Unmanned Aerial Vehicles (UAVs) provide a wide range of opportunities for the service sector, such as last-mile deliveries, surveillance, and data gathering. Finding an optimal collision-free path is necessary to enable UAVs to complete their mission successfully. However, most of the existing path-planning solutions focus on a single UAV and a single target only while overlooking the case of a UAV swarm that collaborates to find collision-free service delivery paths spanning multiple targets or points of interest within a three-dimensional (3D) map. Therefore, to overcome this limitation we propose a novel Multi-UAV Direct Goal Bias Rapidly Exploring Random Trees Star (MDGB-RRT*) algorithm to provide robust path solutions where multiple UAVs traverse a shared 3D urban environment, with each having unique identified goal positions whilst traversing the map with collision-free guarantees. In contrast to RRT*, MDGB-RRT* directly connects the expanding tree to the target location within a predefined search radius, which reduces both the initial pre-validated path length and computation time. The simulation results obtained show that MDGB-RRT* achieves a notable performance advantage compared to existing algorithms for both single and dual-UAV urbanised 3D environment. In addition, MDGB-RRT* maintains its performance advantages with the introduction of two UAVs within the same environment.

Face anti-spoofing detection (FASD) has become a crucial technology due to the alarming advancements in presentation attacks (PAs). As more novel PAs with realistic generative capabilities emerge, improved biometric security solutions are needed to address these evolving threats. In early and foundational work, FASD techniques focused mainly on handcrafted features that were unreliable due to their limited representation capacity. In the recent decade, with the advancements in deep learning and its capabilities for image processing tasks and the emergence of large datasets, improvements in the performance of detecting PAs have been achieved. However, as research progresses rapidly, there is an absence of a comprehensive analysis of detection methods to understand the strengths and weaknesses of different types of approaches. Although various approaches utilise sensors in addition to RGB cameras, in this paper, we focus specifically on RGB camera-based methods and provide a comprehensive review of deep learning-based FASDs, including generalised models developed to date. In addition, the datasets are presented, along with the evaluation protocols and metrics. • This article provides an extensive analysis of deep learning-based approaches in the spatial domain, along with a focused review of frequency domain generalisation methods. • This paper covers the application of GANs for FASD, together with semi-supervised and self-supervised learning methods. Therefore, it provides the reader with state-of-the-art methods for different application scenarios (e.g., unseen domain generalisation and unknown attack detection). • The taxonomy of deep learning-based FASD methods using RGB cameras provides readers with an overview of modern approaches. • The strengths and weaknesses of existing models have also been explored and summarised, providing an overview of their capabilities and limitations.

The scientific community has recently focused on intelligent models for predicting and optimizing EV energy management. Despite numerous studies in energy management optimization, there’s a critical need to address the trade-off between energy consumption and occupant comfort. Existing IoT systems face challenges in data analytics security and authenticity, highlighting the need for contemporary models to overcome data privacy and cost-related issues. This study introduces a smart contract model based on optimization and control modules, aiming to manage energy consumption while satisfying user comfort requirements intelligently. Introducing a smart contract model with hierarchical layers—prediction, optimization, control, and Blockchain—the proposed approach intelligently manages energy consumption while meeting user comfort requirements. Utilizing a Kalman filter for prediction and the BAT algorithm for optimization, the model integrates modules to tailor user preferences and enhance comfort. The synergy between the optimization module and a convolutional FLC enhances system performance, ensuring minimized energy usage and elevated user comfort levels. The study also evaluates the model’s implementation of the Hyperledger Fabric network, assessing outcomes regarding caliper, latency, throughput, and resource utilization.

With the vast development of Internet-of-Things (IoT) ecosystem, various types of information, such as healthcare records and physical resources, are integrated for different types of applications. Due to the sheer number of connected IoT devices, which generate a large amount of data, Distributed Ledger Technology, such as Blockchain and IOTA have been recently applied in developing access control models, yet they involve significant energy due to mining, low throughput, non-scalable, and computational overhead that is not acceptable for IoT resource-constrained devices. In this paper, we propose a Scalable Decentralized and Lightweight Access Control framework (SDAC) by using the IOTA platform. IOTA is an emerging distributed ledger technology that has significant features for IoT, such as zero fees transactions, scalability, security and energy efficiency. The proposed SDAC aims to improve security, authorize, and authenticate users when accessing data by using the IOTA Masked Authenticated Messaging (MAM) protocol. MAM ensures access control by encrypting and granting permission to only authorized users. The experimental results indicate that IOTA MAM is a feasible solution that can be used for managing authorization in the IoT domain.

