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.

In this paper, a resilient control strategy against replay attacks is developed for discrete-time linear systems subject to state and input constraints, bounded disturbances and measurement noises. In particular operating scenarios, where adversaries act on the communication network by maliciously repeating data transmitted from the sensor to the controller, are investigated. The idea is to customize basic model predictive control schemes for detection attack and resilient control action purposes by exploiting set-theoretic and feasibility arguments proper of the receding control horizon philosophy.

In this article, a robust model predictive control strategy is developed for networked cyber-physical systems under false data injections. The main feature of the proposed scheme relies on the capability to mitigate undesired system behaviors due to external malicious actions. This is achieved by showing that set-membership conditions allow us to quickly detect data integrity anomalies and, as a consequence, to implement adequate countermeasures. The core of the resulting solution exploits receding horizon and set-theoretic control ideas so that the resilient nature of the virtual control moves is formally put in light. Simulations on a $B747-100/200$ aircraft model show effectiveness and merits of the proposed resilient control architecture.

? 2016 Published by Elsevier Ltd on behalf of International Federation of Automatic Control.Economic Model Predictive Control is a technique for optimization of economic revenues arising from controlled dynamical processes that has established itself as a variant of standard Tracking Model Predictive Control. It departs from the latter in that arbitrary cost functions are allowed in the formulation of the stage cost. This paper takes a further step in expanding the applicability of Economic Model Predictive Control by illustrating how the paradigm can be adapted in order to accommodate time-varying or parameter-varying costs