Energy is essential for human existence, and its high consumption is a growing concern in today’s technology-driven society. Global initiatives aim to reduce energy consumption and pollution by developing and deploying energy-efficient sensing technologies for long-term monitoring, control, automation, security, and interactions. Wireless Body Area Networks (WBANs) benefit a lot from the continuous monitoring capabilities of these sensing devices, which include medical sensors worn on or implanted in the human body for healthcare monitoring. Despite significant advancements, achieving energy efficiency in WBANs remains a significant challenge. A deep understanding of the WBAN architecture is essential to identify the causes of its energy inefficiency and develop novel energy-efficient solutions. We investigate energy efficiency issues specific to WBANs. We discuss the transformative impact that artificial intelligence and Machine Learning (ML) can have on achieving the energy efficiency of WBANs. Additionally, we explore the potential of emerging technologies such as quantum computing, nano-technology, biocompatible energy harvesting, and Simultaneous Wireless Information and Power Transfer (SWIPT) in enabling energy efficiency in WBANs. We focus on WBANs’ architecture, hardware, and software components to identify key factors responsible for energy consumption in the WBAN environment. Based on our comprehensive review, we introduce an innovative, energy-efficient three-tier architecture for WBANs that employs ML and edge computing to overcome the limitations inherent in existing energy-efficient solutions. Finally, we summarize the lessons learned and highlight future research directions that will enable the development of energy-efficient solutions for WBANs.

With the rapid development of 5G technology, the proportion of video traffic on the Internet is increasing, bringing pressure on the network infrastructure. Edge computing technology provides a feasible solution for optimizing video content distribution. However, the limited edge node cache capacity and dynamic user requests make edge caching more complex. Therefore, we propose a recommendation-driven edge Caching network architecture for the Full life cycle of video streaming (FlyCache) designed to improve users’ Quality of Experience (QoE) and reduce backhaul traffic consumption. FlyCache implements intelligent caching management across three key stages: before-playback, during-playback, and after-playback. Specifically, we introduce a cache placement policy for the before-playback stage, a dynamic prefetching and cache admission policy for the during-playback stage, and a progressive cache eviction policy for the after-playback stage. To validate the effectiveness of FlyCache, we developed a user behavior-driven edge caching simulation framework incorporating recommendation mechanisms. Experiments conducted on the MovieLens and synthetic datasets demonstrate that FlyCache outperforms other caching strategies in terms of byte hit rate, backhaul traffic, and delayed startup rate.

A Mixed Numerology OFDM (MN-OFDM) system is essential in 6G and beyond. However, it encounters challenges due to Inter-Numerology Interference (INI). The upcoming 6G technology aims to support innovative applications with high data rates, low latency, and reliability. Therefore, effective handling of INI is crucial to meet the diverse requirements of these applications. To address INI in MN-OFDM systems, this paper proposes a User-Based Numerology and Waveform (UBNW) approach that uses various OFDM-based waveforms and their parameters to mitigate INI. By assigning a specific waveform and numerology to each user, UBNW mitigates INI, optimizes service characteristics, and addresses user demands efficiently. The required Guard Bands (GB), expressed as a ratio of user bandwidth, vary significantly across different waveforms at an SIR of 25 dB. For instance, OFDM-FOFDM needs only 2.5%, while OFDM-UFMC, OFDM-WOLA, and conventional OFDM require 7.5%, 24%, and 40%, respectively. The time-frequency efficiency also varies between the waveforms. FOFDM achieves 85.6%, UFMC achieves 81.6%, WOLA achieves 70.7%, and conventional OFDM achieves 66.8%. The simulation results demonstrate that the UBNW approach not only effectively mitigates INI but also enhances system flexibility and time-frequency efficiency while simultaneously reducing the required GB.

With technological advancements, high-speed rail has emerged as a prevalent mode of transportation. During travel, passengers exhibit a growing demand for streaming media services. However, the high-speed mobile networks environment poses challenges, including frequent base station handoffs, which significantly degrade wireless network transmission performance. Improving transmission efficiency in high-speed mobile networks and optimizing spatiotemporal wireless resource allocation to enhance passengers’ media experiences are key research priorities. To address these issues, we propose an Adaptive Cross-Layer Optimization Transmission Method with Environment Awareness (ACOTM-EA) tailored for high-speed rail streaming media. Within this framework, we develop a channel quality prediction model utilizing Kalman filtering and an algorithm to identify packet loss causes. Additionally, we introduce a proactive base station handoff strategy to minimize handoff-related disruptions and optimize resource distribution across adjacent base stations. Moreover, this study presents a wireless resource allocation approach based on an enhanced genetic algorithm, coupled with an adaptive bitrate selection mechanism, to maximize passenger Quality of Experience (QoE). To evaluate the proposed method, we designed a simulation experiment and compared ACOTM-EA with established algorithms. Results indicate that ACOTM-EA improves throughput by 11% and enhances passengers’ media experience by 5%.

