Edge computing is a new computing paradigm which distributes tasks to edge networks for processing, it provides effective support for various mobile applications to meet their rapid response requirement. Unmanned Aerial Vehicle (UAV) has been widely used in emergency rescue, mapping, etc., with the advantages of flexible deployment and rapid movement. However, on one hand, mobile applications will be terminated when the battery energy is exhausted. On the other hand, mobile applications will be out of service when mobile devices are out of radio coverage of UAVs. How to achieve low-cost task unloading in the resource limited and location sensitive multi-UAVs edge computing environment is rather challenging. In this paper, we propose a distributed location-aware task offloading scheme, aiming at the above issues. Specifically, we create a nonlinear task allocation problem by combining the limited energy constraints of edge nodes with the random movement of users, where the cost function is divided into static and dynamic costs, respectively. Then, we formulate this problem to a convex optimization one with linear constrains, based on regularization technology. The mathematical proof shows that the scheme can support a parameterized competitive ratio without requiring any prior knowledge of the input task. The simulation results show that the proposed scheme can achieve lower cost edge computing services.INDEX TERMS convex programming,edge computing,task allocation,UAV
Edge computing is used to execute tasks submitted by various mobile applications. However, task offloading to the edge nodes iniatiated by the IoT nodes would cause insider-attack issues. Trust mechanism is an effective method to resist insider-attacks. A trust scheme usually needs a threshold to distinguish between normal nodes and malicious nodes. Unfortunately, how to reasonably determine the threshold of a trust scheme is still an open problem. In this paper, a novel trust scheme based on linear discriminant analysis (LDA) for edge computing is proposed to overcome this problem. First, the trust value of an edge node is calculated based on a trust factor matrix. Second, a difference function of classification model based on LDA is estanblished to to distinguish malicious nodes from normal nodes. Finally, the problem of maximizing the difference function is transformed into the problem of finding the optimal weight. To the best of our knowledge, this is the first work to integrate LDA to solve the problem of trust values’ classification. The simulation results show that our scheme can distinguish malicious nodes from normal nodes with an accuracy of more than 95%, which is much higher than other schemes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.