## 2 Background and Related Work  
Wireless networks are prone, by nature, to suffer from broken end to end communication service. This is due to the intrinsic nature of wireless communication channels that can randomly fade off (there is no way to exactly know in advance when it will faded off), support a high number of active services demanding a high bandwidth, or be subject to strong interferences from other channels or strong collision patterns. All those characteristics could reduce significantly the end to end throughput to the extent that affected MAPs (hereinafter interfered MAPs) links could be broken. A very strong interference of the above characteristics could produce an end to end communication service interruption. This provokes a headache for users consuming multimedia information (streaming), but could be also a big problem for traffic from critical sensors in a smart city (machine to machine or machine to human types of communication). Solutions to the above problem are not simple to obtain, because it requires several levels of the network architecture to cooperate in the solution. That is, it is impossible to solve this problem only at the physical and link level. In general, cross layer techniques have to be applied [29]. We have worked in solutions to this problem for MANETs where over time the communication nodes can dynamically enter and leave the coverage area [30]. Briefly, in the intermediate nodes of the MANET we deployed the OLSR protocol. We configured the OLSR daemon (OLSRd) to calculate the optimal routes defined as the number of attempts by a node on average to successfully transmit a packet to a destination, instead of the number of hops. Using OLSRd, a node can detect changes in the connectivity to its neighbours by injecting and receiving HELLO messages periodically. OLSR is able to quickly reconfigure path breaks using an efficient flooding of control traffic. OLSR messages are not duplicated thanks to its multipoint relays. Finally OLSRd diffuses the topological information necessary to obtain optimal routes in terms of number of hops (this information is periodically renewed). An OLSRd plug-in allows the application level to inject user defined packets (type 200) in intermediate nodes using a default forwarding algorithm [31]. Spreading those messages the application can control the path breaks and interference by reconfiguration of the path. We call that application disruption control relay (dcr). In [32] it is shown that OLSR and HOLSR are efficient enough to reconfigure broken paths, in real time, for streaming services. In [33] it is shown that OLSR can be efficiently used to deploy WMNs over long distances supporting traffic of emergency critical applications in case of natural disasters. In [34] a routing algorithm based on OLSR for finding the best routes according the quality of WiFi links was presented. The simulation results are better than using the Expected Transmission Count metric, but they do not show that this will be optimal for dense networks. For Voice over Internet Protocol (VoIP) [35] efficient policies to assure the low delay data delivery and balancing the traffic among APs are required. We do not deal with these special requirements explicitly. An optimization of OLSR for heterogeneous WMN (APs have multiple wireless interfaces) is the Unified Routing Protocol (URP) that eliminates the redundant diffusion broadcast messages of OLSR [36]. Simulation results in [37] showed that URP is better than OLSR. We do not explicitly consider heterogeneous WMNs, but in case URP allows the usage of dcr it could be easy to contemplate this. According to the above examples of application of OLSR to MANETs and WMNs for different types of traffic, we argue that it is efficient to set up or take down the wireless interfaces of the MAPsaffected by external interferences, and rebuild broken routes efficiently. In the following sections we discuss the main idea about how to apply OLSR and dcr to our wireless network.