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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.

In molecular communication (MC), the motion of information molecules in the medium is usually described by the Brownian motion and governed by the Fick’s laws. However, there are some potential scenarios of MC where the kinetics of information molecules is non-Fickian. In this letter, we investigate one of this kind of MC. The manner of information molecules in the channel is subdiffusion. A three-dimensional MC system with a spherical absorbing receiver is considered. The subdiffusion channel is analyzed. The closed-form expressions of the first hitting probability and its peak time are given. Furthermore, we investigate the performance of MC by timing and amplitude modulation schemes in a subdiffusion channel. The error probability for both modulation schemes is analyzed.

Molecular communications rely on diffusive propagation to transport information, which is attractive for a variety of nano-scale applications. Due to the long-tail channel response, spatial-temporal coding of information may lead to severe inter-symbol interference (ISI). Classical linear signal processing in wireless communications is usually operating with high complexity and high signal-to-noise ratios, whereas signal processing in molecular communication system requires operating in opposite conditions. In this work, we propose a novel signal processing paradigm inspired by the biological principle, which enables low-complexity signal detection in extremely noisy environments. We first propose a non-linear filter inspired by stochastic resonance, which is found in a variety of biological systems, and it can significantly improve the output SNR by converting noise to useful signals. Then, we design a non-coherent detection method, one which exploits the generally transient trend of observed signals (i.e. quick-rising and slow-decaying) rather than hidden channel state information (CSI), thus excluding CSI estimation and involving only summations. Implementation issues are also discussed, including parameters configuration and adaptive threshold. Numerical results show that the proposed bio-inspired scheme can improve the performance remarkably over classical approaches. Even compared with the optimal linear methods, the required SNR of the proposed scheme can be reduced by 7 dB, which reaffirms why it can be used in noisy biological environments. As the first attempt to design bio-inspired molecular signal detectors, the proposed non-linear processing paradigm may provide the great promise to the emerging nano-machine applications.

In the fifth-generation (5G) mobile networks, proactive network optimisation plays an important role in meeting the exponential traffic growth, more stringent service requirements, and to reduce capital and operational expenditure. Proactive network optimisation is widely acknowledged as one of the most promising ways to transform the 5G network based on big data analysis and cloud-fog-edge computing, but there are many challenges. Proactive algorithms will require accurate forecasting of highly contextualised traffic demand and quantifying the uncertainty to drive decision making with performance guarantees. Context in Cyber-Physical-Social Systems (CPSS) is often challenging to uncover, unfolds over time, and even more difficult to quantify and integrate into decision making. The first part of the review focuses on mining and inferring CPSS context from heterogeneous data sources, such as online user-generated-content. It will examine the state-of-the-art methods currently employed to infer location, social behaviour, and traffic demand through a cloud-edge computing framework; combining them to form the input to proactive algorithms. The second part of the review focuses on exploiting and integrating the demand knowledge for a range of proactive optimisation techniques, including the key aspects of load balancing, mobile edge caching, and interference management. In both parts, appropriate state-of-the-art machine learning techniques (including probabilistic uncertainty cascades in proactive optimisation), complexity-performance trade-offs, and demonstrative examples are presented to inspire readers. This survey couples the potential of online big data analytics, cloud-edge computing, statistical machine learning, and proactive network optimisation in a common cross-layer wireless framework. The wider impact of this survey includes better cross-fertilising the academic fields of data analytics, mobile edge computing, AI, CPSS, and wireless communications, as well as informing the industry of the promising potentials in this area.

Optimal operation of hydropower reservoir systems is a classical optimization problem of high dimensionality and stochastic nature. A key challenge lies in improving the interpretability of operation strategies, i.e., the cause–effect relationship between system outputs (or actions) and contributing variables such as states and inputs. This paper reports for the first time a new deep reinforcement learning (DRL) framework for optimal operation of reservoir systems based on deep Q-networks (DQNs), which provides a significant advance in understanding the performance of optimal operations. DQN combines Q-learning and two deep artificial neural networks (ANNs), and acts as the agent to interact with the reservoir system through learning its states and providing actions. Three knowledge forms of learning considering the states, actions, and rewards were constructed to improve the interpretability of operation strategies. The impacts of these knowledge forms and DRL learning parameters on operation performance were analyzed. The DRL framework was tested on the Huanren hydropower system in China, using 400-year synthetic flow data for training and 30-year observed flow data for verification. The discretization levels of reservoir water level and energy output yield contrasting effects: finer discretization of water level improved performance in terms of annual hydropower generated and hydropower production reliability; however, finer discretization of hydropower production can reduce search efficiency, and thus the resulting DRL performance. Compared with benchmark algorithms including dynamic programming, stochastic dynamic programming, and decision tree, the proposed DRL approach can effectively factor in future inflow uncertainties when determining optimal operations and can generate markedly higher hydropower. This study provides new knowledge of the performance of DRL in the context of hydropower system characteristics and data input features, and shows promise for potentially being implemented in practice to derive operation policies that can be updated automatically by learning from new data.

