## 2 Related Work
Several other sensing applications have been proposed by project Urban Atmospheres [17] and the MIT Senseable City Lab [20]. Project SenseWeb proposes a Web-based platform and tools to publish and query sensor data across Internet. Sensors can measure various physical variables, can be static or mobile and carried by humans, or embedded in vehicles or on robots. To hide the heterogeneity and the intermittent connectivity of sensors, sensors are connected to a gateway that provides a uniform interface to communicate with them. Urbanet also focuses on sensing in spontaneous urban networks composed of heterogeneous and potentially mobile sensors. However, unlike the above-mentioned works, it does not rely on central collection points across the Internet that perform data and task management, and act as mediators between users and the network. Urbanet proposes three middleware solutions to develop distributed sensing applications that do not require servers or Internet connectivity; sensing applications running on mobile devices. These solutions, respectively, present the network: (1) as a distributed sensor database that can be queried via a SQL-like language; (2) as a single virtual name space that applications use to access individual resources offered by nodes; and (3) as a client-service model where services migrate to different network nodes to maintain a semantically correct and continuous interaction with clients. The above-mentioned works have drawbacks regarding the opportunistic protocol they implement, as well as Web-based approach they propose. Indeed, similar to CaNet [21], opportunistic protocols used in these works often rely on a simple epidemic (nodes exchange with the other nodes they encounter all the messages they have). However, such an epidemic approach is known to not be the more efficient one, especially when there are many data to exchange; more sophisticated and efficient opportunistic protocols have indeed been proposed to address the issues of the epidemic routing protocol [22]. Moreover, these works propose a Web portal that renders the data that have been collected from the sensors, and do not propose logical extensions of physical objects in the Web that are able to provide a semantic description of these objects and of their functionalities, and that offer means to configure and to control them through Web standards and protocols. However, as mentioned in the Introduction, such an approach offers significant advantages, the physical Mashup being one of them. WoT solutions have been investigated in the agricultural context [23; 24] (and also in the smart home context [25; 26]) to improve the processing of dataflows produced by sensors and to expose API of physical objects in the Web. Agri-IoT [23] is a data analytics platform composed of multiple layers, allowing to communicate with devices (lower layer), to analyze data (intermediate layers) and to present information to end-users either using dashboards or mobile applications (higher layer). In this platform, sensors, and the data they produce, are semantically described using agricultural ontologies. However, again, these solutions do not implement disruption-tolerant or opportunistic networking techniques to cope with the connectivity disruptions that can occur in smart agriculture and farming application domains, and more generally in scenarios involving mobile devices communicating with short range wireless interfaces. In the next section, we present a WoT dedicated platform called ASAWoO. This platform implements opportunistic networking and computing mechanisms, and addresses the drawbacks of above-presented works. Leveraging the Web of Things and the Opportunistic Computing to Build Systems for the Smart Agriculture  
Smart agriculture mainly consists in growing seedlings and plants while preserving natural resources (e.g., water) and limiting crop protection products. To do so, both environmental properties (soil moisture, ambient temperature, sunshine, etc.)