A NullSpaceBased Verification Scheme for Coded Edge Computing against Pollution Attacks Edge computing is attracting more and more attention in recent years to fulfill the requirements of latencycritical and computationintensive applications. By using the coding redundancy, coded edge computing has emerged to optimize the total computation latency. Compared with the servers in cloud computing, edge devices located at the edge of network may not be reliable and trustworthy. In coded edge computing, even one incorrect intermediate result will lead to the incorrect final result. Therefore, considering the low computation capabilities of edge devices and low latency requirements of user, we study the result verification problem for coded edge computing. Specifically, we propose an efficient Orthogonal Mark (OM) verification scheme by the properties of linear space. We also conduct solid theoretical analysis to show the successful verification probabilities under two kinds of attack models, respectively. Finally, we conduct extensive simulations to show the effectiveness of the proposed OM verification scheme when comparing with basic coded edge computing scheme and Decoding Comparison (DC) scheme.

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.