Doctor of Philosophy

dissertationWe are seeing an extensive proliferation of wireless devices including various types and forms of sensor nodes that are increasingly becoming ingrained in our daily lives. There has been a significant growth in wireless devices capabilities as well. This proliferation and rapid growth of wireless devices and their capabilities has led to the development of many distributed sensing and computing applications. In this dissertation, we propose and evaluate novel, efficient approaches for localization and computation offloading that harness distributed sensing and computing in wireless networks. In a significant part of this dissertation, we exploit distributed sensing to create efficient localization applications. First, using the sensing power of a set of Radio frequency (RF) sensors, we propose energy efficient approaches for target tracking application. Second, leveraging the sensing power of a distributed set of existing wireless devices, e.g., smartphones, internet-of-things devices, laptops, and modems, etc., we propose a novel approach to locate spectrum offenders. Third, we build efficient sampling approaches to select mobile sensing devices required for spectrum offenders localization. We also enhance our sampling approaches to take into account selfish behaviors of mobile devices. Finally, we investigate an attack on location privacy where the location of people moving inside a private area can be inferred using the radio characteristics of wireless links that are leaked by legitimate transmitters deployed inside the private area, and develop the first solution to mitigate this attack. While we focus on harnessing distributed sensing for localization in a big part of this dissertation, in the remaining part of this dissertation, we harness the computing power of nearby wireless devices for a computation offloading application. Specially, we propose a multidimensional auction for allocating the tasks of a job among nearby mobile devices based on their computational capabilities and also the cost of computation at these devices with the goal of reducing the overall job completion time and being beneficial to all the parties involved

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 improving information quality in smartphone crowdsensing systems

Given its potential for a large variety of real-life applications, smartphone crowdsensing has recently gained tremendous attention from the research community. Smartphone crowdsensing is a paradigm that allows ordinary citizens to participate in large-scale sensing surveys by using user-friendly applications installed in their smartphones. In this way, fine-grained sensing information is obtained from smartphone users without employing fixed and expensive infrastructure, and with negligible maintenance costs. Existing smartphone sensing systems depend completely on the participants\u27 willingness to submit up-to-date and accurate information regarding the events being monitored. Therefore, it becomes paramount to scalably and effectively determine, enforce, and optimize the information quality of the sensing reports submitted by the participants. To this end, mechanisms to improve information quality in smartphone crowdsensing systems were designed in this work. Firstly, the FIRST framework is presented, which is a reputation-based mechanism that leverages the concept of mobile trusted participants to determine and improve the information quality of collected data. Secondly, it is mathematically modeled and studied the problem of maximizing the likelihood of successful execution of sensing tasks when participants having uncertain mobility execute sensing tasks. Two incentive mechanisms based on game and auction theory are then proposed to efficiently and scalably solve such problem. Experimental results demonstrate that the mechanisms developed in this thesis outperform existing state of the art in improving information quality in smartphone crowdsensing systems --Abstract, page iii

Quantifying User Reputation Scores, Data Trustworthiness, and User Incentives in Mobile Crowd-Sensing

Ubiquity of mobile devices with rich sensory capabilities has given rise to the mobile crowd-sensing (MCS) concept, in which a central authority (the platform) and its participants (mobile users) work collaboratively to acquire sensory data over a wide geographic area. Recent research in MCS highlights the following facts: 1) a utility metric can be defined for both the platform and the users, quantifying the value received by either side; 2) incentivizing the users to participate is a non-trivial challenge; 3) correctness and truthfulness of the acquired data must be verified, because the users might provide incorrect or inaccurate data, whether due to malicious intent or malfunctioning devices; and 4) an intricate relationship exists among platform utility, user utility, user reputation, and data trustworthiness, suggesting a co-quantification of these inter-related metrics. In this paper, we study two existing approaches that quantify crowd-sensed data trustworthiness, based on statistical and vote-based user reputation scores. We introduce a new metric - collaborative reputation scores - to expand this definition. Our simulation results show that collaborative reputation scores can provide an effective alternative to the previously proposed metrics and are able to extend crowd sensing to applications that are driven by a centralized as well as decentralized control

CrowdPickUp:task pick-up in the wild

Abstract. This thesis investigates the feasibility and performance of different types of crowdsourcing tasks picked-up in the wild i.e., situated, location-based and general through the implementation and evaluation of the CrowdPickUp crowdsourcing platform. We describe in detail the implementation process of CrowdPickUp, which we then used in a study where workers could earn coins on the basis of task completion and use their earned coins to buy different available items of their own choice using CrowdPickUp’s web shop integrated within our system. During the study, we recorded the average completion time and accuracy of different crowdsourcing tasks. The key findings show that our platform was able to generate high quality contributions in a composite environment. Finally, we conclude the thesis by discussing the importance and usefulness of different crowdsourcing tasks designed for our crowdsourcing system and our possible future work within the area of crowdsourcing task-pickup system.Tiivistelmä. Tämä diplomityö tutkii joukkouttamisen suorituskykyä ja mahdollisuuksia erityyppisten tehtävien avulla. Tehtävät jaetaan työntekijöille luonnollisissa olosuhteissa paikkasidonnaisesti työssä kehitetyn CrowdPickUp-alustan avulla. Työ kuvailee yksityiskohtaisesti kehitetyn alustan sovelluskehitysprosessin. Tämän jälkeen valmista alustaa käytettiin käyttäjäkokeissa, joissa työntekijät pystyivät ansaitsemaan virtuaalivaluuttaa, jolla pystyi ostamaan erilaisia palkintoja. Kokeen aikana tutkimme ja tallensimme monenlaista tietoa, kuten esimerkiksi suoritetun työn tarkkuutta ja keskimääräistä tehokkuutta. Työn päälöydökset osoittavat, että alustamme kykeni tuottamaan korkealaatuista työtä luonnollisissa olosuhteissa ja ilman tutkijoiden jatkuvaa läsnäoloa. Lopuksi diplomityö keskustelee löydösten ja kehitystyön tärkeyttä sekä soveltuvuutta erilaisten tehtävien suorittamisalustaksi. Lisäksi esittelemme ideoita, joilla työtä voi kehittää eteenpäin entistä hyödyllisemmäksi tutkimusinstrumentiksi