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To combat the challenges of dynamic wireless access, this paper presents ElasticRoute, a practical Software-Defined Networking (SDN) solution designed for Wireless Internet Service Provider (WISP) networks. Unlike many OpenFlow-based approaches, ElasticRoute builds upon Linux’s mature networking stack and leverages the eXpress Data Path (XDP) framework for dynamic flow routing, making it particularly suitable for resource-constrained environments where enterprise-grade SDN equipment might not be viable. The scheme provides Unequal-Cost Multipath forwarding capabilities while maintaining graceful fallback to standard routing behaviour, ensuring network reliability even in failure scenarios. In our test topology, ElasticRoute demonstrated a 49.5% increase in maximum achievable flow compared to regular single-path routing, offering WISPs a pragmatic path toward advanced traffic engineering without the complexity and overhead of traditional SDN solutions.
Federated learning(FL) is a machine learning paradigm designed to protect data privacy and security among multiple clients. It is widely used in industries such as healthcare, finance, and insurance. However, common personalization techniques such as clustering, data augmentation, and knowledge distillation often increase privacy risks or incur high computational costs with methods like homomorphic encryption. To address these challenges, this paper proposes the Secure Personalized Federated Learning (SPFL) algorithm, combining Clustered Federated Learning (CFL) and Oblivious Transfer (OT). Using the Affinity Propagation (AP) clustering algorithm, SPFL groups clients by model similarity to create personalized global models without additional information sharing. OT has achieved efficient delivery of security models. Experimental results show SPFL enhances security, adapts to Non-Independent and Identically Distributed (Non-IID) data, improves global model accuracy by more than 6.5%, and reduces the overall running time under three alpha values by more than 19.82% compared to Paillier encryption.
Existing network simulations often rely on simplistic models that send packets at random intervals, failing to capture the critical role of application-level behaviour. This paper presents a statistical approach that extracts and models application behaviour using probability density functions to generate realistic network simulations. By convolving learned application patterns, the framework produces dynamic, scalable traffic representations that closely mimic real-world networks. The method enables rigorous testing of network monitoring tools and anomaly detection systems by dynamically adjusting application behaviour. It is lightweight, capable of running multiple emulated applications on a single machine, and scalable for analysing large networks where real data collection is impractical. To encourage adoption and further testing, the full code is provided as open-source, allowing researchers and practitioners to replicate and extend the framework for diverse network environments.
Task offloading strategies in Mobile Edge Computing (MEC) aim to reduce computation delay and energy consumption of mobile devices by offloading tasks to edge servers, which is key to improving MEC system performance and user experience. Recent efforts have focused on utilizing deep reinforcement learning (DRL) but DRL-based offloading strategies struggle to achieve optimal decisions in limited iterations due to complexity. Therefore, to address the complexity challenge, this paper proposes a Prune-based Deep reinforcement learning Offloading Algorithm (PDOA) to enhance MEC system performance. First, we construct a dynamic MEC system model and formulate the task offloading problem as a Markov decision process to minimize the total cost of the MEC system. Next, we propose a prune-based DRL offloading algorithm, which prunes DRL models to reduce the complexity and improve learning efficiency, thereby lowering system costs. The experimental results show that PDOA reduces the computational cost of MEC systems significantly compared with other methods and lowers system costs by over 10%. This optimization approach provides a novel research perspective for applying DRL models in MEC.
Networks of today are going through a rapid evolution and there are many emerging areas of information networking and their applications. Heterogeneous networking supported by recent technological advances in low power wireless communications along with silicon integration of various functionalities such as sensing, communications, intelligence, and actuations are emerging as a critically important disruptive computer class based on a new platform, networking structure and interface that enable novel, low-cost and high-volume applications. Several of such applications have been difficult to realize because of many interconnection problems. To fulfill their large range of applications different kinds of networks need to collaborate and wired and next generation wireless systems should be integrated in order to develop high performance computing solutions to problems arising from the complexities of these networks. This book covers the theory, design and applications of computer networks, distributed computing, and information systems.
The aim of the book “Advanced Information Networking and Applications” is to provide latest research findings, innovative research results, methods and development techniques from both theoretical and practical perspectives related to the emerging areas of information networking and applications.
ElasticWISP-NG (Next Generation) presents an innovative approach to dynamic resource provisioning in Wireless Internet Service Provider (WISP) access networks. This paper introduces the early stages of our novel scheme, which builds on the foundational concepts of our previous ElasticWISP model. We focus on the utilisation (and the implicit constraints) of renewable energy sources, and identify where opportunities to reduce network-wide energy consumption exist. Our findings outline the potential of ElasticWISP-NG to transform the accessibility of the Internet in underserved areas, promising significant advances in sustainable and scalable network management.
