# MCCM: An Approach for Connectivity and Coverage Maximization
## 6 Conclusions and Future Works  
The collaboration between UAV and WSN into the Internet of things (IoT), is gaining a lot of attention, as a solution to detect emergency events especially in rural and harsh environments. It guarantees an autonomous network healing and quick actions during long-distance missions. Nonetheless, it contributes to a rural transformation by motivating people to live in rural area and facilitating their daily activities. The main issue in harsh and disaster environments is to maintain connectivity between all the objects (devices) and to maximize the coverage between them. Reducing the mobility of devices is also another issue. Many constraints have been considered and handled in this article such as the distributed environment, the heterogeneity and mobility of devices. The main goal is to keep the movement of devices as minimal as possible to save energy. Another goal is to reduce the overlapping areas covered by different devices and keep it to a small margin to maximize the coverage while maintaining connectivity. We proposed \(MCCM\) a distributed algorithm for coverage maximization, in which each mobile device re-organizes itself to maximize the coverage in its neighborhood. Since the random deployment and the movement of devices, might form isolated groups or \"islands\", we bridge these groups using alpha-shape technique. Simulation results showed an increase in coverage gain and reduction in nodes' displacement and energy consumption, compared to other algorithms in both homogeneous and heterogeneous environments. We noticed the following about \(MCCM\): It may happens that a device \(D_{i}\) trying to maximize the coverage in an island \(\mathcal{N}_{i}\), might meet another device \(D_{j}\) from another island \(\mathcal{N}_{j}\) on his way. The two devices might overlap at their final positions and might not be located in their optimal position with respect to each other (as they belong to two different islands). The reason is that the maximization process applies only inside an island and not when two islands meet. This behavior can be beneficial in scenarios where overlapping gets less priority, while the presence of redundancy enhances network reliability. From the coverage optimization perspective, it is recommended to have the least overlapping area among devices while maximizing coverage. In the future works, we intend to tackle this matter by adapting the \(m\)-ratio and \(\tau_{ij}\) constraints when combining islands together. We also aim to do more experimentation with industrial fields and concrete case studies. Conceptualization, H.M., M.B.H. ; Funding acquisition, G.F.; Investigation, M.B.H., A.G., G.F.; Supervision, A.G., H.M.; Validation, M.B.H. ; Writing-original draft, M.B.H. ; Writing-review and editing, A.G., All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors would like to thank the editors and the anonymous reviewers, for their valuable comments which improved the clarity and quality of the paper. The authors declare no conflict of interest.