Graph Representation of Linear Infrastructure in Smart City IoT Systems The cities infrastructure is being populated with a large number of Internetconnected devices used for monitoring and management to create a Smart City system. A large part of the city infrastructure has linear form, such as e.g. roads, energy or water distribution networks. In this paper, we present a solution for representation, processing and providing to external IT systems the data related to linear infrastructure. We present the idea of representation of such networks in a form of a multigraph that is stored in graph database and the graph generator which merges the data gathered from monitoring devices with network topology and other representative structures. We propose the architecture of a sample linear infrastructure management system based on multigraph representation and discuss how it proposes a performance evaluation method based on data generation on the example of a district heating system.

Modeling and Recovery of Graph Signals and DifferenceBased Signals In this paper, we consider the problem of representing and recovering graph signals with a nonlinear measurement model. We propose a twostage graph signal processing (GSP) framework. First, a GSP representation is obtained by finding the graph filter that best approximates the known measurement function. The new GSP representation enables performing tractable operations over graphs, as well as gaining insights into the signal graphfrequency contents. Then, we formulate the signal recovery problem under the smoothness constraint and derive a regularized leastsquares (LS) estimator, which is obtained by applying the inverse of the approximated graph filter on the nonlinear measurements. In the second part of this paper, we investigate the proposed recovery and representation approach for the special case of graph signals that are influenced by the differences between vertex values only. Differencebased graph signals arise, for example, when modeling power signals as a function of the voltages in electrical networks. We show that any differencebased graph signal corresponds to a filter that lacks the zeroorder filter coefficient, and thus, these signals can be recovered up to a constant by the regularized LS estimator. In our simulations, we show that for the special case of state estimation in power systems the proposed GSP approach outperforms the stateoftheart estimator in terms of total variation.