In the Internet of Things (IoT) domain, end-to-end (E2E) planning tasks require distributed devices to collaboratively train deep models under highly dynamic environments. However, existing federated learning (FL) methods often assume homogeneous communication conditions and static node reliability, leading to suboptimal aggregation performance when confronted with heterogeneous uncertainty sources such as sensing noise, prediction bias, and communication instability. To address this challenge, we propose FedUAP (Federated Uncertainty-Aware End-to-End Planning), a novel framework that dynamically adjusts client contributions based on multi-source uncertainty and network topology information. Specifically, each IoV vehicle node within the broader IoT system estimates three uncertainty factors—prediction uncertainty, sensing uncertainty, and communication uncertainty—to represent its model reliability and transmission stability. A topology-aware weighting module further refines the aggregation by incorporating node connectivity and link quality. In addition, a temporal smoothing strategy is introduced to stabilize weight evolution over successive communication rounds. Extensive experiments on various E2E IoV-centric IoT planning scenarios demonstrate that FedUAP achieves superior convergence stability, communication efficiency, and planning accuracy compared with existing adaptive aggregation and uncertainty-based FL baselines. The proposed approach provides a promising direction toward uncertainty-robust and topology-adaptive federated optimization in large-scale IoT and IoV networks.

Large-scale deep learning models rely on wireless networks for distributed training approaches, which are essential to meet the immense computational and data demands. However, the stochastic nature of wireless environments introduces significant challenges such as variable delays, noise interference, and packet loss, which lead to degraded gradient synchronization and hinder model convergence. In this work, we propose a novel communication-aware distributed training (CADT) framework that integrates real-time channel state information (CSI) feedback into the gradient aggregation process. Unlike conventional methods that assume static or ideal communication conditions, CADT dynamically reweights gradients from each node based on instantaneous channel quality, enabling robust aggregation under adverse wireless conditions. By dynamically adjusting the contribution of each node based on instantaneous channel conditions, CADT effectively compensates for wireless impairments, thereby ensuring more reliable gradient aggregation and significantly improving both convergence speed and final model accuracy. Extensive experiments on CIFAR-10, CIFAR-100, ImageNet, and SVHN using Vision Transformer and ResNet-50 demonstrate that CADT outperforms baseline methods in terms of convergence, accuracy, and communication efficiency. In addition, we provide a rigorous theoretical analysis that establishes convergence guarantees under realistic wireless conditions, thereby advancing the theoretical foundation of distributed optimization in non-ideal communication environments.Our framework offers a practical solution for real-world scenarios such as edge computing, where communication constraints and environmental variability are dominant factors.

With the development of real-time video conferences, interactive multimedia services have proliferated, leading to a surge in traffic. Interactivity becomes one of the main features on future multimedia services, which brings a new challenge to Computer Vision (CV) for communications. In addition, many directions for CV in video, like recognition, understanding, saliency segmentation, coding, and so on, do not satisfy the demands of the multiple tasks of interactivity without integration. Meanwhile, with the rapid development of the foundation models, we apply task-oriented semantic communications to handle them. Therefore, we propose a novel framework, called Real-Time Video Conference with Foundation Model (RTVCFM), to satisfy the requirement of interactivity in the multimedia service. Firstly, at the transmitter, we perform the causal understanding and spatiotemporal decoupling on interactive videos, with the Video Time-Aware Large Language Model (VTimeLLM), Iterated Integrated Attributions (IIA) and Segment Anything Model 2 (SAM2), to accomplish the video semantic segmentation. Secondly, in the transmission, we propose a two-stage semantic transmission optimization driven by Channel State Information (CSI), which is also suitable for the weights of asymmetric semantic information in real-time video, so that we achieve a low bit rate and high semantic fidelity in the video transmission. Thirdly, at the receiver, RTVCFM provides multidimensional fusion with the whole semantic segmentation by using the Diffusion Model for Foreground Background Fusion (DMFBF), and then we reconstruct the video streams. Finally, the simulation result demonstrates that RTVCFM can achieve a compression ratio as high as 95.6%, while it guarantees high semantic similarity of 98.73% in Multi-Scale Structural Similarity Index Measure (MS-SSIM) and 98.35% in Structural Similarity (SSIM), which shows that the reconstructed video is relatively similar to the original video.

