Internet of Agents (IoA) envisions a unified, agent-centric paradigm where heterogeneous large language model (LLM) agents can interconnect and collaborate at scale. Within this paradigm, federated fine-tuning (FFT) serves as a key enabler that allows distributed LLM agents to co-train an intelligent global LLM without centralizing local datasets. However, the FFT-enabled IoA systems remain vulnerable to model poisoning attacks, where adversaries can upload malicious updates to the server to degrade the performance of the aggregated global LLM. This paper proposes a graph representation-based model poisoning (GRMP) attack, which exploits overheard benign updates to construct a feature correlation graph and employs a variational graph autoencoder to capture structural dependencies and generate malicious updates. A novel attack algorithm is developed based on augmented Lagrangian and subgradient descent methods to optimize malicious updates that preserve benign-like statistics while embedding adversarial objectives. Experimental results show that the proposed GRMP attack can substantially decrease accuracy across different LLM models while remaining statistically consistent with benign updates, thereby evading detection by existing defense mechanisms and underscoring a severe threat to the ambitious IoA paradigm.

Uncrewed Aerial Vehicles (UAVs) play a vital role in public safety, especially in monitoring wildfires, where early detection reduces environmental impact. In UAV-Assisted Wildfire Monitoring (UAWM) systems, jointly optimizing the data collection schedule and UAV velocity is essential to minimize the average Age of Information (AoI) for sensory data. Deep Reinforcement Learning (DRL) has been used for this optimization, but its limitations – including low sampling efficiency, discrepancies between simulation and real-world conditions, and complex training – make it unsuitable for time-critical applications such as wildfire monitoring. Recent advances in Large Language Models (LLMs) provide a promising alternative. With strong reasoning and generalization capabilities, LLMs can adapt to new tasks through In-Context Learning (ICL), which enables task adaptation using natural language prompts and example-based guidance without retraining. This paper proposes a novel online Flight Resource Allocation scheme based on LLM-Enabled In-Context Learning (FRSICL) to jointly optimize the data collection schedule and UAV velocity along the trajectory in real time, thereby asymptotically minimizing the average AoI across all ground sensors. Unlike DRL, FRSICL generates data collection schedules and velocities using natural language task descriptions and feedback from the environment, enabling dynamic decision-making without extensive retraining. Simulation results confirm the effectiveness of FRSICL compared to state-of-the-art baselines, namely Proximal Policy Optimization, Block Coordinate Descent, and Nearest Neighbor.

Recent attacks on federated learning (FL) can introduce malicious model updates that can circumvent widely adopted Euclidean distance-based detection methods. This article proposes a novel defense strategy, referred to as LayerCAM-AE, designed to counteract model poisoning in FL. The LayerCAM-AE puts forth a new Layer Class Activation Mapping (LayerCAM) integrated with an autoencoder (AE), significantly enhancing detection capabilities. Specifically, LayerCAM-AE generates a heat map for each local model update, which is then transformed into a more compact visual explanation. The autoencoder processes the LayerCAM heat maps from the local model updates, improving their distinctiveness and increasing the accuracy in spotting anomalous maps and malicious local models. To mitigate the risk of misclassifications in LayerCAM-AE, a voting algorithm is developed, where a local model update is flagged as malicious if its heat maps are consistently suspicious over several communication rounds. Extensive tests on the SVHN and CIFAR-100 datasets are performed under both Independent and Identically Distributed (IID) and non-IID settings in comparison with the state-of-the-art ResNet-50 and REGNETY-800MF defense models. The experimental results show that LayerCAM-AE increases detection rates (Recall: 1.0, Precision: 1.0, FPR: 0.0, Accuracy: 1.0, F1 score: 1.0, AUC: 1.0) and the test accuracy of FL, surpassing both the ResNet-50 and REGNETY-800MF. Our code is available at: https://github.com/jjzgeeks/LayerCAM-AE .

This article proposes a new cyberattack on decentralized federated learning (DFL), named user isolation poisoning (UIP). While following the standard DFL protocol of receiving and aggregating benign local models, a malicious user strategically generates and distributes compromised updates to undermine the learning process. The objective of the new UIP attack is to diminish the impact of benign users by isolating their model updates, thereby manipulating the shared model to reduce the learning accuracy. To realize this attack, we design a novel threat model that leverages an adversarial message-passing graph (MPG) neural network. Through iterative message passing, the adversarial MPG progressively refines the representations (also known as embeddings or hidden states) of each benign local model update. By orchestrating feature exchanges among connected nodes in a targeted manner, the malicious users effectively curtail the genuine data features of benign local models, thereby diminishing their overall influence within the DFL process. The MPG-based UIP attack is implemented in PyTorch, demonstrating that it effectively reduces the test accuracy of DFL by 49.5% and successfully evades existing cosine similarity- and Euclidean distance-based defense strategies.

Accurately recognizing and assessing pain in sheep is key to discern animal health and mitigating harmful situations. However, such accuracy is limited by the ability to manage automatic monitoring of pain in those animals. Facial expression scoring is a widely used and useful method to evaluate pain in both humans and other living beings. Researchers also analyzed the facial expressions of sheep to assess their health state and concluded that facial landmark detection and pain level prediction are essential. For this purpose, we propose a novel weighted graph neural network (WGNN) model to link sheep’s detected facial landmarks and define pain levels. Furthermore, we propose a new sheep facial landmarks dataset that adheres to the parameters of the Sheep Facial Expression Scale (SPFES). Currently, there is no comprehensive performance benchmark that specifically evaluates the use of graph neural networks (GNNs) on sheep facial landmark data to detect and measure pain levels. The YOLOv8n detector architecture achieves a mean average precision ($\mathbf{m A P}$) of $\mathbf{5 9. 3 0 \%}$ with the sheep facial landmarks dataset, among seven other detection models. The WGNN framework has an accuracy of $92.71 \%$ for tracking multiple facial parts expressions with the YOLOv8n lightweight on-board device deployment-capable model.

EdgeIoT represents an approach that brings together mobile edge computing with Internet of Things (IoT) devices, allowing for data processing close to the data source. Sending source data to a server is bandwidth-intensive and may compromise privacy. Instead, federated learning allows each device to upload a shared machine-learning model update with locally processed data. However, this technique, which depends on aggregating model updates from various IoT devices, is vulnerable to attacks from malicious entities that may inject harmful data into the learning process. This paper introduces a new attack method targeting federated learning in EdgeIoT, known as data-independent model manipulation attack. This attack does not rely on training data from the IoT devices but instead uses an adversarial variational graph auto-encoder (AV-GAE) to create malicious model updates by analyzing benign model updates intercepted during communication. AV-GAE identifies and exploits structural relationships between benign models and their training data features. By manipulating these structural correlations, the attack maximizes the training loss of the federated learning system, compromising its overall effectiveness.

This paper focuses on Zero-Trust Foundation Models (ZTFMs), a novel paradigm that embeds zero-trust security principles into the lifecycle of foundation models (FMs) for Internet of Things (IoT) systems. By integrating core tenets, such as least privilege access, continuous verification, data confidentiality, and behavioral analytics into the design, training, and deployment of FMs, ZTFMs can enable secure, privacy-preserving AI across distributed, heterogeneous, and potentially adversarial IoT environments. We present the first structured synthesis of ZTFMs, identifying their potential to transform conventional trust-based IoT architectures into resilient, self-defending ecosystems. Moreover, we propose a comprehensive technical framework, incorporating federated learning (FL), blockchain-based identity management, micro-segmentation, and trusted execution environments (TEEs) to support decentralized, verifiable intelligence at the network edge. In addition, we investigate emerging security threats unique to ZTFM-enabled systems and evaluate countermeasures, such as anomaly detection, adversarial training, and secure aggregation. Through this analysis, we highlight key open research challenges in terms of scalability, secure orchestration, interpretable threat attribution, and dynamic trust calibration. This survey lays a foundational roadmap for secure, intelligent, and trustworthy IoT infrastructures powered by FMs.

