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In wireless networks, consumer experience is important for both short monitoring of the Quality of Experience (QoE) as well as long term customer retainment. Current 4G and 5G networks are not equipped to measure QoE in an automated way, and experience is still reported through traditional customer care and drive-testing. In recent years, large-scale social media analytics has enabled researchers to gather statistically significant data on consumer experience and correlate them to major events such as social celebrations or significant network outages. However, the translational pathway from languages to topic-specific emotions (e.g., sentiment) to detecting anomalies in QoE is challenging. This challenge lies in two issues: (1) the social experience data remains sparsely distributed across space, and (2) anomalies in experience jump across sub-topic spaces (e.g., from data rate to signal strength). Here, we solved these two challenges by examining the spectral space of experience across topics using federated learning (FL) to identify anomalies. This can inform telecom operators to pay attention to potential network demand or supply issues in real time using relatively sparse and distributed data. We use real social media data curated for our telecommunication projects across London and the United Kingdom to demonstrate our results. FL was able to achieve 74-92% QoE anomaly detection accuracy, with the benefit of 30-45% reduce data transfer and preserving privacy better than raw data transfer. European Union funding: 778305

The design of adequate countermeasures against drone's threats needs accurate trajectory estimation to avoid economic damage to the aerospace industry and national infrastructure. As trajectory estimation algorithms need highly accurate physics informed models or off-line learning algorithms, radical innovation in on-line trajectory inference is required. In this paper, a novel drone's physics informed trajectory inference algorithm is proposed. The algorithm constructs a physic informed model and infers the drone's trajectories simultaneously using a closed-loop output error architecture. Two different approaches are proposed based on a physics structure and an admittance filtering model which considers: i) full states measurements and ii) partial states measurements. Stability and convergence of the proposed schemes are assessed using Lyapunov stability theory. Simulations studies are carried out to demonstrate the scope and high inference capabilities of the proposed approach. Royal Academy of Engineering. Office of the Chief Science Adviser for National Security under the UK Intelligence Community Postdoctoral Research Fellowship programme.

Global Navigation Satellite System (GNSS) signal can be blocked when flight vehicles operate in challenging environments such as indoor or adversarial environments. While multi-UAVs are teamed during flight, cooperative localization becomes available to tackle this challenge. Multidimensional Scaling (MDS) method has been well studied for cooperative localization of Wireless Sensor Network (WSN) based on radio frequency (RF) measurement. When noise RF measurement model is lacking, conventional weighted MDS method represents confidence with the measurements by assigning weights relying on distance information between each pair of nodes. In order to process non-distance RF measurements, we present an improved weighted MDS method which applies a novel weighting scheme. In this article, the proposed method conducts velocity estimation for multi-UAV system based on odometry and Frequency Difference of Arrival (FDOA) measurements. Furthermore, an extended Kalman Filter (EKF) algorithm is applied to refine the initial estimation of the MDS method and derive position estimation. Finally, numerical experiments demonstrate the robustness and accuracy of the adaptive MDS-EKF refinement framework for multi-UAV system localization in an unknown dynamic environment lacking measurement noise information. UK Government Foreign, Commonwealth and Development Office: Chevening Scholarship. European Union funding: 778305.

Fall-related injuries at the workplace account for a fair percentage of the global accident at work claims according to Health and Safety Executive (HSE). With a significant percentage of these being fatal, industrial and maintenance workshops have great potential for injuries that can be associated with slips, trips, and other types of falls, owing to their characteristic fast-paced workspaces. Typically, the short turnaround time expected for aircraft undergoing maintenance increases the risk of workers falling, and thus makes a good case for the study of more contemporary methods for the detection of work-related falls in the aircraft maintenance environment. Advanced development in human pose estimation using computer vision technology has made it possible to automate real-time detection and classification of human actions by analyzing body part motion and position relative to time. This paper attempts to combine the analysis of body silhouette bounding box with body joint position estimation to detect and categorize in real-time, human motion captured in continuous video feeds into a fall or a non-fall event. We proposed a standard wide-angle camera, installed at a diagonal ceiling position in an aircraft hangar for our visual data input, and a three-dimensional convolutional neural network with Long Short-Term Memory (LSTM) layers using a technique we referred to as Region Key point (Reg-Key) repartitioning for visual pose estimation and fall detection.