Aiming at the problem of mobile data traffic surge in 5G networks, this paper proposes an effective solution combining massive multiple-input multiple-output techniques with Ultra-Dense Network (UDN) and focuses on solving the resulting challenge of increased energy consumption. A base station control algorithm based on Multi-Agent Proximity Policy Optimization (MAPPO) is designed. In the constructed 5G UDN model, each base station is considered as an agent, and the MAPPO algorithm enables inter-base station collaboration and interference management to optimize the network performance. To reduce the extra power consumption due to frequent sleep mode switching of base stations, a sleep mode switching decision algorithm is proposed. The algorithm reduces unnecessary power consumption by evaluating the network state similarity and intelligently adjusting the agent’s action strategy. Simulation results show that the proposed algorithm reduces the power consumption by 24.61% compared to the no-sleep strategy and further reduces the power consumption by 5.36% compared to the traditional MAPPO algorithm under the premise of guaranteeing the quality of service of users.

This paper studies a cooperative relay transmission system within the framework of Multiple-Input Multiple-Output Radio Frequency/Underwater Optical Wireless Communication (MIMO-RF/UOWC), aiming to establish sea-based heterogeneous networks. In this setup, the RF links obey 𝜅-𝜇 fading, while the UOWC links undergo the generalized Gamma fading with the pointing error impairments. The relay operates under an Amplify-and-Forward (AF) protocol. Additionally, the attenuation caused by the Absorption and Scattering (AaS) is considered in UOWC links. The work yields precise results for the Average Channel Capacity (ACC), Outage Probability (OP), and average Bit Error Rate (BER). Furthermore, to reveal deeper insights, bounds on the ACC and asymptotic results for the OP and average BER are derived. The findings highlight the superior performance of MIMO-RF/UOWC AF systems compared to Single-Input-Single-Output (SISO)-RF/UOWC AF systems. Various factors affecting the Diversity Gain (DG) of the MIMO-RF/UOWC AF system include the number of antennas/apertures, fading parameters of both links, and pointing error parameters. Moreover, while an increase in the AaS effect can result in significant attenuation, it does not determine the achievable DG of the proposed MIMO-RF/UOWC AF relaying system.

In this paper, we analyze the capacity and delay performance of a large-scale Unmanned Aerial Vehicle (UAV)-enabled wireless network, in which untethered and tethered UAVs deployed with content files move along with mobile Ground Users (GUs) to satisfy their coverage and content delivery requests. We consider the case where the untethered UAVs are of limited storage, while the tethered UAVs serve as the cloud when the GUs cannot obtain the required files from the untethered UAVs. We adopt the Ornstein-Uhlenbeck (OU) process to capture the mobility pattern of the UAVs moving along the GUs and derive the compact expressions of the coverage probability and capacity of our considered network. Using tools from martingale theory, we model the traffic at UAVs as a two-tier queueing system. Based on this modeling, we further derive the analytical expressions of the network backlog and delay bounds. Through numerical results, we verify the correctness of our analysis and demonstrate how the capacity and delay performance can be significantly improved by optimizing the percentage of the untethered UAVs with cached contents.

With increasing density and heterogeneity in unlicensed wireless networks, traditional MAC protocols, such as Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) in Wi-Fi networks, are experiencing performance degradation. This is manifested in increased collisions and extended backoff times, leading to diminished spectrum efficiency and protocol coordination. Addressing these issues, this paper proposes a deep-learning-based MAC paradigm, dubbed DL-MAC, which leverages spectrum data readily available from energy detection modules in wireless devices to achieve the MAC functionalities of channel access, rate adaptation, and channel switch. First, we utilize DL-MAC to realize a joint design of channel access and rate adaptation. Subsequently, we integrate the capability of channel switching into DL-MAC, enhancing its functionality from single-channel to multi-channel operations. Specifically, the DL-MAC protocol incorporates a Deep Neural Network (DNN) for channel selection and a Recurrent Neural Network (RNN) for the joint design of channel access and rate adaptation. We conducted real-world data collection within the 2.4 GHz frequency band to validate the effectiveness of DL-MAC. Experimental results demonstrate that DL-MAC exhibits significantly superior performance compared to traditional algorithms in both single and multi-channel environments, and also outperforms single-function designs. Additionally, the performance of DL-MAC remains robust, unaffected by channel switch overheads within the evaluation range.

