Quantify resilience enhancement of UTS through exploiting connect community and internet of everything emerging technologies

This work aims at investigating and quantifying the Urban Transport System (UTS) resilience enhancement enabled by the adoption of emerging technology such as Internet of Everything (IoE) and the new trend of the Connected Community (CC). A conceptual extension of Functional Resonance Analysis Method (FRAM) and its formalization have been proposed and used to model UTS complexity. The scope is to identify the system functions and their interdependencies with a particular focus on those that have a relation and impact on people and communities. Network analysis techniques have been applied to the FRAM model to identify and estimate the most critical community-related functions. The notion of Variability Rate (VR) has been defined as the amount of output variability generated by an upstream function that can be tolerated/absorbed by a downstream function, without significantly increasing of its subsequent output variability. A fuzzy based quantification of the VR on expert judgment has been developed when quantitative data are not available. Our approach has been applied to a critical scenario (water bomb/flash flooding) considering two cases: when UTS has CC and IoE implemented or not. The results show a remarkable VR enhancement if CC and IoE are deploye

Context-based Grouping and Recommendation in MANETs

International audienceWe propose in this chapter a context grouping mechanism for context distribution over MANETs. Context distribution is becoming a key aspect for successful context-aware applications in mobile and ubiquitous computing environments. Such applications need, for adaptation purposes, context information that is acquired by multiple context sensors distributed over the environment. Nevertheless, applications are not interested in all available context information. Context distribution mechanisms have to cope with the dynamicity that characterizes MANETs and also prevent context information to be delivered to nodes (and applications) that are not interested in it. Our grouping mechanism organizes the distribution of context information in groups whose definition is context based: each context group is defined based on a criteria set (e.g. the shared location and interest) and has a dissemination set, which controls the information that can be shared in the group. We propose a personalized and dynamic way of defining and joining groups by providing a lattice-based classification and recommendation mechanism that analyzes the interrelations between groups and users, and recommend new groups to users, based on the interests and preferences of the user