The increasing demand for real-time emergency response systems requires novel approaches to solving traffic congestion, ineffective routing, and cloud dependency delays. This paper describes MAESTRO—a multi-layered hierarchical architecture leveraging Software Defined Networking (SDN) and fog computing to improve ambulances routing in emergency response systems. The architecture integrates cloud, fog, and SDN layers and enables real-time decision making and efficient resource allocation through low-latency communication. This ensures ambulances are dispatched rapidly to accident locations and patients are admitted to hospitals in a timely manner. The fog computing layers are responsible for localized data processing, and the SDN layers dynamically control the routing of network traffic to achieve uninterrupted communication. The proposed algorithms are Ambulance Registration Protocol, Cloud-Fog-SDN Communication Algorithm, and Distributed SDN Traffic-Aware Routing Algorithm which provide continuous monitoring, ambulance-hospital coordination, and adaptive resilient routing in response to changing traffic conditions. Simulations in NS-2 and SUMO demonstrate higher response times, lower network latencies, and greater scalable improvements over traditional systems relying on cloud computing. The results validates MAESTRO’s ability to minimize the impact of congestion, optimize transit time, and deliver predictable routing to streamline emergency services.

Students' performance assessment is crucial in the context of education especially prior to levelling students or recommending personalised learning. In a multicultural environment, multiple factors play a role in students' performance that go beyond the students'-teacher interaction. These factors are related to students' characteristics and experience, learning environment and material as well as the material delivered. Considering the role of these factors in students' performance, this work proposed the use of two multivariate regression algorithms for predicting year 6 school students' performance in math over three semesters being autumn, spring and summer. Principal Component Regression (PCR) and Partial least Square Regression (PLSR) models were constructed and validated for predicting students' performance in end of block assessment over 15 blocks per year: six per autumn semester, six per spring semester and three in summer semester. The results showed that both PCR and PLSR models demonstrated accurate predictions for end of block assessments with correlation coefficient values up to 0.97 and low root mean square error of predictions that was below 5% in most cases. Best performing models were those that assessed fractions, perimeter and geometry and that indicated the strong relationship between the students' factors and end of block assessment. Out of 30 regression models, six models performed poorly and were related to four operations, decimals and ratios. Yet, the performance of students could be predicted accurately and precisely using multivariate regression algorithms. Future work involves evaluating these models to different cohorts of students to determine feasibility of the explored models.

The Industrial Internet of Things (IIoT) generates heterogeneous, high-volume data across diverse devices, posing challenges for anomaly detection while preserving privacy. Traditional federated learning approaches struggle with nonIID data, limited edge resources, and a lack of device-specific adaptation. These limitations often result in suboptimal bias handling, poor personalisation, and unstable convergence in complex IIoT environments. To address these challenges, this paper proposes a novel theoretical Hierarchical Boosting with Bayesian Fusion (HBBF) framework that extends the conventional federated learning paradigm into a three-tier architecture, with multiple devices per edge layer and edge nodes aggregated via a global server layer. Within this hierarchy, each device performs sequential boosting with bias fixation, ensuring that local LightGBM models progressively correct residual errors while maintaining personalised adaptation. The resulting leaf one-hot encoded embeddings from each device/edge are then blended at the edge layer, allowing correlated local knowledge to be synthesised before global Bayesian Aggregation. At the global layer, Bayesian Aggregation fuses the edge-level embeddings and uncertainties, providing a principled probabilistic integration of distributed knowledge. Through this hierarchical design, HBBF enhances robustness and accuracy under heterogeneous data conditions, reduces residual variance through structured bias fixation, and maintains communication efficiency and privacy. Compared to conventional methods like FedAvg and FedPer, HBBF achieves scalable, stable, and high-accuracy federated learning.

Identifying attacks on Internet of Things (IoT) systems through anomaly detection is an effective approach and remains a crucial area of research. The core method involves collecting system-related data during normal operation to establish a baseline of typical behavior and then continuously monitoring for deviations from this baseline. Using system call sequences for anomaly detection is a well-established and important field. System call sequences effectively capture the behavior of a target system at a low level, allowing identification of any changes in this behavior; however, these approaches face several challenges, including high false-positive rates, the need for segmentation of long sequences, and the difficulty of detecting anomalies when the system call data comes from multiple processes. This work presents a novel anomaly-detection approach that uses a position-specific scoring mechanism to analyze the content and structural properties of system call sequences. The proposed approach addresses key challenges in this field, including fixed-length segmentation of system call sequences, predetermined anomaly-detection thresholds, the detection of anomalies in both single and multiple processes, and high false-positive rates. We extensively evaluated the proposed approach using system-call-specific public datasets (ADFA-LD and UNM) of a diverse nature. The performance of the proposed content-based, structure-based, and combined content- and structure-based anomaly-detection methods was evaluated using ten-fold cross-validation. The proposed anomaly-detection approach achieves an impressive detection rate of 1.0, along with exceptionally low false-positive rates of 0.001 and 0.017 when evaluated on the UNM and ADFA-LD datasets, respectively.

Intelligent traffic signal systems, crucial for intelligent transportation systems, have been widely studied and deployed to enhance vehicle traffic efficiency and reduce air pollution. Unfortunately, intelligent traffic signal systems are at risk of data spoofing attack, causing traffic delays, congestion, and even paralysis. In this paper, we reveal a multivehicle collaborative data spoofing attack to intelligent traffic signal systems and propose a collaborative attack sequence generation model based on multiagent reinforcement learning (RL), aiming to explore efficient and stealthy attacks. Specifically, we first model the spoofing attack based on Partially Observable Markov Decision Process (POMDP) at single and multiple intersections. This involves constructing the state space, action space, and defining a reward function for the attack. Then, based on the attack modeling, we propose an automated approach for generating collaborative attack sequences using the Multi‐Actor‐Attention‐Critic (MAAC) algorithm, a mainstream multiagent RL algorithm. Experiments conducted on the multimodal traffic simulation (VISSIM) platform demonstrate a 15% increase in delay time (DT) and a 40% reduction in attack ratio (AR) compared to the single‐vehicle attack, confirming the effectiveness and stealthiness of our collaborative attack.

Abstract Federated learning (FL) is widely used in edge‐cloud collaborative training due to its distributed architecture and privacy‐preserving properties without sharing local data. FLTrust, the most state‐of‐the‐art FL defense method, is a federated learning defense system with trust guidance. However, we found that FLTrust is not very robust. Therefore, in the edge collaboration scenario, we mainly study the poisoning attack on the FLTrust defense system. Due to the aggregation rule, FLTrust, with trust guidance, the model updates of participants with a significant deviation from the root gradient direction will be eliminated, which makes the poisoning effect on the global model not obvious. To solve this problem, under the premise of not being deleted by the FLTrust aggregation rules, we construct malicious model updates that deviate from the trust gradient to the greatest extent to achieve model poisoning attacks. First, we utilize the rotation of high‐dimensional vectors around axes to construct malicious vectors with fixed orientations. Second, the malicious vector is constructed by the gradient inversion method to achieve an efficient and fast attack. Finally, a method of optimizing random noise is used to construct a malicious vector with a fixed direction. Experimental results show that our attack method reduces the model accuracy by 20%, severely undermining the usability of the model. Attacks are also successful hundreds of times faster than the FLTrust adaptive attack method.