In emerging Internet-of-Nano-Things (IoNT) and molecular communication (MC), digital information will be embedded in chemical molecules in order to propagate through complex and absorbing media. However, two main challenges in signal detection are the intensive inter-symbol-interference (ISI) and the severe background noise, which may heavily deteriorate the reliability of molecular communications. This may be even worse when facing unknown channel impulse response (CIR), which is common in the industrial practice of MCs, rendering most of coherent signal detection schemes in traditional wireless communications less attractive. In this paper, we propose an improved non-coherent signal detection scheme, aiming at addressing ISI and noise contamination in the absence of CIR. Specifically, we utilized three non-coherent metrics to characterize transient features of received signals, and furthermore, designed an optimized combination scheme for more reliable signal detections. Given that the significance of each non-coherent metric remains unknown, we devise a self-adaption method premised on the concept of deflection coefficient, which can recursively determine the optimal combination weights. Numerical simulations demonstrate that our proposed scheme significantly improves the detection performance in terms of bit error rate (BER), and outperforms the adaptive threshold detection (ATD) method over 7 dB.

Hyper-dense wireless network deployment is one of the popular solutions to meeting high capacity requirement for 5G delivery. However, current operator understanding of consumer satisfaction comes from call centers and base station quality-of-service (QoS) reports with poor geographic accuracy. The dramatic increase in geo-tagged social media posts adds a new potential to understand consumer satisfaction towards target-specific quality-of-experience (QoE) topics. In our paper, we focus on evaluating users' opinion on wireless service-related topics by applying natural language processing (NLP) to geo-tagged Twitter data. Current generalized sentiment detection methods with generalized NLP corpora are not topic specific. Here, we develop a novel wireless service topic-specific sentiment framework, yielding higher targeting accuracy than generalized NLP frameworks. To do so, we first annotate a new sentiment corpus called SignalSentiWord (SSW) and compare its performance with two other popular corpus libraries, AFINN and SentiWordNet. We then apply three established machine learning methods, namely: Naïve Bayes (NB), Support Vector Machine (SVM), and Recurrent Neural Network (RNN) to build our topic-specific sentiment classifier. Furthermore, we discuss the capability of SSW to filter noisy and high-frequency irrelevant words to improve the performance of machine learning algorithms. Finally, the real-world testing results show that our proposed SSW improves the performance of NLP significantly.

Many engineering, social, and biological complex systems consist of dynamical elements connected via a large-scale network. Monitoring the network's dynamics is essential for a variety of maintenance and scientific purposes. Whilst we understand how to optimally sample a single dynamic element or a non-dynamic graph, we do not possess a theory on how to optimally sample networked dynamical elements. Here, we study nonlinear dynamic graph signals on a fixed complex network. We define the necessary conditions for optimal sampling in the combining time- and graph-domain to fully recover the networked dynamics. We firstly interpret the networked dynamics into a linearized matrix. Then, we prove that the dynamic signals can be sampled and fully recovered if the networked dynamics is stable and their initialization is bandlimited in the graph spectral domain. This new theory directly maps optimal sampling locations and rates to the graph properties and governing nonlinear dynamics. This can inform the optimal placement of experimental probes and sensors on dynamical networks as well as inform the design of each sensor's optimal sampling rate. We motivate the reader with two examples of recovering the networked dynamics for: social population growth and networked protein biochemical interactions with both bandlimited and arbitrary initialization., under review at IEEE (version 2.5)

Information delivery via molecular signals is abundant in nature and potentially useful for industry sensing. Many propagation channels (e.g., tissue membranes and catalyst beds) contain porous medium materials and the impact this has on communication performance is not well understood. Here, communication through realistic porous channels is investigated for the first time via statistical breakthrough curves. Assuming that the number of arrived molecules can be approximated as a Gaussian random variable and using fully resolved computational fluid dynamics results for the breakthrough curves, the numerical results for the throughput, mutual information, error probability, and information diversity gain are presented. Using these numerical results, the unique characteristics of the porous medium channel are revealed., 6 pages; 8 figures; submitted to possible IEEE publication

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

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.

