The term ''Internet-of-Things'' is used as an umbrella keyword for covering various aspects related to the extension of the Internet and the Web into the physical realm, by means of the widespread deployment of spatially distributed devices with embedded identification, sensing and/or actuation capabilities. Internet-of-Things envisions a future in which digital and physical entities can be linked, by means of appropriate information and communication technologies, to enable a whole new class of applications and services. In this article, we present a survey of technologies, applications and research challenges for Internetof-Things.

We study in this report optimal stochastic control issues in delay tolerant networks. We first derive the structure of optimal 2-hop forwarding policies. In order to be implemented, such policies require the knowledge of some system parameters such as the number of mobiles or the rate of contacts between mobiles, but these could be unknown at system design time or may change over time. To address this problem, we design adaptive policies combining estimation and control that achieve optimal performance in spite of the lack of information. We then study interactions that may occur in the presence of several competing classes of mobiles and formulate this as a cost-coupled stochastic game. We show that this game has a unique Nash equilibrium where each class adopts the optimal forwarding policy determined for the single class problem.

Among the novel metrics used to study the relative importance of nodes in complex networks, k-core decomposition has found a number of applications in areas as diverse as sociology, proteinomics, graph visualization, and distributed system analysis and design. This paper proposes new distributed algorithms for the computation of the k-core decomposition of a network, with the purpose of (i) enabling the run-time computation of k-cores in "live" distributed systems and (ii) allowing the decomposition, over a set of connected machines, of very large graphs, that cannot be hosted in a single machine. Lower bounds on the algorithms complexity are given, and an exhaustive experimental analysis on real-world graphs is provided.

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Abstract-Much research has been devoted to maximize the life time of mobile ad-hoc networks. Life time has often been defined as the time elapsed until the first node is out of battery power. In the context of static networks, this could lead to disconnectivity. In contrast, Delay Tolerant Networks (DTNs) leverage the mobility of relay nodes to compensate for lack of permanent connectivity, and thus enable communication even after some nodes deplete their stored energy. One can thus consider the lifetimes of nodes as some additional parameters that can be controlled to optimize the performance of a DTN. In this paper, we consider two ways in which the energy state of a mobile can be controlled. Both listening and transmission require energy, besides each of these has a different type of effect on the network performance. Therefore we consider a joint optimization problem consisting of: i) activation, which determines when a mobile will turn on in order to receive packets, and ii) transmission control, which regulates the beaconing. The optimal solutions are shown to be of the threshold type. The findings are validated through extensive simulations.

Abstract. We study the diffusion of epidemics on networks that are partitioned into local communities. The gross structure of hierarchical networks of this kind can be described by a quotient graph. The rationale of this approach is that individuals infect those belonging to the same community with higher probability than individuals in other communities. In community models the nodal infection probability is thus expected to depend mainly on the interaction of a few, large interconnected clusters. In this work, we describe the epidemic process as a continuous-time individual-based susceptible-infected-susceptible (SIS) model using a first-order mean-field approximation.A key feature of our model is that the spectral radius of this smaller quotient graph (which only captures the macroscopic structure of the community network) is all we need to know in order to decide whether the overall healthy-state defines a globally asymptotically stable or an unstable equilibrium. Indeed, the spectral radius is related to the epidemic threshold of the system.Moreover we prove that, above the threshold, another steady-state exists that can be computed using a lower-dimensional dynamical system associated with the evolution of the process on the quotient graph. Our investigations are based on the graph-theoretical notion of equitable partition and of its recent and rather flexible generalization, that of almost equitable partition.Key words. susceptible-infected-susceptible model, hierarchical networks, graph spectra, equitable and almost equitable partitions AMS subject classifications.1. Introduction. Metapopulation models of epidemics consider the entire population partitioned into communities (also called households, clusters or subgraphs). Such models assume that each community shares a common environment or is defined by a specific relationship (see, e.g., [1,2,3]).Several authors also account for the effect of migration between communities [4,5]. Conversely, the model we are interested in suits better the diffusion of computer viruses or stable social communities, which do not change during the infection period; hence we do not consider migration.In this work, we study the diffusion of epidemics over an undirected graph G = (V, E) with edge set E and node set V . The order of G, denoted N , is the cardinality of V , whereas the size of G is the cardinality of E, denoted L. Connectivity of the graph G is conveniently encoded in the N × N adjacency matrix A. We are interested in the case of networks that can be naturally partitioned into n communities: they are represented by a node set partition π = {V 1 , ..., V n }, i.e., a sequence of mutually disjoint nonempty subsets of V , called cells, whose union is V .The epidemic model adopted in the rest of the paper is a continuous-time Markovian individual-based susceptible-infected-susceptible (SIS) model. In the SIS model a node can be repeatedly infected, recover and yet be infected again. The viral state of a node i, at time t, is thus described by a Bernoulli random var...

Among the novel metrics used to study the relative importance of nodes in complex networks, k-core decomposition has found a number of applications in areas as diverse as sociology, proteinomics, graph visualization, and distributed system analysis and design. This paper proposes new distributed algorithms for the computation of the k-core decomposition of a network, with the purpose of (i) enabling the run-time computation of k-cores in "live" distributed systems and (ii) allowing the decomposition, over a set of connected machines, of very large graphs, that cannot be hosted in a single machine. Lower bounds on the algorithms complexity are given, and an exhaustive experimental analysis on real-world graphs is provided.