Guoliang Xue | Xuanli Lin

Computing an Optimal Entanglement Path with Throughput and Fidelity Considerations

Thu, 27 Aug 2026 00:00:00 +0000

Abstract

Entanglement distribution is a core function of quantum networks essential for operations including teleportation, distributed quantum sensing, and multisite computation. Entanglement throughput and fidelity are two critical performance measures that depend on the quantum transmission along the links and swapping operations at the repeaters along the path. We study the problem of computing a end-to-end entanglement path that satisfies both fidelity and throughput requirements, leveraging qubit buffers at the nodes and considering the sequential swapping order. We show that the general problem of simultaneously satisfying both metrics to be NP-hard, and develop an algorithm to maximize throughput subject to a given fidelity threshold. We introduce the concepts of entanglement probability distribution and path domination and exploit them in the design of our algorithm. Extensive numerical results show that our algorithm can find optimal solutions in networks with thousands of nodes in less than a second. We also describe practical and possible implementation aspects of this algorithm in terms of devices and architecture support.

Published online as an Early Access article on August 27, 2026. Final volume and page numbers have not yet been assigned.

Multi-Pair Fidelity-Aware Rate Allocation in a Quantum Network: Approximation Schemes

Tue, 11 Aug 2026 00:00:00 +0000

Abstract

Entanglement distribution in quantum networks must jointly account for limited link capacities, probabilistic entanglement swapping, and heterogeneous link fidelities. In this paper, we study multi-pair fidelity-aware rate allocation in quantum networks. We formulate three rate-allocation problems: rate sum, rate sum subject to minimum-rate constraints, and max-min fairness. Prior work has studied a special case of the rate sum problem, where all links have identical fidelity. This special case admits a polynomial-time algorithm. We prove that all three problems are NP-hard. We then study optimization versions of these problems which maximize the minimum end-to-end fidelity subject to throughput or fairness requirements. We present fully polynomial-time approximation schemes (FPTAS) for solving these optimization problems. Experiments on randomly generated networks demonstrate the computational effectiveness of the proposed schemes.

Preprint, first submitted 2026-08-11.

Resource-Aware Intrusion Detection in Infrastructure Networks: A Game-Theoretic Approach

Fri, 07 Aug 2026 00:00:00 +0000

Abstract

Infrastructure networks increasingly rely on distributed sensing to detect intrusions before attackers reach valuable assets. Yet sensing devices, communication resources, and edge server capacity are limited, while intelligent attackers can adapt their routes to the deployed defense. Motivated by integrated sensing and communication (ISAC), we study how sensing and processing resources should be allocated under strategic interaction between a defender and an attacker. We formulate their interaction as a graph security game in which the defender deploys sensing actions under resource and false alarm constraints, while the attacker selects routes to valuable targets. We consider simultaneous play and settings in which the attacker observes either a pure defender configuration or a mixed defender strategy. Our analysis characterizes the existence, structure, and computational complexity of the Nash and Stackelberg equilibria, showing how the attacker’s observation of the defense affects equilibrium behavior and when optimal strategies become difficult to compute. We develop algorithms that construct effective pure configurations and refine restricted games for mixed Nash and mixed Stackelberg play. On enumerable instances, their solutions have small mean normalized differences from fully enumerated references; the methods also apply when exhaustive strategy enumeration is impractical. We also identify conditions under which Nash and mixed Stackelberg payoffs are ordered or coincide.

Preprint, first submitted 2026-08-07.

Task Offloading across Unreliable Edge Networks via Distributional Dynamic Programming

Mon, 18 May 2026 00:00:00 +0000

Abstract

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.

AEGIS: Throughput-Guaranteed Resilient Routing via a Conditional Value-at-Risk Approach

Thu, 16 Apr 2026 00:00:00 +0000

Abstract

The past decade has witnessed significant progress in next-generation wireless networks. Resilient routing is essential for maintaining reliability in mission-critical network services, particularly in dynamic and adversarial environments. Traditional traffic engineering (TE) approaches rely on pre-computed paths. Still, they face performance limitations when the number of pre-computed paths is small and scalability challenges when the number is large. This study seeks to answer the fundamental question: “How can we achieve throughput-guaranteed resilient routing under network failures without pre-computing routing paths?” We propose AEGIS, a novel throughput-guaranteed resilient routing scheme leveraging a conditional value-at-risk (CVaR) approach, which proactively guarantees the required throughput under normal conditions and enables recovery during network failures. Specifically, we propose an optimization problem that minimizes the CVaR of total throughput loss across all the failure situations while respecting user budget and network constraints. The above optimization problem is non-differentiable and non-linear; we then reformulate it as an equivalent linear program (LP) and develop an optimal solution. However, the above solution will induce cyclic flows due to resource reservation behaviors. To achieve a more resource-efficient routing, we propose a bisection approach to obtain a CVaR upper bound so that the corresponding routing is acyclic. Extensive numerical evaluations demonstrate the trade-offs among various approaches and highlight the advantages of AEGIS.

