Ontology based Approach for Precision Agriculture

In this paper, we propose a framework of knowledge for an agriculture ontology which can be used for the purpose of smart agriculture systems. This ontology not only includes basic concepts in the agricultural domain but also contains geographical, IoT, business subdomains, and other knowledge extracted from various datasets. With this ontology, any users can easily understand agricultural data links between them collected from many different data resources. In our experiment, we also import country, sub-country and disease entities into this ontology as basic entities for building agricultural linked datasets later

Real-time energy harvesting aided scheduling in UAV-assisted D2D networks relying on deep reinforcement learning

Unmanned aerial vehicle (UAV)-assisted device-to-device (D2D) communications can be deployed flexibly thanks to UAVs’ agility. By exploiting the direct D2D interaction supported by UAVs, both the user experience and network performance can be substantially enhanced at public events. However, the continuous moving of D2D users, limited energy and flying time of UAVs are impediments to their applications in real-time. To tackle this issue, we propose a novel model based on deep reinforcement learning in order to find the optimal solution for the energy-harvesting time scheduling in UAV-assisted D2D communications. To make the system model more realistic, we assume that the UAV flies around a central point, the D2D users move continuously with random walk model and the channel state information encountered during each time slot is randomly time-variant. Our numerical results demonstrate that the proposed schemes outperform the existing solutions. The associated energy efficiency game can be solved in less than one millisecond by an off-the-shelf processor using trained neural networks. Hence our deep reinforcement learning techniques are capable of solving real-time resource allocation problems in UAV-assisted wireless networks