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.

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.

Humanitarian disasters and political violence cause significant damage to our living space. The reparation cost to homes, infrastructure, and the ecosystem is often difficult to quantify in real-time. Real-time quantification is critical to both informing relief operations, but also planning ahead for rebuilding. Here, we use satellite images before and after major crisis around the world to train a robust baseline Residual Network (ResNet) and a disaster quantification Pyramid Scene Parsing Network (PSPNet). ResNet offers robustness to poor image quality and can identify areas of destruction with high accuracy (92\%), whereas PSPNet offers contextualised quantification of built environment damage with good accuracy (84\%). As there are multiple damage dimensions to consider (e.g. economic loss and fatalities), we fit a multi-linear regression model to quantify the overall damage. To validate our combined system of deep learning and regression modeling, we successfully match our prediction to the ongoing recovery in the 2020 Beirut port explosion. These innovations provide a better quantification of overall disaster magnitude and inform intelligent humanitarian systems of unfolding disasters.

The linear inverse problem is fundamental to the development of various scientific areas. Innumerable attempts have been carried out to solve different variants of the linear inverse problem in different applications. Nowadays, the rapid development of deep learning provides a fresh perspective for solving the linear inverse problem, which has various well-designed network architectures results in state-of-the-art performance in many applications. In this paper, we present a comprehensive survey of the recent progress in the development of deep learning for solving various linear inverse problems. We review how deep learning methods are used in solving different linear inverse problems, and explore the structured neural network architectures that incorporate knowledge used in traditional methods. Furthermore, we identify open challenges and potential future directions along this research line., Comment: 60 pages; Publication in (Elsevier) Signal Processing, 2020

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.

Recent studies demonstrate that people are increasingly looking online to assess their health, with reasons varying from personal preferences and beliefs to inability to book a timely appointment with their local medical practice. Records of these activities represent a new source of data about the health of populations, but which is currently unaccounted for by disease surveillance models. This could potentially be useful as evidence of individuals’ perception of bodily changes and self-diagnosis of early symptoms of an emerging disease. We make use of the Experian geodemographic Mosaic dataset in order to extract Type 2 diabetes candidate risk variables and compare their temporal relationships with the search keywords, used to describe early symptoms of the disease on Google. Our results demonstrate that Google Trends can detect early signs of diabetes by monitoring combinations of keywords, associated with searches for hypertension treatment and poor living conditions; Combined search semantics, related to obesity, how to quit smoking and improve living conditions (deprivation) can be also employed, however, may lead to less accurate results.

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.

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.

High speed rail demands precision structural health monitoring in shallow underground environments near embankments. Traditional low frequency wireless channels can communicate with underground sensors, but its large antenna elements are prone to damage from geological stress. In this case study paper, we design a higher frequency compact system and experimentally characterize its performance in-situ, in different soil and moisture conditions that are representative of UK soil conditions near the planned HS2 rail site and weather conditions. Accurate 3D electromagnetic simulation results are also shown to support experimental results and a pathloss model sensitive to soil conditions is developed to inform upcoming high speed rail embankment monitoring deployment. The multi-disciplinary findings presented will also directly inform the depth and data reliability of current high speed rail sensor deployment.

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.

Many post-disaster and post-conflict regions do not have sufficient data on their transportation infrastructure assets, hindering both mobility and reconstruction. In particular, as the number of ageing and deteriorating bridges increases, it is necessary to quantify their load characteristics in order to inform maintenance and asset databases. The load carrying capacity and the design load are considered as the main aspects of any civil structures. Human examination can be costly and slow when expertise is lacking in challenging scenarios. In this paper, we propose to employ deep learning as a method to estimate the load carrying capacity from crowdsourced images. A convolutional neural network architecture is trained on data from over 6000 bridges, which will benefit future research and applications. We observe significant variations in the dataset (e.g. class interval, image completion, image colour) and quantify their impact on the prediction accuracy, precision, recall and F 1 score. Finally, practical optimization is performed by converting multiclass classification into binary classification to achieve a promising field use performance.

