Apollo: Towards factfinding in participatory sensing

2020 IEEE International Conference on Pervasive Computing and Communications (PerCom)

Opportunistic crowdsensing empowers citizens carrying hand-held devices to sense physical phenomena of common interest at a large and fine-grained scale without requiring the citizens' active involvement. However, the resulting uncontrolled collection and upload of the massive amount of contributed raw data incur significant resource consumption, from the end device to the server, as well as challenge the quality of the collected observations. This paper tackles both challenges raised by opportunistic crowdsensing, that is, enabling the resourceefficient gathering of relevant observations. To achieve so, we introduce the BeTogether middleware fostering context-aware, collaborative crowdsensing at the edge so that co-located crowdsensors operating in the same context, group together to share the workload in a cost-and quality-effective way. We evaluate the proposed solution using an implementation-driven evaluation that leverages a dataset embedding nearly one million entries contributed by 550 crowdsensors over a year. Results show that BeTogether increases the quality of the collected data while reducing the overall resource cost compared to the cloud-centric approach.

Journal of Networks, 2013

In this paper we study the problem of sensor data verification in Participatory Sensing (PS) systems using an air quality/pollution monitoring application as a validation example. Data verification, in the context of PS, consists of the process of detecting and removing spatial outliers to properly reconstruct the variables of interest. We propose, implement, and test a hybrid neighborhood-aware algorithm for outlier detection that considers the uneven spatial density of the users, the number of malicious users, the level of conspiracy, and the lack of accuracy and malfunctioning sensors. The algorithm utilizes the Delaunay triangulation and Gaussian Mixture Models to build neighborhoods based on the spatial and non-spatial attributes of each location. This neighborhood definition allows us to demonstrate that it is not necessary to apply accurate but computationally expensive estimators to the entire dataset to obtain good results, as equally accurate but computationally cheaper methods can also be applied to part of the data and obtain good results as well. Our experimental results show that our hybrid algorithm performs as good as the best estimator while reducing the execution time considerably.

Procedia Computer Science, 2011

Participatory sensing enables collection, processing, dissemination and analysis of environmental sensory data by ordinary citizens, through mobile devices. Researchers have recognized the potential of participatory sensing and attempted applying it to many areas. However, participants may submit low quality, misleading, inaccurate, or even malicious data. Therefore, finding a way to improve the data quality has become a significant issue. This study proposes using reputation management to classify the gathered data and provide useful information for campaign organizers and data analysts to facilitate their decisions.

A fundamental step to achieve the Ubiquitous Computing vision is to sense the environment. The research in Wireless Sensor Networks has provided several tools, techniques and algorithms to solve the problem of sensing in limited size areas, such as forests or volcanoes. However, sensing large scale areas, such as large metropolises, countries, or even the entire planet, brings many challenges. For instance, consider the high cost associated with building and managing such large scale systems. Thus, sensing those areas becomes more feasible when people collaborate among themselves using their portable devices (e.g., sensor-enabled cell phones). Systems that enable the user participation with sensed data are named participatory sensing systems. This work analyzes a new type of network derived from this type of system. In this network, nodes are autonomous mobile entities and the sensing depends on whether they want to participate in the sensing process. Based on two datasets of participatory sensing systems, we show that this type of network has many advantages and fascinating opportunities, such as planetary scale sensing at small cost, but also has many challenges, such as the highly skewed spatial-temporal sensing frequency.

Proceedings of the 2018 Designing Interactive Systems Conference

In this paper we examine ways to make data more meaningful and useful for citizens in participatory sensing. Participatory sensing has evolved as a digitally enabled grassroots approach to data collection for citizens with shared concerns. However, citizens often struggle to understand data in relation to their daily lives, and use them effectively. This paper presents a qualitative study on the development of a novel approach to Community Level Indicators (CLIs) during two participatory sensing projects focused on noise pollution. It investigates how CLIs can provide an infrastructure to address challenges in participatory sensing, specifically, making data meaningful and useful for non-experts. Furthermore, we consider how this approach moves towards an ambition of achieving change and impact through participatory sensing and discuss the challenges in this way of working and provide recommendations for future use of CLIs.

