Machine learning-based methods for encrypted traffic classification can be effectively applied to analyze encrypted proxy traffic generated by proxy protocols, which are intermediary protocols used to route network traffic through a remote server. Nonetheless, different encrypted proxy protocols generate distinct traffic patterns, even when they handle the same network behavior. To address these distribution differences, a straight-forward approach is to collect datasets specific to each proxy protocol. However, typical proxy protocols repackage original traffic by encrypting it without payload padding or compression. This leads to a definite characteristic correlation between original and encrypted proxy traffic. We propose an End-to-end Original traffic-based Encrypted Proxy Traffic Classification framework (EO-EPTC) to bridge the distribution gap between original traffic and proxied traffic, enabling the classification of encrypted proxy traffic using a original traffic dataset. EO-EPTC conducts sequence feature alignment to reduce distribution bias and employs a Seq2Seq model to capture the underlying semantics of the proxy protocol, creating a sequence feature transformation model. We apply EO-EPTC to existing encrypted traffic classification models, training them on original traffic to classify proxied traffic. This achieves up to 99.70% accuracy on encrypted proxy traffic, comparable to models trained directly on proxied traffic.

Efficient deployment of thousands of energy-constrained sensor nodes (SNs) in large-scale wireless sensor networks (WSNs) is critical for reliable data transmission and target sensing. This study addresses the Minimum Energy Q-Coverage and C-Connectivity (MinEQC) problem for heterogeneous SNs in three-dimensional environments. MnPF (Metaheuristic–Neural Network Parallel Framework), a two-phase method that can embed most metaheuristic algorithms (MAs) and neural networks (NNs), is proposed to address the above problem. Phase-I partitions the monitoring region via divide-and-conquer and applies NN-based dimensionality reduction to accelerate parallel optimization of local Q-coverage and C-connectivity. Phase-II employs an MA-based adaptive restoration strategy to restore connectivity among subregions and systematically assess how different partitioning strategies affect the number of restoration steps. Experiments with four NNs and twelve MAs demonstrate efficiency, scalability, and adaptability of MnPF, while ablation studies confirm the necessity of both phases. MnPF bridges scalability and energy efficiency, providing a generalizable approach to SN deployment in large-scale WSNs.

With the widespread deployment of Internet of Things (IoT) devices across various domains, security concerns have become increasingly critical. Developing effective intrusion detection systems (IDS) for IoT environments is essential but faces challenges such as ensuring device privacy, addressing inter-class and inter-device heterogeneity, and overcoming the constraints of limited computational resources. This paper presents an innovative federated learning-based approach to address these challenges. First, we designed a federated dynamic sampling algorithm (Fed-DynamicSMOTE) that combines federated learning with the K-means SMOTE oversampling method. This approach addresses the class imbalance and data heterogeneity issues in distributed environments while preserving device privacy. Second, we introduce a lightweight classification algorithm, Light-SqueezeNet, tailored for resource-constrained IoT devices. By introducing residual connections and optimizing the Fire modules, the model’s parameter size is compressed, achieving lightweight and efficient attack detection. Experimental evaluations on the CICIoT2023 and IDS2017 datasets demonstrate that the proposed approach significantly outperforms both traditional centralized methods and conventional federated methods in terms of privacy preservation and intrusion detection accuracy. These findings highlight a promising direction for enhancing IoT security.

Parkinson's disease (PD) is one of the most common neurodegenerative disorders. The increasing demand for high-accuracy forecasts of disease progression has led to a surge in research employing multi-modality variables for prediction. In this review, we selected articles published from 2016 through June 2024, adhering strictly to our exclusion-inclusion criteria. These articles employed a minimum of two types of variables, including clinical, genetic, biomarker, and neuroimaging modalities. We conducted a comprehensive review and discussion on the application of multi-modality approaches in predicting PD progression. The predictive mechanisms, advantages, and shortcomings of relevant key modalities in predicting PD progression are discussed in the paper. The findings suggest that integrating multiple modalities resulted in more accurate predictions compared to those of fewer modalities in similar conditions. Furthermore, we identified some limitations in the existing field. Future studies that harness advancements in multi-modality variables and machine learning algorithms can mitigate these limitations and enhance predictive accuracy in PD progression.

