Large language model (LLM)-based agents are envisioned as cornerstones for autonomous, zero-touch 6G AI-RAN operations. Numerous frameworks adopt LLM-based agents as decision-makers to optimize network configurations, orchestrate resources, and interact with users and connected use cases. However, intrinsic limitations (hallucinations, misaligned human values) and extrinsic adversarial threats (jail-breaks, prompt injections) pose critical risks to network safety, reliability, and privacy—challenges largely overlooked in existing literature. This paper addresses this gap by reviewing state-of-the-art guardrail techniques for 6G AI-RAN. We categorize guardrails across model-level and agent-level layers and map them to common agent application patterns in 6G networks, providing practical foundations for designing trustworthy agentic decision-making frameworks in future 6G AI-RAN systems.

As vehicle autonomy increases, human operators become more susceptible to distractions and a loss of situational awareness (SA) due to cognitive limitations. Rapidly enhancing human SA in hazardous situations is, therefore, critical for timely hazard perception and collision avoidance, particularly in human-vehicle teaming contexts that demand fast, accurate hazard reasoning. This study evaluates the efficiency of a low-cost vibrotactile interface for enhancing hazard SA of human operators when teaming with heterogeneous autonomous vehicles, including both ground and aerial autonomous vehicles. To do so, we evaluate the effectiveness of a vibrotactile interface in challenging time-critical scenarios considering adversarial attacks to better understand the cognitive constraints faced by human operators. Our quantitative analysis, based on the data collected from 39 participants, demonstrates that: first, haptic cues can significantly enhance human hazard SA across various metrics for the ground and aerial scenarios; second, perception of aerial attacks in a 3-D environment is more challenging than ground risk perception.

The rapid proliferation of drones and Wi-Fienabled devices has revolutionized various sectors, including agriculture, entertainment, security, and surveillance. However, this also has magnified the threat space in terms of security, privacy, and efficient spectrum management. Detecting and classifying these devices accurately is crucial to address potential threats to public safety. To alleviate this issue, this paper proposes an advanced signal classification framework to identify drones base on their unique fingerprint. This is done by using spectrogram images of different drones and Wi-Fi devices operating within the 2.4 GHz spectrum which give unique patterns to identify drones fingerprint. The approach combines the features generated by Principal Component Analysis (PCA) with a modulation index to enhance classification accuracy and robustness of different machine learning classifiers. Two tasks are considered in this paper: i) multi-class classification of different drone models and ii) binary classification of drones and Wi-Fi signals. The proposed framework is rigorously tested and challenged using different hyperparameters configurations and ablation studies. The results demonstrate the robustness of the proposed approach in identifying drones accurately.

State estimation is a pivotal element in navigation tasks of autonomous vehicles. This technique is mainly applied when either a required measurement is not available or when the amount of available sensors in the platform are limited. Most of the state estimation algorithms available in on-board control modules use kinematic models as prior model to estimate the states of the autonomous system. However, these simple kinematic models do not consider dynamic terms and physical properties which can lead to biased state estimates. To overcome this issue, this paper proposes a physics-informed state observer for unknown linear systems under partial and noisy measurements. The proposed approach fuses two complementary concepts for state estimation and dynamics identification. The proposed approach is capable to obtain reliable state estimates whilst attenuating the level of noise. Lyapunov stability is used to derive an appropriate update law for the construction of physics-informed estimate model. Simulation studies are given to show the advantages and challenges of the proposed approach.

Due to their flexibility and wide range of applications, UAVs are expected to play an important role in complex urban airspace in the future. However, unpredictable low-level air currents caused by the complexity and variability of local urban design can pose significant risks to the take-off and landing phases. Current high-quality wind profile radars are expensive and only deployed in major airports. The alternative is to conduct large-scale urban modelling of wind using computation fluid dynamics, which relies on a large volume of accurate city and wind profile data. This undermines the future business model of distributed air mobility, e.g., takeoff and land in ad-hoc locations across a city. Therefore, it is crucial to create an approach that is data-efficient and economical. To achieve this, we leverage the abundance of environmental objects that naturally interact with wind, such as trees, flags, and clothing. This initial pilot study aims to address this challenge by examining tree movement using two consecutive techniques: (1) optical flow to extract the natural movement vectors, and (2) deep recurrent neural networks to translate the vectors into wind velocity. The proposed CNN-ConvLSTM model, trained on a video dataset encompassing diverse environmental conditions with ground wind speeds from 0 to 14.6 m/s, extracted visual and motion features from RGB and optical flow images, achieving an 87.42% prediction accuracy in capturing spatiotemporal wind-induced motion patterns. These results suggest the possibility of extending visual anemometer technology to broader scenarios and diverse natural objects, guaranteeing safer UAV operation in complex environments.

Efficient orchestration of AI services in 6G AI-RAN requires well-structured, ready-to-deploy AI service repositories combined with orchestration methods adaptive to diverse runtime contexts across radio access, edge, and cloud layers. Current literature lacks comprehensive frameworks for constructing such repositories and the proposed orchestrators generally over-simplify key orchestration factors compared to real edge computing environments. To fill these gaps, this paper systematically reviews and categorizes critical attributes influencing AI service orchestration in 6G AI-RAN and introduces an open-source, LLM-assisted toolchain that automates service packaging, deployment, and runtime profiling. We validate the proposed toolchain through the Cranfield AI Service repository case study, demonstrating significant automation benefits, reduced manual coding efforts by up to 98%, and the necessity of infrastructure-specific profiling, paving the way for more production-ready service orchestration and provisioning frameworks.

Aerial search and rescue (SAR) rotorcrafts currently need multiple specialist human operators, increasing cost and the risk of downtime due to crew unavailability and mental stress. Autonomy can aid fewer operators performing multiple tasks, but the human operator must maintain situation awareness (SA) of crucial autonomous decisions. A key challenge is the cognitive stress on a multi-tasking human-in-the-loop (HITL) due to the AI agent making decisions without human understanding. Explainable AI (XAI) has often been proposed as a way to explain autonomy decisions, but current XAI solutions doesn’t adapt to real-time human factors in high stress and high stakes situations. Here, we allow an AI agent to perform autonomous rotorcraft navigation, whilst the HITL operator has to perform two simultaneous tasks: (i) search for a target on the ground by toggling an onboard camera, and (ii) maintain SA of the autonomous navigation task through our novel XAI interface. Our novel XAI approach leverages on dimensionality reduction techniques to visualize the reinforcement learning (RL) navigation’s internal states, highlighting patterns in its decision-making process through intuitive interactive clustering on saliency maps. To ensure convergence on performance, we design a two-way interface that allows the human to interpret AI decisions and then give feedback via a Large Language Model to modify the autonomous navigation. Testing demonstrates increased task performance (+43%), while experiencing substantial human reductions in physical demand (-53%), time pressure (-30%), effort (-23%), and frustration (-26%), but at the cost of slightly increased mental demand (+12%).

Autonomous navigation requires training data in diverse transport settings. Accurate street/ground level representation is important to train autonomous driving, tourism planning, environmental protection, and a wide range of sectors. Many parts of the inhabited and most of the uninhabited world lacks street view imagery. Current street image generation can transform satellite imagery into synthetic 3D images, but there is a high level of hallucination. Here, we develop a Neural Gazetteer that integrates semantic narrative data (e.g., review comments and place attributes) to reduce hallucination in generative street-view images. Our novel work flow involves using satellite imagery to extract a geometry projection of the area and then integrating semantic narrative data into a diffusion model to generate realistic street views. We perform a wide range of comparisons with ground truth for urban and rural areas to identify the performance of our approach at both the feature-scale as well as the human perception semantic-scale.

Autonomous systems operating in high-stakes search-and-rescue (SAR) missions must continuously gather mission-critical information while flexibly adapting to shifting operational priorities. We propose CA-MIQ (Context-Aware Max-Information Q-learning), a lightweight dual-critic reinforcement learning (RL) framework that dynamically adjusts its exploration strategy whenever mission priorities change. CA-MIQ pairs a standard extrinsic critic for task reward with an intrinsic critic that fuses state-novelty, information-location awareness, and real-time priority alignment. A built-in shift detector triggers transient exploration boosts and selective critic resets, allowing the agent to re-focus after a priority revision. In a simulated SAR grid-world, where experiments specifically test adaptation to changes in the priority order of information types the agent is expected to focus on, CA-MIQ achieves nearly four times higher mission-success rates than baselines after a single priority shift and more than three times better performance in multiple-shift scenarios, achieving 100% recovery while baseline methods fail to adapt. These results highlight CA-MIQ’s effectiveness in any discrete environment with piecewise-stationary information-value distributions.

Explaining autonomy is becoming a crucial factor in the design of trustworthy autonomous platforms in both transport and smart living sectors. Interpretable reinforcement learning (RL) is an emerging research area that aims to explain why an autonomous platform adopts an action or set of actions. However, the state-of-the-art has focused on the design of explainable tools as independent modules that are not involved in the decision-making process of the RL agent. In this paper, we propose a novel belief-desire-intention RL (BDI-RL) approach that incorporates the explainable module as a belief model that enhances the learning capabilities of the RL as well as actions interpretability. To this end, we combine the merits of Dyna-Q algorithm as backbone RL model and belief maps as explainable element. The combined contribution of these models provides a robust model that emulates better the reasoning process of humans by leveraging beliefs and online agent-environment interactions. Simulations experiments are conducted in a grid environment of different sizes and obstacles. Comparisons are also provided to show the benefits of the proposed methodology.

Human–autonomy teaming in the Low Altitude Economy (LAE) requires operators to manage both ground and aerial autonomous agents under time pressure, spatial uncertainty, and cognitive load. This study investigates how visual and haptic feedback affect operator situational awareness (SA) in simulated collision avoidance tasks involving cars and drones. A high-fidelity virtual environment was built using Unreal Engine 4 and AirSim, with haptic cues delivered through a wearable bHaptics vest. Twenty-two participants performed within-subject trials across visual-only and visual–haptic conditions. Results showed that haptic feedback significantly enhanced SA, particularly in dimensions related to information acquisition and spare mental capacity. Improvements were more consistent in car-based tasks, while drone scenarios exhibited greater inter-individual variability. These findings demonstrate the potential of multimodal interfaces to support cognitive performance and reduce platform-related disparities in operator SA. This work provides empirical evidence for designing adaptive, perception-aware interfaces in safety-critical human–autonomy teaming systems.

As 6G networks evolve to support pervasive AI-driven applications, seamless provisioning of Edge AI services has become increasingly vital. However, current orchestration processes remain fragmented, requiring extensive coordination between AI-powered application developers and the network operators. In this paper, we propose a novel end-to-end orchestration framework that integrates Large Language Model (LLM) agents into O-RAN to automate edge AI service subscription and deployment. Our system translates high-level user intents into orchestrated workflows, including AI model selection, mobility-aware placement, and performance monitoring. We demonstrate the framework via a prototype built on our open-source ORAN simulator, showcasing intelligent, intent-driven AI service provisioning. This work represents a key step toward AI-native, accessible, and scalable service management in 6G.

Mixed-precision computing, a widely applied technique in AI, offers a larger trade-off space between accuracy and efficiency. The recent purposed Mixed-Precision Over-theAir Federated Learning (MP-OTA-FL) enables clients to operate at appropriate precision levels based on their heterogeneous hardware, taking advantages of the larger trade-off space while covering the quantization overheads of the mixed-precision modulation scheme with the OTA aggregation process. A key to further exploring the potential of the MP-OTA-FL framework is the optimization of client precision levels. The choice of precision level hinges on multifaceted factors including hardware capability, potential client contribution, and user satisfaction, among which factors can be difficult to define or quantify.In this paper, we propose a precision planning framework that integrates Retrieval-Augmented Generation (RAG) LLMs and dynamic client profiling to optimize satisfaction and contributions. This includes a hybrid interface for gathering device/user insights and an RAG database storing historical quantization decisions with feedback. Experiments show that our method boosts satisfaction, energy savings, and global model accuracy in MP-OTA-FL systems.