Inherent broadcast characteristics can raise privacy risks of wireless networks. The specifics of antenna ports, antenna types, orientation, and beamforming configurations of a transmitter can be susceptible to manipulation by any device within range when the signal is transmitted wirelessly. Personal and location information of users connected to the transmitter can be intercepted and exploited by malicious actors to track user movements and profile behaviors or launch targeted attacks, thus compromising user privacy and security. In this paper, we propose a novel precoding perturbation approach for privacy preservation in wireless communications. Our approach perturbs the precoding matrix of the transmitter using a Riemannian manifold (RM) structure that adaptively adjusts the magnitude and direction of perturbation based on the geometric properties of the manifold. The approach ensures robust privacy protection while minimizing the distortion of the transmitted signals, thus balancing privacy preservation and data utility. Privacy can be preserved without relying on additional cryptographic mechanisms, resulting in the computational and communication overhead reduction. Our approach operates directly on the transmission of signals, making them inherently secure against eavesdropping and interception. Simulation results underscore the superiority of the approach, showing a 17.21% improvement in privacy preservation while effectively maintaining data utility.

Recent poisoning attacks on federated learning (FL) generate malicious model updates that circumvent widely adopted Euclidean distance-based detection methods. This article proposes a new defense mechanism, namely, GradCAM-AE, against model poisoning attacks on FL, which integrates Gradient-weighted Class Activation Mapping (GradCAM) and autoencoder (AE) to offer a substantially more powerful detection capability compared to existing Euclidean distance-based approaches. Particularly, GradCAM-AE generates a heat map for each uploaded local model update, transforming each local model update into a lower-dimensional, visual representation. An AE further reprojects the GradCAM heat maps of all local module updates with improved distinguishability, thereby accentuating the hidden features of the heat maps and increasing the success rate of identifying anomalous heat maps and malicious local models. A comprehensive evaluation of the proposed GradCAM-AE framework is conducted using the CIFAR-10 and GTSRB datasets under both Independent and Identically Distributed (IID) and Non-IID settings. The ResNet-18 and MobileNetV3-Large models are tested. The results substantiate that GradCAM-AE offers superior detection rates and test accuracy of FL global model, juxtaposed with contemporary state-of-the-art methods. Our code is available at: https://github.com/jjzgeeks/GradCAM-AE .

Accurate and real-time acquisition of vehicular system dynamic states, road surface conditions, and motion states of surrounding participants is crucial for the safety, passenger comfort, and operational efficiency of autonomous vehicles (AVs) and connected automated vehicles (CAVs). In recent years, a significant amount of research has contributed to the field of state estimation for vehicles, roads, and pedestrians. From the systemwide perspective of intelligent transportation systems to a focused view on “vehicle-road-pedestrian”, this survey aims to provide a comprehensive review and summary of recent state estimation techniques for vehicle motion, road surface, and pedestrian motion. A thorough analysis of the reviewed literature, relevant datasets, evaluation metrics, and experimental platforms in this field is also conducted. Finally, existing challenges and future research directions about methods and performance evaluation are further discussed. This survey is expected to contribute to the advancement of research in dynamic state estimation of vehicle-road-pedestrian, thereby facilitating the development of efficient and safe intelligent transportation systems.

Fairness in Federated Learning (FL) is imperative not only for the ethical utilization of technology but also for ensuring that models provide accurate, equitable, and beneficial outcomes across varied user demographics and equipment. This paper proposes a new adversarial architecture, referred to as Adversarial Graph Attention Network (AGAT), which deliberately instigates fairness attacks with an aim to bias the learning process across the FL. The proposed AGAT is developed to synthesize malicious, biasing model updates, where the minimum of Kullback-Leibler (KL) divergence between the user's model update and the global model is maximized. Due to a limited set of labeled input-output biasing data samples, a surrogate model is created, which presents the behavior of a complex malicious model update. Moreover, a graph autoencoder (GAE) is designed within the AGAT architecture, which is trained together with sub-gradient descent to reconstruct manipulatively the correlations of the model updates, and maximize the reconstruction loss while keeping the malicious, biasing model updates undetectable. The proposed AGAT attack is implemented in PyTorch, showing experimentally that AGAT successfully increases the minimum value of KL divergence of benign model updates by 60.9% and bypasses the detection of existing defense models. The source code of the AGAT attack is released on GitHub.

This article puts forth a new training data-untethered model poisoning (MP) attack on federated learning (FL). The new MP attack extends an adversarial variational graph autoencoder (VGAE) to create malicious local models based solely on the benign local models overheard without any access to the training data of FL. Such an advancement leads to the VGAE-MP attack that is not only efficacious but also remains elusive to detection. VGAE-MP attack extracts graph structural correlations among the benign local models and the training data features, adversarially regenerates the graph structure, and generates malicious local models using the adversarial graph structure and benign models' features. Moreover, a new attacking algorithm is presented to train the malicious local models using VGAE and sub-gradient descent, while enabling an optimal selection of the benign local models for training the VGAE. Experiments demonstrate a gradual drop in FL accuracy under the proposed VGAE-MP attack and the ineffectiveness of existing defense mechanisms in detecting the attack, posing a severe threat to FL.

Physical layer security is of paramount importance in vehicular cyber-physical systems, as it safeguards not only the privacy of sensitive data exchanged between vehicles and infrastructure but also ensures the integrity and reliability of the entire transportation network. Key generation plays a crucial role in establishing secure communication channels and facilitating the creation of unique cryptographic keys used for encryption, decryption, and authentication purposes. The secret key generation involves deriving secret bits by harnessing the inherent randomness present within the communication channels. The difficulty lies in precisely evaluating the randomness of the channel to achieve unanimous agreement on secure key generation within an unpredictable environment. In this line, we propose a combinatorial approach involving A2C and LSTM to decrease the key disagreement rate. A2C employs policy and value-based strategies to choose quantization levels predicated on the randomness of wireless channels and LSTM includes the partially observable radio channels and improves the environment. Based on our performance evaluation, the proposed A2C-LSTM method substantially accelerates the convergence rate by $\mathbf{5 0 - 6 0 \%}$ and reduces the Key Disagreement Rate (KDR) by $40 \%$.

Distributed learning is a promising paradigm for future UAV (unmanned aerial vehicle) networks networks and other emerging autonomous unmanned systems. Such distributed learning framework can suit the intrisic decentralized topology of UAV networks, where the UAVs can collaborate to train a global AI model by only exchanging the model parmeters via its peer-to-peer (i.e, inter-UAV) links in a distributed manner. However, with the ever-increasing AI model sizes, the challenges arise from the significant communication overhead for exchanging massive model weights via inter-UAV links in an ad-hoc manner: Previous communication-efficient techniques are mainly designed for conventional federated learning and not easily extendable to the decentralized counterpart. We propose selective link orchestration to minimize communication overhead while ensuring convergence of distributed learning, and prove that the convergence constraint is equivalent to the connectivity of the selected sub-graph. As such, we can reformulate the problem as a link selection problem in graph theory and develop a distributed optimization algorithm based on the modification of the Gallager, Humblet, and Spira’s algorithm. Experimental results on MNIST, Fashion-MNIST, and CIFAR-10 datasets demonstrate up to a $90 \%$ reduction in communication overhead without compromising model accuracy.