Traditional Internet of Things (IoT) architectures that rely on centralized servers for data management and decision-making are vulnerable to security threats and privacy leakage. To address this issue, blockchain has been advocated for decentralized data management in a tamper-resistance, traceable, and transparent manner. However, a major issue that hinders the integration of blockchain and IoT lies in that, it is rather challenging for resource-constrained IoT devices to perform computation-intensive blockchain consensuses such as Proof-of-Work (PoW). Furthermore, the incentive mechanism of PoW pushes lightweight IoT nodes to aggregate their computing power to increase the possibility of successful block generation. Nevertheless, this eventually leads to the formation of computing power alliances, and significantly compromises the decentralization and security of BlockChain-aided IoT (BC-IoT) networks. To cope with these issues, we propose a lightweight consensus protocol for BC-IoT, called Proof-of-Trusted-Work (PoTW). The goal of the proposed consensus is to disincentivize the centralization of computing power and encourage the independent participation of lightweight IoT nodes in blockchain consensus. First, we put forth an on-chain reputation evaluation rule and a reputation chain for PoTW to enable the verifiability and traceability of nodes’ reputations based on their contributions of computing power to the blockchain consensus, and we incorporate the multi-level block generation difficulty as a rewards for nodes to accumulate reputations. Second, we model the block generation process of PoTW and analyze the block throughput using the continuous time Markov chain. Additionally, we define and optimize the relative throughput gain to quantify and maximize the capability of PoTW that suppresses the computing power centralization (i.e., centralization suppression). Furthermore, we investigate the impact of the computing power of the computing power alliance and the levels of block generation difficulty on the centralization suppression capability of PoTW. Finally, simulation results demonstrate the consistency of the analytical results in terms of block throughput. In particular, the results show that PoTW effectively reduces the block generation proportion of the computing power alliance compared with PoW, while simultaneously improving that of individual lightweight nodes. This indicates that PoTW is capable of suppressing the centralization of computing power to a certain degree. Moreover, as the levels of block generation difficulty in PoTW increase, its centralization suppression capability strengthens.

Data trading is a crucial means of unlocking the value of Internet of Things (IoT) data. However, IoT data differs from traditional material goods due to its intangible and replicable nature. This difference leads to ambiguous data rights, confusing pricing, and challenges in matching. Additionally, centralized IoT data trading platforms pose risks such as privacy leakage. To address these issues, we propose a profit-driven distributed trading mechanism for IoT data. First, a blockchain-based trading architecture for IoT data, leveraging the transparent and tamper-proof features of blockchain technology, is proposed to establish trust between data owners and data requesters. Second, an IoT data registration method that encompasses both rights confirmation and pricing is designed. The data right confirmation method uses non-fungible token to record ownership and authenticate IoT data. For pricing, we develop an IoT data value assessment index system and introduce a pricing model based on a combination of the sparrow search algorithm and the back propagation neural network. Finally, an IoT data matching method is designed based on the Stackelberg game. This establishes a Stackelberg game model involving multiple data owners and requesters, employing a hierarchical optimization method to determine the optimal purchase strategy. The security of the mechanism is analyzed and the performance of both the pricing method and matching method is evaluated. Experiments demonstrate that both methods outperform traditional approaches in terms of error rates and profit maximization.

Due to the neglect of the retrieval of communication parameters (including the symbol rate, the symbol timing offset, and the carrier frequency), the existing non-cooperative communication mode recognizers suffer from the generality ability degradation and severe difficulty in distinguishing a large number of modulation modes, etc. To overcome these drawbacks, this paper proposes an efficient communication mode recognizer consisting of communication parameter estimation, the constellation diagram retrieval, and a classification network. In particular, we define a 2-D symbol synchronization metric to retrieve both the symbol rate and the symbol timing offset, whereas a constellation dispersity annealing procedure is devised to correct the carrier frequency accurately. Owing to the accurate estimation of these crucial parameters, high-regularity constellation maps can be retrieved and thus simplify the subsequent classification work. Numerical results show that the proposed communication mode recognizer acquires higher classification accuracy, stronger anti-noise robustness, and higher applicability of distinguishing multiple types, which presents the proposed scheme with vast applicable potentials in non-cooperative scenarios.

