https://xlin.io/tag/edge-computing/Edge ComputingHugoBlox Kit (https://hugoblox.com)en-USMon, 18 May 2026 00:00:00 +0000https://xlin.io/media/icon_hu_ef29f1a521df3b19.pnghttps://xlin.io/tag/edge-computing/https://xlin.io/publication/infocom-26/Mon, 18 May 2026 00:00:00 +0000https://xlin.io/publication/infocom-26/<h2 id="abstract">Abstract</h2> <p>As Internet of Things (IoT) networks evolve toward integrated sensing and communications (ISAC), ensuring reliable resource management at the mobile edge has become increasingly complex. The combination of limited communication and computing resources with unreliable wireless links necessitates highly robust offloading strategies. Traditional approaches that merely maximize expected network utility often fail to account for the stochastic link failures in dynamic sensing systems, resulting in severe utility degradation. To address this, we propose a risk-averse task offloading framework that systematically manages uncertainties in sensing-enabled mobile edge computing. We formulate a joint optimization problem that maximizes expected total task reward, subject to a conditional value-at-risk (CVaR) constraint, effectively mitigating the tail risk of communication task failures. We solve the resulting optimization problem via a novel distributional dynamic programming (DDP) approach. To improve computational efficiency, we integrate Pareto Frontier pruning based on first-order stochastic dominance (FSD) alongside scaling and rounding approximations. Evaluation results confirm that our framework successfully navigates the risk-reward trade-offs in resource-constrained deployments.</p>https://xlin.io/publication/milcom-25-offloading/Mon, 06 Oct 2025 00:00:00 +0000https://xlin.io/publication/milcom-25-offloading/<h2 id="abstract">Abstract</h2> <p>Recent years have witnessed explosive growth in the deployment of IoT devices over tactical edge networks, which demand flexible task offloading strategies to accommodate limited connectivity and computing resources. While many existing works assume direct access to edge servers, such assumptions often break down in adversarial and dynamic environments. This work introduces a novel edge task offloading framework that enables multi-hop communication and enables task execution not only at edge servers but also at relay nodes and local devices. We formulate the problem of joint task offloading, resource allocation, and time frame/slot assignment, under a time-division multiple access (TDMA) uplink scheme, as a nonlinear integer program to maximize total rewards, subject to various communication and resource constraints. To solve it optimally, we first construct a feasibility-aware directed graph that captures valid communication links based on signal-to-noise ratio (SNR) constraints, and then design a dynamic programming (DP) algorithm that integrates slot-minimizing offloading path selection with the assignment of the largest feasible number of time frames for each user to reduce total time slots consumption without violating task completion deadlines. To improve scalability, efficient heuristics are developed using scaling and rounding techniques. Extensive experiments demonstrate that our proposed DP algorithm achieves optimal task offloading and resource allocation, while our heuristic algorithms offer a computationally efficient alternative with competitive performance.</p>