This paper proposes a new visual explanation-based defense mechanism, namely, FedCAMAE, against model poisoning attacks on federated learning (FL), which integrates Layer Class Activation Mapping (LayerCAM) and autoencoder to offer a scientifically more powerful detection capability compared to existing Euclidean distance-based or machine learning-based approaches. Specially, FedCAMAE generates a fine-grained heat map assisted by Layer-CAM for each uploaded local model update, transforming each local model update into a lower-dimensional, visual representation. To accentuate the hidden features of the heat maps, autoencoder is seamlessly embedded into the proposed FedCAMAE, which can refine the the heat maps and enhance their distinguishability, thereby increasing the success rate of identifying anomalous heat maps and malicious local models. We test ResNet-50 and REGNETY-800MF deep learning models with SVHN and CIFAR-100 datasets under Non-Independent and Identically Distributed (Non-IID) setting, respectively. The results demonstrate that Fed-CAMAE offers superior test accuracy of FL global model compared to the state-of-the-art methods. Our code is available at: https://github.com/jjzgeeks/LayerCAM-AE

Allowing for energy sharing among base stations (BSs), we investigate a distributed BS system equipped with renewable power units and energy storage batteries. While offering significant benefits, this raises concerns about privacy protection. By leveraging the Laplace mechanism, our differential privacy (DP)-based method safeguards energy consumption data related to processing data tasks from different BSs. To address a long-term average cost minimization problem, we propose a distributed online algorithm for efficient energy sharing. We demonstrate that the boundary constraints of energy storage batteries can still be satisfied by choosing parameters appropriately. We provide a theoretical bound for the optimality gap and validate the effectiveness of our theoretical results. Numerical results indicate our algorithm can potentially reduce the average costs of BSs by up to 34%.

Recognizing unauthorized Unmanned Aerial Vehicles (UAVs) within designated no-fly zones throughout the day and night is of paramount importance, where the unauthorized UAVs pose a substantial threat to both civil and military aviation safety. However, recognizing UAVs day and night with dual-vision cameras is nontrivial, since red–green–blue (RGB) images suffer from a low detection rate under an insufficient light condition, such as on cloudy or stormy days, while black-and-white infrared (IR) images struggle to capture UAVs that overlap with the background at night. In this paper, we propose a new optical flow-assisted graph-pooling residual network (OF-GPRN), which significantly enhances the UAV detection rate in day and night dual visions. The proposed OF-GPRN develops a new optical fusion to remove superfluous backgrounds, which improves RGB/IR imaging clarity. Furthermore, OF-GPRN extends optical fusion by incorporating a graph residual split attention network and a feature pyramid, which refines the perception of UAVs, leading to a higher success rate in UAV detection. A comprehensive performance evaluation is conducted using a benchmark UAV catch dataset. The results indicate that the proposed OF-GPRN elevates the UAV mean average precision (mAP) detection rate to 87.8%, marking a 17.9% advancement compared to the residual graph neural network (ResGCN)-based approach.

Detecting spoofing attacks on the positions of unmanned aerial vehicles (UAVs) within a swarm is challenging. Traditional methods relying solely on individually reported positions and pairwise distance measurements are ineffective in identifying the misbehavior of malicious UAVs. This paper presents a novel systematic structure designed to detect and mitigate spoofing attacks in UAV swarms. We formulate the problem of detecting malicious UAVs as a localization feasibility problem, leveraging the reported positions and distance measurements. To address this problem, we develop a semidefinite relaxation (SDR) approach, which reformulates the non-convex localization problem into a convex and tractable semidefinite program (SDP). Additionally, we propose two innovative algorithms that leverage the proximity of neighboring UAVs to identify malicious UAVs effectively. Simulations demonstrate the superior performance of our proposed approaches compared to existing benchmarks. Our methods exhibit robustness across various swarm networks, showcasing their effectiveness in detecting and mitigating spoofing attacks. Specifically, the detection success rate is improved by up to 65%, 55%, and 51% against distributed, collusion, and mixed attacks, respectively, compared to the benchmarks.

This paper proposes a novel, data-agnostic, model poisoning attack on Federated Learning (FL), by designing a new adversarial graph autoencoder (GAE)-based framework. The attack requires no knowledge of FL training data and achieves both effectiveness and undetectability. By listening to the benign local models and the global model, the attacker extracts the graph structural correlations among the benign local models and the training data features substantiating the models. The attacker then adversarially regenerates the graph structural correlations while maximizing the FL training loss, and subsequently generates malicious local models using the adversarial graph structure and the training data features of the benign ones. A new algorithm is designed to iteratively train the malicious local models using GAE and sub-gradient descent. The convergence of FL under attack is rigorously proved, with a considerably large optimality gap. Experiments show that the FL accuracy drops gradually under the proposed attack and existing defense mechanisms fail to detect it. The attack can give rise to an infection across all benign devices, making it a serious threat to FL.

The exploitation of radio channels’ inherent randomness for generating secret keys within a vehicular platoon offers a promising approach to securing communications in dynamic and unpredictable environments. The channel-based key generation leverages the fact that the physical characteristics of the radio channel, such as fading, shadowing, and multipath propagation, vary in a complex manner that makes it difficult for external adversaries to predict or replicate. A challenge lies in accurately assessing the channel’s randomness to ensure the generated keys are both secure and consistent across the platooning vehicles, especially in vehicular environments with high mobility and the ever-changing urban landscape. This paper proposes a novel channel-based key generation (DRL-KeyAgree) technique to enhance communication security within vehicular platoons through combinatorial deep reinforcement learning (DRL). DRL-KeyAgree addresses key disagreement among platooning vehicles by training advantage Actor-Critic (A2C), which integrates policy- and value-based strategies to dynamically select optimal quantization intervals adapting to the random wireless channels. Further incorporation of Long Short-Term Memory (LSTM) allows DRL-KeyAgree to capture the characteristics of partially observable radio channels, significantly enhancing the key agreement rate among vehicles. DRL-KeyAgree is rigorously evaluated using the standard National Institute of Standards and Technology (NIST) test suite.

Solar cells have been widely used for offering energy for Internet of Things (IoT) devices. Recently, solar cells have also been used as sensors for context awareness sensing due to their sensitivity to varying lighting conditions. In this article, we are the first to use solar cells for symmetric key generation. To generate symmetric keys, we take advantage of photovoltage measurements generated from solar cells equipped with a pair of IoT devices. Symmetric keys are essential for pairing IoT devices and further securing wireless communication. Despite the sensitivity to varying lighting conditions, challenges still remain for the use of solar cells for key generation, such as time unsynchronisation and noisy measurements. To solve these challenges, we design a novel key generation framework, SolarKey, which includes the starting point detection and a compressed sensing-based two-tier key reconciliation method. Extensive experiments have been conducted to evaluate the performance of our proposed key generation method in various environments, which shows the proposed method can improve the key matching rate by up to 25%. We also conduct security analysis and the randomness test, which shows that SolarKey is resilient to common attacks such as the eavesdropping attack and the imitating attack and sufficiently random.

Unmanned Aerial Vehicle (UAV) swarm can be employed to collect time-sensitive data of ground sensors in remote and hostile areas. Inadequate design of UAVs' trajectories and data collection schedule incur delay and negatively impact the information freshness of ground sensors. This paper aims to jointly optimize the trajectories and data collection schedules of multiple UAVs to minimize the average Age of Information (AoI), adapting to the AoI of the ground sensors, and the trajectories of the UAVs. The optimization is formulated as a multi-agent Markov decision process (MMDP), where network states consist of AoI at the ground sensors and the flight trajectories. In practice, a multi-UAV-assisted sensor network contains a large number of network states and actions in MMDP. Exploring the actions of multiple agents in a large state space results in considerable training uncertainties that destabilize the AoI minimization. For stabilizing the formulated MMDP, we propose an onboard Proximal Policy Optimization-based flight resource allocation scheme (PPO-FRAS), which conducts an on-policy learning to optimize the trajectories of the UAVs and data collection schedule of the ground sensors. Numerical results show that the proposed PPO-FRAS achieves 28% and 59% lower AoI than the existing trajectory planning solution based on Deep Q-Network and the greedy algorithm, respectively.

