Sep 2026· IEEE Transactions on Mobile Computing· Vol 25, pp. 13428-13445· 0 citations· 35 references
Abstract
As mobile applications become increasingly computation-intensive, mobile devices (MDs) face growing limitations due to their constrained computational capabilities and battery life. Collaborative Edge Computing (CEC) has emerged as a promising solution to address these challenges by enabling multiple edge service providers (ESPs) to offer computation offloading services to MDs. As such, a CEC resource trading market is essential for efficient interactions between MDs and ESPs. However, jointly determining the offloading ratios, allocating combinatorial computation and communication resources, and designing appropriate pricing strategies in a dynamic market remains a significant challenge. To this end, we propose a truthful online double auction-based resource allocation mechanism for partial computation offloading (TRAPO) that explicitly accounts for the stochastic nature of both MDs and ESPs. TRAPO first leverages spatial diversity to construct a set of bids for each MD by mapping their task requirements into resource demands through considering MDs’ preferences and partial offloading. Next, we match resource-demanding MDs with resource-supplying ESPs based on adaptive valid price thresholds to maximize social welfare, and calculate the payments of MDs and the rewards of ESPs. Theoretical analyses demonstrate that TRAPO satisfies truthfulness, budget balance, individual rationality, and computational tractability. Simulation experiments further verify the effectiveness and efficiency of TRAPO.
A constraint-aware multi-agent edge collaborative offloading algorithm (CARE-CTDE) that achieves better scheduling performance, resource utilization, and constraint satisfaction than baseline methods in dynamic heterogeneous MEC scenarios, demonstrating its effectiveness and robustness for constrained edge computing systems.
Yuxuan Yang, Hexing Wang, Yang Zhou· Mathematics· 0 citations
An adaptive Beta-policy and delayed-update multi-agent soft actor-critic method, abbreviated as ABDMASAC, which uses a Beta policy to model bounded actions and achieves a better overall trade-off than the selected MASAC-backbone and on-policy MARL baselines under the considered simulation settings.
Zheng Yao, Jie Liu, Changjun Deng et al.· Computers, Materials & C...· 0 citations
Mobile crowdsensing (MCS) leverages distributed mobile users to complete large-scale sensing tasks. With the rapid advancement of sensing capabilities in urban environments, data acquisition has become increasingly efficient and scalable. However, promoting user participation and improving task quality remain significant challenges. While auction-based models in MCS aim to optimize task allocation efficiency and incentivize high-quality data contributions, they often fail to account for the effective evaluation of worker distribution and task quality. To address these limitations, we propose a Reputation-Based Incentive Mechanism Double-Auction Model (RBIM), which considers both uneven worker arrivals and the diminishing returns of task quality. RBIM categorizes tasks based on difficulty levels, allowing workers to choose tasks aligned with their preferences. The task quality is evaluated by integrating platform costs and requester satisfaction, which subsequently influence both worker compensation and reputation scores. Moreover, the payment scheme incorporates reputation-based adjustments to further incentivize reliable participation and enhance task completion rates. Experimental results demonstrate that RBIM consistently outperforms several benchmark algorithms, significantly improving task completion rates, task quality, and overall system utility. Additionally, the proposed mechanism satisfies essential economic properties including individual rationality (IR), truthfulness (TF), and budget feasibility, while maximizing social welfare within given budget constraints (BC).
Simulation results confirm that the proposed JORC framework substantially reduces latency, energy consumption, and overall system cost, while increasing the successful task completion ratio compared to existing baseline approaches.
Tanmay Baidya, S. Moh· Italian National Conference...· 0 citations
This formulation provides a rigorous and tractable framework for distributed spectrum sharing in 6G O-RAN systems, with the potential to support intelligent and adaptive control in future wireless networks.
E. Spyrou, Chrysostomos D. Stylios, V. Kappatos et al.· Future Internet· 0 citations
What if pathology foundation models could do more with less? GigaPath-Flash and GigaTIME-Flash cut computational demands while maintaining strong performance, opening the door to larger studies and broader exploration. The post GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduAug 31, 2026
With millions of users across the world, Julia has been used to conduct cutting-edge research and to design new drugs, jet engines, heat pumps, and more.
MIT News · Artificial Intelligence· news.mit.eduAug 27, 2026
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.