongs. Overlaid upon while more sophisticated than the canonical membership lookup, multiset membership lookup emerges as a pivotal functionality in many computing and networking paradigms. The quest to achieve high-speed, high-accuracy lookup with limited memory cost makes lookup algorithm design a challenging task, particularly when the data items arrive as a stream. In this paper, we devise compact data structures and lookup algorithms that are amendable for hardware implementation, while guaranteeing high lookup accuracy and supporting interactive query processing. We first proposemulti-hash color table, a variant of Bloom filter, to encode subset IDs compactly and map the ID of an item to its subset ID. We further construct a more balanced data structure calledbalanced multi-hash color tableto improve the compactness by integrating the state-of-the-art load balancing technique. We complete our work by addressing the case ofbatch arrivalsand design a batched recording algorithm optimizing the memory efficiency. We give both theoretical and empirical analysis to characterize and evaluate the performance of the proposed algorithms in terms of lookup accuracy, memory and access efficiency.

We formulate and investigate the problem of distributed channel rendezvous in collision-prone wireless networks. Existing researches on this topic are mainly devoted to designing channel hopping sequences, each pair of which can overlap on a common channel within bounded delay. However, this overlap-based canonical rendezvous design does not take into account channel collision, which may render existing rendezvous algorithms fail to achieve bounded delay in collision-prone environment. Motivated by this observation, we formulate and investigate the collision-aware channel rendezvous problem in a generic scenario, where a collision occurs if more than$C$packets overlap in time on a same channel. Our generic formulation allows to model both the baseline single packet reception model with$C=1$and the more sophisticated multiple packet reception model with$C > 1$. We further abstract the collision-aware rendezvous problem as the problem of constructing a robust rendezvous system. We establish the theoretical limit of the problem, guided by which we design a collision-resilient distributed rendezvous algorithm with truly bounded rendezvous delay. We then demonstrate the performance of our rendezvous algorithm both analytically and numerically.

The explosive deployment of the Internet of Things (IoT) brings a massive number of light-weight and energy-limited IoT devices, challenging stable wireless access. Energy-efficient, Frame Slotted Aloha (FSA) recently emerged as a promising MAC protocol for large-scale IoT systems such as Machine to Machine (M2M) and Radio Frequency Identification (RFID). Yet the stability of FSA and how to stabilize it, despite of its fundamental importance on the effective operation in practical systems, have not been systematically addressed. In order to bridge this gap, we devote this paper to designing stable FSA-based access protocol (SFP) to stabilize IoT systems. We first design an additive active node population estimation scheme and use the estimate to set frame size and participation probability for throughput optimization. We then carry out theoretical analysis demonstrating the stability of SFP in the sense of geometric ergodicity of Markov chain derived from dynamics of the active node population and its estimate. Our central theoretical result is a set of closed-form conditions on the stability of SFP. We further conduct extensive simulations whose results confirm our theoretical analysis and demonstrate the effectiveness of SFP.

Backscatter communication with ambient excitations receives great attention recently as it provides a practical battery-free way to convey various IoT data. However, state-of the-art solutions are of low data rates and thus cannot serve highbandwidth applications, e.g., live streaming. This paper presents Hermit Crab, the first WiFi-backscatter system that achieves high-throughput communication for video streaming. The key contribution is a differential decoding algorithm using pilot phase. By doing so, it supports single symbol encoding, which is much faster than multi-symbol encoding of previous systems. In addition, Hermit Crab can recover the production data and tag data at the same time. Through extensive experiments, we show that it achieves throughputs of up to 960 Kbps with 802.1lg ambient signals, which is 7. 6x better than the state-of-the-art system. We also demonstrate that with such good throughputs, it can stably support live streaming of 480p videos at 30 fps.