Wireless communications technology enables us to seamlessly access many multimedia services, e.g., stored multimedia (e.g., video on-demand), live streaming (e.g., Internet live sport networks, Internet radio stations), and real-time interactive streaming (e.g., online games, video conference, e-education), etc. As such, wireless communications technology has rapidly gained a crucial role and become an important aspect of life. Of most concern, however, is the increase in wireless/mobile devices and the huge demand in data rates associated with this. The future 5G cellular network is expected to achieve as much as 1000 times data rate relative to its current 4G counterpart. Data rates are projected to increase by a factor of ten every five years, and with the emerging Internet of Things (IoT) predicted to wirelessly connect trillions of devices across the globe, without novel approaches, future mobile networks (5G) will grind to a halt unless more capacity is created. One of the solutions is the implementation of ultra-dense networks, combining macro-cells and small-cells and exploiting the emerging technologies of millimetre wave (mm-wave) frequency bands and large-scale antennas arrays. The aim of this book is to present new research related to theory or practice of all aspects of ultra-dense networks.

Extracting and detecting spike activities from the fluorescence observations is an important step in understanding how neuron systems work. The main challenge lies in that the combination of the ambient noise with dynamic baseline fluctuation, often contaminates the observations, thereby deteriorating the reliability of spike detection. This may be even worse in the face of the nonlinear biological process, the coupling interactions between spikes and baseline, and the unknown critical parameters of an underlying physiological model, in which erroneous estimations of parameters will affect the detection of spikes causing further error propagation. In this paper, we propose a random finite set (RFS) based Bayesian approach. The dynamic behaviors of spike sequence, fluctuated baseline and unknown parameters are formulated as one RFS. This RFS state is capable of distinguishing the hidden active/silent states induced by spike and non-spike activities respectively, thereby \emph{negating the interaction role} played by spikes and other factors. Then, premised on the RFS states, a Bayesian inference scheme is designed to simultaneously estimate the model parameters, baseline, and crucial spike activities. Our results demonstrate that the proposed scheme can gain an extra $12\%$ detection accuracy in comparison with the state-of-the-art MLSpike method.

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 paper, we tackle the problem of how to locate a single entity with an unknown location in a vast underwater search space. In under-water channels, traditional wave-based signals suffer from rapid distance- and time-dependent energy attenuation, leading to expensive and lengthy search missions. In view of this, we investigate two molecular messaging methods for location discovery: 1) a Rosenbrock gradient ascent algorithm and 2) a chemical encoding messaging method. In absence of explicit diffusion channel knowledge and in presence of diffusion noise, the Rosenbrock method is adapted to account for the blind search process and allow the robot to recover in areas of zero gradient. The two chemical methods are found to offer attractive performance trade-offs in complexity and robustness. Compared to conventional acoustic signals, the chemical methods proposed offers significantly longer propagation distance (1000 km) and longer signal persistence duration (months).

Blackspots are areas of poor signal coverage or service delivery that leads to customer complaints and loss in business revenue. Understanding their spatial-temporal patterns at a high resolution is important for interventions. Conventional methods such as customer helplines, drive-by testing, and network analysis tools often lack the real-time capability and spatial accuracy required. In this paper, we investigate the potential of utilizing geo-tagged Twitter data to uncover blackspots. Here, we apply lexicon and machine-learning natural language processing techniques to over 1.4 million Tweets in London to uncover blackspots for both pre-4G (2012) and post-4G (2016) roll out. It was found that long-term poor signal complaints make up the majority of complaints (86%) pre-4G roll out, but short-term network failure was responsible for most complaints (66%) post-4G roll out.

Relation classification is an important research area in the field of natural language processing (NLP), which aims to recognize the relationship between two tagged entities in a sentence. The noise caused by irrelevant words and the word distance between the tagged entities may affect the relation classification accuracy. In this paper, we present a novel model multi-head attention long short term memory (LSTM) network with filter mechanism (MALNet) to extract the text features and classify the relation of two entities in a sentence. In particular, we combine LSTM with attention mechanism to obtain the shallow local information and introduce a filter layer based on attention mechanism to strength the available information. Besides, we design a semantic rule for marking the key word between the target words and construct a key word layer to extract its semantic information. We evaluated the performance of our model on SemEval-2010 Task8 dataset and KBP-37 dataset. We achieved an F1-score of 86.3% on SemEval-2010 Task8 dataset and F1-score of 61.4% on KBP-37 dataset, which shows that our method is superior to the previous state-of-the-art methods.