Accurate early classification of elephant flows (elephants) is important for network management and resource optimization. Elephant models, mainly based on the byte count of flows, can always achieve high accuracy, but not in a time-efficient manner. The time efficiency becomes even worse when the flows to be classified are sampled by flow entry timeout over Software-Defined Networks (SDNs) to achieve a better resource efficiency. This paper addresses this situation by combining co-training and Reinforcement Learning (RL) to enable a closed-loop classification approach that divides the entire classification process into episodes, each involving two elephant models. One predicts elephants and is retrained by a selection of flows automatically labeled online by the other. RL is used to formulate a reward function that estimates the values of the possible actions based on the current states of both models and further adjusts the ratio of flows to be labeled in each phase. Extensive evaluation based on real traffic traces shows that the proposed approach can stably predict elephants using the packets received in the first 10% of their lifetime with an accuracy of over 80%, and using only about 10% more control channel bandwidth than the baseline over the evolved SDNs.

In the upcoming B5G/6G era, Virtual Reality (VR) over wireless has become a typical application, which is an inevitable trend in the development of video. However, in immersive and interactive VR experiences, VR services typically exhibit high delay, while simultaneously posing challenges for the energy consumption of local devices. To address these issues, this paper aims to improve the performance of VR service in the edge-terminal cooperative system. Specifically, we formulate a joint Caching, Computing, and Communication (3C) VR service policy problem by optimizing the weighted sum of the total VR delivery delay and the energy consumption of local devices. To design the optimal VR service policy, the optimization problem is decoupled into three independent subproblems to be solved separately. To improve the caching efficiency within the network, a Bert-based user interest analysis method is first proposed to accurately characterize the content request behavior. Based on this, a service cost minimum-maximization problem is formulated under the consideration of performance fairness among users. Then, the joint caching and computing scheme is derived for each user with a given allocation of communication resources while a bisection-based communication scheme is acquired with the given information on the joint caching and computing policy. With alternative optimization, an optimal policy for joint 3C based on user interest can be finally obtained. Simulation results are presented to demonstrate the superiority of the proposed user interest-aware caching scheme and the effectiveness of the joint 3C optimization policy while considering user fairness. Our code is available at https://github.com/mrfuqaq1108/Interest-Aware-Joint-3C-Optimization.

In covert communications, joint jammer selection and power optimization are important to improve performance. However, existing schemes usually assume a warden with a known location and perfect Channel State Information (CSI), which is difficult to achieve in practice. To be more practical, it is important to investigate covert communications against a warden with uncertain locations and imperfect CSI, which makes it difficult for legitimate transceivers to estimate the detection probability of the warden. First, the uncertainty caused by the unknown warden location must be removed, and the Optimal Detection Position (OPTDP) of the warden is derived which can provide the best detection performance (i.e., the worst case for a covert communication). Then, to further avoid the impractical assumption of perfect CSI, the covert throughput is maximized using only the channel distribution information. Given this OPTDP based worst case for covert communications, the jammer selection, the jamming power, the transmission power, and the transmission rate are jointly optimized to maximize the covert throughput (OPTDP-JP). To solve this coupling problem, a Heuristic algorithm based on Maximum Distance Ratio (H-MAXDR) is proposed to provide a sub-optimal solution. First, according to the analysis of the covert throughput, the node with the maximum distance ratio (i.e., the ratio of the distances from the jammer to the receiver and that to the warden) is selected as the friendly jammer (MAXDR). Then, the optimal transmission and jamming power can be derived, followed by the optimal transmission rate obtained via the bisection method. In numerical and simulation results, it is shown that although the location of the warden is unknown, by assuming the OPTDP of the warden, the proposed OPTDP-JP can always satisfy the covertness constraint. In addition, with an uncertain warden and imperfect CSI, the covert throughput provided by OPTDP-JP is 80% higher than the existing schemes when the covertness constraint is 0.9, showing the effectiveness of OPTDP-JP.

We propose a Cross-Chain Mapping Blockchain (CCMB) for scalable data management in massive Internet of Things (IoT) networks. Specifically, CCMB aims to improve the scalability of securely storing, tracing, and transmitting IoT behavior and reputation data based on our proposed cross-mapped Behavior Chain (BChain) and Reputation Chain (RChain). To improve off-chain IoT data storage scalability, we show that our lightweight CCMB architecture efficiently utilizes available fog-cloud resources. The scalability of on-chain IoT data tracing is enhanced using our Mapping Smart Contract (MSC) and cross-chain mapping design to perform rapid Reputation-to-Behavior (R2B) traceability queries between BChain and RChain blocks. To maximize off-chain to on-chain throughput, we optimize the CCMB block settings and producers based on a general Poisson Point Process (PPP) network model. The constrained optimization problem is formulated as a Markov Decision Process (MDP), and solved using a dual-network Deep Reinforcement Learning (DRL) algorithm. Simulation results validate CCMB’s scalability advantages in storage, traceability, and throughput. In specific massive IoT scenarios, CCMB can reduce the storage footprint by 50% and traceability query time by 90%, while improving system throughput by 55% compared to existing benchmarks.

