Abstract. More and more applications and functionalities have been introduced to smartphones, but smartphones have limited resources on computation and battery. To enhance the capacity of smartphones, an interesting idea is to use Cloud Computing and virtualization techniques to shift the workload from smartphones to a computational infrastructure. In this paper, we propose a new framework which keeps a mirror for each smartphone on a computing infrastructure in the telecom network. With mirror, we can greatly reduce the workload and virtually expand the resources of smartphones. We show the feasibility of deploying such a framework in telecom networks by protocol design, synchronization study and scalability test. To show the benefit, we introduce two applications where both the computational workload on smartphones and network traffic in telecom networks can be significantly reduced by our techniques.
Abstract-We consider in this paper a networked system of opinion dynamics in continuous time, where the agents are able to evaluate their self-appraisals in a distributed way. In the model we formulate, the underlying network topology is described by a rooted digraph. For each ordered pair of agents (i, j), we assign a function of self-appraisal to agent i, which measures the level of importance of agent i to agent j. Thus, by communicating only with her neighbors, each agent is able to calculate the difference between her level of importance to others and others' level of importance to her. The dynamical system of self-appraisals is then designed to drive these differences to zero. We show that for almost all initial conditions, the trajectory generated by this dynamical system asymptotically converges to an equilibrium point which is exponentially stable.
Abstract-This paper studies the opinion dynamics that result when individuals consecutively discuss a sequence of issues. Specifically, we study how individuals' self-confidence levels evolve via a reflected appraisal mechanism. Motivated by the DeGroot-Friedkin model, we propose a Modified DeGrootFriedkin model which allows individuals to update their selfconfidence levels by only interacting with their neighbors and in particular, the modified model allows the update of selfconfidence levels to take place in finite time without waiting for the opinion process to reach a consensus on any particular issue. We study properties of this Modified DeGroot-Friedkin model and compare the associated equilibria and stability with those of the original DeGroot-Friedkin model. Specifically, for the case when the interaction matrix is doubly stochastic, we show that for the modified model, the vector of individuals' selfconfidence levels converges to a unique nontrivial equilibrium which for each individual is equal to 1 n , where n is the number of individuals. This implies that eventually individuals reach a democratic state. I. INTRODUCTIONOver the years, the advancement in information technology has enabled individuals to be more closely connected and the rapid expansion of online social networks has provided huge amount of data available for analysis regarding how individuals interact over networks. Consequently, much research attention has been drawn to understand how an individual's opinion evolves over time, in particular, how to model the underlying process of opinion formation [1], [2], [3]. There has been increasing interest in developing models of opinion dynamics to capture individuals' interaction, and some of these as relevant to the topic of this paper will be mentioned later. In the literature, two main approaches have been adopted on how each individual updates her opinion:In social sciences, quite a few models have been proposed for opinion dynamics. Notable among them are the three classical models, namely, the DeGroot model [1], the Friedkin-Johnsen model [2], and the Krause model [3]. In the DeGroot model, each individual has a fixed set of neighbors and the local interaction is captured by taking the convex combination of her own opinion and the opinions of her neighbors at each time step. The model can be extended naturally to the case in which the neighbor sets change over time. The Friedkin-Johnsen model is a variation of the DeGroot model in which each individual is assumed to adhere to her initial opinion to a certain degree, which brings
Abstract-We consider in this paper a networked system of opinion dynamics in continuous time, where the agents are able to evaluate their self-appraisals in a distributed way. In the model we formulate, the underlying network topology is described by a rooted digraph. For each ordered pair of agents (i, j), we assign a function of self-appraisal to agent i, which measures the level of importance of agent i to agent j. Thus, by communicating only with her neighbors, each agent is able to calculate the difference between her level of importance to others and others' level of importance to her. The dynamical system of self-appraisals is then designed to drive these differences to zero. We show that for almost all initial conditions, the trajectory generated by this dynamical system asymptotically converges to an equilibrium point which is exponentially stable.
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