Privacy Management and Optimal Pricing in People-Centric Sensing

With the emerging sensing technologies such as mobile crowdsensing and Internet of Things (IoT), people-centric data can be efficiently collected and used for analytics and optimization purposes. This data is typically required to develop and render people-centric services. In this paper, we address the privacy implication, optimal pricing, and bundling of people-centric services. We first define the inverse correlation between the service quality and privacy level from data analytics perspectives. We then present the profit maximization models of selling standalone, complementary, and substitute services. Specifically, the closed-form solutions of the optimal privacy level and subscription fee are derived to maximize the gross profit of service providers. For interrelated people-centric services, we show that cooperation by service bundling of complementary services is profitable compared to the separate sales but detrimental for substitutes. We also show that the market value of a service bundle is correlated with the degree of contingency between the interrelated services. Finally, we incorporate the profit sharing models from game theory for dividing the bundling profit among the cooperative service providers.Comment: 16 page

Scenarios of Electromobility. Cross ferilisation and Dissemination of Best Practices and Researches within EU Policies Webinar proceedings

La pubblicazione riporta gli esiti del webinar incentrato sull'user center design dei veicoli elettrici, delle loro infrastrutture di ricarica e sulle sperimentazioni dei veicoli elettrici leggeri nei sistemi di trasporto urbano di Torino e Venaria Reale (IT), Villach (Austria) e Calvià (Spagna). La pubblicazione e il seminario sono parte del progetto STEVE, finanziato dal programma europeo Horizon2020, e incentrato sulla sperimentazione di modelli di mobilità elettrica leggera nelle aree urbane. Il progetto ha coinvolto città, piccole e medie imprese e università di sette paesi europei. Urban Lab ha collaborato con la Città di Torino a delineare le raccomandazioni rivolte ai decision makers in materia di pianificazione della mobilità urbana, emerse dai risultati dei tre anni di progetto