## 6 Conclusions  
This work has proposed the Internet of cooperative agents architecture (X-IoCA), a cloud robotics architecture that enables the cooperation of robotic and human agents in a common mission. Although there are different potential fields of application for such an architecture (agriculture, logistics, industrial, etc. ), our implementation is focused on search and rescue: SAR-IoCA. This implementation is based on two fundamental entities: an Internet of robotic things (IoRT) and a feedback information system (FIS). The IoRT includes several sensors embedded on heterogeneous agents (human and robots), a hybrid and heterogeneous network of wireless transceivers (H2 WTN), based on LoRa and BLE technologies, and an ROS network. The IoRT is connected to the FIS through the fog, by means of multi-access edge computing (MEC) centers and the cloud (running a virtual machine with ROS integrated). Thus, the FIS is divided in two parts: an SAR-FIS (including a path planner for UGVs considering the risks detected by a LoRa H-WSN) and a ROS-FIS (for real-time monitoring and processing of information published throughout the ROS network). Both parts are hosted in cloud and local edges, taking advantage of the location awareness. All the elements of the SAR-IoCA implementation rely on a pilot 5G network which provide high-bandwidth and low-latency communications to the SAR agents. SAR-IoCA was validated in a realistic exercise in cooperation with first responders. The successful completion of the mission implied the collaboration of three UGVs, a UAV and two human teams, coordinated from a forward control center (FCC) located close to the operation area, thus acting as a local edge. In addition, a remote base control center (BCC) acts as a cloud edge, performing the same coordination role. The information to drive the decisions at the MEC centers was acquired from the IoRT deployed on the operation area, being essential video (from the ground and from the air), positioning and tracking in real time and environmental information. Quality of the signals (RSSI and SNR) from the H2 WTN (LoRa and BLE transceivers) and physiological parameters of a firefighter during the performance of his tasks was also obtained. The information received from the H2 WTN made it possible to identify areas of interest by means of:  
* BLE beacons, distributed throughout the area of operations, which emulate victims and can be detected from BLE scanners on board different robots (UGVs and UAV). * LoRa sensor-nodes, distributed throughout the area of operations, to detect risks to which SAR teams must respond. SAR-FIS, which integrates a global path planner, receives this sensory information and plans a path for a UGV to travel to the location of the SG involved in the detection of a sensory event associated with a risk. Having a hybrid network, with static and mobile nodes, allowed for the acquisition of information from locations initially outside the coverage of the static concentrator-nodes, in the case of the LoRa H-WSN. The proposed X-IoCA architecture and, in particular, SAR-IoCA require the use of a high-bandwidth, low-latency communication system. The availability of the 5G pilot network provided by Vodafone and Huawei satisfactorily supported the architecture presented. The teleoperation of the UGVs from the MEC centers was realized through the visualization of on-board cameras on the robots, via 5G. It also favored the exploration of the terrain from UAVs and UGVs, as well as the deployment of real-time cloud computing applications, such as tessellation. It also enabled the integration of an IoT network. Future lines of work will aim to increase the level of integration obtained with this architecture. Currently, SAR-FIS accesses the onboard cameras through requests to web servers hosted on public sockets, requiring the use of additional PCs at the FCC and the BCC to monitor the information published by ROS nodes. Integrating ROS-FIS in SAR-FIS would eliminate that requirement, simplifying the deployment and operation of the system.