This paper explores multi-person pose estimation for reducing the risk of airborne pathogens. The recent COVID-19 pandemic highlights these risks in a globally connected world. We developed several techniques which analyse CCTV inputs for crowd analysis. The framework utilised automated homography from pose feature positions to determine interpersonal distance. It also incorporates mask detection by using pose features for an image classification pipeline. A further model predicts the behaviour of each person by using their estimated pose features. We combine the models to assess transmission risk based on recent scientific literature. A custom dashboard displays a risk density heat-map in real time. This system could improve public space management and reduce transmission in future pandemics. This context agnostic system and has many applications for other crowd monitoring problems.

One of the most used Position, Navigation, and Timing (PNT) technology of the 21st century is Global Navigation Satellite Systems (GNSS). GNSS signals are affected by urban canyons that limit Line-Of-Sight (LOS) and increase position ambiguity. Smart cities are expected to adopt autonomous Unmanned Aerial Vehicles (UAV) operations for critical missions such as the transportation of organs that are time-sensitive. Therefore, techniques to mitigate Non-Line-Of-Sight (NLOS) interference are required for improved positioning accuracy. This paper proposes a Gated Recurrent Unit-based (GRU) multipath detection algorithm that uses pseudorange, ephemerides, Doppler shift, Carrier-To-Noise Ratio (C/N0), and elevation data from each satellite to determine whether multipath is present. Signals from the satellite classified as multipath are then flagged and ignored for Position, Velocity, and Timing (PVT) calculations until they are deemed as LOS. The classification algorithm is developed and tested on Spirent GSS7000 to generate GNSS Radio Frequency (RF). OKTAL-SE Sim3D is used to simulate urban canyon environments in which signals propagate from the satellite to the receiver. RF signals are then transmitted to a Ublox F9P GNSS receiver that can receive GPS and GLONASS signals which are processed to output PVT information. The data collected is used to train the GRU to classify received signals as no multipath or multipath. From performance evaluation, GRU outperforms decision tree, K-Nearest Neighbor (KNN) classifiers, and Support Vector Machines (SVM). Furthermore, comparing GRU with SVM, a 50% increase in accuracy is observed with a 95% error of 0.85 m for GRU compared to 1.78 m for SVM.

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.

Molecular signaling is ubiquitous across scales in nature and finds useful applications in precision medicine and heavy industry. Characterizing noise in communication systems is essential to understanding its information capacity. To date, research in molecular nano communication (MNC) primarily considers the molecular dynamics within the medium, where various forms of stochastic effects generate noise. However, in many real-world scenarios, external effects can also influence molecular dynamics and cause noise. Here, the noise due to the temperature fluctuations from incident electromagnetic (EM) radiation is considered, with applications ranging from cell signaling to chemical engineering. EM radiation and subsequent molecular absorption cause temperature fluctuations which affect molecular dynamics and can be considered as an exogenous noise source for MNC. In this paper, the probability density function of the radiation absorption noise (RAN) is analyzed and to demonstrate applicability, we include characteristics of different tissues of the human body. Furthermore, the closed-form expression of error probability (EP) for MNC under the radiation noise is derived. Numerical analysis is demonstrated on different tissues of the human body: skin, brain, and blood, as well as the polarization factor of incident EM radiation is demonstrated. The coupling relationship between the radiation frequency and the intrinsic impedance of the human body on the PDF of radiation absorption noise is presented. This is useful for understanding how mutual information changes with external radiation sources.

Precise timing over timestamped packet exchange communication is an enabling technology in the mission-critical industrial Internet of Things, particularly when satellite-based timing is unavailable. The main challenge is to ensure timing accuracy when the clock synchronisation 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 synchronisation between an arbitrary pair of clocks is modelled 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 modelled as disturbances. A static output controller is adopted to adjust the drifting clock. The H∞ robust control design solution is proposed to guarantee that the ratio between the modulus of synchronisation precision and the magnitude of the disturbances is always less than a given value. Therefore, the proposed time synchronisation protocol is robust against the disturbances, which means that the impacts of drifting frequency and delays on the synchronisation accuracy are limited. The one-hour experimental results demonstrate that the proposed R-PkCOs protocol can realise time synchronisation with the precision of six microseconds 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.

In this paper we show how Shannon entropy, an intuitive and versatile measure of uniformity of a probability distribution, can be adapted to quantify the heterogeneity of land use and population density in and around human settlements. Using a raster data set of estimates of historical population density and land use, we show that local entropy measures capture salient aspects of the evolution of urban systems. Through the case studies of the UK, India, and Italy we reconnect the temporal evolution of the measures to some of the main socioeconomic and political changes and epidemic events these countries went through during the last three centuries. We argue that the diffusion of technological innovations is more apparently correlated to changes in the measures than epidemic events in themselves. We discuss the potential significance and limitations of this finding in understanding changes in urban systems in the context of the ongoing COVID-19 pandemic.