Mobile edge computing (MEC) enables the Internet of Things (IoT) with seamless integration of multiple application services. Federated learning is increasingly considered to improve training accuracy in MEC-IoT while circumventing the disclosure of private data, where the IoT nodes collaboratively train a machine learning model without disclosing their private data. In this paper, we propose a new cyber-epidemic attack that progressively manipulates federated learning and reduces the training accuracy of the benign MEC-IoT. The proposed cyber-epidemic attack explores adversarial graph autoencoders (GACE) to generate malicious local model updates that extract correlated features with the benign local and global models. The proposed GACE attack epidemically infects all the benign IoT nodes along with the training iterations in federated learning, while highly enhancing concealment of the attack.

Unmanned aerial vehicles (UAVs) have been increasingly considered as aerial servers in mobile edge computing (MEC) to assist mission-critical computation tasks of edge ground nodes. The tasks are buffered at the ground node, while the task offloading is scheduled by the UAV. When one ground node in MEC is scheduled to offload its tasks, other unselected ground nodes’ tasks could expire and be cancelled. To maximize the offloaded tasks to the UAV, this paper proposes a new joint optimization of cruise control and task offloading scheduling, which synthetically takes into account the computation capacity and battery energy of the ground nodes, and the speed limit of the UAV. Given a large and unknown network state and action space, a new deep reinforcement learning (DRL) framework based on graph neural networks (GNN) is developed to train online the continuous cruise control of the UAV and the task offloading schedule. Particularly, GNN explores feature correlations of network states to supervise the action training of the UAV in DRL. We implement the proposed GNN-DRL framework on Google Tensorflow. Extensive numerical results show that GNN-DRL improves the task offloading rate by 43%, compared to the DRL solution without GNN.

Federated learning (FL) is increasingly considered to circumvent the disclosure of private data in mobile edge computing (MEC) systems. Training with large data can enhance FL learning accuracy, which is associated with non-negligible energy use. Scheduled edge devices with small data save energy but decrease FL learning accuracy due to a reduction in energy consumption. A trade-off between the energy consumption of edge devices and the learning accuracy of FL is formulated in this proposed work. The FL-enabled twin-delayed deep deterministic policy gradient (FL-TD3) framework is proposed as a solution to the formulated problem because its state and action spaces are large in a continuous domain. This framework provides the maximum accuracy ratio of FL divided by the device’s energy consumption. A comparison of the numerical results with the state-of-the-art demonstrates that the ratio has been improved significantly.

In recent years, the Internet of Things (IoT) has been studied in the context of the Metaverse to provide users with immersive cyber-virtual experiences in mixed-reality environments. This survey introduces six typical IoT applications in the Metaverse, including collaborative healthcare, education, smart city, entertainment, real estate, and socialization. In the IoT-inspired Metaverse, we also comprehensively survey four pillar technologies that enable augmented reality (AR) and virtual reality (VR), namely, responsible artificial intelligence (AI), high-speed data communications, cost-effective mobile edge computing (MEC), and digital twins. According to the physical-world demands, we outline the current industrial efforts and seven key requirements for building the IoT-inspired Metaverse: immersion, variety, economy, civility, interactivity, authenticity, and independence. In addition, this survey describes the open issues in the IoT-inspired Metaverse, which need to be addressed to eventually achieve the convergence of physical and cyber worlds.

In recent years, ubiquitous semantic Metaverse has been studied to revolutionize immersive cyber-virtual experiences for augmented reality (AR) and virtual reality (VR) users, which leverages advanced semantic understanding and representation to enable seamless, context-aware interactions within mixed-reality environments. This survey focuses on the intelligence and spatiotemporal characteristics of four fundamental system components in ubiquitous semantic Metaverse, i.e., artificial intelligence (AI), spatiotemporal data representation (STDR), Semantic Internet of Things (SIoT), and semantic-enhanced digital twin (SDT). We thoroughly survey the representative techniques of the four fundamental system components that enable intelligent, personalized, and context-aware interactions with typical use cases of the ubiquitous semantic Metaverse, such as remote education, work and collaboration, entertainment and socialization, healthcare, and e-commerce marketing. Furthermore, we outline the opportunities for constructing the future ubiquitous semantic Metaverse, including scalability and interoperability, privacy and security, performance measurement and standardization, as well as ethical considerations and responsible AI. Addressing those challenges is important for creating a robust, secure, and ethically sound system environment that offers engaging immersive experiences for the users and AR/VR applications.

In wireless powered sensor networks (WPSN), data of ground sensors can be collected or relayed by an unmanned aerial vehicle (UAV) while the battery of the ground sensor can be charged via wireless power transfer. A key challenge of resource allocation in UAV-aided WPSN is to prevent battery drainage and buffer overflow of the ground sensors in the presence of highly dynamic lossy airborne channels which can result in packet reception errors. Moreover, state and action spaces of the resource allocation problem are large, which is hardly explored online. To address the challenges, a new data-driven deep reinforcement learning framework, DDRL-RA, is proposed to train flight resource allocation online so that the data packet loss is minimized. Due to time-varying airborne channels, DDRL- RA firstly leverages long short-term memory (LSTM) with precollected offline datasets for channel randomness predictions. Then, Deep Deterministic Policy Gradient (DDPG) is studied to control the flight trajectory of the UAV, and schedule the ground sensor to transmit data and harvest energy. To evaluate the performance of DDRL-RA, a UAV-ground sensor testbed is built, where real-world datasets of channel gains are collected. DDRL-RA is implemented on Tensorflow, and numerical results show that DDRL-RA achieves 19% lower packet loss than other learning-based frameworks.

The handover process in an integrated terrestrial-satellite network (ITSN) faces many security threats, such as eavesdropping, impersonating, replaying, and privacy leakage due to the link exposure and network heterogeneity of ITSN. This paper proposes a lightweight and privacy-preserving ITSN handover authentication mechanism based on elliptic curve cryptography to eliminate the security threats. Specifically, we propose to directly authenticate mobile users at access nodes and on a batch basis, hence avoiding backhaul and concurrent authentication overhead. User anonymous identity and private information encryption are adopted to protect users’ privacy. A regular key update scheme is developed to mitigate a risk of private key breach. As demonstrated by a formal security proof based on the AVISPA tool and security analysis, the proposed mechanism resists various attacks (e.g., replay attacks and impersonation attacks). Numerical results show that our mechanism is more efficient than the standard and the existing alternatives. The proposed mechanism has the great potential to secure the handover processes of emerging ITSN.

In this paper, we demonstrate a new ultra-wideband (UWB) local-ization testbed, which tracks a UWB tag and estimates locations of obstacles based on channel impulse response measurements. An-chor nodes that are developed with off-the-shelf Decawave DW1000 UWB transceivers are deployed to cover the area of interest. The testbed is implemented and preliminary experiments are carried out to estimate the location of the object by analyzing channel impulse response strength of the UWB tag.

