Yanchao Zhang | Xuanli Lin

Multi-Hop Relay-Aided Task Offloading for Tactical Edge Computing

Mon, 06 Oct 2025 00:00:00 +0000

Abstract

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.

Joint Optimization of Task Offloading and Resource Allocation in Tactical Edge Networks

Mon, 28 Oct 2024 00:00:00 +0000

Abstract

Recent years have witnessed explosive growth in deploying IoT devices over tactical edge networks. Due to the limited computing resources of local edge devices, there is an urgent need to offload the computing tasks to edge servers. A central question here is “How can we design offloading strategies and resource allocation plans so that all the computing resource constraints of servers and communication constraints between devices and servers are satisfied?” In this paper, we formulate the problem of jointly optimizing task offloading and resource allocation (JOA) in tactical edge networks as maximizing the total task completion rewards subject to various resource constraints. We design two algorithms to solve the JOA problem. The first is an exact algorithm using a search tree, where the branch-cutting criterion is well-crafted based on the property of the JOA problem. The second is a meta-heuristic algorithm based on the simulated annealing approach. We conduct numerical evaluations and demonstrate that the proposed exact algorithm can obtain the optimal task offloading strategy and resource allocation plan. The heuristic algorithm can efficiently provide competitive performance.

Most Vulnerable Attack Trace in a Probabilistic Attack Graph

Mon, 28 Oct 2024 00:00:00 +0000

Abstract

In this paper, we investigate the properties and computation of attack traces on probabilistic attack graphs, a model that integrates probability into traditional attack graphs to reflect the varying exploitability of network vulnerabilities. We introduce the Most Vulnerable Attack Trace (MVAT) problem, which aims to identify the attack trace with the highest cumulative success probability for an attacker. To address this problem, we propose both an exact algorithm and a heuristic algorithm, each designed to navigate the complexities introduced by cycles in the attack graph. Our exact algorithm explores all possible sequences of node selections to ensure the optimal attack trace, while our heuristic algorithm efficiently approximates the MVAT in polynomial time. We evaluate the performance of our algorithms using an extensive dataset, demonstrating the usefulness of the proposed algorithms.