## 7 Conclusions and Future Works  
Service discovery is one of the most important mechanisms in an _ad hoc_ network. Thanks to service discovery, service providers can announce their services in a dynamic way, and clients can discover and access those services in an autonomous manner.  
The aim of this paper was to define a service discovery solution based on the one described for OLSRv1, but for the current version of the OLSR protocol. We have added a new TLV structure to the OLSR v2 protocol for service advertisements. We have also introduced some improvements to the service discovery mechanism, such as checking the _Topology Information Base_ of each node to decrease the false service discovery ratio. One important part of the work was to evaluate by simulation whether the new version behaved better than the previous one in terms of overhead, time needed to find a service, service discoveries and false service discovery ratio when the mobility and the services of the network increased.  
The simulation results showed that SDM over OLSRv2 is superior to SDM over OLSRv1 regarding the parameters described above. The service discovery delay decreases; the service discovery ratio is better; and the false service discovery ratio is reduced. Then, the improvements of OLSRv2 as a routing protocol also have a major impact on the performance of the service discovery protocol.  
We also compared the performance of service discovery with AODV reactive routing protocol. We found that integrating service discovery with OLSRv2 gives better results in terms of service route acquisition latency regardless of the network size. In terms of service discovery, we do not find too many differences between both protocols, except when we limit service discovery request flooding scope in AODV. However, in terms of network overhead, SD-AODV gives better results than SD-OLSRv2, except when service discovery requests are broadcast throughout the entire network. SD-AODV gives better results at the expense of increased service acquisition latency. Overall, we conclude that integration of service discovery with OLSRv2 gives better results than integration with AODV for large and high mobility networks and, mainly, for networks where real-time services are desired. This will become more significant as the size of the network increases, due to proactive networks' characteristics.  
For the future, we plan to test the proposed solution with larger networks. In OLSR, the denser and larger a network is, the more optimization is achieved. Therefore, it is expected to get better results in larger networks compared to the ones shown here.  
We want to work on a real implementation of our protocol, including a new service description and advanced matchmaking algorithms to deal with the proliferation of new complex services in IoT environments. For reaching this objective, we will take into account the works on the Semantic Webof Things (SWoT) [51] of Information Systems Laboratory (SisInfLab), Information Systems Research Group ([[http://sisinflab.poliba.it](http://sisinflab.poliba.it)]([http://sisinflab.poliba.it](http://sisinflab.poliba.it))).  
On the other hand, we want to add additional extensions to service discovery integrated into OLSRv2, to support QoS-aware service selection. The goal of QoS-aware service selection is to select the node that optimize some QoS attributes, among other nodes offering equivalent services.  
Finally, security issues are one of the open lines we are working on. MANETs are finding a place in real-world deployments, and it is fundamental to preserve the network connectivity from possible vulnerabilities and attacks. Along these lines, we are researching how nodes can select the most efficient security services in OLSRv2 with the minimum power consumption, the lowest packet overhead and the highest possible security level associated with the communications.