Personalized recommendation is deemed ubiquitous. Indeed, it has been applied to several online services (e.g., E-commerce, advertising, and social media applications, to name a few). Learning unknown user preferences from user-provided data lies at the core of modern collaborative filtering recommender systems. However, there is an incentive for malicious attackers to manipulate the learned preferences, which could affect business decision making, by injecting poisoned data. In the face of such a poisoning attack, while previous works have proposed a number of defense methods succeeding in other machine learning (ML) tasks, little is effective for collaborative filtering (CF). Thereof, we present a new defense scheme called poison-tolerant collaborative filtering (PTCF), which is highly robust against poisoning attacks on collaborative filtering. Different from the defenses that remove outliers or search a min-loss subset, the PTCF scheme enables collaborative filtering on an attacked training dataset while guarantees system's availability and integrity. We evaluate extensively the PTCF scheme on a public dataset (Jester) and two real-world datasets (Movie and E-Shopping), and demonstrate that the PTCF scheme is significantly effective in providing robustness.

Recent advances in deep learning (DL) have brought tremendous gains in signal modulation classification. However, DL-based classifiers lack transparency and interpretability, which raises concern about model's reliability and hinders the wide deployment in real-word applications. While explainable methods have recently emerged, little has been done to explain the DL-based signal modulation classifiers. In this work, we propose a novel model-agnostic explainer, Model-Agnostic Signal modulation classification Explainer (MASE), which provides explanations for the predictions of black-box modulation classifiers. With the subsequence-based signal interpretable representation and in-distribution local signal sampling, MASE learns a local linear surrogate model to derive a class activation vector, which assigns importance values to the timesteps of signal instance. Besides, the constellation-based explanation visualization is adopted to spotlight the important signal features relevant to model prediction. We furthermore propose the first generic quantitative explanation evaluation framework for signal modulation classification to automatically measure the faithfulness, sensitivity, robustness, and efficiency of explanations. Extensive experiments are conducted on two real-world datasets with four black-box signal modulation classifiers. The quantitative results indicate MASE outperforms two state-of-the-art methods with 44.7% improvement in faithfulness, 30.6% improvement in robustness, and 44.1% decrease in sensitivity. Through qualitative visualizations, we further demonstrate the explanations of MASE are more human interpretable and provide better understanding into the reliability of black-box model decisions.

Vision is a vital sense that plays a pivotal role in our understanding of the world. The majority of our external information is acquired through our visual system, which significantly impacts various aspects of our lives, including mobility, cognitive abilities, access to information, and how we interact with both our surroundings and other individuals. Hence, individuals who need assisted living due to visual challenges are left behind and rely on human-driven image captioning services to make sense of their surroundings. In response to this challenge, we have developed a proof-of-concept system that integrates a large language model like ChatGPT to provide assistance to individuals with visual impairments in their daily lives through the utilisation of image captioning techniques. Our proposed model leverages the image captioning technique to describe the user’s environment. It is a fusion of concepts from Deep Learning and the Internet of Things, enabling it to provide more informative and enriched image captions. In this process, ChatGPT is stimulated to generate increasingly detailed and informative descriptions of images, allowing users to gain a deeper understanding of their surroundings. Our findings show that the proposed system generates captions that are contextually relevant to the visual content. These captions can assist individuals in various day-today activities, contributing to an improved quality of life.

Abstract Federated Learning (FL) is suitable for the application scenarios of distributed edge collaboration of the Internet of Things (IoT). It can provide data security and privacy, which is why it is widely used in the IoT applications such as Industrial IoT (IIoT). Latest research shows that the federated learning framework is vulnerable to poisoning attacks in the case of an active attack by the adversary. However, the existing backdoor attack methods are easy to be detected by the defence methods. To address this challenge, we focus on edge‐cloud synergistic FL clean‐label attacks. Unlike common backdoor attack, to ensure the attack's concealment, we add a small perturbation to realize the clean label attack by judging the cosine similarity between the gradient of the adversarial loss and the gradient of the normal training loss. In order to improve the attack success rate and robustness, the attack is implemented when the global model is about to converge. The experimental results verified that 1% of poisoned data could make an attack successful with a high probability. Our method maintains stealth while performing model poisoning attacks, and the average Peak Signal‐to‐Noise Ratio (PSNR) of poisoning images reaches over 30 dB, and the average Structural SIMilarity (SSIM) is close to 0.93. Most importantly, our attack method can bypass the Byzantine aggregation defence.

Industry 5.0 is aimed at merging the cognitive computing capabilities of deep neural networks (DNNs) with human resourcefulness in collaborative operations. DNNs have been widely used in Industrial Artificial Intelligence of Things (Industrial AIoT) systems. However, DNNs are vulnerable to adversarial attacks, which bring a considerable risk to Industrial AIoT systems. The adversary uses adversarial examples crafted on the local ensemble model to attack black-box target of Industrial AIoT systems, resulting in catastrophic consequences. It is essential to study ensemble adversarial attack and defense strategies in black-box scenarios. Nevertheless, current ensemble attacks' performance is limited by the diversity of local models and ensemble strategies, and defensive strategies are inefficient. To solve these problems, we propose two novel deep fusion methods from both an attacker's and a defender's perspective. For initiating attacks, we propose deep fusion attack. The erosion models are applied to compensate for local models' insufficiency in diversity. We fuse erosion models in the output space, and the feature space simultaneously and continuously accumulate historical gradients to retain adversarial information, thereby improving transferability. Extensive experimental results show that our approach achieves superior performance in black-box attacks, and the average success rate of our attack reaches a compelling 87.4%. For constructing defenses, we propose deep fusion defense, using a fusion of multiple predictions with erosion models as a novel approach. We successfully increase the model's robustness by more than 90% on the ImageNet dataset.

Although the global shipping industry is experiencing a productivity revolution due to the adoption of IoTs (Internet of Things), the dependence on complex data transmission and interactive centers is also increasing, which makes the IoT-enabled Maritime Transportation Systems (MTS) one of the most valuable but vulnerable industries against network security attacks. To guarantee the transmission security of confidential data, an important alternative in an untrustworthy IoT-enabled MTS is to apply the covert timing channels. This paper mainly introduces the construction of covert timing channel with low bit shifting rate and high reliability by multi-stage verification and error correction. For the covert timing channel schemes realized by active packet loss, the packet loss noise interferes with the channel's reliability. However, due to the constraints of stealthiness, the active packet loss ratio during covert communication is low, so more effective reliable strategies are needed to reduce noise interference. In the excellent scenario, when the bit error rate is lower than 0.08%, the transmission performance is kept at 0.49 bps. In the good scenario with strong network noise, although this method loses some performance, it can still maintain the transmission performance of 0.2 bps under the condition of bit error rate less than 1%, which effectively proves the effectiveness of multi-stage verification and error correction.

With the rapid development of mobile technology, it is very convenient to share people’s current locations by checking-in on Location-Based Social Networks (LBSNs). Using users’ check-in histories to study mobility preferences and recommend new locations is a typical application to LBSNs. Most existing models explore reasonable representations for users and locations. However, a lack of behavioral mobility modeling would hamper a better understanding of users’ mobility patterns. This paper proposes a location recommendation model to serve the personalized LBSNs application, called Spatio-temporal Individual mobility graph encoding network with Group Mobility Assistance (SIGMA). We design a spatio-temporal interaction enhanced graph neural network to encode the mobility graphs to represent individual mobility behaviors. Furthermore, we provide a novel stacked scoring approach to generate the recommendation score by combining the stacked individual mobility graphs with the group influences. We conduct extensive experiments on two real-world LBSNs data, Foursquare and Gowalla. The result demonstrates SIGMA outperforms ten state-of-the-art models and further confirms that both the individual and the group mobility behaviors play essential roles in the practical scenario of location recommendation.