In the Internet of things (IoT), security and privacy issues are a fundamental challenge determining the successful implementation of many IoT applications. Distributed ledger technology (e.g., Blockchain) offers a great promise to solve these issues. Blockchain-based solutions support security and privacy, yet they involve significant energy due to mining, low throughput, and computational overhead that is not acceptable for IoT resource-constrained devices. In this paper, we propose an energy-efficient Privacy-Preserving Distributed Intelligence approach (PPDI) by adopting the IOTA technology. IOTA is an emerging distributed ledger technology that allows for zero fees transactions for the IoT. The proposed PPDI aims to address the privacy issues in the IoT by using the IOTA Masked Authenticated Messaging (MAM) protocol. MAM ensures privacy by encrypting and granting permission to authorized users to access data. This paper presents a healthcare scenario that demonstrate how IOTA MAM can be used to address the privacy issue in the IoT. The experimental results clearly show that IOTA MAM is a feasible solution that can be used to solve privacy related issues in the IoT domain.

This study investigates enhancing student learning and performance by exploring student motivation through the use of learning analytics. A mixed-methods approach will be used to collect data from Computer Science students within the UK higher education sector. The collected data will be analyzed using thematic analysis to develop a theoretical framework that will be tested subsequently using structural equation modeling. The identification of student motivation factors helps tutors and learning analysts to better understand student learning motivation and adapt their learning practices accordingly.

The noticeable growth in the adoption of Internet of Things (IoT) technologies, has led to the generation of large amounts of data usually from sensor devices. When dealing with massive amounts of data, it is very common to observe databases with large amounts of missing values. This is a challenge for data miners because various methods for data analysis only work well on complete databases. A popular way to deal with this challenge is to fill-in (impute) missing values using adequate estimation techniques. Unfortunately, a good number of existing methods rely on all the observed values in the entire dataset to estimate missing values, which significantly causes unfavourable effects (low accuracy and high complexity) on imputed results. In this paper, we propose a novel imputation technique based on data clustering and a robust selection of adequate imputation equations for each missing datapoint. We evaluate our proposed method using six University of California Irvine (UCI) datasets, and relevant comparison with five recently proposed imputation methods. The results presented showed that the performance of the proposed imputation method is comparable with the Local Similarity Imputation (LSI) technique in terms of imputation accuracy, but is significantly less complex than all the existing methods identified.

Sentiment analysis mines people's opinions and attitudes regarding a certain issue from source materials. Recently, it has drawn significant attention in a number of application areas. The sentiment analysis of healthcare in general and that of users' drug experience in particular could shed significant light on how to improve public health and make the right decisions. However, one of the major challenges in sentiment classification lies in the very large number of extracted features. Fuzzy-rough feature selection provides a means by which discrete or real-valued noisy data can be effectively reduced without human intervention. This paper proposes an implementation for automatic sentiment classification of drug reviews employing fuzzy rough feature selection. Experimental results demonstrate that the employment of fuzzy-rough feature selection can indeed significantly reduce the complexity of feature space and the classification run-time overheads while maintaining classification accuracy.

The significant growth and adoption of Internet of Things (IoT) solutions has led to tremendous increase in the generation of data. The need for high speed data processing has become very important to meet with the ever increasing volume and velocity of IoT data, due to the large scale and distributed nature of IoT infrastructure and networks. Present cloud based technologies are struggling to meet up with these needs for real time data processing in the midst of enormous amounts of data. The success of bitcoin has inspired more research in the application of Distributed ledger technologies in various domains. The decentralized nature of these platforms have enabled security and privacy of data in previous research and their architecture has a potential for enabling large scale decentralized data processing. In this paper, we identify some open areas of research in the use of distributed ledger technology and propose a framework for storing, analyzing and ensuring the security of large volumes of IoT data.

