Computer Science

Fast IPv6 scanning is challenging in the field of network measurement as it requires exploring the whole IPv6 address space but limited by current computational power. Researchers propose to obtain possible active target candidate sets to probe by algorithmically analyzing the active seed sets. However, IPv6 addresses lack semantic information and contain numerous addressing schemes, leading to the difficulty of designing effective algorithms. In this paper, we introduce our approach 6VecLM to explore achieving such target generation algorithms. The architecture can map addresses into a vector space to interpret semantic relationships and uses a Transformer network to build IPv6 language models for predicting address sequence. Experiments indicate that our approach can perform semantic classification on address space. By adding a new generation approach, our model possesses a controllable word innovation capability compared to conventional language models. The work outperformed the state-of-the-art target generation algorithms on two active address datasets by reaching more quality candidate sets.

This paper aims to propose a three-dimensional (3D) point process that can be employed to generally deploy unmanned aerial vehicles (UAVs) in a large-scale cellular network and tractably analyze the fundamental network-wide performances of the network. This 3D point process is devised based on a 2D marked Poisson point process in which each point and its random mark uniquely correspond to the projection and the altitude of each point in the 3D point process, respectively. We elaborate on some important statistical properties of the proposed 3D point process and use them to tractably analyze the coverage performances of a UAV-enabled cellular network wherein all the UAVs equipped with multiple antennas are served as aerial base stations. The downlink coverage of the UAV-enabled cellular network is found and its closed-form results for some special cases are explicitly derived as well. Furthermore, the fundamental limits achieved by cell-free massive antenna array are characterized when coordinating all the UAVs to jointly perform non-coherent downlink transmission. These findings are validated by numerical simulation.

Cognitive radio networks (CRNs) is the next generation of wireless communication. This type of network requires efficent spectrum allocation methods. This paper presents a new meta-heuristic evolutionary method for solving the channel allocation problem in an ad hoc network context. The suggested method is based on a graph-theoretic model and seeks a solution for the spectrum allocation problem in a clustered ad hoc network topology.The method is referred to as imperialist competitive algorithm (ICA)and provides a scheme for allocating the available channels to cluster heads maximizing spectrum efficiency and minimizes co-channel interference. The suggested methods are tested for several scenarios; the performance of the ICA-based scheme is compared with the genetic algorithm based scheme.

Volunteer computing uses Internet-connected devices (laptops, PCs, smart devices, etc.), in which their owners volunteer them as storage and computing power resources, has become an essential mechanism for resource management in numerous applications. The growth of the volume and variety of data traffic in the Internet leads to concerns on the robustness of cyberphysical systems especially for critical infrastructures. Therefore, the implementation of an efficient Intrusion Detection System for gathering such sensory data has gained vital importance. In this paper, we present a comparative study of Artificial Intelligence (AI)-driven intrusion detection systems for wirelessly connected sensors that track crucial applications. Specifically, we present an in-depth analysis of the use of machine learning, deep learning and reinforcement learning solutions to recognize intrusive behavior in the collected traffic. We evaluate the proposed mechanisms by using KD'99 as real attack data-set in our simulations. Results present the performance metrics for three different IDSs namely the Adaptively Supervised and Clustered Hybrid IDS (ASCH-IDS), Restricted Boltzmann Machine-based Clustered IDS (RBC-IDS) and Q-learning based IDS (QL-IDS) to detect malicious behaviors. We also present the performance of different reinforcement learning techniques such as State-Action-Reward-State-Action Learning (SARSA) and the Temporal Difference learning (TD). Through simulations, we show that QL-IDS performs with 100% detection rate while SARSA-IDS and TD-IDS perform at the order of 99.5%.

Cellular Internet of Things (IoT) is considered as de facto paradigm to improve the communication and computation systems. Cellular IoT connects massive number of physical and virtual objects to the Internet using cellular networks. The latest generation of cellular networks, e.g. fifth-generation (5G), use evolutionary and revolutionary technologies to notably improve the performance of wireless networks. However, given the envisioned new use-cases, e.g., holographic communication, and the ever-increasing deployment of massive smart-physical end-devices in IoT, the volume of network traffic has considerably raised, and therefore, the current generation of mobile networks cannot wholly meet the ever-increasing demands. Hence, it is envisioned that the next generation, sixth generation (6G) networks, need to play a critical role to alleviate such challenges in IoT by providing new communication services, network capacity, and ultra-low latency communications (uRLLC). In this paper, first, the need for 6G networks is discussed. Then, the potential 6G requirements and trends, as well as the latest research activities related to 6G are introduced e.g., Tactile Internet and Terahertz (THz). Furthermore, the key performance indicators, applications, new services, and the potential key enabling technologies for 6G networks are presented. Finally, several potential unresolved challenges for future 6G networks are presented.

