关键词： integrated communication and sensing   mobile edge computing   deep reinforcement learning   robust design   computation uncertainty  

摘要： In this paper, we consider an integrated sensing, communication, and computation (ISCC) system to alleviate the spectrum congestion and computation burden problem. Specifically, while serving communication users, a base station (BS) actively engages in sensing targets and collaborates seamlessly with the edge server to concurrently process the acquired sensing data for efficient target recognition. A significant challenge in edge computing systems arises from the inherent uncertainty in computations, mainly stemming from the unpredictable complexity of tasks. With this consideration, we address the computation uncertainty by formulating a robust communication and computing resource allocation problem in ISCC systems. The primary goal of the system is to minimize total energy consumption while adhering to perception and delay constraints. This is achieved through the optimization of transmit beamforming, offloading ratio, and computing resource allocation, effectively managing the trade-offs between local execution and edge computing. To overcome this challenge, we employ a Markov decision process (MDP) in conjunction with the proximal policy optimization (PPO) algorithm, establishing an adaptive learning strategy. The proposed algorithm stands out for its rapid training speed, ensuring compliance with latency requirements for perception and computation in applications. Simulation results highlight its robustness and effectiveness within ISCC systems compared to baseline approaches.

关键词： Task analysis   Servers   Delays   Multi-access edge computing   Uncertainty   Mobile handsets   Information sharing   Bandit feedback   mobile edge computing   multi-user offloading   uncertain system-side information  

摘要： Mobile edge computing facilitates users to offload computation tasks to edge servers for meeting their stringent delay requirements. Previous works mainly explore task offloading when system-side information is given (e.g., server processing speed, cellular data rate), or centralized offloading under system uncertainty. But both generally fall short of handling task placement involving many coexisting users in an uncertain environment. In this paper, we develop a multi-user offloading framework considering unknown yet stochastic system-side information to enable a decentralized user-initiated service placement under overlapping server coverage. Specifically, we formulate the dynamic task placement as an online multi-user multi-armed bandit process, and propose a decentralized epoch based offloading (DEBO) to optimize user rewards which are subjected under network delay. We consider both cases without and with neighboring edge feedback once users' tasks are processed, where the latter incorporates system-side information sharing among edge servers for an enhanced task placement. For both cases, we show that DEBO can gradually deduce the optimal user-server assignment during dynamic offloading, thereby achieving a close-to-optimal service performance and tight O(log(2)T) regret. Moreover, we generalize DEBO to various common scenarios such as unknown reward gap, dynamic entering or leaving of clients, and fair reward distribution, while further exploring when users' offloaded tasks require heterogeneous computing resources. Particularly, we accomplish a sub-linear regret for each of these instances. Real measurements based evaluations corroborate the superiority of our offloading schemes over state-of-the-art approaches in optimizing delay-sensitive rewards.

关键词： Decoding   Task analysis   Energy consumption   Eavesdropping   Wireless networks   Signal to noise ratio   NOMA   Mobile edge computing   non-orthogonal multiple access   semi-grant-free transmission   scheduling  

摘要： This letter investigates a non-orthogonal multiple access (NOMA) based mobile edge computing system with semi-grant-free (SGF) transmission, where a grant-based user (GBU) wants to securely offload its data to a computational access point (CAP) for processing with a time deadline in the presence of an eavesdropper, and a grant-free user (GFU) is scheduled from a group of GFUs to simultaneously offload its data to the CAP for processing based on SGF-NOMA and at the same time interfere with the eavesdropper to improve the GBU secrecy performance. Two GFU scheduling strategies, where one schedules the GFU with the maximum communication channel gain, and the other schedules the GFU with the maximum jamming channel gain, are proposed. The closed-form exact expressions of the system success probability are derived, from which the impacts of various system parameters are obtained. Simulation results confirm the theoretical results and show that the performance comparison between the two strategies depends on the system parameters such as the offloaded data size.