Load balancing in open radio access networks (O-RAN) is critical for ensuring efficient resource utilization, and the user’s experience by evenly distributing network traffic load. Current research mainly focuses on designing load-balancing algorithms to allocate resources while overlooking the cascade stability of load balancing, which is critical to prevent endless handover. The main challenge to analyse the cascade stability lies in the difficulty of establishing an accurate mathematical model to describe the process of load balancing due to its nonlinearity and high-dimensionality. In our previous theoretical work, a simplified general dynamic function was used to analyze the stability. However, it is elusive whether this function is close to the reality of the load balance process. To solve this problem, 1) a data-driven method is proposed to identify the dynamic model of the load balancing process according to the real-time traffic load data collected from the radio units (RUs); 2) the stability condition of load balancing process is established for the identified dynamics model. Based on the identified dynamics model and the stability condition, the RAN Intelligent Controller (RIC) can control RUs to achieve a desired load-balancing state while ensuring cascade stability.

Over-the-Air Federated Learning (OTA-FL) is a privacy-preserving distributed learning mechanism, by aggregating updates in the electromagnetic channel rather than at the server. A critical research gap in existing OTA - FL research is the assumption of homogeneous client computational bit precision. While in real world application, clients with varying hardware resources may exploit approximate computing (AxC) to operate at different bit precisions optimized for energy and computational efficiency. Model updates with varying precisions among clients present a significant challenge for OTA - FL, as they are incompatible with the wireless modulation superposition process. Here, we propose an mixed-precision OTA-FL framework of clients with multiple bit precisions, demonstrating the following innovations: (i) the superior trade-off for both server and clients within the constraints of varying edge computing capabilities, energy efficiency, and learning accuracy requirements compared to homogeneous client bit precision, and (ii) a multi-precision gradient modulation scheme to ensure compatibility with OTA aggregation and eliminate the overheads of precision conversion. Through case study with real world data, we validate our modulation scheme that enables AxC based mixed-precision OTA-FL. In comparison to homogeneous standard precision of 32-bit and 16-bit, our framework presents more than 10% in 4-bit ultra low precision client performance and over 65 % and 13 % of energy savings respectively. This demonstrates the great potential of our mixed-precision OTA-FL approach in heterogeneous edge computing environments.

Abstract Online social networks (OSNs) provide a platform for individuals to share information, exchange ideas, and build social connections beyond in-person interactions. For a specific topic or community, opinion leaders are individuals who have a significant influence on others’ opinions. Detecting opinion leaders and modeling influence dynamics is crucial as they play a vital role in shaping public opinion and driving conversations. Existing research have extensively explored various graph-based and psychology-based methods for detecting opinion leaders, but there is a lack of cross-disciplinary consensus between definitions and methods. For example, node centrality in graph theory does not necessarily align with the opinion leader concepts in social psychology. This review paper aims to address this multi-disciplinary research area by introducing and connecting the diverse methodologies for identifying influential nodes. The key novelty is to review connections and cross-compare different multi-disciplinary approaches that have origins in: social theory, graph theory, compressed sensing theory, and control theory. Our first contribution is to develop cross-disciplinary discussion on how they tell a different tale of networked influence. Our second contribution is to propose trans-disciplinary research method on embedding socio-physical influence models into graph signal analysis. We showcase inter- and trans-disciplinary methods through a Twitter case study to compare their performance and elucidate the research progression with relation to psychology theory. We hope the comparative analysis can inspire further research in this cross-disciplinary area.

Wildfires pose major challenges to natural ecosystems and smart living due to its destructive nature. Unmanned Aerial vehicles (UAVs) or drones have been used to support fire fighter in identifying vulnerable areas and the detection of people that need assistance. Most of the current solutions use path planning approaches under simple and deterministic environments that fail to model the dynamic nature of fire. Furthermore, the localisation of victims is assumed to be known which is unrealistic in disaster-like scenarios. To alleviate this issue, this paper proposes a novel search and rescue (SAR) application using drones. A multi-agent deep Q-network is designed to train a fleet of UAVs to search for people and evacuate them in a wildfire scenario. A realistic forest environment is designed that considers variations in vegetation and fire propagation. This helps to challenge RL algorithms to be more adaptive to changes in the environment due to the evolution of fire. Extensive simulation experiments are conducted to show the advantages and effectiveness of the proposed approach.

The cooperative nature of drone swarms poses risks in the smooth operation of services and the security of national facilities. The control objective of the swarm is, in most cases, occluded due to the complex behaviors observed in each drone. It is paramount to understand which is the control objective of the swarm, whilst understanding better how they communicate with each other to achieve the desired task. To solve these issues, this article proposes a physics-informed multiagent inverse reinforcement learning (PI-MAIRL) that: 1) infers the control objective function or reward function from observational data and 2) uncover the network topology by exploiting a physics-informed model of the dynamics of each drone. The combined contribution enables to understand better the behavior of the swarm, whilst enabling the inference of its objective for experience inference and imitation learning. A physically uncoupled swarm scenario is considered in this study. The incorporation of the physics-informed element allows to obtain an algorithm that is computationally more efficient than model-free IRL algorithms. Convergence of the proposed approach is verified using Lyapunov recursions on a global Riccati equation. Simulation studies are carried out to show the benefits and challenges of the approach.

Recently, the emergence of Internet of Things (IoT) devices has posed a challenge for securing information and avoiding attacks. Most of the cryptography solutions are based on physical layer security (PLS), whose idea is to fully exploit the properties of wireless channel state information (CSI) for generating symmetric keys between two communication nodes. However, accurate channel estimation is vulnerable for attackers and relies on powerful signal processing capability, which is not suitable for low-power IoT devices. In this article, we expect to apply graph layer security (GLS) to exploit the common features of physical dynamics detected by IoT sensors placed in networked systems to generate keys for data encryption and decryption, which we believe is a new frontier to security for both industry and academic research. We propose a distributed key generation algorithm based on multi-agent deep reinforcement learning (MADRL) approach, which enables communication nodes to cooperatively generate symmetric keys based on their locally detected physical dynamics (e.g., water/gas/oil/electrical pressure/flow/voltage) with low computational complexity and without information exchange. In order to demonstrate the feasibility, we conduct and evaluate our key generation algorithm in both a simulated and real water distribution network. The experimental results show that the proposed algorithm has considerable performance in terms of randomness, bit agreement rate (BAR), and so on.

Drones are set to penetrate society across transport and smart living sectors. While many are amateur drones that pose no malicious intentions, some may carry deadly capability. It is crucial to infer the drone's objective to prevent risk and guarantee safety. In this article, a policy error inverse reinforcement learning (PEIRL) algorithm is proposed to uncover the hidden objective of drones from online data trajectories obtained from cooperative sensors. A set of error-based polynomial features are used to approximate both the value and policy functions. This set of features is consistent with current onboard storage memories in flight controllers. The real objective function is inferred using an objective constraint and an integral inverse reinforcement learning (IRL) batch least-squares (LS) rule. The convergence of the proposed method is assessed using Lyapunov recursions. Simulation studies using a quadcopter model are provided to demonstrate the benefits of the proposed approach.

In Underwater Acoustic Sensor Networks (UASNs), the robustness of network is greatly affected by complex marine environments when implementing multi-hop data transmission. Factors such as the underwater acoustic channel and dynamic topological changes induced by multi-layered oceanic vortices exacerbate this influence. However, there is currently a research gap in the specific area of robustness optimization for UASNs. Existing studies on robustness optimization are unsuitable for UASNs as they neglect the considerations of the marine environment and node characteristics (e.g., residual energy). In this work, we propose PRobust, a percolation-based robustness optimization model for UASNs. PRobust consists of two distinct phases: percolation modeling and bottleneck optimization. In the percolation modeling phase, we incorporate both node and edge features, considering the physical and topological properties, and introduce a novel approach for calculating link quality. In the bottleneck optimization phase, we devise a graph theory-based method to identify bottlenecks, leveraging the flow information recorded by nodes to improve the accuracy of bottleneck discovery. Moreover, we integrated time slots and a current movement model into the proposed model, allowing its applicability to dynamically changing UASNs. Extensive simulation results indicate that, compared to existing methods, PRobust significantly enhances network robustness and performance with the same overhead after bottleneck optimization.

Reconfigurable intelligent surfaces (RIS) can both help and hinder the physical layer secret key generation (PL-SKG) of communications systems. Whilst a legitimate RIS can yield beneficial impacts, including increased channel randomness to enhance PL-SKG, a malicious RIS can poison legitimate channels and crack almost all existing PL-SKGs. In this work, we propose an adversarial learning framework that addresses Man-in-the-middle RIS (MITM-RIS) eavesdropping which can exist between legitimate parties, namely Alice and Bob. First, the theoretical mutual information gap between legitimate pairs and MITM-RIS is deduced. From this, Alice and Bob leverage adversarial learning to learn a common feature space that assures no mutual information overlap with MITM-RIS. Next, to explain the trained legitimate common feature generator, we aid signal processing interpretation of black-box neural networks using a symbolic explainable AI (xAI) representation. These symbolic terms of dominant neurons aid the engineering of feature designs and the validation of the learned common feature space. Simulation results show that our proposed adversarial learning- and symbolic-based PL-SKGs can achieve high key agreement rates between legitimate users, and is further resistant to an MITM-RIS Eve with the full knowledge of legitimate feature generation (NNs or formulas). This therefore paves the way to secure wireless communications with untrusted reflective devices in future 6G.

Explaining what does a data-driven control algorithm learns play a crucial role for safety critical control of autonomous platforms in transportation. This is more acute in reinforcement learning control algorithms, where the learned control policy depends on various factors that are hidden within the data. Explainable artificial intelligence methods have been used to explain the outcomes of machine learning methods by analysing input-output relations. However, data-driven control does not pose a simple input-output mapping and hence, the resulting explanations lack depth. To deal with this issue, this paper proposes a explainable data-driven control method that allows to understand what the data-driven method is learning from the data. The model is composed by a Q-learning algorithm enhanced by a dynamic mode decomposition with control (DMDc) algorithm for state-transition function estimation. Both the Q-learning and DMDc provides the elements that are learned from the data and allow the construction of counterfactual explanations. The proposed approach is robust and does not require hyperparameter tuning. Simulation experiments are conducted to observe the benefits and challenges of the method.

Data-driven proactive network optimisation is critical for 5G advanced and 6G, allowing operators to dynamically allocate cellular spectrum reuse in anticipating for demand surges. Current approaches to traffic prediction are largely temporal correlation based. We know causal inference of key factors can help to improve prediction accuracy for spike traffic events and identify pathways to improve services. Current causal inference identify stationary independent variables, but real environments have open challenges: (i) dynamic and heterogeneous causal maps, (ii) cascade partially observable variables, and/or (iii) have coupled / confounding relationships. Currently there is no research that dynamically configures the causal relationship according to emerging real-time data and shares inference outcomes across the data sharing and computing continuum of Open-RAN (ORAN) architecture. Here, we use both real cellular network traffic and social event triggers to perform nonlinear causal inference as an rApp: Predictability Improvement (PI), Conditional Mutual Information (CMI), and Convergent Cross Map (CCM). This causal knowledge is then shared across the ORAN to be embedded in traffic prediction xApps: hard causal embedding to Recurrent Neural Network (RNN) and soft causal feature embedding to a Gaussian Processes (GP). The results show a significant accuracy improvement (93-99%) over baseline non-causal correlated prediction (76-94%) and blind multi-variate approaches (87-95%). This work paves the way to causal proactive network optimisation.

Cascade stability of load balancing is critical for ensuring high efficiency service delivery and preventing undesirable handovers. In energy efficient networks that employ diverse sleep mode operations, handing over traffic to neighbouring cells’ expanded coverage must be done with minimal side effects. Current research is largely concerned with designing distributed and centralized efficient load balancing policies that are locally stable. There is a major research gap in identifying largescale cascade stability for networks with heterogeneous load balancing policies arising from diverse plug-and-play sleep mode policies in ORAN, which will cause heterogeneity in the network stability behaviour. Here, we investigate whether cells arbitrarily connected for load balancing and having an arbitrary number undergoing sleep mode can: (i) synchronize to a desirable load-balancing state, and (ii) maintain stability. For the first time, we establish the criterion for stability and prove its validity for any general load dynamics and random network topology. Whilst its general form allows all load balancing and sleep mode dynamics to be incorporated, we propose an ORAN architecture where the network service management and orchestration (SMO) must monitor new load balancing policies to ensure overall network cascade stability.