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 emission 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.

High resolution social media data presents an opportunity to better understand people's behavioural patterns and sentiment. Whilst significant work has been conducted in various targeted social contexts, very little is understood about differentiated behaviour in different industrial sectors. In this paper, we present results on how social media usage and general sentiment vary across the geographic and industry sector landscape. Unlike existing studies, we use a novel geocomputational approach to link location specific Twitter data with business sectors by leveraging the UK Standard Industrial Classification Code (SIC Code). Our baseline results for the Greater London area identifies Construction, Real Estate, Transport and Financial Services industries consistently have stronger Twitter footprints. We go on to apply natural language processing (NLP) techniques to understand the prevailing sentiment within each business sector and discuss how the evidence can contribute towards de-biasing Twitter data. We believe this research will prove a valuable surveillance tool for policy makers and service providers to monitor ongoing sentiment in different industry sectors, perceive the impact of new policies and can be used as a low cost alternative to survey methods in organisational studies.

In this paper, the throughput maximization of millimeter-wave (mm-Wave) ultra-dense networks (UDN) using dynamic spectrum sharing (DSS) is considered. Most of the existing works only allow temporal-domain access and admit at most one user at each time slot, resulting in significant under-utilization of spectrum resource, which will be less attractive to mm-wave UDN applications. A generalized temporal-spatial sharing scheme is proposed in this paper for UDN by exploiting the location information of incumbent devices, where multiple users are allowed to access each channel simultaneously via spatial separations. For distributed applications, the global information exchange among secondary users (SU) tends to be impractical, given the unaffordable signaling overhead and latency. Thus, a non-cooperative game with fine-grained two-dimensional reuse is formulated, which leads to a more efficient access strategy. It is then proved to be an ordinary potential game (OPG), which guarantees the existence of the strategy Nash equilibrium (NE). Finally, an improved decentralized reinforcement learning algorithm is designed, with which SUs can learn from wireless environments and adapt towards to a NE point, relying on the individual observation and the historical action-reward (rather than the global information exchanging). The convergence efficiency of the new scheme is also rigorously proved. Numerical simulations are provided to validate the performances of the proposed schemes.\ud

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 study, 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 (MED) 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.\ud

In this letter, we provide a tractable theoretical framework for analysing the performance of device-to-device (D2D) communications in the presence of co-channel interference from other D2D and conventional cellular (CC) transmissions. In particular, we consider multi-hop D2D using the shortest-path-routing (SPR) algorithm in both the uplink (UL) and the downlink (DL) channels. Closed-form expressions for the number of hops and the outage probability are presented. Our analytical and numerical results both show that while the D2D links are reasonably reliable (outage probability 25%). Accordingly, we exploit and provide insights into the trade-off between D2D and CC transmission reliability.

Molecular communications (MC) has been studied as a bio-inspired information carrier for micro-scale and nano-scale environments. On the macro-scale, it can also be considered as an alternative to electromagnetic (EM) wave based systems, especially in environments where there is significant attenuation to EM wave power. This paper goes beyond the unbounded free space propagation to examine three macro-scale environments: the pipe, the knife edge, and the mesh channel. Approximate analytical expressions shown in this paper demonstrate that MC has an advantage over EM wave communications when: (i) the EM frequency is below the cut-off frequency for the pipe channel, (ii) the EM wavelength is considerably larger than the mesh period, and (iii) when the receiver is in the high diffraction loss region of an obstacle.

Modelling enemy networks in a way that reveals their key entities and links is important when disrupting complex networks with high redundancy. In this paper, we examine how best to attack such networks under a limited ground intelligence constraint. The key modelling contribution is to include both the heterogeneity of the node functions and the dynamics of recuperation after destruction. Through identifying the core nodes, the results show that ground intelligence should focus on locating and attacking high degree nodes, yielding a 41% reduction in conflict length over random opportunistic targeting and a 23% reduction over specialist targeting. Even when difficult to replace specialists are considered, targeting high degree nodes that can recuperate quickly, remains the most effective method of attack. The impact is to allow military forces to more effectively target enemy nodes that will cause functional paralysis and create further collapses.