Proceedings of the 11th international conference on Information Processing in Sensor Networks - IPSN '12, 2012

This paper addresses the challenge of truth discovery from noisy social sensing data. The work is motivated by the emergence of social sensing as a data collection paradigm of growing interest, where humans perform sensory data collection tasks. A challenge in social sensing applications lies in the noisy nature of data. Unlike the case with wellcalibrated and well-tested infrastructure sensors, humans are less reliable, and the likelihood that participants' measurements are correct is often unknown a priori. Given a set of human participants of unknown reliability together with their sensory measurements, this paper poses the question of whether one can use this information alone to determine, in an analytically founded manner, the probability that a given measurement is true. The paper focuses on binary measurements. While some previous work approached the answer in a heuristic manner, we offer the first optimal solution to the above truth discovery problem. Optimality, in the sense of maximum likelihood estimation, is attained by solving an expectation maximization problem that returns the best guess regarding the correctness of each measurement. The approach is shown to outperform the state of the art fact-finding heuristics, as well as simple baselines such as majority voting.

IEEE Globecom, 2017

Data credibility is a crucial issue in mobile crowd sensing (MCS) and, more generally, people-centric Internet of Things (IoT). Prior work takes approaches such as incentive mechanism design and data mining to address this issue, while overlooking the power of crowds itself, which we exploit in this paper. In particular, we propose a cross validation approach which seeks a validating crowd to verify the data credibility of the original sensing crowd, and uses the verification result to reshape the original sensing dataset into a more credible posterior belief of the ground truth. Following this approach, we design a specific cross validation mechanism, which integrates four sampling techniques with a privacy-aware competency-adaptive push (PACAP) algorithm and is applicable to time-sensitive and quality-critical MCS applications. It does not require redesigning a new MCS system but rather functions as a lightweight "plug-in", making it easier for practical adoption. Our results demonstrate that the proposed mechanism substantially improves data credibility in terms of both reinforcing obscure truths and scavenging hidden truths.

Science China Information Sciences, 2021

Participatory sensing is a promising approach with which people contribute sensory information to form a body of knowledge. In practice, people may have different ways to engage in a participatory sensing campaign. For example, there are several possible routes from a participant's home to her office, where a route can be seen as a set of space-temporal coordinates, and measurements can be taken at these coordinates. To coordinate participation routes to collect more valuable information with a limited number of participants, a further concept, informative participatory sensing (IPS) has been developed recently. However, existing IPS systems lack incentive mechanisms to fight against the strategic behaviours of self-interested users. Hence, we propose a formal model of IPS where a service provider can coordinate individual schedules of self-interested participants. As the problem of informative path coordination is NP-hard, the well-known mechanism, Vickrey-Clarke-Groves (VCG) will be computationally inefficient to solve our problem. Given this, we design a sequentially sorting mechanism (SSM) for the model to allocate the schedules and determine the bonuses for these participants, and we then theoretically prove that SSM is computationally efficient, individually rational, profitable and truthful. Furthermore, we empirically evaluate our route allocation method in simulations and show that it significantly outperforms several benchmark approaches.

2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), 2015

2012 Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, 2012

In this paper we describe our experiences in applying the concept of participatory sensing to environmental monitoring. We have run pilot trials for air quality, water quality and plant disease monitoring. In these pilots, users have reported their personal observations or measurements of various environmental phenomena, using special locationbased applications in their mobile phones. We found a relevant correlation between algae observations by untrained citizens and by professionals, which supports the feasibility of participatory sensing as a complementary information source for algae monitoring. One key issue in collecting useful participatory datasets is managing to motivate people for acting as mobile environmental sensors. Other important issues discussed in the paper include privacy preservation and reliability of user observations.