Multi-robot collaborative SLAM, as a key direction in Internet of Things (IoT) applications, faces the critical challenge of achieving accurate self and mutual pose estimation. Existing distributed pose optimization methods often suffer from unbalanced computational allocation and insufficient utilization of inter-robot information. To address these limitations, we propose a balanced and robust distributed pose optimization framework that integrates three complementary layers: inter-robot global transformation, intra-robot factor graph, and real-time pose estimation. Firstly, a flexible load-aware bidirectional global pose optimization module is designed to achieve inter-robot adaptive computational balance. Next, a multi-source evaluated local pose optimization module is introduced, which allows high-precision robots to assist low-precision robots for accurate intra-robot factor graph refinement on different scenes. Then, a geometric observation-driven degenerate pose correction module is developed to improve observation precision and real-time pose estimation accuracy in degraded environments. Together, these modules establish a hierarchical optimization architecture that achieves inter-robot computational balance, intra-robot refined pose, and real-time degeneration correction. Both simulation and real-world experiments demonstrate that the proposed framework consistently outperforms state-of-the-art methods in terms of computational balance and robustness.

While Graph Neural Networks and Transformers have shown promise in predicting molecular properties, they struggle with directly modeling complex many-body interactions. Current methods often approximate interactions like three- and four-body terms in message passing, while attention-based models, despite enabling direct atom communication, are typically limited to triplets, making higher-order interactions computationally demanding. To address the limitations, we introduce MABNet, a geometric attention framework designed to model four-body interactions by facilitating direct communication among atomic quartets. This approach bypasses the computational bottlenecks associated with traditional triplet-based attention mechanisms, allowing for the efficient handling of higher-order interactions. MABNet achieves state-of-the-art performance on benchmarks like MD22 and SPICE. These improvements underscore its capability to accurately capture intricate many-body interactions in large molecules. By unifying rigorous many-body physics with computational efficiency, MABNet advances molecular simulations for applications in drug design and materials discovery, while its extensible framework paves the way for modeling higher-order quantum effects.

Virtual Screening (VS) is vital for drug discovery but struggles with low hit rates and high computational costs. While Active Learning (AL) has shown promise in improving the efficiency of VS, traditional methods rely on inflexible and handcrafted heuristics, limiting adaptability in complex chemical spaces, particularly in balancing molecular diversity and selection accuracy. To overcome these challenges, we propose GLARE, a reinforced active learning framework that reformulates VS as a Markov Decision Process (MDP). Using Group Relative Policy Optimization (GRPO), GLARE dynamically balances chemical diversity, biological relevance, and computational constraints, eliminating the need for inflexible heuristics. Experiments show GLARE outperforms state-of-the-art AL methods, with a 64.8% average improvement in Enrichment Factors (EF). Additionally, GLARE enhances the performance of VS foundation models like DrugCLIP, achieving up to an 8-fold improvement in EF$_{0.5\\%}$ with as few as 15 active molecules. These results highlight the transformative potential of GLARE for adaptive and efficient drug discovery.

This paper tackles a notable security loophole in tag-based Physical-Layer Authentication (PLA) when faced with cooperative attacks, highlighting its significance due to two main reasons. First, while attackers may only observe the tag amid noise, they can reduce the noise effect through multiple observations. Furthermore, several cooperative attackers can orchestrate more sophisticated attacks, achieving goals beyond the reach of a single attacker. In this paper, we propose an enhanced PLA scheme, designated as the Enhanced Detection of Cooperative Attacks (EDCA) scheme, to counter these cooperative threats. The basic idea of the EDCA scheme involves utilizing a unique noise characteristic induced by replayed signals to identify and thwart cooperative attacks, preventing attackers from accumulating multiple observations of the same tag. We theoretically analyze the proposed scheme over fading channels and derive closed-form expressions for its performance. Implementation and rigorous evaluation of the EDCA scheme are carried out to compare its performance with prior schemes. Simulation results confirm the alignment of theoretical predictions with empirical outcomes. The proposed scheme not only can effectively detect cooperative attacks but also provide a better detection performance.

In this paper, we are concerned about the problem of guaranteeing both privacy and security in a Location-Based Service (LBS) system, where a challenging scenario involving cooperative attack is considered. Since prior Physical-Layer Authentication (PLA) schemes do not consider cooperative attack, their security significantly declines under such attacks. We propose two privacy-preserving PLA schemes: the Privacy-Preserving Physical-Layer Authentication using Noise Variance (PPPLA-NV) scheme and the Privacy-Preserving Physical-Layer Authentication using Multiple Channel Responses (PPPLA-MCR) scheme, which significantly improve the privacy-preserving performance under a cooperative attack. Note that the proposed schemes protect not only user’s identity information but also data message. We theoretically analyze the performance of the proposed schemes, derive their closed-form expressions, and provide a theoretical comparison between both proposed schemes. We implement the proposed schemes and conduct extensive performance comparisons through simulations. Experimental results show a perfect match between the theoretical and simulation results. From the experimental results, we observe that if the overhead is not the priority, the PPPLA-MCR scheme is the best option; otherwise, the PPPLA-NV scheme may be a better option.

