# H2O: Secure Interactions in IoT via Behavioral Fingerprinting
## 5 Evaluating the Performances of H2O Network  
In this section, we describe the experimental campaign we carried out in order to test the feasibility of our proposal. This set of experiments are also useful to tune the parameters of our approach and determine the best configuration for the different application scenarios. We leveraged on the online dataset provided by [66]. This dataset is based on real IoT objects available in the city of Santander ([[https://www.smartsantander.eu/](https://www.smartsantander.eu/)]([https://www.smartsantander.eu/](https://www.smartsantander.eu/)), accessed on 30 April 2021). This old Spanish seaport on the north coast became one of the first smart city in the world in 2010. During that period, indeed, 12,500 sensors have been placed in and around the city's downtown district, to measure and monitor a lot of things from the number of available parking spaces to the amount of trash in containers, or crowds on the sidewalks. In addition, sensors on public vehicles were added to measure air pollution levels and traffic conditions. The dataset objects are categorized following the typologies and data model introduced in the FIWARE Data Models ([[https://www.fiware.org/developers/data-models](https://www.fiware.org/developers/data-models)]([https://www.fiware.org/developers/data-models](https://www.fiware.org/developers/data-models)), accessed on 30 April 2021). This kind of data models has been thought to enable data portability for different applications in the IoT context, such as: Smart Cities, Smart Environment, Smart Sensoring, Smart Energy, Smart Water and so on. The dataset collects the information of both private mobile devices, which are carried by the users during their movements, and static objects, left in a single location (e.g., the users' house). Among the former category of private mobile devices there are: smartphones, cars, tablets, smart fitness devices and smartwatches. The latter category includes: personal computers, smart printers and home sensors. Also public devices of the city of Santander are available in the dataset, such as: environment and weather monitoring objects, transportation vehicles (i.e., taxis or buses), digital indicators, garbage trucks, city street lights, parking locations and security alarms or traffic monitoring devices. Users' movement is simulated through a mobility model called Small World in Motion (SWIM) [67; 68]. SWIM generates traces for ad-hoc networking having the same statistical properties of real traces. The parameters used for this simulation along with some numeric of the dataset are shown in the Table 3. The map in Figure 6 is a graphical representation of the objects' positions in the Santander city used for our experiments. For the sake of presentation, in this figure, the object position has been scaled down to a unitary square area [66]. To carry out our experimental evaluation of the performance and features of our approach, we built a simulator using Python language on an Ubuntu Server equipped with 32 GB of RAM and a 8 core CPU at 5.10 GHz. Our first experiment is devoted to the comparison of our approach with a naive one, in terms of the load generated for the H2O, that is the number of transactions sent in the communications. According to the naive approach, each object sends data to another one in the network directly, without applying any strategy to assess if the target of its communication is reliable or not. Figure 6: The maps of smart things of Santander used in our experiments. Observe that the distribution follows a power law which is a typical distribution of social systems [69]. According to this distribution, we can observe that most of the nodes have a number of neighbors around \(6\), whereas a very small percentage of nodes have up to \(100\) contacts. With that said, we focus on the network after the _safe starting phase_. During this phase some nodes can be hacked or corrupted, moreover, new transactions between nodes not holding the fingerprint model can be established.