Mobile phone usage has changed significantly over the past few years\ud and smartphone data usage is still not well understood on a statistically\ud significant scale. This Letter analyses 2.1 million smartphone usage\ud data values and explore the current wireless downlink–uplink\ud demand asymmetry for different time periods and across different\ud radio access networks. The current data demand over 2G networks\ud remains largely symmetric with strong temporal variations, whereas\ud the demand over 3G networks is asymmetric with surprisingly weak\ud temporal variations is shown here.

Device-to-Device (D2D) communications is 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, we show that this is not the case. In this paper, we show that by understanding the location of the user, an interference-aware routing algorithm can be devised. We propose an adaptive Interference-Aware-Routing (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 optimization in wireless systems, which is particularly important for increasingly dense and semantic-aware deployments.

In multi-cellular organisms, molecular signaling spans multiple distance scales and is essential to tissue structure and functionality. Molecular communications is increasingly researched and developed as a key subsystem in the Internet-of-Nano-Things paradigm. While short range microscopic diffusion communications is well understood, longer range channels can be inefficient and unreliable. Static and mobile relays have been proposed in both conventional wireless systems and molecular communication contexts. In this paper, our main contribution is to analyze the information delivery energy efficiency of bacteria mobile relays. We discover that these mobile relays offer superior energy efficiency compared with pure diffusion information transfer over long diffusion distances. This paper has widespread implications ranging from understanding biological processes to designing new efficient synthetic biology communication systems.\ud

The energy efficiency of systems in general determines its operational sustainability. Harvesting energy is a crucial technology for a variety of wireless systems that have limited access to a reliable electricity supply or recharging sources. As such, these devices need to harvest electricity from alternative sources such as the natural environment or wireless signals. A variety of wireless systems and devices fit this profile, from relatively power-hungry macro-base stations deployed in remote regions, to nano-scale sensors in vivo environments. The wide range of devices transverse multiple length scales and communicate across distance scales that vary by 9 orders of magnitude (from km to microns). It remains unclear what set of energy harvesting technologies are suitable. This chapter will review state-of-the-art technologies that allow multi-scale wireless devices to simultaneous harvest energy and transmit data.

Molecular communication via diffusion (MCvD) is inherently an energy efficient transportation paradigm, which requires no external energy during molecule propagation. Inspired by the fact that the emitted molecules have a finite probability to reach the receiver, this letter introduces an energy efficient scheme for the information molecule synthesis process of MCvD via a simultaneous molecular information and energy transfer (SMIET) relay. With this SMIET capability, the relay can decode the received information as well as generate its emission molecules using its absorbed molecules via chemical reactions. To reveal the advantages of SMIET, approximate closed-form expressions for the bit error probability and the synthesis cost of this two-hop molecular communication system are derived and then validated by particle-based simulation. Interestingly, by comparing with a conventional relay system, the SMIET relay system can be shown to achieve a lower minimum bit error probability via molecule division, and a lower synthesis cost via molecule type conversion or molecule division.

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#x002F;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.

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.

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 considers the energy harvesting potential from ambient electromagnetic waves in the radio frequency (RF) communications spectrum. Because of chaotic positioning nature of cells, handsets and hotspots, the analysis employs stochastic geometry theory to derive the mean and probability distribution of the received RF radiation energy available for harvesting. The mean and density function of energy harvested, as a function of transmitter deployment density, spectrum and pathloss characteristics are given. The novel benefit is that the derived expressions serve as a design guideline for RF harvesting solutions. For a given power harvesting and reliability requirement, the expressions in this paper can yield insight into which set and how many RF spectrum bands to harvest.

Femto-cells have been proposed as a throughput boosting solution for indoor users, but a key challenge is how to resolve the interference between neighboring cells. This paper proposes a distributed self-deployment solution to improve the indoor throughput. This investigation considers where to optimally place a femto-cell in a multi-room indoor environment. The novelty of the research is that a closed-form Femto-cell placement expression has been derived, which can maximize the throughput of the indoor building given knowledge of a few key statistical network parameters. The benefit compared to blind placement is that it can achieve a throughput improvement of 20 to 50%. The solution has a high placement error tolerance of 10% of building size. Another benefit is that the optimal solution can maintain an approximately constant level of throughput, irrespective of the indoor building size.