Data heterogeneity is the obstacle for the resource sharing on Semantic Web (SW), and ontology is regarded as a solution to this problem. However, since different ontologies are constructed and maintained independently, there also exists the heterogeneity problem between ontologies. Ontology matching is able to identify the semantic correspondences of entities in different ontologies, which is an effective method to address the ontology heterogeneity problem. Due to huge memory consumption and long runtime, the performance of the existing ontology matching techniques requires further improvement. In this work, an extended compact genetic algorithm‐based ontology entity matching technique (ECGA‐OEM) is proposed, which uses both the compact encoding mechanism and linkage learning approach to match the ontologies efficiently. Compact encoding mechanism does not need to store and maintain the whole population in the memory during the evolving process, and the utilization of linkage learning protects the chromosome’s building blocks, which is able to reduce the algorithm’s running time and ensure the alignment’s quality. In the experiment, ECGA‐OEM is compared with the participants of ontology alignment evaluation initiative (OAEI) and the state‐of‐the‐art ontology matching techniques, and the experimental results show that ECGA‐OEM is both effective and efficient.

With the rapidly increasing spectrum demand by multimedia applications, the limitation of the spectrum resource restricts the improvement of the performance for communication systems. The cognitive spectrum utilization scenario can solve this problem by sharing the licensed spectrum of primary users with secondary users under specific constrains. In this paper, we consider the uplink resource allocation problem in cognitive satellite network, where cognitive satellite users exploit the spectrum allocated to terrestrial networks as primary users. In order to control the interference to the primary users caused by cognitive users and achieve a fair allocation with considerable total capacity, we detailedly investigate the joint transmit power and bandwidth allocation problem with reasonable system model we proposed. We propose a combined resource management architecture to improve computational efficiency, and formulate the resource allocation problem as a cooperative bargaining game based on game theory. The near optimal joint resource allocation is derived on dual domain of original problem, and a cooperative resource allocating algorithm is proposed based on subgradient method. From simulation results, several important concluding remarks are obtained as follows: a) The proposed algorithm has a considerable convergence rate, and distributed computation can further improve computational efficiency; b) The multiuser and multi-beam diversity can improve total capacity, while interference constrains and the limitation of resource limits the performance boundary; c) Compared with existing methods, the proposed algorithm is Pareto optimal, which can achieve a better trade-off between fairness among users and total capacity of the whole network.

With the rapid growth of e-commerce and ongoing innovations in the logistics industry, intelligent unmanned logistics warehousing systems have emerged to significantly enhance operational efficiency and reduce costs. In these systems, the two critical stages of order assignment and path planning are interconnected through racks in the order picking process. However, prior research has largely overlooked their joint optimization. In this paper, we investigate the real time task planning problem (RTTP) in intelligent unmanned logistics warehousing, where racks are dynamically assigned to orders arriving in real time, and robots are responsible for delivering the racks to workstations according to planned paths, with the goal of jointly minimizing total order processing time and travel costs. To solve the RTTP, we first design a joint optimization evaluation indicator and propose a joint optimization task planning (JOTP) algorithm. Furthermore, we innovatively introduce a reinforcement learning-based approach (JOTP-RL) by modeling order selection as a partially observable Markov decision process (POMDP), and employing the Q-Mix algorithm to solve it. To enhance path planning efficiency, we optimize the improved THA\(^*\) algorithm by eliminating redundant calculations and accounting for congestion times. Finally, extensive experiments conducted on two datasets demonstrate that our proposed algorithms significantly outperform the baseline and state-of-the-art methods, achieving superior efficiency and effectiveness in both execution time and task optimization.

With the rapid development of Internet of Vehicles technology, communication and data exchange between vehicles have become an important part of modern traffic management. A content placement and edge collaborative caching solution based on deep reinforcement learning is proposed in this paper, aiming to address the data processing and storage challenges faced by Internet of Vehicles systems. Utilizing the collaborative caching between smart vehicles and roadside units employs deep reinforcement learning methods to find and design a collaborative caching solution for the Internet of Vehicles edge. It uses content segmentation technology to divide and cache content fragments in advance to reduce the central server load and network pressure, thereby adapting to the randomness of vehicle mobility and communication duration. The experimental results show that the proposed scheme can effectively reduce the load on the central server, reduce network latency, and improve cache hit rate, providing a flexible and efficient solution for real-time communication and data exchange in the Internet of Vehicles system.