Software configuration requires that the user sets appropriate values to specified variables, known as configuration parameters, which potentially affect the behaviors of software system. It is an essential means for software reliability, but how to ensure correct configurations remains a great challenge, especially when a large number of parameter settings are involved. Existing studies on misconfiguration diagnosis treat all configurations independently, ignoring the constraints and correlations among different configurations. In this article, we reveal the phenomenon of multi-misconfigurations and present a tool, MMD, for multi-misconfigurations diagnosis. Specifically, MMD consists of two modules: Correlated Configurations Analysis and Primary Misconfigurations Diagnosis. The former determines the correlation among each pair of configurations by analyzing the control and data flows related to each configuration. The latter is responsible for collecting a list of configurations ranked according to their suspiciousness. Combining the outputs of two modules, MMD is able to assist the user in multi-misconfigurations diagnosis. We evaluate MMD on seven popular Java projects: Randoop, Soot, Synoptic, Hdfs, Hbase, Yarn, and Zookeeper. MMD identifies 510 configuration correlations with a 4.9% false positive rate. Furthermore, it effectively diagnoses 22 multi-misconfigurations collected from StackOverflow, outperforming two state-of-the-art baselines.

Abstract The release of millions of financial documents, which has been known as the ‘WikiLeaks’ of the financial world (a.k.a. ‘Panama Papers’), has dragged global attention in how highly structured means applied by some of the elite to conceal their financial assets. Consequently, significant financial corruption allegations were raised. We concentrate on a somewhat overlooked region, the Middle East and North Africa (MENA) region. This study aims to use social network analytics to study the information contained in these documents. We are checking the major players in the MENA’s trends and patterns to determine if it matches the known economic powers. The analysis reveals that while the constructed network enjoys some typical characteristics, many interesting observations and properties are worth discussing. Specifically, using the extracted network consisting of 62 987 nodes and 84 692 edges, our social network analysis finding shows that, perhaps surprisingly, the nodes or the social network are not necessarily directly correlated with perceived economic influence.

Summary Discrete Tchebichef polynomials (DTPs) and their moments are effectively utilized in different fields such as video and image coding, pattern recognition, and computer vision due to their remarkable performance. However, when the moments order becomes large (high), DTPs prone to exhibit numerical instabilities. In this article, a computationally efficient and numerically stable recurrence algorithm is proposed for high order of moment. The proposed algorithm is based on combining two recurrence algorithms, which are the recurrence relations in the and ‐directions. In addition, an adaptive threshold is used to stabilize the generation of the DTP coefficients. The designed algorithm can generate the DTP coefficients for high moment's order and large signal size. By large signal size, we mean the samples of the discrete signal are large. To evaluate the performance of the proposed algorithm, a comparison study is performed with state‐of‐the‐art algorithms in terms of computational cost and capability of generating DTPs with large polynomial size and high moment order. The results show that the proposed algorithm has a remarkably low computation cost and is numerically stable, where the proposed algorithm is 27 times faster than the state‐of‐the‐art algorithm.

Nowadays, autonomous electric vehicles (AEVs) are increasingly popular due to low resource consumption, low pollutant emission, and high efficiency. In practice, Vehicle-to-Grid (V2G) networks supply energy power to EVs to ensure the usage of EVs. However, there are still certain security and privacy concerns in V2G connections, such as identity impersonation and message manipulation. Additionally, the widespread usage of EVs brings significant pressure on the power grid, leading to undesirable effects like voltage deviations if EVs’ charging is not well coordinated. In this article, to tackle these issues, we design a novel load-balanced secure and private EV charging framework named load-balanced secure and private framework (LbSP) for secure, private, and efficient EV charging with a minimal negative effect on the existing power grid. It assures reliable and efficient charging services by a lightweighted encryption technique. Also, it balances the energy consumption of power grids via an online pricing strategy that minimizes load variance by optimizing energy prices in real time. Moreover, it preserves users’ privacy while not affecting online pricing using an advanced differential privacy technique. Furthermore, LbSP deploys on an edge-cloud structure for fast response and more precise pricing, where clouds balance overall load consumption by online price optimization while edges gather data for clouds and respond to charging requests from EVs. The evaluation results show that the proposed framework ensures secure and private EV charging, balances energy load consumption, and preserves users’ privacy.

Big data, due to its promotion for industrial intelligence, has become the cornerstone of the Industry 4.0 era.Federated learning, proposed by Google, can effectively integrate data from different devices and different domains to train models under the premise of privacy preservation. Unfortunately, this new training paradigm faces security risks both on the client side and server side. This article proposes a new federated learning scheme to defend from client-side malicious uploads (e.g., backdoor attacks). In addition, we use cryptography techniques to prevent server-side privacy attacks (e.g., membership inference). Thesecure partial aggregationprotocol we designed improves the privacy and robustness of federated learning. The experiments show that models can achieve high accuracy of over 90% with a proper upload proportion, while the accuracy of the backdoor attack decreased from 99.5% to 0% with the best result. Meanwhile, we prove that our protocol can disable privacy attacks.

The federated learning provides an effective solution to train collaborative models over a large scale of participated Industrial Internet of Things (IIoT) applications with the help of a global server, building an intelligent life. However, the federated learning is vulnerable to the backdoor attack from strong malicious participants. The backdoor attack is inconspicuous and may result in devastating consequences. To resist the attack on IIoT applications, we propose the federated backdoor filter defense that can identify backdoor inputs and restore the data to availability by theblur-label-flippingstrategy. We build multiple filters with eXplainable AI models on the server and send them to clients randomly, preventing advanced attackers from evading the defense. Our backdoor filters show significant backdoor recognition with the accuracy up to 99%. After the implementation of the blur-label-flipping strategy, victim's local model on suspicious backdoor samples can achieve the accuracy up to 88%.

Numerous applications in 3D underwater sensor networks (UWSNs), such as pollution detection, disaster prevention, animal monitoring, navigation assistance, and submarines tracking, heavily rely on accurate localization techniques. However, due to the limited batteries of sensor nodes and the difficulty for energy harvesting in UWSNs, it is challenging to localize sensor nodes successfully within a short sensor node lifetime in an unspecified underwater environment. Therefore, we propose the Adaptive Energy-Efficient Localization Algorithm (Adaptive EELA) to enable energy-efficient node localization while adapting to the dynamic environment changes. Adaptive EELA takes a fuzzy game-theoretic approach, whereby the Stackelberg game is used to model the interactions among sensor and anchor nodes in UWSNs and employs the adaptive neuro-fuzzy method to set the appropriate utility functions. We prove that a socially optimal Stackelberg–Nash equilibrium is achieved in Adaptive EELA. Through extensive numerical simulations under various environmental scenarios, the evaluation results show that our proposed algorithm accomplishes a significant energy reduction, e.g., 66% lower compared to baselines, while achieving a desired performance level in terms of localization coverage, error, and delay.

The current Global Positioning System (GPS) has been heavily criticized by several parties (e.g., drivers and researchers) due to the delay in disseminating en-route data to drivers ahead of time. This issue resulted in poor rates of accuracy of data (i.e., traffic status), which eventually causes inefficient transportation. According to National Institute of Standards and Technology, the delay is caused mainly by the transmission medium. This paper presents a new distributed fog-based vehicles navigation system for efficient transportation. The proposed system uses closer-to-the-source data (aka drivers) nodes (i.e., fogs) as a new transmission medium for disseminate the road details to subscribed drivers who are connected to those nodes. A package delivery scenario has been implemented to show the impact of using fog nodes in reducing the navigation time and improving efficiency. Those fog nodes gather and broadcast real-time traffic data from/to drivers within the area they cover. In addition, a new road navigation simulator has been designed and developed to simulate the proposed system. The simulation results show superior performance of the new navigation system in terms of travel time and in-situ road data provisioning to drivers.

