# Experimental Evaluation of an IoT-Based Platform for Maritime Transport Services
## 2 Related Works  
The adoption of innovative technologies such as cloud, IoT, artificial intelligence and edge computing in maritime transport platforms has been tackled in several works in the literature [11; 12]. More specifically, in ref. [13], the authors conduct an exhaustive analysis of the digital transformation in container shipping, describing its significant benefits and the challenges it brings that require constant improvements. In ref. [14],the authors provide a survey on the enabling technologies for maritime communication networks including hybrid satellite-terrestrial networks and maritime IoT data collection and integration, which are the technologies we also adopt in this work. However, several open challenges for an efficient IoT-based data collection and processing in maritime transport still need to be tackled in the literature [15]. In fact, a large amount of data is generated from different sources and in different formats in maritime transport, which are difficult to process and require advanced tools and techniques to analyze and utilize them. In addition, various stakeholders involved in cargo transports rely and take decisions based on these data, which can lead to several advantages such as efficiency, increased safety and resource utilization, but can also bring up some issues such as low reliability, current high processing latency and the need to protect collected data from unauthorized users. Moreover, as the number of digital sensors generating IoT traffic in the maritime context is rapidly increasing [16], a variety of issues related to this traffic management arise going from ships collision avoidance [17], ships fuel consumption management [18] and vessels real-time monitoring (e.g., position, height of bridges, sailing routes, etc.) [19]. The use of IoT devices fosters the technological advancement of maritime transportation, but introduces several novel risks that can considerably impact the maritime industry such as the high cost of implementation, the accuracy of data analysis in IoT systems [20] and cyber-security threats [21]. In this work, we rely on robust NB-IoT devices that allow transmitting data from maritime containers located on ships to sophisticated applications in the cloud with minimum packet loss and low latency. Regarding the tracking issue, currently, Radio Frequency Identification (RFID) technologies are used for monitoring cargo flow in the logistics chain, where a 'tag' attached to a container is interrogated by a reader with radio waves [22]. Thus, no real-time tracking is performed, as we propose in this work, and the container is tracked at precise points in the supply chain. Differently, cargo shipping companies are developing proprietary and \"siloed\" real-time tracking systems [23; 24] intrinsically not scalable to future scenario evolutions, in contrast with the open and standard layer that we offer with our proposed Maritime Platform. In fact, on the one hand, the OneM2M is a standardized framework for an IoT Service Layer and provides a vendor-independent software middleware with open and standard interfaces with IoT devices as well as related on-top applications. On the other hand, the NB-IoT network can be accessed regardless of the device manufacturer, provided that roaming agreements have been established among operators. Finally, few research initiatives undertook an experimental evaluation of IoT traffic management in the context of maritime transport. The VITAL-5G project offers a virtualized 5G environment for the experimentation of a set of Transport and Logistics (T&L) facilities including warehouses, hubs and ports [25]. The virtual platform enables the testing and validation of T&L Network Applications (NetApps) in real-life conditions, utilizing 5G connectivity. However, the focus of the project is on the deployment and usage of the NetApps while in this work we consider a more complex testbed including a satellite network, which brings further challenges.