Networked devices, such as wearable devices, laptops, smart home appliances, etc., are ubiquitous nowadays. To secure communication among those devices, symmetric keys are widely used because of their feasibility in resource-constrained networked devices. The ob-servations of sensors from independent devices have been adopted for symmetric key generation. The identical biometrics information or environment interference has been observed by sensors, and their corresponding patterns are used for key generation. Pop-ular signals from networked devices are inertial measurements, sound, wireless signals, etc. The existing sensor-based key gen-eration solutions use the same type of sensors for both devices. Different from the existing solutions, we are the first to propose a cross-sensor symmetric key generation system i2Key, where two devices collect inertial measurements from a motion sensor and inaudible sound from a microphone, respectively. A new coding framework is designed for general key generation. We also pro-pose an efficient and accurate time synchronisation method for key generation. Additionally, a multi-tier key reconciliation method is suggested to improve key generation performance. By using the proposed architecture, the key generation rate is improved by up to approximately 40% compared with the situation without using it. We also perform security analysis and randomness analysis over the proposed method.

In this paper, we propose a new ultra-wideband (UWB)-based simultaneous localization and wall-mapping (SLAM) system, which adopts two-way ranging optimization on UWB anchor and tag nodes to track the target's real-time movement in an unknown area. The proposed UWB-based SLAM system captures time difference of arrival (TDoA) of the anchor nodes' signals over a line-of-sight propagation path and reflected paths. The real-time location of the UWB tag is estimated according to the real-time TDoA measurements. To minimize the estimation error resulting from background noise in the two-way ranging, a Least Squares Method is implemented to minimize the estimation error for the localization of a static target, while Kalman Filter is applied for the localization of a mobile target. An experimental testbed is built based on off-the-shelf UWB hardware. Experiments validate that a reflector, e.g., a wall, and the UWB tag can be located according to the two-way ranging measurement. The localization accuracy of the proposed SLAM system is also evaluated, where the difference between the estimated location and the ground truth trajectory is less than 1 meter.

Unmanned aerial vehicles (UAVs) can be employed to collect sensory data in remote wireless sensor networks (WSNs). Due to UAV’s maneuvering, scheduling a sensor device to transmit data can overflow data buffers of the unscheduled ground devices. Moreover, lossy airborne channels can result in packet reception errors at the scheduled sensor. This article proposes a new deep reinforcement learning-based flight resource allocation framework (DeFRA) to minimize the overall data packet loss in a continuous action space. DeFRA is based on deep deterministic policy gradient (DDPG), optimally controls instantaneous headings and speeds of the UAV, and selects the ground device for data collection. Furthermore, a state characterization layer, leveraging long short-term memory (LSTM), is developed to predict network dynamics, resulting from time-varying airborne channels and energy arrivals at the ground devices. To validate the effectiveness of DeFRA, experimental data collected from a real-world UAV testbed and energy harvesting WSN are utilized to train the actions of the UAV. Numerical results demonstrate that the proposed DeFRA achieves a fast convergence while reducing the packet loss by over 15%, as compared to the existing deep reinforcement learning solutions.

This article puts forth an aerial edge Internet of Things (EdgeIoT) system, where an unmanned aerial vehicle (UAV) is employed as a mobile-edge server to process mission-critical computation tasks of ground Internet of Things (IoT) devices. When the UAV schedules an IoT device to offload its computation task, the tasks buffered at the other unselected devices could be outdated and have to be canceled. We investigate a new joint optimization of UAV cruise control and task offloading allocation, which maximizes tasks offloaded to the UAV, subject to the IoT device’s computation capacity and battery budget, and the UAV’s speed limit. Since the optimization contains a large solution space while the instantaneous network states are unknown to the UAV, we propose a new deep-graph-based reinforcement learning framework. An advantage actor–critic (A2C) structure is developed to train the real-time continuous actions of the UAV in terms of the flight speed, heading, and the offloading schedule of the IoT device. By exploring hidden representations resulting from the network feature correlation, our framework takes advantage of graph neural networks (GNNs) to supervise the training of UAV’s actions in A2C. The proposed graph neural network-enabled A2C (GNN-A2C) framework is implemented with Google Tensorflow. The performance analysis shows that GNN-A2C achieves fast convergence and reduces considerably the task missing rate in aerial EdgeIoT.

Federated learning (FL) has been increasingly considered to preserve data training privacy from eavesdropping attacks in mobile-edge computing-based Internet of Things (EdgeIoT). On the one hand, the learning accuracy of FL can be improved by selecting the IoT devices with large data sets for training, which gives rise to a higher energy consumption. On the other hand, the energy consumption can be reduced by selecting the IoT devices with small data sets for FL, resulting in a falling learning accuracy. In this article, we formulate a new resource allocation problem for privacy-preserving EdgeIoT to balance the learning accuracy of FL and the energy consumption of the IoT device. We propose a new FL-enabled twin-delayed deep deterministic policy gradient (FL-DLT3) framework to achieve the optimal accuracy and energy balance in a continuous domain. Furthermore, long short-term memory (LSTM) is leveraged in FL-DLT3 to predict the time-varying network state while FL-DLT3 is trained to select the IoT devices and allocate the transmit power. Numerical results demonstrate that the proposed FL-DLT3 achieves fast convergence (less than 100 iterations) while the FL accuracy-to-energy consumption ratio is improved by 51.8% compared to the existing state-of-the-art benchmark.

Thanks to flexible deployment and excellent maneuverability, autonomous drones are regarded as an effective means to enable aerial data capture in large-scale wireless sensor networks with limited to no cellular infrastructure, e.g., smart farming in a remote area. A key challenge in drone-assisted sensor networks is that the autonomous drone’s maneuvering can give rise to buffer overflows at the ground sensors and unsuccessful data collection due to lossy airborne channels. In this paper, we propose a new deep deterministic policy gradient based maneuver control (DDPG-MC) scheme which minimizes the overall data packet loss through online training instantaneous headings and patrol velocities of the drone, and the selection of the ground sensors for data collection in a continuous action space. Moreover, the maneuver control of the drone and communication schedule is formulated as an absorbing Markov chain, where network states consist of battery energy levels, data queue backlogs, timestamps of the data collection, and channel conditions between the ground sensors and the drone. An experience replay memory is utilized onboard at the drone to store the training experiences of the maneuver control and communication schedule at each time step. Numerical results demonstrate that the proposed DDPG-MC achieves 15.2 and 47.6 percent lower packet loss rate than deep Q-learning-based flight control and non-learning scheduling policies, respectively.

Unmanned Aerial Vehicles (UAVs) can collaborate to collect and relay data for ground sensors in remote and hostile areas. In multi-UAV-assisted wireless sensor networks (MA-WSN), the UAVs' movements impact on channel condition and can fail data transmission, this situation along with newly arrived data give rise to buffer overflows at the ground sensors. Thus, scheduling data transmission is of utmost importance in MA-WSN to reduce data packet losses resulting from buffer overflows and channel fading. In this paper, we investigate the optimal ground sensor selection at the UAVs to minimize data packet losses. The optimization problem is formulated as a multi-agent Markov decision process, where network states consist of battery levels and data buffer lengths of the ground sensor, channel conditions, and waypoints of the UAV along the trajectory. In practice, an MA-WSN contains a large number of network states, while the up-to-date knowledge of the network states and other UAVs' sensor selection decisions is not available at each agent. We propose a Multi-UAV Deep Reinforcement Learning based Scheduling Algorithm (MUAIS) to minimize the data packet loss, where the UAVs learn the underlying patterns of the data and energy arrivals at all the ground sensors. Numerical results show that the proposed MUAIS achieves at least 46 % and 35% lower packet loss than an optimal solution with single-UAV and an existing non-learning greedy algorithm, respectively.