Symbiotic radio (SR) has emerged as a spectrum and energy-efficient paradigm to support massive Internet of Things connections. This paper investigates secure transmission in a multi-antenna artificial noise (AN)-assisted SR network under both parasitic SR (PSR) and commensal SR (CSR) setups, with a particular focus on the challenges posed by the presence of a passive eavesdropper. Specifically, the transmitter allocates part of its power for AN generation to disrupt the eavesdropper deliberately without affecting the legitimate receiver. To evaluate the secrecy performance in both setups, new approximate closed-form expressions for the secrecy outage probability in primary and backscatter links are derived using the Gauss-Chebyshev quadrature method. The secrecy diversity orders of the system are studied by analyzing the asymptotic behaviours in the high signal-to-noise ratio regime. Furthermore, the secrecy performance under imperfect channel state information is investigated to evaluate the robustness of the proposed scheme in practical scenarios. Monte Carlo simulations are performed to validate the correctness and effectiveness of the analytical results, which demonstrate that the CSR setup provides stronger secrecy performance than the PSR setup in primary signal decoding.

Perimeter security system based on distributed optical fiber sensor network plays a key role in the monitoring and protection of restricted areas and large industrial areas. At present, most of the distributed intrusion signal recognition algorithms rely on manual feature extraction methods and traditional classifiers such as traditional support vector machines, which generally have low recognition efficiency and accuracy. To solve these problems, a convolutional prototype network DWTCPLnet is proposed in this paper. Firstly, the original onedimensional intrusion interference signal is decomposed into five approximate coefficients in the frequency domain by discrete wavelet transform (DWT), and then combined with the original signal to form a new two-dimensional data. This two-dimensional data is then entered into DWT-CPLnet for training. At the same time, the training process of the network is restricted by the metric space of prototype learning. The experimental results indicate that the average recognition accuracy of DWT-CPLnet in 6 types of common intrusion disturbance signals (three natural disturbances: wind blowing, light rain, heavy rain; three manmade disturbances: knocking, impacting and slapping) can reach 99.59%, and also has the ability to identify unknown classes to meet the actual monitoring needs.

The management of computing resources through the computing power network (CPN) has gradually become a focal point of research. With the development of the 6th generation (6G) mobile networks, some promising technologies such as satellite-terrestrial integrated network (STIN) and smart endogenous network driven by artificial intelligence (AI) are increasingly being applied in Industrial Internet of Things (IIoT). However, several issues in current studies are worthy of attention: 1) the large number of devices powered by battery in IIoT, 2) the complex environments of communication, 3) the finite computing resources for task data processing. To cope with these challenges, a satellite-terrestrial integrated computing power network (STICPN) framework is introduced in this article. Within this framework, a task offloading link selection scheme is proposed, which minimizes the delay and the consumption of energy. The task offloading optimization problem is modeled as a Markov decision process (MDP). Meanwhile, deep reinforcement learning (DRL) algorithm is employed to adapt to the dynamic states of environment. Specifically, a dueling double deep Q network (D3QN) is used to make optimal decisions and delay as well as energy consumption can be reduced significantly. Moreover, the D3QN-based scheme extends the usage time of IIoT devices. The simulation results indicate that the proposed scheme outperforms comparison schemes significantly.

Mission-critical applications are of significant importance to sixth generation (6G)’s massive and ubiquitous Internet of things (IoT) communications. The mission-critical applications mostly fall within the scope of finite blocklength (FBL), and in order to assess the information freshness, age of information (AoI) has been introduced. However, packet error is inevitable in the FBL regime, which exerts impacts on the time for successful packet transmission, and thus increases the AoI. To optimize the performance of AoI, the management of queue packets is an effective way. Motivated by optimizing the AoI performance in the FBL regime, we consider a system equipped with a single buffer, and propose two schemes of packet management in this article. We subsequently derive the closed-form expressions for the average AoI and the average peak AoI and we discuss the relationship between AoI and the factors, i.e. the blocklength, data generation rate and signal-to-noise ratio. Afterwards, we give the optimal blocklength expression associated with the optimal AoI. In order to examine the information timeliness in the network under the proposed schemes, the closed-form expressions of the average delay are deduced. The simulation results validate the theoretical analysis and demonstrate the advantage of the proposed scheme in terms of the performance of AoI, delay, and their trade-off.

