Visible to the public Biblio

Filters: Keyword is Underwater Internet of Things  [Clear All Filters]
2022-05-06
Kalyani, Muppalla, Park, Soo-Hyun.  2021.  Ontology based routing path selection mechanism for underwater Internet of Things. 2021 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia). :1—5.
Based on the success of terrestrial Internet of Things (IoT), research has started on Underwater IoT (UIoT). The UIoT describes global network of connected underwater things that interact with water environment and communicate with terrestrial network through the underwater communication technologies. For UIoT device, it is important to choose the channel before transmission. This paper deals with UIoT communication technologies and ontology based path selection mechanism for UIoT.
Qi, Xingyue, Lin, Chuan, Wang, Zhaohui, Du, Jiaxin, Han, Guangjie.  2021.  Proactive Alarming-enabled Path Planning for Multi-AUV-based Underwater IoT Systems. 2021 Computing, Communications and IoT Applications (ComComAp). :263—267.
The ongoing expansion of underwater Internet of Things techniques promote diverse categories of maritime intelligent systems, e.g., Underwater Acoustic Sensor Networks (UASNs), Underwater Wireless Networks (UWNs), especially multiple Autonomous Underwater Vehicle (AUV) based UWNs have produced many civil and military applications. To enhance the network management and scalability, in this paper, the technique of Software-Defined Networking (SDN) technique is introduced, leading to the paradigm of Software-Defined multi-AUV-based UWNs (SD-UWNs). With SD-UWNs, the network architecture is divided into three functional layers: data layer, control layer, and application layer, and the network administration is re-defined by a framework of software-defined beacon. To manage the network, a control model based on artificial potential field and network topology theory is constructed. On account of the efficient data sharing ability of SD-UWNs, a proactive alarming-enabled path planning scheme is proposed, wherein all potential categories of obstacle avoidance scenes are taken into account. Evaluation results indicate that the proposed SD-UWN is more efficient in scheduling the cooperative network function than the traditional approaches and can secure exact path planning.