Integrated Sensing and Communication (ISAC) is envisioned as a promising technology for Sixth-Generation (6G) wireless communications, which enables simultaneous high-rate communication and high-precision target localization.compared to independent sensing and communication modules, dual-function ISAC could leverage the strengths of both communication and sensing in order to achieve cooperative gains. When considering the communication core network, ISAC system facilitates multiple communication devices to collaborate for networked sensing. This paper investigates such kind of cooperative ISAC systems with distributed transmitters and receivers to support non-connected and multi-target localization. Specifically, we introduce a Time of Arrival (TOA) based multi-target localization scheme, which leverages the bi-static range measurements between the transmitter, target, and receiver channels in order to achieve elliptical localization. To obtain the low-complexity localization, a two-stage search-refine localization methodology is proposed. In the first stage, we propose a Successive Greedy Grid-Search (SGGS) algorithm and a Successive-Cancellation-List Grid-Search (SCLGS) algorithm to address the Measurement-to-Target Association (MTA) problem with relatively low computational complexity. In the second stage, a linear approximation refinement algorithm is derived to facilitate high-precision localization. Simulation results are presented to validate the effectiveness and superiority of our proposed multi-target localization method.

Efficient and safe information exchange between vehicles can reduce the probability of road accidents, thereby improving the driving experience of vehicles in Vehicular Ad Hoc Networks (VANETs). This paper proposes a group management algorithm with trust and mobility evaluation to address the enormous pressure on VANETs topology caused by high-speed vehicle movement and dynamic changes in the direction of travel. This algorithm utilizes historical interactive data to mine the fusion trust between vehicles. Then, combined with fusion mobility, the selection of center members and information maintenance of group members is achieved. Furthermore, based on bilinear pairing, an encryption protocol is designed to solve the problem of key management and update when the group structure changes rapidly, ensuring the safe forwarding of messages within and between groups. Numerical analysis shows that the algorithm in the paper ensures group stability and improves performance such as average message delivery rate and interaction delay.

Low-light images often have defects such as low visibility, low contrast, high noise, and high color distortion compared with well-exposed images. If the low-light region of an image is enhanced directly, the noise will inevitably blur the whole image. Besides, according to the retina-and-cortex (retinex) theory of color vision, the reflectivity of different image regions may differ, limiting the enhancement performance of applying uniform operations to the entire image. Therefore, we design a Hierarchical Flow Learning (HFL) framework, which consists of a Hierarchical Image Network (HIN) and a normalized invertible Flow Learning Network (FLN). HIN can extract hierarchical structural features from low-light images, while FLN maps the distribution of normally exposed images to a Gaussian distribution using the learned hierarchical features of low-light images. In subsequent testing, the reversibility of FLN allows inferring and obtaining enhanced low-light images. Specifically, the HIN extracts as much image information as possible from three scales, local, regional, and global, using a Triple-branch Hierarchical Fusion Module (THFM) and a Dual-Dconv Cross Fusion Module (DCFM). The THFM aggregates regional and global features to enhance the overall brightness and quality of low-light images by perceiving and extracting more structure information, whereas the DCFM uses the properties of the activation function and local features to enhance images at the pixel-level to reduce noise and improve contrast. In addition, in this paper, the model was trained using a negative log-likelihood loss function. Qualitative and quantitative experimental results demonstrate that our HFL can better handle many quality degradation types in low-light images compared with state-of-the-art solutions. The HFL model enhances low-light images with better visibility, less noise, and improved contrast, suitable for practical scenarios such as autonomous driving, medical imaging, and nighttime surveillance. Outperforming them by PSNR = 27.26 dB, SSIM = 0.93, and LPIPS = 0.10 on benchmark dataset LOL-v1. The source code of HFL is available at https://github.com/Smile-QT/HFL-for-LIE.

