Deep LearningBased Detection of Fake Task Injection in Mobile Crowdsensing Mobile crowdsensing (MCS) is a ubiquitous sensing paradigm where builtin sensors of smart mobile devices are empowered to acquire sensory data in lieu of dedicating large scale sensing infrastructures. One of the most crucial problems in mobile crowdsensing is the injection of fake sensing tasks to clog the energy, computing, storage and sensing resources of participating devices. In this paper, we present solutions that leverage deep networks to analyze the tasks submitted to MCS platforms. To this end, we model offtheshelf deep learning models, namely Deep Autoencoder (DeepAE), Restricted Boltzmann Machine (RBM) and Deep Belief Network (DBN) in order to detect and filter out illegitimate tasks submitted to MCS campaigns. For the same purpose, we also utilize a Deep Multilayer Perceptron (DeepMLP) network instead of the well known Multilayer Perceptron. Through numerical results on MCS data, we show that DeepMLP outperforms its counterparts with 0.963 precision and 0.964 recall in the detection of fake sensing tasks.

Incentivizing for Truth Discovery in Edgeassisted Largescale Mobile Crowdsensing The recent development of humancarried mobile devices has promoted the great development of mobile crowdsensing systems. Most existing mobile crowdsensing systems depend on the crowdsensing service of the deep cloud. With the increasing scale and complexity, there is a tendency to enhance mobile crowdsensing with the edge computing paradigm to reduce latency and computational complexity, and improve the expandability and security. In this paper, we propose an integrated solution to stimulate the strategic users to contribute more for truth discovery in the edgeassisted mobile crowdsensing. We design an incentive mechanism consisting of truth discovery stage and budget feasible reverse auction stage. In truth discovery stage, we estimate the truth for each task in both deep cloud and edge cloud. In budget feasible reverse auction stage, we design a greedy algorithm to select the winners to maximize the quality function under the budget constraint. Through extensive simulations, we demonstrate that the proposed mechanism is computationally efficient, individually rational, truthful, budget feasible and constant approximate. Moreover, the proposed mechanism shows great superiority in terms of estimation precision and expandability.