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

Recommender Systems for Online and Mobile Social Networks: A survey

Recommender Systems (RS) currently represent a fundamental tool in online services, especially with the advent of Online Social Networks (OSN). In this case, users generate huge amounts of contents and they can be quickly overloaded by useless information. At the same time, social media represent an important source of information to characterize contents and users' interests. RS can exploit this information to further personalize suggestions and improve the recommendation process. In this paper we present a survey of Recommender Systems designed and implemented for Online and Mobile Social Networks, highlighting how the use of social context information improves the recommendation task, and how standard algorithms must be enhanced and optimized to run in a fully distributed environment, as opportunistic networks. We describe advantages and drawbacks of these systems in terms of algorithms, target domains, evaluation metrics and performance evaluations. Eventually, we present some open research challenges in this area

Path Optimization Techniques for Trusted Routing in Mobile Ad-Hoc Networks: An Interplay Between Ordered Semirings

In this paper, we formulate the problem of trusted routing as a transaction of services over a complex networked environment. We present definitions from service-oriented environments which unambiguously capture the difference between trust and reputation relations. We show that the trustworthiness measures associated with these relations have a linear order embedded in them. Identifying this order structure permits us to treat the trusted routing problem as a bi-objective path optimization problem. Further, we present polynomial time solutions to obtain the optimal routing paths in various biobjective settings. In developing these algorithms, we identify an interesting semiring decomposition principle that yields a distributed solutio

Flow-based reputation: more than just ranking

The last years have seen a growing interest in collaborative systems like electronic marketplaces and P2P file sharing systems where people are intended to interact with other people. Those systems, however, are subject to security and operational risks because of their open and distributed nature. Reputation systems provide a mechanism to reduce such risks by building trust relationships among entities and identifying malicious entities. A popular reputation model is the so called flow-based model. Most existing reputation systems based on such a model provide only a ranking, without absolute reputation values; this makes it difficult to determine whether entities are actually trustworthy or untrustworthy. In addition, those systems ignore a significant part of the available information; as a consequence, reputation values may not be accurate. In this paper, we present a flow-based reputation metric that gives absolute values instead of merely a ranking. Our metric makes use of all the available information. We study, both analytically and numerically, the properties of the proposed metric and the effect of attacks on reputation values

STEP-UP: Enabling low-cost IMU sensors to predict the type of dementia during everyday stair climbing

Posterior Cortical Atrophy is a rare but significant form of dementia which affects peoples’ visual ability before their memory. This is often misdiagnosed as an eyesight rather than brain sight problem. This paper aims to address the frequent, initial misdiagnosis of this disease as a vision problem through the use of an intelligent, cost-effective, wearable system, alongside diagnosis of the more typical Alzheimer’s Disease. We propose low-level features constructed from the IMU data gathered from 35 participants, while they performed a stair climbing and descending task in a real-world simulated environment. We demonstrate that with these features the machine learning models predict dementia with 87.02% accuracy. Furthermore, we investigate how system parameters, such as number of sensors, affect the prediction accuracy. This lays the groundwork for a simple clinical test to enable detection of dementia which can be carried out in the wild

The Internet of Everything

In the era before IoT, the world wide web, internet, web 2.0 and social media made people’s lives comfortable by providing web services and enabling access personal data irrespective of their location. Further, to save time and improve efficiency, there is a need for machine to machine communication, automation, smart computing and ubiquitous access to personal devices. This need gave birth to the phenomenon of Internet of Things (IoT) and further to the concept of Internet of Everything (IoE)