Mobile edge computing (MEC) has been considered as a promising technology to provide seamless integration of multiple application services. Federated learning (FL) is carried out at edge clients in MEC for privacy-preserving training of data processing models. Despite that the edge clients with small data payloads consume less energy on FL training, the small data payload gives rise to a low learning accuracy due to insufficient input to the FL training. Inadequate selection of the edge clients can result in a large energy consumption at the edge clients, or a low learning accuracy of the FL training. In this paper, a new FL-based client selection optimization is proposed to balance the trade-off between energy consumption of the edge clients and the learning accuracy of FL. We first show that this optimization problem is NP-complete. Next, we propose a FL-based energy-accuracy balancing heuristic algorithm to approximate the optimal client selection in polynomial time. The numerical results show the advantage of our proposed algorithm.

Autonomous UAV cruising is gaining attention due to its flexible deployment in remote sensing, surveillance, and reconnaissance. A critical challenge in data collection with the autonomous UAV is the buffer overflows at the ground sensors and packet loss due to lossy airborne channels. Trajectory planning of the UAV is vital to alleviate buffer overflows as well as channel fading. In this work, we propose a Deep Deterministic Policy Gradient based Cruise Control (DDPG-CC) to reduce the overall packet loss through online training of headings and cruise velocity of the UAV, as well as the selection of the ground sensors for data collection. Preliminary performance evaluation demonstrates that DDPG-CC reduces the packet loss rate by under 5% when sufficient training is provided to the UAV.

In this paper, we present a practical secret key management for data relay security of bluetooth-connected drones. Time-varying received signal strengths between the drones and the ground sensing nodes are quantized to generate the secret key pairs, where the quantization interval is adjusted to reduce the number of mismatched secret key bits. To validate the key management performance, a multihop aerial relay system testbed is developed based on the MX400 drone platform and the bluetooth low energy radio transceiver.

Employing unmanned aerial vehicles (UAVs) as aerial data collectors in Internet-of-Things (IoT) networks is a promising technology for large-scale environment sensing. A key challenge in UAV-aided data collection is that UAV maneuvering gives rise to buffer overflow at the IoT node and unsuccessful transmission due to lossy airborne channels. This article formulates a joint optimization of flight cruise control and data collection schedule to minimize network data loss as a partially observable Markov decision process (POMDP), where the states of individual IoT nodes can be obscure to the UAV. The problem can be optimally solvable by reinforcement learning, but suffers from the curse of dimensionality and becomes rapidly intractable with the growth in the number of IoT nodes. In practice, a UAV-aided IoT network contains a large number of network states and actions in POMDP while the up-to-date knowledge is not available at the UAV. We propose an onboard deep Q-network-based flight resource allocation scheme (DQN-FRAS) to optimize the online flight cruise control of the UAV and data scheduling given outdated knowledge on the network states. Numerical results demonstrate that DQN-FRAS reduces the packet loss by over 51%, as compared to existing nonlearning heuristics.

Thanks to flexible deployment and excellent maneuverability, autonomous drones have been recently considered as an effective means to act as aerial data relays for wireless ground devices with limited or no cellular infrastructure, e.g., smart farming in a remote area. Due to the broadcast nature of wireless channels, data communications between the drones and the ground devices are vulnerable to eavesdropping attacks. This article develops BloothAir, which is a secure multi-hop aerial relay system based on Bluetooth Low Energy ( BLE ) connected autonomous drones. For encrypting the BLE communications in BloothAir, a channel-based secret key generation is proposed, where received signal strength at the drones and the ground devices is quantized to generate the secret keys. Moreover, a dynamic programming-based channel quantization scheme is studied to minimize the secret key bit mismatch rate of the drones and the ground devices by recursively adjusting the quantization intervals. To validate the design of BloothAir, we build a multi-hop aerial relay testbed by using the MX400 drone platform and the Gust radio transceiver, which is a new lightweight onboard BLE communicator specially developed for the drone. Extensive real-world experiments demonstrate that the BloothAir system achieves a significantly lower secret key bit mismatch rate than the key generation benchmarks, which use the static quantization intervals. In addition, the high randomness of the generated secret keys is verified by the standard NIST test, thereby effectively protecting the BLE communications in BloothAir from the eavesdropping attacks.

Device-free context awareness is important to many applications. There are two broadly used approaches for device-free context awareness, i.e., video-based and radio-based. Video-based approaches can deliver good performance, but privacy is a serious concern. Radio-based context awareness applications have drawn researchers' attention instead, because it does not violate privacy and radio signal can penetrate obstacles. The existing works design explicit methods for each radio-based application. Furthermore, they use one additional step to extract features before conducting classification and exploit deep learning as a classification tool. Although this feature extraction step helps explore patterns of raw signals, it generates unnecessary noise and information loss. The use of raw CSI signal without initial data processing was, however, considered as no usable patterns. In this article, we are the first to propose an innovative deep learning–based general framework for both signal processing and classification. The key novelty of this article is that the framework can be generalised for all the radio-based context awareness applications with the use of raw CSI. We also eliminate the extra work to extract features from raw radio signals. We conduct extensive evaluations to show the superior performance of our proposed method and its generalisation.

In Unmanned Aerial Vehicle (UAV)-assisted Wireless Powered Internet of Things (IoT), the UAV is employed to charge the IoT nodes remotely via Wireless Power Transfer (WPT) and collect their data. A key challenge of resource management for WPT and data collection is preventing battery drainage and butter overflow of the ground IoT nodes in the presence of highly dynamic airborne channels. In this paper, we consider the resource management problem in practical scenarios, where the UAV has no a-prior information on battery levels and data queue lengths of the nodes. We formulate the resource management of UAV-assisted WPT and data collection as Markov Decision Process (MDP), where the states consist of battery levels and data queue lengths of the IoT nodes, channel qualities, and positions of the UAV. A deep Q-learning based resource management is proposed to minimize the overall data packet loss of the IoT nodes, by optimally deciding the IoT node for data collection and power transfer, and the associated modulation scheme of the IoT node.

In this paper, a multi-hop airborne system is built based on Bluetooth 5 connected autonomous drones to relay real-time data of Internet of Things (IoT). A new lightweight Onboard Bluetooth Transceiver (OBT) is developed for reliable drone-to-drone and drone-to-ground communications. A graphical user interface is presented to monitor real-time flight trajectory of the drones and end-to-end data delivery. Outdoor experiments are conducted in real world to test autonomous flight control of the drones and received signal strength of the OBT communications.

In Unmanned Aerial Vehicle (UAV)-enabled wireless powered sensor networks, a UAV can be employed to charge the ground sensors remotely via Wireless Power Transfer (WPT) and collect the sensory data. This paper focuses on trajectory planning of the UAV for aerial data collection and WPT to minimize buffer overflow at the ground sensors and unsuccessful transmission due to lossy airborne channels. Consider network states of battery levels and buffer lengths of the ground sensors, channel conditions, and location of the UAV. A flight trajectory planning optimization is formulated as a Partial Observable Markov Decision Process (POMDP), where the UAV has partial observation of the network states. In practice, the UAV-enabled sensor network contains a large number of network states and actions in POMDP while the up-to-date knowledge of the network states is not available at the UAV. To address these issues, we propose an onboard deep reinforcement learning algorithm to optimize the realtime trajectory planning of the UAV given outdated knowledge on the network states.

For assisting data communications in human-unfriendly environments, Unmanned Aerial Vehicles (UAVs) are employed to relay data for ground sensors thanks to UAVs' flexible deployment, high mobility, and line-of-sight communications. In UAV relay networks, energy efficient data relay is critical due to limited battery of the ground sensing devices. In this paper, we propose a butter-aware transmission scheduling optimization to minimize the energy consumption of the ground devices under constraints of butter overflows and energy cost fairness on the ground devices. Moreover, we show that the problem is NP-complete and propose a heuristic algorithm to approximate the optimal scheduling solution in polynomial time. The performance of the proposed algorithm is evaluated in terms of network sizes, packet arrival rates, and fairness of the energy consumption. Numerical results confirm that the proposed scheduling algorithm reduces the energy consumption of the ground devices in a fair fashion, while the butter overflow constraint holds.

