In this paper, we present the Smart City Architecture developed in the context of the ARTEMIS JU SP3 SOFIA project. It is an Event Driven Architecture that allows the management and cooperation of heterogeneous sensors for monitoring public spaces. The main components of the architecture are implemented in a testbed on a subway scenario with the objective to demonstrate that our proposed solution, can enhance the detection of anomalous events and simplify both the operators tasks and the communications to passengers in case of emergency.

The mobile phone market lacks a satisfactory location technique that is accurate, but also economical and easy to deploy. Current technology provides high accuracy, but requires substantial technological and financial investment. In this paper we present the results of experiments intended to asses the accuracy of inexpensive Cell-ID location technique and its suitability for the provisioning of location based services. We first evaluate the accuracy of Cell-ID in urban, suburban and highway scenarios (both in U.S. and Italy), we then introduce the concepts of discovery-accuracy and discovery-noise to estimate the impact of Positioning accuracy on the quality of resource discovery services. Experiments show that the accuracy of Cell-ID is not satisfactory as a general solution. In contrast we show how Cell-ID can be effectively exploited to implement more effective and efficient voice location-based services

Plants sense their environment by producing electrical signals which in essence represent changes in underlying physiological processes. These electrical signals, when monitored, show both stochastic and deterministic dynamics. In this paper, we compute 11 statistical features from the raw non-stationary plant electrical signal time series to classify the stimulus applied (causing the electrical signal). By using different discriminant analysis-based classification techniques, we successfully establish that there is enough information in the raw electrical signal to classify the stimuli. In the process, we also propose two standard features which consistently give good classification results for three types of stimuli—sodium chloride (NaCl), sulfuric acid (H ₂ SO ₄ ) and ozone (O ₃ ). This may facilitate reduction in the complexity involved in computing all the features for online classification of similar external stimuli in future.

The assessment of environmental pollution levels is a complex and expensive task that public administration and often also private entities are willing or forced to take over. Focusing on the assessment of environmental noise pollution in urban areas, we provide qualitative considerations and experimental results to show the feasibility of wireless sensor networks to be used in this context. We present a prototype for the collection and logging of noise pollution data based on the Tmote invent prototyping platform, using which we performed indoor and outdoor noise pollution measurements. We build upon these first experimental results to depict the potentials and limits of currently available wireless sensor networks prototyping platforms to be used as noise pollution sensors. Furthermore, we present tinyLAB, a Matlab-based tool developed in the context of this work, which enables real-time acquisition, processing and visualization of data collected in wireless sensor networks.