## 7 Conclusions  
In this paper, we have considered a novel method for improving the connectivity of mobile wireless robot nodes. Specifically, we have proposed a robust stochastic optimization (RSO) algorithm, using a stochastic gradient ascent (SGA) optimization method, for balancing and boosting the received signal strength (RSS) between two wireless nodes by optimizing the position of the relay node. Related work was referred to and compared with the proposed method throughout the development of the algorithm and its implementation. For the implementation of this algorithm, we devised a multi-sensor setup for the measurement of the radio signal strengths (RSS). The sensors are spatially distributed on the relay node, and we apply dedicated pre-processing techniques, such as an exponential moving average temporal filtering and an averaging spatial filtering. In principle, the proposed method can be implemented in any mobile robot platform. The effectiveness of the proposed solution for connectivity improvement was validated, both with simulations and field experiments in a CERN facility, using a mobile robot platform as the relay node and two stand-alone Raspberry Pi computers as the server and client nodes. Using the theoretical optimal solution as a measure of success, simulations and field experiments yielded a success rate of 88% and 78%, respectively. Although this leaves room for improvement, the algorithm considerably improved the RSS and the end-to-end network capacity in the non-successful cases nonetheless. It should be noted that the optimum positions in theory and practice might be different due to obstacles in the environment. Even though the proposed RSO algorithm cannot guarantee a global optimum position for the relay node due to physical (hardware) constraints and performance assessment limitations, it always guarantees improvement in the RSS and the network capacity, which are the main objectives in this research.  
A fundamental advantage of the proposed solution is the redundancy in physical and algorithmic terms, which gives an increase in robustness. This robustness is needed in hostile environments where fail-safe operations of mobile robots are of supreme importance. The proposed methods are, however,  
Figure 18: Change in the goodput values of server-relay, relay-client and end-to-end (server-client) links in one of the trials.  
not only intended to be deployed on ground wireless nodes (field robots), but also on unmanned aerial nodes, such as in quad-rotors.  
Future work thus includes the implementation of the current developments in dynamic environments and improvement of the algorithm performance by integrating it with simultaneous localization and mapping (SLAM) algorithms for better obstacle detection and management. Extension to multi-tier architectures is also an avenue for future research.  
This research project has been supported by a Marie Curie Initial Training Network of the European Community's Seventh Framework Program under Contract Number PITN-GA-2010-264336-PURESAFE.  
This work was conceived of, formulated and performed by [PERSON], being the main author, with remarkable inputs from [PERSON]. [PERSON] greatly assisted with the experiments. The rest of the authors helped in the supervising and directing of this research.  
The authors declare no conflict of interest.