In a platoon-based vehicular cyber-physical system (PVCPS), a lead vehicle that is responsible for managing the platoon’s moving directions and velocity periodically disseminates control messages to the vehicles that follow. Securing wireless transmissions of the messages between the vehicles is critical for privacy and confidentiality of the platoon’s driving pattern. However, due to the broadcast nature of radio channels, the transmissions are vulnerable to eavesdropping. In this article, we propose a cooperative secret key agreement (CoopKey) scheme for encrypting/decrypting the control messages, where the vehicles in PVCPS generate a unified secret key based on the quantized fading channel randomness. Channel quantization intervals are optimized by dynamic programming to minimize the mismatch of keys. A platooning testbed is built with autonomous robotic vehicles, where a TelosB wireless node is used for onboard data processing and multi-hop dissemination. Extensive real-world experiments demonstrate that CoopKey achieves significantly low secret bit mismatch rate in a variety of settings. Moreover, the standard NIST test suite is employed to verify randomness of the generated keys, where the p-values of our CoopKey pass all the randomness tests. We also evaluate CoopKey with an extended platoon size via simulations to investigate the effect of system scalability on performance.

In intelligent transportation systems, wireless connected vehicles moving in platoons can improve roads' throughput. For managing driving status of the platoon, a lead vehicle transmits driving information to following autonomous vehicles by using multi-hop data dissemination. We study a novel data dissemination protocol which investigates a chain-based transmit rate control to reduce data dissemination latency. The optimal resource allocation algorithm is formulated to minimize the total dissemination latency of the platoon under guaranteed bit error rates, and can be judiciously reformulated and solved using standard optimization techniques. A novel dynamic programming algorithm is presented to solve the platooning resource allocation optimization, which uses backward induction to significantly reduce the resource allocation complexity. In addition, we interpret the vehicular platoon as one-dimensional Markov chain, and derive a closed form of dissemination latency. Simulations are carried out to evaluate the performance of the proposed dynamic programming algorithm. The numerical results show that our algorithm achieves optimal solutions with cutting off the complexity by orders of magnitude, while improving dissemination rate in the vehicular platoon.

In a vehicular platoon, the lead vehicle that is responsible for managing the platoon's moving directions and velocity periodically disseminates messages to the following automated vehicles in a multi-hop vehicular network. However, due to the broadcast nature of wireless channels, vehicle-to-vehicle (V2V) communications are vulnerable to eavesdropping and message modification. Generating secret keys by extracting the shared randomness in a wireless fading channel is a promising way for V2V communication security. We study a security scheme for platoon-based V2V communications, where the platooning vehicles generate a shared secret key based on the quantized fading channel randomness. To improve conformity of the generated key, the probability of secret key agreement is formulated, and a novel secret key agreement algorithm is proposed to recursively optimize the channel quantization intervals, maximizing the key agreement probability. Numerical evaluations demonstrate that the key agreement probability achieved by our security protocol given different platoon size, channel quality, and number of quantization intervals. Furthermore, by applying our security protocol, it is shown that the probability that the encrypted data being cracked by an eavesdropper is less than 5%.

Privacy preservation is critical for control information dissemination in Platoon-based Vehicular Cyber-Physical Systems (PVCPS). However, the vehicular communication is vulnerable to wireless eavesdropping attack and message modification, due to broadcast nature of radio channels. In this poster, we present a secret key generation testbed for PVCPS security, which is built based on off-the-shelf autonomous robotic vehicles and TelosB wireless transceivers. A cooperative secret key agreement (CoopKey) scheme is demonstrated for encrypting/decrypting the disseminated control messages. To unify the secret key generated by the vehicles, CoopKey explores received signal strength (RSS) measurements and channel estimation on the inter-node radio channel. In addition, a Python-based user interface is also implemented to show real-time bit mismatch rate of CoopKey.

This paper considers that a legitimate UAV tracks suspicious UAVs' flight for preventing intended crimes and terror attacks. To enhance tracking accuracy, the legitimate UAV proactively eavesdrops suspicious UAVs' communication via sending jamming signals. A tracking algorithm is developed for the legitimate UAV to track the suspicious flight by comprehensively utilizing eavesdropped packets, angle-of-arrival and received signal strength of the suspicious transmitter's signal. A new co-simulation framework is implemented to combine the complementary features of optimization toolbox with channel modeling (in Matlab) and discrete event-driven mobility tracking (in NS3). Moreover, numerical results validate the proposed algorithms in terms of tracking accuracy of the suspicious UAVs' trajectory.

We consider the problem of data collection from a network of energy harvesting sensors, applied to tracking mobile assets in rural environments. Our application constraints favor a fair and energy-aware solution, with heavily duty-cycled sensor nodes communicating with powered base stations. We study a novel scheduling optimization problem for energy harvesting mobile sensor network, that maximizes the amount of collected data under the constraints of radio link quality and energy harvesting efficiency, while ensuring a fair data reception. We show that the problem is NP-complete and propose a heuristic algorithm to approximate the optimal scheduling solution in polynomial time. Moreover, our algorithm is flexible in handling progressive energy harvesting events, such as with solar panels, or opportunistic and bursty events, such as with Wireless Power Transfer. We use empirical link quality data, solar energy, and WPT efficiency to evaluate the proposed algorithm in extensive simulations and compare its performance to state-of-the-art. We show that our algorithm achieves high data reception rates, under different fairness and node lifetime constraints.

In a vehicular platoon, a lead vehicle that is responsible for managing the platoon's moving directions and velocity periodically disseminates control commands to following vehicles based on vehicle-to-vehicle communications. However, reducing command dissemination latency with multiple vehicles while ensuring successful message delivery to the tail vehicle is challenging. We propose a new linear dynamic programming algorithm using backward induction and interchange arguments to minimize the dissemination latency of the vehicles. Furthermore, a closed form of dissemination latency in vehicular platoon is obtained by utilizing Markov chain with M/M/1 queuing model. Simulation results confirm that the proposed dynamic programming algorithm improves the dissemination rate by at least 50.9%, compared to similar algorithms in the literature. Moreover, it also approximates the best performance with the maximum gap of up to 0.2 second in terms of latency.

With the increasing quality of service (QoS) demands for indoor multimedia applications, millimeter wave (mmWave) communications are emerging as a promising candidate for the wireless personal area networks (WPANs). On the one hand, it has the advantage of providing several-Gbps transmission rate. However, due to the unique characteristics in 60-GHz frequency band, such as high propagation loss, beamforming is fully exploited for mmWave links to achieve directional transmission and reception. In this paper, we propose a novel QoS aware scheduling algorithm for concurrent transmission in mmWave WPANs based on coalition game. First, we formulate the problem of concurrent transmission scheduling into a non-convex integer programming problem. Then, we propose a coalition game based algorithm to maximize the number of flows satisfying the corresponding QoS requirements, while improving the network resource utilization effectively. Besides, our proposed algorithm converges to a Nash-stable equilibrium with greatly reduced complexity. Through extensive simulations under various system parameters, we demonstrate our scheme achieves better network performance in terms of the throughput and the number of flows scheduled successfully, compared with existed protocols.

This paper presents a suboptimal approach for resource allocation of massive MIMO‐OFDMA systems for high‐speed train (HST) applications. An optimization problem is formulated to alleviate the severe Doppler effect and maximize the energy efficiency (EE) of the system. We propose to decouple the problem between the allocations of antennas, subcarriers, and transmit powers and solve the problem by carrying out the allocations separately and iteratively in an alternating manner. Fast convergence can be achieved for the proposed approach within only several iterations. Simulation results show that the proposed algorithm is superior to existing techniques in terms of system EE and throughput in different system configurations of HST applications.