In most indoor localization systems deployed on commodity WiFi infrastructure, channel state information (CSI) data is usually transmitted over multiple subcarriers of different frequencies. An observation is that there exists a certain subcarrier that can best estimate the location of the target. Based on it, we propose MobiFi to leverage deep learning to automatically select the best subcarrier. MobiFi mainly consists of two steps: First, a lightweight end-to-end Convolution Neural Network (CNN) is taken as the backbone network to extract features and do classification while avoiding serious overfitting. After selecting the best subcarrier by the first two steps, MobiFi calculates the AoA estimation and corresponding location estimation in the same way as SpotFi. Since the backbone network is lightweight, MobiFi can realize near real-time on mobile devices with guaranteed localization performance. Extensive experiments show that MobiFi is comparable to SpotFi; both methods achieve a median AoA estimation error of 8.6° and median location estimation error of 1. 5m in an indoor office scenario. At the same time, MobiFi which consumes less than 0. 21s and 1. 7s on Personal Computer (PC) and mobile devices respectively is 5 times faster than SpotFi. Particularly, because MobiFi enables real-time localization on mobile devices, it provides an economical solution for some cases where a central server is replaced by a mobile device.

With the rapid development of radio-frequency identification (RFID) technology, the ever-increasing research effort has been dedicated to devising various RFID-enabled services. The missing event detection, the functionality of detecting missing objects, is one of the most important services in many Internet-of-Things applications such as inventory management. Prior detection protocols only work in single-tagged RFID systems and would waste much time on repeated checks on one object in the emerging multitagged systems where each object is attached by multiple tags, leaving efficient detection in the new scenario unaddressed. To bridge the gap, this article is devoted to detecting missing multitagged objects. The key technicality is to build a filter from a subset of tags instead of whole in prior works to avoid repeated detections of one object and reduce detection time. Specifically, we first provide a basic solution based on the Bloom filter which can specify only tags in the chosen subset to participate in the final detection. To further improve time efficiency, we propose an advanced protocol that exploits tag ID knowledge and sparsity of slots mapped by only tags in the chosen subset to build a more compact compressive filter. Moreover, a composite vector is used to efficiently coordinate tags to report its presence. We conduct theoretical analysis on optimum protocol parameters and extensive simulations to verify the feasibility of the protocols. The results show that the advanced protocol achieves more than$2\times $performance gain in terms of time efficiency over the Bloom filter-based basic protocol.

With rapid development of radio frequency identification (RFID) technology, ever-increasing research effort has been dedicated to devising various RFID-enabled services. The missing tag identification, which is to identify all missing tags, is one of the most important services in many Internet-of-Things applications such as inventory management. Prior work on missing tag detection all rely on hash functions implemented at individual tags. However, in reality hash functions are not supported by commercial off-the-shelf (COTS) RFID tags. To bridge this gap between theory and practice, this paper is devoted to detecting missing tags with COTS Gen2 devices. We first introduce a point-to-multipoint protocol, named P2M that works in an analog frame slotted Aloha paradigm to interrogate tags and collect their electronic product codes (EPCs). A missing tag will be found if its EPC is not present in the collected ones. To reduce time cost of P2M resulted from tag response collisions, we further present a collision-free point-to-point protocol, named P2P that selectively specifies a tag to reply with its EPC in each slot. If the EPC is not received, this tag is regarded to be missing. We develop two bitmask selection methods to enable the selective query while reducing communication overhead. We implement P2M and P2P with COTS RFID devices and evaluate their performance under diverse settings.

Ever-increasing research efforts have been dedicated to radio frequency identification (RFID) systems, such as finding top-k, elephant groups, and missing-tag detection. While group labeling, which is how to tell tags their associated group data, is the common prerequisite in many RFID applications, its efficiency is not well optimized due to the transmission of useless data with only one seed used. In this paper, we introduce a unified protocol called GLMS which employs multiple seeds to construct a composite indicator vector (CIV), reducing the useless transmission. Technically, to address Seed Assignment Problem (SAP) arising during building CIV, we develop an approximation algorithm (AA) with a competitive ratio 0.632 by globally searching for the seed contributing to the most useful slot. We then further design two simplified algorithms through local searching, namely c-search-I and its enhanced version c-search-II, reducing the complexity by one order of magnitude while achieving comparable performance. We conduct extensive simulations to demonstrate the superiority of our approaches.