The recognition of hand palm print through veins is one of the promising biometric techniques, which has received great interest lately due to its accuracy in identifying individuals. Although the literature witnessed several techniques and devel-opments to deal with the problem of identifying people through the veins in the palm, the technology is still in its infancy. In this research, we propose our palm print recognition model which use convolution neural networks preceded by the pre-processing stages to optimise the data and to extract the important regions. The pre-processing helped in extracting the vein pattern which feed into the proposed convolution neural network model. The CASIA database has been used; it contains 7200 images taken form 100 people based on 6 wavelengths (940 nm, 850 nm, 700 nm, 630 nm, 460 nm, and white). The model has been tested with all wavelengths in the database. AlexNet is used for benchmarking. The results show that our approach using the proposed pre-processing has helped to surpass AlexNet in terms of performance, speed, and accuracy.

Education providers are increasingly using artificial techniques for predicting students' performance based on their interactions in Virtual Learning Environments (VLE). In this paper, the Open University Learning Analytics Dataset (OULAD), which contains student demographic information, assessment scores, number of clicks in the virtual learning environment and final results, etc, has been used to predict student performance. Various techniques such as standardisation and normalisation have been employed in the pre-processing stage. Spearman's correlation coefficient is used to measure the correlation between the activity types and the students' final results to determine the importance of the activities. Deep learning has been utilised to predict students' performance based on their engagement in the VLE. The empirical results show that our model has the ability to accurately predict student academic performance.

Summary Despite the elasticity and pay‐per‐use benefits of cloud computing (aka fifth utility computing), organizations adopting clouds could be locked into single cloud providers, which is not always a “pleasant” experience when these providers stop operations. This is a serious concern for those organizations that who would like to deploy (core) business processes on the cloud along with tapping into these two benefits. To address the lock‐into concern, this paper proposes an approach for decomposing business processes into fragments that would run over multiple clouds and hence multiple providers. To develop fragments, the approach considers both restrictions over owners of business processes and potential competition among cloud providers. On the one hand, restrictions apply to each task in a business process and are specialized into budget to allocate, deadline to meet, and exclusivity to request. On the other hand, competition leads cloud providers to offer flexible pricing policies that would cater to the needs and requirements of each process owner. A policy handles certain clouds' properties referred to as limitedness, non‐renewability, and non‐shareability that impact the availability of cloud resources and hence the whole fragmentation. For instance, a non‐shareable resource could delay other processes should the current process do not release this resource on time. During fragmentation, interactions between owners of processes and providers of clouds happen according to two strategies referred to as global and partial. The former collects offers about cloud resources from all providers, while the latter collects such details from particular providers. To evaluate these strategies' pros and cons, a system implementing them, as well as demonstrating the technical feasibility of the fragmentation approach using credit‐application case study, is also presented in the paper. The system extends BPMN2‐modeler Eclipse plugin and supports interactions of processes' owners with clouds' providers that result to identifying the necessary fragments with focus on cost optimization.

The expansion of information technology infrastructure is encountered with Advanced Persistent Threats (APTs), which can launch data destruction, disclosure, modification, and/or Denial of Service attacks by drawing upon vulnerabilities of software and hardware. Moving Target Defense (MTD) is a promising risk mitigation technique that replies to APTs via implementing randomisation and dynamic strategies on compromised assets. However, some MTD techniques adopt the blind random mutation, which causes greater performance overhead and worse defense utility. In this paper, we formulate the cyber-attack and defense as a dynamic partially observable Markov process based on dynamic Bayesian inference. Then we develop an Inference-Based Adaptive Attack Tolerance (IBAAT) system , which includes two stages. In the first stage, a forward–backward algorithm with a time window is employed to perform a security risk assessment. To select the defense strategy, in the second stage, the attack and defense process is modelled as a two-player general-sum Markov game and the optimal defense strategy is acquired by quantitative analysis based on the first stage. The evaluation shows that the proposed algorithm has about 10% security utility improvement compared to the state-of-the-art.

Road damages have caused numerous fatalities, thus the study of road damage detection, especially hazardous road damage detection and warning is critical for traffic safety. Existing road damage detection systems mainly process data at cloud, which suffers from a high latency caused by long-distance. Meanwhile, supervised machine learning algorithms are usually used in these systems requiring large precisely labeled data sets to achieve a good performance. In this article, we propose EcRD: an edge-cloud-based road damage detection and warning framework, that leverages the fast-responding advantage of edge and the large storage and computation resources advantages of cloud. There are three main contributions in this article: we first propose a simple yet efficient road segmentation algorithm to enable fast and accurate road area detection. Then, a light-weighted road damage detector is developed based on gray level co-occurrence matrix features at edge for rapid hazardous road damage detection and warning. Furthermore, a multitypes road damage detection model is introduced for long-term road management at cloud, embedded with a novel image generator based on cycle-consistent adversarial networks which automatically generates images with labels to further improve road damage detection accuracy. By comparing with the state-of-the-art, we demonstrate that the proposed EcRD can accurately detect both hazardous road damages at edge and multitypes road damages at cloud. Besides, it is around 579 times faster than cloud-based approaches without affecting users' experience and requiring very low storage and labeling cost.

With the advent of the era of the Internet of Things (IoT), a large number of interconnected smart devices form a huge network. The network can be abstracted as a graph, and we propose to identify similar IoT devices in different networks by graph alignment. However, most methods rely on prelabeled cross-network node pairs such as anchor links, which are difficult to obtain due to personal privacy and security restrictions, especially in IoT. In addition, existing network entity alignment methods focus on individual pairs of nodes but ignore the tightly connected group structure in the network, which is a significant feature of IoT devices. In this article, we propose a method of hierarchical unsupervised network alignment (HUNA) to identify similar IoT devices in different networks by a deep learning approach. First, we propose an unsupervised network alignment method based on cycle adversarial networks (UNA), which utilizes the adversarial characteristics of cycle adversarial networks to achieve entity alignment under unsupervised conditions. Second, we further expand the model by carefully designing the group structure aggregation optimization module to aggregate the nodes with closely related attributes and structures into a coarse-grained node and align the coarse-grained nodes. Finally, we evaluate HUNA with real and synthetic data sets. Experimental results show that this method can improve the accuracy of node alignment by 10% and perform well in terms of parameter sensitivity.

Abstract Security authentication is one of the most important levels of information security. Nowadays, human biometric techniques are the most secure methods for authentication purposes that cover the problems of older types of authentication like passwords and pins. There are many advantages of recent biometrics in terms of security; however, they still have some disadvantages. Progresses in technology made some specific devices, which make it possible to copy and make a fake human biometric because they are all visible and touchable. According to this matter, there is a need for a new biometric to cover the issues of other types. Brainwave is human data, which uses them as a new type of security authentication that has engaged many researchers. There are some research and experiments, which are investigating and testing EEG signals to find the uniqueness of human brainwave. Some researchers achieved high accuracy rates in this area by applying different signal acquisition techniques, feature extraction and classifications using Brain–Computer Interface (BCI). One of the important parts of any BCI processes is the way that brainwaves could be acquired and recorded. A new Signal Acquisition Strategy is presented in this paper for the process of authorization and authentication of brain signals specifically. This is to predict image memorability from the user’s brain to use mental imagery as a visualization pattern for security authentication. Therefore, users can authenticate themselves with visualizing a specific picture in their minds. In conclusion, we can see that brainwaves can be different according to the mental tasks, which it would make it harder using them for authentication process. There are many signal acquisition strategies and signal processing for brain-based authentication that by using the right methods, a higher level of accuracy rate could be achieved which is suitable for using brain signal as another biometric security authentication.