State estimation of autonomous platforms is a crucial element in the design and test of perception algorithms. The nonlinear nature of autonomous platforms makes hard to design accurate state estimation algorithms without using linearization techniques, large amount of data or knowledge of the physical parameters of the platform. This paper reports a novel state estimation algorithm of autonomous platforms. The proposed approach is based on a physics informed recurrent neural network (PIRNN) that combines the power of recurrent nets with an estimate structure of the autonomous platform model. This estimated model regularises the weights’ manifold of the network for the accurate estimation of the states. Boundedness of the proposed PIRNN is verified using Lyapunov stability theory as long as the physics-informed signals satisfy a persistent of excitation condition. Simulations are conducted to test the PIRNN model and show its benefits and challenges.

In recent years, robots and autonomous systems have become increasingly integral to our daily lives, offering solutions to complex problems across various domains. Their application in search and rescue (SAR) operations, however, presents unique challenges. Comprehensively exploring the disaster-stricken area is often infeasible due to the vastness of the terrain, transformed environment, and the time constraints involved. Traditional robotic systems typically operate on predefined search patterns and lack the ability to incorporate and exploit ground truths provided by human stakeholders, which can be the key to speeding up the learning process and enhancing triage. Addressing this gap, we introduce a system that integrates social interaction via large language models (LLMs) with a hierarchical reinforcement learning (HRL) framework. The proposed system is designed to translate verbal inputs from human stakeholders into actionable RL insights and adjust its search strategy. By leveraging human-provided information through LLMs and structuring task execution through HRL, our approach not only bridges the gap between autonomous capabilities and human intelligence but also significantly improves the agent's learning efficiency and decision-making process in environments characterised by long horizons and sparse rewards.

Drones have become a crucial element in current wildfire and smoke detection applications. Several deep learning architectures have been developed to detect fire and smoke using either colour-based methodologies or semantic segmentation techniques with impressive results. However, the computational demands of these models reduce their usability on memory-restricted devices such as drones. To overcome this memory constraint whilst maintaining the high detection capabilities of deep learning models, this paper proposes two lightweight architectures for fire and smoke detection in forest environments. The approaches use the Deeplabv3+ architecture for image segmentation as baseline. The novelty lies in the incorporation of vision transformers and a lightweight convolutional neural network architecture that heavily reduces the model complexity, whilst maintaining state-of-the-art performance. Two datasets for fire and smoke segmentation, based on the Corsican, FLAME, SMOKE5K, and AI-For-Mankind datasets, are created to cover different real-world scenarios of wildfire to produce models with better detection capabilities. Experiments are conducted to show the benefits of the proposed approach and its relevance in current drone-based wildfire detection applications.

Explaining what a reinforcement learning (RL) control agent learns play a crucial role in the safety critical control domain. Most of the approaches in the state-of-the-art focused on imitation learning methods that uncover the hidden reward function of a given control policy. However, these approaches do not uncover what the RL agent learns effectively from the agent-environment interaction. The policy learned by the RL agent depends in how good the state transition mapping is inferred from the data. When the state transition mapping is wrongly inferred implies that the RL agent is not learning properly. This can compromise the safety of the surrounding environment and the agent itself. In this paper, we aim to uncover the elements learned by data-driven RL control agents in a special class of discrete-time linear autonomous systems. Here, the approach aims to add a new explainable dimension to data-driven control approaches to increase their trust and safe deployment. We focus on the classical data-driven Q-learning algorithm and propose an explainable Q-learning (XQL) algorithm that can be further expanded to other data-driven RL control agents. Simulation experiments are conducted to observe the effectiveness of the proposed approach under different scenarios using several discrete-time models of autonomous platforms.

The design of accurate trajectory prediction algorithms is crucial to implement adequate countermeasures against drones with anomalous performances. Wrong predictions may cause high-false-positives that compromise safety in national infrastructures. In this article, a physics informed reservoir computing (PIRC) scheme for drone trajectory prediction is proposed. The approach is comprised of two main complementary learning algorithms that enhance the prediction and generalization capabilities: 1) a standard reservoir computing scheme for high-dimensional encoding exploitation and 2) a nonlinear control scheme that gives a physical feedback to the reservoir weights to ensure the prediction error is minimized. The nonlinear control scheme is modeled by the prediction error dynamics and a feedback linearization controller. Two different PIRC schemes are proposed which preserve the reservoir properties and enhance the prediction robustness. Lyapunov stability theory is used to verify the boundedness and convergence of the proposed algorithms. Simulation studies and comparisons are given to verify the proposed approach.

Cyber-physical systems, such as smart transportation, face security threats from both digital and physical realms. Recently, Mobility-as-a-Service (MaaS) has emerged as a novel transportation concept, offering passengers access to diverse mobility services via a unified platform. Central to this system is the smart MaaS coordinator, tasked with tailoring services to passengers based on their profiles and behaviors. However, the coordination of heterogeneous passengers introduces vulnerabilities, enabling malicious entities to exploit the system by impersonating priority passengers with falsified information. Effective detection mechanisms require a deep understanding of the spoofing process. This paper investigates threats to the smart MaaS coordinator, unveiling a new reinforcement learning-based attack named the passenger spoofing attack, which aims to mitigate the risk of inadvertently exposing MaaS vulnerabilities post-deployment. This attack leverages feedback from actions and experiences to manipulate system profitability and passenger satisfaction by generating false passenger information. Furthermore, our research reveals that multi-agent reinforcement learning, accounting for spatial distribution among malicious agents and passengers, strengthens the attack. Through simulations based on datasets from New York City and synthetic sources, we demonstrate that the attack can significantly reduce 70% of profit and 50% of passenger satisfaction. Spatial analysis indicates an effective distance of approximately two nodes from the origin or destination. This study enriches our comprehension of the vulnerabilities inherent in smart coordinators within MaaS, enabling the development of robust countermeasures against malicious actors.

Mobility-as-a-service (MaaS) is a new transport model that combines multiple transport modes in a single platform. Dynamic passenger behavior based on past experiences requires reinforcement-based optimization of MaaS services. Deep reinforcement learning (DRL) may improve passenger satisfaction by offering the most appropriate transport services based on individual passenger experiences and preferences. However, this produces a new privacy risk to the MaaS platform using the centralized DRL method. Information leakage will occur if the platform is not carefully designed with privacy-preserving mechanisms. In this paper, we propose a federated deep deterministic policy gradient (FDDPG) that maximizes passenger satisfaction and MaaS long-term profit while preserving privacy. We enforce an equally weighted experience sampling mechanism to prevent sampling bias such that the solution quality of FDDPG is statistically equivalent to the centralized algorithm. During the model training and inference, information is processed locally, and only the gradients are shared, which prevents information leakage to any semi-honest participants and eavesdroppers. Secure aggregation protocol in line with the dynamic property of the mobile agent is also used in the gradient sharing step to ensure that the algorithm is prevented from inference attacks. We perform experiments on New York City-based real-world and synthetic scenarios. The results show that the proposed FDDPG can improve the MaaS profit and passenger satisfaction by about 90% and 15%, respectively, and maintain stable training against agent dropout. Our approach and findings could enhance MaaS utility as well as facilitate passenger trust and participation in MaaS and other data-driven transportation systems.

Increased dynamic drone usage has increased complexity in aerial navigation and often demands distributed local deconfliction. Due to the high velocities and few landmarks, robust deconfliction relies on precise positioning and synchronization. However, intentional spoofing attacks aimed at inducing navigation conflicts threaten the reliability of conventional techniques. Here, we address these concerns by establishing a baseline on the impact of novel conflict-inducing spoofing attacks on existing geometric navigation methods. Based on the impact of the attacks on the navigation, reinforcement learning (RL) strategy is used to counter the effects of spoofing attacks. In order to counter the effect of spoofing in randomized dynamic airspace conditions, a zero-sum action-robust (ZSAR) RL based on mixed Nash equilibrium objective is used. The proposed methodology yields an improved number of conflict-free paths while reducing average conflicts compared to existing state of the art RL strategies, thus making it suitable for deploying autonomous aircrafts.

The rapid growth of autonomous systems (ASs) with data sharing means new cybersecurity methods have to be developed for them. Existing computational complexity-based cryptography does not have information-theoretical bounds and poses threats to superior computational attackers. This post-quantum cryptography issue indeed motivated the rapid advances in using common physical layer properties to generate symmetrical cipher keys (known as PLS). However, PLS remains sensitive to attackers (e.g., jamming) that destroy its prerequisite wireless channel reciprocity. When ASs are in cooperative tasks (e.g., rescuing searching, and formation flight), they will behave cooperatively in the control layer. Inspired by this, we propose a new security mechanism called control layer security (CLS), which exploits the correlated but unobservable states of cooperative ASs to generate symmetrical cipher keys. This idea is then realized in the linearized UAV cooperative control scenario. The theoretical correlation coefficients between Alice's and Bob's states are computed, based on which common feature selection and key quantization steps are designed. The results from simulation and real UAV experiments show i) an approximately 90% key agreement rate is achieved, and ii) even an Eve with the known observable states and systems fails to estimate the unobservable states and the secret keys relied upon, due to the multiple-to-one mapping from unobservable states (pitch, roll and yaw angles) to the observable states (3D trajectory). This demonstrates CLS as a promising candidate to secure the communications of ASs, especially in the adversarial radio environment with attackers that destroys the prerequisite for current PLS.

Proliferation of cheaper autonomous system prototypes has magnified the threat space for attacks across the manufacturing, transport, and smart living sectors. An accurate trajectory inference algorithm is required for monitoring and early detection of autonomous misbehavior and to take relevant countermeasures. This article presents a trajectory inference algorithm based on a CLOE approach using partial states measurements. The approach is based on a physics informed state parameteterization that combines the main advantages of state estimation and identification algorithms. Noise attenuation and parameter estimates convergence are obtained if the output trajectories fulfill a persistent excitation condition. Known and unknown desired reference/destination cases are considered. The stability and convergence of the proposed approach are assessed via Lyapunov stability theory under the fulfillment of a persistent excitation condition. Simulation studies are carried out to verify the effectiveness of the proposed approach.

Proliferation of autonomous systems have increased the threat space and the economic risk in several national infrastructures, e.g., at airports. Therefore, reliable detection of their intention is paramount to ensure smooth operation of national services and societal safety. This article reports a data-driven trajectory intent prediction algorithm which is based on a linear model structure of the autonomous system dynamics obtained from a dynamic mode decomposition algorithm. The model computation is enhanced by two sources of physics informed knowledge associated to the energy functional. Two different prediction algorithms that consider fixed or time-varying references are designed in terms of the availability of control input measurements. Rigorous theoretical results are provided to support the approach using matrix decomposition and optimization techniques. Simulation and experimental studies are carried out to verify the effectiveness of the proposal.

Drones are prone to abuse due to their low cost and their pool of potential illegal applications that can compromise safety of national infrastructures and facilities. Hence, drone detection and predict its behaviour is crucial to ensure smooth operation of services. In this paper, an unsupervised/supervised statistical learning algorithm for drone behaviour prediction is proposed. The algorithm is based on drone detection data collected from any radar or RF- sensor. The architecture of the approach is comprised of two stages: i) the first stage attempts to study the drone detection data using either unsupervised or supervised learning methods to model low dimensional expert's features, and ii) in the second stage a real time drone behaviour predictor model is proposed based on the Kolmogorov-Smirnov and Wasserstein distances. Simulation studies using synthetic data obtained from the AirSim simulator are given to provide the evidence-base for future improvements in the field of drone behaviour prediction.

Climate change affects human liveability and may increase the likelihood of armed violence. However, the precise repercussions on social cohesion and conflict are difficult to model, and several socio-economic mechanisms exist between local climate changes and conflict, and are often hidden to us. Nonetheless, we offer an exploratory data analysis in this paper at a global scale, on the relationship between diverse climate indicators and conflict. Here we investigate potential basic causal models between climate change and conflict, including the causal direction, causal lag, and causal strength. We use historical climate and extreme environmental event data from the past 50 years across the world to identify geographic region-specific causal indicators. The initial broad findings are: (1) rainfall is a reasonably general indicator of conflict, (2) there are fragile regions which exhibit a strong causal link between extreme climate variations and conflict (predominantly in Africa and South Asia), and 3. there exists a common time lag of the causality between the climate variations and the conflict in many regions, which is worth further study.

