72,334 research outputs found
Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges
Participatory sensing is a powerful paradigm which takes advantage of
smartphones to collect and analyze data beyond the scale of what was previously
possible. Given that participatory sensing systems rely completely on the
users' willingness to submit up-to-date and accurate information, it is
paramount to effectively incentivize users' active and reliable participation.
In this paper, we survey existing literature on incentive mechanisms for
participatory sensing systems. In particular, we present a taxonomy of existing
incentive mechanisms for participatory sensing systems, which are subsequently
discussed in depth by comparing and contrasting different approaches. Finally,
we discuss an agenda of open research challenges in incentivizing users in
participatory sensing.Comment: Updated version, 4/25/201
Mechanisms for Risk Averse Agents, Without Loss
Auctions in which agents' payoffs are random variables have received
increased attention in recent years. In particular, recent work in algorithmic
mechanism design has produced mechanisms employing internal randomization,
partly in response to limitations on deterministic mechanisms imposed by
computational complexity. For many of these mechanisms, which are often
referred to as truthful-in-expectation, incentive compatibility is contingent
on the assumption that agents are risk-neutral. These mechanisms have been
criticized on the grounds that this assumption is too strong, because "real"
agents are typically risk averse, and moreover their precise attitude towards
risk is typically unknown a-priori. In response, researchers in algorithmic
mechanism design have sought the design of universally-truthful mechanisms ---
mechanisms for which incentive-compatibility makes no assumptions regarding
agents' attitudes towards risk.
We show that any truthful-in-expectation mechanism can be generically
transformed into a mechanism that is incentive compatible even when agents are
risk averse, without modifying the mechanism's allocation rule. The transformed
mechanism does not require reporting of agents' risk profiles. Equivalently,
our result can be stated as follows: Every (randomized) allocation rule that is
implementable in dominant strategies when players are risk neutral is also
implementable when players are endowed with an arbitrary and unknown concave
utility function for money.Comment: Presented at the workshop on risk aversion in algorithmic game theory
and mechanism design, held in conjunction with EC 201
Game Theoretic Approaches to Massive Data Processing in Wireless Networks
Wireless communication networks are becoming highly virtualized with
two-layer hierarchies, in which controllers at the upper layer with tasks to
achieve can ask a large number of agents at the lower layer to help realize
computation, storage, and transmission functions. Through offloading data
processing to the agents, the controllers can accomplish otherwise prohibitive
big data processing. Incentive mechanisms are needed for the agents to perform
the controllers' tasks in order to satisfy the corresponding objectives of
controllers and agents. In this article, a hierarchical game framework with
fast convergence and scalability is proposed to meet the demand for real-time
processing for such situations. Possible future research directions in this
emerging area are also discussed
Understanding collaboration in volunteer computing systems
Volunteer computing is a paradigm in which devices participating in a distributed environment share part of their resources to help others perform their activities. The effectiveness of this computing paradigm depends on the collaboration attitude adopted by the participating devices. Unfortunately for software designers it is not clear how to contribute with local resources to the shared environment without compromising resources that could then be required by the contributors. Therefore, many designers adopt a conservative position when defining the collaboration strategy to be embedded in volunteer computing applications. This position produces an underutilization of the devices’ local resources and reduces the effectiveness of these solutions. This article presents a study that helps designers understand the impact of adopting a particular collaboration attitude to contribute with local resources to the distributed shared environment. The study considers five collaboration strategies, which are analyzed in computing environments with both, abundance and scarcity of resources. The obtained results indicate that collaboration strategies based on effort-based incentives work better than those using contribution-based incentives. These results also show that the use of effort-based incentives does not jeopardize the availability of local resources for the local needs.Peer ReviewedPostprint (published version
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