Reconfigurable Intelligent Surface (RIS) is regarded as a cutting-edge technology for the development of future wireless communication networks with improved frequency efficiency and reduced energy consumption. This paper proposes an architecture by combining RIS with Generalized Spatial Modulation (GSM) and then presents a Multi-Residual Deep Neural Network (MR-DNN) scheme, where the active antennas and their transmitted constellation symbols are detected by sub-DNNs in the detection block. Simulation results demonstrate that the proposed MR-DNN detection algorithm performs considerably better than the traditional Zero-Forcing (ZF) and the Minimum Mean Squared Error (MMSE) detection algorithms in terms of Bit Error Rate (BER). Moreover, the MR-DNN detection algorithm has less time complexity than the traditional detection algorithms.

High-quality services in today’s mobile networks require stable delivery of bandwidth-intensive network content. Multipath QUIC (MPQUIC), as a multipath protocol that extends QUIC, can utilize multiple paths to support stable and efficient transmission. The standard coupled congestion control algorithm in MPQUIC synchronizes these paths to manage congestion, meeting fairness requirements and improving transmission efficiency. However, current algorithms’ Congestion Window (CWND) reduction approach significantly decreases CWND upon packet loss, which lowers effective throughput, regardless of the congestion origin. Furthermore, the uncoupled Slow-Start (SS) in MPQUIC leads to independent exponential CWND growth on each path, potentially causing buffer overflow. To address these issues, we propose the CC-OLIA, which incorporates Packet Loss Classifcation (PLC) and Coupled Slow-Start (CSS). The PLC distinguishes between congestion-induced and random packet losses, adjusting CWND reduction accordingly to maintain throughput. Concurrently, the CSS module coordinates CWND growth during the SS, preventing abrupt increases. Implementation on MININET shows that CC-OLIA not only maintains fair performance but also enhances transmission efficiency across diverse network conditions.

To facilitate cross-domain data interaction in the Industrial Internet of Things (IIoT), establishing trust between multiple administrative domains is essential. Although blockchain technology has been proposed as a solution, current techniques still suffer from issues related to efficiency, security, and privacy. Our research aims to address these challenges by proposing a lightweight, trusted data interaction scheme based on blockchain, which reduces redundant interactions among entities. We enhance the traditional Practical Byzantine Fault Tolerance (PBFT) algorithm to support lightweight distributed consensus in large-scale IIoT scenarios. Introducing a composite digital signature algorithm and incorporating veto power minimizes resource consumption and eliminates ineffective consensus operations. The experimental results show that, compared with PBFT, our scheme reduces latency by 27.2%, thereby improving communication efficiency and resource utilization. Furthermore, we develop a lightweight authentication technique specifically for cross-domain IIoT, leveraging blockchain technology to achieve distributed collaborative authentication. The performance comparisons indicate that our method significantly outperforms traditional schemes, with an average authentication latency of approximately 151 milliseconds. Additionally, we introduce a trusted federated learning (FL) algorithm that ensures comprehensive trust assessments for devices across different domains while protecting data privacy. Extensive simulations and experiments validate the reliability of our approach.

Semantic Communication (SemCom) can significantly reduce the transmitted data volume and keep robustness. Task-oriented SemCom of images aims to convey the implicit meaning of source messages correctly, rather than achieving precise bit-by-bit reconstruction. Existing image SemCom systems directly perform semantic encoding and decoding on the entire image, which has not considered the correlation between image content and downstream tasks or the adaptability to channel noise. To this end, we propose a content-aware robust SemCom framework for image transmission based on Generative Adversarial Networks (GANs). Specifically, the accurate semantics of the image are extracted by the semantic encoder, and divided into two parts for different downstream tasks: Regions of Interest (ROI) and Regions of Non-Interest (RONI). By reducing the quantization accuracy of RONI, the amount of transmitted data volume is reduced significantly. During the transmission process of semantics, a Signal-to-Noise Ratio (SNR) is randomly initialized, enabling the model to learn the average noise distribution. The experimental results demonstrate that by reducing the quantization level of RONI, transmitted data volume is reduced up to 60.53% compared to using globally consistent quantization while maintaining comparable performance to existing methods in downstream semantic segmentation tasks. Moreover, our model exhibits increased robustness with variable SNRs.

Federated Learning (FL) has become a popular training paradigm in recent years. However, stragglers are critical bottlenecks in an Internet of Things (IoT) network while training. These nodes produce stale updates to the server, which slow down the convergence. In this paper, we studied the impact of the stale updates on the global model, which is observed to be significant. To address this, we propose a weighted averaging scheme, FedStrag, that optimizes the training with stale updates. The work is focused on training a model in an IoT network that has multiple challenges, such as resource constraints, stragglers, network issues, device heterogeneity, etc. To this end, we developed a time-bounded asynchronous FL paradigm that can train a model on the continuous inflow of data in the edge-fog-cloud continuum. To test the FedStrag approach, a model is trained with multiple stragglers scenarios on both Independent and Identically Distributed (IID) and non-IID datasets on Raspberry Pis. The experiment results suggest that the FedStrag outperforms the baseline FedAvg in all possible cases.

