## 1 Introduction  
Wireless sensor networks (WSN) are conformed by a set of interconnected spatially-distributed sensors. These nodes capture information from the environment within their detection range and report to a sink node (SN) that redirects data to the appropriate destination. To achieve this goal, every node needs to be connected to the sink node, directly or through one or more intermediate nodes. The sink node: (i) does not move; (ii) is connected to a main power supply; and (iii) redirects information to an external network. Performance in WSN can be measured in terms of a number of parameters (see Section 3). Specifically, connectivity and throughput depend largely on deployment [1]. Optimal deployment is often not viable, specially if WSN need to cover large, dynamic areas. Instead, WSN often rely on redundant, mesh topologies. However, the deployment of large WSN is still complex [2]. Furthermore, nodes in WSN are prone to failure due to: (i) component malfunctioning; (ii) battery depletion; (iii) environmental factors; and (iv) man-caused factors [3]. Network management techniques include topology management [4]. The main goal of topology management is to achieve sustainable coverage while maintaining connectivity, i.e., to have the largest possible area within the sensing range of at least one sensor and keep at least one communication path between each sensor and the sink node. When sensor failures create holes in the coverage area and/or disconnect full clusters of functional nodes, topology management includes self-diagnostic and self-healing functions. Power control techniques [5] could be applied in such situations to increase the transmission range at the expense of a higher power consumption. Many commercial radio transceiversallow one to adjust the transmission power levels at runtime, but only within a restricted interval, so power control techniques are usually combined with more complex approaches [6]. Some of them are based on strategically adding some redundant nodes so that there is more than one routing path between every pair of sensors in the network (k-vertex connectivity) [7]. The main challenge in these cases is the complexity of planning node deployment using a reduced number of redundant nodes. Alternatively, other methods propose spares for critical nodes. These spares could be passive extra nodes or active nodes nearby the critical ones. In this second case, it is necessary to select as the replacement the node that would cause the least degradation in the network [8]. Some methods work in a reactive way: rather than planning over the full topology, nodes are rearranged according to local criteria, like preserving connectivity or minimizing coverage loss in a given area. In these cases, algorithms can work with as little as one-hop information to detect the failure of critical nodes and decide the best strategy to recover from it [6]. In its simplest implementation, a failed node could be replaced by its nearest uncritical neighbor [9], whereas more complex approaches aim at minimizing movement overhead in order to extend network lifetime [10]. Other proposals deal with routing information to provide short paths between remaining nodes, as well [11]. These techniques are typically used in mobile WSN (MWSN), where nodes have some degree of mobility. MWSN can autonomously deploy themselves and also adjust their positions if part of the network fails (self-healing) [12]. MWSN are useful for long-term monitorization of large areas and also for emergency deployment of communication networks. In both cases, mobile nodes allow adaptation to changing conditions, including the specifics of the area and also the lifetime of the different nodes. Multiple robot systems (MRS) are adequate for MWSN [3; 13; 14]. In these systems, robots move to achieve the best possible coverage using all living nodes. Some approaches for deployment in WSN are based on deliberative algorithms to optimize efficiency [15; 16; 17; 18] and also for self-healing [3; 19].