We are delighted to present this novel special issue, which emphasizes semantic eSystems and their engineering methods, techniques, and tools. “eSystems” are those interdisciplinary cost-effective and interconnected solutions that leverage advanced information and communication technologies' techniques and tools to gain competitive advantage. Recent years have witnessed increasing interest in the design and development of various eSystem-based applications, ranging from real-world to machine and robotic applications. An essential feature in eSystems is their intelligent ability to “speak” and interact with one another to support function “extensibility” so that complex interactions could be established with other eSystems. Therefore, connecting disparate eSystems “on the fly” necessitates engineering a dialog channel such that it enables data fusion, exchange, and link in a unified and understandable way. While data emitted from eSystems are of different formats, sizes, and types, adopting semantic data technologies is a natural way to address these differences. Simply put, semantics is about agreeing on a common understanding of data that need to be exchanged between systems and between humans and systems. This special issue received 22 submissions, which were aligned with the theme of semantic eSystems' methods, techniques, and tools. Those submissions came from diverse researchers from academia, industry, and individuals from all over the globe. First, all submissions were screened closely by the editors to check their suitability with the special issue's list of topics. Second, after multiple rounds of peer review, only 11 high-quality submissions were accepted for publication in this special issue. Those accepted papers address the semantic eSystems theme from different perspectives including, for instance, efficient semantic–visual indexing model for large-scale image retrieval in cloud environment, trilateration-based indoor localization engineering technique for visible light communication system, graph-based system to enable efficient transformation of enterprise infrastructures, recognizing physical activities having complex interclass variations using semantic data of smartphone, spatiotemporal-based sentiment analysis on tweets for risk assessment of an event using the deep-learning approach, sentiment-based eSystem using hybridized fuzzy and deep neural network for measuring customer satisfaction, data fusion analysis for emotion recognition with thermal image and Internet of Thing devices, unified framework to manage cybersecurity and safety in manufacturing industry, graph-based convolutional neural network stock price prediction with leading indicators, author classification using transfer learning and predicting stars in coauthor networks, and DNA signal analysis tool: intelligent noise suppression window filter. The authors express their sincere thanks to the Editor-in-Chief for allowing them to organize this special issue. The editorial office staff members are excellent and are thanked for their support. The authors are also thankful to all the contributors who made this special issue possible and to the reviewers for their thoughtful contributions.

Medical Internet of Things are generating a big volume of data to enable smart medicine that tries to offer computer-aided medical and healthcare services with artificial intelligence techniques like deep learning and clustering. However, it is a challenging issue for deep learning, and clustering algorithms to analyze large medical data because of their high computational complexity, thus hindering the progress of smart medicine. In this article, we present an incremental high-order possibilistic c-means algorithm (IHoPCM) on a cloud-edge computing system to achieve medical data coclustering of multiple hospitals in different locations. Specifically, each hospital employs the deep computation model to learn a feature tensor of each medical data object on the local edge computing system, and then uploads the feature tensors to the cloud computing platform. The high-order possibilistic c-means algorithm is performed on the cloud system for medical data clustering on uploaded feature tensors. Once the new medical data feature tensors are arriving at the cloud computing platform, the incremental high-order possibilistic c-means algorithm (IHoPCM) is performed on the combination of the new feature tensors and the previous clustering centers to obtain clustering results for the feature tensors received to date. In this way, repeated clustering on the previous feature tensors is avoided to improve the clustering efficiency. In the experiments, we compare different algorithms on two medical datasets regarding clustering accuracy and clustering efficiency. Results show that the presented IHoPCM method achieves great improvements over the compared algorithms in clustering accuracy and efficiency.

Federated learning (FL) enables a large number of clients to collaboratively train a global model through sharing their gradients in each synchronized epoch of local training. However, a centralized server used to aggregate these gradients can be compromised and forge the result in order to violate privacy or launch other attacks, which incurs the need to verify the integrity of aggregation. In this work, we explore how to design communication-efficient and fast verifiable aggregation in FL. We propose VeriFL, a verifiable aggregation protocol, with O(N) (dimension-independent) communication and O(N+ d) computation for verification in each epoch, where N is the number of clients and d is the dimension of gradient vectors. Since d can be large in some real-world FL applications (e.g., 100K), our dimension-independent communication is especially desirable for clients with limited bandwidth and high-dimensional gradients. In addition, the proposed protocol can be used in the FL setting where secure aggregation is needed or there is a subset of clients dropping out of protocol execution. Experimental results indicate that our protocol is efficient in these settings.

Android as an operating system is now increasingly being adopted in industrial information systems, especially with cyber-physical systems (CPS). This also puts Android devices onto the front line of handling security-related data and conducting sensitive behaviors, which could be misused by the increasing number of polymorphic and metamorphic malicious applications targeting the platform. The existence of such malware threats, therefore, call for more accurate identification and surveillance of sensitive Android app behaviors, which is essential to the security of CPS and Internet of Things (IoT) devices powered by Android. Nevertheless, achieving dynamic app behavior monitoring and identification on real CPS powered by Android is challenging because of restrictions from the security and privacy model of the platform. In this article, the authors investigate how the latest advances in deep learning could address this security problem with better accuracy. Specifically, a deep learning engine is proposed that detects sensitive app behaviors by classifying patterns of system-wide statistics, such as available storage space and transmitted packet volume, using a customized deep neural network based on existing models called Encoder and ResNet. Meanwhile, to handle resource limitations on typical CPS and IoT devices, sparse learning is adopted to reduce the amount of valid parameters in the trained neural network. Evaluations show that the proposed model outperforms a well-established group of baselines on time series classification in identifying sensitive app behaviors with background noise and the targeted behaviors potentially overlapping.

Configuration errors are among the dominant causes of system faults for the industrial automation and control systems (IACS). It is difficult to detect and correct such errors of IACS as there are various kinds of systems and devices with miscellaneous configuration specifications. In this article, we first propose a streaming algorithm to keep all the configuration changes in the limited memory space. When making a new configuration change, another novel streaming algorithm is proposed to search and return all the similar historical changes, which can be used to validate this new one. So far, we are the first to model the configuration changes of IACS as a data stream and apply the streaming similarity search in correcting configuration errors while overcoming the inherent unbounded-memory bottleneck. The theoretical correctness and complexity analyses are presented. Experiments with real and synthetic datasets confirm the theoretical analyses and demonstrate the effectiveness of the proposed method in preventing misconfigurations of IACS.

As an effective tool, roadside digital billboard advertising is widely used to attract potential customers (e.g., drivers and passengers passing by the billboards) to obtain commercial profit for the advertiser, i.e., the attracted customers' payment. The commercial profit depends on the number of attracted customers, hence the advertiser needs to adopt an effective advertising strategy to determine the advertisement switching policy for each digital billboard to attract as many potential customers as possible. Whether a customer could be attracted is influenced by numerous factors, such as the probability that the customer could see the billboard and the degree of his/her interests in the advertisement. Besides, cooperation and competition among all digital billboards will also affect the commercial profit. Taking the above factors into consideration, we formulate the dynamic advertising problem to maximize the commercial profit for the advertiser. To address the problem, we first extract potential customers' implicit information by using the vehicular data collected by Mobile CrowdSensing (MCS), such as their vehicular trajectories and their preferences. With this information, we then propose an advertising strategy based on multi-agent deep reinforcement learning. By using the proposed advertising strategy, the advertiser could determine the advertising policy for each digital billboard and maximize the commercial profit. Extensive experiments on three real-world datasets have been conducted to verify that our proposed advertising strategy could achieve the superior commercial profit compared with the state-of-the-art strategies.