With the development of intelligent transportation systems, vehicular edge computing (VEC) has played a pivotal role by integrating computation, storage, and analytics closer to the vehicles. VEC represents a paradigm shift towards real-time data processing and intelligent decision-making, overcoming challenges associated with latency and resource constraints. In VEC scenarios, the efficient scheduling and allocation of computing resources are fundamental research areas, enabling real-time processing of vehicular tasks and intelligent decision-making. This paper provides a comprehensive review of the latest research in Deep Reinforcement Learning (DRL)-based task scheduling and resource allocation in VEC environments. Firstly, the paper outlines the development of VEC and introduces the core concepts of DRL, shedding light on their growing importance in the dynamic VEC landscape. Secondly, the state-of-the-art research in DRL-based task scheduling and resource allocation is categorized, reviewed, and discussed. Finally, the paper discusses current challenges in the field, offering insights into the promising future of VEC applications within the realm of intelligent transportation systems.

The integration among 6G communication networks, power grids, and transportation systems is emerging as a promising paradigm to achieve mutual benefits among autonomous-driving electric vehicle (EV) users, communication operators, and power grids. Task offloading strategies for autonomous driving and the traveling patterns of EVs can induce communication load fluctuation within 6G network, which subsequently influences energy flow in power grid. Conversely, electricity price from the power grid affects EV charging/discharging strategies, impacting traffic flow and autonomous driving task offloading within the 6G network. Based on the interdependencies among the three networks, this paper constructs a communication-power-transportation coupling network with 6G base stations (BSs) and fast charge stations (FCSs) acting as coupling hubs. Besides, a spatio-temporal electricity price model considering spatial traffic distribution and temporal load fluctuation is developed. Moreover, the optimization problem is formulated to jointly coordinate FCS selection, bidirectional charging/discharging power regulation, task offloading decisions, and route selection strategies to maximize demand response quality of experience (QoE), grid stability and balance under the constraint of autonomous driving quality of service (QoS). Then, a knowledge transfer collaboration-based spatio-temporal EV task offloading, energy, and traffic management joint optimization algorithm is proposed, which improves the optimization performance through knowledge transfer collaboration among EV. Finally, simulation results validate the performance improvement of the proposed algorithm in demand response QoE, grid stability and balance, and autonomous driving QoS.

In intelligent transportation systems, data routing in vehicle-to-everything (V2X) networks is key to ensuring efficient information transfer among vehicles, pedestrians, and infrastructure. The quality of data routing directly affects communication efficiency and system performance. However, data routing in V2X networks often faces potential security threats, which may lead to communication interruption, data delay, or information loss. Unreliable routing fails to meet the communication Quality of Service (QoS) requirements for V2X networks. Therefore, this article proposes a joint scheme that combines trust prediction and attack resistance to ensure reliable routing in V2X networks. First, this scheme employs a fuzzy control-based trust evaluation method to provide direct trust indicators. Second, a trust prediction method based on deep belief networks is utilized to evaluate vehicle status. A classification scheme based on the trust levels is used to filter candidate sets for network repair to help the network resist malicious behavior. Finally, a novel routing decision function is introduced to plan reliable routes. Routes planned on the basis of this function not only meet the basic requirements of reliable routing but are also suitable for routing requirements in different scenarios, such as minimizing transmission latency. The experimental results show that, compared with the three baseline schemes, this scheme improves the accuracy and false alarm rate on the UNSW-NB15 dataset by 2.94% and 6.31%, respectively, and this scheme also performs better in terms of the data reception rate and transmission delay rate in actual application scenarios.

We are delighted to introduce the fifth part of the Special Issue on intelligent autonomous transportation systems with 6G, which aims to provide the scientific community with a comprehensive overview of innovative technologies, advanced architectures, and potential challenges for the 6G-supported intelligent autonomous transport systems. Forty-two papers were selected for publication in this issue. All the papers were rigorously evaluated according to the standard reviewing process of IEEE Transactions on Intelligent Transportation Systems. The evaluation process considered originality, technical quality, presentational quality, and overall contribution. We will introduce these articles and highlight their main contributions in the following.

Network traffic classification is crucial for optimal network resource management. Several network traffic classification methods have been proposed, e.g., Deep Packet Inspection (DPI), and machine learning-based network traffic classification. Each approach is generally efficient for a certain class of network traffic. However, there is no one-fit-all method, i.e., no method offers the best performance for all types of network traffic. In this paper, we propose a hybrid network traffic classification technique that uses a combination of DPI and machine learning to identify and classify the network traffic into different Quality of Service (QoS) classes. The traffic is first identified through the DPI module, and the unidentified traffic then goes through the machine learning module, offering a classification accuracy of more than 98%. The results are evaluated based on the combination of DPI and different machine learning methods, e.g. supervised and unsupervised learning algorithms.

Artificial intelligence has been utilized to analyze patients' electroencephalograms (EEG) to diagnose depression. However, attackers can deduce patients' privacy after analyzing patients' EEG time series. Therefore, researchers propose to operate ciphertext calculation in depression diagnosis models based on homomorphic encryption. Nevertheless, homomorphic encryption requires consistent private keys during training, which could result in other participants decrypting the ci-phertexts. Additionally, existing EEG-based models neglect the relationship among electrode positions during EEG acquisition. To address these issues, we propose a novel training strategy for the depression diagnosis model based on fully homomorphic en-cryption (FHE) and electrode topology. Specifically, we establish a training strategy that prioritizes the privacy of patients' EEG data without compromising the cost-effectiveness of the diagnosis model. Furthermore, we incorporate the attention mechanism of electrode topology into our model to improve its performance and verify the relationship among topology locations. Our proposed model outperforms the original convolution neural network model, achieving higher accuracy in depression diagnosis and identifying virtual electrode channels for the first time.

Federated learning (FL) has been deployed on low earth orbit (LEO) satellites Internet of Things (IoT), where learning models can be trained collaboratively, thus preserving IoT data privacy without centralizing. However, the efficiency of FL is significantly hindered by the straggler that cause maximum latency. The data allocation strategy that parallelizes learning could potentially increase efficiency of FL for LEO satellite IoT networks since multiple LEO satellites can access a terrestrial IoT gateway concurrently. However, modeling and optimizing data allocation poses a significant challenge. To address this challenge, this paper proposes a collaborative learning method with latency-aware data allocation for LEO satellite IoT networks. Particularly, we formulate the data allocation strategy as an optimization problem of minimizing the maximum latency which is the sum of training time of the learning model and signal propagation delay, while considering the constraint of limited energy at each satellite. Next, we use a line search sequential quadratic programming (SQP) method to decompose the problem into a sequence of quadratic programming (QP) subproblems, which are further solved by the active-set algorithm. Simulation results show that nearly the half of the maximum latency per round can be decreased and the procedure of convergence is accelerated about 25 % in a large LEO satellite constellation with 1000 satellites and 10 IoT gateways.

Maneuvering-target tracking has always been an important and challenge work because the unknown and changeable motion-models can easily lead to the failure of model-driven target tracking. Recently, many neural network methods are proposed to improve the tracking accuracy by constructing direct mapping relationships from noisy observations to target states. However, limited by the coverage of training data, those data-driven methods suffer other problems, such as weak generalization abilities and unstable tracking effects. In this paper, a digital twin system for maneuvering-target tracking is built, and all kinds of simulated data are created with different motion-models. Based on those data, the features of noisy observations and their relationship to target states are found by two specially designed neural networks: one eliminates the observation noises and the other one predicts the target states according to the noise-limited observations. Combining the above two networks, the state prediction method is proposed to intelligently predict targets by understanding the information of motion-model hidden in noisy observations. Simulation results show that, in comparison with the state-of-the-art model-driven and data-driven methods, the proposed method can correctly and timely predict the motion-models, increase the tracking generalization ability and reduce the tracking root-mean-squared-error by over 50% in most of maneuvering-target tracking scenes.

We are delighted to introduce the third part of the Special Section on Intelligent Autonomous Transportation Systems with 6G, which aims to provide the scientific community with a comprehensive overview of innovative technologies, advanced architectures, and potential challenges for the 6G-supported Intelligent Autonomous Transport Systems. Twenty articles were selected for publication in this issue. All the articles were rigorously evaluated according to the standard reviewing process of the IEEE Transactions on Intelligent Transportation Systems. The evaluation process considered originality, technical quality, presentational quality, and overall contribution. We will introduce these articles and highlight their main contributions in the following.

We are delighted to introduce the fourth part of the Special Issue on Intelligent Autonomous Transportation Systems ith 6G, hich aims to provide the scientific community ith a comprehensive overvie of innovative technologies, advanced architectures, and potential challenges for the 6G-supported Intelligent Autonomous Transport Systems. Forty-three papers ere selected for publication in this issue. All the papers ere rigorously evaluated according to the standard revieing process of the IEEE Transactions on Intelligent Transportation Systems. The evaluation process considered originality, technical quality, presentational quality, and overall contribution. e ill introduce these articles and highlight their main contributions in the folloing.

Recently, we have experienced an incredible surge of interest in connected and autonomous vehicles and related enabling technologies, which are expected to revolutionize future Intelligent Transportation Systems (ITS). This surging demand and popularity of ITS with the Internet of Vehicles technology has led to a tremendous rise in the number of connected vehicles. Driven by this massive number of connected vehicles, and the stringent requirements of autonomous vehicles and data-intensive applications such as ultralow latency, high reliability, and high security, intelligent transportation systems are rapidly moving to the 6G networks. The 6G-supported ITS is expected to be a transformative factor for both society and the economy by delivering unprecedented, seamless, reliable, efficient massive connectivity to millions of users and connected vehicles.

In the near future, the 6G-supported Intelligent Transportation System (ITS) is expected to be a transformative factor for both society and the economy by delivering unprecedented, seamless, reliable, efficient massive connectivity to millions of users and connected vehicles. This Special Issue aims to provide the scientific community with a comprehensive overview of innovative technologies, advanced architectures, and potential challenges for the 6G-supported Intelligent Autonomous Transport System. Twenty articles were selected for publication in the second part of the issue. All the articles were rigorously evaluated according to the standard reviewing process of the IEEE Transactions on Intelligent Transportation Systems. The evaluation process considered factors pertaining to originality, technical quality, presentational quality, and overall contribution. We will introduce these articles and highlight their main contributions in the following.

Non-orthogonal multiple access (NOMA) promises to fulfill the fast-growing connectivities in future Internet of Things (IoT) using abundant multiple-access signatures. While explicitly notifying the utilized NOMA signatures causes large signaling cost, blind signature classification naturally becomes a low-cost option. To accomplish signature classification for NOMA, we study both likelihood- and feature-based methods. A likelihood-based method is firstly proposed and showed to be optimal in the asymptotic limit of the observations, despite high computational complexity. While feature-based classification methods promise low complexity, efficient features are non-trivial to be manually designed. To this end, we resort to artificial intelligence (AI) for deep learning-based automatic feature extraction. Specifically, our proposed deep neural network for signature classification, namely DeepClassifier, establishes on the insights gained from the likelihood-based method, which contains two stages to respectively deal with a single observation and aggregate the classification results of an observation sequence. The first stage utilizes an iterative structure where each layer employs a memory-extended network to explicitly exploit the knowledge of signature pool. The second stage incorporates the straight-through channels within a deep recurrent structure to avoid information loss of previous observations. Experiments show that DeepClassifier approaches the optimal likelihood-based method with a reduction of 90% complexity.

With the continuous progress of communication quality, the wireless vehicular networks (WVN) will surely be-come an inevitable part of future smart cities. Inside WVN where context is complicated and stochastic, quality of service (QoS) acts as the core concern for broad users. And reliable prediction towards traffic in WVN is essentially an important demand to ensure QoS. Conventionally, related methods mainly focus one side to establish robust prediction models, possessing some limitations. To bridge such gap, model integration may be an intuitive and promising solution. This paper proposes QoS-aware reliable traffic prediction model under WVN (TP-WVN). Firstly, two typical prediction models are used as fundamental learners, which can capture the spatial correlations from different angles. Then, regression model is selected as the integrator to combine base models together. Simulative experiments on a real-world dataset are conducted to evaluate the proposal, and results show that the TP-WVN is able to realize reliable QoS-aware prediction compared with baseline methods.

The evolution of deep learning has promoted the popularization of smart devices. However, due to the insufficient development of computing hardware, the ability to conduct local training on smart devices is greatly restricted, and it is usually necessary to deploy ready-made models. This opacity makes smart devices vulnerable to deep learning backdoor attacks. Some existing countermeasures against backdoor attacks are based on the attacker’s ignorance of defense. Once the attacker knows the defense mechanism, he can easily overturn it. In this paper, we propose a Trojaning attack defense framework based on moving target defense(MTD) strategy. According to the analysis of attack-defense game types and confrontation process, the moving target defense model based on signaling game was constructed. The simulation results show that in most cases, our technology can greatly increase the attack cost of the attacker, thereby ensuring the availability of Deep Neural Networks(DNN) and protecting it from Trojaning attacks.

Driven by the Internet-of-Things (IoT)-enabled massively data-intensive applications, such as virtual-augmented-reality-based gaming, ultramassive machine-type communications, holographic rendering and high-precision communications, multiway teleconferencing, etc., there is a need for technological advancements and evolutions for wireless communications beyond the fifth-generation (5G) networks. The wireless data traffic is estimated to reach 4394 EB by 2030 (Source: ITU), and 5G will be unable to provide support to most of these advanced applications. Here, 6G is expected to extend the 5G capabilities to very high levels where millions of connected devices and applications could operate seamlessly with high data rates and low latency.

As a new energy saving approach for green communications, energy harvesting (EH) could be suitable technique to facilitate massive connections for large number of devices in such networks. The spectrum shortage occurs in huge number of devices which access with small-cell and macro-cell networks. To tackle these challenges, we develop a tractable framework relying on prominent techniques such as non-orthogonal multiple access (NOMA), antenna selection and energy harvesting. In this paper, we aim at practical scenarios of small cell networks by jointly evaluating capable of interference management and EH. We benefit from transmission approaches including full duplex (FD) and bi-directional transmission to improve the main performance system metrics such as outage probability and throughput. Three useful schemes are explored by considering EH and inter-cell interference. We derive the closed-form and asymptotic expressions for system metrics. We then perform extensive simulations with different system configurations to confirm the effectiveness of the proposed small-cell NOMA systems.

In the last decade, the number of vehicles worldwide has increased every year, and this growth is projected to continue unabated. Thus, the congestions, incidents, and environmental pollution which are caused by the increasing number of road vehicles and traffics have resulted in hundreds of millions of losses and become a major challenge to the sustainable development of recent human society. Both academia and industry have already reached a consensus that vehicular communication is a vital element to extend the sensing ability of vehicles for ensuring safety driving. Unfortunately, current vehicular communication cannot meet the security, reliability, and effectiveness and other needs of ITS. The industry needs a more intelligent vehicular communication to support secure and reliable transmission of data. Therefore, the research community has to focus more on enhanced and completely new communication techniques.

Motivated by multiple-mode orthogonal frequency division multiplexing with index modulation (MM-OFDM-IM) and multiple-input multiple-output (MIMO) technologies, a new modulation technique named multiple-mode MIMO with IM (MM-MIMO-IM) is proposed, which is implemented over the virtual parallel channels resulting from singular value decomposition (SVD) of the MIMO channel. Moreover, coordinate interleaved technique is employed to improve its diversity gain. Compared with traditional MIMO systems, the spectral efficiency (SE) of MM-MIMO-IM systems is significantly improved due to the introduction of the mode permutation. We further extend MM-MIMO-IM to in-phase/quadrature (I/Q) dimension, yielding MM-MIMO-IM-IQ, which achieves a double SE with respect to MM-MIMO-IM. Asymptotically tight upper bounds on bit error rates (BERs) of the proposed schemes are derived to evaluate their performance. Monte Carlo simulation results demonstrate the advantages of the proposed schemes.