As novel applications spring up in future network scenarios, the requirements on network service capabilities for differentiated services or burst services are diverse. Aiming at the research of collaborative computing and resource allocation in edge scenarios, migrating computing tasks to the edge and cloud for computing requires a comprehensive consideration of energy consumption, bandwidth, and delay. Our paper proposes a collaboration mechanism based on computation offloading, which is flexible and customizable to meet the diversified requirements of differentiated networks. This mechanism handles the terminal's differentiated computing tasks by establishing a collaborative computation offloading model between the cloud server and edge server. Experiments show that our method has more significant improvements over regular optimization algorithms, including reducing the execution time of computing tasks, improving the utilization of server resources, and decreasing the terminal's energy consumption.

With the growing computer vision applications, lots of videos are transmitted for content analysis, the way to allocate resources can affect the performance of video content analysis. For this purpose, the traditional resource allocation schemes for video transmission in vehicular networks, such as qualityof-service (QoS) based or quality-of-experience (QoE) based schemes, are no longer optimal anymore. In this paper, we propose an efficient content driven resource allocation scheme for vehicles equipped with cameras under bandwidth constraints in order to improve the video content analysis performance. The proposed resource allocation scheme is based on maximizing the quality-of-content (QoC), which is related to the content analysis performance. A QoC based assessment model is first proposed. Then, the resource allocation problem is converted to a solvable convex optimization problem. Finally, simulation results show the better performance of our proposed scheme than the existing schemes like QoE based schemes.

Failures in optical network backbone can cause significant interruption in internet data traffic. Hence, it is very important to reduce such network outages. Prediction of such failures would be a step forward to avoid such disruption of internet services for users as well as operators. Several research proposals are available in the literature which are applications of data science and machine learning techniques. Most of the techniques rely on significant amount of real time data collection. Network devices are assumed to be equipped to collect data and these are then analysed by different algorithms to predict failures. Every network element which is already deployed in the field may not have these data gathering or analysis techniques designed into them initially. However, such mechanisms become necessary later when they are already deployed in the field. This paper proposes a Bayesian network based failure prediction of network nodes, e.g., routers etc., using very basic information from the log files of the devices and applying power law based data augmentation to complement for scarce real time information. Numerical results show that network node failure prediction can be performed with high accuracy using the proposed mechanism.

Reconfigurable Intelligent Surface (RIS) has emerged as one of the key technologies for 6G in recent years, which comprise a large number of low-cost passive elements that can smartly interact with the impinging electromagnetic waves for performance enhancement. However, optimally configuring massive number of RIS elements remains a challenge. In this paper, we present a novel digital-twin framework for RIS-assisted wireless networks which we name it Environment-Twin (Env-Twin). The goal of the Env-Twin framework is to enable automation of optimal control at various granularities. In this paper, we present one example of the Env-Twin models to learn the mapping function between the RIS configuration with measured attributes for the receiver location, and the corresponding achievable rate in an RIS-assisted wireless network without involving explicit channel estimation or beam training overhead. Once learned, our Env-Twin model can be used to predict optimal RIS configuration for any new receiver locations in the same wireless network. We leveraged deep learning (DL) techniques to build our model and studied its performance and robustness. Simulation results demonstrate that the proposed Env-Twin model can recommend near-optimal RIS configurations for test receiver locations which achieved close to an upper bound performance that assumes perfect channel knowledge. Our Env-Twin model was trained using less than 2% of the total receiver locations. This promising result represents great potential of the proposed Env-Twin framework for developing a practical RIS solution where the panel can automatically configure itself without requesting channel state information (CSI) from the wireless network infrastructure.

Interactions between the intra- and inter-domain routing protocols received little attention despite playing an important role in forwarding transit traffic. More precisely, by default, IGP distances are taken into account by BGP to select the closest exit gateway for the transit traffic (hot-potato routing). Upon an IGP update, the new best gateway may change and should be updated through the (full) re-convergence of BGP, causing superfluous BGP processing and updates in many cases. We propose OPTIC (Optimal Protection Technique for Inter-intra domain Convergence), an efficient way to assemble both protocols without losing the hot-potato property. OPTIC pre-computes sets of gateways (BGP next-hops) shared by groups of prefixes. Such sets are guaranteed to contain the post-convergence gateway after any single IGP event for the grouped prefixes. The new optimal exits can be found through a single walk-through of each set, allowing the transit traffic to benefit from optimal BGP routes almost as soon as the IGP converges. Compared to vanilla BGP, OPTIC's structures allow it to consider a reduced number of entries: this number can be reduced by 99\% for stub networks. The update of OPTIC's structures, which is not required as long as border routers remain at least bi-connected, scales linearly in time with its number of groups.