In a rechargeable wireless sensor network, the data packets are generated by sensor nodes at a specific data rate, and transmitted to a base station. Moreover, the base station transfers power to the nodes by using Wireless Power Transfer (WPT) to extend their battery life. However, inadequately scheduling WPT and data collection causes some of the nodes to drain their battery and have their data buffer overflow, while the others waste their harvested energy, which is more than they need to transmit their packets. In this paper, we investigate a novel optimal scheduling strategy, called Scheduled WPT (SWPT), aiming to minimize data packet loss from a network of wireless powered sensor nodes by jointly considering the sensor nodes' energy consumption and data queue state information. The scheduling problem is formulated by a MDP model, assuming that the complete states of each sensor node are well known by the base station. This presents the best effort performance of the scheduling that can be collected in a wireless powered sensor network. The simulation results show that, in terms of network throughput and packet loss rate, the proposed scheduling model significantly improves the network performance.

This paper studies the wireless surveillance of a hybrid automatic repeat request (HARQ) based suspicious communication link over Rayleigh fading channels. We propose a proactive eavesdropping approach, where a half-duplex monitor can opportunistically jam the suspicious link to exploit its potential retransmissions for overhearing more efficiently. In particular, we consider that the suspicious link uses at most two HARQ rounds for transmitting the same data packet, and we focus on two cases without and with HARQ combining at the monitor receiver. In both cases, we aim to maximize the successful eavesdropping probability at the monitor, by adaptively allocating the jamming power in the first HARQ round according to fading channel conditions, subject to an average jamming power constraint. For both cases, we show that the optimal jamming power allocation follows a threshold-based policy, and the monitor jams with constant power when the eavesdropping channel gain is less than the threshold. Numerical results show that the proposed proactive eavesdropping scheme achieves higher successful eavesdropping probability than the conventional passive eavesdropping, and HARQ combining can help further improve the eavesdropping performance.

We consider a wireless information surveillance in UAV network, where a legitimate unmanned aerial vehicle (UAV) proactively eavesdrops communication between two suspicious UAVs. However, challenges arise due to lossy airborne channels and limited power of the UAV. In this paper, we study an emerging legitimate eavesdropping paradigm that the legitimate UAV improves the eavesdropping performance via jamming the suspicious communication. Moreover, a power efficient legitimate eavesdropping scheme, PELE, is proposed to maximize the number of eavesdropped packets from the legitimate UAV while maintaining a target signal to interference plus noise ratio at the suspicious link. Numerical results are shown to validate the performance of PELE. Additionally, four typical fading channel models are applied to the network so as to investigate their impact on PELE.

Space utilization are important elements for a smart city to determine how well public space are being utilized. Such information could also provide valuable feedback to the urban developer on what are the factors that impact space utilization. The spatial and temporal information for space utilization can be studied and further analyzed to generate insights about that particular space. In our research context, these elements are translated to part of big data and Internet of things (IoT) to eliminate the need of on site investigation. However, there are a number of challenges for large scale deployment, eg. hardware cost, computation capability, communication bandwidth, scalability, data fragmentation, and resident privacy etc. In this paper, we designed and prototype a Renewable Wireless Sensor Network (RWSN), which addressed the aforementioned challenges. Finally, analyzed results based on initial data collected is presented.

Airborne relaying can extend wireless sensor networks (WSNs) to remote human-unfriendly terrains. However, lossy airborne channels and limited battery of unmanned aerial vehicles (UAVs) are critical issues, adversely affecting success rate and network lifetime, especially in real-time applications. We propose an energy-efficient cooperative relaying scheme which extends network lifetime while guaranteeing the success rate. The optimal transmission schedule of the UAVs is formulated to minimize the maximum (min-max) energy consumption under guaranteed bit error rates, and can be judiciously reformulated and solved using standard optimisation techniques. We also propose a computationally efficient suboptimal algorithm to reduce the scheduling complexity, where energy balancing and rate adaptation are decoupled and carried out in a recursive alternating manner. Simulation results confirm that the suboptimal algorithm cuts off the complexity by orders of magnitude with marginal loss of the optimal network yield (throughput) and lifetime. The proposed suboptimal algorithm can also save energy by 50 percent, increase network yield by 15 percent, and extend network lifetime by 33 percent, compared to the prior art.

Airborne relaying is of potential to extend wireless sensor networks (WSN) to human-unfriendly terrains. Challenges arise due to lossy airborne channels and limited battery of unmanned aerial vehicles (UAVs). We propose an energy-efficient relaying scheme to overcome the challenges. A swarm of UAVs are deployed to listen to remote sensors from distributed locations, improving packet reception over lossy channels. UAVs report their reception qualities to the base station where the optimal schedule with guaranteed success rates and balanced energy consumption can be generated. Such scheduling is an NP-hard binary integer programming. We develop a suboptimal solution by decoupling the processes of energy balancing and data rate adjustment. Simulations confirm that, in terms of network yield, our method is indistinguishable to the NP-hard optimal solution, 15% higher than greedy algorithms. Our method can reduce the complexity by orders of magnitude, and extend network lifetime by 33%.

We consider the problem of data collection from a large-scale energy harvesting sensors in smart city. We study a novel scheduling optimisation model for WSN with Wireless Power Transfer (WPT), that maximises the amount of collected data under constraints of radio link quality and WPT efficiency, while ensuring a fair data reception. A WPT-WSN testbed is built and preliminary results show that the WPT efficiency is jointly affected by distance between WPT transmitter and receiver, and their antenna orientation.

Aerial wireless sensor and actor networks are composed of multiple unmanned aerial vehicles. An actor node in the network has the capabilities of both acting on the environment and also performing networking functionalities for sensor nodes. Thus, positioning of actors is critical for the efficient data collection. In this paper, we propose an actor positioning strategy, which utilizes a hybrid antenna model that combines the complimentary features of an isotropic omni radio and directional antennas. We present a distributed algorithm for fast neighbor discovery with the hybrid antenna. The omni module of the hybrid antenna is used to form a self organizing network and the directional module is used for reliable data transmission. Extensive simulations show that our protocol improves the packet reception ratio by up to 50% compared to omnidirectional antenna. Moreover, the network reorganization delay is also reduced. The tradeoff between coverage and reorganization delay is also illustrated.

An AWSN composed of bird-sized Unmanned Aerial Vehicles (UAVs) equipped with sensors and wireless radio, enables low cost high granularity three-dimensional sensing of the physical world. The sensed data is relayed in real-time over a multi-hop wireless communication network to ground stations. The following characteristics of an AWSN make effective multi-hop communication challenging - (i) frequent link disconnections due to the inherent dynamism (ii) significant inter-node interference (iii) three dimensional motion of the UAVs. In this paper, we investigate the use of a hybrid antenna to accomplish efficient neighbor discovery and reliable communication in AWSNs. We propose the design of a hybrid Omni Bidirectional ESPAR (O-BESPAR) antenna, which combines the complimentary features of an isotropic omni radio (360 degree coverage) and directional ESPAR antennas (beamforming and reduced interference). Control and data messages are transmitted separately over the omni and directional modules of the antenna, respectively. Moreover, a communication protocol is presented to perform fast neighbor discovery and beam steering. We present results from extensive simulations then consider three different real-world AWSN application scenarios and empirical aerial link characterization and show that the proposed antenna design and protocol reduces the packet loss rate, as compared to a single omni or ESPAR antenna.