Global epidemic propagation rate and structure are strongly coupled with the international air transport network. Due to the network structure, countries are desynchronized in the way infections arise, transported, mitigated, and re-infect again. A global lockdown is detrimental to the international economy, and many argue that unless the whole world is evenly vaccinated, we cannot return to pre-COVID lives. The current challenge is that new waves of re-infection are spreading, and vaccination will take many months to materialise across the world. Here, we show how certain small airports (0.1% of global) contribute much more to the epidemic spread process, irrespective of the actual spreading mechanics. We use trophic coherence as a metric for network stability in directed graphs (e.g., recurring network source of reinfection). We find that the air transport network has a trophic coherence similar to a random expectation (99% of airports form a tightly looped network) and practically all networks need to be equally protected to ensure global security.

Multi-scale molecular communication (MC) employs the characteristics of information molecules for information exchange. The received signal in MC inevitably encounters severe inter-symbol interference and signal-dependent noise due to the stochastic diffusion mechanism. Focusing on the critical signal detection in MC, first this article reviews the commonly used mod-el-based detectors and exposes their limitations in practical implementation. Then the emerging data-driven detectors that can make up for some deficiencies of the model-based detectors are presented. Despite the black-box nature of the data-driven detectors, the explainable artificial intelligence can be further investigated for the performance improvement of transparency and trust. Finally, some open research issues and future research directions in receiver design are discussed.

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.

Named entity recognition (NER) is an essential part of natural language processing tasks. Chinese NER task is different from the many European languages due to the lack of natural delimiters. Therefore, Chinese Word Segmentation (CWS) is usually regarded as the first step of processing Chinese NER. However, the word-based NER models relying on CWS are more vulnerable to incorrectly segmented entity boundaries and the presence of out-of-vocabulary (OOV) words. In this paper, we propose a novel character-based Gated Convolutional Recurrent neural network with Attention called GCRA for Chinese NER task. In particular, we introduce a hybrid convolutional neural network with gating filter mechanism to capture local context information and a highway neural network after LSTM to select characters of interest. The additional gated self-attention mechanism is used to capture the global dependencies from different multiple subspaces and arbitrary adjacent characters. We evaluate the performance of our proposed model on three datasets, including SIGHAN bakeoff 2006 MSRA, Chinese Resume, and Literature NER dataset. The experiment results show that our model outperforms other state-of-the-art models without relying on any external resources like lexicons and multi-task joint training.

The sixth generation (6G) of wireless systems are likely to operate in environments and scales that wireless services have not penetrated effectively. Many of these environments are not suitable for efficient data bearing wave propagation. Molecular signals have the potential to deliver information by exploiting both new modulation mechanisms via chemical encoding and new multi-scale propagation physics. While the fusion of biophysical models and communication theory has rapidly advanced the molecular communication field, there is a lack of real-world macro-scale applications. Here, we introduce application areas in defense and security, ranging from underwater search and rescue to covert communications; and cyber-physical systems, such as using molecular signals for health monitoring in underground networked systems. These engineering applications not only demand new wireless communication technologies ranging from DNA encoding to molecular graph signal processing, but also demonstrate the potential for molecular communication to contribute in traditional but challenging engineering areas. Together, it is increasingly believed that molecular communication can be a new physical layer for 6G, accessing and extracting data from extreme wave-denied environments.\ud

Wireless traffic prediction is a fundamental enabler to proactive network optimisation in beyond 5G. Forecasting extreme demand spikes and troughs due to traffic mobility is essential to avoiding outages and improving energy efficiency. Current state-of-the-art deep learning forecasting methods predominantly focus on overall forecast performance and do not offer probabilistic uncertainty quantification (UQ). Whilst Gaussian Process (GP) models have UQ capability, it is not able to predict extreme values very well. Here, we design a feature embedding (FE) kernel for a GP model to forecast traffic demand with extreme values. Using real 4G base station data, we compare our FE-GP performance against both conventional naive GPs, ARIMA models, as well as demonstrate the UQ output. For short-term extreme value prediction, we demonstrated a 32\% reduction vs. S-ARIMA and 17\% reduction vs. Naive-GP. For long-term average value prediction, we demonstrated a 21\% reduction vs. S-ARIMA and 12\% reduction vs. Naive-GP. The FE kernel also enabled us to create a flexible trade-off between overall forecast accuracy against peak-trough accuracy. The advantage over neural network (e.g. CNN, LSTM) is that the probabilistic forecast uncertainty can inform us of the risk of predictions, as well as the full posterior distribution of the forecast.

