This paper reports the results of a recently concluded R&D project, SCALS (Smart Cities Adaptive Lighting System), which aimed at the development of all hardware/software components of an adaptive urban smart lighting architecture allowing municipalities to manage and control public street lighting lamps. The system is capable to autonomously adjust street lamps’ brightness on the basis of the presence of vehicles (busses/trucks, cars, motorcycles and bikes) and/or pedestrians in specific areas or segments of the streets/roads of interest to reduce the energy consumption. The main contribution of this work is to design a low cost smart lighting system and, at same time, to define an IoT infrastructure where each lighting pole is an element of a network that can increase their amplitude. More generally, the proposed smart infrastructure can be viewed as the basis of a wider technological architecture aimed at offering value-added services for sustainable cities. The smart architecture combines various sub-systems (local controllers, motion sensors, video-cameras, weather sensors) and electronic devices, each of them in charge of performing specific operations: remote street segments lamp management, single street lamp brightness control, video processing for vehicles motion detection and classification, wireless and wired data exchanges, power consumptions analysis and traffic evaluation. Two pilot sites have been built up in the project where the smart architecture has been tested and validated in real scenarios. Experimental results show that energy savings of up to 80% are possible compared to a traditional street lamp system.

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A fault detection and isolation (FDI) filter design method is proposed for linear parameter-varying (LPV) systems, which are subject to abrupt changes in their structure. Such a phenomenon is modeled by a finite state Markov chain whose outcome is supposed to be directly available along with its rates transition matrix. The FDI filter is designed as a bank of H − /H ∞ Luenberger observers, derived by optimizing frequency conditions that ensure guaranteed levels of disturbance rejection, fault sensitivity and are capable to discriminate anomalous events belonging to different fault classes. It is proved that, by resorting to stochastic stability concepts, the design method can be recast as a linear matrix inequality programming program in the observer bank gains. The resulting residual generator is a jump parameterdependent observer jointly exploiting the available measures on the deterministic plant parameter and on the instantaneous Markov chain realization. An FDI threshold logic is also proposed in order to reduce the generation of false alarms. The effectiveness of the design technique is illustrated via a numerical example.

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