Network data constantly evolves with new network applications and protocols. There is a need for robust techniques to detect anomalous behaviour. Offline models trained with static data lose validity when new variants of traffic emerge. They require retraining but the need for ground truth and lengthy training times make this task challenging. Meanwhile, online models which detect outliers in streaming data are susceptible to the curse of dimensionality and natural variability. Today’s anomalies may be tomorrow’s new traffic and existing methods do not provide a way to differentiate between them. We propose a framework that makes the most of both approaches: an offline deep learning model extracts features of normal traffic and provides a bias for an online outlier detection model to select data for training. The online model retains its previously learnt knowledge and retrains itself with new data. Online thresholds are updated in a drifting manner and the Mann-Whitney U test is incorporated to prevent inaccurate updates. We perform analysis on the scores, develop heuristics to detect new traffic and evaluate using three deep learning models and four outlier detection methods on the UNSW-NB15 and CTU-13 datasets. The framework improves upon any individual offline or online models in isolation.
The challenge of anomaly detection is to obtain an accurate understanding of expected behaviour which is intensified when the data are distributed heterogeneously. Transmitting raw data to a central site incurs high communication overhead and raises privacy issues. The concept of Edge AI allows computation to be performed at the edge site allowing for quick decision making in mission critical scenarios such as self-driving cars. A model is learnt locally and its parameters are transmitted and aggregated. However, existing methods of aggregation do not account for variance and heterogeneous distribution of data. They also do not consider edge constraints such as limited computational, memory and communication capabilities of edge devices. In this work, a fully Bayesian approach is employed by means of a Bayesian Random Vector Functional Link AutoEncoder being incorporated with Expectation Propagation for distributed training. Our anomaly detection system operates without any transmission of raw data, is robust under inhomogeneous network densities and under uneven and biased data distributions. It allows for asynchronous updates to converge in a few iterations and is a relatively simple neural network addressing edge constraints without compromising on performance as compared to existing more complex models.
Internet Service Providers need to deploy and maintain many wireless sites in isolated or inaccessible terrain to provide Internet connectivity to rural communities. Addressing failures at such sites can be very expensive, both in identifying the fault, and also in the repair or rectification. Data monitoring can be useful, to spot anomalies and predict a fault (and possibly pre-empt it altogether), or to locate and isolate it quickly once it causes an issue for the network. There might be hundreds of variables to be monitored in principle, but only a few of significance for detecting faults. Here, in a case study involving a Wireless Internet Service Provider (WISP) in a rural area, we first illustrate a bottom-up approach to the identification of variables likely to be of use in an automatic anomaly detector. For the purpose of this study, the detector consists of an autoencoder neural network with weights optimized by machine learning (ML). We then show how the cause of an anomaly can be derived from indirect measurements, and use the model to learn relationships between certain variables.
With the advancement in technology, normal network traffic is becoming more heterogeneous. In this scenario, the problem of detecting anomalies is intensified. In the literature, offline methods see more data and can be optimised to achieve lower false positive rates. However, they cannot readily adapt to changing network conditions or capture concept-drift. This necessitates an incremental online learning model. On the other hand, online training is easily affected by noise. In this paper, we propose a hybrid Online Offline system in which the Offline model retains general characteristics of network traffic while the Online model continuously learns. The Offline model acts as a bias for the Online model to select new data to learn from. The Online model retains its knowledge and adapts to the changing ground truth. They are put to work together to detect anomalies. We implement this idea with an Online Support Vector Machine (SVM) which retains its support vectors and shifts its decision boundary guided by an Offline Radius Nearest Neighbor (Rad-NN). The method is evaluated on the NSL-KDD 2009 dataset. This relatively simple model achieves over 95% accuracy on known anomalies and over 60% detection rate on most of the unknown anomalies.
Bufferbloat, or excessive queuing delay under load, is a noticeable quality of service degradation that occurs when latency-sensitive traffic experiences the effects of increased packet buffering delays in network devices. This phenomenon leads to increased latency and reduced network performance, particularly affecting real-time voice and video traffic, online gaming, and other interactive applications that demand low-latency at all times. Bufferbloat also poses serious risks to future ambitions of latency-critical applications such as telesurgery, autonomous vehicles, and virtual reality, as it undermines network consistency and creates significant operational issues for service providers to manage. Bufferbloat is especially detrimental to Wireless Internet Service Providers (WISPs), due to their often ad-hoc and dynamic nature. To better characterise the prevalence of bufferbloat, we analyse a real-world WISP network and propose "Polus", a framework for detecting and characterising adverse network conditions caused by the phenomenon.
