Mapping Accessible Paths in the City Using Collective Intelligence

New information and communication technologies (ICTs) have an increasingly stronger role in people\u27s lives, especially after the commoditization of smartphones. They affect many aspects of everyday life, including urban mobility. Some applications, including Waze, benefit from the collective intelligence (CI) of the crowds to gather the information they need to provide users with good advice on the routes to follow. But they are mainly focused on roads and streets, giving little information on the quality of sidewalks, which are essential to pedestrians, people on wheelchairs and blind people. With the intention to improve the mobility of citizens with special needs, we developed the prototype of an application that allows users themselves to update accessibility maps, tagging obstacles and also indicating the existence of resources that contribute to improve the mobility of people with special needs in urban spaces. Tests in a controlled environment helped to debug the application’s functionalities, before members of the intended target group of users were finally exposed to it. Results are promising, as users were able to include relevant data by themselves and seem motivated to keep doing so, due a sense of utility, social facilitation or simply due to altruism, as anticipated by the CI literature. One unexpected outcome was that impaired users are more excited about the potential the application has to give visibility to the challenges they face than with the actual improvement it can bring to their mobility

An Efficient Collaboration and Incentive Mechanism for Internet-of-Vehicles (IoVs) with Secured Information Exchange Based on Blockchains

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordWith the rapid development of Internet-of-Things (IoT), mobile crowdsensing, i.e., outsourcing sensing tasks to mobile devices or vehicles, has been proposed to address the problem of data collection in the scenarios such as smart city. Despite its benefits for a wide range of applications, mobile crowdsensing lacks an efficient incentive mechanism, restricting the development of IoT applications, especially for Internet-ofVehicles (IoV) – a typical example of IoT applications; this is because vehicles are usually reluctant to participate these sensing tasks. Moreover, in practice some sensing tasks may arrive suddenly (called an emergent task) in the IoV environment, but the resources of a single vehicle may be insufficient to handle, and thus multi-vehicles collaboration is required. In this case, the incentive mechanisms for the participation of multiple vehicles and the task scheduling for their collaborations are collectively needed. To address this important problem, we firstly propose a new model for the scenario of two vehicles collaboration, considering the situation of emergent appearance of a task. In this model, for a general sensing task, we propose a bidding mechanism to better encourage vehicles to contribute their resources, and the tasks for those vehicles are scheduled accordingly. Secondly, for an emergent task, a novel time-window based method is devised to manage the tasks among vehicles and to incent the vehicles to participate. Finally, we develop a blockchain framework to achieve the secured information exchange through smart contract for the proposed models in IoV.National Key Research and Development Program of ChinaNational Natural Science Foundation of China (NSFC)Purple Mountain Laboratory: Networking, Communications and SecurityAcademician Expert Workstation of Bitvalue Technology (Hunan) Company Limite

A Stackelberg Game Approach Towards Socially-Aware Incentive Mechanisms for Mobile Crowdsensing (Online report)

Mobile crowdsensing has shown a great potential to address large-scale data sensing problems by allocating sensing tasks to pervasive mobile users. The mobile users will participate in a crowdsensing platform if they can receive satisfactory reward. In this paper, to effectively and efficiently recruit sufficient number of mobile users, i.e., participants, we investigate an optimal incentive mechanism of a crowdsensing service provider. We apply a two-stage Stackelberg game to analyze the participation level of the mobile users and the optimal incentive mechanism of the crowdsensing service provider using backward induction. In order to motivate the participants, the incentive is designed by taking into account the social network effects from the underlying mobile social domain. For example, in a crowdsensing-based road traffic information sharing application, a user can get a better and accurate traffic report if more users join and share their road information. We derive the analytical expressions for the discriminatory incentive as well as the uniform incentive mechanisms. To fit into practical scenarios, we further formulate a Bayesian Stackelberg game with incomplete information to analyze the interaction between the crowdsensing service provider and mobile users, where the social structure information (the social network effects) is uncertain. The existence and uniqueness of the Bayesian Stackelberg equilibrium are validated by identifying the best response strategies of the mobile users. Numerical results corroborate the fact that the network effects tremendously stimulate higher mobile participation level and greater revenue of the crowdsensing service provider. In addition, the social structure information helps the crowdsensing service provider to achieve greater revenue gain.Comment: Submitted for possible journal publication. arXiv admin note: text overlap with arXiv:1711.0105