There have been significant advances in the field of Internet of Things (IoT) recently, which have not always considered security or data security concerns: A high degree of security is required when considering the sharing of medical data over networks. In most IoT-based systems, especially those within smart-homes and smart-cities, there is a bridging point (fog computing) between a sensor network and the Internet which often just performs basic functions such as translating between the protocols used in the Internet and sensor networks, as well as small amounts of data processing. The fog nodes can have useful knowledge and potential for constructive security and control over both the sensor network and the data transmitted over the Internet. Smart healthcare services utilise such networks of IoT systems. It is therefore vital that medical data emanating from IoT systems is highly secure, to prevent fraudulent use, whilst maintaining quality of service providing assured, verified and complete data. In this article, we examine the development of a Cognitive Fog (CF) model, for secure, smart healthcare services, that is able to make decisions such as opting-in and opting-out from running processes and invoking new processes when required, and providing security for the operational processes within the fog system. Overall, the proposed ensemble security model performed better in terms of Accuracy Rate, Detection Rate, and a lower False Positive Rate (standard intrusion detection measurements) than three base classifiers (K-NN, DBSCAN, and DT) using a standard security dataset (NSL-KDD).

Cloud services are often distributed across several data centers requiring new scalable approaches to efficiently perform searching to reduce the energy and price cost of fulfilling requests. Multiagent-based systems have arisen as a powerful technique for improving distributed processing on a wide scale, which can operate in environments where partial observability is the norm and the cost of prolonged search can be exponential. In this paper, we present a multiagent-based service composition approach, using agent-matchmakers and agent-representatives, for the efficient retrieval of distributed services and propagation of information within the agent network to reduce the amount of brute-force search. Our extensive simulation results indicate that by introducing localized agent-based memory searches, the amount of actions (with their associated energy costs) can be reduced by over 50 percent which results in a lower energy cost per composition request.

Ground-level ozone concentration is one of the main concerns for air pollution, due to the negative impacts on human health, animals, foliage, climate and the whole ecosystem. The aim of this paper is to reduce the influential outliers by including weightages within robust method to avoid the bias of the model. The influential outliers from x-space (predictors) have been identified using leverage values. Furthermore, Cook's distance and standardized residual have been computed to clarify the influential outliers from both of x-space and y-direction. S-estimation and MM-estimation have been introduced as a new approach for reducing the influential outliers from x-space and both of y-direction and x-space respectively. The comparison between the robust method and the ordinary least square method shows that, the accuracy measures of the robust method have been improved by around 0.94% (D+1), 0.56% (D+2) and 1.85% (D+3) respectively.

While the usage of Artificial Intelligence and Machine Learning Software (AI/MLS) in defensive cybersecurity has received considerable attention, there remains a noticeable research gap on their offensive use. This paper reviews the defensive usage of AI/MLS in cybersecurity and then presents a survey of its offensive use. Inspired by the System-Fault-Risk (SFR) framework, we categorize AI/MLS-powered cyberattacks by their actions into seven categories. We cover a wide spectrum of attack vectors, discuss their practical implications and provide some recommendations for future research.

The primary aim of Vehicular Ad hoc NETworks (VANET) is to enhance traffic safety by enabling frequent broadcasting of location information between vehicles. In VANET safety applications, a vehicle requires to broadcast messages, which usually contain its location information, every (1-10 Hz) with other vehicles in its communication area (300m) to facilitate cooperative awareness. This would arise privacy issues because vehicles are vulnerable to tracking attacks via their locations. To prevent long-term linking, many privacy schemes have adopted a silent period in which a vehicle stops sharing its locations for a period. However, silent periods could have a negative impact on safety applications as an accident could have happened if a vehicle stop sharing its locations with other neighbours. Thus, in this paper, we first discuss three privacy schemes (RSP, SLOW and CAPS), which adopted silent periods but in different concepts. Then, we improve the privacy and safety level of CAPS. A privacy simulator PREXT is used to evaluate and compare the performance of schemes.

Summary The rapid proliferation of Internet of things (IoT) devices, such as smart meters and water valves, into industrial critical infrastructures and control systems has put stringent performance and scalability requirements on modern Supervisory Control and Data Acquisition (SCADA) systems. While cloud computing has enabled modern SCADA systems to cope with the increasing amount of data generated by sensors, actuators, and control devices, there has been a growing interest recently to deploy edge data centers in fog architectures to secure low‐latency and enhanced security for mission‐critical data. However, fog security and privacy for SCADA‐based IoT critical infrastructures remains an under‐researched area. To address this challenge, this contribution proposes a novel security “toolbox” to reinforce the integrity, security, and privacy of SCADA‐based IoT critical infrastructure at the fog layer. The toolbox incorporates a key feature: a cryptographic‐based access approach to the cloud services using identity‐based cryptography and signature schemes at the fog layer. We present the implementation details of a prototype for our proposed secure fog‐based platform and provide performance evaluation results to demonstrate the appropriateness of the proposed platform in a real‐world scenario. These results can pave the way toward the development of a more secure and trusted SCADA‐based IoT critical infrastructure, which is essential to counter cyber threats against next‐generation critical infrastructure and industrial control systems. The results from the experiments demonstrate a superior performance of the secure fog‐based platform, which is around 2.8 seconds when adding five virtual machines (VMs), 3.2 seconds when adding 10 VMs, and 112 seconds when adding 1000 VMs, compared to the multilevel user access control platform.

Fog-to-fog communication has been introduced to deliver services to clients with minimal reliance on the cloud through resource and capability sharing of cooperative fogs. Current solutions assume full cooperation among the fogs to deliver simple and composite services. Realistically, each fog might belong to a different network operator or service provider and thus will not participate in any form of collaboration unless self-monetary profit is incurred. In this paper, we introduce a fog collaboration approach for simple and complex multimedia service delivery to cloud subscribers while achieving shared profit gains for the cooperating fogs. The proposed work dynamically creates short-term service-level agreements (SLAs) offered to cloud subscribers for service delivery while maximizing user satisfaction and fog profit gains. The solution provides a learning mechanism that relies on online and offline simulation results to build guaranteed workflows for new service requests. The configuration parameters of the short-term SLAs are obtained using a modified tabu-based search mechanism that uses previous solutions when selecting new optimal choices. Performance evaluation results demonstrate significant gains in terms of service delivery success rate, service quality, reduced power consumption for fog and cloud datacenters, and increased fog profits.

Oblivious RAM (ORAM) is important for applications that require hiding of access patterns. However, most of existing implementations of ORAM are very expensive, which are infeasible to be deployed in lightweight devices, like the terminal equipment for Internet of Things (IoT). In this article, we focus on how to apply the expensive ORAM to protect access pattern in IoT devices and propose an ORAM scheme supporting thin-client, called “ThinORAM”, under non-colluding clouds. Our proposed scheme removes complicated computations in the client side and requires only O(1) communication cost with a reasonable response time. We further show how to securely deploy ThinORAM in the fog computing environment to achieve oblivious data access with minimum client cost. Experiments show that our scheme can eliminate most of the client storage and reduce the cloud-cloud bandwidth by 2×, with 3× faster response time, when compared to the best scheme that aims at reducing client side overheads.