In the increasingly critical field of aerial communication, unmanned aerial vehicles (UAVs) have gained significant attention as prominent representatives, and accurate air-to-air (A2A) channel modeling plays a pivotal role in the design and evaluation of reliable communication systems. This paper presents a A2A channel model for UAV communications. It introduces a quasi-deterministic approach to address limitations in existing modeling frameworks. The proposed model uses a truncated ellipsoid to capture the distribution of scatterers in A2A scenarios and, for the first time, incorporates rooftop specular reflection (RSR). Power correction factors, based on the UAV’s airframe structure, position, and posture, are introduced to provide a comprehensive and realistic depiction of the A2A communication channel. The performance of proposed model is assessed by simulating key statistical channel characteristics and comparing with other alternatives. The simulations illustrate how channel behavior is influenced by factors such as flight level, flight trajectory, and UAV posture. The results show that RSR leads to the channel hardening effect, while airframe occlusion causes the received signal power to vary gradually with changes in UAV’s position and posture. The validity of the model is confirmed through comparison with measurement data and ray-tracing results, proving its accuracy and practical application.

Unmanned aerial vehicle (UAV)-to-ground (U2G) channel models play a decisive role in the design, optimization, and evaluation of communication systems between UAV and ground terminal. This paper proposes a three-dimensional (3D) model for U2G communication channels, enhanced with ultra-wideband (UWB) features and frequency non-stationarity. This model integrates large-scale and small-scale fading components, introducing bandwidth-dependent path numbers and the UAV posture matrix for realistic scenario representation. It encompasses specific UWB U2G channel phenomena such as the channel hardening, UAV 3D movements, and posture variation effect. The channel parameters, including spatial large-scale parameters (LSPs), bandwidth-correlated path numbers, delay-posture-correlated path power, and frequency-correlated path phase, are generated to capture channel non-stationary characteristics across time and frequency domains. Employing ray-tracing (RT) for the path number and optimization methods for the path delay, the proposed model ensures reliable parameter evolution. The proposed model is assessed through key statistical properties, including space-time-frequency correlation functions, power delay profile, root-mean-square delay spread, Doppler power spectrum density, and the energy variance. It is demonstrated that both posture and bandwidth variations have crucial effects on channel characteristics. The validity and practicability of this research is demonstrated by comparing the simulated outcomes with the measurement data.

The multi-connectivity enabled user association (MCUA) represents a promising technology to overcome the unreliability of millimeter-wave (mmWave) links, which are plagued by severe path loss, frequent cross-cell handover, and vulnerability to blockage by obstacles. This paper aims to address the essential challenges in MCUA, i.e., the user associations and power allocations. Specifically, we investigate the joint optimization of MCUA and power allocation from the perspective of the trade-off between downlink rates for different users, taking into account the mutual constraints on each user's downlink rate in resource-limited systems. To solve this nonlinear mixed integer multi-objective optimization problem, we first employ the weighted-sum method to scalarize it as a single-objective optimization problem (SOOP) and relax the binary association variables to real ones. Then we develop an iterative algorithm based on the convex-concave procedure to solve this relaxed SOOP. Numerical results are presented to demonstrate the effectiveness of the proposed algorithms.

This paper proposes a joint Service function chain - virtual network functions (SFC-VNF) deployment scheme based on SFC mapping requirements in 5G systems, aiming to enhance server resource utilization in the physical network while minimizing link consumption. Given the NP-hard nature of this problem, an improved Sine Cosine Algorithm (ISCA) is developed to address it, specifically in the form of the ISCA-SFC deployment algorithm. Extensive experiments were conducted on two distinct physical networks to demonstrate the superior performance of SFC-VNF compared to traditional deployment approaches. Additionally, ISCA-SFC was compared with alternative mechanisms based on four other heuristic algorithms, with experimental results confirming its superior performance.