Secret key generation in physical layer security exploits the unpredictable random nature of wireless channels. The millimeter-wave (mmWave) channels have limited multipath and channel randomness in static environments. In this paper, for mmWave secret key generation of physical layer security, we use a reconfigurable intelligent surface (RIS) to induce randomness directly in wireless environments, without adding complexity to transceivers. We consider RIS to have continuous individual phase shifts (CIPS) and derive the RIS-assisted reflection channel distribution with its parameters. Then, we propose continuous group phase shifts (CGPS) to increase the randomness specifically at legal parties. Since the continuous phase shifts are expensive to implement, we analyze discrete individual phase shifts (DIPS) and derive the corresponding channel distribution, which is dependent on the quantization bit. We then derive the secret key rate (SKR) to evaluate the randomness performance. With the simulation results verifying the analytical results, this work explains the mathematical principles and lays a foundation for future mmWave evaluation and optimization of artificial channel randomness.

Wireless networks like many multi-user services have to balance limited resources in real-time. In 6G, increased network automation makes consumer trust crucial. Trust is reflect in both a personal emotional sentiment as well as a physical understanding of the transparency of AI decision making. Whilst there has been isolated studies of consumer sentiment to wireless services, this is not well linked to the decision making engineering. Likewise, limited recent research in explainable AI (XAI) has not established a link to consumer perception.Here, we develop a Quality-of-Trust (QoT) KPI that balances personal perception with the quality of decision explanation. That is to say, the QoT varies with both the time-varying sentiment of the consumer as well as the accuracy of XAI outcomes. We demonstrate this idea with an example in Neural Water-Filling (N-WF) power allocation, where the channel capacity is perceived by artificial consumers that communicate through Large Language Model (LLM) generated text feedback. Natural Language Processing (NLP) analysis of emotional feedback is combined with a physical understanding of N-WF decisions via meta-symbolic XAI. Combined they form the basis for QoT. Our results show that whilst the XAI interface can explain up to 98.9% of the neural network decisions, a small proportion of explanations can have large errors causing drops in QoT. These drops have immediate transient effects in the physical mistrust, but emotional perception of consumers are more persistent. As such, QoT tends to combine both instant physical mistrust and long-term emotional trends.

The increasing utilization of unmanned aerial vehicles (UAVs) in advanced air mobility (AAM) necessitates highly automated conflict resolution and collision avoidance strategies. Consequently, reinforcement learning (RL) algorithms have gained popularity in addressing conflict resolution strategies among UAVs. However, increasing digitization introduces challenges related to packet drop constraints and various adversarial cyber threats, rendering AAM fragile. Adversaries can introduce perturbations into the system states, reducing the efficacy of learning algorithms. Therefore, it is crucial to systematically investigate the impact of increased digitization, including adversarial cyber-threats and packet drop constraints to study the fragile characteristics of AAM infrastructure. This study examines the performance of artificial intelligence(AI) based path planning and conflict resolution strategies under different adversarial and stochastic packet drop constraints in UAV systems. The fragility analysis focuses on the number of conflicts, collisions and fuel consumption of the UAVs with respect to its mission, considering various adversarial attacks and packet drop constraint scenarios. The safe deep q-networks (DQN) architecture is utilized to navigate the UAVs, mitigating the adversarial threats and is benchmarked with vanilla DQN using the necessary metrics. The findings are a foundation for investigating the necessary modification of learning paradigms to develop antifragile strategies against emerging adversarial threats.

Precise timing over timestamped packet-exchange communication is an enabling technology in the mission-critical industrial Internet of Things (IIoT), particularly when satellite-based timing is unavailable. The main challenge is to ensure timing accuracy when the clock synchronization system is subject to disturbances caused by the drifting frequency, time-varying delay, jitter, and timestamping uncertainty. In this work, a robust packet-coupled oscillators (R-PkCOs) protocol is proposed to reduce the effects of perturbations manifested in the drifting clock, timestamping uncertainty, and delays. First, in the spanning-tree clock topology, time synchronization between an arbitrary pair of clocks is modeled as a state-space model, where clock states are coupled with each other by one-way timestamped packet exchange (referred to as packet coupling), and the impacts of both drifting frequency and delays are modeled as disturbances. A static output controller is adopted to adjust the drifting clock. The$H_{\infty }$robust control design solution is proposed to guarantee that the ratio between the modulus of synchronization precision and the magnitude of the disturbances are always less than a given value. Therefore, the proposed time synchronization protocol is robust against the disturbances, which means that the impacts of drifting frequency and delays on the synchronization accuracy are limited. The one-hour experimental results demonstrate that the proposed R-PkCO’s protocol can realize time synchronization with the precision of 6$\mu \text{s}$in a 21-node IEEE 802.15.4 network. This work has widespread impacts in the process automation of automotive, mining, oil, and gas industries.

Uncovering the reward function of optimal controllers is crucial to determine the desired performance that an expert wants to inject to a certain dynamical system. In this paper, a reward inference algorithm of discrete-time expert's controllers is proposed. The approach is inspired by the complementary mechanisms of the striatum, neocortex, and hippocampus for decision making and experience transference. These systems work together to infer the reward function associated to expert's controller using the complementary merits of data-driven and online learning methods. The proposed approach models the neocortex system as two independent learning algorithms given by a Q-learning algorithm and a gradient identification rule. The hippocampus is modelled by a least-squares update rule that extracts the relation from the states and control inputs of the expert's data. The striatum is modelled by an inverse optimal control algorithm which iteratively finds the hidden reward function. Lyapunov stability theory is used to show the stability and convergence of the proposed approach. Simulation studies are given to demonstrate the effectiveness of the proposed complementary learning algorithm.

Abstract Multimodal transportation systems require an effective journey planner to allocate multiple passengers to transport operators. One example is mobility-as-a-service, a new mobility service that integrates various transport modes through a single platform. In such a multimodal and diverse journey planning problem, accommodating heterogeneous passengers with different and dynamic preferences can be challenging. Furthermore, passengers may behave based on experiences and expectations, in the sense that the transport experience affects their state and decision of the next transport service. Current methods of treating each journey planning optimization as a non-time varying single experience problem cannot adequately model passenger experience and memories over many journeys over time. In this paper, we model passenger experience as a Markov model where prior experiences have a transient effect on future long-term satisfaction and retention rate. As such, we formulate a multi-objective journey planning problem that considers individual passenger preferences, experiences, and memories. The proposed approach dynamically determines utility weights to obtain an optimal journey plan for individual passengers based on their status. To balance the profit received by each transport operator, we present a variant-based proportional fairness. Our experiments using real-world and synthetic datasets show that our approach enhances passenger satisfaction, compared to baseline methods. We demonstrate that the overall profit is increased by 2.3 times, resulting in a higher retention rate caused by higher satisfaction levels. Our proposed approach can facilitate the participation of transport operators and promote passenger acceptance of MaaS.

Abstract Increased drone proliferation in civilian and professional settings has created new threat vectors for airports and national infrastructures. The economic damage for a single major airport from drone incursions is estimated to be millions per day. Due to the lack of balanced representation in drone data, training accurate deep learning drone detection algorithms under scarce data is an open challenge. Existing methods largely rely on collecting diverse and comprehensive experimental drone footage data, artificially induced data augmentation, transfer and meta-learning, as well as physics-informed learning. However, these methods cannot guarantee capturing diverse drone designs and fully understanding the deep feature space of drones. Here, we show how understanding the general distribution of the drone data via a generative adversarial network (GAN), and explaining the under-learned data features using topological data analysis (TDA) can allow us to acquire under-represented data to achieve rapid and more accurate learning. We demonstrate our results on a drone image dataset, which contains both real drone images as well as simulated images from computer-aided design. When compared to random, tag-informed and expert-informed data collections (discriminator accuracy of 94.67%, 94.53% and 91.07%, respectively, after 200 epochs), our proposed GAN-TDA-informed data collection method offers a significant 4% improvement (99.42% after 200 epochs). We believe that this approach of exploiting general data distribution knowledge from neural networks can be applied to a wide range of scarce data open challenges.

Molecular communication is attractive to the emerging nano-scale communication systems. Traditionally, a detector recovers the information from only the concentration of single messenger molecule, while ignoring the variation of multiple participants in biochemical reaction. In this paper, we propose a non-coherent signal detector, by fully exploiting this ubiquitous biochemical diversity property of multiple reacting molecules. After extracting the channel state information (CSI) independent non-coherent features of received signals, the dynamical transient characteristics of messenger, reactant and product molecules are all utilized to implement the diversity detection, thus formulating a functional single-input multiple-output (SIMO) system via reaction-diffusion communication that has been barely considered before. We design both hard and soft combination strategies to attain the potential diversity gain arise from the dynamical co-variation of participants. Theoretical analysis and numerical simulations are provided to demonstrate the advantages of our detector. Compared with conventional detectors that use only single messenger molecule, the bit error rate (BER) of is substantially reduced. Moreover, the BER performances of our non-coherent detector are even better than coherent maximum a posteriori (MAP) detector that requires accurate CSI estimation, which confirms the dramatic diversity gain provided by our detector. It would have great potentials in reliable nano-scale communications.

While autonomous systems can be used for a variety of beneficial applications, they can also be used for malicious intentions and it is mandatory to disrupt them before they act. So, an accurate trajectory inference algorithm is required for monitoring purposes that allows to take appropriate countermeasures. This article presents a closed-loop output error approach for trajectory inference of a class of linear systems. The approach combines the main advantages of state estimation and parameter identification algorithms in a complementary fashion using online data and an estimated model, which is constructed by the state and parameter estimates, that inform about the physics of the system to infer the followed noise-free trajectory. Exact model matching and estimation error cases are analyzed. A composite update rule based on a least-squares rule is also proposed to improve robustness and parameter and state convergence. The stability and convergence of the proposed approaches are assessed via the Lyapunov stability theory under the fulfilment of a persistent excitation condition. Simulation studies are carried out to validate the proposed approaches.

The development of reconfigurable intelligent surfaces (RIS) has recently advanced the research of physical layer security (PLS). Beneficial impacts of RIS include but are not limited to offering a new degree-of-freedom (DoF) for key-less PLS optimization, and increasing channel randomness for physical layer secret key generation (PL-SKG). However, there is a lack of research studying how adversarial RIS can be used to attack and obtain legitimate secret keys generated by PL-SKG. In this work, we show an Eve-controlled adversarial RIS (Eve-RIS), by inserting into the legitimate channel a random and reciprocal channel, can partially reconstruct the secret keys from the legitimate PL-SKG process. To operationalize this concept, we design Eve-RIS schemes against two PL-SKG techniques used: (i) the CSI-based PL-SKG, and (ii) the two-way cross multiplication based PL-SKG. The channel probing at Eve-RIS is realized by compressed sensing designs with a small number of radio-frequency (RF) chains. Then, the optimal RIS phase is obtained by maximizing the Eve-RIS inserted deceiving channel. Our analysis and results show that even with a passive RIS, our proposed Eve-RIS can achieve a high key match rate with legitimate users, and is resistant to most of the current defensive approaches. This means the novel Eve-RIS provides a new eavesdropping threat on PL-SKG, which can spur new research areas to counter adversarial RIS attacks.

Trajectory inference is a hard problem when states measurements are noisy and if there is no high-fidelity model available for estimation; this may arise into high-variance and biased estimates results. This article proposes a physics informed trajectory inference of a class of nonlinear systems. The approach combines the advantages of state and parameter estimation algorithms to infer the trajectory that follows the nonlinear system using online noisy state measurements. The algorithm is composed of a parallel estimated model constructed in terms of a low-pass filter parameterization. The estimated model defines a physics informed model that infers the trajectory of the real nonlinear system with noise attenuation capabilities. The parameters of the estimated model are updated by a closed-loop output error identification algorithm which uses the estimated states instead of the noisy measurements to avoid biased estimation. Stability and convergence of the proposed technique is assessed using Lyapunov stability theory. Simulations studies are carried out under different scenarios to verify the effectiveness of the proposed inference algorithm.

