Generalizing multi-agent reinforcement learning (MARL) to accommodate variations in problem configurations remains a critical challenge in real-world applications, where even subtle differences in task setups can cause pre-trained policies to fail. To address this, we propose Context-Aware Identity Generation (CAID), a novel framework to enhance MARL performance under the Contextual MARL (CMARL) setting. CAID dynamically generates unique agent identities through the agent identity decoder built on a causal Transformer architecture. These identities provide contextualized representations that align corresponding agents across similar problem variants, facilitating policy reuse and improving sample efficiency. Furthermore, the action regulator in CAID incorporates these agent identities into the action-value space, enabling seamless adaptation to varying contexts. Extensive experiments on CMARL benchmarks demonstrate that CAID significantly outperforms existing approaches by enhancing both sample efficiency and generalization across diverse context variants.

Radio frequency identification (RFID) system has been extensively employed to track missing items by affixing them with RFID tags. Many practical applications require to efficiently identify missing events for a specific subset of system tags (called key tags) due to their elevated importance. Existing methods primarily aim to identify all tags, which makes it challenging to specifically identify key tags because of interference from other non-key tags (called ordinary tags). In light of this, several key tag identification methods follow a two-step scheme that filters ordinary tags first and then identifies key tags. Nevertheless, this wastes too much time on tag filtering, resulting in low time efficiency. This paper presents a novel missing key tag identification protocol with two creative designs to gain high efficiency. First, we develop a novel verification technique that can rapidly determine the presence or absence of key tags amid the scenarios with both key tags and ordinary ones. By combining the ON-OFF Keying modulation, we could verify multiple key tags in a single slot, thereby reducing the total slots required. Second, we design a new selection technique that efficiently selects the unverified key tags for further verification, while filtering out the verified key tags and irrelevant ordinary tags to avoid redundant data transmission. Additionally, we present an enhancement protocol that leverages a preselection technique to avoid collecting useless tag responses, further boosting efficiency. We carry out rigorous theoretical analysis to optimize the performance of the proposed protocols. Both simulations and practical experiments demonstrate that our method is markedly superior to state-of-the-art solutions.

Radio frequency identification (RFID) system has been widely employed in warehouse management and supply logistics. A fundamental systematic functionality is to determine the presence or absence of tagged items, referred to as missing tag identification. Although this research has attracted exten-sive attention, prior methods predominantly address scenarios involving a single user. In this paper, we extend the research to more common multi-user scenarios, which raise two concerns: time sensitivity and privacy protection. We propose a novel Parallel Missing tag Identification protocol (PaMI) that leverages lightweight and anonymous bit-vector techniques to specify the data and order of tag responses. This effectively prevents both intra- and inter-user tag collisions, enabling the identification of missing tags across multiple users in one shot while safeguarding user privacy. We carry out a comprehensive theoretical analysis to optimize the proposed method's performance. Extensive experiments show that our protocol strikingly outperforms state-of - the-art baselines.

Federated learning (FL) provides a promising solution to meet the requirements of data privacy and security in intelligent transportation systems (ITS), which enables edge devices and road side units (RSUs) to collaboratively train learning models without exposing the raw data. However, the deep leakage from gradients (DLG) still leads to the risk of divulging the original data. Meanwhile, the existing gradient protection methods based on secure multi-party computation (SMC) result in huge communication overheads and latency, which are difficult to satisfy the real-time demands of both FL and the diverse services in ITS. Focusing on improving edge data security in ITS, this paper proposes an enhanced federated learning (FL) model (SemBroc-RF) with reinforcement learning, which considers the advantages of both end-to-end homomorphic encryption (HE) and SMC. To reduce communication overheads and strengthen data security in RSUs and end devices simultaneously, a partially encrypted secure multi-party broadcast computation algorithm (SemBroc) is designed, which achieves the time complexity O(n) by constructing the decoding function and sharing the gradients among the local models. To improve the model accuracy by gradients aggregation, a FL algorithm (GreFLa) with reinforcement learning is proposed based on the adaptive assigned weight of the local gradients. Theoretical analysis and detailed simulation results verify that SemBroc-RF can effectively prevent gradient leakage. On the MNIST and CIFAR-10 datasets, compared with the benchmark, the accuracy of SemBroc-RF is increased by 3.63% and 1.35%, and the training round of SemBroc-RF is reduced by 70.8% and 45.6%, respectively.

RFID systems have been widely deployed for various applications such as inventory control, retail management, and object tracking. In many scenarios, coarse-grained system management of tag categories is more common than traditional system management of individual tags. Existing works on tag category management require knowing category IDs in advance, however, few protocols have been specially designed to obtain category IDs in multi-category RFID systems. In this paper, we proposeCIP, a time-efficient protocol specially designed to collect category IDs in multi-category RFID systems. The challenge in solving this problem is to avoid repeatedly collecting category IDs from multiple tags belonging to the same category. We address this challenge by squeezing tags in the same category into the same time slot and letting them reply to the reader simultaneously. Considering that the reader may receive signals from different tags in a slot, we exploit the Manchester coding to decode the aggregated signals. With this design, CIP collects each category ID only once and thus achieves high time efficiency by avoiding redundant identification of the same category ID. We further enhance CIP by utilizing the lightweight vectors to specify the order of tags’ replying and avoid useless slots. Rigorous theoretical analysis is presented to optimize the performance of CIP and guidelines on how to set optimal parameters to minimize the overall execution time are given. We conduct extensive experiments to evaluate the performance of our protocols. The experimental results show that our protocols can significantly improve time efficiency by 67 percent when compared with the state-of-the-art solutions.

