Luis G. Jaimes

A location-based incentive mechanism for participatory sensing systems with budget constraints

Participatory sensing (PS) systems rely on the willingness of mobile users to participate in the collection and reporting of data using a variety of sensors either embedded or integrated in their cellular phones. However, this new data collection paradigm has not been very successful yet mainly because of the lack of incentives for participation. Although several incentive schemes have been proposed to encourage user participation, none has used location information and imposed budget and coverage constraints, which will make the scheme more realistic and efficient. We propose a recurrent reverse auction incentive mechanism with a greedy algorithm that selects a representative subset of the users according to their location given a fixed budget. Compared to existing mechanisms, our incentive scheme improves the area covered by more than 60 percent acquiring a more representative set of samples after every round while maintaining the same number of active users in the system and spending the same budget.

A Survey of Incentive Techniques for Mobile Crowd Sensing

Crowd sensing (CS) is an approach to collecting many samples of a phenomena of interest by distributing the sampling across a large number of individuals. While any one individual may not provide sufficient samples, aggregating samples across many individuals provides high-quality, high-coverage measurements of the phenomena. Thus, for participatory sensing to be successful, one must motivate a large number of individuals to participate. In this work, we review a variety of incentive mechanisms that motivate people to contribute to a CS effort. We then establish a set of design constraints or minimum requirements that any incentive mechanism for CS must have. These design constrains are then used as metrics to evaluate those approaches and determine their advantages and disadvantages. We also contribute a taxonomy of CS incentive mechanisms and show how current systems fit within this taxonomy. We conclude with the identification of new types of incentive mechanisms that require further investigation.

Trends in Mobile Cyber-Physical Systems for health Just-in time interventions

Advances in pervasive computing, machine learning, and human activity recognition are changing preventive health care. Emerging paradigms, such as Mobile Cyber-Physical System (MCPS) and Just-in-time interventions (JITI), allow patients to take health monitoring, diagnosis, therapy and treatments beyond traditional medical settings. These paradigms empower patients by delivering health care at any place and at any time. MCPS provides the necessary engineering support to enable JITI systems to work in an autonomous way. In this work, we review the recent trends in the design of Mobile Cyber-Physical systems for Just-in-time interventions (MCP-JITI), and the different engineering concepts behind this paradigm. Finally, we discuss a set of necessary requirements or design issues to successfully deploy in real world scenarios. This discussion is driven by the description of the MCP-JITI architecture and the interconnections among its components.

A crowd sensing incentive algorithm for data collection for consecutive time slot problems

Crowd sensing (CS) is a new sensing paradigm that takes advantage of the availability of mobile devices almost in every place. In this type of system, the mobile phones users are asked to use their resources such as data plan, energy and time, in order to collect and transmit data to a central infrastructure. Since participants usually do not receive a direct benefit from the system, the incentive mechanisms are required in order to encourage peoples participation in the system. This paper presents a new incentive mechanism for CS based on reverse auctions and the maximization of the variance in the location of selected participants under a budget constraint. The proposed mechanism aims to assure a good coverage of the area of interest as well as the sufficient number of participants in order to guarantee a good quality of information provided to the final user. The experiments present the good performance of the proposed mechanism in terms of number of active participants, budget utilization and coverage of the area of interest.

CALMA, an algorithm framework for mobile just in time interventions

Advancements in ubiquitous computing are rapidly changing preventative health care. These quick changes allow not only to track in real time the heath of an individual, but also to react to any anomalies that may indicate the need of help. This new health care paradigm (i.e., Just-in-time interventions) allows to support treatments and deliver help anytime and anywhere. In this work, we take a first step in modeling a flexible mechanism for choosing the most effective intervention from a set of available ones. We model this optimization problem as a Markov Decision Process (MDP) and we solve it by using Value Iteration in an online fashion. We show that the policy found by our algorithms for selecting interventions, at least doubles the efficiency achieved by a random policy, in minimizing the average number of interventions to relieve a patient from a condition such as stress.

Privacy-Preserving Mechanisms for Crowdsensing: Survey and Research Challenges

Crowdsensing (CS) is a new data collection paradigm based on the willingness of people to utilize their mobile devices to sense and transmit data of interest. Given the large amount of cellular users, mobile sensor networks will be able to collect enough data to address large-scale societal problems in a fast, easy, and cost-effective manner. One important issue in CS is that of privacy; without appropriate privacy-preserving mechanisms, many users will not be willing to participate in the data collection process. This paper presents the state-of-the-art in privacy-preserving mechanisms for CS systems. After a general description of CS systems and their main components, this paper addresses the most important issues to consider in the design, implementation, and evaluation of privacy-preserving mechanisms. Then, following a new taxonomy, the most important mechanisms available in the literature are described and qualitatively evaluated. Finally, this paper presents research challenges that should be addressed in order to improve the performance of future privacy-preserving mechanisms for CS systems.

PREVENTER, a Selection Mechanism for Just-in-Time Preventive Interventions

This paper examines just-in-time adaptive interventions (JITAIs) for stress, a pervasive and affective computing application with significant implications for long-term health and quality of life. We discuss fundamental components needed to enabling JITAIs based for one kind of affect data stress. Chronic stress has significant long-term behavioral and physical health consequences, including an increased risk of cardiovascular disease, cancer, anxiety and depression. This paper conducts post-hoc experiments and simulations to demonstrate feasibility of both real-time stress forecasting and stress intervention adaptation and optimization. Using physiological data collected by ten individuals in the natural environment for one week, we show 1) that simple Hidden Markov Models (HMMs) can be used to forecast physiological measures of stress with up to 3 minutes in advance; and 2) Q-Learning (QL) combined with eligibility traces could be used by an affective computing system to adapt and deliver any number and type of interventions in response to changes in affect. Our hope is that this work will take us one step closer to using pervasive devices to assist in the daily management of chronic stress and other affect-related challenges.

A cooperative incentive mechanism for recurrent crowd sensing

Crowd sensing (CS) is an approach that consists of collecting many samples of a phenomena of interest by distributing the sampling process across a large number of individuals. In this work, we address the effect of cooperation among individuals by modeling a recurrent CS task as a repeated game. In this game, participants are the players of the corresponding game, and every round of the CS task is considered as a single-shot game which is repeated over time. In this model, participants compete and cooperate with each other in order to sell their samples. We represent the participants evolutionary behaviors by a graph network in which all the individuals make utilities in the long run. We show that although a pure competition approach faces problems such as the continuous drop-out of participants and the raise of prices of samples, this hybrid approach keeps the prices of samples low while maintaining the required number of participants.

Increasing Trust in Personal Informatics Tools

Personal Informatics (PI) systems help individuals collect and reflect on personal physiological, behavioral and/or contextual data. Typically, these systems offer users interactive visualizations that allow meaningful exploration of the data. Through this exploration, PI systems have great potential to facilitate self-reflection and encourage behavior change.

An Incentive Mechanism for Crowdsensing Markets With Multiple Crowdsourcers

In this paper, we design an incentive mechanism for data collection in smart cities. We propose an incentive mechanism for crowdsensing with multiple crowdsourcers. We model the incentive mechanism as a noncooperative game. We consider two different pricing mechanisms when the crowdsourcers fixed the rewards in advance, and when the crowdsourcers dynamically set the rewards in order to maximize their own utilities. A discrete time dynamic inspired by the well known best response dynamic, called elite strategy dynamics, is proposed to compute a Nash equilibrium of the modeled game. Comprehensive simulations were presented to evaluate the performance of the proposed incentive mechanism.