Individuals modify their opinions towards a topic based on their social interactions. Opinion evolution models conceptualize the change of opinion as a uni -dimensional continuum, and the effect of influence is built by the group size, the network structures, or the relations among opinions within the group. However, how to model the personal opinion evolution process under the effect of the online social influence as a function remains unclear. Here, we show that the uni -dimensional continuous user opinions can be represented by compressed high-dimensional word embeddings, and its evolution can be accurately modelled by an ordinary differential equation (ODE) that reflects the social network influencer interactions. We perform our analysis on 87 active users with corresponding influencers on the COVID-19 topic from 2020 to 2022. The regression results demonstrate that 99% of the variation in the quantified opinions can be explained by the way we model the connected opinions from their influencers. Our research on the COVID-19 topic and for the account analysed shows that social media users primarily shift their opinion based on influencers they follow (e.g., model explains for 99% variation) and self-evolution of opinion over a long time scale is limited.

In this paper, a cost inference algorithm for discrete-time systems using human-behaviour learning is pro-posed. The approach is inspired in the complementary learning that exhibits the neocortex, hippocampus, and striatum learning systems to achieve complex decision making. The main objective is to infer the hidden cost function from expert's data associated to the hippocampus (off-policy data) and transfer it to the neocortex for policy generalization (on-policy data) in different systems and environments. The neocortex is modelled by a Q-learning and a least-squares identification algorithms for on-policy learning and system identification. The cost inference is obtained using a one-step gradient descent rule and an inverse optimal control algorithm. Convergence of the cost inference algorithm is discussed using Lyapunov recursions. Simulations verify the effectiveness of the approach.

The causal mechanism between climate and political violence is fraught with complex mechanisms. Current quantitative causal models rely on one or more assumptions: (1) the climate drivers persistently generate conflict, (2) the causal mechanisms have a linear relationship with the conflict generation parameter, and/or (3) there is sufficient data to inform the prior distribution. Yet, we know conflict drivers often excite a social transformation process which leads to violence (e.g., drought forces agricultural producers to join urban militia), but further climate effects do not necessarily contribute to further violence. Therefore, not only is this bifurcation relationship highly non-linear, there is also often a lack of data to support prior assumptions for high resolution modeling. Here, we aim to overcome the aforementioned causal modeling challenges by proposing a neural forward-intensity Poisson process (NFIPP) model. The NFIPP is designed to capture the potential non-linear causal mechanism in climate induced political violence, whilst being robust to sparse and timing-uncertain data. Our results span 20 recent years and reveal an excitation-based causal link between extreme climate events and political violence across diverse countries. Our climate-induced conflict model results are cross-validated against qualitative climate vulnerability indices. Furthermore, we label historical events that either improve or reduce our predictability gain, demonstrating the importance of domain expertise in informing interpretation.

Reconfigurable intelligent surfaces (RIS) have been shown to improve the secret key rate (SKR) for physical layer secret key generation (PL-SKG), by using the programmable phase shifts to increase reciprocal channel entropy. Most current studies consider the role of RIS on passive eavesdroppers (Eves) and overlook active attackers, especially the pilot spoofing attacks (PSA). For PSA in PL-SKG setups, this is implemented by Eve sending an amplified pilot sequence simultaneously with legitimate user Alice. With the increase of the spoofing amplifying factor, the channel probing results at Bob and Eve become similar, thereby enabling Eve to generate shared secret key with Bob. In this work, we analyze how RIS can positively or negatively affect the PL-SKG under pilot spoofing. To do so, we theoretically express the legitimate and spoofing SKRs in terms of the RIS phase shifts. Leveraging this, the closed-form theoretical upper bounds of both legitimate and spoofing SKRs are deduced, which lead to two further findings. First, the legitimate SKR upper-bound does not vary with RIS phase shift vector, but reduces drastically with the increase of the spoofing amplifying factor. This suggests the limited effect of RIS against PL-SKG spoofing, since the legitimate SKR has a hard limit, which cannot be surpassed by adjusting RIS phase and reflecting power, but can even be 0 with properly assigned spoofing amplifying factor. Second, the spoofing SKR upper-bound shows a large gap from the non-optimized SKR, which indicates a potential for RIS phase optimization.

Molecular communication and the Internet of Nanothings (IoNT) are emerging research hotspots recently, which show great potential in biomedical applications inside the human body. However, how to transmit information from inside body IoNTs to outside devices is seldomly studied. It is well known that the nervous system is responsible for perceiving the external environment and controlling the feedback signals. It exactly works like an interface between the external and internal environment. Inspired by this, this article proposes a novel concept that one can use the modified nervous system to communicate between IoNT devices andin vitroequipments. In our proposed system, nanomachines transmit signals via stimulating the nerve fiber by the electrode. Then, the signals transmit along nerve fibers and muscle fibers. Finally, they cause changes in surface electromyography (sEMG) signals, which can be decoded by the body surface receiver. This article presents the framework of this entire through-body communication system. Each part of the framework is also mathematically modeled. The error probability and mutual information of the system are derived from the communication theory perspective, which are evaluated and analyzed through numerical results. This study can pave the way for the connection of IoNTin vivoto external networks.

With significantly dynamic characteristics of the new aerial users, the emerging cellular-enabled unmanned aerial vehicle (UAV) communication paradigm raises great challenges to current research of UAV applications. As far as the robust channel allocation is concerned, the high mobility of UAV nodes and the unexpected disturbance of external environment would render most existing methods which rely on definite information and are vulnerable to dynamic environment, become less attractive or even invalid. In this paper, we particularly investigate a cellular-enabled mesh UAV network exploiting partially overlapping channels (POCs), and propose a distributed fuzzy space based learning scheme for POCs allocation to combat the dynamic environment. Rather than the perfect channel state information (CSI) assumption, the dynamic and uncertain CSI of UAVs is characterized by fuzzy number. On this basis, the allocation process can be implemented in a mapped fuzzy space. Integrating fuzzy-logic and game based learning, we formulate the problem of POCs assignment as a fuzzy payoffs game (FPG), and demonstrate the existence of fuzzy Nash equilibrium for our designed FPG. Then, with the derived priority vector in the fuzzy space, the equilibrium solution can be achieved by the proposed algorithm. Numerical simulations demonstrate the advantages of our new scheme.

In recent robotic applications, a critical need is to simultaneously detect communication (emission state) and estimate its trajectory. Whilst wireless sensor observations are useful, they are often uncertain due to the stochastic communication bursts and robot mobility. Over-sampling the information environment can incur excessive radio interference and energy usage. Therefore, one challenge is how to improve the efficiency of sensing under sparse and dynamic information, and make accurate inference on the robot's location. Here, we design a novel mixed detection and estimation (MDE) scheme to enhance both the accuracy and the efficiency by exploiting the mobility pattern correlations. Relying on a Markov state-space model, dynamic behaviors of robot's communication state and movement are formulated. A two-stage sequential Bayesian scheme, premised on random finite set (RFS), is developed to detect and estimate the involved unknown states. Specifically, in order to counteract the probability likelihood disappearance (caused by no information emission) and improve robustness to ambient noise, a sequential pre-filtering technique is designed, which can refine local observations and thereby significantly improve the accuracy of the system. We validate the proposed MDE scheme via both theoretical analysis and numerical simulations, demonstrating it would improve both the detection and estimation accuracy and efficiency.

Infrastructure monitoring is critical for safe operations and sustainability. Like many networked systems, water distribution networks (WDNs) exhibit both graph topological structure and complex embedded flow dynamics. The resulting networked cascade dynamics are difficult to predict without extensive sensor data. However, ubiquitous sensor monitoring in underground situations is expensive, and a key challenge is to infer the contaminant dynamics from partial sparse monitoring data. Existing approaches use multi-objective optimization to find the minimum set of essential monitoring points but lack performance guarantees and a theoretical framework. Here, we first develop a novel Graph Fourier Transform (GFT) operator to compress networked contamination dynamics to identify the essential principal data collection points with inference performance guarantees. As such, the GFT approach provides the theoretical sampling bound. We then achieve under-sampling performance by building auto-encoder (AE) neural networks (NN) to generalize the GFT sampling process and under-sample further from the initial sampling set, allowing a very small set of data points to largely reconstruct the contamination dynamics over real and artificial WDNs. Various sources of the contamination are tested, and we obtain high accuracy reconstruction using around 5%–10% of the network nodes for known contaminant sources, and 50%–75% for unknown source cases, which although larger than that of the schemes for contaminant detection and source identifications, is smaller than the current sampling schemes for contaminant data recovery. This general approach of compression and under-sampled recovery via NN can be applied to a wide range of networked infrastructures to enable efficient data sampling for digital twins.

Current state-of-the-art neural network explanation methods (e.g. Saliency maps, DeepLIFT, LIME, etc.) focus more on the direct relationship between NN outputs and inputs rather than the NN structure and operations itself, hence there still exists uncertainty over the exact role played by neurons. In this paper, we propose a novel neural network structure with Kolmogorov-Arnold Superposition Theorem based topology and Gaussian Processes based flexible activation function to achieve partial explainability of the neuron inner reasoning. The model feasibility is verified in a case study on binary classification of the banknotes.

Molecular communication (MC) is an emerging communication paradigm, where the information is carried via the patterns of molecules that are mainly governed by the diffusion process. Current MC literature concentrates on the time-domain analysis, while the signal analysis in other domains may facilitate the MC research. To this end, this paper performs the frequency-domain analysis by deriving the frequency response of the diffusion-based MC channels, manifesting an explicitly low-compass characteristic. The energy of the channel impulse response in the diffusion-based MC is also derived, and the corresponding bandwidth definition is proposed, which determines the sampling frequency for the one-shot diffusive channel impulse response in MC. The results in this work lay the foundation for the frequency-domain signal processing in diffusion-based MC channels.

Wireless traffic prediction is a fundamental enabler to proactive network optimisation in 5G and beyond. Forecasting extreme demand spikes and troughs is essential to avoiding outages and improving energy efficiency. However, current forecasting methods predominantly focus on overall forecast performance and/or do not offer probabilistic uncertainty quantification. Here, we design a feature embedding (FE) kernel for a Gaussian Process (GP) model to forecast traffic demand. The FE kernel enables us to trade-off overall forecast accuracy against peak-trough accuracy. Using real 4G base station data, we compare its performance against both conventional GPs, ARIMA models, as well as demonstrate the uncertainty quantification output. The advantage over neural network (e.g. CNN, LSTM) models is that the probabilistic forecast uncertainty can directly feed into decision processes in optimisation modules.

In recent years, molecular communication and internet of nanothings (IoNTs) are studied intensively for potential biomedical applications inside the human body. However, the communication through the human body, which could exchange data between the IoNTs and the outside human body, is seldomly studied. It is known that the neural system can send signals and receive feedback between the inside body and the outside body. Based on that, this paper proposes a novel concept that people can utilize and modify the existing neural system to transmit signals from the IoNTs to the outside. The nanomachine sends signals via stimulating the nerve fiber by electrodes. The signals propagate through nerves and generate surface electromyography (sEMG) signals which can be used as information received by body surface receiver. The framework of the entire through body communication system is presented and each part under the framework is modeled. The communication performance is evaluated. The study will pave the way for the implementation of connecting the in-body IoNTs with the outside networks.

Increasingly available high resolution geospatial wireless demand data is available from high density base stations, wireless localisation, and geo-tagged social media posts. Mapping the evolving spatiotemporal demand is critical for a wide range of infrastructure services, including future network planning and operations. However, monitoring geospatial data demand across a whole city is computationally and financially expensive. Here, we show that geospatial traffic demand data from both 0.4 million Twitter posts and 3.2 million base stations records can be compressed to spatially invariant points in London. These points correspond to major sources of human movement activity that act as either facilitators (e.g. public multi-modal transport hubs) or drivers (e.g. tourist attractions and business hubs). This demonstrates that by monitoring these spatially invariant critical points, we can obtain an accurate understanding of the human demand dynamics elsewhere in the city. Indeed, the operator which maps the dynamics between these points uncover the latent human connected dynamics embedded in complex urban ecosystems. We use both the latest signal processing technique of Graph Fourier Transform (GFT) and a AutoDecoder inspired deep learning neural network to demonstrate spatially invariant compression and both error-free and noisy recovery. These promising results show that we can exploit the connected structure of complex cities to dramatically reduce data monitoring.

