AoI-Guaranteed Incentive Mechanism for Mobile Crowdsensing With Freshness Concerns

Xu, Yin; Xiao, Mingjun; Zhu, Yu; Wu, Jie; Zhang, Sheng; Zhou, Jing

doi:10.1109/tmc.2023.3285779

Cited by 6 publications

(3 citation statements)

References 59 publications

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“…We compare the proposed contract-based incentive mechanism under asymmetric information (CA) with other incentive mechanisms, including the Stackelberg game-based incentive mechanism under asymmetric information (SA) [ 33 ]. For the SA mechanism, the utilities of MTs and the SP are as follows:

and

where

.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…This results in enhanced stability and faster convergence in simulations. Xu et al focused on incentive design for mobile crowd sensing, integrating the AoI metric for data freshness [ 33 ]. Their two-tiered Stackelberg game-based approach offers AoI-centric incentives and a DRL-driven method for uncertain utility parameters.…”

Section: Related Workmentioning

confidence: 99%

“…Delay AoI UAV Interference OFDMA NOMA [19] System [20] System [25] System [21] System [17,18] System [22] System [26] System [27] System [31] Incentive [28,29] Incentive [23,24] Incentive [32,33] Incentive…”

Section: Consumptionmentioning

confidence: 99%

See 2 more Smart Citations

Enhancing Data Freshness in Air-Ground Collaborative Heterogeneous Networks through Contract Theory and Generative Diffusion-Based Mobile Edge Computing

Sun,

Chen

2023

Sensors

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Mobile edge computing is critical for improving the user experience of latency-sensitive and freshness-based applications. This paper provides insights into the potential of non-orthogonal multiple access (NOMA) convergence with heterogeneous air–ground collaborative networks to improve system throughput and spectral efficiency. Coordinated resource allocation between UAVs and MEC servers, especially in the NOMA framework, is addressed as a key challenge. Under the unrealistic assumption that edge nodes contribute resources indiscriminately, we introduce a two-stage incentive mechanism. The model is based on contract theory and aims at optimizing the utility of the service provider (SP) under the constraints of individual rationality (IR) and incentive compatibility (IC) of the mobile user. The block coordinate descent method is used to refine the contract design and complemented by a generative diffusion model to improve the efficiency of searching for contracts. During the deployment process, the study emphasizes the positioning of UAVs to maximize SP effectiveness. An improved differential evolutionary algorithm is introduced to optimize the positioning of UAVs. Extensive evaluation shows our approach has excellent effectiveness and robustness in deterministic and unpredictable scenarios.

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and

where

.…”

Section: Simulation Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%