## 5 Conclusions  
This work has proposed a reactive behavior-based algorithm for deployment and self-healing in MWSN using autonomous robots. The algorithm is based on locally balancing three forces for  
Figure 16: Power evolution in time: (**a**) mean power in the network; (**b**) mean transmitted packets; (**c**) transmitted packets’ standard deviation. each robot: (a) repulsion from nearby obstacles to avoid collisions and remain in the work area; (b) repulsion from nearby nodes to expand the network; (c) attraction to gateway nodes to prevent loss of connectivity. The algorithm is valid for geographic and also for hierarchical routing if we define roles to determine which forces affect a given node. Parameters in our nodes have been heuristically adjusted using a reduced number of physical robots equipped with a radio chip and a range sensor. Afterwards, we have tested the algorithm in large networks using the Player/Stage simulation environment. Tests have been performed in environments with and without obstacles to check how adaptable the proposed algorithm is to environment layout. Results have been evaluated in terms of network life and coverage in time. We have tested the algorithm both for geographic and hierarchical routing, although our routing algorithms are very simple because routing was not the target of this work. The main advantages of our algorithm with respect to other approaches [4] are the following ones. Our algorithm is valid for multi-node failure. It is fully reactive, so no knowledge about the topology of the full network nor about the layout of the environment is required: nodes estimate their motion in terms of the relative distance to their (one-hop) neighbors and nearby obstacles detected with an onboard short-range sensor. Unlike in game theory-based approaches, no absolute localization information is required. Furthermore, the algorithm does not assume that there is a direct path free of obstacles during movement. The computational load is reduced and distributed among the nodes; this increases resistance to failure. Discrimination between different types of nodes, if necessary, is automatic: nodes reposition themselves depending uniquely on local conditions and their intended role. Additional constraints can simply be added as additional forces in the SPF algorithm. On the other hand, since the proposed algorithm is fully reactive, its performance may be sub-par with respect to algorithms that optimize factors like coverage, average distance traveled by nodes, number of hops to sink, etc. Furthermore, the estimation of node to node distance is based on RSS from nodes in the transmission range. In the real world, returned values would be affected by localization errors. Although collisions are handled by on-board range sensors, relative distances between nodes and, especially, distance to the deployment boundaries would be affected by these errors, as well. Our main conclusions are the following ones. The proposed algorithm provides good results in terms of deployment and self-healing. Differences between scenarios with or without obstacles are not significant, meaning that the algorithm adapts well to the layout of the environment. Furthermore, it steadily adapts to the loss of nodes in the network, as well. Geographic routing-based networks usually provide better coverage, but their expected life is much lower. This drawback could be mitigated by using more efficient routing algorithms. However, these routing algorithms would also increase signaling traffic, so further study would be needed. Similarly, coverage could be improved in hierarchical routing-based networks if we allowed nodes to travel further from each other, but then transmission power would also need to be increased. The proposed algorithm is suitable for deployment and self-healing in large, dynamic, unstructured areas, although its performance depends on acceptable node localization.