UAV enabled terrestrial wireless networks enables targeted user-centric service provisioning to en-richen both deep urban coverage and target various rural challenge areas. However, UAVs have to balance the energy consumption of flight with the benefits of wireless capacity delivery via a high dimensional optimisation problem. Classic reinforcement learning (RL) cannot meet this challenge and here, we propose to use deep reinforcement learning (DRL) to optimise both aggregate and minimum service provisioning. In order to achieve a trusted autonomy, the DRL agents have to be able to explain its actions for transparent human-machine interrogation. We design a Double Dueling Deep Q-learning Neural Network (DDDQN) with Prioritised Experience Replay (PER) and fixed Q-targets to achieve stable performance and avoid over-fitting, offering performance gains over naive DQN algorithms. We then use a big data driven case study and found that UAVs battery size determines the nature of its autonomous mission, ranging from an efficient exploiter of one hotspot (100% reward gain) to a stochastic explorer of many hotspots (60-150% reward gain). Using a variety of telecom and social media data, we infer driving Quality-of-Experience (QoE) and Quality-of-Service (QoS) metrics that are in contention with UAV power and communication constraints. Our greener UAVs (30-40% energy saved) address both quantitative QoS and qualitative QoE issues. Partial interpretability in the reinforcement learning is achieved using data features extracted in the hidden layers, offering an initial step for explainable AI (XAI) connecting machine intelligence with human expertise.

Underwater communication is vital for a variety of defence and scientific purposes. Current optical and sonar based carriers can deliver high capacity data rates, but their range and reliability is hampered by heavy propagation loss. A vertical Molecular Communication via Buoyancy (MCvB) channel is experimentally investigated here, where the dominant propagation force is buoyancy. Sequential puffs representing modulated symbols are injected and after the initial loss of momentum, the signal is driven by buoyancy forces which apply to both upwards and downwards channels. Coupled with the complex interaction of turbulent and viscous diffusion, we experimentally demonstrate that sequential symbols exhibit a Gaussian velocity spatial distribution. Our experimental results use Particle Image Velocimetry (PIV) to trace molecular clusters and infer statistical characteristics of their velocity profile. We believe our experimental paper's results can be the basis for long range underwater vertical communication between a deep sea vehicle and a surface buoy, establishing a covert and reliable delay-tolerant data link. The statistical distribution found in this paper is akin to the antenna pattern and the knowledge can be used to improve physical security.

Multi-channel optimisation relies on accurate channel state information (CSI) estimation. Error distributions in CSI can propagate through optimisation algorithms to cause undesirable uncertainty in the solution space. The transformation of uncertainty distributions differs between classic heuristic and Neural Network (NN) algorithms. Here, we investigate how CSI uncertainty transforms from an additive Gaussian error in CSI into different power allocation distributions in a multi-channel system. We offer theoretical insight into the uncertainty propagation for both Water-filling (WF) power allocation in comparison to diverse NN algorithms. We use the Kullback-Leibler divergence to quantify uncertainty deviation from the trusted WF algorithm and offer some insight into the role of NN structure and activation functions on the uncertainty divergence, where we found that the activation function choice is more important than the size of the neural network.

Water-filling (WF) is a well-established iterative solution to optimal power allocation in parallel fading channels. Slow iterative search can be impractical for allocating power to a large number of OFDM sub-channels. Neural networks (NN) can transform the iterative WF threshold search process into a direct high-dimensional mapping from channel gain to transmit power solution. Our results show that the NN can perform very well (error 0.05%) and can be shown to be indeed performing approximate WF power allocation. However, there is no guarantee on the NN is mapping between channel states and power output. Here, we attempt to explain the NN power allocation solution via the Meijer G-function as a general explainable symbolic mapping. Our early results indicate that whilst the Meijer G-function has universal representation potential, its large search space means finding the best symbolic representation is challenging.

In emerging Internet-of-Nano-Thing (IoNT), information will be embedded and conveyed in the form of molecules through complex and diffusive medias. One main challenge lies in the long-tail nature of the channel response causing inter-symbol-interference (ISI), which deteriorates the detection performance. If the channel is unknown, existing coherent schemes (e.g., the state-of-the-art maximum a posteriori, MAP) have to pursue complex channel estimation and ISI mitigation techniques, which will result in either high computational complexity, or poor estimation accuracy that will hinder the detection performance. In this paper, we develop a novel high-dimensional non-coherent detection scheme for molecular signals. We achieve this in a higher-dimensional metric space by combining different non-coherent metrics that exploit the transient features of the signals. By deducing the theoretical bit error rate (BER) for any constructed high-dimensional non-coherent metric, we prove that, higher dimensionality always achieves a lower BER in the same sample space, at the expense of higher complexity on computing the multivariate posterior densities. The realization of this high-dimensional non-coherent scheme is resorting to the Parzen window technique based probabilistic neural network (Parzen-PNN), given its ability to approximate the multivariate posterior densities by taking the previous detection results into a channel-independent Gaussian Parzen window, thereby avoiding the complex channel estimations. The complexity of the posterior computation is shared by the parallel implementation of the Parzen-PNN. Numerical simulations demonstrate that our proposed scheme can gain 10dB in SNR given a fixed BER as 10-4, in comparison with other state-of-the-art methods.

Efficient neighbor discovery in vehicular ad hoc networks is crucial to a number of applications such as driving safety and data transmission. The main challenge is the high mobility of vehicles. In this paper, we proposed a new algorithm for quickly discovering neighbor node in such a dynamic environment. The proposed rapid discovery algorithm is based on a novel mobility prediction model using Kalman filter theory, where each vehicular node has a prediction model to predict its own and its neighbors' mobility. This is achieved by considering the nodes' temporal and spatial movement features. The prediction algorithm is reinforced with threshold triggered location broadcast messages, which will update the prediction model parameters, and improve the efficiency of the neighbor discovery algorithm. Through extensive simulations, the accuracy, robustness, and efficiency properties of our proposed algorithm are demonstrated. Compared with other methods of neighbor discovery, which are frequently used in HP-AODV, ARH, and ROMSG, the proposed algorithm needs the least overheads and can reach the lowest neighbor error rate while improving the accuracy rate of neighbor discovery. In general, the comparative analysis of different neighbor discovery methods in routing protocol is obtained, which shows that the proposed solution performs better than HP-AODV, ARH, and ROMSG.

Information embedded in the fluid dynamic properties undergo stochastic behaviour when propagating from transmitter (Tx) to receiver (Rx). This is due to the high dimensionality and continuous dynamic forces of the environment, which erodes the achievable mutual information. Quantifying the statistical noise distribution and mutual information with respect to the key fluid dynamic parameters is important to molecular communication. Here, we empirically study macro- scale molecular signal propagation using a planar laser induced fluorescence (PLIF) method. We first statistically characterize both the additive and jitter noise distribution. We show that mutual information is maximized under certain transmission strategies and varies with the receiver size. The statistical results can benefit future studies to analyse the impact on communication reliability, and design superior modulation coding schemes.

Recent advances in meta-materials offer the prospect of deploying smart surfaces, or intelligent reflecting surfaces (IRS), that can manipulate electromagnetic (EM) channels and expand their achievable capacity. In this paper, we investigate the programmable channel of multi-user multiple input and multiple output (MU-MIMO) transmission and beamforming using meta-surface with multiple elements. We first model the MU-MIMO channel, and the optimal solution is derived based on the proposed multi-user Linearly Constrained Minimum Variance (MU-LCMV) beamformer. The beam pattern is analyzed, which shows that a single set of optimal weights can form multiple interference-free beams with redundant beams to be formed to achieve the multi-stream MIMO transmission in typical configurations. The mathematical relationships of the beams are derived with different surface configurations. Extensive simulations verify the results. This work is fundamental and can potentially enhance any state-of-the-art wireless communication systems ranging from transceiver design, system and architecture design, network deployment, and self-organizing-network operations.

Molecular communication underpins biological system coordination across multiple spatial and temporal scales. Whilst significant research has focused on micro-scale diffusion dominated channels, far less is understood of macro-scale flow dominated channels. The latter introduces complex fluid dynamic forces, one of which is turbulent diffusion. Molecular Communication via Turbulent Diffusion (MCvTD) more accurately reflects realistic molecular channels in both pheromone signaling and chemical engineering. Current literature assumes linear combining between sequential molecular signals, but this assumption may not hold when turbulence is introduced. Here, we use computational fluid dynamics (CFD) simulation to show that sequential MCvTD signals do indeed linearly combine. This is a non-trivial and non-intuitive result and our conclusion allows the research field to leverage on existing linear combining signal analysis. To ensure robustness of our results, we test for the received signal strength and Inter-Symbol-Interference (ISI) under different concentrations, co-flow rate, and the information sequence. Also, we introduce a basis for the channel model in a way that for any k sequential signals in which k ≥ 4, by understanding the 1 ≤ k ≤ 3 signals and the last signal, we can represent the other signals. We expect these results to be useful to both molecular communication and biological signaling researchers.

On-line conflict can lead to and manifest itself in real-time emotional distress and radical behaviour. Whilst the topics are diverse, one of the most challenging and relatively under-explored topics is in real conflict landscapes. Many such places have high ethnolinguistic diversity with multiple principal and hybrid language groups. Here, we examine how on-line social network debates unfold for the recent Russian intervention in Ukraine. We use Natural Language Processing (NLP) to map the evolving Reddit social network, showing rich structural and sentiment signal evolution. Whilst relatively straightforward for well-resourced languages, NLP tasks for ethno-linguistic fictionalised areas with 22 languages including various lingua franca is challenging, and require proprietary methods. Yet, it is in this linguistic and real-world landscape that we uncover politically sensitive posts. We demonstrate how we can extract clear topic groups, echo chambers, and create the data that will enable us to track the sentiment of users and the role they play both within and between echo chambers.

In this paper, we consider diffusion-based molecular communication with and without drift between two static nano-machines. We employ type-based information encoding, releasing a single molecule per information bit. At the receiver, we consider an asynchronous detection algorithm which exploits the arrival order of the molecules. In such systems, transposition errors fundamentally undermine reliability and capacity. Thus, in this paper, we study the impact of transpositions on the system performance. Toward this, we present an analytical expression for the exact bit error probability (BEP) caused by transpositions and derive computationally tractable approximations of the BEP for diffusion-based channels with and without drift. Based on these results, we analyze the BEP when background is not negligible and derive the optimal bit interval that minimizes the BEP. Simulation results confirm the theoretical results and show the error and goodput performance for different parameters such as block size or noise generation rate.

Dynamic spectrum sharing will facilitate the interference coordination in device-to-device (D2D) communications. In the absence of network level coordination, the timing synchronization among D2D users will be unavailable, leading to inaccurate channel state estimation and device detection, especially in time-varying fading environments. In this paper, we design an asynchronous device detection/discovery framework for cognitive-D2D applications, which acquires timing drifts and dynamical fading channels when directly detecting the existence of a proximity D2D device (e.g. or primary user). To model and analyze this, a new dynamical system model is established, where the unknown timing deviation follows a random process, while the fading channel is governed by a discrete state Markov chain. To cope with the mixed estimation and detection problem, a novel sequential estimation scheme is proposed, using the conceptions of statistic Bayesian inference and random finite set. By tracking the unknown states (i.e. varying time deviations and fading gains) and suppressing the link uncertainty, the proposed scheme can effectively enhance the detection performance. The general framework, as a complimentary to a network-aided case with the coordinated signaling, provides the foundation for development of flexible D2D communications along with proximity-based spectrum sharing.

