GraphSTGAN:Situation understanding network of slow-fast high maneuvering targets for maritime monitor services of IoT data

With the rapid growth of the maritime Internet of Things(IoT)devices for Maritime Monitor Services(MMS),maritime traffic controllers could not handle a massive amount of data in time.For unmanned MMS,one of the key technologies is situation understanding.However,the presence of slow-fast high maneuvering targets and track breakages due to radar blind zones make modeling the dynamics of marine multi-agents difficult,and pose significant challenges to maritime situation understanding.In order to comprehend the situation accurately and thus offer unmanned MMS,it is crucial to model the complex dynamics of multi-agents using IoT big data.Nevertheless,previous methods typically rely on complex assumptions,are plagued by unstructured data,and disregard the interactions between multiple agents and the spatial-temporal correlations.A deep learning model,Graph Spatial-Temporal Generative Adversarial Network(GraphSTGAN),is proposed in this paper,which uses graph neural network to model unstructured data and uses STGAN to learn the spatial-temporal dependencies and interactions.Extensive experiments show the effectiveness and robustness of the proposed method.

Convergence of Broadband and Broadcast/Multicast in Maritime Information Networks

Recently,the fifth-generation(5G)of wireless networks mainly focuses on the terrestrial applications.However,the well-developed emerging technologies in 5G are hardly applied to the maritime communications,resulting from the lack of communication infrastructure deployed on the vast ocean,as well as different characteristics of wireless propagation environment over the sea and maritime user distribution.To satisfy the expected plethora of broadband communications and multimedia applications on the ocean,a brand-new maritime information network with a comprehensive coverage capacity in terms of all-hour,all-weather,and all-sea-area has been expected as a revolutionary paradigm to extend the terrestrial capacity of enhanced broadband,massive access,ultra-reliable,and low-latency to the vast ocean.Further considering the limited available resource of maritime communication infrastructure,the convergence of broadband and broadcast/multicast can be regarded as a possible yet practical solution for realizing an efficient and flexible resource configuration with high quality of services.Moreover,according to such multi-functionality and all-coverage maritime information network,the monitoring and sensing of vast ocean area relying on massive Ocean of Things and advanced radar techniques can be also supported.Concerning these issues above,this study proposes a Software Defined Networking(SDN)based Maritime Giant Cellular Network(MagicNet)architecture for broadband and multimedia services.Based on this network,the convergence techniques of broadband and broadcast/multicast,and their supporting for maritime monitoring and marine sensing are also introduced and surveyed.