The advancement of 6G wireless communication technology has facilitated the integration of Vehicular Ad-hoc Networks (VANETs). However, the messages transmitted over the public channel in the open and dynamic VANETs are vulnerable to malicious attacks. Although numerous researchers have proposed authentication schemes to enhance the security of Vehicle-to-Vehicle (V2V) communication, most existing methodologies face two significant challenges: (1) the majority of the schemes are not lightweight enough to support real-time message interaction among vehicles; (2) the sensitive information like identity and position is at risk of being compromised. To tackle these issues, we propose a lightweight dual authentication protocol for V2V communication based on Physical Unclonable Function (PUF). The proposed scheme accomplishes dual authentication between vehicles by the combination of Zero-Knowledge Proof (ZKP) and MASK function. The security analysis proves that our scheme provides both anonymous authentication and information unlinkability. Additionally, the performance analysis demonstrates that the computation overhead of our scheme is approximately reduced 23.4% compared to the state-of-the-art schemes. The practical simulation conducted in a 6G network environment demonstrates the feasibility of 6G-based VANETs and their potential for future advancements.

Existing wireless networks are flooded with video data transmissions, and the demand for high-speed and low-latency video services continues to surge. This has brought with it challenges to networks in the form of congestion as well as the need for more resources and more dedicated caching schemes. Recently, Multi-access Edge Computing (MEC)-enabled heterogeneous networks, which leverage edge caches for proximity delivery, have emerged as a promising solution to all of these problems. Designing an effective edge caching scheme is critical to its success, however, in the face of limited resources. We propose a novel Knowledge Graph (KG)-based Dueling Deep Q-Network (KG-DDQN) for cooperative caching in MEC-enabled heterogeneous networks. The KG-DDQN scheme leverages a KG to uncover video relations, providing valuable insights into user preferences for the caching scheme. Specifically, the KG guides the selection of related videos as caching candidates (i.e., actions in the DDQN), thus providing a rich reference for implementing a personalized caching scheme while also improving the decision efficiency of the DDQN. Extensive simulation results validate the convergence effectiveness of the KG-DDQN, and it also outperforms baselines regarding cache hit rate and service delay.

The Internet of Things (IoT) has gained substantial attention in both academic research and real-world applications. The proliferation of interconnected devices across various domains promises to deliver intelligent and advanced services. However, this rapid expansion also heightens the vulnerability of the IoT ecosystem to security threats. Consequently, innovative solutions capable of effectively mitigating risks while accommodating the unique constraints of IoT environments are urgently needed. Recently, the convergence of Blockchain technology and IoT has introduced a decentralized and robust framework for securing data and interactions, commonly referred to as the Internet of Blockchained Things (IoBT). Extensive research efforts have been devoted to adapting Blockchain technology to meet the specific requirements of IoT deployments. Within this context, consensus algorithms play a critical role in assessing the feasibility of integrating Blockchain into IoT ecosystems. The adoption of efficient and lightweight consensus mechanisms for block validation has become increasingly essential. This paper presents a comprehensive examination of lightweight, constraint-aware consensus algorithms tailored for IoBT. The study categorizes these consensus mechanisms based on their core operations, the security of the block validation process, the incorporation of AI techniques, and the specific applications they are designed to support.

Existing Generalized Receive Spatial Modulation (GRSM) with Symbol-Level Precoding (SLP) forces the received signals (excluding noise) at unintended antennas to be zero, which restricts the generation of strong constructive interference to intended receive antennas and thus limits the performance improvement over conventional GRSM with Zero-Forcing (ZF) precoding. In this paper, we propose a novel GRSM-SLP scheme that relaxes the zero receive power constraint and achieves superior performance by integrating Intelligent Reflecting Surfaces (IRSs). Specifically, our advanced GRSM-RSLP jointly exploits SLP at the transmitter and passive beamforming at the IRS to maximize the power difference between intended and unintended receive antennas, where the received signals at unintended antennas are relaxed to lie in a sphere centered at origin with a preset radius that depends on the Signal-to-Noise Ratio (SNR) value. The precoding matrix and passive beamforming vectors are optimized alternately by considering both phase shift keying and quadrature amplitude modulation signaling. It is worth emphasizing that GRSM-RSLP is a universal solution, also applicable to systems without IRS, although it performs better in IRS-assisted systems. We finally conduct extensive simulations to prove the superiority of GRSM-RSLP over GRSM-ZF and GRSM-SLP. Simulation results show that the performance of GRSM-RSLP is significantly influenced by the number of unintended antennas, and the larger the number, the better its performance. In the best-case scenario, GRSM-RSLP can achieve SNR gains of up to 10.5 dB and 12.5 dB over GRSM-SLP and GRSM-ZF, respectively.