Molecular communications convey information via diffusion propagation. The inherent long-tail channel response causes severe inter-symbol interference, which may seriously degrade signal detection performances. Traditional linear signal detection techniques, unfortunately, require both high complexity and a high signal-to-noise (SNR) ratio to operate. In this paper, we proposed a new non-linear signal processing paradigm inspired by the biological systems that achieves low-complexity signal detection even in low SNR regimes. First, we introduce a stochastic resonance inspired non-linear filtering scheme for molecular communications, and show that it significantly improves the output SNR by transforming the noise energy into useful signals. Second, we design a novel non-coherent detector by exploiting the transient features of molecular signaling, which are independent of channel response and involves only lowcomplexity linear summation operations. Numerical simulations show that this new scheme can improve the detection performance remarkably (approx. 7dB gain), even when compared against linearly optimal coherent methods. This is one of the first attempts to demodulate molecular signals from an entirely biological point of view, and the designed non-linear noncoherent paradigm will provide significant potential to the design and future implementation of nano-systems in noisy biological environments.

In molecular communication, the heavy tail nature of molecular signals causes inter-symbol interference (ISI). Because of this, it is difficult to decrease symbol periods and achieve high data rate. As a probable solution for ISI mitigation, enzymes were proposed to be used since they are capable of degrading ISI molecules without deteriorating the molecular communication. While most prior work has assumed an infinite amount of enzymes deployed around the channel, from a resource perspective, it is more efficient to deploy a limited amount of enzymes at particular locations and structures. This paper considers carrying out such deployment at two structures-around the receiver (Rx) and/or the transmitter (Tx) site. For both of the deployment scenarios, channels with different system environment parameters, Tx-to-Rx distance, size of enzyme area, and symbol period, are compared with each other for analyzing an optimized system environment for ISI mitigation when a limited amount of enzymes are available. \n

Device‐to‐device (D2D) communications are widely seen as an efficient network capacity scaling technology. The co‐existence of D2D with conventional cellular (CC) transmissions causes unwanted interference. Existing techniques have focused on improving the throughput of D2D communications by optimising the radio‐resource management and power allocation. However, very little is understood about the impact of the route selection of the users and how optimal routing can reduce interference and improve the overall network capacity. In fact, traditional wisdom indicates that minimising the number of hops or the total path distance is preferable. Yet, when interference is considered, the authors show that this is not the case. In this study, they show that by understanding the location of the user an interference‐aware‐routing (IAR) algorithm can be devised. They propose an adaptive IAR algorithm that on average achieves a 30% increase in hop distance, but can improve the overall network capacity by 50% whilst only incurring a minor 2% degradation to the CC capacity. The analysis framework and the results open up new avenues of research in location‐dependent optimisation in wireless systems, which is particularly important for increasingly dense and semantic‐aware deployments.

In this paper we address a new problem of predicting affect and well-being scales in a real-world setting of heterogeneous, longitudinal and non-synchronous textual as well as non-linguistic data that can be harvested from on-line media and mobile phones. We describe the method for collecting the heterogeneous longitudinal data, how features are extracted to address missing information and differences in temporal alignment, and how the latter are combined to yield promising predictions of affect and well-being on the basis of widely used psychological scales. We achieve a coefficient of determination (R^2) of 0.71-0.76 and a correlation coefficient of 0.68-0.87 which is higher than the state-of-the art in equivalent multi-modal tasks for affect.

Information delivery using chemical molecules is an integral part of biology at multiple distance scales and has attracted recent interest in bioengineering and communication. The collective signal strength at the receiver (i.e., the expected number of observed molecules inside the receiver), resulting from a large number of transmitters at random distances (e.g., due to mobility), can have a major impact on the reliability and efficiency of the molecular communication system. Modeling the collective signal from multiple diffusion sources can be computationally and analytically challenging. In this paper, we present the first tractable analytical model for the collective signal strength due to randomly-placed transmitters, whose positions are modelled as a homogeneous Poisson point process in three-dimensional (3D) space. By applying stochastic geometry, we derive analytical expressions for the expected number of observed molecules at a fully absorbing receiver and a passive receiver. Our results reveal that the collective signal strength at both types of receivers increases proportionally with increasing transmitter density. The proposed framework dramatically simplifies the analysis of large-scale molecular systems in both communication and biological applications.

This work studies the impact of time-synchronization in molecular timing (MT) channels by analyzing three different modulation techniques. The first requires transmitter-receiver synchronization and is based on modulating information on the release timing of information particles. The other two are asynchronous and are based on modulating information on the relative time between two consecutive releases of information particles using indistinguishable or distinguishable particles. All modulation schemes result in a system that relate the transmitted and the received signals through an additive noise, which follows a stable distribution. As the common notion of the variance of a signal is not suitable for defining the power of stable distributed signals (due to infinite variance), we derive an expression for the geometric power of a large class of stable distributions, and then use this result to characterize the geometric signal-to-noise ratio (G-SNR) for each of the modulation techniques. In addition, for binary communication, we derive the optimal detection rules for each modulation technique. Numerical evaluations indicate that the bit error rate (BER) is constant for a given G-SNR, and the performance gain obtained by using synchronized communication is significant. Yet, it is also shown that by using two distinguishable particles per bit instead of one, the BER of the asynchronous technique can approach that of the synchronous one.

Energy‐efficient wireless communication is important for wireless devices with a limited battery life and cannot be recharged. In this study, a bit allocation algorithm to minimise the total energy consumption for transmitting a bit successfully is proposed for a two‐way orthogonal frequency‐division multiplexing relay system, whilst considering the constraints of quality‐of‐service and total transmit power. Unlike existing bit allocation schemes, which maximise the energy efficiency (EE) by measuring ‘bits‐per‐Joule’ with fixed bidirectional total bit rates constraint and no power limitation, their scheme adapts the bidirectional total bit rates and their allocation on each subcarrier with a total transmit power constraint. To do so, they propose an idea to decompose the optimisation problem. The problem is solved in two general steps. The first step allocates the bit rates on each subcarrier when the total bit rate of each user is fixed. In the second step, the Lagrangian multipliers are used as the optimisation variants, and the dimension of the variant optimisation is reduced from 2 N to 2, where N is the number of subcarriers. They also prove that the optimal point is on the bounds of the feasible region, thus the optimal solution could be searched through the bounds.

Molecular communications via diffusion (MCvD) represents a relatively new area of wireless data transfer with especially attractive characteristics for nanoscale applications. Due to the nature of diffusive propagation, one of the key challenges is to mitigate inter-symbol interference (ISI) that results from the long tail of channel response. Traditional coherent detectors rely on accurate channel estimations and incur a high computational complexity. Both of these constraints make coherent detection unrealistic for MCvD systems. In this paper, we propose a low-complexity and noncoherent signal detector, which exploits essentially the local convexity of the diffusive channel response. A threshold estimation mechanism is proposed to detect signals blindly, which can also adapt to channel variations. Compared to other noncoherent detectors, the proposed algorithm is capable of operating at high data rates and suppressing ISI from a large number of previous symbols. Numerical results demonstrate that not only is the ISI effectively suppressed, but the complexity is also reduced by only requiring summation operations. As a result, the proposed noncoherent scheme will provide the necessary potential to low-complexity molecular communications, especially for nanoscale applications with a limited computation and energy budget.

Device-to-device (D2D) communications utilizing licensed spectrum have been considered a promising technology to improve cellular network spectral efficiency and offload local traffic from cellular base stations (BSs). In this paper, we develop Iunius: a peer-to-peer (P2P) system based on harvesting data in a community utilizing multi-hop D2D communications. The Iunius system optimizes D2D communications for P2P local file sharing, improves user experience, and offloads traffic from the BSs. The Iunius system features cross-layer integration of: 1) a wireless P2P protocol based on the BitTorrent protocol in the application layer; 2) a simple centralized routing mechanism for multi-hop D2D communications; 3) an interference cancellation technique for conventional cellular (CC) uplink communications; and 4) a radio resource management scheme to mitigate the interference between CC and D2D communications that share the cellular uplink radio resources while maximizing the throughput of D2D communications. Simulation results show that the proposed Iunius system can increase the cellular spectral efficiency, reduce the traffic load of BSs, and improve the data rate and energy saving for mobile users.

In this work, we consider diffusion-based molecular communication timing channels. Three different timing channels are presented based on three different modulation techniques, i.e., i) modulation of the release timing of the information particles, ii) modulation on the time between two consecutive information particles of the same type, and iii) modulation on the time between two consecutive information particles of different types. We show that each channel can be represented as an additive noise channel, where the noise follows one of the subclasses of stable distributions. We provide expressions for the probability density function of the noise terms, and numerical evaluations for the probability density function and cumulative density function. We also show that the tails are longer than Gaussian distribution, as expected.

Wave-based signals have been successful in reliably and efficiently transferring data between two or more well defined points (e.g., known location area). However, it is challenged when the transmitter is hidden and the receivers are absent. Essentially, the transmitter and the receivers have no location knowledge of each other. We demonstrate that unlike wave-based transmissions, the total molecular energy doesn't monotonically degrade as a function of time. This paper uses a bio-inspired method of communicating data from a hidden transmitter to a group of absent receivers. A specialized molecular communication system is designed, including how to embed vital location information in the structure of a heterogeneous biochemical molecule. Like message in a bottle, there is a growing probability of receiving the location message over a period of several years. The only caveat is that there is an initial delay of a few hours to days, depending on the proximity of the rescue team to the crash site. This will provide an attractive alternative to current wave-based communications for delay-tolerant crash recovery.

In molecular communication information is conveyed through chemical signals. In this work, we have considered a novel communication scheme where information is encoded in chemical barcodes, through use of persistent chemical tags. We have assumed that this information is already encoded in the environment, and we have devised a robotic platform for reading the chemical tag. We have performed many experiments to find the optimal encoding scheme and an algorithm for reading and decoding the chemically tagged information. We have demonstrated that chemical tags can be decoded using simple algorithms and inexpensive, off-the-shelf sensors. Finally, we have evaluated and presented the bit error rate performance of our devised algorithm.

Multi-hop cooperative communications have been considered to increase the coverage range of device-to-device (D2D) communications underlaying cellular networks. Whilst network coding (NC) has been proven as an efficient technique to improve the throughput of ad-hoc networks, it has not been adapted to D2D networks. In this paper, we propose a NC-D2D system, in which we apply NC into the multi-hop D2D communications to enhance its throughput. We then develop a radio resource management (RRM) mechanism that jointly optimises the power control and subchannel allocation for NC-D2D communications. The proposed RRM maximises the throughput of NC-D2D communications while guaranteeing the quality of service (QoS) requirements of conventional cellular (CC) UEs. We also investigate how caching capabilities at relay nodes would impact the system performance. Simulation results show that by employing NC and our proposed RRM mechanism, the throughput of multi-hop D2D communications is significantly improved and the time required for exchanging large data files between a D2D pair is dramatically reduced as compared to existing multi-hop D2D schemes.

This paper presents the results of a joint investigation conducted between academia and industry. The investigation focused on modelling and reducing the energy expenditure of a 4G LTE network in a London case-study area, using data sets from an existing 3G network deployment. In the first part of the paper, different multi-cell network modelling approaches were compared, including: stochastic and linear geometry, hexagonal cell layout with wrap- around, and realistic network topology. The impact of terrain and clutter is also examined. It was found that they had a similar performance profile (80%) correlation, and a back-off factor to translate theoretical to measured results can account for most of the differences. The second part of the paper focuses on effective techniques that can be used to reduce energy consumption. Sleep mode and heterogeneous networks are combined and an ERG of 15-46% is achieved.

This paper addresses the dichotomy that exists in the analysis of wireless channels, which exists between employing: the tractable Shannon capacity bound and the accurate simulated capacity of modulation and coding schemes (MCSs). By considering the effects of mutual information saturation in modulation schemes and the efficiency of error correction codes, the paper proposes a new tractable capacity expression that can accurately represent any MCS and adaptive combinations. To demonstrate this methodology, the paper considers the adaptive MCS capacity for the Long-Term-Evolution (LTE) system. The potential benefit is that system-level optimization can employ the more accurate capacity expression. In the second part of the paper, the proposed capacity expression is applied to optimize power allocation to the parallel channels of a MIMO system. The results show that when mutual information saturation occurs, neither the water-filling nor channel inversion schemes are optimal. In fact the optimal power allocation is a combination of the two aforementioned schemes, switching adaptively between their relative merits.

