194 research outputs found
Joint Data compression and Computation offloading in Hierarchical Fog-Cloud Systems
Data compression has the potential to significantly improve the computation
offloading performance in hierarchical fog-cloud systems. However, it remains
unknown how to optimally determine the compression ratio jointly with the
computation offloading decisions and the resource allocation. This joint
optimization problem is studied in the current paper where we aim to minimize
the maximum weighted energy and service delay cost (WEDC) of all users. First,
we consider a scenario where data compression is performed only at the mobile
users. We prove that the optimal offloading decisions have a threshold
structure. Moreover, a novel three-step approach employing convexification
techniques is developed to optimize the compression ratios and the resource
allocation. Then, we address the more general design where data compression is
performed at both the mobile users and the fog server. We propose three
efficient algorithms to overcome the strong coupling between the offloading
decisions and resource allocation. We show that the proposed optimal algorithm
for data compression at only the mobile users can reduce the WEDC by a few
hundred percent compared to computation offloading strategies that do not
leverage data compression or use sub-optimal optimization approaches. Besides,
the proposed algorithms for additional data compression at the fog server can
further reduce the WEDC
On the View-and-Channel Aggregation Gain in Integrated Sensing and Edge AI
Sensing and edge artificial intelligence (AI) are two key features of the
sixth-generation (6G) mobile networks. Their natural integration, termed
Integrated sensing and edge AI (ISEA), is envisioned to automate wide-ranging
Internet-of-Tings (IoT) applications. To achieve a high sensing accuracy,
multi-view features are uploaded to an edge server for aggregation and
inference using an AI model. The view aggregation is realized efficiently using
over-the-air computing (AirComp), which also aggregates channels to suppress
channel noise. At its nascent stage, ISEA still lacks a characterization of the
fundamental performance gains from view-and-channel aggregation, which
motivates this work. Our framework leverages a well-established distribution
model of multi-view sensing data where the classic Gaussian-mixture model is
modified by adding sub-spaces matrices to represent individual sensor
observation perspectives. Based on the model, we study the End-to-End sensing
(inference) uncertainty, a popular measure of inference accuracy, of the said
ISEA system by a novel approach involving designing a scaling-tight uncertainty
surrogate function, global discriminant gain, distribution of receive
Signal-to-Noise Ratio (SNR), and channel induced discriminant loss. We prove
that the E2E sensing uncertainty diminishes at an exponential rate as the
number of views/sensors grows, where the rate is proportional to global
discriminant gain. Given channel distortion, we further show that the
exponential scaling remains with a reduced decay rate related to the channel
induced discriminant loss. Furthermore, we benchmark AirComp against equally
fast, traditional analog orthogonal access, which reveals a sensing-accuracy
crossing point between the schemes, leading to the proposal of adaptive
access-mode switching. Last, the insights from our framework are validated by
experiments using real-world dataset.Comment: 13 pages, 8 figure
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