Given the sensitivity of the potential WSN applications and because of resource limitations, key management emerges as a challenging issue for WSNs. One of the main concerns when designing a key management scheme is the network scalability. Indeed, the protocol should support a large number of nodes to enable a large scale deployment of the network. In this paper, we propose a new highly scalable key management scheme for WSNs which provides a good secure connectivity coverage. For this purpose, we make use for the first time of the unital design theory. We show that the basic mapping from unitals to key pre-distribution allows to achieve an extremely high network scalability. Nonetheless, this naive mapping does not guarantee a high key sharing probability. Therefore, we propose an enhanced unital-based key pre-distribution scheme providing high network scalability and good key sharing probability lower bounded by 1 − e −1 ≈ 0.632. We conduct analytical analysis and simulations to compare our solution to main existing ones regarding different criteria including storage overhead, network scalability, network connectivity, average secure path length and network resiliency. The obtained results show that our approach enhances considerably the network scalability while providing high secure connectivity coverage and good overall performances. Moreover, the obtained results show that at equal network size, our solution reduces significantly the storage overhead compared to main existing solutions.

Air pollution has become a major issue of modern megalopolis because of industrial emissions and increasing urbanization along with traffic jams and heating/cooling of buildings. Monitoring urban air quality is therefore required by municipalities and by the civil society. Current monitoring systems rely on reference sensing stations that are precise but massive, costly and therefore seldom. In this paper, we focus on an alternative or complementary approach, with a network of low cost and autonomic wireless sensors, aiming at a finer spatiotemporal granularity of sensing. Generic deployment models of the literature are not adapted to the stochastic nature of pollution sensing. Our main contribution is to design integer linear programming models that compute sensor deployments capturing both the coverage of pollution under time-varying weather conditions and the connectivity of the infrastructure. We evaluate our deployment models on a real data set of Greater London. We analyze the performance of the proposed models and show that our joint coverage and connectivity formulation is tight and compact, with a reasonable enough execution time. We also conduct extensive simulations to derive engineering insights for effective deployments of air pollution sensors in an urban environment.