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Some of the most widely deployed IoT devices in urban areas are smartphones in the possession of urban individuals. Their proliferation has led to the emergence of crowdsensing/crowdsourcing services, where humans collect data about their environment (using phones), and servers aggregate the data for various application purposes of interest. With the emergence of social media, a common alternative...
This paper develops a simplified dependency model for sources on social networks that is shown to improve the quality of fact-finding -- assessing veracity of observations shared on social media. Recent literature developed a mathematical approach for exploiting social networks, such as Twitter, as noisy sensor networks that report observations on the state of the physical world. It was shown that...
The paper develops a recursive state estimator for social network data streams that allows exploitation of social networks, such as Twitter, as sensor networks to reliably observe physical events. Recent literature suggested using social networks as sensor networks leveraging the fact that much of the information upload on the former constitutes acts of sensing. A significant challenge identified...
Online social communities often exhibit complex relationship structures, ranging from close friends to political rivals. As a result, persons are influenced by their friends and foes differently. Future network applications can benefit from integrating these structural differences in propagation schemes through socially aware sensors. In this paper, we introduce a propagation model for such social...
This paper presents the system level performance evaluation for energy-detection based cooperative spectrum sensing in cognitive radio networks. Three performance criteria are quantitively analyzed for cooperative spectrum sensing. First, the average error probability is determined given fixed amplifier gains for a fixed number of secondary users by considering all possible channel realizations. Second,...
This paper addresses optimal spectrum sensing in cognitive radio networks considering its system level cost that accounts for the local processing cost of sensing (sample collection and energy calculation at each secondary user) as well as the transmission cost (forwarding energy statistic from secondary users to fusion center). The optimization problem solves for the appropriate number of samples...
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