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

Multi-round Master-Worker Computing: a Repeated Game Approach

We consider a computing system where a master processor assigns tasks for execution to worker processors through the Internet. We model the workers decision of whether to comply (compute the task) or not (return a bogus result to save the computation cost) as a mixed extension of a strategic game among workers. That is, we assume that workers are rational in a game-theoretic sense, and that they randomize their strategic choice. Workers are assigned multiple tasks in subsequent rounds. We model the system as an infinitely repeated game of the mixed extension of the strategic game. In each round, the master decides stochastically whether to accept the answer of the majority or verify the answers received, at some cost. Incentives and/or penalties are applied to workers accordingly. Under the above framework, we study the conditions in which the master can reliably obtain tasks results, exploiting that the repeated games model captures the effect of long-term interaction. That is, workers take into account that their behavior in one computation will have an effect on the behavior of other workers in the future. Indeed, should a worker be found to deviate from some agreed strategic choice, the remaining workers would change their own strategy to penalize the deviator. Hence, being rational, workers do not deviate. We identify analytically the parameter conditions to induce a desired worker behavior, and we evaluate experi- mentally the mechanisms derived from such conditions. We also compare the performance of our mechanisms with a previously known multi-round mechanism based on reinforcement learning.Comment: 21 pages, 3 figure

Human Computation and Economics

This article is devoted to economical aspects of Human Computation (HC) and to perspectives of HC in economics. As of economical aspects of HC, it is first observed that much of what makes HC systems effective is economical in nature suggesting that complexity being reconsidered as a “HC complexity” and the conception of efficient HC systems as a “HC economics”. This article also points to the relevance of HC in the development of standard software and to the importance of competition in HC systems. As of HC in economics, it is first argued that markets can be seen as HC systems avant la lettre. Looking more closely at financial markets, the article then points to a speed differential between transactions and credit risk awareness that compromises the efficiency of financial markets. Finally, a HCbased credit risk rating is proposed that, overcoming the afore mentioned speed differential, holds promise for better functioning financial markets

Lower Bounds on Implementing Robust and Resilient Mediators

We consider games that have (k,t)-robust equilibria when played with a mediator, where an equilibrium is (k,t)-robust if it tolerates deviations by coalitions of size up to k and deviations by up to $t$ players with unknown utilities. We prove lower bounds that match upper bounds on the ability to implement such mediators using cheap talk (that is, just allowing communication among the players). The bounds depend on (a) the relationship between k, t, and n, the total number of players in the system; (b) whether players know the exact utilities of other players; (c) whether there are broadcast channels or just point-to-point channels; (d) whether cryptography is available; and (e) whether the game has a $k+t)-punishment strategy; that is, a strategy that, if used by all but at most$k+t$ players, guarantees that every player gets a worse outcome than they do with the equilibrium strategy