With the emerging sensing technologies, such as mobile crowdsensing and Internet of Things, people-centric data can be efficiently collected and used for analytics and optimization purposes. These data are 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 with 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.
People are the primary focus in people-centric sensing, which has applications in transportation systems @cite_23 , assistive healthcare @cite_18 , and urban monitoring @cite_26 , just to name a few. In this section, we first review related work on pricing models in sensing and communication systems. Then, we discuss the crucial issue of privacy awareness in people-centric sensing. Finally, we review related work on pricing and incentive mechanisms for mobile crowdsensing.
@cite_18 @cite_26 @cite_23
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As the domains of pervasive computing and sensor networking are expanding, there is an ongoing trend towards assistive living and healthcare support environments that can effectively assimilate these technologies according to human needs. Most of the existing research in assistive healthcare follows a more passive approach and has focused on collecting and processing data using a static-topology and an application-aware infrastructure. However, with the technological advances in sensing, computation, storage, and communications, a new era is about to emerge changing the traditional view of sensor-based assistive environments where people are passive data consumers, with one where people carry mobile sensing elements involving large volumes of data related to everyday human activities. This evolution will be driven by people-centric sensing and will turn mobile phones into global mobile sensing devices enabling thousands new personal, social, and public sensing applications. In this paper, we discuss our vision for people-centric sensing in assistive healthcare environments and study the security challenges it brings. This highly dynamic and mobile setting presents new challenges for information security, data privacy and ethics, caused by the ubiquitous nature of data traces originating from sensors carried by people. We aim to instigate discussion on these critical issues because people-centric sensing will never succeed without adequate provisions on security and privacy. To that end, we discuss the latest advances in security and privacy protection strategies that hold promise in this new exciting paradigm. We hope this work will better highlight the need for privacy in people-centric sensing applications and spawn further research in this area. Copyright © 2011 John Wiley & Sons, Ltd. People-centric urban sensing systems (PC-USSs) refer to using human-carried mobile devices such as smartphones and tablets for urban-scale distributed data collection, analysis, and sharing to facilitate interaction between humans and their surrounding environments. A main obstacle to the widespread deployment and adoption of PC-USSs are the privacy concerns of participating individuals as well as the concerns about data integrity. To tackle this open challenge, this paper presents the design and evaluation of VPA, a novel peer-to-peer based solution to verifiable privacy-preserving data aggregation in PC-USSs. VPA achieves strong user privacy by letting each user exchange random shares of its datum with other peers, while at the same time ensures data integrity through a combination of Trusted Platform Module and homomorphic message authentication code. VPA can support a wide range of statistical additive and non-additive aggregation functions such as Sum, Average, Variance, Count, Max Min, Median, Histogram, and Percentile with accurate aggregation results. The efficacy and efficiency of VPA are confirmed by thorough analytical and simulation results. For the last two decades, intelligent transportation systems (ITS) have emerged as an efficient way of improving the performance of transportation systems, enhancing travel security, and providing more choices to travelers. A significant change in ITS in recent years is that much more data are collected from a variety of sources and can be processed into various forms for different stakeholders. The availability of a large amount of data can potentially lead to a revolution in ITS development, changing an ITS from a conventional technology-driven system into a more powerful multifunctional data-driven intelligent transportation system (D2ITS) : a system that is vision, multisource, and learning algorithm driven to optimize its performance. Furthermore, D2ITS is trending to become a privacy-aware people-centric more intelligent system. In this paper, we provide a survey on the development of D2ITS, discussing the functionality of its key components and some deployment issues associated with D2ITS Future research directions for the development of D2ITS is also presented.