Computational geometry

Big data are widely recognized as being one of the most powerful drivers to promote productivity, improve efficiency, and support innovation. It is highly expected to explore the power of big data and turn big data into big values. To answer the interesting question whether there are inherent correlations between the two tendencies of big data and green challenges, a recent study has investigated the issues on greening the whole life cycle of big data systems. This paper would like to discover the relations between the trend of big data era and that of the new generation green revolution through a comprehensive and panoramic literature survey in big data technologies toward various green objectives and a discussion on relevant challenges and future directions.

Big Data Meet Green Challenges: Big Data Toward Green Applications

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.

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

The air quality in urban areas is a major concern in modern cities due to significant impacts of air pollution on public health, global environment, and worldwide economy. Recent studies reveal the importance of micro-level pollution information, including human personal exposure and acute exposure to air pollutants. A real-time system with high spatio-temporal resolution is essential because of the limited data availability and non-scalability of conventional air pollution monitoring systems. Currently, researchers focus on the concept of The Next Generation Air Pollution Monitoring System (TNGAPMS) and have achieved significant breakthroughs by utilizing the advance sensing technologies, MicroElectroMechanical Systems (MEMS) and Wireless Sensor Network (WSN). However, there exist potential problems of these newly proposed systems, namely the lack of 3D data acquisition ability and the flexibility of the sensor network. In this paper, we classify the existing works into three categories as Static Sensor Network (SSN), Community Sensor Network (CSN) and Vehicle Sensor Network (VSN) based on the carriers of the sensors. Comprehensive reviews and comparisons among these three types of sensor networks were also performed. Last but not least, we discuss the limitations of the existing works and conclude the objectives that we want to achieve in future systems.

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A Survey of Wireless Sensor Network Based Air Pollution Monitoring Systems

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.

A Survey of Incentive Techniques for Mobile Crowd Sensing

This article presents P-Sense, a participatory sensing application for air pollution monitoring and control. The paper describes in detail the system architecture and individual components of a successfully implemented application. In addition, the paper points out several other research-oriented problems that need to be addressed before these applications can be effectively implemented in practice, in a large-scale deployment. Security, privacy, data visualization and validation, and incentives are part of our work-in-progress activities

P-Sense: A participatory sensing system for air pollution monitoring and control

This paper presents the design and implementation of an Internet of Thing (IoT)-based system for indoor localization using Bluetooth Low Energy (BLE) technology. Our solution consists of two main systems: an acquisition system and a central server, under the Client-Server paradigm and the IoT philosophy. We report the development of different modules: measurement (Bluetooth beacons), data aggregation and transmission, storage, web-interface and cloud services for data, and results visualization. The localization mechanism of the proposed system is based on a simple location algorithm stemming from the Received Signal Strength (RSS) footprinting method, which allow us to detect reference zones inside closed environments. We performed real experiments in order to assess the proposed system. This paper reports the design, implementation and evaluation of an IoT-based system for indoor location using Bluetooth Low Energy (BLE) technology.

IoT-based system for indoor location using bluetooth low energy

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.

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

Data interpolation for participatory sensing systems

This paper presents an approach for explosive-landmine detection by designing, developing and integrating a Ground Penetrating Radar (GPR) onboard an autonomous aerial drone. Our goal is twofold: f...

Diego Mendez

Papers

P-Sense: A participatory sensing system for air pollution monitoring and control

IoT-based system for indoor location using bluetooth low energy

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

Data interpolation for participatory sensing systems

An integrated aerial system for landmine detection: SDR-based Ground Penetrating Radar onboard an autonomous drone