This paper addresses the critical challenge of securing two-way Time-of-Arrival (ToA) localization in Wireless Sensor Networks (WSNs) against cooperative distance-enlargement attacks. Existing schemes often rely on heuristic test statistics, are limited to specific attack strategies e.g., Amplify and Forward (AF), or require collaboration among multiple anchor nodes, which hinders their effectiveness against sophisticated adversaries and often incurs high communication overhead. We propose two novel detection schemes based on signal characteristics that overcome these limitations, offering both high accuracy and low complexity. The first scheme, termed the Efficient Detection Scheme of Distance-Enlargement Attacks using Log-Likelihood Ratio (EDDEA-LLR), leverages the Neyman-Pearson (NP) lemma to derive an optimal test statistic for scenarios involving AF attacks. Recognizing the limitations of existing schemes and the EDDEA-LLR scheme in countering Decode and Forward (DF) attacks, we introduce the Efficient Detection Scheme of Distance-Enlargement Attacks by Embedding a Secret Tag (EDDEA-EST). This scheme exploits embedded secret tags within the challenge signal to effectively detect DF attacks. Importantly, the proposed schemes do not require collaboration among multiple anchor nodes, eliminating the need for additional communication and featuring low computational complexity. We provide a rigorous theoretical analysis, deriving closed-form expressions for the detection performance of both schemes. Through extensive simulations, we demonstrate the superiority of the proposed schemes over existing methods in terms of detection accuracy, robustness against both AF and DF attacks, and comprehensive performance considering communication overhead.

This paper addresses two issues of a contact-tracing system with edge-based wireless communication techniques: to locate close contacts and to provide confidentiality-preserving communications. In this paper, we propose the Confidentiality-Preserving Contact-Tracing (CPCT) system with multiple wireless edge-based nodes. The CPCT system consists of three stages: registration, authentication, and confidentiality-preserving communication stages. We propose two Physical-Layer Authentication (PLA) schemes for the authentication stage of the CPCT system: the Coordinate-based Location PLA (CLP) scheme using the estimated coordinates and the Time-of-Arrival (ToA) based Location PLA (TLP) scheme using the estimated ToAs. We propose a distributed encryption scheme for the confidentiality-preserving communication stage of the CPCT system named the Distributed Channel Impulse Response (CIR)-based Encryption (DCE) scheme. We provide the theoretical analysis of the proposed schemes and derive their closed-form expressions. We implement the proposed schemes and conduct extensive performance comparisons through simulations. We observe that the theoretical results perfectly match the corresponding simulation results. Moreover, the proposed PLA schemes provide higher authentication performance than the prior PLA scheme, while the proposed encryption scheme provides higher confidentiality performance than the prior encryption scheme.

This paper addresses the issue of detecting the use of information hiding at anti-copying 2D barcodes. Prior hidden information detection schemes have their roots in either heuristic-based or Machine Learning (ML). However, prior heuristics-based schemes lack a rigorous theoretical analysis. Prior ML-based information schemes lack robustness because a printed 2D barcode is very much environmentally dependent. Thus, an information hiding detection scheme trained in one environment often does not work well in another environment. In this paper, we propose two hidden information detection schemes for existing anti-copying 2D barcodes. The first scheme directly uses the pixel distance to detect the use of an information hiding scheme in a 2D barcode, referred to as the Pixel Distance Based Detection (PDBD) scheme. The second scheme first calculates the variance of raw signal and the covariance between the recovered and raw signals, and then based on the variance results, detects the use of information hiding scheme in a 2D barcode, referred to as the Pixel Variance Based Detection (PVBD) scheme. Moreover, we design advanced Illegitimately-Copying (IC) attacks to evaluate the security of two existing anti-copying 2D barcodes. We conduct extensive performance comparisons among the proposed schemes and prior schemes under different capturing devices,e.g., different scanners or camera phones. Our experimental results show that the PVBD scheme can correctly detect the existence of hidden information at both the 2LQR code and the LCAC 2D barcode. Moreover, the successful attacking probability of the proposed IC attacks achieves 0.6538 for the 2LQR code and 1 for the LCAC 2D barcode.

This paper is concerned with the security problem of Time of Arrival (ToA) based localization schemes in a wireless sensor network (WSN) with multiple attackers and this paper focuses on defending against external attacks, especially under cooperative external attackers. The prior scheme for defending against the attacks in the localization scheme often introduce high communication overhead and their security relies on the capability of the attackers. In this paper, we propose a lightweight secure ToA-based localization scheme in a WSN by exploiting the noise feature caused by external distance attacks. In comparison with the prior scheme, our scheme provides lower communication overhead and a higher level of security. We theoretically analyze the performance of the proposed scheme over fading channels and derive the closed-form expressions. We implemented our scheme and conducted extensive performance comparisons through simulations. Our experimental results show that the closed-form expressions for the detection performance perfectly match with their simulation results as we expected. The communication overhead of the proposed scheme is saved by 72.8% than that of the prior scheme for different numbers of anchors and is independent of the times of measurements.

In this paper, to overcome the drawbacks of prior approaches for defending against Illegally-Copying (IC) attacks, such as low generality, high cost, and high overhead, we propose a Low-Cost Anti-Copying (LCAC) 2D barcode by exploiting the difference between the noise characteristics of legal and illegal channels. An embedding strategy is proposed, and for a variant of this strategy, we also perform a corresponding analysis. To accurately evaluate the performance of our approach, a theoretical model of the noise in an illegal channel is established by using a generalized Gaussian distribution. By comparing the experimental results based on various printers, scanners, and mobile phones, it can be found that the sample histogram and fitting curve of the theoretical model match well, so it can be concluded that the theoretical model works well. To evaluate the security of the proposed LCAC code, in addition to the Direct-Copying (DC) attack, an improved version, called the Synthesized-Copying (SC) attack, is also considered in this paper. Based on the theoretical model, we build a prediction function to optimize the parameters of our approach. Parameters optimization seeks a tradeoff between the production cost and the cost of IC attacks. The experimental results show that the proposed LCAC code with two printers and two scanners can detect DC attacks effectively and resist SC attacks up to the access of 14 legal copies.