RFID technology has been widely applied in a range of applications such as inventory control, warehouse management and supply chain logistics. Many practical applications need to search a given set of tags (calledwanted tags) to determine which of them are present in the system, which is usually calledtag searching. Existing tag searching protocols suffer performance bottleneck and the time efficiency and need to be improved. The bottleneck stems from two factors. First, the existing methods validate the wanted tags in a random way due to the randomness of the hash function, which unavoidably generate many useless slots. Second, in order to achieve the predefined reliability requirement, the existing methods have to repeatedly validate target tags multiple times. In this paper, we design new tag searching techniques of compact exclusive validation that break through the existing performance bottleneck from two aspects. First, our protocols avoid slot waste. Different from random tag validation, our protocols validate tags orderly by mapping the wanted tags and the slots in a one-to-one manner, which makes the reader be able to validate tags in every slot. Second, our protocols avoid repeated tag responding. By combining two lightweight indicators, we rapidly filter out non-wanted tags so that there is no interference when validating the wanted tags. Hence, we need to validate each wanted tag only once, which avoids redundant validation and greatly improves time efficiency. Our protocols work with the assumption that the rough number of tags in the system can be obtained by using existing estimation algorithms, but they do not need to know exactly which tags are in the system. Theoretical analysis illustrates our methods achieve linear time complexity. Extensive experimental results show that, our best protocol can improve the time efficiency by up to 81 percent when compared with the-state-of-art solution.

Weakly-supervised domain adaptation has been introduced to address the source domain with label noise or/and feature noise. However, the existing weakly-supervised domain adaptation methods only work under ideal assumptions, which assume either the annotated data of the target domain can be accessed or the noise rate of all the classes is identical and already known. This limits their practical application. To tackle this, we propose a universal weakly-supervised domain adaptation method called PDCAS which relaxes the ideal assumptions and makes it more general. Specially, PD- CAS includes two stages: progressive distillation and domain alignment. In progressive distillation, we iteratively distill out potentially corrected samples whose annotated labels are consistent with the prediction of model. By exploiting intrinsic similarity to extract initial corrected samples, this process does not need any supervision. In domain alignment, besides taking the global feature distributions into consideration, we also adopt Class-Aligned Sampling which balances the samples for both source and target domains to alleviate the shift of label distributions. Extensive experiments on Office-31 and Office-Home datasets demonstrate the effectiveness and robustness of our method compared to state-of-the-art methods.

By integrating the micro-sensor on RFID tags to obtain the environment information, the sensor-augmented RFID system greatly supports the applications that are sensitive to environment. To quickly collect the information from all tags, many researchers dedicate on well arranging tag replying orders to avoid the signal collisions. Compared to from all tags, collecting information from a part of tags (i.e., target tags) is more challenging because the collecting process is interfered by useless replying from non-target tags. The existing works of target tag information collection are designed for single reader systems. However, they cannot work efficiently in more common multi-reader scenarios, where each reader lacks knowledge of tag distribution among all readers. In this paper, we propose time-efficient protocols to collect target tag information in multi-reader systems. The high efficiency of our protocol is enabled by two novel designs. First, we develop a technique that quickly detects and silences non-target tags without a priori knowledge of which tags are in the readers' interrogation regions. Second, we design an allocation vector to efficiently arrange the replying order of only target tags. Different from previous bit-vector based approaches that make use of only singleton slots, our allocation vector approach also makes use of collision slots to speed up target tag information collection. We further propose an enhancement protocol which can reconcile the collision slots with higher probability and therefore collect information from more target tags simultaneously. The extensive simulation results demonstrate that our protocols significantly outperform the state-of-the-art protocols in terms of time-efficiency.

Localization plays a pivotal role in wireless sensor networks. Many range-based localization algorithms have been proposed for 2-D sensor networks or densely deployed 3-D sensor networks. However, range-based localization in sparse 3-D sensor networks is still a challenging problem, because the sparseness of the network makes it difficult to obtain a proper order of nodes to be sequentially localized. The patch-and-stitching localization strategy can conquer the sparseness problem in 2-D networks, but for 3-D networks it is still unknown how to uniquely merge two patches when there are not enough common nodes. In this paper, we solve this challenging problem by deriving the conditions under which two subnetworks can be uniquely merged. In the proposed approach, we treat the translation parameters as unknowns and form a set of equations with which the unknowns can be uniquely solved. The novelty of our algorithm also lies in that we exploit both common nodes and connecting edges among adjacent subnetworks to merge them, resulting in very high chances that two subnetworks can be merged. We conduct extensive simulation experiments to evaluate the performance of the proposed algorithm. The results show that the proposed algorithm could localize more than 90% of nodes in sparse 3-D networks with average node degree of 11 and anchor ratio of 5%, while the best existing solution can localize only 52% of nodes in the same situation.