While computing task offloading in mobile edge computing (MEC) has been extensively studied, existing research has primarily focused on the mobility-awareness arising from opportunistic contact between edge devices. However, in a crowdsourced MEC system, it is essential to incentivize edge users to relocate for offloading tasks to crowdsourced edge devices. This movement-aware task offloading is particularly important for the crowdsourced system operation and has not yet been thoroughly explored. Moreover, the situation becomes more complex when users are socially connected and device-to-device (D2D)-enabled, yet few works have considered these characteristics in combination, particularly from an economic incentive perspective. Therefore, a new incentive framework is needed to analyze the economic issues comprehensively. In this work, we focus on designing a truthful incentive mechanism, where socially-connected D2D users can be incentivized to move around for offloading tasks. To truthfully elicit private information, we model the resource allocation between edge devices and users as a multiseller multibuyer double auction mechanism with realistic MEC constraints, such as delay and storage limitation. Theoretically, we show that the proposed mechanism is computationally efficient and achieves desirable economic properties, including truthfulness, individual rationality, and budget balance. Simulations demonstrate that the mechanism achieves good system efficiency, with a performance improvement of 20% compared to state-of-the-art baselines.

A Truthful Incentive Mechanism for Movement-Aware Task Offloading in Crowdsourced Mobile Edge Computing Systems

Stackelberg Game-Based Bandwidth Allocation and Resource Pricing for Multi-User in MEC System

Energy-Efficient UAV Scheduling and Probabilistic Task Offloading for Digital Twin-Empowered Consumer Electronics Industry

Unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) networks have greatly practical significance for improving the computation capacity of ground terminal devices (TDs) in Internet of Things (IoT), while the triggered energy consumption becomes a non-negligible critical issue. In this article, we propose a novel MEC framework assisted by multiple UAVs. Our goal is to balance the computation capacity and the energy consumption to maximize the network utility, by jointly optimizing power control, UAV-TD pairing, computation resource allocation, and UAVs' 3D trajectory. In particular, a flexible fairness-aware mechanism is set to maintain the fairness among these TDs. To solve the problem, we characterize the expected offloading rate on the basis of statistical channel information. Then, we propose a joint optimization algorithm based on the successive convex approximation technique and the block coordinate descent structure. The proposed trajectory optimization algorithm caters the UAV-MEC missions as the proposed algorithm is dedicated for solving the constrained UAV 3D path planning problems, and the results can be implemented in practice. Numerical results demonstrate the tradeoff between the computation capacity and the energy consumption, and validate the superiority of the proposed algorithm in the terms of improving the network utility.

3D Multi-UAV Computing Networks: Computation Capacity and Energy Consumption Tradeoff

With the popularity of vehicular communication systems and mobile edge vehicle networking, intelligent transportation applications arise in Internet of Vehicles (IoVs), which are latency-sensitive, computation-intensive, and requiring sufficient computing and communication resources. To satisfy the requirements of these applications, computation offloading emerges as a new paradigm to utilize idle resources on vehicles to cooperatively complete tasks. However, there exist several obstacles for realizing successful task offloading among vehicles. For one thing, extra cost such as communication overhead and energy consumption occur when a task is offloaded on a service vehicle, it is unlikely to expect the service vehicle will contribute its resources without any reward. For another, since there are many vehicles around, both user vehicles and service vehicles are trying to strike a balance between cost and profit, through matching the perfect service/user vehicles and settled with optimal offloading plan that is beneficial to all parties. To solve these issues, this work focuses on the design of effective incentive mechanisms to stimulate vehicles with idle resources to actively participate in the offloading process. A fuzzy logic-based dynamic pricing strategy is proposed to accurately evaluate the cost of a vehicle for processing the task, which provides insightful guidance for finding the optimal offloading decision. Meanwhile, the compete and cooperation relationships among vehicles are thoroughly investigated and modeled as a two-stage Stackelberg game. Particularly, this work emphasizes the social attributes of vehicles and their effect on the offloading decision making process, multiple key properties such as the willingness of UV to undertake the task locally, the reputation of UV and the satisfaction of SV for the allocated task proportion, are carefully integrated in the design of the optimization problem. A distributed algorithm with applicable complexity is proposed to solve the problem and to find the optimal task offloading strategy. Extensive simulations are conducted on real-world scenarios and results show that the proposed mechanism achieves significant performance advantages in terms of vehicles' utilities, cost, completion delay under varied network and channel environment, which justifies the effectiveness and efficiency of this work. 

Cost-aware Task Offloading in Vehicular Edge Computing: A Stackelberg Game Approach

We study the intelligent offloading problem for a multiple unmanned aerial vehicle (multi-UAV)-assisted mobile edge computing (MEC) system in a MEC scenario where a natural disaster has damaged the edge server. The study has two steps. First, the task offloading destination is determined by minimizing the total energy consumption of the multiple UAVs in the system. We propose the server selection game-theoretic (SSGT) algorithm and demonstrate its convergence through simulation experiments. Second, we propose an offloading incentive mechanism to price computing resources for a single UAV-MEC server. Considering the UAV’s power consumption and mobile users’ willingness, we model the interaction between the UAV-MEC server and mobile users as a Stackelberg game. We prove the existence of a Nash equilibrium by theoretical analysis and experimental verification and design the multi-round iterative game (MRIG) algorithm based on arithmetic descent to achieve the optimal solution, i.e., the utility tradeoff between the UAV-MEC server and mobile users. Finally, the simulation results show that our proposed scheme can increase the value of overall user satisfaction (SoU) more than other schemes, which proves that the incentive mechanism of resource pricing can supply computing power support for ground mobile users in a UAV-assisted MEC system more effectively. 

Stackelberg Game-Based Intelligent Offloading Incentive Mechanism for a Multi-UAV-Assisted Mobile Edge Computing System

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Xiaoli Yang

Author Tools

Chat about Author

Papers

Stackelberg Game-Based Intelligent Offloading Incentive Mechanism for a Multi-UAV-Assisted Mobile Edge Computing System