In this paper, we investigate a Reconfigurable Intelligent Surface (RIS)-assisted Free-Space Optics-Radio Frequency (FSO-RF) mixed dual-hop communication system for Unmanned Aerial Vehicles (UAVs). In the first hop, a source UAV transmits data to a relay UAV using the FSO technique. In the second hop, the relay UAV forwards data to a destination Mobile Station (MS) via an RF channel, with the RIS enhancing coverage and performance. The relay UAV operates in a Decode-and-Forward (DF) mode. As the main contribution, we provide a mathematical performance analysis of the RIS-assisted FSO-RF mixed dual-hop UAV system, evaluating outage probability, Bit-Error Rate (BER), and average capacity. The analysis accounts for factors such as atmospheric attenuation, turbulence, geometric losses, and link interruptions caused by UAV hovering behaviors. To the best of our knowledge, this is the first theoretical investigation of RIS-assisted FSO-RF mixed dual-hop UAV communication systems. Our analytical results show strong agreement with Monte Carlo simulation outcomes. Furthermore, simulation results demonstrate that RIS significantly enhances the performance of UAV-aided mixed RF/FSO systems, although performance saturation is observed due to uncertainties stemming from UAV hovering behavior.

Unmanned Aerial Vehicles (UAVs) have been considered to have great potential in supporting reliable and timely data harvesting for Sensor Nodes (SNs) from an Internet of Things (IoT) perspective. However, due to physical limitations, UAVs are unable to further process the harvested data and have to rely on terrestrial servers, thus extra spectrum resource is needed to convey the harvested data. To avoid the cost of extra servers and spectrum resources, in this paper, we consider a UAV-based data harvesting network supported by a Cell-Free massive Multiple-Input-Multiple-Output (CF-mMIMO) system, where a UAV is used to collect and transmit data from SNs to the central processing unit of CF-mMIMO system for processing. In order to avoid using additional spectrum resources, the entire bandwidth is shared among radio access networks and wireless fronthaul links. Moreover, considering the limited capacity of the fronthaul links, the compress-and-forward scheme is adopted. In this work, in order to maximize the ergodically achievable sum rate of SNs, the power allocation of ground access points, the compression of fronthaul links, and also the bandwidth fraction between radio access networks and wireless fronthaul links are jointly optimized. To avoid the high overhead introduced by computing ergodically achievable rates, we introduce an approximate problem, using the large-dimensional random matrix theory, which relies only on statistical channel state information. We solve the nontrivial problem in three steps and propose an algorithm based on weighted minimum mean square error and Dinkelbach’s methods to find solutions. Finally, simulation results show that the proposed algorithm converges quickly and outperforms the baseline algorithms.

Data privacy leakage has always been a critical concern in cloud-based Internet of Things (IoT) systems. Dynamic Symmetric Searchable Encryption (DSSE) with forward and backward privacy aims to address this issue by enabling updates and retrievals of ciphertext on untrusted cloud server while ensuring data privacy. However, previous research on DSSE mostly focused on single keyword search, which limits its practical application in cloud-based IoT systems. Recently, Patranabis (NDSS 2021) [1] proposed a groundbreaking DSSE scheme for conjunctive keyword search. However, this scheme fails to effectively handle deletion operations in certain circumstances, resulting in inaccurate query results. Additionally, the scheme introduces unnecessary search overhead. To overcome these problems, we present CKSE, an efficient conjunctive keyword DSSE scheme. Our scheme improves the oblivious shared computation protocol used in the scheme of Patranabis, thus enabling a more comprehensive deletion functionality. Furthermore, we introduce a state chain structure to reduce the search overhead. Through security analysis and experimental evaluation, we demonstrate that our CKSE achieves more comprehensive deletion functionality while maintaining comparable search performance and security, compared to the oblivious dynamic cross-tags protocol of Patranabis. The combination of comprehensive functionality, high efficiency, and security makes our CKSE an ideal choice for deployment in cloud-based IoT systems.