The Internet of Things (IoT) makes attractive services available to smart objects and humans. To aim this, IoT devices need high sensing, reasoning, and real-time acting capabilities that can be also obtained by promoting adaptive forms of cooperation machine-to-machine among smart objects. The convergence of IoT and multiagent systems also relies on the association between software agents with IoT devices for exploiting their social attitude of interacting and cooperating for services. However, the choice of reliable partners for cooperation can be very difficult when IoT devices migrate across different environments, where the most part of their members will be unreferenced with respect to their trustworthiness. It is well known that agents reputation can be a viable aspect to consider to form social groups; therefore, a possible solution to this problem is to form groups of agents in each IoT environment, based on their social capabilities. In this respect, the first contribution of this paper is represented by a reputation model focused on building the reputation capital of each agent. Second, an algorithm capable to form groups of agents in IoT environments on the basis of their reputation capital was designed. Finally, since in this contest, it is important to spread reliable and certified information about the device/agent reputation in a distributed environment, the third contribution is represented by the adoption of the blockchain technology to certify the reputation capital. Some experiments we have performed show that the model is capable to detect almost all the misleading agents if their percentage is under a high enough threshold, and that good results in term of group composition are obtained. Moreover, the simulations show that, by adopting our model, malicious devices always pay for services significantly more than honest ones. We argue that the individual reputation capital of devices and, consequently, the overall reputation capital of the IoT community can take benefit from the adoption of the proposed approach.

In Smart Factories, Automated Guided Vehicles(AGVs) accomplish heterogeneous tasks as moving objects, restoring connectivity or performing different manufacturing activities into production-lines. These kinds of devices combine several capabilities, as artificial intelligence (visual and speech recognition, automatic fault detecting, pro-active behavior) and mobility,into the so-called "mobile intelligence". A typical scenario is represented by a workshop with a large number of mobile intelligent devices with associated agents, mutually interacting on their behalf. Here, to satisfy some basic requirements as effectiveness and efficiency, it is often necessary to organize ad hoc teams of free-moving vehicles, sensors and smart devices to reach a given target. Therefore, a specific issue consists inadequately representing the reciprocal agent/device trustworthiness for advantaging such team formation processes in a smart factory environment. To this end, in this paper(i)we define a trust measure based on reliability and reputation of AGVs, which are computed based on the feedbacks released for the AGVs activities in the factory;(ii)we design a framework exploiting the defined trust measures to support the formation of virtual,temporary and trust-based teams of mobile intelligent devices;and(iii)we present a set of experimental results highlighting that the proposed trust framework can improve the workshop performance in terms of effectiveness and efficiency

Several models have been proposed in the past for representing both reliability and reputation. However, we remark that a crucial point in the practical use of these two measures is represented by the possibility of suitably combining them to support the agent's decision. In the past, we proposed a reliability–reputation model, called RRAF, that allows the user to choose how much importance to give to the reliability with respect to the reputation. However, RRAF shows some limitations, namely: (i) The weight to assign to the reliability versus reputation is arbitrarily set by the user, without considering the system evolution; (ii) the trust measure that an agent a perceives about an agent b is completely independent of the trust measure perceived by each other agent c, while in the reality the trust measures are mutually dependent. In this paper, we propose an extension of RRAF, aiming at facing the limitations above. In particular, we introduce a new trust reputation model, called TRR, that considers, from a mathematical viewpoint, the interdependence among all the trust measures computed in the systems. Moreover, this model dynamically computes a parameter measuring the importance of the reliability with respect to the reputation. Some experiments performed on the well-known ART(Agent Reputation and Trust) platform show the significant advantages in terms of effectiveness introduced by TRR with respect to RRAF.

Flying vehicles are receiving more and more attention and are becoming an opportunity to start a new urban mobility paradigm. The most interesting feature of flying cars is the expected opportunity they could offer to reduce congestion, traffic jams and the loss of time to move between origin/destination pairs in urban contexts. In this perspective, urban air mobility might meet the concept of “sustainable mobility”, intended as the ideal model of a transport system that minimizes the environmental impacts by maximizing efficiency and travel speed. For transport engineering planning issues, further knowledge is required in this field to understand the effects that a possible urban air mobility system, including the ground traffic component, could have in terms of sustainable mobility in the above meaning. This paper contributes to this topic by providing an analysis of different urban flying car scenarios by using an agent-based approach with different traffic conditions. The preliminary results obtained on some test networks and focusing on travel cost effects suggest that the expected advantages the flying car will depend on trip origin/destination points, average distances travelled in the urban contexts and the location of transition nodes, which are introduced as interchange nodes between aerial and ground mode.

Graph robustness--the ability of a graph to preserve its connectivity after the loss of nodes and edges--has been extensively studied to quantify how social, biological, physical, and technical systems withstand to external damages. In this paper, we prove that graph robustness can be quickly estimated through the Randic index, a parameter introduced in chemistry to study organic compounds. We prove that Erdos-Renyj (ER) graphs are a good specimen of robust graphs because they lack of a clear modular structure; we derive an analytical expression for the Randic index of ER graphs and use ER graphs as an effective term of comparison to decide about graph robustness. Experiments on real datasets from different domains (scientific collaboration networks, content-sharing systems, co-purchase networks from an e-commerce platform, and a road network) show that real-life large graphs are more robust than ER ones with the same number of nodes and edges. We also observe that if node degree distribution closely follows a power law, then few edges contribute for more than half of the Randic index, thus indicating that the selective removal of those edges has devastating impact on graph robustness. Finally, we describe sampling-based algorithms to efficiently but accurately approximate the Randic index.