Probabilistic Registration for Large-Scale Mobile Participatory Sensing

International audienceOne of the main benefits of mobile participatory sensing becoming a reality is the increased knowledge it will provide about the real world, as it is expected to rely on a large number of smart and mobile devices. Nowadays, those devices have the ability to host different types of sensors that will be incorporated in every aspect of our daily lives. However, given the constantly increasing number of capable mobile devices, any participatory sensing approach should be, first and foremost, scalable. To address this challenge, we present an approach to decrease the participation of devices (in sensing tasks) in a manner that does not compromise the accuracy of the real-world information while increasing the efficiency of the overall system. To reduce the number of the devices involved, we present a probabilistic registration approach enabling only a subset of devices to register their services while still providing adequate coverage of the deployment area. Our techniques, based on a re- alistic human mobility model, allow devices to decide whether or not to register their sensing services depending on the probability of other, equivalent devices being present at the locations of their expected path. We present the design and implementation of a registration middleware based on our techniques, using which mobile devices can base their registration decision. Through experiments performed on real and simulated datasets, we show that our approach scales, while not sacrificing significant amounts of sensing coverage

Service-Oriented Middleware for Large-Scale Mobile Participatory Sensing

International audienceIn this paper, we introduce MobIoT, a service-oriented middleware that enables large-scale mobile participatory sensing. Scalability is achieved by limiting the participation of redundant sensing devices. Precisely, MobIoT allows a new device to register its services only if it increases the sensing coverage of a physical attribute, along its expected path, for the set of registered devices. We present the design and implementation of MobIoT, which mobile devices use to determine their registration decision and become accessible for their services. Through experiments performed on real datasets, we show that our solution scales, while meeting sensing coverage requirements

CSWA: Aggregation-Free Spatial-Temporal Community Sensing

In this paper, we present a novel community sensing paradigm -- {C}ommunity {S}ensing {W}ithout {A}ggregation}. CSWA is designed to obtain the environment information (e.g., air pollution or temperature) in each subarea of the target area, without aggregating sensor and location data collected by community members. CSWA operates on top of a secured peer-to-peer network over the community members and proposes a novel \emph{Decentralized Spatial-Temporal Compressive Sensing} framework based on \emph{Parallelized Stochastic Gradient Descent}. Through learning the \emph{low-rank structure} via distributed optimization, CSWA approximates the value of the sensor data in each subarea (both covered and uncovered) for each sensing cycle using the sensor data locally stored in each member's mobile device. Simulation experiments based on real-world datasets demonstrate that CSWA exhibits low approximation error (i.e., less than $0.2 ^\circ$C in city-wide temperature sensing task and $10$ units of PM2.5 index in urban air pollution sensing) and performs comparably to (sometimes better than) state-of-the-art algorithms based on the data aggregation and centralized computation.Comment: This paper has been accepted by AAAI 2018. First two authors are equally contribute

Sharing in the Rain: Secure and Efficient Data Sharing for the Cloud

Cloud storage has rapidly become a cornerstone of many businesses and has moved from an early adopters stage to an early majority, where we typically see explosive deployments. As companies rush to join the cloud revolution, it has become vital to create the necessary tools that will effectively protect users' data from unauthorized access. Nevertheless, sharing data between multiple users' under the same domain in a secure and efficient way is not trivial. In this paper, we propose Sharing in the Rain – a protocol that allows cloud users' to securely share their data based on predefined policies. The proposed protocol is based on Attribute-Based Encryption (ABE) and allows users' to encrypt data based on certain policies and attributes. Moreover, we use a Key-Policy Attribute-Based technique through which access revocation is optimized. More precisely, we show how to securely and efficiently remove access to a file, for a certain user that is misbehaving or is no longer part of a user group, without having to decrypt and re-encrypt the original data with a new key or a new policy

Let Opportunistic Crowdsensors Work Together for Resource-efficient, Quality-aware Observations

International audienceOpportunistic 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 resource-efficient 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 work load in a cost- and quality-effective way. We evaluate the proposed solution using an implementation-driven evaluation that leverages a dataset embedding nearly 1 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

Location Privacy in Spatial Crowdsourcing

Spatial crowdsourcing (SC) is a new platform that engages individuals in collecting and analyzing environmental, social and other spatiotemporal information. With SC, requesters outsource their spatiotemporal tasks to a set of workers, who will perform the tasks by physically traveling to the tasks' locations. This chapter identifies privacy threats toward both workers and requesters during the two main phases of spatial crowdsourcing, tasking and reporting. Tasking is the process of identifying which tasks should be assigned to which workers. This process is handled by a spatial crowdsourcing server (SC-server). The latter phase is reporting, in which workers travel to the tasks' locations, complete the tasks and upload their reports to the SC-server. The challenge is to enable effective and efficient tasking as well as reporting in SC without disclosing the actual locations of workers (at least until they agree to perform a task) and the tasks themselves (at least to workers who are not assigned to those tasks). This chapter aims to provide an overview of the state-of-the-art in protecting users' location privacy in spatial crowdsourcing. We provide a comparative study of a diverse set of solutions in terms of task publishing modes (push vs. pull), problem focuses (tasking and reporting), threats (server, requester and worker), and underlying technical approaches (from pseudonymity, cloaking, and perturbation to exchange-based and encryption-based techniques). The strengths and drawbacks of the techniques are highlighted, leading to a discussion of open problems and future work

SPACE-TA: cost-effective task allocation exploiting intradata and interdata correlations in sparse crowdsensing

Data quality and budget are two primary concerns in urban-scale mobile crowdsensing. Traditional research on mobile crowdsensing mainly takes sensing coverage ratio as the data quality metric rather than the overall sensed data error in the target-sensing area. In this article, we propose to leverage spatiotemporal correlations among the sensed data in the target-sensing area to significantly reduce the number of sensing task assignments. In particular, we exploit both intradata correlations within the same type of sensed data and interdata correlations among different types of sensed data in the sensing task. We propose a novel crowdsensing task allocation framework called SPACE-TA (SPArse Cost-Effective Task Allocation), combining compressive sensing, statistical analysis, active learning, and transfer learning, to dynamically select a small set of subareas for sensing in each timeslot (cycle), while inferring the data of unsensed subareas under a probabilistic data quality guarantee. Evaluations on real-life temperature, humidity, air quality, and traffic monitoring datasets verify the effectiveness of SPACE-TA. In the temperature- monitoring task leveraging intradata correlations, SPACE-TA requires data from only 15.5% of the subareas while keeping the inference error below 0.25°C in 95% of the cycles, reducing the number of sensed subareas by 18.0% to 26.5% compared to baselines. When multiple tasks run simultaneously, for example, for temperature and humidity monitoring, SPACE-TA can further reduce ∼10% of the sensed subareas by exploiting interdata correlations