Integrated sensing and communication (ISAC) has emerged as a pivotal technology for next-generation mobile networks to embed sensing function on communication waveforms. A major challenge in ISAC is the effective integration of sensing and communication functions. Addressing this, this paper introduces a dual-functional waveform design that builds on the existing orthogonal frequency division multiplexing (OFDM) waveform. Unlike prior approaches that generally sacrifice communication performance to enhance sensing performance, our design contains a null-space sensing precoder that utilizes the null space of the communication channel to project additional sensing signals, thus improving the sensing functionality of the OFDM waveform without degrading any communication performance. We formulate a waveform optimization problem aimed at maximizing the sensing performance under the null-space sensing precoder and then propose a majorization-minimization (MM)-based waveform design algorithm. Additionally, to meet the real-time communication requirement in practice, we analyze the intrinsic characteristics of the high-performance sensing waveform and then develop a low-complexity waveform design algorithm. Simulation results show that the proposed MM-based algorithm can dramatically improve sensing performance without incurring any additional sensing power and degrading the communication performance. Furthermore, the low-complexity algorithm achieves substantial improvements in the sensing performance with much reduced computational complexity.

For large-scale multi-objective evolutionary algorithms (LSMOEAs), it has been a major challenge to efficiently obtain accurate evolutionary directions in the ultra-high-dimensional decision space to produce high-quality offspring. To this hand, this paper proposes an algorithm, namely, a directed quick search guided large-scale multi-objective evolutionary algorithm (QSLMOA). This algorithm contains three main parts: a directional vector-based sampling strategy, a quick search guided directed reproduction strategy, and an environment selection. In each generation, the proposed sampling strategy determines a set of directional solutions to construct the direction vectors which are used to guide the search directions. The sampling strategy significantly reduces the search space and improves the sampling efficiency in the early stages. On the other hand, our proposed reproduction strategy introduces the directional information, and with their assistance, the inferior ones of solutions in the combined population can rapidly converge to the elite ones, which can speed up the convergence rate. Finally, the elitist non-dominated sorting is adopted as the environment selection to obtain the parent population of the next generation. Comprehensive experiments verify that QSLMOA performs the best compared to the state-of-the-art LSMOEAs for nine large-scale multi-objective benchmark problems LSMOP1-LSMOP9 with up to three objectives and 5000 decision variables.

The increasing number of objectives poses a great challenge upon many-objective optimization algorithms (MaOOAs) when solving many-objective optimization problems (MaOOPs), since it is rather difficult to obtain well-distributed solutions with tight convergence. To efficiently improve the ability of solving MaOOPs, this paper proposes a hierarchical clustering-based cooperative multi-population many-objective optimization algorithm (C2MP-MaOOA). Specifically, a hierarchical clustering-based population division strategy is proposed in C2MP-MaOOA, which is able to effectively optimize different regions of the Pareto front (PF) regardless of its shape, so as to maintain population diversity and accelerate convergence. Any single-objective optimizer can be applied in C2MP-MaOOA to optimize a subpopulation. To comprehensively evaluate the performance of C2MP-MaOOA, it was compared with eight state-of-the-art existing algorithms and two variants of C2MP-MaOOA on 63 MaOOPs selected from DTLZ, MaF, and WFG benchmark suites. The results indicate that C2MP-MaOOA has the best overall performance for each benchmark suite, which demonstrates that C2MP-MaOOA is quite competitive in solving MaOOPs.

To address the issue of imbalanced datasets classification, this study explores how different oversampling algorithms and imbalance ratios affect the performance of classification algorithms. Two oversampling algorithms, including random oversampling and Synthetic Minority Oversampling Technique (SMOTE), are used to adjust the imbalance ratio of the training dataset to 999:1, 99:1, 9:1, 3:1, and 1:1. Four classification methods, including the Convolutional Neural Network, Vision Transformer, XGBoost and CatBoost, are evaluated using performance metrics such as precision, recall, AUC, and F2-Score. We conduct more than 240 experiments and observe that oversampling ratio has a significant positive impact on AUC and recall rate, but a negative impact on precision. The study also identifies the best oversampling algorithm and imbalance ratio for each classification algorithm. It is noteworthy that the Vision Transformer algorithm used in this study has not been employed in previous research on imbalanced data classification.

