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

Location reliability and gamification mechanisms for mobile crowd sensing

People-centric sensing with smart phones can be used for large scale sensing of the physical world by leveraging the sensors on the phones. This new type of sensing can be a scalable and cost-effective alternative to deploying static wireless sensor networks for dense sensing coverage across large areas. However, mobile people-centric sensing has two main issues: 1) Data reliability in sensed data and 2) Incentives for participants. To study these issues, this dissertation designs and develops McSense, a mobile crowd sensing system which provides monetary and social incentives to users. This dissertation proposes and evaluates two protocols for location reliability as a step toward achieving data reliability in sensed data, namely, ILR (Improving Location Reliability) and LINK (Location authentication through Immediate Neighbors Knowledge). ILR is a scheme which improves the location reliability of mobile crowd sensed data with minimal human efforts based on location validation using photo tasks and expanding the trust to nearby data points using periodic Bluetooth scanning. LINK is a location authentication protocol working independent of wireless carriers, in which nearby users help authenticate each other’s location claims using Bluetooth communication. The results of experiments done on Android phones show that the proposed protocols are capable of detecting a significant percentage of the malicious users claiming false location. Furthermore, simulations with the LINK protocol demonstrate that LINK can effectively thwart a number of colluding user attacks. This dissertation also proposes a mobile sensing game which helps collect crowd sensing data by incentivizing smart phone users to play sensing games on their phones. We design and implement a first person shooter sensing game, “Alien vs. Mobile User”, which employs techniques to attract users to unpopular regions. The user study results show that mobile gaming can be a successful alternative to micro-payments for fast and efficient area coverage in crowd sensing. It is observed that the proposed game design succeeds in achieving good player engagement

Seamless Interactions Between Humans and Mobility Systems

As mobility systems, including vehicles and roadside infrastructure, enter a period of rapid and profound change, it is important to enhance interactions between people and mobility systems. Seamless human—mobility system interactions can promote widespread deployment of engaging applications, which are crucial for driving safety and efficiency. The ever-increasing penetration rate of ubiquitous computing devices, such as smartphones and wearable devices, can facilitate realization of this goal. Although researchers and developers have attempted to adapt ubiquitous sensors for mobility applications (e.g., navigation apps), these solutions often suffer from limited usability and can be risk-prone. The root causes of these limitations include the low sensing modality and limited computational power available in ubiquitous computing devices. We address these challenges by developing and demonstrating that novel sensing techniques and machine learning can be applied to extract essential, safety-critical information from drivers natural driving behavior, even actions as subtle as steering maneuvers (e.g., left-/righthand turns and lane changes). We first show how ubiquitous sensors can be used to detect steering maneuvers regardless of disturbances to sensing devices. Next, by focusing on turning maneuvers, we characterize drivers driving patterns using a quantifiable metric. Then, we demonstrate how microscopic analyses of crowdsourced ubiquitous sensory data can be used to infer critical macroscopic contextual information, such as risks present at road intersections. Finally, we use ubiquitous sensors to profile a driver’s behavioral patterns on a large scale; such sensors are found to be essential to the analysis and improvement of drivers driving behavior.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163127/1/chendy_1.pd

Spatial crowdsourcing with mobile agents in vehicular networks

In the last years, the automotive industry has shown interest in the addition of computing and communication devices to cars, thanks to technological advances in these fields, in order to meet the increasing demand of “connected” applications and services. Although vehicular ad hoc networks (VANETs) have not been fully developed yet, they could be used in a near future as a means to provide a number of interesting applications and services that need the exchange of data among vehicles and other data sources. In this paper, we propose a spatial crowdsourcing schema for the opportunistic collection of information within an interest area in a city or region (e.g., measures about the environment, such as the concentration of certain gases in the atmosphere, or information such as the availability of parking spaces in an area), using vehicular ad hoc communications. We present a method that exploits mobile agent technology to accomplish the distributed collection and querying of data among vehicles in such a scenario. Our proposal is supported by an extensive set of realistic simulations that prove the feasibility of the approach

Privacy and trust in the internet of vehicles

The Internet of Vehicles aims to fundamentally improve transportation by connecting vehicles, drivers, passengers, and service providers together. Several new services such as parking space identification, platooning and intersection control--to name just a few--are expected to improve traffic congestion, reduce pollution, and improve the efficiency, safety and logistics of transportation. Proposed end-user services, however, make extensive use of private information with little consideration for the impact on users and third parties (those individuals whose information is indirectly involved). This article provides the first comprehensive overview of privacy and trust issues in the Internet of Vehicles at the service level. Various concerns over privacy are formalised into four basic categories: personal information privacy, multi-party privacy, trust, and consent to share information. To help analyse services and to facilitate future research, the main relevant end-user services are taxonomised according to voluntary and involuntary information they require and produce. Finally, this work identifies several open research problems and highlights general approaches to address them. These especially relate to measuring the trade-off between privacy and service functionality, automated consent negotiation, trust towards the IoV and its individual services, and identifying and resolving multi-party privacy conflicts

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

Edge Intelligence : Empowering Intelligence to the Edge of Network

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe

Edge Intelligence : Empowering Intelligence to the Edge of Network

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe