Unmanned aerial vehicles (UAVs) are increasingly integrated into modern communication and sensing infrastructures, making onboard intrusion detection systems (IDSs) essential for resilience. While recent research has explored adversarial threats to UAV IDSs, most studies focus on white-box settings or assume full access to training data and model parameters. This paper investigates the vulnerability of machine learning (ML)based UAV IDSs to black-box adversarial evasion attacks, where the adversary lacks access to model parameters. We utilize two fused cyber-physical datasets collected from UAV testbeds: one to train the operator IDS and one as a surrogate dataset to train the attacker model, mimicking black-box conditions. We evaluate ML-based IDS models under two classes of black-box attacks: (i) non-query-based attacks, which rely on transferring perturbations from a surrogate model, and (ii) query-based attacks, which iteratively craft adversarial examples using only output feedback from the operator model, without requiring a surrogate model, making them more practical. Experimental results show that shallow ML models suffer detection rate (DR) degradations of up to 85% when subject to black-box evasion attacks compared to classic attacks (i.e., false data injection attacks), while deep ML models exhibit more moderate DR declines of up to 64%. Query-based evasion attacks result in a greater degradation in DR compared to non-query-based attacks of up to 67%, highlighting the elevated risk posed by query-based threats in black-box settings.

Power grids serve as the backbone of critical infrastructures, enabling the efficient control and distribution of electricity. Subsequently, the number of cyber attacks against power grids continues to grow, especially during times of international conflict and uncertainty. Thus, we must continue to further the research that secures and ensures the stability of power grids. Related research has made progress to this end through the application of artificial intelligence-based systems, but such works suffer from the following limitations: (a) they strongly focus on attack detection, neglecting attack classification, (b) they produce complex systems requiring large amounts of computation without concern for reducing complexity, and (c) they lack concern for how the systems can be adapted when new attacks arise. These limitations motivate our work, where we develop efficient and high-performing classification systems that are adaptable to new attacks. Specifically, we develop diverse benign and attack datasets consisting of cyber and physical layer data using our power system testbed. Additionally, we adopt SHapley Additive exPlanations to reduce the total number of features required to accurately classify attacks by 83% while maintaining a superior accuracy of 97%. Lastly, we use transfer learning to enhance fine-tuning and adapt our classification system to classify new cyber attacks in power grid environments.

The emergence of 6G-enabled Vehicle-to-Everything (V2X) networks has created unprecedented demand for ultra-reliable, low-latency spectrum allocation across heterogeneous entities including vehicles, IoT devices, and industrial systems. Current spectrum allocation methods suffer from exponential computational complexity, extensive information sharing requirements, and poor scalability in dense networks. This paper proposes AMADRL (Attention-based Multi-Agent Deep Reinforcement Learning), a novel framework employing dual critic networks with multi-head self-attention mechanisms for intelligent spectrum allocation. The dual critic architecture resolves individual-collective optimization conflicts through local critics for independent entity optimization and a global critic with attention-based coordination. Our approach significantly reduces information sharing requirements while handling heterogeneous QoS demands across diverse entity types. Comprehensive experimental evaluation comparing AMADRL against state-of-the-art baselines including MADDPG, MAAC, QMIX, attention-based methods (A-DDPG, MHA-DQN), and game-theoretic approaches reveals that AMADRL achieves superior performance across multiple metrics including spectrum utilization efficiency, interference mitigation, and network scalability, while preserving user privacy and satisfying strict latency constraints required by safety-critical and industrial use cases.

Unmanned Aerial Vehicle (UAV) swarms’ ability to dynamically adapt to communication topologies brings both great potential and significant security risks. Traditional intrusion detection systems (IDS) are designed for UAVs with fixed swarm topology and fail to secure dynamic topologies with constantly evolving communication patterns. The lack of datasets that reflect swarm behaviors across varying topologies further hinders the development of IDS. Moreover, these IDS typically focus on temporal information, overlooking the crucial spatial relationships within the UAV swarm. To address these challenges, we developed a testbed of six UAVs configured to communicate in six distinct topological graphs where each UAV acts as a node and communicates with its immediate neighbors. We then executed various cyberattacks, such as false data injection (FDI), evil twin, replay, and denial-of-service (DoS) attacks, and collected the data under both normal and attack conditions. We propose a graph neural network (GNN)-based IDS that explicitly incorporates spatial and temporal information patterns to detect intrusions. This paper seeks to answer the following questions: (a) Does exploiting spatio-temporal correlations improve detection compared to IDS trained solely on temporal data? (b) Can we develop a generalized IDS capable of detecting intrusions irrespective of the specific swarm communication topology? (c) Does the spatio-temporal correlation improve detection capabilities when the range of swarm topological data is expanded in training and the IDS is tested on unseen/new topology? Through extensive experiments on varying swarm topologies and comparison with traditional deep neural network models, we assess the effectiveness of the topology-aware GNN-based IDS in securing UAV swarm communications.

The growing popularity of electric vehicles (EVs) has rendered public EV charging stations (EVCSs) vital for alleviating range anxiety and supporting long-distance travel. However, recent studies reveal security vulnerabilities in EVs and EVCSs against attacks. This paper addresses these security concerns by introducing injection attacks on the front-end Vehicle-to-Grid (V2G) communication using the ISO 15118 protocol. Malicious EV owners or compromised EVCS supply equipment can inject harmful packets, potentially leading to runtime modifications and malware attacks. To counter this threat, we propose an innovative bidirectional recurrent attentive graph neural network (BiRAGNN)-based intrusion detection system (IDS) that dynamically captures spatiotemporal aspects and the bidirectional flow of information, while leveraging an attention mechanism to effectively detect injection attacks within EVs and EVCSs. The BiRAGNN model is founded on a probabilistic charging graph of real cities. Other deep learning and graph-based IDSs are also investigated as evaluation benchmarks. The IDSs are examined against a standalone system (from a single EVCS) and multi-node systems (from 8, 50, and 100-node EVCSs), all with packet-level, flow-level, and fused packet and flow-level data. The proposed BiRAGNN-based IDS offers a detection accuracy of 99% on the 100-node fused dataset, surpassing the benchmarks by$5 - 8\%$, offering EVs and public EVCSs resilience against cyber threats.

Controlled quantum teleportation (CQT) is a key technique that allows quantum information to be transmitted over a quantum network under the control of a network administrator/ firewall. Existing CQT protocols rely on complex entanglement structures or increased number of qubits to enhance the administrator’s ability to block unauthorized communication, referred to as the control level, which poses practical challenges on current quantum hardware due to decoherence and the fragility of quantum states. In contrast, we propose efficient single and dual controller protocols that improve the control level, while using practical and experimentally feasible entanglement resources such as GHZ and GHZ-like states. The proposed protocols adopt quantum hiding to enhance the network’s control level, whether the receiver is compliant or non-compliant with the protocol. The dual-controller design further enables the integration of demilitarized zones (DMZs) into quantum networks, providing isolated intermediate regions jointly governed by two controllers and strengthening security boundaries. Our results demonstrate that the single-controller protocol achieves 87.5% control, reflecting a 75% improvement over the standard CQT scheme, while the dual-controller protocol achieves 98.44% control, corresponding to a 96.88% improvement. This improvement is achieved at a low cost of 2−9% reduction in the rate of successful teleportation when tested in noisy environments. In addition, both protocols maintain high state-averaged teleportation fidelity across randomly generated input states. When benchmarked against existing protocols, the proposed schemes demonstrate superior performance by achieving the highest efficiency while using the least amount of quantum resources.

Polarization shift keying (PolarSK) has been demonstrated as a spectrally efficient single-radio-frequency (RF) multiple-input multiple-output (MIMO) technique. We herein further reveal a pivotal characteristic of PolarSK: The inherent robustness to hardware impairments, which provides a theoretical guarantee for practical implementation. Specifically, we propose the first performance degradation evaluation framework for the PolarSK system under transmitter hardware impairments, which incorporates typical hardware impairments including in-phase/quadrature (I/Q) imbalance, nonlinearity, and phase noise. We derive the analytical average bit error probability (ABEP) expression of PolarSK under hardware impairments, along with the asymptotic ABEP floor. The theoretical derivations on both the approximate ABEP and the asymptotic floor are verified in 3GPP channels. Compared to state-of-the-art modulation systems in single-RF MIMO, the PolarSK system exhibits superior robustness against ABEP degradation under non-ideal hardware conditions, achieving up to 8 times lower ABEP.

With the rise of Software-Defined Networking (SDN) for managing traffic and ensuring seamless operations across interconnected devices, challenges arise when SDN controllers share infrastructure with deep learning (DL) workloads. Resource contention between DL training and SDN operations, especially in latency-sensitive IoT environments, can degrade SDN's responsiveness and compromise network performance. Federated Learning (FL) helps address some of these concerns by decentralizing DL training to edge devices, thus reducing data transmission costs and enhancing privacy. Yet, the computational demands of DL training can still interfere with SDN's performance, especially under the continuous data streams characteristic of IoT systems. To mitigate this issue, we propose REDUS (Resampling for Efficient Data Utilization in Smart-Networks), a resampling technique that optimizes DL training by prioritizing misclassified samples and excluding redundant data, inspired by AdaBoost. REDUS reduces the number of training samples per epoch, thereby conserving computational resources, reducing energy consumption, and accelerating convergence without significantly impacting accuracy. Applied within an FL setup, REDUS enhances the efficiency of model training on resource-limited edge devices while maintaining network performance. In this paper, REDUS is evaluated on the CICIoT2023 dataset for IoT attack detection, showing a training time reduction of up to 72.6% with a minimal accuracy loss of only 1.62%, offering a scalable and practical solution for intelligent networks.

Aerial base stations (ABSs) are expected to play a major role in 5G+ wireless networks where unmanned aerial vehicles (UAVs) serve as flying base stations to provide wireless coverage. As the utilization of ABSs continues to expand, ensuring their security and resilience against malicious attacks emerges as a vital concern. In this paper, we demonstrate successful false data injection attacks on a real UAV testbed, leading the UAV offcourse, which can impact the ABS coverage. Current research efforts focus on designing intrusion detection systems (IDS) tailored specifically for UAVs. However, the impact of adversarial attacks on UAV IDS is largely overlooked in the literature. Our results herein show that evasion attacks pose a significant threat, capable of deteriorating IDS model detection accuracy by 20 %. In this paper, we propose adopting cyber-physical fused datasets to train our proposed unsupervised sequential ensemble learningbased models to improve IDS robustness against evasion attacks. Our results, based on a practical UAV testbed and considering a wide range of evasion attacks, demonstrate that the proposed ensemble of a Transformer autoencoder and long short-term memory recurrent neural network reduces accuracy deterioration to 3 % when combined with the physical and cyber fused features.

Network slicing enables multiple virtual networks on shared 6G infrastructure, but dynamic resource allocation across Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Machine Type Communications (mMTC) services remains challenging. We present a hybrid Convolutional Neural Network-Long Short-Term Memory Architecture (CNN-LSTM) framework with service-specific utility functions that optimize resources while ensuring Quality of Service (QoS) guarantees under dynamic conditions. Our approach integrates spatial pattern recognition with temporal prediction, incorporating constraint measurement and lightweight optimization. Experimental results on a testbed with 100 base stations and 10,000 users demonstrate superior performance over state-of-the-art methods. The framework achieves significant improvements in resource utilization, QoS satisfaction, and energy efficiency with real-time inference capability. Convergence analysis validates system stability, confirming practical deployment feasibility for latency-critical 6G applications.

The evolution of 6G wireless networks requires robust and adaptive communication systems that can handle dynamic indoor environments. Accurate prediction of the terahertz (THz) channel is a key enabler of this adaptability, enabling proactive decisions such as beamforming and handover. Because traffic levels (i.e., user densities) fluctuate, the underlying channel statistics change over time, resulting in concept drift that can deteriorate the performance and generalization ability of deep learning (DL) models if they are tested against mismatched conditions (i.e., on a traffic level other than the one used for training). This paper introduces a novel framework for generalizable THz channel prediction, enabled by fusing environmental sensing with AI-assisted wireless communication to mitigate concept drift and maintain generalization. First, we investigate the use of DL-based channel prediction models tailored to specific traffic levels—light, moderate, and dense—and evaluate their performance under mismatched conditions. Results show up to 51% performance deterioration when models are exposed to mismatched traffic scenarios. To mitigate this issue, a traffic-aware channel prediction framework is proposed, comprising three stages: people counting using sensing technologies; quantization of the user count into traffic levels; and dynamic selection of the corresponding DL model. Simulation results demonstrate that integrating accurate sensing technologies, particularly vision-based systems, significantly reduces prediction deterioration to as low as 4%. The proposed framework’s adaptability ensures reliable channel prediction by aligning model selection with real-time traffic conditions, which highlights the potential of fusing environmental sensing with AI-assisted wireless communication to enhance the robustness of future 6G networks.

Electromagnetic interference (EMI) still poses a serious threat to reliable wireless communication, particularly in crowded and hostile electromagnetic environments. While space-time block coding (STBC) and other conventional diversity techniques have been extensively employed to mitigate multipath fading, their ability to mitigate EMI is inherently limited, especially when interference uniformly affects every component of the antenna. The spectral efficiency of Fluid Antenna (FA)-enabled Multiple-Input Multiple-Output (MIMO) systems can be significantly enhanced by employing Index Modulation (IM). However, current FA-enabled IM (FAIM)-aided MIMO systems suffer from considerable performance degradation due to strong spatial correlation in the wireless channel, which is caused by the dense port distribution of the FA. In this paper, we propose an effective approach that integrates a Fluid Antenna System (FAS) with STBC to enhance system robustness in the presence of EMI. Simulation results show that integrating an FAS into a dual-antenna receiver employing STBC significantly improves spectral efficiency and reliability under EMI. At low signal-to-noise ratios (SNRs), the presence of EMI reduces Bit Error Rate (BER) performance by more than 12% when compared to the traditional arrangement without EMI. In contrast to the system affected by EMI without FAS, the BER curve closely resembles the scenario without EMI when FAS selection is used, resulting in a ~10% BER reduction at Eb/N0= 6 dB. Similarly, the achievable rate bridges the performance gap caused by EMI by improving by more than 1.5 bps/Hz over the SNR range. These findings confirm that FAS is effective in reducing EMI and improving communication in unfriendly settings.

6G networks are expected to significantly benefit from advanced wireless local area technologies such as Wireless Gigabit (WiGig), which operates in the 60 GHz frequency band. This band supports extremely high data rates and low latency, making it ideal for next-generation wireless applications such as the metaverse and holograms. However, WiGig signals are highly susceptible to attenuation from physical obstructions, resulting in frequent handovers and connectivity disruptions. Traditional reactive handover mechanisms are often slow due to latency in decision-making and processing overhead. However, proactive handover strategies that leverage channel prediction can enhance network reliability and improve the quality of service. This paper investigates the feasibility of using statistical methods, specifically the auto-regressive integrated moving average (ARIMA) model, to predict the received signal strength indicator (RSSI) in real-world indoor WiGig environments. Our results indicate that ARIMA exhibits poor predictive accuracy, with a root mean square error (RMSE) of 15 dBm, which may trigger inaccurate handover decisions by initiating handovers under strong signal conditions or failing to respond under weak ones. To overcome this shortcoming, we propose an ensemble learning-based channel prediction approach utilizing the random forest (RF) algorithm. Our results show that the RF model significantly outperforms ARIMA by effectively capturing the nonlinear dynamics of real-world indoor WiGig channels. Specifically, the RF model achieves a 90% reduction in both mean absolute error and RMSE, and a 99% reduction in mean squared error, offering a promising solution for robust proactive handover management in 6G networks.

The dynamic nature of user mobility and density in indoor WiGig networks poses a significant challenge to seamless handover, particularly in the 60 GHz band, where small and closely clustered channel gain values hinder effective decision-making. To address this, we propose a generalizable deep reinforcement learning (DRL)-based handover that integrates a novel reward function designed to amplify channel gain differentials, thereby improving the convergence speed and decision accuracy of learning agents. We investigate the performance of state-of-the-art DRL algorithms—Deep Q-Network (DQN), Proximal Policy Optimization (PPO), and Advantage Actor-Critic (A2C)—enhanced through advanced hyperparameter-tuning techniques, including grid search, random search, optuna, and hyperopt. Among these, DQN combined with grid search yields the best overall performance, surpassing A2C by 48% in average rolling reward and achieving 32% faster convergence than PPO.To assess generalization, we evaluate the merged DQN agent trained across varying user density scenarios (1–8 users) against expert agents specialized for individual densities and a high-density-trained agent tested across all scenarios. Our results reveal that the merged agent exhibits robust and consistent performance across all densities, indicating strong generalization capability. In contrast, the high-density agent suffers performance degradation of up to 13% when exposed to unseen scenarios, underscoring its limited adaptability. While expert agents perform optimally within their specific environments, their deployment complexity renders them impractical for real-time systems. These findings highlight the importance of training DRL agents across diverse scenarios to achieve scalable and generalizable handover solutions in dense and dynamic WiGig networks.

Emerging wireless local area networks, such as WiGig that operate in the extremely high-frequency band (60 GHz) hold significant potential for the development of next-generation 6G networks by offering high throughput and low latency. However, the 60 GHz band is prone to severe signal degradation due to channel blockages, leading to frequent handovers and challenges in maintaining seamless connectivity. Reactive handover strategies can result in service delays due to overhead and decision-making latency. To tackle these issues, proactive approaches that utilize machine learning (ML) and deep learning (DL) are becoming increasingly popular for network optimization in WiGig networks. However, existing ML/DL models are often tailored to specific network environments, making them susceptible to concept drift — a phenomenon where even minor environmental changes can significantly degrade network performance due to incorrect decision-making. This paper investigates scenarios and environmental changes that can trigger concept drift in WiGig networks. We conduct real-world experiments to analyze the statistical behavior of received signal strength, highlighting the potential for concept drift. Based on our findings, we propose a direction for identifying concept drift in WiGig networks.

Autonomous vehicles generate massive sensor data daily but operate as isolated intelligence units due to privacy constraints and network limitations. Current centralized machine learning approaches face critical barriers including compliance issues, high bandwidth costs, and latency constraints preventing real-time safety decisions. While Federated Learning (FL) enables collaborative training without raw data sharing and 6G networks promise ultra-low latency, a fundamental mismatch exists between FL's dynamic computational demands and 6G's static resource allocation mechanisms. This paper presents Predictive FL-Aware Network Slicing (PFANS), a novel framework that integrates real-time convergence modeling with proactive 6 G slice reconfiguration for autonomous vehicle networks. PFANS predicts FL computational demands multiple training rounds in advance and automatically reconfigures network slices before bottlenecks occur. Experimental results demonstrate superior resource utilization efficiency, significantly faster convergence compared to baseline approaches, and excellent handover success rates with minimal context migration times. The framework achieves state-of-theart prediction accuracy while introducing negligible network overhead, establishing effective adaptive resource management for next-generation vehicular networks.

Cell-free networks have emerged as a new paradigm for beyond-5G networks, offering uniform coverage and improved control over interference. However, scalability poses a challenge in full cell-free networks, where all access points (APs) serve all users. This challenge is addressed by user-centric clustering, where each user is served by a subset of APs, reducing complexity while maintaining coverage. In this paper, we provide an analysis of the relation between the user-centric clustering and pilot assignment problems in cell-free networks, and introduce a formulation which decouples both problems enabling each to be solved independently. We present a general problem formulation for the user-centric clustering problem, allowing the use of diverse per-user and network-wide performance metrics. Specifically, we focus on one instance of this framework, utilizing per-user spectral efficiency and network-wide sum spectral efficiency (SE) as metrics. Additionally, we formulate the pilot assignment problem to minimize overall channel estimation error while considering the user-centric clusters in evaluating the desirability of pilot assignments, which leads to better performing solutions. Both problems are classified as binary nonlinear programs that are at least NP-hard. To solve these optimization problems, our proposed methodology employs sample average approximation coupled with surrogate optimization for the user-centric clustering problem and utilizes the genetic algorithm for the pilot assignment problem. Numerical experiments demonstrate that the optimized solutions surpass baseline solutions, leading to significant improvements in spectral efficiency.

The convergence of Intelligent Reflecting Surfaces (IRS) and Terahertz (THz) communications represents a transformative advancement for sixth-generation (6G) wireless networks, yet presents unprecedented challenges in system optimization. This paper addresses the critical challenge of joint optimization between IRS phase shifts and THz resource allocation in dynamic Internet of Things (IoT) environments, focusing on real-time adaptation to rapidly changing channel conditions. We propose a novel Adaptive Online Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework that leverages dynamic experience weighting to automatically adjust learning based on detected environmental changes. Our approach incorporates a multi-resolution buffer structure that balances recent observations with historical patterns, enabling both rapid adaptation and long-term optimization while considering the unique characteristics of THz-band propagation and IRS reflection patterns. The framework employs explicit coordination protocols between IRS controllers and resource managers, significantly improving convergence in non-stationary environments. Comprehensive simulations using realistic THz channel models and practical IRS configurations demonstrate that our proposed framework achieves a 45% improvement in system throughput, a 38% reduction in end-to-end latency, and a 30% enhancement in energy efficiency compared to conventional optimization approaches. More significantly, our solution demonstrates unprecedented adaptation capabilities, recovering 90% of optimal performance within 5 ms after abrupt environmental changes a critical requirement for future 6G networks. The framework maintains robust performance under diverse conditions, including high user mobility scenarios and adverse atmospheric conditions, while exhibiting linear computational scaling with increasing IRS elements (tested up to 512 elements). These results establish the viability of Adaptive Online MADDPG-based joint IRS-THz optimization for practical 6G deployments, particularly in dynamic IoT environments where traditional communication approaches face significant limitations.

The concept of integrating reconfigurable intelligent surfaces (RISs) into visible light communications (VLC) has already been realized through metasurfaces such as adjustable-angle reflectors or liquid crystal tiles with variable refractive indices. However, current research neglects the power consumption and control signaling overhead required to drive these RIS tiles, potentially resulting in the overheads of RIS outweighing its benefits. Considering that some RISs will be blocked in indoor mobile environments and the blockage patterns will change over time, which results in concept drift for management strategy, this paper proposes a dynamic RIS tile exclusion strategy adaptive to indoor mobility. Such an RIS controller deactivates less efficient RIS tiles based on channel conditions, which reduces signaling and power consumption costs, thereby, enhancing the overall system efficiency. Since this decision problem is highly non-convex and non-linear, and involves RIS-assisted time-varying visible light channels that cannot be characterized by general mathematical models, this paper proposes a maximum entropy deep reinforcement learning-based method to optimize the strategy. In the validation scenarios, compared to the tile exclusion strategy at fixed time intervals, the proposed adaptive strategy improves the RIS utilization efficiency by over 100%. Moreover, it only takes less than$53.55~\mu $s to execute the trained strategy on a low-cost micro-controller unit. The generalization ability of the learned strategy is also proven as no transfer learning or adaption is needed when facing a new environment.

As a sustainable participant in the modernization of transportation systems, electric vehicles (EVs) call for a well-planned charging infrastructure. To meet the ever-increasing charging demands of EVs, an efficient dynamic spatio-temporal allocation strategy of charging stations (CSs) is necessary. With newly allocated CSs, additional distributed generators (DGs) are required to compensate for the load increase. Given a budget to be allocated over a certain time horizon, we formulate the joint spatio-temporal CSs and DGs planning problem as a multi-objective optimization problem. During each planning period, the allocation strategy aims at minimizing the total power generation costs and CSs/DGs installation costs while satisfying budgetary and power constraints and ensuring a minimum level for the charging requests satisfaction rate. In this regard, we first predict the future power demand of EVs using a graph convolutional neural network (GCNN). Then, using the power demand forecast, we obtain the optimal number and locations of CSs and DGs at each time stage using reinforcement learning. A case study of the proposed allocation strategy over 6 time stages for the 2000-bus power grid of Texas coupled with 720 initially existing CSs is presented to illustrate the performance of the planning strategy.

Dynamic wireless charging (DWC) facilitates energy transfer from the electric grid to moving electric vehicles (EVs) via charging pads (CPs) positioned along roadways. To maximize satisfied charging requests, given the limited supply capacity, dynamic charging coordination is required to determine suitable CPs for mobile EVs. Charging coordination necessitates EV owners to share their information (i.e., the identities and locations) with charging service providers (CSPs) to allocate the best CP for charging. However, charging coordination raises privacy concerns due to the exchange of private information. Moreover, a fast authentication mechanism is then required between EVs and CPs to initiate the charging process. In addition to the privacy limitation, existing DWC strategies lack the presence of multiple CSPs, which is a crucial aspect given the significant growth of the EV market. Consequently, centralization arises, with a single CSP overseeing the entire network. This paper proposes a semi-decentralized privacy-preserving networking strategy utilizing a specially designed consortium blockchain to support dynamic charging coordination, authentication, and billing while ensuring user anonymity and data unlinkability. Our proposed strategy leverages a novel semi-decentralized K-times group signature scheme and distributed random number generators to achieve privacy and decentralization. Simulation results showed that the proposed method reduces the EV authentication time to 0.1 ms while limiting storage requirements to just 4 MB per block at each EV. Additionally, the proposed strategy showed improved security and privacy features when compared with IBM’s privacy-preserving blockchain (Identity Mixer).

This paper explores secret key generation in 5G and beyond LiFi networks using visible light in the downlink and infrared in the uplink. Unlike the existing works, we focus on a realistic indoor environment with multi-user mobility. Given inaccuracies in high-frequency channel models, we introduce the first deep learning model that combines the channel probing and quantization phases to generate initial secret keys with a minimal key disagreement rate (KDR) of 16% between the uplink and downlink, leading to a key generation rate (KGR) of 79 bits/s after information reconciliation. We show that LiFi channel statistics suffer from concept drifts with user density changes in the room. This increases the KDR by 28% - 44% and the generated keys fail to pass the NIST randomness tests. As a countermeasure, we introduce a voting ensemble model that mitigates concept drifts, maintaining a stable 16% KDR, 79 bits/s KGR, and passing NIST tests, despite the varying user densities.

Accurate channel prediction using deep learning (DL) algorithms can address the challenges of terahertz (THz) propagation, such as atmospheric absorption and object scattering, by enabling proactive handover and beamforming. However, indoor environments are inherently dynamic, with factors like occupancy level variations causing the channel characteristics to change over time. This phenomenon, known as concept drift, can severely degrade the DL model performance used in channel prediction. This paper investigates the impact of indoor occupancy level variations on the generalization ability of state-of-the-art DL models for THz channel prediction. We identify three distinct occupancy levels (low, medium, and high) within the THz indoor channel. Our results demonstrate that the state-of-the-art DL models exhibit limited generalization capabilities, with performance deterioration in prediction accuracy ranging from 4−62%. We propose a robust two-stage framework to mitigate concept drift in THz channel prediction. The first stage predicts the indoor occupancy level from the THz wireless signal, which is a multi-class classification problem. Due to the reoccurring concept of occupancy levels, the second stage contains a pool of models in a sleeping mode based on a hybrid convolutional neural network (CNN) long-short-term memory (LSTM) architecture. One of these DL expert models is activated for channel prediction based on the occupancy level predicted from the previous stage. Our framework demonstrates superior generalization by limiting the performance deterioration from 62% due to concept drift to ≤ 9%. This represents an 85% reduction in performance deterioration compared to the existing state-of-the-art DL models.

This paper explores the vulnerability of wireless secret key generation (WSKG) to eavesdropping in a dynamic indoor light-fidelity (LiFi) network. It analyzes the channel impulse response (CIR) similarities of two moving user equipments (UEs) across scenarios with two, four, and eight UEs. We observe that as the number of UEs increases, the similarity in CIR also rises, due to the proximal movement patterns among UEs. Specifically, the similarity rate peaks at 70% when eight UEs enter the room; it then drops to 24% during the wandering phase and rises again to 80% as UEs exit the room. Consequently, an eavesdropper among the eight UEs is able to generate 27% of a legitimate UE’s secret key, it significantly reduces the key’s complexity, decreasing the number of possible keys that need to be tested to break the encryption and making it easier to predict the remainder of the key. To mitigate this issue, we introduce a novel approach that utilizes a generative adversarial network (GAN) to artificially manipulate the CIR, thereby reducing the effectiveness of eavesdropping by adding noise into the observed CIR. This method effectively reduces the CIR similarity to a negligible 1%, thus ensuring the integrity of WSKG against eavesdropping threats.

The increasing reliance on unmanned aerial vehicles (UAVs) has escalated the associated cyber risks. While machine learning has enabled intrusion detection systems (IDSs), current IDSs do not incorporate cyber-physical UAV features, which limits their detection performance. Additionally, the lack of public UAV’s cyber and physical datasets to develop IDS hinders further research. Therefore, this paper proposes a novel IDS fusing UAV cyber and physical features to improve detection capabilities. First, we developed a testbed that includes UAV, controller, and data collection tools to execute cyber-attacks and gather cyber and physical data under normal and attack conditions. We made this dataset publicly available. The dataset covers a range of cyber-attacks including denial-of-service, replay, evil twin, and false data injection attacks. Then, machine learning-based IDSs fusing cyber and physical features were trained to detect cyber-attacks using support vector machines, feedforward neural networks, recurrent neural networks with long short-term memory cells, and convolutional neural networks. Extensive experiments were conducted on varying complexity and range of attack training data to explore whether (a) fusion of cyber and physical features enhances detection performance compared to cyber or physical features alone, (b) fusion enhances detection when IDS is trained on a single attack type and tested on unseen attacks of varying complexity, (c) fusion enhances performance when the range of attack training data increases and models are tested on unseen attacks. Answering these research questions provides insights into IDS capabilities using cyber, physical, and cyber-physical features under different conditions.

Reconfigurable intelligent surfaces (RIS) experience considerable control overhead, particularly problematic in mobile multi-user programmable wireless environments (PWEs). This paper presents a strategy that dynamically excludes shadowed RIS tiles from supporting non-line-of-sight mmWave communications, reducing overheads and enhancing RIS usage efficiency. Spatio-temporal variations caused by crowd mobility necessitate the dynamic adaption of this strategy. The primary challenge lies in identifying optimal occasions for updating RIS tile exclusion decisions, which must strike a balance between improving achievable channel gain (performance) and power consumption as well as controlling overhead (cost) associated with decision updates. Given the absence of a general mmWave channel model, this paper applies deep reinforcement learning (DRL) for managing the RIS exclusion strategy. DRL caters to the susceptibility of mmWave channels to concept drift, where spatio-temporal variations in crowd mobility alter the probability distribution of RIS channel gains and outages. To counter concept drift and generate a universal RIS exclusion strategy for any indoor mmWave environment, we propose an adaptive exclusion update mechanism powered by DRL. This mechanism utilizes hierarchical decision decomposition, reward signal embedding, and a fusion of concept drift and temporal features due to crowd mobility, enabling efficient adaptation to environmental changes. Extensive cross-validation confirms the agent’s impressive generalization ability, directly applicable to varied environments. This adaptive mechanism, despite containing only 2, 300 learnable parameters, achieves more than a two-fold increase in efficiency relative to static exclusion timing methods. Furthermore, decision execution, based on a low-cost RIS controller, only takes a few tens of nanoseconds, showcasing practicality and efficiency.

Recent efforts have promoted programmable wireless environments (PWEs) to enhance the reception quality in high-frequency bands via reconfigurable intelligent surfaces (RISs). However, relevant research efforts are limited to setups with stationary users. This paper shows that crowd mobility in indoor PWEs induces spatio-temporal shadows on the surfaces, resulting in spatio-temporal sparsity in channel gains due to signal blockages. This overlooked aspect impacts the operation strategy of PWEs as the shadowed RIS tiles would contribute to the overheads while offering almost no improvement to the reception quality. Hence, this paper proposes an optimal strategy that excludes the shadowed tiles, which maximizes the utilization efficiency of RISs while minimizing the overheads. Since signal blockage is tied with the details of user mobility, a general model does not exist to identify such shadowed tiles for exclusion. Hence, we follow a data-driven approach that capitalizes on a realistic indoor mobility model and ray-tracing to generate the channel data. However, conventional ray-tracing presents high complexity that hinders data generation. So, we propose an approach to identify the shadow regions with a nine-order of magnitude reduction in complexity to efficiently generate the channel data. Furthermore, we present two exclusion strategies that offer guaranteed and best-effort quality-of-service support, and each can identify the tiles to be excluded via a search method with a complexity of$\mathcal {O}(N)$for$N$tiles. The results indicate that the proposed strategies reduce the overheads by$45-50\%$while maintaining optimal service quality in various environments, operation frequencies, and user and access point density.

This paper addresses the user-centric clustering and pilot assignment problems in cell-free networks, recognizing the need to solve both problems simultaneously. The motivation of this research stems from the absence of benchmarks, general formulations, and the reliance on subjectively designed objective functions and heuristic algorithms prevalent in existing literature. To tackle these challenges, we formulate stochastic non-linear binary integer programs for both the user-centric clustering and pilot assignment problems. We specifically design the pilot assignment formulation to incorporate user-centric clusters when evaluating the desirability of pilot assignments, resulting in improved efficiency. To solve the problems, the proposed methodology employs sample average approximation coupled with surrogate optimization for the user-centric clustering problem and the genetic algorithm for the pilot assignment problem. Numerical experiments demonstrate that the optimized solutions outperform baseline solutions, leading to significant gains in spectral efficiency.

This study investigates the performance of visible light communications (VLCs) in the presence of blockages. An indoor office scenario with a single VLC access point serving the user nodes in the presence of human blockages is examined. System performance is assessed through closed-form expressions for outage probability and symbol error rate for binary phase shift keying and quadrature amplitude modulation. A deep neural network for symbol detection is deployed at the receiver. Performance metrics illustrate that the blockages cause significant impact on signal detection. Computer simulations corroborate the correctness of the obtained analytical expressions.

False data injection attacks (FDIAs) on smart power grids’ measurement data present a threat to system stability. When malicious entities launch cyberattacks to manipulate the measurement data, different grid components will be affected, which leads to failures. For effective attack mitigation, two tasks are required: determining the status of the system (normal operation/under attack) and localizing the attacked bus/power substation. Existing mitigation techniques carry out these tasks separately and offer limited detection performance. In this paper, we propose a multi-task learning-based approach that performs both tasks simultaneously using a graph neural network (GNN) with stacked convolutional Chebyshev graph layers. Our results show that the proposed model presents superior system status identification and attack localization abilities with detection rates of 98.5−100% and 99 − 100%, respectively, presenting improvements of 5 − 30% compared to benchmarks.

The mmWave WiGig frequency band can support high throughput and low latency emerging applications. In this context, accurate prediction of channel gain enables seamless connectivity with user mobility via proactive handover and beamforming. Machine learning techniques have been widely adopted in literature for mmWave channel prediction. However, the existing techniques assume that the indoor mmWave channel follows a stationary stochastic process. This paper demonstrates that indoor WiGig mmWave channels are non-stationary where the channel’s cumulative distribution function (CDF) changes with the user’s spatio-temporal mobility. Specifically, we show significant differences in the empirical CDF of the channel gain based on the user’s mobility stage, namely, room entering, wandering, and exiting. Thus, the dynamic WiGig mmWave indoor channel suffers from concept drift that impedes the generalization ability of deep learning-based channel prediction models. Our results demonstrate that a state-of-the-art deep learning channel prediction model based on a hybrid convolutional neural network (CNN) long-short-term memory (LSTM) recurrent neural network suffers from a deterioration in the prediction accuracy by 11–68% depending on the user’s mobility stage and the model’s training. To mitigate the negative effect of concept drift and improve the generalization ability of the channel prediction model, we develop a robust deep learning model based on an ensemble strategy. Our results show that the weight average ensemble-based model maintains a stable prediction that keeps the performance deterioration below 4%.

This article proposes using communication pipelining to enhance the convergence speed of federated learning in mobile edge computing applications. Due to limited wireless subchannels, a subset of the total clients is scheduled in each iteration of federated learning algorithms. On the other hand, the scheduled clients wait for the slowest client to finish its computation. We propose to first cluster the clients based on the time they need per iteration to compute the local gradients of the federated learning model. Then, we schedule a mixture of clients from all clusters to send their local updates in a pipelined manner. In this way, instead of just waiting for the slower clients to finish their computations, more clients can participate in each iteration. While the time duration of a single iteration does not change, the proposed method can significantly reduce the number of required iterations to achieve a target accuracy. We provide a generic formulation for optimal client clustering under different settings, and we analytically derive an efficient algorithm for obtaining the optimal solution. We also provide numerical results to demonstrate the gains of the proposed method for different data sets and deep learning architectures.

This paper investigates wireless secret key generation in 5G+ indoor LiFi networks operating in the visible light band in the downlink and the infrared band in the uplink. Unlike the existing research, this paper aims to characterize the impact of indoor user mobility on the ability to generate identical secret keys between the user and the base station. Hence, we first generate mobility traces that reflect realistic human behavior in an indoor setup to capture the sensitivity of the optical channels to random blockage and transceiver-misorientation events associated with user mobility. Next, we generate the corresponding channel impulse response and adopt guard band quantization and information reconciliation to extract identical uplink and downlink keys. Then, we examine a set of spatial and temporal features related to the channel probing rate, key disagreement rate, and key generation rate, along with their statistical distributions. Further, we study the randomness of the key following the NIST randomness tests. Our study demonstrated that the handover strategy significantly impacts the randomness of the key and its generation rate.

Cities around the world are increasingly promoting electric vehicles (EV) to reduce and ultimately eliminate greenhouse gas emissions. A huge number of EVs will put unprecedented stress on the power grid. To efficiently serve the increased charging load, these EVs need to be charged in a coordinated fashion. One promising coordination strategy is vehicle-to-vehicle (V2V) charging coordination, enabling EVs to sell their surplus energy in an ad-hoc, peer to peer manner. This paper introduces an Information Centric Networking (ICN)-based protocol to support ad-hoc V2V charging coordination (V2V-CC). Our evaluations demonstrate that V2V-CC can provide added flexibility, fault tolerance, and reduced communication latency than a conventional centralized cloud based approach. We show that V2V-CC can achieve a 93% reduction in protocol completion time compared to a conventional approach. We also show that V2V-CC also works well under extreme packet loss, making it ideal for V2V charging coordination.

During pandemics, diagnostic tests are essential to provide quick treatment of patients and limit the disease spread. The high demand for testing resources can stress the healthcare system. Thus, a remote collection of symptoms and reporting the results via an automated diagnostic system is highly desirable. However, such a system is challenged by privacy and scalability issues. Hence, we propose a sharded blockchain-based system that (a) introduces a set of shards that distributes the testing load among a group of local nodes (LNs), hence, offering high scalability for country-wide adoption, (b) uses ring signatures and unique random identifiers to ensure the anonymity of the users and the unlinkability of test requests, hence, supporting privacy-preservation, (c) deploys a detection strategy at the LNs based on deep neural networks, which is implemented on smart contracts, hence, enabling autonomous diagnosis, and (d) provides healthcare entities with authorized access to the symptoms and test results, hence, enabling efficient data sharing that supports future research. We provide an implementation of the proposed system and our experimental results demonstrate the high scalability and privacy of the system while achieving a testing accuracy up to 90%. We present a case study for U.S. wide deployment showing that a total daily test request of 2, 407, 462 can be performed and reported in 11 minutes compared to 63 days in absence of sharding. Moreover, sharding decreased the user storage requirement to be 0.18 MB at maximum instead of 723 MB without sharding.

Dynamic wireless charging of electric vehicles (EVs) enables the exchange of power between a mobile EV and the electricity grid via a set of charging pads (CPs) deployed along the road. Accordingly, dynamic charging coordination can be introduced for a group of mobile EVs to specify where each EV can charge (i.e., from which CPs). This coordination mechanism maximizes the satisfied charging requests given the limited available energy supply. Upon specifying the optimal set of pads for a given EV, a fast authentication mechanism is required between the EV and the CPs to start the charging process. However, both the coordination and authentication mechanisms require exchanging private information, e.g., EV identities and locations. Hence, there is a need for a strategy that enables privacy-preservation in dynamic charging via supporting: (i) user anonymity and (ii) data unlinkability. In this paper, we propose a decentralized and scalable networking strategy based on a specially designed private blockchain that can support the privacy requirements of dynamic charging coordination, authentication, and billing. The proposed networking strategy relies on group signature and distributed random number generators to support the desirable features. Simulation results demonstrate the efficiency and low complexity of the proposed blockchain-based networking strategy.

Demand side management (DSM) presents an effective tool to regulate customers’ energy consumption. However, DSM requires customer-utility interaction based on fine-grained data, which jeopardizes the privacy of the customers. To overcome such a threat, several proposals are made based on noise addition, encryption techniques, or extra hardware. Unfortunately, these solutions either impact the integrity of the data, incur additional computational complexity, or add extra cost. Recently, blockchain has been adopted to support smart grid applications while promoting customer’s anonymity. However, special measures are yet to be taken to support both customer’s anonymity and data unlinkability, hence preserving the customer’s privacy. This paper achieves this goal by proposing a novel networking strategy based on a private blockchain. The proposed strategy employs a group signature to ensure the customer’s anonymity and data unlinkability. Further, a novel decentralized random number generation scheme is proposed to support customer-utility interaction for DSM, billing, and auditing while ensuring data unlinkability. We present an implementation of the proposed blockchain-based strategy, investigate its scalability, and provide low-bound empirical expressions on computational time and storage overhead.

Electricity theft cyber-attacks pose significant threats to smart power grids. In these attacks, malicious customers hack into their smart meters and manipulate the integrity of their energy consumption readings to reduce their electricity bills. Recently, machine learning techniques have been successfully employed to detect such cyber-attacks. However, the developed detectors have been tested against simple attacks. In this paper, we investigate the performance of electricity theft detectors against evasion attacks that are designed to reduce the reported value of the energy consumption and at the same time fool the machine learning-based detector model via adversarial samples. Furthermore, we propose a strong evasion attack that significantly degrades the performance of a set of benchmark detectors. Our results reveal that evasion attacks can deteriorate the detection rate (DR) and false alarm (FA) rate by ~ 20%. To address such evasion attacks, we propose an ensemble learning-based detector that integrates auto-encoder with attention (AEA), long-short-term-memory (LSTM), and feed forward deep neural networks. The developed detector maintains a stable detection performance against evasion attacks with a deterioration in performance by only 1 − 5% in DR and FA.

Next-generation wireless networks rely on unused high spectrum bands, such as mmWave and visible light, to support high-speed communications. However, such bands suffer from frequent outages of line-of-sight (LoS) signals, which deteriorates the communication's quality. Thus, recent efforts have promoted programmable wireless environments (PWEs), which employs reconfigurable intelligent surfaces (RIS) that can redirect non-LoS (NLoS) signals towards the user to enhance reception quality. Unfortunately, these efforts are limited so far to setups with stationary users. We show in this paper that crowd mobility induces spatio-temporal sparseness across the wireless channels in an indoor layout. As a result, shadowed regions will exist on the walls of the room due to signal blockage. Hence, random allocations of passive RIS tiles across the room will not result in the utmost gains and will unnecessarily complicate the communication system. Therefore, we propose an efficient data driven approach that capitalizes on a realistic indoor mobility model to allocate RIS tiles across the room in a way that maximizes their exploitation efficiency. Our results indicate that efficient design of PWEs can reduce the number of required passive RIS tiles by 70% while maintaining high service quality.

Optical wireless networks, especially those relying on visible light communications, suffer from severe deterioration in signal's quality when the line-of-sight (LOS) link is absent due to user's mobility. In order to enable efficient resource management within such networks, reliable prediction of LOS link outage is essential. Towards this objective, this article proposes a data-driven approach based on deep machine learning techniques to predict the outage events in an LOS link. First, we present a framework to generate sufficient data representing the channel gain in mobile optical wireless networks that consist of visible light communications in the downlink and infrared communications in the uplink. Using the developed dataset, we propose a channel predictor that forecasts the burst outages or signal recoveries in the upcoming frames using a deep recurrent neural network that implements long-short-term-memory (LSTM) units. To achieve this goal, we propose a low-complexity approach to reduce the data sparsity due to the user's mobility by abstracting and densifying the channel state sequence. For a one second prediction interval, the proposed prediction framework achieves an event hit rate of 91.55% for abrupt outages with an average event timing error of 79 ms, and 83.19% for recoveries from outages with 145 ms timing error. This timing error is on the same order of magnitude with the coherence time of the optical wireless channel. Therefore, this predictor is very useful in developing efficient resource management strategies in such optical networks.

Smart grids are faced with the challenge of meeting the ever increasing load demands of electric vehicles (EVs). To provide acceptable charging services, operators need to be equipped with an efficient charging stations (CSs) planning strategy. Unfortunately, existing planning solutions are quite limited. They normally rely on standard IEEE bus systems or power grids that are specific to certain cities. In this paper, using stochastic geometry, we formulate the CSs planning on a stochastic geometry-based power grid model, that we previously showed to mimic real-world power grids. We study the effect of the density of EVs and the technical charging constraints on the minimum number of CSs to install.

The tight interaction between power grids and communication networks supports the advanced functionalities of smart grids and enables a more efficient utilization of the assets within the power system. Towards this objective, it is of utmost importance to develop a model that reflects such an interaction. Unfortunately, existing cyber-physical interdependent models are conceptual or are specific to certain regions and do not consider the spatial information and correlation of electrical elements. To address these limitations, in this paper, we propose an interdependent communication-power network model using tools from stochastic geometry, which takes into consideration the spatial locations of electrical buses and the required service quality within the communication network. A step-by-step process on building a communication network on top of the power grid while satisfying the average queuing delay and queuing stability constraints is described. A case study for a realization of this generative interdependent model is presented.

This article investigates a heterogeneous network (HetNet) consisting of an overlapped coverage of radio frequency (RF) and optical wireless communication (OWC) to support human connectivity to the Internet of Things (IoT) in the fifth generation and beyond (5G+) mobile networks. Such a HetNet-based IoT benefits from the high throughput of the OWC and the high reliability of the RF communications. To ensure a reliable link with Quality-of-Service (QoS) guarantee in terms of network delay and throughput, vertical handovers are triggered within the HetNet. A cross-layer data-driven approach is adopted to reach optimal handover decisions and tackle the challenges associated with the mobility and reliability of IoT. As mobile time-varying wireless channel gains in optical links are not publicly available, we first present a realistic model for the mobile optical channel that reflects the nature of human mobility, and hence, a useful model that features the spatial-temporal patterns of indoor mobile channels is proposed. Using the created data set, we then present a data-driven algorithm that predicts abrupt outages in Line-of-Sight (LOS) optical links and evaluates the optical channel quality through deep learning. Given the resulting LOS link outage prediction in OWC, a reinforcement-learning-based approach is proposed to implement optimal vertical handover decisions with the QoS guarantee. The proposed handover decision algorithm learns to make a tradeoff between the outage risk and the cost of excessive handovers. The numerical results demonstrate considerable improvement in overall latency and handover rate under indoor mobility for bidirectional links.

Link instability induced by users' mobility is one of the challenges of optical wireless communications (OWC) inherited from the propagation nature of light. Hence, good understanding of the optical channel characteristics in dynamic environments plays a vital role in developing robust resource management strategies in OWC networks. Unfortunately, it is quite difficult to collect accurate indoor optical channel data in dynamic environments. In addition, indoor trajectory dataset is not publicly available. To overcome such limitations, this paper proposes a mobile terminal-centric analytical framework that captures the propagation channel characteristics in a mobile OWC network whose downlink is based on visible light and uplink is based on infrared light. We abstract the nature of human behavior by integrating both macro and micro mobility patterns. These patterns are then used to realize the spatio-temporal characteristics of optical wireless channels under long-term environment-confined mobility. The statistics derived from the developed framework indicate that the mobile line-of-sight (LOS) channel gain follows space-time-dependent multiple-peak Nakagami distributions, whereas the non-line-of-sight (NLOS) channel gain adheres to various space-time-dependent single peak distributions under different indoor layouts. The overall distribution of NLOS bandwidth follows space-time-dependent multiple-peak log-logistic distributions in downlinks and space-time-dependent generalized log-logistic distributions in uplinks. Our investigation demonstrates that the indoor layout and the user's environment-confined mobility pattern significantly impact the LOS dynamics but present limited impact on NLOS components. Motivated by the need for better channel models for mobile OWC, the proposed framework fills up an important gap in literature and help the research community to understand better the indoor optical wireless channel characteristics.

This paper focuses on the development of energy efficient visible light communication (VLC) networks. More specifically, since the quality-of-service and energy cost are key parameters in designing energy efficient networks, this paper optimizes the VLC base station (BS) density to minimize the area power consumption (APC) under an outage probability constraint. Using stochastic geometry, approximations of the outage probability of VLC networks are first introduced. The derived approximations are applicable to an arbitrary field-of-view at photodiodes and present low computational complexities. Leveraging the derived analytical results, a low complexity algorithm to find the VLC BS density that minimizes the APC of VLC networks is then proposed. The numerical simulations corroborate the tightness of the approximations on the outage probability and confirm that the proposed algorithm exhibits almost identical performance as the algorithm that exhaustively searches the optimal BS density.

In this paper, we propose privacy-preserving fine-grained data retrieval schemes for mobile social networks (MSNs). The schemes enable users to retrieve data from other users who are interested in some topics related to a subject of interest. We define a subject to be a broad term that can cover many fine-grained topics, e.g., History can be a subject and World War I can be a topic. We consider centralized and decentralized network models. Our centralized scheme allows users to securely outsource data to a server such that the server matches the users who are interested in same topic(s) and have defined social attributes with privacy preservation. Searchable encryption scheme and a proposed cryptography construct are used to enable the server to match the topics and attributes without knowing any private information. By using the social attributes, users can prescribe the other users who can be connected to. We also propose a decentralized scheme that can be used when there is no connection to the server, i.e, shortage of Internet connectivity. The scheme leverages friends-of-friends relationship and transferable trust concept, where each user trusts his friends and the friends of friends. If a friend is not interested in the requested subject, he/she can link him/her to his/her friends without knowing the requested subject to preserve privacy. Our schemes use Bloom filters to store the topics of interest to reduce the storage and communication overhead. This is important because the number of fine-grained topics can be large. Different techniques to store the topics in the filter are proposed and investigated. Performance metrics are proposed and evaluated using real implementations. Our analysis and implementation results demonstrate that our schemes can preserve the privacy of the MSN users with high performance.

This paper focuses on the development of energy efficient hybrid networks consisting of radio frequency (RF) base stations (BSs) and visible light communication (VLC) BSs. More specifically, since the quality-of-service and energy cost are key parameters in designing energy efficient networks, this paper optimizes the RF BS and VLC BS intensities to minimize the area power consumption (APC) under an outage probability constraint. Using stochastic geometry, approximations of the outage probability of VLC networks, which are applicable to an arbitrary field-of-view at photodiodes and present low computational complexities, are first introduced. Leveraging the derived analytical results, a low complexity algorithm to find the VLC BS intensity that minimizes the APC of VLC networks is then proposed. Furthermore, algorithms to identify the intensities of RF BSs and VLC BSs for energy efficient hybrid RF/VLC networks via one-dimensional search methods are also developed. The numerical simulations corroborate the tightness of the approximations on the outage probability and confirm that the proposed algorithms exhibit almost identical performances as the algorithms that exhaustively search the optimal BS intensities. Finally, it is shown that the hybrid RF/VLC networks achieve a lower outage probability with a reduced APC compared to the RF-only networks and VLC-only networks.

Modern smart grids rely on advanced metering infrastructure (AMI) networks for monitoring and billing purposes. However, such an approach suffers from electricity theft cyberattacks. Different from the existing research that utilizes shallow, static, and customer-specific-based electricity theft detectors, this paper proposes a generalized deep recurrent neural network (RNN)-based electricity theft detector that can effectively thwart these cyberattacks. The proposed model exploits the time series nature of the customers' electricity consumption to implement a gated recurrent unit (GRU)-RNN, hence, improving the detection performance. In addition, the proposed RNN-based detector adopts a random search analysis in its learning stage to appropriately fine-tune its hyper-parameters. Extensive test studies are carried out to investigate the detector's performance using publicly available real data of 107,200 energy consumption days from 200 customers. Simulation results demonstrate the superior performance of the proposed detector compared with state-of-the-art electricity theft detectors.

Advanced metering infrastructure (AMI) networks are vulnerable against electricity theft cyber attacks. Different from the existing research that exploits shallow machine learning architectures for electricity theft detection, this paper proposes a deep neural network (DNN)-based customer-specific detector that can efficiently thwart such cyber attacks. The proposed DNN-based detector implements a sequential grid search analysis in its learning stage to appropriately fine tune its hyper-parameters, hence, improving the detection performance. Extensive test studies are carried out based on publicly available real energy consumption data of 5000 customers and the detector's performance is investigated against a mixture of different types of electricity theft cyber attacks. Simulation results demonstrate a significant performance improvement compared with state-of-the-art shallow detectors.

In this paper, we develop optimal energy scheduling algorithms for optical Poisson channels with energy harvesting devices. The objective is to maximize the channel sum-rate, assuming that the side information of energy harvesting states for K time slots is known a priori, and the battery capacity and the maximum energy consumption in each time slot are bounded. The problem is formulated as a convex optimization problem with O(K) constraints making it hard to solve using a general convex solver since the computational complexity of a generic convex solver is exponential in the number of constraints. This paper gives an efficient energy scheduling algorithm that has a computational complexity of O(K2). The proposed algorithm is also shown to be optimal. The proposed energy schedule is piece-wise constant, which changes when the battery overflows or depletes. Numerical results depict significant improvement of the optimal strategy over benchmark strategies.

In literature, dynamic planning (base station (BS) on-off switching) is proposed as an efficient approach for energy saving at a low call traffic load condition. All related research efforts aim to employ dynamic planning to save energy for the network operators while satisfying the quality-of-service (QoS) for downlink mobile users. On the other hand, although switching off a BS can save energy for network operators and satisfy the downlink QoS, it may lead to associating uplink mobile users with a faraway BS, and hence, degrade the uplink QoS. Such an impact of dynamic planning on uplink service quality is not well investigated in literature. In this paper, we aim to quantify this impact by considering two QoS metrics, namely, user required throughput and call dropping probability due to mobile terminal battery depletion for uplink data calls. Simulation results demonstrate that BS switching off decisions that do not account for the uplink QoS requirements can result in severe service quality degradation.

In this paper, a novel devices-to-device (Ds2D) communication paradigm is proposed to enable green (energy efficient) wireless networks. Different from the conventional D2D communications, the sink device establishes simultaneous associations with multiple source devices for file download using its multiple radio interfaces and the multi-homing technique. In such a networking setting, we propose a network-controlled algorithm for optimal selection of source devices and their respective radio interfaces to support green Ds2D communications. Simulation results demonstrate that the proposed Ds2D communication paradigm under optimal selection of source devices and radio interfaces presents an improved energy efficiency performance compared with the conventional D2D communications, and leads to a lower energy consumption per source device.

In this paper, we propose a privacy-preserving power charging coordination scheme. Each energy storage unit (ESU) should send a charging request to an aggregator. The request does not reveal any private information to the aggregator. The aggregator forwards the requests to a charging controller that can know enough data to run a charging coordination scheme, but it cannot link the data to particular ESUs. Temporal charging coordination scheme is then proposed based on a modified knapsack problem formulation. The goal is to maximize the amount of power delivered to the ESUs before the charging requests expire without exceeding the available maximum charging capacity. Our simulation results demonstrate that both the optimal charging coordination and the privacy-aware charging coordination exhibit an improved performance compared with a first-come-first-serve charging coordination. More importantly, the privacy-aware scheme offers an attractive trade-off between the charging coordination performance and privacy preservation.

In this paper, we propose a trust-based and privacy-preserving fine-grained data retrieval scheme for mobile social networks (MSNs). The scheme enables users to create a log of trusted users who store (or are interested in) some topics related to a subject of interest. A subject is a broad term that can cover many fine-grained topics. In creating logs, we leverage friends-of-friends relationships and transferrable trust concept. Each user trusts its friends and the friends of friends. If a friend is not interested in a subject, he can help his friend in creating the log by linking the friend to his friends without knowing the subject to preserve privacy. In order to reduce the storage and computation overhead, we use Bloom filters to store the topics. A distinctive feature in our scheme is that it can query users who possess a fine-grained topic, rather than querying users who are interested in the broad subject but they may not have the specific topic of interest. We analyze the security and privacy of our scheme and evaluate the communication and computation overhead.

Developing energy efficient wireless communication networks has become crucial due to the associated environmental and financial benefits. Visible light communication (VLC) has emerged as a promising candidate for achieving energy efficient wireless communications. Integrating VLC with radio frequency (RF)-based wireless networks has improved the achievable data rates of mobile users. In this paper, we investigate the energy efficiency benefits of integrating VLC with RF-based networks in a heterogeneous wireless environment. We formulate and solve the problem of power and bandwidth allocation for energy efficiency maximization of a heterogeneous network composed of a VLC system and an RF communication system. Then, we investigate the impact of the system parameters on the energy efficiency of the mixed RF/VLC heterogeneous network. Numerical results are conducted to corroborate the superiority in performance of the proposed hybrid system. The impact of hybrid system parameters on the overall energy efficiency is also quantified.

Dynamic planning has been used by network operators for energy saving by switching ON-OFF base stations (BSs) according to the traffic load condition while providing quality-of-service (QoS) guarantee for mobile users. In the literature, research efforts focus mainly on satisfying the QoS of downlink mobile users. However, the impact of dynamic planning on the QoS of uplink mobile users is overlooked. A dynamic planning approach that relies only on the downlink performance of mobile users to determine the BS switching decision can result in deactivating nearby small cells and associating uplink mobile users to a faraway macro BS. Consequently, uplink mobile users may suffer from QoS degradation due to the longer transmission distance. Hence, while saving energy for the network operators and satisfying the downlink QoS, the uplink QoS could be violated. In this paper, we propose a balanced dynamic planning approach that accounts for QoS requirements both in the uplink and downlink to specify the BS switching decision. The switching decision is formulated as a two-timescale average reward Markov decision process with finite horizon. Due to computational complexity, sub-optimal algorithms are proposed. Simulation results demonstrate the trade-off between energy saving and QoS guarantee.