Molecular Communication (MC) is a promising micro-scale technology that enables wireless connectivity in electromagnetically challenged conditions. The signal processing approaches in MC are different from conventional wireless communications as molecular signals suffer from severe inter-symbol interference (ISI) and signal-dependent counting noise due to the stochastic diffusion process of the information molecules. One of the main challenges in MC is the high computational complexity of the existing time-domain ISI mitigation schemes that display a third-order polynomial or even exponential growth with the ISI length, which is further exasperated under the high symbol rate case. For the first time, we develop a frequency-domain equalization (FDE) with lower complexity, capable of achieving independence from the ISI effects. This innovation is grounded in our characterization of the channel frequency response of diffusion signals, facilitating the design of receiver sampling strategies. However, the perfect counting noise power is unavailable in the optimal minimum mean square error (MMSE) equalizer. We address this issue by exploiting the statistical information of the transmit signal and decision feedback for noise power estimation, designing novel MMSE equalizers with low complexity. The FDE for MC is successfully developed with its immunity to ISI effects, and its signal processing cost has only a logarithmic growth with symbol length in each block.

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.

This method and data paper sets out the macro-scale experimental techniques to acquire fluid dynamic knowledge to inform molecular communication performance and design. Fluid dynamic experiments capture latent features that allow the receiver to detect coherent signal structures and infer transmitted parameters for optimal decoding. This paper reviews two powerful fluid dynamical measurement methodologies that can be applied beneficially in the context of molecular signal tracking and detection techniques. The two methods reviewed are Particle Image Velocimetry (PIV) and Planar Laser-Induced Fluorescence (PLIF). Step-by-step procedures for these techniques are outlined as well as comparative evaluation in terms of performance accuracy and practical complexity is offered. The relevant data is available on IEEE DataPort to help in better understanding of these methods.

Nano-machines have the potential to achieve targeted drug delivery, improving efficacy and reducing side-effects and wastage. Coordinating multiple nano-machines require communication capability, and molecular communications (MC) is recognised as a key technology under the Internet-of-Nano Things (IoNT) ecosystem. Whilst secure communication is critical, it is limited by the low computational capability of nano-machines. Here, we leverage on the rapid advances in radio physical layer security (PLS) to propose cipher keys generated from dynamic molecular channel statistics for encryption. Like PLS, codewords do not need to be exchanged and a public codeword library is not required, decreasing the computational and architectural burden for ad-hoc nano-machine coordination. Our results show that we can achieve eavesdropper key disagreement rate (KDR) of 5-7 × higher than our intended transmission channel, demonstrating that any potential malicious eavesdropper cannot decipher the message and hence cannot go on to perform malicious actions.

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.

As 5G mobile networks are bringing about global societal benefits, the design phase for 6G has started. Evolved 5G and 6G will need sophisticated AI to automate information delivery simultaneously for mass autonomy, human machine interfacing, and targeted healthcare. Trust will become increasingly critical for 6G as it manages a wide range of mission-critical services. As we migrate from traditional mathematical model-dependent optimization to data-dependent deep learning, the insight and trust we have in our optimization modules decrease. This loss of model explainability means we are vulnerable to malicious data, poor neural network design, and the loss of trust from stakeholders and the general public -- all with a range of legal implications. In this review, we outline the core methods of explainable artificial intelligence (XAI) in a wireless network setting, including public and legal motivations, definitions of explainability, performance vs. explainability trade-offs, and XAI algorithms. Our review is grounded in case studies for both wireless PHY and MAC layer optimization and provide the community with an important research area to embark upon.

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.

Uncertainty and interconnectedness in complex engineering and engineered systems such as power-grids and telecommunication networks are sources of vulnerability compromising the resilience of these systems. Conditions of uncertainty and interconnectedness change over time and depend on emerging socio-technical contexts, thus conventional methods which can conduct normative, descriptive and prescriptive assessment of complex engineering and engineered systems resilience are limited. This paper brings together contributions of experts in complex engineering and engineered systems who have identified six methods, three each for uncertainty and interconnectedness, which form the foundational methods for knowing complex engineering and engineered systems resilience. The paper has reviewed how these methods contribute to overcoming uncertainty or interconnectedness and how they are implemented using case studies in order to illustrate essential approaches to enhancing resilience. It is hoped that this approach will allow the subject to be quantified and best practice standards to develop.