Multi-connectivity enabled user associations (MCUA) has been believed to be a promising method to enhance the connection between user equipments and base stations in ultra-dense millimeter wave (mmWave) networks. In this paper, the optimal MCUA is investigated from the user-side perspective with the objective of maximizing the overall downlink rate while satisfying the QoS requirements of each user. In view the terribly huge computational cost required by centralized MCUA methods, in this paper, we develop a distributed multi-agent deep reinforcement (MADRL) model to search for the optimal MCUA policy. In the proposed MADRL-MCUA, each UE is regarded as an independent agent and determines the its own association policy according to its own observed benefits and the feedback from the mmWave base stations. Experiment results are presented to demonstrate the effectiveness of the proposed method.

Energy-efficient optimal power allocation (OPA) has always been an essential optimization for multi-user cooperative diversity systems. However, most of the existing works have mainly focused on optimizing the system’s overall energy efficiency (EE) while neglecting to maximize the EE of each user jointly which is an inherent demand for multi-user systems. To this end, in this letter, we investigate the quality-of-service constrained EE-OPA to maximize the EE for each user simultaneously in a multi-user decode-and-forward cooperative system by a multi-objective optimization approach. This constrained multi-objective optimization problem (MOOP) is solved by jointly leveraging the weighted Tchebycheff method and the Dinkelbach method, however, with a considerably high computational complexity. In order to reduce the computational complexity while still obtaining near-optimal solutions, we further proposed a machine learning approach to solve the MOOP. Specifically, we setup an Elman neural network to model and learn the multi-objective EE-OPA (MO-EE-OPA) for a given multi-user DF cooperative network. Numerical results show that the Elman network can output near-optimal solutions with dramatically low computational complexity.

The millimeter wave (mmWave) spectrum has been involved in the fifth generation wireless systems (5G) for its enormous spectrum resources. However, signals over mmWave band suffer from severe path loss and are vulnerable to be blocked by obstacles due to the extremely high frequency, which greatly degrades the quality of transmission and connection reliability. One of the promising ways to overcome this problem is the multi-connectivity technology, which allows a user in an mmWave network to associate with multiple mmWave base stations (mBSs). And for multi-connectivity, there are two essential challenges: one is the optimal user association, that is, which are the most appropriate mBSs a user should be connected to, and the other is the optimal power allocation of each connection. In view of this, this paper aims to jointly optimize the user association and optimal power allocation for multi-connectable mmWave networks. Different from most existing works, the optimization objectives in our work are three-fold, namely maximizing the overall energy-efficiency and meanwhile balancing the achievable rates among all users and the load among all mBSs, respectively, under the QoS constraint. Considering that this multi-objective problems is a mixed integer programming and NP-hard, this paper proposes a method based on the novel non-dominated sorting genetic algorithm II (NSGA-II) to obtain the near-optimal solutions. Simulation results show that the proposed scheme enables optimal multi-connectivity enabled user associations and power allocations simultaneously in ultra-dense mmWave networks and yields EE-fairness tradeoff.

Facing the challenges brought by the surge in the demand for mobile data traffic and increasingly scarce spectrum resources, two well-known channel access mechanisms named as duty-cycle muting (DCM) and listen- before-talk (LBT) have been proposed. In this article, we propose a novel adaptive hybrid channel access scheme which takes advantages of both mechanisms. Based on the WiFi traffic and the available licensed spectrum resource, our proposal can adaptively adjust the important parameters, such as the back-off window size and the duty-cycle time fraction, while ensuring fair and harmonious network coexistence between the WiFi and LTE-U systems. It can realize the flexible handoff between the DCM and LBT mechanisms to meet the requirements of different markets as well. Moreover, joint transmission power and spectrum resource allocation is also studied to improve the spectral efficiency on both licensed and unlicensed bands. The effectiveness of the proposed scheme is finally validated by numerical simulations.

