4,186 research outputs found
Quality of Information in Mobile Crowdsensing: Survey and Research Challenges
Smartphones have become the most pervasive devices in people's lives, and are
clearly transforming the way we live and perceive technology. Today's
smartphones benefit from almost ubiquitous Internet connectivity and come
equipped with a plethora of inexpensive yet powerful embedded sensors, such as
accelerometer, gyroscope, microphone, and camera. This unique combination has
enabled revolutionary applications based on the mobile crowdsensing paradigm,
such as real-time road traffic monitoring, air and noise pollution, crime
control, and wildlife monitoring, just to name a few. Differently from prior
sensing paradigms, humans are now the primary actors of the sensing process,
since they become fundamental in retrieving reliable and up-to-date information
about the event being monitored. As humans may behave unreliably or
maliciously, assessing and guaranteeing Quality of Information (QoI) becomes
more important than ever. In this paper, we provide a new framework for
defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the
current state-of-the-art on the topic. We also outline novel research
challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN
A Formal Framework for Modeling Trust and Reputation in Collective Adaptive Systems
Trust and reputation models for distributed, collaborative systems have been
studied and applied in several domains, in order to stimulate cooperation while
preventing selfish and malicious behaviors. Nonetheless, such models have
received less attention in the process of specifying and analyzing formally the
functionalities of the systems mentioned above. The objective of this paper is
to define a process algebraic framework for the modeling of systems that use
(i) trust and reputation to govern the interactions among nodes, and (ii)
communication models characterized by a high level of adaptiveness and
flexibility. Hence, we propose a formalism for verifying, through model
checking techniques, the robustness of these systems with respect to the
typical attacks conducted against webs of trust.Comment: In Proceedings FORECAST 2016, arXiv:1607.0200
Trust models in ubiquitous computing
We recapture some of the arguments for trust-based technologies in ubiquitous computing, followed by a brief survey of some of the models of trust that have been introduced in this respect. Based on this, we argue for the need of more formal and foundational trust models
REPUTATION COMPUTATION IN SOCIAL NETWORKS AND ITS APPLICATIONS
This thesis focuses on a quantification of reputation and presents models which compute reputation within networked environments. Reputation manifests past behaviors of users and helps others to predict behaviors of users and therefore reduce risks in future interactions. There are two approaches in computing reputation on networks- namely, the macro-level approach and the micro-level approach. A macro-level assumes that there exists a computing entity outside of a given network who can observe the entire network including degree distributions and relationships among nodes. In a micro-level approach, the entity is one of the nodes in a network and therefore can only observe the information local to itself, such as its own neighbors behaviors. In particular, we study reputation computation algorithms in online distributed environments such as social networks and develop reputation computation algorithms to address limitations of existing models. We analyze and discuss some properties of reputation values of a large number of agents including power-law distribution and their diffusion property. Computing reputation of another within a network requires knowledge of degrees of its neighbors. We develop an algorithm for estimating degrees of each neighbor. The algorithm considers observations associated with neighbors as a Bernoulli trial and repeatedly estimate degrees of neighbors as a new observation occurs. We experimentally show that the algorithm can compute the degrees of neighbors more accurately than a simple counting of observations. Finally, we design a bayesian reputation game where reputation is used as payoffs. The game theoretic view of reputation computation reflects another level of reality in which all agents are rational in sharing reputation information of others. An interesting behavior of agents within such a game theoretic environment is that cooperation- i.e., sharing true reputation information- emerges without an explicit punishment mechanism nor a direct reward mechanisms
Trust and Reputation in Multi-Agent Systems
Multi-Agent systems (MAS) are artificial societies populated with
distributed autonomous agents that are intelligent and rational.
These self-independent agents are capable of independent decision
making towards their predefined goals. These goals might be common
between agents or unique for an agent. Agents may cooperate with
one another to facilitate their progresses. One of the fundamental
challenges in such settings is that agents do not have a full
knowledge over the environment and regarding their decision making
processes, they might need to request other agents for a piece of
information or service. The crucial issues are then how to rely on
the information provided by other agents, how to consider the
collected data, and how to select appropriate agents to ask for
the required information. There are some proposals addressing how
an agent can rely on other agents and how an agent can compute the
overall opinion about a particular agent. In this context, the
trust value reflects the extent to which agents can rely on other
agents and the reputation value represents public opinion about a
particular agent. Existing approaches for reliable information
propagation fail to capture the dynamic relationships between
agents and their influence on further decision making process.
Therefore, these models fail to adapt agents to frequent
environment changes. In general, a well-founded trust and
reputation system that prevents malicious acts that are emerged by
selfish agents is required for multi-agent systems. We propose a
trust mechanism that measures and analyzes the reliability of
agents cooperating with one another. This mechanism concentrates
on the key attributes of the related agents and their
relationships. We also measure and analyze the public reputation
of agents in large-scale environments utilizing a sound reputation
mechanism. In this mechanism, we aim at maintaining a public
reputation assessment in which the public actions of agents are
accurately under analysis. On top of the theoretical analysis, we
experimentally validate our trust and reputation approaches
through different simulations. Our preliminary results show that
our approach outperforms current frameworks in providing accurate
credibility measurements and maintaining accurate trust and
reputation mechanisms
Group Minds and the Case of Wikipedia
Group-level cognitive states are widely observed in human social systems, but
their discussion is often ruled out a priori in quantitative approaches. In
this paper, we show how reference to the irreducible mental states and
psychological dynamics of a group is necessary to make sense of large scale
social phenomena. We introduce the problem of mental boundaries by reference to
a classic problem in the evolution of cooperation. We then provide an explicit
quantitative example drawn from ongoing work on cooperation and conflict among
Wikipedia editors, showing how some, but not all, effects of individual
experience persist in the aggregate. We show the limitations of methodological
individualism, and the substantial benefits that come from being able to refer
to collective intentions, and attributions of cognitive states of the form
"what the group believes" and "what the group values".Comment: 21 pages, 6 figures; matches published versio
Dynamics of Information Diffusion and Social Sensing
Statistical inference using social sensors is an area that has witnessed
remarkable progress and is relevant in applications including localizing events
for targeted advertising, marketing, localization of natural disasters and
predicting sentiment of investors in financial markets. This chapter presents a
tutorial description of four important aspects of sensing-based information
diffusion in social networks from a communications/signal processing
perspective. First, diffusion models for information exchange in large scale
social networks together with social sensing via social media networks such as
Twitter is considered. Second, Bayesian social learning models and risk averse
social learning is considered with applications in finance and online
reputation systems. Third, the principle of revealed preferences arising in
micro-economics theory is used to parse datasets to determine if social sensors
are utility maximizers and then determine their utility functions. Finally, the
interaction of social sensors with YouTube channel owners is studied using time
series analysis methods. All four topics are explained in the context of actual
experimental datasets from health networks, social media and psychological
experiments. Also, algorithms are given that exploit the above models to infer
underlying events based on social sensing. The overview, insights, models and
algorithms presented in this chapter stem from recent developments in network
science, economics and signal processing. At a deeper level, this chapter
considers mean field dynamics of networks, risk averse Bayesian social learning
filtering and quickest change detection, data incest in decision making over a
directed acyclic graph of social sensors, inverse optimization problems for
utility function estimation (revealed preferences) and statistical modeling of
interacting social sensors in YouTube social networks.Comment: arXiv admin note: text overlap with arXiv:1405.112
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