Direct Acyclic Graph (DAG)-based ledger and the corresponding consensus algorithm has been identified as a promising technology for Internet of Things (IoT). Compared with Proof-of-Work (PoW) and Proof-of-Stake (PoS) that have been widely used in blockchain, the consensus mechanism designed on DAG structure (simply called as DAG consensus) can overcome some shortcomings such as high resource consumption, high transaction fee, low transaction throughput and long confirmation delay. However, the theoretic analysis on the DAG consensus is an untapped venue to be explored. To this end, based on one of the most typical DAG consensuses, Tangle, we investigate the impact of network load on the performance and security of the DAG-based ledger. Considering unsteady network load, we first propose a Markov chain model to capture the behavior of DAG consensus process under dynamic load conditions. The key performance metrics, i.e., cumulative weight and confirmation delay are analysed based on the proposed model. Then, we leverage a stochastic model to analyse the probability of a successful double-spending attack in different network load regimes. The results can provide an insightful understanding of DAG consensus process, e.g., how the network load affects the confirmation delay and the probability of a successful attack. Meanwhile, we also demonstrate the trade-off between security level and confirmation delay, which can act as a guidance for practical deployment of DAG-based ledgers.

Wireless body area networks (WBANs) that offer various medical applications have received considerable attention in recent years. Due to limited energy of sensors, duty-cycling technique is employed to prolong the network lifetime. However, it results in long delivery delay and suffers from reliability issues. In this paper, we introduce an efficient and reliable sleep scheduling scheme from the perspective of constructing m-fold dominating set (DS), where m is the number of links from a node outside DS to those in DS. The key idea is to activate partial nodes at each frame to form a DS which can guarantee the network reliability such that the other nodes can fall asleep to save energy. Technically, we formulate the sleep scheduling in a WBAN as a problem of constructing minimum weighted m-fold DS, which is proven NP-hard. We first design an H(m + δ)-approximation algorithm, namely global approximation algorithm, by globally picking the optimal node based on a polymatroid function, where H(·) is the Harmonic number and δ is the maximum node degree. Then, we propose a simplified 1 +ln (mδ)-approximation algorithm, referred to as local approximation algorithm, to reduce computational complexity and execution rounds. We further conduct extensive simulations to confirm the superiority of our proposed algorithms.

Tag search, which is to find a particular set of tags in a radio frequency identification (RFID) system, is a key service in such important Internet-of-Things applications as inventory management. When the system scale is large with a massive number of tags, deterministic search can be prohibitively expensive, and probabilistic search has been advocated, seeking a balance between reliability and time efficiency. Given a failure probability$\frac {1}{\mathcal {O}(K)}$, where$K$is the number of tags, state-of-the-art solutions have achieved a time cost of$\mathcal {O}(K \log K)$through multi-round hashing and verification. Further improvement, however, faces a critical bottleneck of repetitively verifying each individual target tag in each round. In this paper, we present an efficient tree-based tag search (TTS) that approaches$\mathcal {O}(K)$through batched verification. The key novelty of TTS is to smartly hash multiple tags into each internal tree node and adaptively control the node degrees. It conducts bottom–up search to verify tags group by group with the number of groups decreasing rapidly. Furthermore, we design an enhanced tag search scheme, referred to as TTS+, to overcome the negative impact of asymmetric tag set sizes on time efficiency of TTS. TTS+ first rules out partial ineligible tags with a filtering vector and feeds the shrunk tag sets into TTS. We derive the optimal hash code length and node degrees in TTS to accommodate hash collisions and the optimal filtering vector size to minimize the time cost of TTS+. The superiority of TTS and TTS+ over the state-of-the-art solution is demonstrated through both theoretical analysis and extensive simulations. Specifically, as reliability demand on scales, the time efficiency of TTS+ reaches nearly 2 times at most that of TTS.