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.

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.

Many urban infrastructures contain complex dynamics embedded in spatial networks. Monitoring using Internet-of-Things (IoT) sensors is essential for ensuring safe operations. An open challenge is given an existing sensor network, where best to collect the minimum amount of representative data. Here, we consider an urban underground water distribution network (WDN) and the problem of contamination detection. Existing topology-based approaches link complex network (e.g. Laplacian spectra) to optimal sensing selections, but neglects the underpinning fluid dynamics. Alternative data-driven approaches such as compressed sensing (CS) offer limited data reduction.In this work, we introduce a principal component analysis based Graph Fourier Transform (PCA-GFT) method, which can recover the full networked signal from a dynamic subset of sensors. Specifically, at each time step, we are able to predict which sensors are needed for the next time step. We do so, by exploiting the spatial-time correlations of the WDN dynamics, as well as predicting the sensor set needed using sparse coefficients in the transformed domain. As such, we are able to significantly reduce the number of samples compared with CS approaches. The drawback lies in the computational complexity of a data collection point (DCP) updating the PCA-GFT operator at each time-step. The experimental results show that, on average, with nearly 40% of the sensors reported, the proposed PCA-GFT method is able to fully recover the networked dynamics.\ud

Targeted drug delivery system is believed as one of the most promising solutions for cancer treatment due to its low-dose requirement and less side effects. However, both passive targeting and active targeting rely on systemic blood circulation and diffusion, which is actually not the real "active" drug delivery. In this paper, an ant-behavior inspired nanonetwork composing of intelligent nanomachines is proposed. A big intelligent nanomachine take small intelligent nanomachines and drugs to the vicinity of of the tumor area. The small intelligent nanomachines can coordinate with each other to find the most effective path to the tumor cell for drug transportation. The framework and mechanism of this cooperative network are proposed. The route finding algorithm is presented. The convergence performance is analytically analyzed where the influence of the factors such as molecule degradation rate, home-destination distance, number of small nanomachines to the convergence is presented. Finally the simulation results validate the effectiveness of the proposed mechanism and analytical analysis.

Many embedded networks are difficult to monitor, such as water distribution networks (WDNs). A key challenge is how to use minimum sparse sensors to measure contamination and transmit contamination data to a hub for system analysis. Existing approaches deploy sensors using multi-objective optimisation and transmit the data using ground penetrating waves or fixed-line access. Here, for the first time, we introduce a novel molecular communication relay system, which is able to transmit the data report to the hub via the water-flow of WDN itself, and avoids the complex ground penetrating techniques. A water flow data-driven Graph Fourier Transform (GFT) sampling method is designed to inform the invariant orthogonal locations for deploying the molecular relay sensors. Each sensor encodes information via a DNA molecule that enables the common hub to reconstruct the full contamination information. Numerical simulation validates the proposed system, providing a pathway to integrate MC into macro-scale Digital Twin platforms for infrastructure monitoring.

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 \textit{single} set of optimal weights can form \textit{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.

Geo-tagged social media data is a key input to many smart city application areas, ranging from mapping consumer demand to understanding location dependent well-being. The sparsity in geo-tagged data, especially in certain cities, means that there is a lack of dynamic spatial point process models for social media data. Having statistically representative spatial models can enable proxy models that improve our understanding of human patterns in urban and suburban areas. Here, we analyse a data set of more than 400,000 Tweets in London to create a spatial point process model of Tweet clusters. We model Tweet clusters as a Poisson Cluster Process. We then track how the point process parameter and spatial entropy evolve over time to create a generative model usable for others, as well as discuss its relevance to urban dynamics and smart city applications.

Mobile wireless networks underpin digital economies and smart cities. Local and national scale network failures cause widespread social and economic impact. Self-reporting of consumer experience on social media platforms can inform operators. This paper investigates an innovative method to detect the consumer experience to outage events in both temporal and spatial domain using Twitter data. We use a variety of natural language processing (NLP) analysis to detect the consumer sentiment from a custom made dictionary and using naive Bayes classifier. We propose a hybrid geo-information extraction that sequentially extracts the geo-location from a priority list. A case study upon recent UK wide mobile network failure has been implemented in this paper. The results show that our proposed hybrid geo-information extraction system has been able to increase data size and accuracy of geo labelled Tweets. Also, our system can successfully detect this network issue in both time and location, which is validated by the national newspaper reports on this issue.

