Digital Communications and Networks最新文献

Nearly all real-world networks are complex networks and usually are in danger of collapse. Therefore, it is crucial to exploit and understand the mechanisms of network attacks and provide better protection for network functionalities. Network dismantling aims to find the smallest set of nodes such that after their removal the network is broken into connected components of sub-extensive size. To overcome the limitations and drawbacks of existing network dismantling methods, this paper focuses on network dismantling problem and proposes a neighbor-loop structure based centrality metric, NL, which achieves a balance between computational efficiency and evaluation accuracy. In addition, we design a novel method combining NL-based nodes-removing, greedy tree-breaking and reinsertion. Moreover, we compare five baseline methods with our algorithm on ten widely used real-world networks and three types of model networks including Erdös-Rényi random networks, Watts-Strogatz small-world networks and Barabási-Albert scale-free networks with different network generation parameters. Experimental results demonstrate that our proposed method outperforms most peer methods by obtaining a minimal set of targeted attack nodes. Furthermore, the insights gained from this study may be of assistance to future practical research into real-world networks.

Energy limitation of traditional Wireless Sensor Networks (WSNs) greatly confines the network lifetime due to generating and processing massive sensing data with a limited battery. The energy harvesting WSN is a novel network architecture to address the limitation of traditional WSN. However, existing coverage and deployment schemes neglect the environmental correlation of sensor nodes and external energy with respect to physical space. Comprehensively considering the spatial correlation of the environment and the uneven distribution of energy in energy harvesting WSN, we investigate how to deploy a collection of sensor nodes to save the deployment cost while ensuring the target perpetual coverage. The Confident Information Coverage (CIC) model is adopted to formulate the CIC Minimum Deployment Cost Target Perpetual Coverage (CICMTP) problem to minimize the deployed sensor nodes. As the CICMTP is NP-hard, we devise two approximation algorithms named Local Greedy Threshold Algorithm based on CIC (LGTA-CIC) and Overall Greedy Search Algorithm based on CIC (OGSA-CIC). The LGTA-CIC has a low time complexity and the OGSA-CIC has a better approximation rate. Extensive simulation results demonstrate that the OGSA-CIC is able to achieve lower deployment cost and the performance of the proposed algorithms outperforms GRNP, TPNP and EENP algorithms.

In the era of rapid development of Internet of Things (IoT), numerous machine-to-machine technologies have been applied to the industrial domain. Due to the divergence of IoT solutions, the industry is faced with a need to apply various technologies for automation and control. This fact leads to a demand for an establishing interworking mechanism which would allow smooth interoperability between heterogeneous devices. One of the major protocols widely used today in industrial electronic devices is Modbus. However, data generated by Modbus devices cannot be understood by IoT applications using different protocols, so it should be applied in a couple with an IoT service layer platform. oneM2M, a global IoT standard, can play the role of interconnecting various protocols, as it provides flexible tools suitable for building an interworking framework for industrial services. Therefore, in this paper, we propose an interworking architecture between devices working on the Modbus protocol and an IoT platform implemented based on oneM2M standards. In the proposed architecture, we introduce the way to model Modbus data as oneM2M resources, rules to map them to each other, procedures required to establish interoperable communication, and optimization methods for this architecture. We analyze our solution and provide an evaluation by implementing it based on a solar power management use case. The results demonstrate that our model is feasible and can be applied to real case scenarios.

The security of Federated Learning (FL)/Distributed Machine Learning (DML) is gravely threatened by data poisoning attacks, which destroy the usability of the model by contaminating training samples, so such attacks are called causative availability indiscriminate attacks. Facing the problem that existing data sanitization methods are hard to apply to real-time applications due to their tedious process and heavy computations, we propose a new supervised batch detection method for poison, which can fleetly sanitize the training dataset before the local model training. We design a training dataset generation method that helps to enhance accuracy and uses data complexity features to train a detection model, which will be used in an efficient batch hierarchical detection process. Our model stockpiles knowledge about poison, which can be expanded by retraining to adapt to new attacks. Being neither attack-specific nor scenario-specific, our method is applicable to FL/DML or other online or offline scenarios.

