Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN

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

Real-Time Urban Weather Observations for Urban Air Mobility

Cities of the future will have to overcome congestion, air pollution and increasing infrastructure cost while moving more people and goods smoothly, efficiently and in an eco-friendly manner. Urban air mobility (UAM) is expected to be an integral component of achieving this new type of city. This is a new environment for sustained aviation operations. The heterogeneity of the urban fabric and the roughness elements within it create a unique environment where flight conditions can change frequently across very short distances. UAM vehicles with their lower mass, more limited thrust and slower speeds are especially sensitive to these conditions. Since traditional aviation weather products for observations and forecasts at an airport on the outskirts of a metropolitan area do not translate well to the urban environment, weather data for low-altitude urban airspace is needed and will be particularly critical for unlocking the full potential of UAM. To help address this need, crowdsourced weather data from sources prevalent in urban areas offer the opportunity to create dense meteorological observation networks in support of UAM. This paper considers a variety of potential observational sources and proposes a cyber-physical system architecture, including an incentive-based crowdsensing application, which empowers UAM weather forecasting and operations

Delivering IoT Services in Smart Cities and Environmental Monitoring through Collective Awareness, Mobile Crowdsensing and Open Data

The Internet of Things (IoT) is the paradigm that allows us to interact with the real world by means of networking-enabled devices and convert physical phenomena into valuable digital knowledge. Such a rapidly evolving field leveraged the explosion of a number of technologies, standards and platforms. Consequently, different IoT ecosystems behave as closed islands and do not interoperate with each other, thus the potential of the number of connected objects in the world is far from being totally unleashed. Typically, research efforts in tackling such challenge tend to propose a new IoT platforms or standards, however, such solutions find obstacles in keeping up the pace at which the field is evolving. Our work is different, in that it originates from the following observation: in use cases that depend on common phenomena such as Smart Cities or environmental monitoring a lot of useful data for applications is already in place somewhere or devices capable of collecting such data are already deployed. For such scenarios, we propose and study the use of Collective Awareness Paradigms (CAP), which offload data collection to a crowd of participants. We bring three main contributions: we study the feasibility of using Open Data coming from heterogeneous sources, focusing particularly on crowdsourced and user-contributed data that has the drawback of being incomplete and we then propose a State-of-the-Art algorith that automatically classifies raw crowdsourced sensor data; we design a data collection framework that uses Mobile Crowdsensing (MCS) and puts the participants and the stakeholders in a coordinated interaction together with a distributed data collection algorithm that prevents the users from collecting too much or too less data; (3) we design a Service Oriented Architecture that constitutes a unique interface to the raw data collected through CAPs through their aggregation into ad-hoc services, moreover, we provide a prototype implementation

Proximity as a Service via Cellular Network-Assisted Mobile Device-to-Device

PhD ThesisThe research progress of communication has brought a lot of novel technologies to meet the multi-dimensional demands such as pervasive connection, low delay and high bandwidth. Device-to-Device (D2D) communication is a way to no longer treat the User Equipment (UEs) as a terminal, but rather as a part of the network for service provisioning. This thesis decouples UEs into service providers (helpers) and service requesters. By collaboration among proximal devices, with the coordination of cellular networks, some local tasks can be achieved, such as coverage extension, computation o oading, mobile crowdsourcing and mobile crowdsensing. This thesis proposes a generic framework Proximity as a Service (PaaS) for increasing the coverage with demands of service continuity. As one of the use cases, the optimal helper selection algorithm of PaaS for increasing the service coverage with demands of service continuity is called ContAct based Proximity (CAP). Mainly, fruitful contact information (e.g., contact duration, frequency, and interval) is captured, and is used to handle ubiquitous proximal services through the optimal selection of helpers. The nature of PaaS is evaluated under the Helsinki city scenario, with movement model of Points Of Interest (POI) and with critical factors in uencing the service demands (e.g., success ratio, disruption duration and frequency). Simulation results show the advantage of CAP, in both success ratio and continuity of the service (outputs). Based on this perspective, metrics such as service success ratio and continuity as a service evaluation of the PaaS are evaluated using the statistical theory of the Design Of Experiments (DOE). DOE is used as there are many dimensions to the state space (access tolerance, selected helper number, helper access limit, and transmit range) that can in uence the results. A key contribution of this work is that it brings rigorous statistical experiment design methods into the research into mobile computing. Results further reveal the influence of four factors (inputs), e.g., service tolerance, number of helpers allocated, the number of concurrent devices supported by each helper and transmit range. Based on this perspective, metrics such as service success ratio and continuity are evaluated using DOE. The results show that transmit range is the most dominant factor. The number of selected helpers is the second most dominant factor. Since di erent factors have di erent regression levels, a uni ed 4 level full factorial experiment and a cubic multiple regression analysis have been carried out. All the interactions and the corresponding coe cients have been found. This work is the rst one to evaluate LTE-Direct and WiFi-Direct in an opportunistic proximity service. The contribution of the results for industry is to guide how many users need to cooperate to enable mobile computing and for academia. This reveals the facts that: 1, in some cases, the improvement of spectrum e ciency brought by D2D is not important; 2, nodal density and the resources used in D2D air-interfaces are important in the eld of mobile computing. This work built a methodology to study the D2D networks with a di erent perspective (PaaS)