It is estimated that there will be approximately 26 to 30 billion Internet of Things (IoT) devices connected to the Internet by 2020. This presents research challenges in areas such as data processing, infrastructure scalability, and privacy. Several studies have demonstrated the benefits of using distributed intelligence to overcome these challenges. This article reviews existing state-of-the-art distributed intelligence approaches in IoT and focuses on the motivations and challenges for distributed intelligence in IoT. We propose a potential solution based on IOTA (Tangle), a platform that enables highly scalable transaction-based data exchange amongst large quantities of smart things in a peer-to-peer manner, together with mobile agents to support distributed intelligence. Challenges and future research directions are also discussed.

As the location-based services market is growing, the plethora of navigation applications is strongly increasing. The solutions are made for single purposes and platforms. And the communication and information exchange in between, for example, pedestrian navigation applications from different solution providers, remain non-existent. To fill this gap, we developed a software architecture. The architecture abstraction enables easy and modular replacement of map, object, method, user, communications and sensor implementations between applications for many navigation purposes and technology platforms. The prototype design was tested using inertial, GNSS and UWB technologies on a MATLAB environment using multiple users, methods and sensors.

We describe a hybrid-resolution version of the UKESM earth system model that reduces the model's computational costs by using high resolution for simulated resolved atmospheric dynamics and physical parameterizations, and a lower resolution for chemistry and aerosol calculations. Initial evaluations of its scientific performance are encouraging. We are currently working on coupling the hybrid-resolution atmosphere-chemistry-aerosol model to the ocean component of the full coupled system.

Summary The evolution of communication protocols, sensory hardware, mobile and pervasive devices, alongside social and cyber‐physical networks, has made the Internet of things (IoT) an interesting concept with inherent complexities as it is realised. Such complexities range from addressing mechanisms to information management and from communication protocols to presentation and interaction within the IoT. Although existing Internet and communication models can be extended to provide the basis for realising IoT, they may not be sufficiently capable to handle the new paradigms that IoT introduces, such as social communities, smart spaces, privacy and personalisation of devices and information, modelling and reasoning. With interaction models in IoT moving from the orthodox service consumption model, towards an interactive conversational model, nature‐inspired computational models appear to be candidate representations. Specifically, this research contests that the reactive and interactive nature of IoT makes chemical reaction‐inspired approaches particularly well suited to such requirements. This paper presents a chemical reaction‐inspired computational model using the concepts of graphs and reflection, which attempts to address the complexities associated with the visualisation, modelling, interaction, analysis and abstraction of information in the IoT. Copyright © 2013 John Wiley & Sons, Ltd.

Opportunistic Networks are a specific type of wireless ad hoc network where there is an absence of a continuous end-to-end path. The proliferation of mobile devices with Wi-Fi capability creates opportunities to forward packets by utilizing nodes as they present themselves. Such a dynamic networking environment enables services to be advertised by propagating from device to device, in order that all users in an area receive them. However, excessive propagation of service advertisements consumes energy from mobile devices, whilst also degrading the users' experience if they receive adverts for services that are misaligned with their personal interests. In this article we propose an architecture for a protocol and an algorithm that facilitates the matching of relevant service adverts with interested recipients in an Opportunistic Networking environment, whilst serving to minimize energy consumption.

The ability to seamlessly bridge clouds across organisational and administrative boundaries will play a vital role in establishing the utility of cloud computing for large-scale collaborative processes. Managing human and environmental contexts across geographical, network and administrative boundaries is a process that can benefit from a federation of cloud platforms. In the absence of a mature standard that defines access, control, management and coordination mechanisms between clouds in a federation, we explore these issues through a use-case of managing the dissemination and consumption of contextual information. The use-case is driven by the deployment of a broker-based context provisioning system, for homogenous cloud deployments that reside in different administrative domains. The discussion is driven by the aim to highlight key issues and challenges for enabling cloud federation for large-scale context provisioning, which forms the main contribution of this article.

We re-examine the concept of peer autonomy from an agent-oriented perspective and identify rational behaviours that can be embedded within complex P2P networks. Emergent behaviour is considered in relation to the environmental conditions presented by P2P networks, and we propose a classification of social characteristics that facilitate the mapping and subsequent exchange of peer experiences between nodes.