关键词： Mobile edge computing   Deep learning   Light-weight computing   Image semantic segmentation  

摘要： In the field of remote sensing image interpretation, automatically extracting water body information from high-resolution images is a key task. However, facing the complex multi-scale features in high-resolution remote sensing images, traditional methods and basic deep convolutional neural networks are difficult to effectively capture the global spatial relationship of the target objects, resulting in incomplete, rough shape and blurred edges of the extracted water body information. Meanwhile, massive image data processing usually leads to computational resource overload and inefficiency. Fortunately, the local data processing capability of edge computing combined with the powerful computational resources of cloud centres can provide timely and efficient computation and storage for high-resolution remote sensing image segmentation. In this regard, this paper proposes PMNet, a lightweight deep learning network for edge-cloud collaboration, which utilises a pipelined multi-step aggregation method to capture image information at different scales and understand the relationships between remote pixels through horizontal and vertical spatial dimensions. Also, it adopts a combination of multiple decoding branches in the decoding stage instead of the traditional single decoding branch. The accuracy of the results is improved while reducing the consumption of system resources. The model obtained F1-score of 90.22 and 88.57 on Landsat-8 and GID remote sensing image datasets with low model complexity, which is better than other semantic segmentation models, highlighting the potential of mobile edge computing in processing massive high-resolution remote sensing image data.

关键词： Mobile edge computing  

摘要： Context: Cloud-based black-box model deployment faces challenges related to latency and privacy due to data transmission across Wide Area Networks. On the other hand, Mobile-based black-box deployment prioritizes privacy at the expense of higher latency due to limited computational resources. To address these issues, Edge AI enables the deployment of black-box models across Mobile, Edge, and Cloud devices using a wide range of operators able to distribute a model’s components, terminate inference early, or even quantize a model’s computations, offering latency and privacy benefits. Existing surveys classify Edge AI model inference techniques into eight families, including Quantization, Early Exiting, and Partitioning, but they often treat these operators in isolation, overlooking their potential synergies and practical integration in real-world scenarios. Deciding what combination of operators to use across the Edge AI tiers to achieve specific latency and model performance requirements is still an open question for MLOps Engineers. Objective: This study aims to empirically assess the accuracy vs inference time trade-off of different black-box Edge AI deployment strategies, i.e., combinations of deployment operators and deployment tiers. Method: In this paper, we conduct inference experiments involving three deployment operators (i.e., Partitioning, Quantization, Early Exit), three deployment tiers (i.e., Mobile, Edge, Cloud) and their combinations on four widely-used Computer-Vision models to investigate the optimal strategies from the point of view of MLOps developers. The analysis is conducted in a containerized environment using CUDA for Cloud GPU acceleration and ONNX for model interoperability, covering a wide range of network bandwidths. Results: Our findings suggest that Edge deployment using the hybrid Quantization + Early Exit operator could be preferred over Non-Hybrid operators (Quantization/Early Exit on Edge, Partition on Mobile-Edge) when faster laten

关键词： Mobile edge computing  

摘要： Given flexible mobility, rapid deployment, and low cost, unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) shows great potential to compensate for the lack of terrestrial edge computing coverage. However, limited battery capacity, computing and spectrum resources also pose serious challenges for UAV-enabled MEC, which shorten the service time of UAVs and degrade the quality of experience (QoE) of user devices (UDs) without effective control approach. In this work, we consider a UAV-enabled MEC scenario where a UAV serves as an aerial edge server to provide computing services for multiple ground UDs. Then, a joint task offloading, resource allocation, and UAV trajectory planning optimization problem (JTRTOP) is formulated to maximize the QoE of UDs under the UAV energy consumption constraint. To solve the JTRTOP that is proved to be a future-dependent and NP-hard problem, an online joint optimization approach (OJOA) is proposed. Specifically, the JTRTOP is first transformed into a per-slot real-time optimization problem (PROP) by using the Lyapunov optimization framework. Then, a two-stage optimization method based on game theory and convex optimization is proposed to solve the PROP. Simulation results validate that the proposed approach can achieve superior system performance compared to the other benchmark schemes. Copyright © 2024, The Authors. All rights reserved.

