Internet of Things (IoT) has gained great popularity in various fields including smart warehouse and intelligent manufacturing. As a building block of IoT network, the Radio Frequency IDentification (RFID) technology enables a large and ever-increasing number of physical objects to be monitored across the Internet via tag identification. Efficiently managing massive tags in RFID systems becomes an important research issue for IoT networks. This paper focuses on a fundamental management problem -tag-sorting, which is to (1) put a set S of identified tags into a certain order by informing each tag t ∈ S of a unique integer ∆(t) ∈ {1, 2, • • • , |S|}, and meanwhile (2) keep unidentified tags from receiving any of these integers. For RFID systems, it is critical to solve this problem as quickly as possible in the sense that, once sorted, every identified tag t ∈ S can be manipulated via t's log 2 (|S|)-bit integer significantly shorter than t's 96-bit long tag-ID (log 2 (|S|) 96), boosting efficiency substantially. The existing works of literature, however, fails to solve this problem rapidly, as they accomplish (1) and (2) separately by using aloha-like protocols and Bloom filters, which incur a long communication time far from the optimum. In this paper, we overcome this drawback by proposing a protocol P sort capable of solving the problem fastly. In particular, this protocol is built with a novel data structure and communication scheme to achieve (1) and (2) simultaneously by using a communication time proven to be much less than the stateof-the-art protocols. The simulation results demonstrate the competence of P sort in achieving about 1.4× speedup than the state-of-the-art solutions.INDEX TERMS IoT network, edge server, RFID systems, tag management, tag-sorting, unidentified tag, identified tag.
Tag-selection problem, which selects a set of wanted tags from a tag population, is vital for boosting efficiencies of the real-time applications in RFID systems. However, prior arts for the problem can not be applied to RFID systems directly, given that they either require additional computing functions implemented in tag's chips or require a time-consuming pre-process with a large communication cost. This paper studies the tag-selection problem and propose an efficient Electronic Product Code (EPC)-based tag selection method with theoretical analysis. In particular, firstly, we prove a nontrivial lower bound of communication overhead for a protocol which is capable of solving the tag-selection problem. Secondly, we propose an efficient protocol, denoted by TagSP, which only uses the ''select'' command (a mandatory command that all RFID tags shall support) and EPC. The proposed TagSP can be applied directly into offthe-shelf RFID systems with a communication overhead close to the lower bound. Extensive simulations are conducted and the simulation results show TagSP's superiority compared with existing protocols. INDEX TERMS RFID systems, tag selection, lower bound of communication overhead, EPC.
Accurate prediction of future events brings great benefits and reduces losses for society in many domains, such as civil unrest, pandemics, and crimes. Knowledge graph is a general language for describing and modeling complex systems. Different types of events continually occur, which are often related to historical and concurrent events. In this paper, we formalize the future event prediction as a temporal knowledge graph reasoning problem. Most existing studies either conduct reasoning on static knowledge graphs or assume knowledges graphs of all timestamps are available during the training process. As a result, they cannot effectively reason over temporal knowledge graphs and predict events happening in the future. To address this problem, some recent works learn to infer future events based on historical event-based temporal knowledge graphs. However, these methods do not comprehensively consider the latent patterns and influences behind historical events and concurrent events simultaneously. This paper proposes a new graph representation learning model, namely Recurrent Event Graph ATtention Network (RE-GAT), based on a novel historical and concurrent events attention-aware mechanism by modeling the event knowledge graph sequence recurrently. More specifically, our RE-GAT uses an attention-based historical events embedding module to encode past events, and employs an attentionbased concurrent events embedding module to model the associations of events at the same timestamp. A translation-based decoder module and a learning objective are developed to optimize the embeddings of entities and relations. We evaluate our proposed method on four benchmark datasets. Extensive experimental results demonstrate the superiority of our RE-GAT model comparing to various baselines, which proves that our method can more accurately predict what events are going to happen.
Abstract. Accurate effectively to submarine CGF modeling is to ensure that ships latent simulation submarine force in the basis of physical authenticity and intelligent behavior. In submarine CGF model based on the generic framework for submarine CGF physical modeling and behavioral modeling has carried on the analysis and research, put forward in the submarine CGF behavior model based on Petri net tactical rules introduced in front predicate formula of behavior modeling method, improves the accurate description in the battlefield of submarine CGF behavior ability. On STAGE simulation environment verification results show that the method is reasonable and effective.
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