Entropy-Aware Task Offloading in Mobile Edge Computing
- URL: http://arxiv.org/abs/2603.16949v1
- Date: Mon, 16 Mar 2026 18:02:00 GMT
- Title: Entropy-Aware Task Offloading in Mobile Edge Computing
- Abstract summary: This work focuses on the privacy issues induced from wireless communication, namely, usage pattern and location privacy.<n>The effects of these privacy concerns on the task offloading Markov Decision Process (MDP) is addressed and the MDP is solved using a Deep Recurrent Q-Netwrok (DRQN)
- Score: 5.72960498613472
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Mobile Edge Computing (MEC) technology has been introduced to enable could computing at the edge of the network in order to help resource limited mobile devices with time sensitive data processing tasks. In this paradigm, mobile devices can offload their computationally heavy tasks to more efficient nearby MEC servers via wireless communication. Consequently, the main focus of researches on the subject has been on development of efficient offloading schemes, leaving the privacy of mobile user out. While the Blockchain technology is used as the trust mechanism for secured sharing of the data, the privacy issues induced from wireless communication, namely, usage pattern and location privacy are the centerpiece of this work. The effects of these privacy concerns on the task offloading Markov Decision Process (MDP) is addressed and the MDP is solved using a Deep Recurrent Q-Netwrok (DRQN). The Numerical simulations are presented to show the effectiveness of the proposed method.
Related papers
- Efficient Quantum-resistant Delegable Data Analysis Scheme with Revocation and Keyword Search in Mobile Cloud Computing [11.086273195946996]
We propose an efficient quantum-resistant delegable data analysis scheme with revocation and keyword search.<n>In the proposed scheme, an authorised mobile device can perform keyword searches and compute inner product values over encrypted data.<n>Our scheme is formally proven secure in the standard model against quantum attacks.
arXiv Detail & Related papers (2025-12-15T02:07:52Z) - Intra-DP: A High Performance Collaborative Inference System for Mobile Edge Computing [67.98609858326951]
Intra-DP is a high-performance collaborative inference system optimized for deep neural networks (DNNs) on mobile devices.<n>It reduces per-inference latency by up to 50% and energy consumption by up to 75% compared to state-of-the-art baselines.<n>The evaluation demonstrates that Intra-DP reduces per-inference latency by up to 50% and energy consumption by up to 75% compared to state-of-the-art baselines.
arXiv Detail & Related papers (2025-07-08T09:50:57Z) - Collaborative Inference over Wireless Channels with Feature Differential Privacy [57.68286389879283]
Collaborative inference among multiple wireless edge devices has the potential to significantly enhance Artificial Intelligence (AI) applications.
transmitting extracted features poses a significant privacy risk, as sensitive personal data can be exposed during the process.
We propose a novel privacy-preserving collaborative inference mechanism, wherein each edge device in the network secures the privacy of extracted features before transmitting them to a central server for inference.
arXiv Detail & Related papers (2024-10-25T18:11:02Z) - Homomorphic Encryption-Enabled Federated Learning for Privacy-Preserving Intrusion Detection in Resource-Constrained IoV Networks [20.864048794953664]
This paper proposes a novel framework to address the data privacy issue for Federated Learning (FL)-based Intrusion Detection Systems (IDSs) in Internet-of-Vehicles (IoVs) with limited computational resources.
We first propose a highly-effective framework using homomorphic encryption to secure data that requires offloading to a centralized server for processing.
We develop an effective training algorithm tailored to handle the challenges of FL-based systems with encrypted data.
arXiv Detail & Related papers (2024-07-26T04:19:37Z) - The Security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective [64.36680481458868]
Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network's edge.
This paper provides a survey of security and privacy in MEC from the perspective of Artificial Intelligence (AI)
We focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI.
arXiv Detail & Related papers (2024-01-03T07:47:22Z) - Differentially Private Deep Q-Learning for Pattern Privacy Preservation
in MEC Offloading [76.0572817182483]
attackers may eavesdrop on the offloading decisions to infer the edge server's (ES's) queue information and users' usage patterns.
We propose an offloading strategy which jointly minimizes the latency, ES's energy consumption, and task dropping rate, while preserving pattern privacy (PP)
We develop a Differential Privacy Deep Q-learning based Offloading (DP-DQO) algorithm to solve this problem while addressing the PP issue by injecting noise into the generated offloading decisions.
arXiv Detail & Related papers (2023-02-09T12:50:18Z) - Mobile Edge Computing, Metaverse, 6G Wireless Communications, Artificial
Intelligence, and Blockchain: Survey and Their Convergence [14.855306407950058]
This paper investigates the computational paradigms used to meet the stringent requirements of modern applications.
The application scenarios of MEC in mobile augmented reality (MAR) are provided.
This survey presents the motivation of MEC-based Metaverse and introduces the applications of MEC to the Metaverse.
arXiv Detail & Related papers (2022-09-28T14:54:06Z) - AI-Empowered Data Offloading in MEC-Enabled IoV Networks [40.75165195026413]
This article surveys research studies that use AI as part of the data offloading process, categorized based on four main issues: reliability, security, energy management, and service seller profit.
Various challenges to the process of offloading data in a MEC-enabled IoV network have emerged, such as offloading reliability in highly mobile environments, security for users within the same network, and energy management to keep users from being disincentivized to participate in the network.
arXiv Detail & Related papers (2022-03-31T09:31:53Z) - Distributed Reinforcement Learning for Privacy-Preserving Dynamic Edge
Caching [91.50631418179331]
A privacy-preserving distributed deep policy gradient (P2D3PG) is proposed to maximize the cache hit rates of devices in the MEC networks.
We convert the distributed optimizations into model-free Markov decision process problems and then introduce a privacy-preserving federated learning method for popularity prediction.
arXiv Detail & Related papers (2021-10-20T02:48:27Z) - Multi-Agent Reinforcement Learning Based Coded Computation for Mobile Ad
Hoc Computing [6.94732606123235]
We introduce a novel coded computation scheme based on multi-agent reinforcement learning (MARL)
MARL has many promising features such as adaptability to network changes, high efficiency and robustness to uncertain system disturbances.
Comprehensive simulation studies demonstrate that the proposed approach can outperform state-of-the-art distributed computing schemes.
arXiv Detail & Related papers (2021-04-15T15:50:57Z) - Communication Efficient Federated Learning with Energy Awareness over
Wireless Networks [51.645564534597625]
In federated learning (FL), the parameter server and the mobile devices share the training parameters over wireless links.
We adopt the idea of SignSGD in which only the signs of the gradients are exchanged.
Two optimization problems are formulated and solved, which optimize the learning performance.
Considering that the data may be distributed across the mobile devices in a highly uneven fashion in FL, a sign-based algorithm is proposed.
arXiv Detail & Related papers (2020-04-15T21:25:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.