This paper considers device-to-device (D2D) network with Simultaneous Wireless Information and Power Transfer (SWIPT) enabled devices to ensure self-sustained communication in situations like disasters. Such direct link networks can ensure connectivity with devices having drained back-up, when trapped in collapsed infrastructure, through mutual sharing of energy on RF link. To guarantee successful execution of SWIPT session for an isolated device in wake of disasters, it is pertinent to select a reliable peer with ultimate aim to maximize link Energy Efficiency (EE). In practice, Energy Harvesting (EH) is not achievable after Information Decoding (ID); however, it has been made possible through splitting the signal in the time domain. Selection of D2D peer for self-sustained communication with an objective to maximize EE through optimum time based splitting of signal has not been extensively studied. In this paper to manifest the aforesaid goal, we worked out a joint problem of peer association and time switching ratio allocation with an objective to maximize the EE for a device contained under collapsed infrastructure. We propose an Energy efficient Peer Selection and Time switching Ratio Allocation (EPS-TRA) algorithm to solve the proposed mixed integer problem. Numerical results validate our proposed approach in acquiring better EE when compared with Uniform Allocation Scheme of time slots for EH & ID. Furthermore, results explain how EE of the link varies with the choice of constrained variables i.e., data rate and harvested energy.

Fog computing is a promising network paradigm in the IoT area as it has a great potential to reduce processing time for time-sensitive IoT applications. However, fog can get congested very easily due to fog resources limitations in term of capacity and computational power. In this paper, we tackle the issue of fog congestion through a request offloading algorithm. The result shows that the performance of fogs nodes can be increased be sharing fog's overload over several fog nodes. The proposed offloading algorithm could have the potential to achieve a sustainable network paradigm and highlights the significant benefits of fog offloading for the future networking paradigm.

Intracranial pressure (ICP) could be an indicator of a neurological disorder known as hydrocephalus, which is currently managed by shunting procedure. This paper investigates the current advances of shunting valves and provides an overview of ICP readings interpretation from a medical point of view with reference to Alder Hey hospital in Liverpool, UK. Moreover, this paper helps to express ICP readings using advanced data science approach and prepares for implementing intelligent approaches as an alternative pathway to improve the use of ICP within the current medical system. It is assumed that this paper would help specialists and non-specialists in an informative way to comprehend ICP readings. It also allows combining ICP reading with other parameters to derive a proper action with respect to patients with hydrocephalus.

With the emerge of Internet of Things, billions of devices and humans are connected directly or indirectly to the internet. This significant growth in the number of connected devices rises the needs for a new development for the current network paradigm (e.g., cloud computing). The new network paradigm, such as fog computing, along with its related edge computing paradigms, are seen as promising solutions for handling the large volume of securely-critical and delay-sensitive data that is being produced by the IoT nodes. In this paper, we give a brief overview on the IoT related computing paradigms, including their similarities and differences as well as challenges. Next, we provide a summary of the challenges and processing and storage capabilities of each network paradigm.

The construction sector, which has a long history to use visualisation to envisage proposed designs and project delivery, is beginning to see the benefits of augmented reality and agile project management methodologies. This study investigated the benefits of augmented reality and agile project management methodologies. Convergent design method was considered valuable and the most straightforward for this study, as different types of quantitative and qualitative data were required to be collected and analysed. The participants drawn from the construction sector revealed a number of augmented and agile determinants that facilitated the delivery of construction and integration of project teams. The participants suggested that the proposed ARGILE framework increases client understanding of the tasks output, increases client involvement and collaboration with the project team. It was further established that the proposed ARGILE framework enhances project time management, embeds the client and empowers multidisciplinary team, increases collaboration and communication.

Outdoor mobility of Visually Impaired People (VIPs) has always been challenging due to the dynamically varying scenes and environmental states. Variety of systems have been introduced to assist VIPs' mobility that include sensor mounted canes and use of machine intelligence. However, these systems are not reliable when used to navigate the VIPs in dynamically changing environments. The associated challenges are the robust sensing and avoiding diverse types of obstacles, dynamically modelling the changing environmental states (e.g. moving objects, road-works), and effective communication to interpret the environmental states and hazards. In this paper, we propose an intelligent wearable auditory display framework that will process real-time video and multi-sensor data streams to: a) identify the type of obstacles, b) recognize the surrounding scene/objects and corresponding attributes (e.g. geometry, size, shape, distance from user), c) automatically generate the descriptive information about the recognized obstacle/objects and attributes, d) produce accurate, precise and reliable spatial information and corresponding instructions in audio-visual form to assist and navigate VIPs safely with or without the assistance of traditional means.

Medical images and videos are utilized to discover, diagnose and treat diseases. Managing, storing, and retrieving stored images effectively are considered important topics. The rapid growth of multimedia data, including medical images and videos, has caused a swift rise in data transmission volume and repository size. Multimedia data contains useful information; however, it consumes an enormous storage space. Therefore, high processing time for that sheer volume of data will be required. Image and video applications demand for reduction in computational cost (processing time) when extracting features. This paper introduces a novel method to compute transform coefficients (features) from images or video frames. These features are used to represent the local visual content of images and video frames. We compared the proposed method with the traditional approach of feature extraction using a standard image technique. Furthermore, the proposed method is employed for shot boundary detection (SBD) applications to detect transitions in video frames. The standard TRECVID 2005, 2006, and 2007 video datasets are used to evaluate the performance of the SBD applications. The achieved results show that the proposed algorithm significantly reduces the computational cost in comparison to the traditional method.

Traditional network routing protocol exhibits high statics and singleness, which provide significant advantages for the attacker. There are two kinds of attacks on the network: active attacks and passive attacks. Existing solutions for those attacks are based on replication or detection, which can deal with active attacks; but are helpless to passive attacks. In this paper, we adopt the theory of network coding to fragment the data in the Software-Defined Networks and propose a network coding-based resilient multipath routing scheme. First, we present a new metric named expected eavesdropping ratio to measure the resilience in the presence of passive attacks. Then, we formulate the network coding-based resilient multipath routing problem as an integer-programming optimization problem by using expected eavesdropping ratio. Since the problem is NP-hard, we design a Simulated Annealing-based algorithm to efficiently solve the problem. The simulation results demonstrate that the proposed algorithms improve the defense performance against passive attacks by about 20% when compared with baseline algorithms.

Sensitive data protection is vital for mobile users. An effective way is to store sensitive data in the hidden volume of mobile devices with Plausibly Deniable Encryption (PDE) systems. Typically, PDE creates a hidden volume inside the outer volume. However, existing PDE systems could lose data, due to overriding the hidden volume, and significantly waste physical storage, because of the fixedly reserved area for the hidden volume. In this paper, we present MobiGyges to solve the above problems. MobiGyges leverages the Thin Pool to coordinate the storage allocation for both the outer volume and the hidden volume which avoids data override and prevents data loss. Moreover, it improves the storage efficiency by virtualizing the total physical storage into small storage blocks and utilizing the blocks for the outer volume and the hidden volume, which eliminates the reserved area. We implement MobiGyges prototype on Google Nexus 6P with LineageOS 13 operating system. Experimental results show that MobiGyges avoids data loss and improves storage utilization up to 31% compared with existing works.

The major objective of evolution towards 5G networks is to support increasing number of end devices with stringent latency and high bandwidth requirements. Software Defined Networking (SDN) and Network Function Virtualization (NFV) are paving the way in supporting such requirements of 5G networks. In this paper, we propose a seamless mobility management for the users when they move from a SDN controller coverage to another in 5G heterogeneous networks. Our proposed solution utilizes the key-value Distributed Hash Table (DHT) to catch users' mobility in a distributed SDN controller to address scalability and seamlessness, where mobile device may join and leave between different associated SDN controllers. The proposed solution allows to select an appropriate AP with cooperation of mobile devices and controllers, so that network performance can be maximized and users' demand can be met in a dynamically changing of network condition. The performance evaluated using OMNeT++ simulator imparts that the solution introduced in this paper can successfully reduce handover latency around 50% compared to the conventional mobility management solutions.

