# A Multi-Technology Communication Platform for Urban Mobile Sensing
## 6 Conclusions  
Our aim was to develop, implement, and evaluate a multi-technology opportunistic platform for environmental data gathering over smart cities. To that end, this work provided the design and the development of each component constituting the platform: the several network elements, the implemented LoRa MAC protocol, and the proposition of two urban-centric forwarding strategies for an opportunistic and delay-tolerant network.  
For the platform's evaluation we resorted to a real application scenario over the city of Aveiro. Considering moliceiros as the mobile entities of our multi-technology opportunistic platform, we were able to evaluate our platform's multi-technology capabilities over three consecutive days, with an acquisition of gathered information for a total period of more than 20 h. Several important elements were considered during this evaluation: the multi-technology concurrency (between LoRa and WiFi); the performance and behavior of the medium access control protocol for LoRa; and the performance and behavior of the opportunistic and delay-tolerant network considering our proposed forwarding strategies.  
The obtained results showed that, regarding the LoRa technology, we had some losses of packets through this communication interface. However, those losses were not due to poor performance of our LoRa MAC protocol, but to the existence of geographical areas that belong to the moliceiro's routes where there is a lack of connectivity to any of the LoRa gateways that we had already installed in the city. On the other hand, regarding the WiFi and the opportunistic and delay-tolerant network, we evaluated our two proposed forwarding strategies: Contacts-Based Controlled Replication with Neighborhood Classification and Mobility-Based Controlled Replication with Neighborhood Classification. These strategies' evaluations showed that the introduced mechanisms, loop avoidance and congestion minimization, were capable of reducing the network overhead (and thus, the network resources consumption) through the prevention of packet loops and through more selective replication based on the past history of the packet traveled hops, respectively. Also, the neighborhood classification mechanisms allowed the replication of data packets only for the most qualified neighbors. With a more selective choice of the next hop, lower network congestion is achieved without compromising the delivery ratio.  
Figure 20: Network overhead results.  
Therefore, we were able to implement and assess our smart city platform over a real application scenario. As future steps we aim to further assess our platform with different real application scenarios, over larger evaluation periods and considering a more dense and populated network.  
This work was funded by the European Regional Development Fund (FEDER), through the Competitiveness and Internationalization Operational Programme (COMPETE 2020) of the Portugal 2020 framework, Project MOBIWISE, POCI-01-0145- FEDER-016426.  
[PERSON] designed the network elements, implemented the forwarding algorithms, and analyzed the results. [PERSON] and [PERSON] designed the forwarding algorithms. [PERSON], [PERSON], and [PERSON] prepared the evaluation scenarios. [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] wrote the paper. [PERSON] and [PERSON] supervised the entire research process.  
The authors declare no conflict of interest.