# A Novel Routing Scheme for Creating Opportunistic Context-Virtual Networks in IoT Scenarios +
## 2 Related Work  
Opportunistic networking is a field where huge efforts have been made by the research community during the last decade [15]. However, the emergence of IoT networks opens a way to integrate both paradigms into one promising area: opportunistic IoT. These networks are formed by a large number of fixed IoT devices, spread in a given physical area, gathering information from the environment, and generating useful data for mobile applications. The opportunistic IoT concept was first introduced in [7]. In that work, the inherently close relationship between humans and the opportunistic connection of smart things was justified, while it was discussed how effective protocols on data dissemination can be proposed considering the impact of human behavior and mobility patterns. Since opportunistic communities are formed based on the opportunistic contact nature of humans or animals, using opportunistic networks in IoT environments can result in very efficient and dynamic IoT system deployments. As an example, the use of opportunistic IoT networks is very appropriate for emergency scenarios such as natural disasters (e.g., hurricanes, earthquakes, tsunamis, etc. ), since they are infrastructure-less. Nodes can communicate with each other even with the lack of 4G/5G coverage, by using the store-carry-and-forward method, where paths between nodes are dynamically built. This aspect is very relevant, since the critical objective in emergency situations is to ensure that data generated in the disaster area are correctly delivered to the group in charge of coordinating the emergency response. In [16], an analysis of the performance of opportunistic routing protocols in emergency scenarios was presented. Specific parameters, such as the number of victims, the number of mobile nodes, or the volume of data generated were compared in order to analyze their impact on the performance of routing protocols. Another area of application where opportunistic IoT networking can be easily deployed is wildlife tracking. In this scenario, the main objective is to monitor the activity of wild animals on a remote and geographically-large habitat, where heterogeneous IoT devices are deployed both on the ground or carried by the animals to be monitored. The challenges of real-time wildlife monitoring systems are energy efficiency, a high delivery ratio, and low latency. The authors of [17] proposed a wildlife monitoring system network architecture by using a Low Power Wide Area Network (LWPAN) with LoRa technology for collection stations and opportunistic mobile networks exploiting BLE capabilities on animals' collars. Also in the field of passive healthcare, the opportunistic IoT paradigm can be used to create a pervasive system of intelligent devices to cooperatively gather, process, and send information on patients' lifestyle and the context around them without requiring major changes in their behavior [18; 19; 20]. However, several issues must be addressed in order to provide opportunistic IoT with reliable performance, e.g., the interoperability among different standards and communication technologies, or neighbor discovery in a scenario with heterogeneous IoT devices. In [21], a mobile gateway software architecture to support IoT interoperability through a smartphone-centric application was proposed. Although the proposed architecture was validated on a real testbed without excessively using hardware resources such as CPU and memory, there were limitations related to energy consumption. The authors of [22] presented a classification and a taxonomy of neighbor discovery protocols on IoT scenarios, whose the main goal was to exploit their knowledge about mobility patterns to improve the efficiency in the discovery process. Finally, in [23], the authors proposed a routing algorithm based on trajectory prediction for identifying a mobility model by analyzing the historical mobility characteristics of the nodes and selecting the ones with the best metrics, reducing the packet loss rate, and avoiding excessive energy consumption.