This paper investigates the AP deployment optimization for wireless local area networks (WLANs) within non-uniform service areas. Specifically, this paper proposes a genetic algorithm (GA) based AP deployment scheme to jointly optimize the location and the transmit power of each AP under the constraint of full coverage. Different from most existing works, the proposed scheme takes the non-uniform service area where there are various obstacles within it into fully consideration. Sufficient simulations have been done to evaluate the performance of the GA-based deployment scheme by comparing with the uniform deployment scheme. Simulation results indicate that the total transmit power can be significantly reduced by leveraging the proposed scheme while guaranteeing the full coverage of the desired service area. At the same time, the overlap rate of the WLAN can also be significantly reduced by the proposed scheme.

Linear network coded cooperation (LNCC) is a new technology that combines linear network coding and cooperative communication. LNCC can dramatically reduce system outage probability and hence increase spectrum efficiency. The outage performance considering path loss is essential for further system optimizations for LNCC, such as optimal power allocation and optimal relay selection. However, as far as we know, this issue still remains open. In this paper, we investigate the outage performance of a two-slot multiuser LNCC system in many-to-one communication mode. We fully consider the effect of pathloss in the process of theoretical analysis and theoretically derive the tight approximated outage probability of LNCC system. And the analytic results are verified by Monte Carlo simulations. Numerical results show that two-slot LNCC system with path loss is able to greatly improve the outage performance.

In multi-user cooperative diversity systems, energy-efficient optimal power allocation (OPA) is an essential problem. However, most of the existing works only focus on optimizing the overall system energy efficiency (EE), rather than the individual EE for each user. In this paper, we investigate the OPA in multi-user amplify-and-forward cooperative systems aiming at maximizing the EE for each individual user while satisfying the quality-of-service (QoS) requirements. We utilize the multi-objective optimization framework to formulate the power allocation problem. Due to the NP-hardness of the problem, we further employ the non-dominated sorting genetic algorithm II (NSGA-II) to find the Pareto optimal solutions to the multi-objective optimization problem. Simulation results demonstrate that the proposed algorithm can obtain well-distributed Pareto optimal solutions, as well as yield fast convergence and flexible EE tradeoff.

In shifted Luby transform (SLT) codes, robust soltion distribution (RSD) degree distribution was conducted with shifted rounding to derive shifted RSD (SRSD) degree distribution based on partial information. In shifted rounding process, the large shift of corresponding probability distribution destroyed belief propagation decoding rule, so the decoding symbols increased. Meanwhile, the overlarge probability distribution value of degree k resulted in the increase of decoding symbols. In this work, traditional SRSD degree distribution was developed to improved SRSD (I‐SRSD) degree distribution function by decreasing degree shift of rounding and limiting probability distribution of degree k . Theoretical analysis and experimental results show that SLT codes by I‐SRSD degree distribution can decrease decoding symbols as well as encoding and decoding complexity.

The robustness of inter‐session network coding is still an open issue in lossy wireless networks. The traditional XOR based network coding cannot work well if the overhearing is unperfect. Especially, the coding node cannot know the overheard information in time. In this paper, we consider a robust network coding method, namely R 2 NC which uses random linear network coding to encode packets together in the inter‐session level, to resist the unperfect overhearing problem. With this method, coding node can always know the solvability of coded packets without the knowledge of overheard information. We analyse the performance of R 2 NC method with both lossy links of output and overhearing in the classic X ‐topology model, and give a necessary condition for the existence of coding gain. Finally, we design an optimal coding algorithm and a relay selection algorithm for R 2 NC to achieve its maximal transmission efficiency. Through ns‐2 simulations, we demonstrate that R 2 NC plays a good performance in terms of throughput, delay and overhead, and is robust against losses on output and overhearing links.

Outage probability is one of the most important performance measures for cooperative communication systems. And the closed-form solution on exact outage probability is essential for many further studies, such as optimal power allocation and rate control. However, as far as we know, the issue remains open for multiuser linear network coded cooperation (LNCC), which is a technology integrating linear network coding into cooperative communication. Consequently, in this paper, we investigate the multiuser LNCC system with multiple cooperation time slots and many-to-one communication pattern. All the possible outage scenarios are fully considered and the closed-form solution of the system's exact outage probability is theoretically derived. In order to obtain the diversity order of the LNCC system, the asymptotic outage probability is also analysed. The theoretical analyses are verified by plenty of Monte Carlo simulations. In order to demonstrate the benefits introduced by LNC to the cooperative system, the outage performance comparison between the LNCC and the traditional Decode-and-Forward (DF) cooperation system is carried out. The results show that given the same number of users and cooperation time slots, the LNCC system's outage probability is greatly lower than that of the traditional DF cooperation system.