Swarm unmanned aerial vehicles (UAVs) are critical to a number of smart city applications, such as distributed sensing and disaster relief. Connected UAVs often operate in complex environments where the communication channels suffer fading loss. Achieving full spatial diversity is important for reliable communication, but cannot be guaranteed at any given time. Here, we show that delay tolerant transmission via caching can enable full diversity. To achieve this, we improve the decode-and-forward cooperative relaying scheme, where the number of successful relay UAVs increases with each additional cooperation stage. We demonstrate a trade-off between the achievable bit error rate and the necessary delay via caching. The results show that a small delay enabled by UAV caching can enable full diversity at SNR levels 10–$;100 \times$ lower in most mobile swarm cases. The proposed protocol can dynamically trade-off transmission reliability with caching capacity and enable reliable UAV swarm coordination in complex environments.

Accessibility to banking is rapidly changing due to the onset of new digital technologies. In urban areas, multiple overlapping banking channels are available, ranging from physical ATMs to digital broadband. However, city scale analysis of accessibility to different demographic groups is lacking. Understanding this can inform smart city design and prioritize the deployment of digital infrastructure, whilst delaying any decommissioning of legacy physical branches. Here, for the first time, we present a short paper on our analysis of UK’s two largest cities, showing different banking accessibility results and their relation to social electronic inclusion classes. In particular, we show how poor accessibility occurs in areas with higher retired demographics and near social deprivation, and how the urban population of Birmingham and London are better equipped and more socially ready for digital transformation than rural areas.

Distributed power control in wireless networks faces challenges related to its stability. When perfect information of channel states and transmitting agents are available, previous work has shown that the stability conditions can be known. When there is uncertainty over the parameter space, stability is not well understood. In this work, we study the impact of parameter uncertainty and network structure on the stability and scalability of a well known distributed power control, namely the Foschini-Miljanic algorithm. More specifically, we derive probabilistic conditions with respect to the parameters of the channel distributions for which the system is stable. Furthermore, we study the effects of these parameters for different node distribution on the plane. Numerical examples validate our theoretical results.

Mobile molecular communication (MC) attracts much attention in recent years where mobile nanomachines exchange information using molecules. In this paper, we consider a diffusion-based mobile MC system consisting a pair of diffusive nanomachines. Due to the Brownian motion of nanomachines, the distance between them is a stochastic process. In this paper, its probability density function (PDF) is derived by characterizing nanomachines' motion as Wiener process. Besides, the initial distance between nanomachines is a significant parameter of diffusive mobile MC systems. With the knowledge of initial distance, the expected channel impulse response (CIR) can be obtained and the detection threshold can be set in advance. A novel two-step scheme is proposed to estimate the initial distance by maximum likelihood (ML) estimation. Firstly, the releasing distance is estimated based on observations of the number of received molecules. Secondly, the estimation of the releasing distance is used as an observation to estimate the initial distance by ML estimation. The performance of proposed scheme is evaluated via particle-based simulation of the Brownian motion.

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.

The persistent awareness of dynamic links quality is of critical importance to shared access systems, e.g., promoting sensing accuracy or even enabling seamless sharing, which remains yet as a challenging task. In this paper, we consider the shared access in heterogeneous small-cells, which are likely to be the main infrastructure components by utilizing the carrier aggregation (CA) scheme, potentially combining both licensed and unlicensed bands. We suggest a promising sensing scheme, which permits the simultaneous assessment of multiple links quality when detecting vacant spectrum, and hence is able to solve the long-standing hidden terminal problem. We incorporate an additional objective of acquiring dynamic link state information of multiple incumbent transmitters/receivers, and thereby, structure spectrum sensing as a mixed classification and estimation problem. The main challenge is that multiple channels may become similar with each other, and existing joint estimation schemes are unable to distinguish which incumbent is active and what its associated channel state is. To address this indistinguishable problem and alleviate the error propagation in recursive estimations, a novel alternating Bernoulli filtering algorithm is proposed. By fully exploiting the group transitional behavior of incumbents, a multiple-chains inference framework is developed. Numerical simulations validate our new scheme. Occupancy states of multiple incumbents can be identified accurately, by tracking their links quality and exploiting the underlying dynamics, which lays the foundation for more effective shared access.