In recent years, cities have become more and more connected. Readings of the electricity usage are being performed wirelessly. In order to reduce the energy consumption, lampposts became intelligent, while the garbage trucks communicate with dust bins in order to plan the garbage collection tours. These networks often lack the infrastructure and sensor nodes rely on a battery with a limited capacity. In this thesis, I analyze the issues of multi-hop wireless networks in smart cities. First, I study the significance and impact of network topologies on network performance. More precisely, through simulations and an experimental study, I show that the placement of nodes impacts the performance of geographic routing algorithms. Then, I propose a set of clustering algorithms for wireless sensor networks that optimize the lifetime of the network. The key hypothesis is that a cluster-head is consuming more energy than a regular node. This set of algorithms, named BLAC, creates multi-hop clusters in which each cluster-head is the root of a tree. The main idea is that the role of each cluster-head should be assigned regarding the remaining energy of nodes and their neighborhood. These algorithms have been tested through simulations based on realistic city topologies with simulation parameters resembling the real world. In the end, I propose a routing algorithm for large scale smart cities that combines two geographic routing techniques. It relies on the hypothesis that only a fraction of nodes in the network are aware of their positions. I show that it is possible to achieve the performance close to classical routing algorithms, even under this hypothesis.
