The Collaborative Internet of Things (C-IoT) is an emerging paradigm that involves many communities with the idea of cooperating in data gathering and service sharing. Many fields of application, such as Smart Cities and environmental monitoring, use the concept of crowdsensing in order to produce the amount of data that such IoT scenarios need in order to be pervasive. In our paper we introduce an architecture, namely SenSquare, able to handle both the heterogeneous data sources coming from open IoT platform and crowdsensing campaigns, and display a unified access to users. We inspect all the facets of such a complex system, spanning over issues of different nature: we deal with heterogeneous data classification, Mobile Crowdsensing (MCS) management for environmental data, information representation and unification, IoT service composition and deployment. We detail our proposed solution in dealing with such tasks and present possible methods for meeting open challenges. Finally, we demonstrate the capabilities of SenSquare through both a mobile and a desktop client.

This paper proposes deployment strategies for consumer unmanned aerial vehicles (UAVs) to maximize the stationary coverage of a target area and to guarantee the continuity of the service through energy replenishment operations at ground charging stations. The three main contributions of our work are as follows. 1) A centralized optimal solution is proposed for the joint problem of UAV positioning for a target coverage ratio and scheduling the charging operations of the UAVs that involves travel to the ground station. 2) A distributed game-theory-based scheduling strategy is proposed using normal-form games with rigorous analysis on performance bounds. Furthermore, a bio-inspired scheme using attractive/repulsive spring actions are used for distributed positioning of the UAVs. 3) The cost-benefit tradeoffs of different levels of cooperation among the UAVs for the distributed charging operations is analyzed. This paper demonstrates that the distributed deployment using only 1-hop messaging achieves approximation of the centrally computed optimum, in terms of coverage and lifetime.

In this paper, we illustrate the design of a cross-layered MAC and clustering solution for supporting the fast propagation of broadcast messages in a vehicular ad hoc network (VANET). A distributed dynamic clustering algorithm is proposed to create a dynamic virtual backbone in the vehicular network. The vehicle-members of the backbone are responsible for implementing an efficient messages propagation. The backbone creation and maintenance are proactively performed aiming to balance the stability of backbone connections as well as the cost/efficiency trade-off and the hops-reduction when forwarding broadcast messages. A fast multi-hop MAC forwarding mechanism is defined to exploit the role of backbone vehicles, under a cross-layered approach. Simulation results show the effectiveness of the mutual support of proactive clustering and MAC protocols for efficient dissemination of broadcast messages in VANETs.

This paper is motivated by the concept that the successful, effective, and sustainable implementation of the smart city paradigm requires a close cooperation among researchers with different, complementary interests and, in most cases, a multidisciplinary approach. It first briefly discusses how such a multidisciplinary methodology, transversal to various disciplines such as architecture, computer science, civil engineering, electrical, electronic and telecommunication engineering, social science and behavioral science, etc., can be successfully employed for the development of suitable modeling tools and real solutions of such sociotechnical systems. Then, the paper presents some pilot projects accomplished by the authors within the framework of some major European Union (EU) and national research programs, also involving the Bologna municipality and some of the key players of the smart city industry. Each project, characterized by different and complementary approaches/modeling tools, is illustrated along with the relevant contextualization and the advancements with respect to the state of the art.

In most countries the share of circulating electric vehicles (EV) is less than 1%. Beside cost issues, the detrimental factors that most discourage people from purchasing EVs are their limited range and the rather poor coverage area of the recharging infrastructure. This clearly indicates the need for higher investments in vehicular technologies and infrastructures; however, software services and applications also play a major role in the transition to electric mobility, e.g. to mitigate EV driver anxiety. In this paper we illustrate the recipe proposed by the European Internet of Energy (IoE) project toward interoperable services for large-scale EV mobility scenarios: it consists of a Service-Oriented Architecture (SoA) to collect and integrate data coming from the heterogeneous actors of the EV scenario thanks to a semantic data tier. On top of it, we describe the implementation of an advanced Route Planning service to compute the optimal path towards a destination according to user-defined parameters and goals; differently from other approaches, our tool integrates reservation mechanisms of charging slots, so that a complete planning of the itinerary can be produced. Effectiveness of the route planning service is evaluated on large-scale EV scenarios (e.g. the Italian Emilia-Romagna region) thanks to an innovative emulation-simulation platform.