We revisit the opportunistic scheduling problem in which a server opportunistically serves multiple classes of users under time-varying multi-state Markovian channels. The aim of the server is to find an optimal policy minimizing the average waiting cost of those users. Mathematically, the problem can be recast to a restless multiarmed bandit one, and a pivot to solve restless bandit by the Whittle index approach is to establish indexability. Despite the theoretical and practical importance of the Whittle index policy, the indexability is still open for opportunistic scheduling in the heterogeneous multi-state channel case. To fill this gap, we mathematically identify a set of sufficient conditions on a channel state transition matrix under which the indexability is guaranteed and consequently, the Whittle index policy is feasible. Furthermore, we obtain the closed-form Whittle index by exploiting the structural property of the channel state transition matrix. For a generic channel state transition matrix, we propose an eigenvalue-arithmetic-mean scheme to obtain the corresponding approximate matrix which satisfies the sufficient conditions, and consequently can get an approximate Whittle index. This paper constitutes a small step toward solving the opportunistic scheduling problem in its generic form involving multi-state Markovian channels and multi-class users.

Multihop wireless networking is a key enabling technology for interconnecting a vast number of IoT devices. Measurement is fundamental to various network operations including management, diagnostics, and optimization. Out-of-band measurement approaches use external sniffers to monitor the network traffic passively, and they provide detailed information about the network. However, existing approaches do not carefully consider lossy and correlated links which are common in low-power wireless networks, resulting in unsatisfactory packet capture ratio and low measurement quality. In this paper, we present NetVision, a practical out-of-band measurement system with special consideration for sniffer deployment. By explicitly considering link quality and link correlation, we are able to achieve a high measurement quality while minimizing the deployment cost. We formulate the sniffer deployment problem as an optimization problem and propose efficient algorithms for solving this problem. We further design a set of instructions and APIs to simplify a variety of common measurement tasks. We implement NetVision on the TinyOS/TelosB platform and evaluate its performance extensively both in simulation and an indoor testbed with 80 TelosB nodes. Results show that NetVision is accurate, generic, and robust. Three typical case studies demonstrate that NetVision can facilitate various measurement and debugging tasks.

We consider the multichannel opportunistic access problem, in which a user decides, at each time slot, which channel to access among multiple Gilbert-Elliot channels in order to maximize his aggregated utility (e.g., the expected transmission throughput) given that the observation of channel state is error-prone. The problem can be cast into a restless multiarmed bandit problem which is proved to be PSPACE-Hard. An alternative approach, given the problem hardness, is to look for simple channel access policies. Whittle index policy is a very popular heuristic for restless bandits, which is provably optimal asymptotically and has good empirical performance. In the case of imperfect observation, the traditional approach of computing the Whittle index policy cannot be applied because the channel state belief evolution is no more linear, thus rendering the indexability of our problem open. In this paper, we mathematically establish the indexability and establish the closed-form Whittle-index, based on which index policy can be constructed. The major technique in our analysis is a fixed point based approach which enable us to divide the belief information space into a series of regions and then establish a set of periodic structures of the underlying nonlinear dynamic evolving system, based on which we devise the linearization scheme for each region to establish indexability and compute the Whittle index for each region.

Searching for a particular group of tags in an RFID system is a key service in such important Internet-of-Things applications as inventory management. When the system scale is large with a massive number of tags, deterministic search can be prohibitively expensive, and probabilistic search has been advocated, seeking a balance between reliability and time efficiency. Given a failure probability [1/(O(K))], where K is the number of tags, state-of-the-art solutions have achieved a time cost of O(K log K) through multi-round hashing and verification. Further improvement however faces a critical bottleneck of repetitively verifying each individual target tag in each round. In this paper, we present a novel Tree-based Tag Search (TTS) that approaches O (K) through batched verification. TTS smartly hashes multiple tags into each internal tree node and adaptively controls the node degrees. It conducts bottom-up search to verify tags group by group with the number of groups decreasing rapidly. We derive the optimal hash code length and node degrees to accommodate hash collisions, and demonstrate the superiority of TTS through both theoretical analysis and extensive simulations. In particular, we show that, with increasing reliability demand and system size, TTS achieves an even higher performance gain, making it a highly scalable solution.