Dynamic Flow Routing and Scheduling for Time-Critical Network Services

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

Abstract

A key challenge in next-generation networks is providing intelligent network services to support time-sensitive applications such as AR/VR and the Internet of Military Things (IoMT). This work aims to answer the question: “How can we design a network service scheduling and flow routing scheme to satisfy the stringent demand and deadline requirements of tactical network applications?” We take a time-expanded graph-based approach to this study. Specifically, given a time-expanded graph constructed from an original network graph, we propose an optimization problem to find a subset of network services to maximize their total completion rewards such that the data of all services in this subset can be transmitted to their destination nodes by their deadlines. Unfortunately, solving this problem directly is challenging since it is a mixed-integer program. We propose a heuristic algorithm based on a sequential rounding approach where a linear programming (LP) feasibility problem is iteratively solved to update the network service subset, guided by a relaxed reward maximization problem that prioritizes high-reward requests. To further improve efficiency, we fine-tune the above LP feasibility problem from a static flow perspective, enabling fast feasibility checks on the original graph, and design the corresponding efficient heuristic algorithm. The simulation results demonstrate the trade-off among different approaches and validate the effectiveness of the proposed algorithms.

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.

Entanglement Distribution in LEO Satellite-based Dynamic Quantum Networks

Sun, 08 Dec 2024 00:00:00 +0000

Abstract

Recent advances in space quantum communications envision Low Earth Orbit (LEO) satellites for global entanglement distribution. Entanglement distribution in such a network requires considerations such as satellite mobility, ground station mobility due to the Earth’s rotation, inter-satellite links, and multiple orbital shells, all of which have not been thoroughly studied in the networking literature. We ameliorate this deficit by defining a system model which accounts for all of the aforementioned factors. Using this system model, we formulate the dynamic optimal entanglement distribution (DOED) problem. We convert the DOED problem in a dynamic physical network to an instance of the problem in a static logical graph, the latter of which can be used to solve the former. We obtain a reduced logical graph from a logical graph, which can be used to reduce the complexity of solving the DOED problem. We propose two polynomial-time greedy algorithms for computing entanglement paths, as well as an integer linear programming (ILP)-based algorithm as a benchmark. We present evaluation results to demonstrate the advantages of our model and algorithms.

Max-min Hub Pricing in Payment Channel Networks

Sun, 08 Dec 2024 00:00:00 +0000

Abstract

Payment Channel Networks (PCNs) offer an efficient off-chain alternative to the blockchain for transactions. Router nodes in PCNs facilitate transactions between non-adjacent nodes in exchange for a fee. PCN topology tends to be centralized, with a select number of routers known as hubs dominating all payment services. The fee-setting choices of hubs in order to maximize their revenue present fertile grounds for the study of PCN communications and economics. In this paper, we conduct a comprehensive analysis of the Hub Price-Setting (HPS) game. In particular, we define approximate Best Response strategies (ϵ-BR) as well as approximate Nash equilibria (ϵ-NE). We prove that for any ϵ > 0, an ϵ-BR always exists, and can be computed in polynomial time. We also prove that for some ϵ > 0, an ϵ-NE may not exist. We furthermore introduce the notion of conservative estimate and present a max-min approach to the HPS game. Extensive evaluation results demonstrate the power of our proposed approach.

Inferring User Activities from Internet of Things (IoT) Connected Device Events Using Machine Learning Based Algorithms

Thu, 05 Dec 2024 00:00:00 +0000

Abstract

An Internet-of-Things (IoT) learning framework (IoT learning framework) may train AI models to infer user activities from IoT connected device events using machine learning based algorithms. According to such an example, processing circuitry obtains a training dataset indicating IoT device events and extracts representative user activity patterns from the sequences of IoT device events. In such an example, processing circuitry trains the AI model to learn an optimal subset of the sequences of IoT device events corresponding to a smallest quantity of the sequences of IoT device events to predict user activities with accuracy that satisfies a threshold and outputs the AI model. According to such an example, processing circuitry may obtain new data indicating new sequences of IoT device events and generates output indicating one or more user activities predicted by the AI model to have occurred based on the new sequences of IoT device events.

Guoliang Xue, Yinxin Wan, Xuanli Lin, Kuai Xu, and Feng Wang. “Inferring User Activities from Internet of Things (IoT) Connected Device Events Using Machine Learning Based Algorithms.” U.S. Patent Application Publication US20240403648A1, published December 5, 2024.

This record is a published patent application. Application 18/734,240 was filed June 5, 2024, with a provisional application 63/506,316 filed June 5, 2023. The assignee listed on the publication is the Arizona Board of Regents on Behalf of Arizona State University.

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.