Convergecast is probably the most common communication style in wireless sensor networks (WSNs). And network coding (NC) is a promising concept to improve throughput or reliability of convergecast. Most of the existing works have mainly focused on exploiting these benefits without considering its potential adverse effect. In this paper, we argue that network coding may not always benefit convergecast. This viewpoint is discussed within four scenarios: The network-coding-aided (NC-aided) and the none-network-coding (none-NC) convergecast schemes with or without automatic repeat request (ARQ) mechanisms. The most concerned performance metrics, including packet collection rate, energy consumption and end-to-end delay, are investigated. Theoretical analyses and simulation results show that the way network coding operates, i.e., conscious overhearing and the prerequisite of successfully decoding, could naturally diminish its advantages in convergecast. And NC-aided convergecast schemes may even be inferior to none-NC ones when the wireless link delivery ratio is high enough. The conclusion drawn in this paper casts a new light on how to effectively apply network coding to practical WSNs.

Network coding is one of the most promising techniques to increase the reliability and reduce the energy consumption for wireless sensor networks (WSNs). However, most of the previous works mainly focus on the network coding for multi-cast or uni- cast in WSNs, in spite of the fact that the converge-cast is the most common communication style in WSNs. In this paper, we investigate, for the first time as far as we know, the feasibility of acquiring network coding benefits in converge-cast, and we present that with the ubiquitous convergent structures self-organized during converge-casting in the network, the reliability benefits can be obtained by applying linear network coding. We theoretically derive the network coding benefits obtained in a general convergent structure, and simulations are conducted to validate our theoretical analysis. The results reveal that the network coding can improve the network reliability considerably.

In this paper, an energy-efficient optimal relay selection strategy which is jointly optimized with the energy-efficient optimal power allocation solution for AF cooperative transmission is proposed. The relay selection criterion is the distance to the optimal relay locations where the minimum transmission power of the source, the relay or their total can be achieved. To determine those most energy-efficient relay locations, a universal algorithm with low computational complexity and easy implementation is also presented in this paper. The simulations are conducted to validate our theoretical analysis. The results show that with the relay selected by the proposed strategy, the cooperative transmission can achieve considerably high energy-efficiency.

To solve the problems occurring in the traditional precision agriculture such as poor real-time data acquisition, small monitoring coverage area, excessive manpower requirement etc., this paper designs and deploys an environment monitoring system for precise agriculture based on wireless sensor networks in a red bayberry greenhouse located on a hillside. This system can automatically collect the temperature, humidity, illumination, voltage and other parameters of the deployment zone, and transmit the data to the remote server via GPRS in real time. This system also includes a web-based platform integrated with Google Maps to release the greenhouse environmental status and provide real-time voice and SMS alarm service. Since the experimental area is lack of mains supply, the system is powered by solar and storage batteries. The experiment result shows that the low-cost system has strong scalability, and can provide real-time, stable and accurate service for precise agriculture.

Modern Communication Technologies course is a specialized course for information-related but non-communication majors. Firstly, this paper introduces the features of this course and the syllabus of the theory and practice teaching. Then, to address the issues exist in the practice teaching, this paper proposes three ideas including college-enterprise co-construction labs, leading virtual instruments technologies and network simulation technologies into the practice teaching, and organizing course-related visits.

In this paper, we proposed an adaptive low latency MAC protocol for sensor networks based on cross-layer architecture, named LLS-MAC (Low Latency Sensor MAC). The new protocol is inspired by S-MAC. The advancement of LLS-MAC in packet end-to-end delay is mainly due to the continual data transmission scheme presented in this paper. For achieving this scheme, a cross-layer design approach is adopted in LLS-MAC. Simulations have been done to evaluate the performance of the proposed new protocol, by which we can find out that LLS-MAC can really reduce packet end-to-end delay greatly in case of busy networks compared with S-MAC.