Haptic is the modality that complements traditional multimedia, i.e., audiovisual, to evolve the next wave of innovation at which the Internet data stream can be exchanged to enable remote skills and control applications. This will require ultra-low latency and ultra-high reliability to evolve the mobile experience into the era of Digital Twin and Tactile Internet. While the 5th generation of mobile networks is not yet widely deployed, Long-Term Evolution (LTE-A) latency remains much higher than the 1 ms requirement for the Tactile Internet and therefore the Digital Twin. This work investigates an interesting solution based on the incorporation of Software-defined networking (SDN) and Multi-access Mobile Edge Computing (MEC) technologies in an LTE-A network, to deliver future multimedia applications over the Tactile Internet while overcoming the QoS challenges. Several network scenarios were designed and simulated using Riverbed modeler and the performance was evaluated using several time-related Key Performance Indicators (KPIs) such as throughput, End-2-End (E2E) delay, and jitter. The best scenario possible is clearly the one integrating MEC and SDN approaches, where the overall delay, jitter, and throughput for haptics- attained 2 ms, 0.01 ms, and 1000 packets per second. The results obtained give clear evidence that the integration of, both SDN and MEC, in LTE-A indicates performance improvement, and fulfills the standard requirements in terms of the above KPIs, for realizing a Digital Twin/Tactile Internet-based system.

Hybrid Power-line/Visible-light Communication (HPVC) network has been one of the most promising Cooperative Communication (CC) technologies for constructing Smart Home due to its superior communication reliability and hardware efficiency. Current research on HPVC networks focuses on the performance analysis and optimization of the Physical (PHY) layer, where the Power Line Communication (PLC) component only serves as the backbone to provide power to light Emitting Diode (LED) devices. So designing a Media Access Control（MAC) protocol remains a great challenge because it allows both PLC and Visible Light Communication (VLC) components to operate data transmission, i.e., to achieve a true HPVC network CC. To solve this problem, we propose a new HPC network MAC protocol (HPVC MAC) based on Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) by combining IEEE 802.15.7 and IEEE 1901 standards. Firstly, we add an Additional Assistance (AA) layer to provide the channel selection strategies for sensor stations, so that they can complete data transmission on the selected channel via the specified CSMA/CA mechanism, respectively. Based on this, we give a detailed working principle of the HPVC MAC, followed by the construction of a joint analytical model for mathematical-mathematical validation of the HPVC MAC. In the modeling process, the impacts of PHY layer settings (including channel fading types and additive noise feature), CSMA/CA mechanisms of 802.15.7 and 1901, and practical configurations (such as traffic rate, transit buffer size) are comprehensively taken into consideration. Moreover, we prove the proposed analytical model has the solvability. Finally, through extensive simulations, we characterize the HPVC MAC performance under different system parameters and verify the correctness of the corresponding analytical model with an average error rate of 4.62% between the simulation and analytical results.

The Autonomous Underwater Glider (AUG) is a kind of prevailing underwater intelligent internet vehicle and occupies a dominant position in industrial applications, in which path planning is an essential problem. Due to the complexity and variability of the ocean, accurate environment modeling and flexible path planning algorithms are pivotal challenges. The traditional models mainly utilize mathematical functions, which are not complete and reliable. Most existing path planning algorithms depend on the environment and lack flexibility. To overcome these challenges, we propose a path planning system for underwater intelligent internet vehicles. It applies digital twins and sensor data to map the real ocean environment to a virtual digital space, which provides a comprehensive and reliable environment for path simulation. We design a value-based reinforcement learning path planning algorithm and explore the optimal network structure parameters. The path simulation is controlled by a closed-loop model integrated into the terminal vehicle through edge computing. The integration of state input enriches the learning of neural networks and helps to improve generalization and flexibility. The task-related reward function promotes the rapid convergence of the training. The experimental results prove that our reinforcement learning based path planning algorithm has great flexibility and can effectively adapt to a variety of different ocean conditions.

