Idalides J. Vergara-Laurens

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.

Privacy, quality of information, and energy consumption in Participatory Sensing systems

Participatory Sensing (PS) is a new data collection paradigm based on the voluntary participation of many cellular users equipped with smart applications, a large diversity of sensors, and Internet connectivity at all times. Although many PS-based applications can be foreseen to solve interesting and useful problems, many of them have not been fully implemented and used in practice because of privacy concerns. Compounding the problem, privacy-preserving mechanisms introduce additional issues. For example, one of the most important problems is that of the quality of the information provided by the PS system to the final users. The problem is that, in order to protect the privacy of the users, most privacy-preserving mechanisms modify their real locations, which makes the reported data as if it had been measured from a different location, introducing noise or false information in the system and to the final users. Another important problem is that of the energy consumption. Privacy-preserving mechanisms consume extra energy and users are not very willing to use PS applications if they drain their batteries considerably faster. This paper proposes a hybrid privacy-preserving mechanism that combines anonymization, data obfuscation, and encryption techniques to increase the quality of information and privacy protection without increasing the energy consumption in a significant manner. A new algorithm is proposed that dynamically changes the cell sizes of the grid of the area of interest according to the variability of the variable of interest being measured and chooses different privacy-preserving mechanisms depending on the size of the cell. In small cells, where users can be identified easier, the algorithm uses encryption techniques to protect the privacy of the users and increase the quality of the information, as the reported location is the real location. On the other hand, anonymization and data obfuscation techniques are used in bigger cells where the variability of the variable of interest is low and therefore it is more important to protect the real location (privacy) of the user. We evaluated our hybrid approach and other privacy-preserving mechanisms using a real PS system for air pollution monitoring. Our experiments show the better performance of the proposed hybrid mechanism and the existing trade-offs in terms of privacy, quality of information to the final user, and energy consumption.

Preserving privacy while reducing power consumption and information loss in LBS and participatory sensing applications

Participatory sensing 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. Users agree to use their cellular phone resources to sense and transmit the data of interest because these data will be used to address a collective problem that otherwise would have been very difficult to solve. However, this new data collection paradigm has not been very successful yet mainly because of privacy concerns. Without adequate privacy-preserving mechanisms most users are not willing to participate. Although several schemes have been proposed in the literature, none of them offers a complete solution, and instead, trade offs exist. For example, anonymization-based schemes change the real location of the users, and therefore preserve their privacy, but they might not be precise enough for certain applications. On the other hand, encryption-based schemes, since they do not modify the real location of the user, are very accurate and serve well all applications; however, they are very costly in terms of energy consumption. In this paper we present a scheme that combines the good properties of both approaches to reduce the energy consumption of encryption-based schemes as well as the noise added by anonymization-based schemes. Our simulation results show that the proposed scheme in fact achieves the desired objectives of reducing the energy consumption and information loss while allowing the application to track the users accurately.

On the Interactions between Privacy-Preserving, Incentive, and Inference Mechanisms in Participatory Sensing Systems

In Participatory Sensing (PS) systems people agree to utilize their cellular phone resources to sense and transmit the data of interest. Although PS systems have the potential to collect enormous amounts of data to discover and solve new collective problems, they have not been very successful in practice, mainly because of lack of incentives for participation and privacy concerns. Therefore, several incentive and privacy-preserving mechanisms have been proposed. However, these mechanisms have been traditionally studied in isolation overseeing the interaction between them. In this paper we include a model and implement several of these mechanisms to study the interactions and effects that they may have on one another and, more importantly, on the quality of the information that the system provides to the final user. Our experiments show that privacy-preserving mechanisms and incentive mechanisms may in fact affect each other’s performance and, more importantly, the quality of the information to the final user.

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.

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.

Fall detection system for the elderly

According with the World Health Organization, Falls are the second leading cause of accidental or unintentional injury deaths worldwide. Adults older than 65 suffer the greatest number of fatal falls. Therefore, the quality of life of older people can be improved by using automatic fall detection systems. This paper presents a fall detection system that monitors in real-time an older adult. The system defines two major components: a wearable device and a cell phone. The wearable has the capability of communicating with a cell phone can be located in a 100ft radius. Once, the wearable device detects a fall, it sends an alert to the cell phone; then the cell phone alerts to the emergency contacts defined by the user. The main idea is to avoid the need of carrying the cell phone every time. In addition, our system has a panic button that can be used in order to alert the emergency contacts in the event that the user feels that a fall may happen.

A-PIE: An algorithm for preserving privacy, quality of information, and energy consumption in Participatory Sensing Systems

Abstract We present A-PIE, a hybrid privacy-preserving mechanism for Participatory Sensing Systems that provides a high level of privacy protection as well as a high quality of information while minimizing the energy consumption. A-PIE takes into consideration the variability of the variable of interest to identify clusters, and divides the target area in cells of different sizes. A-PIE applies anonymization or double-encryption to balance privacy protection, quality of information and energy consumption based on the cell’s size. Extensive experimentation, using a real air monitoring system, shows the superior performance of the proposed mechanism when compared with most important privacy-preserving mechanisms.

PAT: A power-aware decision tree algorithm for mobile activity recognition

Mobile context recognition attempts to infer the context of a mobile phone user. Machine learning algorithms exhibit high classification accuracy in these applications. Most approaches are very power-inefficient because they record data from all sensors at all time. Intelligently cycling sensors could greatly improve the power efficiency of context recognition services. We propose a decision tree-based machine learning algorithm which optimizes not only on classification accuracy, but also on data retrieval costs based on power efficiency. We show that in a simple physical activity recognition application, the use of this new algorithm results in a significant decrease in power consumption while maintaining a high classification accuracy.