Following the tremendous success of 2G and 3G mobile networks and the fast growth of 4G, the next generation mobile networks (5G) was proposed aiming to provide infinite networking capability to mobile users. Differentiated from 4G, benefits offered by 5G is much more than the increased maximum throughput. It aims to involve and benefit from many current technical advances, including Internet of Things (IoT). As the IoT integrates many heterogeneous networks, such as wireless sensor networks, wireless local area networks, mobile communication networks (3G/4G/LTE/5G), wireless mesh networks, and wearable health care systems, it is critical to design self-organizing and smart protocols for heterogeneous ad hoc networks in various IoT applications, such as cyber-physical systems, cloud computing for heterogeneous ad hoc networks, large-scale sensor networks, data acquisition from distributed smart devices, green communication and applications, environmental monitoring and control, etc. Moreover, based on the survey conducted by the World Health Organization, the world will lack 12.9 million health care workers by 2035. Hence, it is important to develop wearable health care systems to perform self-health monitoring. In general, wearable health care systems demands low power consumption and high measurement accuracy. Smart technologies including green electronics, green radios, fuzzy neural approaches, and intelligent signal processing techniques play important roles for the developments of the wearable health care systems. This Special Issue aims at providing a forum to discuss the recent advances on 5G and beyond mobile technologies and applications for IoT.

The cloud radio access network (C-RAN) emerges as one of the future solutions to handle the ever-growing data traffic, which is beyond the physical resources of current mobile networks. The C-RAN decouples the traffic management operations from the radio access technologies, leading to a new combination of a virtualized network core and a fronthaul architecture. This new resource coordination provides the necessary network control to manage dense Long-Term Evolution (LTE) networks overlaid with femtocells. However, the energy expenditure poses a major challenge for a typical C-RAN that consists of extended virtualized processing units and dense fronthaul data interfaces. In response to the power efficiency requirements and dynamic changes in traffic, this paper proposes C-RAN solutions and algorithms that compute the optimal backup topology and network mapping solution while denying interfacing requests from low-flow or inactive femtocells. A graph-coloring scheme is developed to label new formulated fronthaul clusters of femtocells using power as the performance metric. Additional power savings are obtained through efficient allocations of the virtualized baseband units (BBUs) subject to the arrival rate of active fronthaul interfacing requests. Moreover, the proposed solutions are used to reduce power consumption for virtualized LTE networks operating in the Wi-Fi spectrum band. The virtualized network core use the traffic load variations to determine those femtocells who are unable to transmit to switch them off for additional power savings. The simulation results demonstrate an efficient performance of the given solutions in large-scale network models.

The papers in this special section provides a forum to present recent advances on 5G mobile communications f(5G) tactile Internet. The Internet, which was created to provide resilient and interoperable communication across the globe, evolved to transport a vast amount of content with which to enrich our real-life experience. Pervasive ultra-broadband, programmable networks, and cost reduction of IT systems are paving the way to new services and commoditization of telecommunications infrastructure while lowering entry barriers for new players and giving rise to new value chains. Today, it provides a depth of information and social sophistication that rivals the real world. The Tactile Internet, the next evolutionary step, will enable remote, real-time physical interactionwith real and virtual objects, creating a two-way interactive experience in which boundaries between the real world and virtual world will blur.

Smart grid is a next generation electricity network that transmits electricity to end users and enable two way digital communication for providing remote reading and other advanced metering functions. For Smart grid domains to interact with each other, a reliable communication is required. This reliable two-way communication can be achieved with the use of small cells technology (femto, pico, etc), relay, or any of the transmission scheme proposed in 5G systems. With the use of femto cells and relay nodes in the network, the allocation of resources for the purpose of smart grid communication needs to be done efficiently. Power being one of the resource has been studied for its allocation in a femto cell network for Smart grid communication, however, transmission reliability and interference is generally ignored. In this paper, we investigate the problem of power allocation in a Neighborhood Area Network (NAN) with femto cell and relay technology as a communication mechanism. We formulate the problem as a power minimization problem such that the transmission reliability is ensured. Our performance evaluation shows that the average transmit power required for transmission through femto cell is 99% less as compared with cooperative transmission.