Exploring Machine Learning Algorithms for User Activity Inference from IoT Network Traffic

Tue, 26 Sep 2023 00:00:00 +0000

Abstract

The availability of ubiquitous and heterogeneous Internet-of-Things (IoT) devices in smart homes and their interactions with users provide a unique opportunity to monitor, understand, recognize, learn, and infer user activities for safety monitoring, connected health, energy saving as well as other disruptive services. Our analysis on IoT network traffic from smart homes with a variety of IoT devices has discovered that user activities often trigger overlapping traffic waves from multiple IoT devices that are deployed near the activities. This insight leads us to adopt wavelet analysis to decompose IoT network traffic in smart homes into low, middle, and high frequency bands that distinguish IoT traffic waves triggered by user activities from background noises such as heartbeat signals between IoT devices and cloud servers. Subsequently, we extract a broad range of traffic features from these IoT traffic waves and explore supervised machine learning (ML) algorithms to classify various user activities with these features. Based on the labelled user activities and IoT network traffic data collected from real smart home environments, our experiments have demonstrated that the ML-based algorithms are able to use IoT network traffic to accurately infer various user activities in smart homes.

Best Paper Award Recipient

Extracting Spatial Information of IoT Device Events for Smart Home Safety Monitoring

Fri, 19 May 2023 00:00:00 +0000

Abstract

Smart home IoT devices have been widely deployed and connected to many home networks for various applications such as intelligent home automation, connected healthcare, and security surveillance. The network traffic traces generated by IoT devices have enabled recent research advances in smart home network measurement. However, due to the cloud-based communication model of smart home IoT devices and the lack of traffic data collected at the cloud end, little effort has been devoted to extracting the spatial information of IoT device events to determine where a device event is triggered. In this paper, we examine why extracting IoT device events’ spatial information is challenging by analyzing the communication model of the smart home IoT system. We propose a system named IoTDuet for determining whether a device event is triggered locally or remotely by utilizing the fact that the controlling devices such as smartphones and tablets always communicate with cloud servers with relatively stable domain name information when issuing commands from the home network. We further show the importance of extracting spatial information of IoT device events by exploring its applications in smart home safety monitoring.

IoT System Vulnerability Analysis and Network Hardening with Shortest Attack Trace in a Weighted Attack Graph

Tue, 09 May 2023 00:00:00 +0000

Abstract

In recent years, Internet of Things (IoT) devices have been extensively deployed in edge networks, including smart homes and offices. Despite the exciting opportunities afforded by the advancements in the IoT, it also introduces new attack vectors and vulnerabilities in the system. Existing studies have shown that the attack graph is an effective model for performing system-level analysis of IoT security. In this paper, we study IoT system vulnerability analysis and network hardening. We first extend the concept of attack graph to weighted attack graph and design a novel algorithm for computing a shortest attack trace in a weighted attack graph. We then formulate the network hardening problem. We prove that this problem is NP-hard, and then design an exact algorithm and a heuristic algorithm to solve it. Extensive experiments on 9 synthetic IoT systems and 2 real-world smart home IoT testbeds demonstrate that our shortest attack trace algorithm is robust and fast, and our heuristic network hardening algorithm is efficient in producing near optimal results compared to the exact algorithm.

Best Paper Award Recipient

Inferring User Activities from IoT Device Events in Smart Homes: Challenges and Opportunities

Mon, 05 Sep 2022 00:00:00 +0000

Abstract

The ubiquitous deployment of IoT devices in smart homes has led to growing research interests in studying the home network traffic for various applications such as network measurements, device profiling, and IoT device event inference. Recent studies have shown that user activities can be inferred from a home network using extracted device event logs. However, existing solutions for user activity inference such as IoTMosaic and E2AP have limitations when handling ambiguities caused by device malfunctions. In this paper, we first identify the challenges faced by the existing user activity inference algorithms and the root causes of their poor performances on certain types of inputs. We then show that useful information can still be obtained even in situations where device malfunctions introduce ambiguities in user activity patterns. We achieve so by designing an extension to the existing algorithms. We also apply our extension in a digital forensics application. Our extensive experimental evaluations demonstrate that our solutions can effectively provide insights to user activity inference despite the presence of indistinguishable user activity patterns.

An Effective Machine Learning Based Algorithm for Inferring User Activities From IoT Device Events

Wed, 20 Jul 2022 00:00:00 +0000

Abstract

The rapid and ubiquitous deployment of Internet of Things (IoT) in smart homes has created unprecedented opportunities to automatically extract environmental knowledge, awareness, and intelligence. Many existing studies have adopted either machine learning approaches or deterministic approaches to infer IoT device events and/or user activities from network traffic in smart homes. In this paper, we study the problem of inferring user activity patterns from a sequence of device events by first deterministically extracting a small number of representative user activity patterns from the sequence of device events, then applying unsupervised learning to compute an optimal subset of these user activity patterns to infer user activity patterns. Based on extensive experiments with sequences of device events triggered by 2,959 real user activities and up to 30,000 synthetic user activities, we demonstrate that our scheme is resilient to device malfunctions and transient failures/delays, and outperforms the state-of-the-art solution.