Wireless Sensor Network (WSN) is a distributed sensor network composed a large number of nodes with low cost, low performance and self-management. The special structure of WSN brings both convenience and vulnerability. For example, a malicious participant can launch attacks by capturing a physical device. Therefore, node authentication that can resist malicious attacks is very important to network security. Recently, blockchain technology has shown the potential to enhance the security of the Internet of Things (IoT). In this paper, we propose a Blockchain-empowered Authentication Scheme (BAS) for WSN. In our scheme, all nodes are managed by utilizing the identity information stored on the blockchain. Besides, the simulation experiment about worm detection is executed on BAS, and the security is evaluated from detection and infection rate. The experiment results indicate that the proposed scheme can effectively inhibit the spread and infection of worms in the network.

A significant demand rises for energy-efficient deep neural networks to support power-limited embedding devices with successful deep learning applications in IoT and edge computing fields. An accurate energy prediction approach is critical to provide measurement and lead optimization direction. However, the current energy prediction approaches lack accuracy and generalization ability due to the lack of research on the neural network structure and the excessive reliance on customized training dataset. This paper presents a novel energy prediction model, NeurstrucEnergy. NeurstrucEnergy treats neural networks as directed graphs and applies a bi-directional graph neural network training on a randomly generated dataset to extract structural features for energy prediction. NeurstrucEnergy has advantages over linear approaches because the bi-directional graph neural network collects structural features from each layer's parents and children. Experimental results show that NeurstrucEnergy establishes state-of-the-art results with mean absolute percentage error of 2.60%. We also evaluate NeurstrucEnergy in a randomly generated dataset, achieving the mean absolute percentage error of 4.83% over 10 typical convolutional neural networks in recent years and 7 efficient convolutional neural networks created by neural architecture search. Our code is available at https://github.com/NEUSoftGreenAI/NeurstrucEnergy.git.

In wireless communication networks, mobile users in overlapping areas may experience severe interference, therefore, designing effective Interference Management (IM) methods is crucial to improving network performance. However, when managing multiple disturbances from the same source, it may not be feasible to use existing IM methods such as Interference Alignment (IA) and Interference Steering (IS) exclusively. It is because with IA, the aligned interference becomes indistinguishable at its desired Receiver (Rx) under the cost constraint of Degrees-of-Freedom (DoF), while with IS, more transmit power will be consumed in the direct and repeated application of IS to each interference. To remedy these deficiencies, Interference Alignment Steering (IAS) is proposed by incorporating IA and IS and exploiting their advantages in IM. With IAS, the interfering Transmitter (Tx) first aligns one interference incurred by the transmission of one data stream to a one-dimensional subspace orthogonal to the desired transmission at the interfered Rx, and then the remaining interferences are treated as a whole and steered to the same subspace as the aligned interference. Moreover, two improved versions of IAS, i.e., IAS with Full Adjustment at the Interfering Tx (IAS-FAIT) and Interference Steering and Alignment (ISA), are presented. The former considers the influence of IA on the interfering user-pair's performance. The orthogonality between the desired signals at the interfered Rx can be maintained by adjusting the spatial characteristics of all interferences and the aligned interference components, thus ensuring the Spectral Efficiency (SE) of the interfering communication pairs. Under ISA, the power cost for IS at the interfered Tx is minimized, hence improving SE performance of the interfered communication-pairs. Since the proposed methods are realized at the interfering and interfered Txs cooperatively, the expenses of IM are shared by both communication-pairs. Our in-depth simulation results show that joint use of IA and IS can effectively manage multiple disturbances from the same source and improve the system's SE.