With rapid development of radio frequency identification (RFID) technology, ever-increasing research effort has been dedicated to devising various RFID-enabled services. The key tag monitoring, which is to detect anomaly of key tags, is one of the most important services in such important Internet-of-Things applications as inventory management. Yet prior work assumes that all tags are armed with hashing functionality and a reader would report channel states in every slot, which is not supported by commercial off-the-shelf (COTS) RFID tags and readers. To bridge this gap, this paper is devoted to enabling key tag monitoring service with COTS devices. In particular, we introduce two anomaly monitoring protocols to detect whether there is any key tag absent from the system. The first protocol employs Q-query that works in an analog frame slotted Aloha paradigm to interrogate tags and collect tag IDs. An anomaly event will be found if at least one key tag ID is not present in the collected ones. To reduce time cost of the first protocol resulted from tag collisions, we present a collision-free method that uses select-query to specify a key tag to reply in each slot. Once there is no response in a slot, the specified key tag is regarded as a missing tag. We conduct experiments to evaluate two protocols.

We investigate the opportunistic scheduling problem where a server opportunistically serves multiple classes of users under time varying multi-state Markovian channels. The aim of the server is to find an optimal policy minimizing the average waiting cost of users. Mathematically, the problem can be cast to a restless bandit one, and a pivot to solve restless bandit by index policy is to establish indexability. We mathematically propose a set of sufficient conditions on channel state transition matrix, and consequently, the index policy is feasible. Our work consists of a small step toward solving the opportunistic scheduling problem in its generic form involving multi- state Markovian channels and multi-class users.

We formulate and study a broadcast problem arising in multi-channel duty cycling wireless body area networks (WBANs), where the sink needs to broadcast the control message to all sensor nodes. The objective is to design robust multichannel wake-up schedule with minimum worst-case broadcast delay while guaranteeing the full broadcast diversity regardless of clock drifts and asymmetric duty cycles. To that end, we first derive the lower-bound of worst-case broadcast delay with full diversity of any broadcast protocol and then design a multichannel broadcast protocol (MCB) that satisfies the performance requirement for the latency and diversity. Finally, the simulation results demonstrate the capability of MCB of ensuring successful broadcast delivery on every channel within the theoretical worst-case broadcast delay, even under asymmetric duty cycles and any amount of clock drifts.

Ever-increasing research effort has been dedicated to multi-group radio frequency identification (RFID) systems where all tags are partitioned into multiple groups, such as group-level queries, and multi-group missing tag detection. However, it is assumed in the existing work that all tags know their individual group IDs, which thus leaves group labeling problem unaddressed. To tackle the under-investigated problem, this paper is devoted to devising an efficient group labeling protocol to inform each tag of its corresponding group ID fast and accurately. To this end, we employ multiple seeds to build a Composite Indicator Vector (CIV) indicating the assigned seed in each slot, which reduces transmissions of useless information and thus improves time efficiency. Specifically, we first theoretically show that the Seed Assignment Problem (SAP) arising in establishing the CIV is NP-hard and then develop a myopic approximation algorithm. Finally, the simulation results confirm the superiority of the proposed protocol over the state-of-the-art solution in terms of time efficiency.

We formulate and study a broadcast problem arising in multichannel duty-cycling wireless body area networks (WBANs) which the sink needs to broadcast control information to all sensor nodes on or implanted in the human body. Despite its fundamental importance for the network configuration and secure key management, the multichannel broadcast problem is largely unaddressed in duty-cycling WBANs. In this paper, we devise novel 2-D scheduling specifying the rule of channel hopping and wake-up time slot selection, which achieves the order-minimal worst-case broadcast delay while guaranteeing the full broadcast diversity regardless of clock drifts and asymmetric duty cycles and channel perceptions. Specifically, we first employ the Chinese remainder theorem to design an effective multichannel broadcast (MCB) algorithm and further propose improved MCB that enhances the granularity of MCB in matching actual duty cycles and number of channels, reducing the theoretically worst-case broadcast delay of MCB by up to 75%. We demonstrate the performance of the proposed algorithms through theoretical analysis and extensive simulations.

