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

System and Method for Truth Discovery in social media Big Data

Within the span of enormous information and the coming of numerous advancements in the communication technologies, at every tick of the clock, enormous sums of information is produced from different sources. One such source of data generation is social media. However, such data carries much of the noisy, uncertain, and untrustworthy data. In this way, finding independable information from loud information is one of the characteristic challenges of huge information focusing on the esteem characteristic of enormous information. Therefore, in this article, an attempt is made to target a few challenges arriving from “misinformation spread”, “data sparsity” or the “long-tail wonder” in the domain of social media data analytics. The study uses an instance from the Online Social Network (OSN) datasets to develop scalable to wide-range social sensing by consolidating Scalable Robust Trust Discovery (SRTD) plots to address the mentioned challenges utilizing the distributed parallel computing framework. The dataset picked for investigation includes 128,483 tweets which incorporates 20% deception, 80% retweets bringing about 0.05 milliseconds utilizing Spark parallel processing