This paper presents an energy efficient and quality of service aware dynamic cell zooming algorithm for dense heterogeneous networks. The exponential growth of mobile data traffic would lead to dense deployment of small base stations and eventually higher energy consumption in Fifth Generation (5G) wireless networks. We formulate a dynamic cell zooming and base stations sleep optimization algorithm for dense heterogeneous networks as a Linear Programming (LP) problem in order to not only minimize the system power consumption but also to guarantee the quality of service to end user. This is possible by optimally zooming the coverage area of macro base stations and small cells based upon real time traffic conditions. We characterize the optimal as well as provide an approximate solution, which, however, performs very closely to the optimum. The extensive performance evaluation of our proposed dynamic cell zooming algorithm shows that our proposed algorithm can significantly decrease both system energy consumption and outage probability.

This paper investigates energy efficient in vivo communication with multi-source nodes. In such a network, the in vivo sensor nodes transmit their sensing information to an on-body destination node. Due to the associated high path loss with implant devices, on-body relay nodes can be used to convey the information bits from the in vivo source nodes to the on-body destination node in an energy efficient manner. In this context, the paper objective is to select the optimal on-body relay locations that result in the minimum per bit average energy consumption for the in vivo networks using the minimum number of on-body relay nodes. The problem is formulated as an integer program that can be efficiently solved using commercial optimization solvers. Numerical results demonstrate the significant improvement in energy consumption and quality-of-service (QoS) support, when on-body relays are optimally located.

In this paper, energy efficient uplink communications are investigated for battery-constrained mobile terminals (MTs) with service quality requirements and multi-homing capabilities. A heterogeneous wireless medium is considered, where MTs communicate with base stations (BSs) and access points (APs) of different networks with overlapped coverage. Different from the existing works, we develop a quality of service (QoS)-based optimization framework for joint uplink bandwidth and power allocation to maximize energy efficiency for a set of MTs with multi-homing capabilities. The proposed framework is implemented in a decentralized architecture, through coordination among BSs/APs of different networks and MTs, which is a desirable feature when different networks are operated by different service providers. A suboptimal framework is presented with a reduced computational complexity as compared with the optimal framework. Simulation results demonstrate the improved performance of both the optimal and suboptimal frameworks over a state-of-the-art benchmark.

A vehicle-to-vehicle (V2V) (dis)charging strategy can provide charging plans for gridable electric vehicles (GEVs), aiming to offload the heavy power loads from the electric power system. However, designing an efficient online V2V (dis)charging strategy to achieve optimal energy utilization is still an open issue. In this paper, we propose a semi-distributed online V2V (dis)charging strategy at a swapping station based on price control. Specifically, based on the electricity price control strategy, GEVs are motivated to contribute to a V2V energy transaction due to expected high revenue for discharging GEVs and low cost for charging GEVs. The Oligopoly game and Lagrange duality optimization techniques are exploited to address the associated optimal V2V (dis)charging strategies. Simulation results are presented to demonstrate the performance of the proposed V2V (dis)charging strategy.

This paper investigates energy efficient uplink communications for battery-constrained mobile terminals (MTs). We consider a heterogeneous wireless medium where MTs communicate with base stations (BSs) and access points (APs) of different networks with overlapped coverage. Unlike the existing research, we develop a joint bandwidth and power allocation framework that maximizes energy efficiency for a set of MTs, in different service areas, with best effort service and multi-homing capabilities. The problem formulation captures the heterogeneity of the medium, in terms of different service areas, channel conditions, available resources at BSs/APs of different networks, and different available maximum power at the MTs. In addition, the framework is implemented in a decentralized manner which is desirable in a case that different networks are operated by different service providers. Simulation results are presented to demonstrate the performance of the proposed framework.

In this paper, an energy management sub-system is proposed for mobile terminals (MTs) to support a sustainable multi-homing video transmission, over the call duration, in a heterogeneous wireless access medium. Through statistical video quality guarantee, the MT can determine a target video quality lower bound that can be supported for a target call duration. The target video quality lower bound captures the MT available energy at the beginning of the call, the time varying bandwidth availability and channel conditions at different radio interfaces, the target call duration, and the video packet characteristics in terms of distortion impact, delay deadlines, and video packet encoding statistics. The MT then adapts its energy consumption to support at least the target video quality lower bound during the call. Simulation results demonstrate the superior performance of the proposed framework over two benchmarks and some performance trade-offs.

In this paper, radio resource allocation in a heterogeneous wireless access medium is investigated. Mobile terminals (MTs) are equipped with multiple radio interfaces and are assumed to have multi-homing capabilities. A novel algorithm, namely prediction based resource allocation algorithm, is proposed for the resource allocation. Unlike the existing solutions in literature, the proposed algorithm does not require a central resource manager to perform the radio resource allocation. The MT plays an active role in the resource allocation operation by requesting a bandwidth share from each available network based on the available resources at the network, such that the total allocated bandwidth from different networks satisfies the MT service requirement. The proposed algorithm is suitable for implementation in a dynamic environment with call arrivals and departures, and relies on network cooperation to perform the decentralized radio resource allocation in an efficient manner. Simulation results are presented to investigate the performance tradeoffs of the proposed algorithm.

This paper studies video transmission using a multi-homing service in a heterogeneous wireless access medium. We propose an energy and content aware video transmission framework that incorporates the energy limitation of mobile terminals (MTs) and the quality-of-service (QoS) requirements of video streaming applications, and employs the available opportunities in a heterogeneous wireless access medium. In the proposed framework, the MT determines the transmission power for the utilized radio interfaces, selectively drops some packets under the battery energy limitation, and assigns the most valuable packets to different radio interfaces in order to minimize the video quality distortion. First, the problem is formulated as MINLP which is known to be NP-hard. Then we employ a piecewise linearization approach and solve the problem using a cutting plane method which reduces the associated complexity from MINLP to a series of MIPs. Finally, for practical implementation in MTs, we approximate the video transmission framework using a two-stage optimization problem. Numerical results demonstrate that the proposed framework exhibits very close performance to the exact problem solution. In addition, the proposed framework, unlike the existing solutions in literature, offers a choice for desirable trade-off between the achieved video quality and the MT operational period per battery charging.

In this paper, radio resource allocation for mobile terminals (MTs) in a heterogeneous wireless access medium is investigated. Unlike the existing solutions in literature, two types of services are considered in this paper, namely single-network and multi-homing services. In single-network services, an MT is assigned to the best available wireless network, while in multi-homing services an MT utilizes all available wireless access networks simultaneously. With the presence of both services in the heterogeneous wireless access medium, the radio resource allocation objective is of twofold: We aim to find the optimal assignment of MTs with single-network service to the available wireless access networks and to determine the corresponding optimal bandwidth allocation to the MTs with single-network and multi-homing services. The radio resource allocation problem is formulated to guarantee the service quality for both service types. Numerical results are presented to demonstrate the performance of the proposed radio resource allocation scheme.

In this paper, radio resource allocation in a heterogeneous wireless access medium is studied. Mobile terminals (MTs) are assumed to have multi-homing capabilities. Both constant bit rate and variable bit rate services are considered. A novel algorithm is developed for the resource allocation. Unlike existing solutions in literature, the proposed algorithm is distributed in nature, such that each network base station / access point can perform its own resource allocation to support the MTs according to their service classes. The coordination among different available wireless access networks' base stations is established via the MT multiple radio interfaces in order to provide the required bandwidth to each MT. A priority mechanism is employed, so that each network gives a higher priority on its resources to its own subscribers as compared to other users. Numerical results demonstrate the validity of the proposed algorithm.

This paper studies radio resource allocation for mobile terminals (MTs) in a heterogeneous wireless access medium. Unlike the existing solutions in literature, we consider the simultaneous presence of both single-network and multi-homing services in the networking environment. In single-network services, an MT is assigned to the best wireless access network available at its location. On the other hand, in multi-homing services, an MT utilizes all available wireless access networks simultaneously. The objective of the radio resource allocation is of twofold: to determine the optimal assignment of MTs with single-network service to the available wireless access networks, and to find the corresponding optimal bandwidth allocation to the MTs with single-network and multi-homing services. We develop a sub-optimal decentralized implementation of the radio resource allocation, which relies on network cooperation to perform the allocation in a dynamic environment in an efficient manner. The MT plays an active role in the resource allocation operation, whether by selecting the best available wireless network for single-network services or by determining the required bandwidth share from each available network for multi-homing services. Simulation results are presented to demonstrate the performance of the proposed algorithm.