We consider the channel access problem arising in opportunistic scheduling over fading channels, cognitive radio networks, and server scheduling. The multi-channel communication system consists of N channels. Each channel evolves as a time-nonhomogeneous multi-state Markov process. At each time instant, a user chooses M channels to transmit information, and obtains some reward, i.e., throughput, based on the states of the chosen channels. The objective is to design an access policy, i.e., which channels should be accessed at each time instant, such that the expected accumulated discounted reward is maximised over a finite or infinite horizon. The considered problem can be cast into a restless multi-armed bandit (RMAB) problem, which is PSPACE-hard, with the optimal policy usually intractable due to the exponential computation complexity. Hence, a natural alternative is to consider the easily implementable myopic policy that only maximises the immediate reward but ignores the impact of the current strategy on the future reward. In this paper, we perform an analytical study on the performance of the myopic policy for the considered RMAB problem, and establish a set of closed-form conditions to guarantee the optimality of the myopic policy.

Radio frequency identification technology has been widely used in missing tag detection to reduce and avoid inventory shrinkage. In this application, promptly finding out the missing event is of paramount importance. However, the existing missing tag detection protocols cannot efficiently handle the presence of a large number of unexpected tags whose IDs are not known to the reader, which shackles the time efficiency. To deal with the problem of detecting missing tags in the presence of unexpected tags, this paper introduces a two-phase Bloom filter-based missing tag detection (BMTD) protocol. The proposed BMTD exploits Bloom filter in sequence to first deactivate the unexpected tags and then test the membership of the expected tags, thus dampening the interference from the unexpected tags and considerably reducing the detection time. Moreover, the theoretical analysis of the protocol parameters is performed to minimize the detection time of the proposed BMTD and achieve the required reliability simultaneously. In addition, we derive a critical threshold on the unexpected tag size for the execution of first phase in BMTD. Extensive experiments are then conducted to evaluate the performance of the proposed BMTD. The results demonstrate that the proposed BMTD significantly outperforms the state-of-the-art solutions.

Frame Slotted Aloha (FSA) protocol has been widely applied in Radio Frequency Identification (RFID) systems as the de facto standard in tag identification. However, very limited work has been done on the stability of FSA despite its fundamental importance both on the theoretical characterization of FSA performance and its effective operation in practical systems. In order to bridge this gap, we devote this paper to investigating the stability properties of p-persistent FSA by focusing on two physical layer models of practical importance, the models with single packet reception and multipacket reception capabilities. Technically, we model the FSA system backlog as a Markov chain with its states being backlog size at the beginning of each frame. The objective is to analyze the ergodicity of the Markov chain and demonstrate its properties in different regions, particularly the instability region. By employing drift analysis, we obtain the closed-form conditions for the stability of FSA and show that the stability region is maximized when the frame length equals the number of packets to be sent in the single packet reception model and the upper bound of stability region is maximized when the ratio of the number of packets to be sent to frame length equals in an order of magnitude the maximum multipacket reception capacity in the multipacket reception model. Furthermore, to characterize system behavior in the instability region, we mathematically demonstrate the existence of transience of the backlog Markov chain. Finally, the analytical results are validated by the numerical experiments.

We formulate and study a missing tag detection problem arising in multiple-group, multiple-region radio frequency identification (RFID) systems, where a mobile reader needs to detect whether there is any missing event for each group of tags. The problem we tackle is to devise missing tag detection protocols with minimum execution time while guaranteeing the detection reliability requirement for each group. By leveraging the technique of Bloom filter, we develop a suite of three missing tag detection protocols, each decreasing the execution time compared to its predecessor by incorporating an improved version of the Bloom filter design and parameter tuning. By sequentially analyzing the developed protocols, we gradually iron out an optimum detection protocol that works in practice.