Content caching at small base stations has been considered as an efficient way to alleviate the use of expensive backhauling. In this paper, we study the caching content update for a cache- enabled heterogeneous network. Through analyzing a benchmark content update policy, the problem of content consistency, which is caused by the distinct update time at each caching entity, is firstly revealed. As well, the close form representation of the consistency probability in a mobile environment is derived. Furthermore, a deterministic content update strategy is investigated, where the trade-off between consistency probability and storage cost is characterized. Detailed simulations are provided to support our analysis, as well as present the impacts of moving speed of mobile terminals and transmission bandwidth for content delivery on the consistency performance.

Following the tremendous success of 2G and 3G mobile networks and the fast growth of 4G, the next generation mobile networks (5G) was proposed aiming to provide infinite networking capability to mobile users. Differentiated from 4G, a benefit offered by 5G is much more than the increased maximum throughput. It aims to involve and benefit from many current technical advances including Industrial Internet of Things (IIoT). As the IIoT integrates many heterogeneous networks, such as Wireless Sensor Networks (WSNs), Wireless Local Area Networks (WLANs), Mobile Communication Networks (3G/4G/LTE/5G), Wireless Mesh Networks (WMNs) and wearable health care systems, it is critical to design self-organizing and smart protocols for heterogeneous ad hoc networks in various IoT applications, such as cyber-physical systems, cloud computing for heterogeneous ad hoc networks, large-scale sensor networks, data acquisition from distributed smart devices, green communication and applications, environmental monitoring and control, etc. Moreover, based on the survey conducted by the World Health Organization, the world will lack 12.9 million healthcare workers by 2035. Hence, it is important to develop wearable healthcare systems to perform self-health monitoring. In general, wearable healthcare systems demands low power consumption and high measurement accuracy. Smart technologies including green electronics, green radios, fuzzy neural approaches and intelligent signal processing techniques play important roles in the developments of the wearable healthcare systems. Therefore, this special issue provides a forum to discuss the recent advances on 5G and beyond mobile technologies and applications for IIoT.

5G research shows more potential attention to mobile communications in information intensive industrial sectors such as power utility. In smart grid context, employing licensed assisted access (LAA) allows smart grid operators to transfer utility data between different sites using the unlicensed and licensed bands. This can play a crucial role in improving efficiency, sustainability, stability, and to meet the quality of service (QoS) requirements of different smart grid consumer requests. Considering the unlicensed band, there is a strong need to develop new LAA unlicensed access technology that can improve spectrum acceptability compared to conventional Wi-Fi to meet the high volumes of information in smart grids. In this paper, we investigate the spectrum allocation techniques required to exploit smart grid requirements by setting a minimum bit error rate (BER) threshold while evaluating the availability of white holes in the unlicensed band. Simulation results confirm the advantages of the proposed scheme in allocating more resources to LAA unlicensed users subject to their load requirements. This paper provides a new method for intelligent spectrum allocation to support the communication requirements of smart grid networks.

Power consumption analysis is the first step in the process of dimensioning the cell size for any mobile network. Therefore, improving power utilization emerges as one of the major challenges to the 3rd Generation Partnership Project (3GPP) Long-Term Evolution (LTE) Release 12 coordinated multipoint (CoMP) transmission and reception scheme. This paper proposes new models for heterogeneous deployment of CoMP macrocells overlaid with the emerging neighborhood femtocell network in order to meet the power efficiency requirements. Two case studies are mathematically analyzed: Firstly, neighborhood femtocells are allocated at the macrocell edge line in order to reduce the ultimate range of CoMP transmission while coverage is extended using low power femtocell transmissions. Secondly, femtocells are deployed at selected CoMP intra-cell regions in order to improve neighborhood coverage; transmissions are coupled between femtocells and CoMP to provide the necessary converge across the cell area and transfer connections from macrocell to low power femtocells. A comparative study is performed to show the power efficiency obtained through scaling the network area at both cell-edge and sub-cell areas. The analysis show new strategies for deploying small cells under CoMP macrocell umbrella in order to minimize the power consumption figures compared to only macrocell based network.