PROTECT: Proximity-based Trust-advisor using Encounters for Mobile Societies

Many interactions between network users rely on trust, which is becoming particularly important given the security breaches in the Internet today. These problems are further exacerbated by the dynamics in wireless mobile networks. In this paper we address the issue of trust advisory and establishment in mobile networks, with application to ad hoc networks, including DTNs. We utilize encounters in mobile societies in novel ways, noticing that mobility provides opportunities to build proximity, location and similarity based trust. Four new trust advisor filters are introduced - including encounter frequency, duration, behavior vectors and behavior matrices - and evaluated over an extensive set of real-world traces collected from a major university. Two sets of statistical analyses are performed; the first examines the underlying encounter relationships in mobile societies, and the second evaluates DTN routing in mobile peer-to-peer networks using trust and selfishness models. We find that for the analyzed trace, trust filters are stable in terms of growth with time (3 filters have close to 90% overlap of users over a period of 9 weeks) and the results produced by different filters are noticeably different. In our analysis for trust and selfishness model, our trust filters largely undo the effect of selfishness on the unreachability in a network. Thus improving the connectivity in a network with selfish nodes. We hope that our initial promising results open the door for further research on proximity-based trust

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

Exploration of location-based services adoption

As mobile technologies become more ubiquitous in the general population, it is reasonable to assume that individuals will consume services and software to enhance their aspirations and entertainment desires. This paper discusses a controlled experiment to explore aspects of user perceptions of their use of location-based services. This study examines a location-based service prototype experiment and analysis based on the UTAUT model. The results show significant indicators that suggest behavior patterns of early adopters of location-based services are being observed. We discuss these influences and attempt to explain their significance. Moreover, more curiously we discuss why some of our model was unsupported and postulate why

AMISEC: Leveraging Redundancy and Adaptability to Secure AmI Applications

Security in Ambient Intelligence (AmI) poses too many challenges due to the inherently insecure nature of wireless sensor nodes. However, there are two characteristics of these environments that can be used effectively to prevent, detect, and confine attacks: redundancy and continuous adaptation. In this article we propose a global strategy and a system architecture to cope with security issues in AmI applications at different levels. Unlike in previous approaches, we assume an individual wireless node is vulnerable. We present an agent-based architecture with supporting services that is proven to be adequate to detect and confine common attacks. Decisions at different levels are supported by a trust-based framework with good and bad reputation feedback while maintaining resistance to bad-mouthing attacks. We also propose a set of services that can be used to handle identification, authentication, and authorization in intelligent ambients. The resulting approach takes into account practical issues, such as resource limitation, bandwidth optimization, and scalability

Privacy in crowdsourcing:a systematic review

The advent of crowdsourcing has brought with it multiple privacy challenges. For example, essential monitoring activities, while necessary and unavoidable, also potentially compromise contributor privacy. We conducted an extensive literature review of the research related to the privacy aspects of crowdsourcing. Our investigation revealed interesting gender differences and also differences in terms of individual perceptions. We conclude by suggesting a number of future research directions.</p