We consider the channel access problem arising in opportunistic scheduling over fading channels, cognitive radio networks, and server scheduling. The multi-channel communication system consists of N channels. Each channel evolves as a time-nonhomogeneous multi-state Markov process. At each time instant, a user chooses M channels to transmit information. Some reward depending on the states of the chosen channels is obtained for each transmission. The objective is to design an access policy that maximizes the expected accumulated discounted reward over a finite or infinite horizon. The considered problem can be cast into a restless multi-armed bandit (RMAB) problem with PSPACE-hardness. A natural alternative is to consider the easily implementable myopic policy. In this paper, we perform an theoretical analysis on the considered RMAB problem, and establish a set of closed-form conditions to guarantee the optimality of the myopic policy.

Tag population estimation has recently attracted significant research attention due to its paramount importance on a variety of radio-frequency identification (RFID) applications. However, most, if not all, of the existing estimation mechanisms are proposed for the static case where tag population remains constant during the estimation process, thus leaving the more challenging dynamic case unaddressed, despite the fundamental importance of the latter case on both the theoretical analysis and the practical application. In order to bridge this gap, we devote this paper to designing a generic framework of stable and accurate tag population estimation schemes based on the Kalman filter for both the static and dynamic RFID systems. Technically, we first model the dynamics of RFID systems as discrete stochastic processes and leverage the techniques in the extended Kalman filter and cumulative sum control chart to estimate tag population for both the static and dynamic systems. By employing the Lyapunov drift analysis, we mathematically characterize the performance of the proposed framework in terms of estimation accuracy and convergence speed by deriving the closed-form conditions on the design parameters under which our scheme can stabilize around the real population size with bounded relative estimation error that tends to zero with exponential convergence rate.

Frame Slotted Aloha (FSA) protocol has been widely applied in Radio Frequency Identification (RFID) systems as the defacto standard in tag identification. However, very limited work has been done on the stability of FSA despite its fundamental importance both on the theoretical characterisation of FSA performance and its effective operation in practical systems. In order to bridge this gap, we devote this paper to investigating the stability properties of FSA by focusing on two physical layer models of practical importance, the models with single packet reception and multipacket reception capabilities. Technically, we model the FSA system backlog as a Markov chain with its states being backlog size at the beginning of each frame. The objective is to analyze the ergodicity of the Markov chain and demonstrate its properties in different regions, particularly the instability region. By employing drift analysis, we obtain the closed-form conditions for the stability of FSA and show that the stability region is maximised when the frame length equals the backlog size in the single packet reception model and when the ratio of the backlog size to frame length equals in an order of magnitude the maximum multipacket reception capacity in the multipacket reception model. Furthermore, to characterise system behavior in the instability region, we mathematically demonstrate the existence of transience of the backlog Markov chain.

Opportunistic network is a type of challenged network where an end-to-end path between the source and the destination doesn't exist. The dissemination of the data relies on the encounters of nodes. Link duration time is a main factor in determining the transmission capacity between two encounter nodes in the opportunistic network. Besides, inter-contact time plays a key role in forwarding algorithms and has an obvious effect on the delivery delay. In this paper, according to statistical analysis and numerical methods, statistical characteristics of wireless link in random waypoint (RWP) are analyzed from aspects of contact duration time and inter-contact time with different moving speed and transmission radiuses of the nodes. Complementary Cumulative Distribution Functions (CCDF) of the contact duration time and inter-contact time of the nodes are provided by numerical methods.

Opportunistic networks are the emerging networks featured by partitions, long disconnections, and topology instability, where the message propagation depends on the cooperation of nodes to fulfill a “store-carry-and-forward” fashion. But due to constrained energy and buffer, some nodes may behave selfishly, which will involve damage to the existing routing approaches and seriously degrade the performance of opportunistic networks. Aiming at the above problem, this paper proposes a novel bargaining based incentive protocol (BIP) for opportunistic networks, which exploits two-person bargaining model and allows a node to pay and charge according to its state and the attributes of messages. In addition, the proposed BIP protocol can tackle the issue of blind cooperation when the resources are very scarce. Extensive simulation results demonstrate the effectiveness and the practicality of the proposed BIP protocol in terms of high delivery ratio, low energy consumption and small average delay.