In cellular networks, balancing the throughput among users is important to achieve a uniform Quality-of-Service (QoS). This can be accomplished using a variety of cross-layer techniques. In this paper, the authors investigate how the down-tilt of base-station (BS) antennas can be adjusted to maximize the user throughput fairness in a heterogeneous network, considering the impact of both a dynamic user distribution and capacity saturation of different transmission techniques. Finding the optimal down-tilt in a multi-cell interference-limited network is a complex problem, where stochastic channel effects and irregular antenna patterns has yielded no explicit solutions and is computationally expensive. The investigation first demonstrates that a fixed tilt strategy yields good performances for homogeneous networks, but the introduction of HetNet elements adds a high level of sensitivity to the tilt dependent performance. This means that a HetNet must have network-wide knowledge of where BSs, access-points and users are. The paper also demonstrates that transmission techniques that can achieve a higher level of capacity saturation increases the optimal down-tilt angle. A distributed reinforcement learning algorithm is proposed, where BSs do not need knowledge of location data. The algorithm can achieve convergence to a near-optimal solution rapidly (6-15 iterations) and improve the throughput fairness by 45-56% and the energy efficiency by 21-47%, as compared to fixed strategies. Furthermore, the paper shows that a tradeoff between the optimal solution convergence rate and asymptotic performance exists for the self-learning algorithm.

This study considers the theoretical and realistic performance of a traffic aware sleep mode mechanism in the cellular network. The study devises a sleep mode mechanism that maximises the energy saving of the network, while maintaining both the user throughput and reliability performance. The analysis combines recent developments in stochastic geometry and packet data modelling, as well as a simulation of a real cellular network. The main contribution is to show that accurate theoretical modelling of realistic network data can allow deterministic sleep mode triggers to be devised. This is shown to be highly applicable to a real network, reducing energy consumption by 15–60% throughout a day and the annual operational expenditure by 4%.

This paper addresses the challenge of how to reduce the energy consumption of a multi-cell network under a dynamic traffic load. The body of investigation first shows that the energy reduction upper-bound for transmission improving techniques is hardware-limited, and the bound for infrastructure reduction is capacity-limited. The paper proposes a novel cell expansion technique, where the coverage area of cells can expand and contract based on the traffic load. This is accomplished by switching off low load cell-sites and compensating for the coverage loss by expanding the neighboring cells through antenna beam tilting. The multi-cell coordination is resolved by using either a centralized controller or a distributed self-organizing-network (SON) algorithm. The analysis demonstrates that the proposed distributed algorithm is able to exploit flexibility and performance uncertainty through reinforced learning and improves on the centralized solution. The combined energy saving benefit of the proposed techniques is up to 50% compared to a reference deployment and 44% compared with alternative state-of-the-art dynamic base-station techniques.

This paper presents closed-form capacity expressions for interfere-limited relay channels. Existing theoretical analysis has primarily focused on Gaussian relay channels, and the analysis of interference-limited relay deployment has been confined to simulation based approaches. The novel contribution of this paper is to consolidate on these approaches by proposing a theoretical analysis that includes the effects of interference and capacity saturation of realistic transmission schemes. The performance and optimization results are reinforced by matching simulation results. The benefit of this approach is that given a small set of network parameters, the researcher can use the closed-form expressions to determine the capacity of the network, as well as the deployment parameters that maximize capacity without committing to protracted system simulation studies. The deployment parameters considered in this paper include the optimal location and number of relays, and resource sharing between relay and base-stations. The paper shows that the optimal deployment parameters are pre-dominantly a function of the saturation capacity, pathloss exponent and transmit powers. Furthermore, to demonstrate the wider applicability of the theoretical framework, the analysis is extended to a multi-room indoor building. The capacity improvements demonstrated in this paper show that deployment optimization can improve capacity by up to 60% for outdoor and 38% for indoor users. The proposed closed-form expressions on interference-limited relay capacity are useful as a framework to examine how key propagation and network parameters affect relay performance and can yield insight into future research directions.

Wireless communications has been recognized as a key enabler to the growth of the future economy. There is an unprecedented growth in data volume and the associated energy consumption. The challenge addressed in this paper is how to meet the growth in data traffic, whilst reducing both the cost and energy consumed. The paper shows that small cell deployments can significantly reduce energy consumption (30%), but increase the network cost (14%). The novel characterization of the tradeoff between Capacity, Energy and Cost (CEC) is of importance to researchers and operators.

This paper analyzes the fundamental tradeoff between the total power consumption and the downlink capacity in an interference limited LTE network. In order to achieve significant energy savings, a re-deployment of the cell-sites is needed. The paper employs a novel power consumption and capacity tradeoff to assist the process of re-deployment and show that up to 75% energy can be saved. The implication of this paper's results has a significant impact both on commercial revenue and the environment by reducing up to 24 power plants world wide.

This paper addresses the challenge of designing a cellular network that can meet a dynamic range of offered traffic loads at a low energy level. Cellular networks are conventionally deployed to meet a high traffic load and can be energy inefficient at lower loads. The paper proposes a novel cell expansion technique that can reduce total energy consumption by up to 46% depending on the offered load. Dynamic cell expansion switches off a certain pattern of cell-sites in accordance with traffic load and allows neighbouring cells to compensate by expanding their coverage. A key novelty is achieving this via creating inner and outer cell regions using vertical sectorization and adaptively tilting outer cell antennas to expand and contract cell coverage. Furthermore, a management service is proposed to handle contention between competing cells. The gains achieved compared to existing techniques is 22% higher and the results of this paper can have a profound impact both on the operators' revenue and global environment, reducing up to 11 power plants world wide.

Inter-cell interference (ICI) is a key limiting factor to the general performance of a multi-cell multi-user radio access network. The channel quality of cell edge users is greatly impaired by ICI owing to the fact that cell edge users are furthest away from the their serving base station and closest to the interfering base stations. As a result the Quality of Service (QoS) and energy efficiency of the E-UTRAN is primarily dependant on the cell edge users. Firstly, we propose a new Time Domain Packet Scheduling criterion that endeavours to reduce the variation in the energy performance, of the users, in a temporal sense. The proposed criteria aims to strike a balance between two user prioritisation criteria that result in energy performance at the two extremes of the energy consumption range. The paper shows that this improves the mean energy efficiency of the E-UTRAN. Secondly, we introduce an energy optimisation algorithm to complement the Time Domain Packet Scheduler. The new energy aware packet scheduling criteria is compared against the established throughput based proportional fair scheduler with uniform power allocation and is shown to produce 20% Energy Reduction Gains (ERG) without compromising the spectral efficiency and QoS performance.

The objective of this paper is to provide the methodology and results that is used to evaluate a scalable energy performance comparison of LTE-femtocells and 802.11n with both SISO and MIMO antenna configurations. The performance is derived in a multi-user and multi-cell wireless communication system. A system level LTE-femtocell simulator has been developed and an analytical model to evaluate 802.11n network performances has been proposed. It is shown that 1 Femtocell Access Point consistently perform better than 1 802.11n Access Point. This may be explained by the centralised resource allocation and channel access methods used by femtocells compared to distributed methods and CSMA/CA used by 802.11 networks. Generally, SISO deployment is more energy efficient than Alamouti MIMO 2×2.

The purpose of this paper is to show that the indoor downlink capacity and energy efficiency can be significantly improved by optimising the indoor location of femtocell. This body of investigation is done in the presence of outdoor interference and the simulation results are backed up by a novel theoretical framework employing convex optimisation. Moreover, this optimisation problem has been demonstrated to be meaningful in the context of considering capacity saturation of realistic modulation and coding schemes. The results yield insight into the relationship between the outdoor and indoor aspects of the cellular network, as well as the propagation parameters. The paper shows that a mean capacity improvement of up to 20% can be made with optimal placement, which translates to an operational energy reduction of 8%. Moreover, it has been shown that the optimisation does not significantly degrade the performance of outdoor network. The global impact of this work is that 1.6 TWh can be saved globally, which amounts to the energy produced by two 1000 MW power plants.

Inter-cell interference (ICI) is a key limiting factor to the radio frequency (RF) energy performance of a multi-cell multi-user radio access network (RAN). The channel quality of the cell edge users is greatly impaired by ICI owing to the fact that cell edge users are furthest away from their serving base stations (BSs) and closest to the interfering BSs. Consequently the BS is compelled to allocate more physical resource blocks [PRBs (In LTE a PRB spans 12 sub-carriers each with a bandwidth of 15 kHz over a 0.5 ms time slot)] to the cell edge users in order to meet their quality-of-service (QoS) targets. The study proposes a novel ICI management technique that mitigates the effects of ICI through a sequential game play between cells in the E-UTRAN [Evolved Universal Mobile Telecommunications Systems (UMTS) terrestrial radio access network] based on the instantaneous cell offered load. The proposed technique is shown to produce greater user channel quality improvements of 4 dB greater than the state-of-the-art ICI management techniques. The channel quality improvements result in a utilisation of less PRBs, RF and radio head energy which translate to energy reduction gains of 34 and 45% at low- and high-offered loads, respectively. In addition the proposed scheme does not require a central processing entity such as a radio network controller and can be implemented in unplanned self-organising networks.

We propose a novel relationship that characterizes the fundamental tradeoff between the capacity and the outage performance of a multi-user cooperative network. As far as we are aware, no such tradeoff has previously been explicitly investigated in cooperative networks that utilize realistic modulation and channel codes. We show that increased repetition cooperation maps to regions of increased transmission reliability but degrades the system capacity. Careful system design is essential to balance reliability and capacity performance and this can only be achieved through optimization with the aid of these tradeoff curves. We present both theoretical and simulation results on the tradeoffs. Furthermore, we also optimize the tradeoff relationship by employing closed-form optimized partner selection and power allocation schemes, and show that large gains can be made in both outage performance and capacity compared with blind cooperation. The proposed methodology presented in this paper can also be extended to address different system scenarios and performance metrics.

Distributed spatial diversity systems, such as multi-relay and multi-user networks, have drawn significant attention from the research community. However, their feasibility in a practical environment remains an open concern. Most notably, how to optimize cooperation between multiple users with individual interests and how practical systems issues can erode diversity gains. Whilst existing information theoretical analysis may yield insightful bounds, they provide an inadequate solution for optimal power allocation in realistic systems due to the mutual information saturation of non-Gaussian inputs. In our work, we use feasible modulation and error correction codes to implement a system and demonstrate how multiple users cannot only improve their performance through cooperation, but also optimize their performance through power allocation with imperfect feedback. We do so, by considering an evolution game theoretic (EGT) approach, whereby the status-quo between users change with each decision.

We consider decode-and-forward cooperative networks and we derive analytical expressions as well as tractable asymptotic approximations for the outage probability of a network node. Our analysis sheds more light on the interplay between the channel conditions, the network size and the adopted transmission scheme, and provides a useful tool for the design of cooperative networks.

In this paper, we consider cooperative networks of users that implement an unselfish protocol to decode and forward packets of their partners. In unselfish cooperation, a user that has successfully retrieved the source data of a partner, will unconditionally assist that partner in its transmission to the destination. We use a threshold-based model to derive an analytical expression for the end-to-end packet error probability and we validate our approach comparing theoretical to simulation results. Furthermore, we explore the interplay between various network parameters, such as the number of users, the transmission scheme and the quality of the interconnecting channels, and we briefly discuss the impact of power allocation on the overall performance.

We derive an adaptive power control method for a collaborative network utilizing partner selection that aims to minimize the frame error rate (FER). We model a decode-and-forward (DF) collaborative network under block fading conditions, which contains M independent users utilizing codes, whose performance can be expressed by a signal to noise (SNR) threshold, such as turbo codes. We show that partner selection can reduce system complexity and power allocation can improve the FER performance. We use both a search method as well as a convex deterministic method to demonstrate our power allocation scheme. This research extends other work in which adaptive power allocation is only applied to limited scenarios. We conclude that power control can greatly benefit a DF collaborative network in a block fading environment.

We derive theoretical bit and frame error rate expressions for decode-and-forward (DF) collaborative networks containing M users, employing a variety of block codes over a Rayleigh block faded channel. With the aid of these expressions, we explore the performance of adaptive power control for such systems. This extends previous work by optimizing power allocation for all relay and direct channels. We further extend our work to a variety of cooperation mechanisms and we conclude that power control can greatly benefit a DF collaborative network in a fading environment.