Unmanned aerial systems (UAS) are becoming a common tool for aerial sensing applications. Nevertheless, sensed data need further processing before becoming useful information. This processing requires large computing power and time before delivery. In this paper, we present a parallel architecture that includes an unmanned aerial vehicle (UAV), a small embedded computer on board, a communication link to the Internet, and a cloud service with the aim to provide useful real-time information directly to the end-users. The potential of parallelism as a solution in remote sensing has not been addressed for a distributed architecture that includes the UAV processors. The architecture is demonstrated for a specific problem: the counting of olive trees in a crop field where the trees are regularly spaced from each other. During the flight, the embedded computer is able to process individual images on board the UAV and provide the total count. The tree counting algorithm obtains an $F_1$ score of $99.09\%$ for a sequence of ten images with 332 olive trees. The detected trees are geolocated and can be visualized on the Internet seconds after the take-off of the flight, with no further processing required. This is a use case to demonstrate near real-time results obtained from UAS usage. Other more complex UAS applications, such as tree inventories, search and rescue, fire detection, or stock breeding, can potentially benefit from this architecture and obtain faster outcomes, accessible while the UAV is still on flight.

Collaborative tools based on the Web/Internet infrastructure such as E-mail, discussion groups, video/audio conferencing and virtual campuses have been proposed and implemented in many distance learning scenarios. Grids as Internet-wide resource infrastructures that enable access to large resource pools should permit new applications and learning activities. We propose ULabGrid as a new architecture that enables educators to design collaborative, distant laboratories for undergraduate students using the Grid infrastructure. We describe here the prototype that is being developed and present the results of our efforts to date.

Grid resource discovery service is a fundamental problem that has been the focus of research in the recent past. We propose a scheme that has essential characteristics for efficient, self-configuring and fault-tolerant resource discovery and is able to handle dynamic attributes, such as memory capacity. Our approach consists of an overlay network with a hypercube topology connecting the grid nodes and a scalable, fault-tolerant, self-configuring and adaptive search algorithm. Every grid node keeps a small routing table of only log ₂ N entries. The search algorithm is executed in less than (log ₂ N +1) time steps and each grid node is queried only once. By design, the algorithm improves the probability of reaching all working nodes in the system even in the presence of non-alive nodes (inaccessible, crashed or heavily loaded nodes). We analyze the static resilience of the approach presented, which is the measure of how well the algorithm can discover resources without having to update the routing tables. This is done before the routing recovery is processed in order to reconfigure the overlay to avoid non-alive nodes. The results show that our approach has a significantly high static resilience for a grid environment.

Recent developments in unmanned aerial systems (UAS) provide new opportunities in remote sensing application. In contrast to satellite and conventional (manned) aerial tasks, UAS flights can be operated in a very short period of time. UAS can also be more specifically focused toward a given task such as crop reconnaissance or electric line tower inspection. For some applications, the delivery time of the remote sensing results is crucial. The current three-phase procedure of data acquisition, data downloading and data processing, performed sequentially in time, represents a drawback that reduces the benefits of using unmanned aerial systems. In this paper, we present a parallel processing strategy, based on queuing theory, in which the data processing phase is performed on board in parallel with data acquisition. The unmanned aerial system payload has been enlarged with low-cost, lightweight, multi-core boards to facilitate remote sensing data processing during flight. The storage of the raw sensing data is also done for possible further analysis; however, the ultimate decision support information can be seamless delivered to the customer upon landing. Furthermore, text alarms and limited imagery can also be provided during flight.

Grid resource discovery service is a fundamental problem that has been the focus of research in the recent past. We propose a scheme that presents essential characteristics for efficient, self-configuring and fault-tolerant resource discovery and is able to handle dynamic attributes, such as memory capacity. Our approach consists of an overlay network with a hypercube topology connecting the grid nodes and a scalable, fault-tolerant, self-configuring search algorithm. By design, the algorithm improves the probability of reaching all working nodes in the system even in the presence of non-alive nodes (inaccessible, crashed or nodes loaded by heavy traffic). We analyze the static resilience of the approach presented, which is the measure of how well the algorithm can discover resources without having to update the routing tables. The results show that the presented approach has significantly high static resilience.