This chapter reports on a family of nature-inspired algorithms that were successfully used to deploy a drone-based wireless sensor network into (simulated) indoor environments. The algorithms are designed to work in a decentralised manner, with each drone operating autonomously, using only local (subjective) information about a drone’s own position as well as the inferred positions of its neighbours. The aim of the chapter is not to propose these algorithms but to highlight a currently under-represented research area in the field of Internet of Drones, namely the study of group behaviour for collectives (swarms) of cyber-physical devices (here: drones). The availability of swarms of devices has only recently grown to a level where the deployment of such collectives is actually becoming a reality. Even today, for most researchers operating swarms of drones is still impossible simply due to the lack of legal guidelines and frameworks. In the near future we will see increasing reports in the literature reporting results collected not on the basis of simulations but generated from the actual deployment of a swarm in the real world. The case this chapter makes is that the time has come to start looking at such swarms of cyber-physical systems with the tools and metrics used in theoretical biology, such as finer-grained simulation that acknowledges inter-individual variability.