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.

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.

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.

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$\text{E2AP}$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.

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.