关键词： service management   sequential decision making   task offloading   mobile edge computing   optimal stopping theory  

摘要： Mobile edge computing (MEC) paradigm has emerged to improve the quality of service & experience of applications deployed in close proximity to end-users. Due to their restricted computational and communication resources, MEC nodes can provide access to a portion of the entire set of services and data gathered. Therefore, there are several obstacles to their management. Keeping track of all the services offered by the MEC nodes is challenging, particularly if their demand rates change over time. Received tasks (such as, analytics queries, classification tasks, and model learning) require services to be invoked in real MEC use-case scenarios, e.g., smart cities. It is not unusual for a node to lack the necessary services or part of them. Undeniably, not all the requested services may be locally available;thus, MEC nodes must deal with the timely and appropriate choice of whether to carry out a service replication (pull action) or tasks offloading (push action) to peer nodes in a MEC environment. In this study, we contribute with a novel time-optimized mechanism based on the optimal stopping theory, which is built on the cost-based decreasing service demand rates evidenced in various service management situations. Our mechanism tries to optimally solve the decision-making dilemma between pull and push action. The experimental findings of our mechanism and its comparative assessment with other methods found in the literature showcase the achieved optimal decisions with respect to certain cost-based objective functions over dynamic service demand rates.

关键词： Mobile edge computing   wireless sensor network   Japanese teaching platform   remote interactive learning   microprocessor   platform user roles   practical teaching   database management   interaction performance  

关键词： Authentication   Threshold signature   Authorization update   Mobile edge computing  

摘要： The widespread adoption of fifth-generation mobile networks has spurred the rapid advancement of mobile edge computing (MEC). By decentralizing computing and storage resources to the network edge, MEC significantly enhances real-time data access services and enables efficient processing of large-scale dynamic data on resource-limited devices. However, MEC faces considerable security challenges, particularly in cross-domain service environments, where every device poses a potential security threat. To address this issue, this paper proposes a secure cross-domain authentication scheme based on a threshold signature tailored to MEC's multi-subdomain nature. The proposed scheme employs a (t,n) threshold mechanism to bolster system resilience and security, catering to large-scale, dynamic, and decentralized MEC scenarios. Additionally, the proposed scheme features an efficient authorization update function that facilitates the revocation of malicious nodes. Security analysis confirmed that the proposed scheme satisfies unforgeability, collusion resistance, non-repudiation and forward security. Theoretical evaluation and experimental simulation verify the effectiveness and feasibility of the proposed scheme. Compared with existing schemes, the proposed scheme has higher computational performance while implementing secure authorization updates.

关键词： Mobile edge computing  

摘要： Placing applications in mobile edge computing servers presents a complex challenge involving many servers, users, and their requests. Existing algorithms take a long time to solve high-dimensional problems with significant uncertainty scenarios. Therefore, an efficient approach is required to maximize the quality of service while considering all technical constraints. One of these approaches is machine learning, which emulates optimal solutions for application placement in edge servers. Machine learning models are expected to learn how to allocate user requests to servers based on the spatial positions of users and servers. In this study, the problem is formulated as a two-stage stochastic programming. A sufficient amount of training records is generated by varying parameters such as user locations, their request rates, and solving the optimization model. Then, based on the distance features of each user from the available servers and their request rates, machine learning models generate decision variables for the first stage of the stochastic optimization model, which is the user-to-server request allocation, and are employed as independent decision agents that reliably mimic the optimization model. Support Vector Machines (SVM) and Multi-layer Perceptron (MLP) are used in this research to achieve practical decisions from the stochastic optimization models. The performance of each model has shown an execution effectiveness of over 80%. This research aims to provide a more efficient approach for tackling high-dimensional problems and scenarios with uncertainties in mobile edge computing by leveraging machine learning models for optimal decision-making in request allocation to edge servers. These results suggest that machine-learning models can significantly improve solution times compared to conventional approaches. Copyright © 2024, The Authors. All rights reserved.