Due to a rapidly increasing aging population and its associated challenges in health and social care, Ambient Assistive Living has become the focal point for both researchers and industry alike. The need to manage or even reduce healthcare costs while improving the quality of service is high government agendas. Although, technology has a major role to play in achieving these aspirations, any solution must be designed, implemented and validated using appropriate domain knowledge. In order to overcome these challenges, the remote real-time monitoring of a person’s health can be used to identify relapses in conditions, therefore, enabling early intervention. Thus, the development of a smart healthcare monitoring system, which is capable of observing elderly people remotely, is the focus of the research presented in this paper. The technology outlined in this paper focuses on the ability to track a person’s physiological data to detect specific disorders which can aid in Early Intervention Practices. This is achieved by accurately processing and analysing the acquired sensory data while transmitting the detection of a disorder to an appropriate career. The finding reveals that the proposed system can improve clinical decision supports while facilitating Early Intervention Practices. Our extensive simulation results indicate a superior performance of the proposed system: low latency (96% of the packets are received with less than 1 millisecond) and low packets-lost (only 2.2% of total packets are dropped). Thus, the system runs efficiently and is cost-effective in terms of data acquisition and manipulation.

In recent years, the UK’s emergency call and response has shown elements of great strain as of today. The strain on emergency call systems estimated by a 9 million calls (including both landline and mobile) made in 2014 alone. Coupled with an increasing population and cuts in government funding, this has resulted in lower percentages of emergency response vehicles at hand and longer response times. In this paper, we highlight the main challenges of emergency services and overview of previous solutions. In addition, we propose a new system call Smart Hospital Emergency System (SHES). The main aim of SHES is to save lives through improving communications between patient and emergency services. Utilising the latest of technologies and algorithms within SHES is aiming to increase emergency communication throughput, while reducing emergency call systems issues and making the process of emergency response more efficient. Utilising health data held within a personal smartphone, and internal tracked data (GPU, Accelerometer, Gyroscope etc.), SHES aims to process the mentioned data efficiently, and securely, through automatic communications with emergency services, ultimately reducing communication bottlenecks. Live video-streaming through real-time video communication protocols is also a focus of SHES to improve initial communications between emergency services and patients. A prototype of this system has been developed. The system has been evaluated by a preliminary usability, reliability, and communication performance study.

With high learner withdrawal rates in the setting of MOOC platforms, the early identification of at-risk student groups has become increasingly important. Although many prior studies consider the dropout issue in form of a sequence classification problem, such works address only a limited set of behavioural dynamics, typically recorded as sequence of weekly interval, neglecting important contextual factors such as assignment deadlines that may be important components of student latent engagement. In this paper we, therefore, aim to investigate the use of Gaussian Mixture Models for the incorporation such important dynamics, providing an analytical assessment of the influence of latent engagement on students and their subsequent risk of leaving the course. Additionally, linear regression and, k-nearest neighbors classifiers were used to provide a performance comparison. The features used in the study were constructed from student behavioural records, capturing activity over time, which were subsequently organized into six-time intervals, corresponding to assignment submission dates. Results obtained from the classification procedure yielded an F1-Measure of 0.835 for the Gaussian Mixture Model, indicating that such an approach holds promise for the identification of at-risk students within the MODe setting.

Chronic Kidney Disease is a serious lifelong condition that induced by either kidney pathology or reduced kidney functions. Early prediction and proper treatments can possibly stop, or slow the progression of this chronic disease to end-stage, where dialysis or kidney transplantation is the only way to save patient's life. In this study, we examine the ability of several machine-learning methods for early prediction of Chronic Kidney Disease. This matter has been studied widely; however, we are supporting our methodology by the use of predictive analytics, in which we examine the relationship in between data parameters as well as with the target class attribute. Predictive analytics enables us to introduce the optimal subset of parameters to feed machine learning to build a set of predictive models. This study starts with 24 parameters in addition to the class attribute, and ends up by 30 % of them as ideal sub set to predict Chronic Kidney Disease. A total of 4 machine learning based classifiers have been evaluated within a supervised learning setting, achieving highest performance outcomes of AUC 0.995, sensitivity 0.9897, and specificity 1. The experimental procedure concludes that advances in machine learning, with assist of predictive analytics, represent a promising setting by which to recognize intelligent solutions, which in turn prove the ability of predication in the kidney disease domain and beyond.

Telecom companies are facing a serious problem of customer churn due to exponential growth in the use of telecommunication based services and the fierce competition in the market. Customer churns are the customers who decide to quit or switch use of the service or even company and join another competitor. This problem can affect the revenues and reputation of the telecom company in the business market. Therefore, many Customer Churn Prediction (CCP) models have been developed; however these models, mostly study in the context of within company CCP. Therefore, these models are not suitable for a situation where the company is newly established or have recently adopted the use of advanced technology or have lost the historical data relating to the customers. In such scenarios, Just-In-Time (JIT) approach can be a more practical alternative for CCP approach to address this issue in cross-company instead of within company churn prediction. This paper has proposed a JIT approach for CCP. However, JIT approach also needs some historical data to train the classifier. To cover this gap in this study, we built JIT-CCP model using Cross-company concept (i.e., when one company (source) data is used as training set and another company (target) data is considered for testing purpose). To support JIT-CCP, the cross-company data must be carefully transformed before being applied for classification. The objective of this paper is to provide an empirical comparison and effect of with and without state-of-the-art data transformation methods on the proposed JIT-CCP model. We perform experiments on publicly available benchmark datasets and utilize Naive Bayes as an underlying classifier. The results demonstrated that the data transformation methods improve the performance of the JIT-CCP significantly. Moreover, when using well-known data transformation methods, the proposed model outperforms the model learned by using without data transformation methods.

In this paper, a novel application of machine learning algorithms including Neural Network architecture is presented for the prediction of flood severity. Floods are considered natural disasters that cause wide-scale devastation to areas affected. The phenomenon of flooding is commonly caused by runoff from rivers and precipitation, specifically during periods of extremely high rainfall. Due to the concerns surrounding global warming and extreme ecological effects, flooding is considered a serious problem that has a negative impact on infrastructure and humankind. This paper attempts to address the issue of flood mitigation through the presentation of a new flood dataset, comprising 2000 annotated flood events, where the severity of the outcome is categorised according to 3 target classes, demonstrating the respective severities of floods. The paper also presents various types of machine learning algorithms for predicting flood severity and classifying outcomes into three classes, normal, abnormal, and high-risk floods. Extensive research indicates that artificial intelligence algorithms could produce enhancement when utilised for the pre-processing of flood data. These approaches helped in acquiring better accuracy in the classification techniques. Neural network architectures generally produce good outcomes in many applications, however, our experiments results illustrated that random forest classifier yields the optimal results in comparison with the benchmarked models.

Within the Internet of Things (IoT) era, a big volume of data is generated/gathered every second from billions of connected devices. The current network paradigm, which relies on centralised data centres (a.k.a. Cloud computing), becomes impractical solution for IoT data storing and processing due to the long distance between the data source (e.g., sensors) and designated data centres. In other words, by the time the data reaches a far data centre, the importance of the data would be vanished. Therefore, the network topologies have been evolved to permit data processing and storage at the edge of the network, introducing what so-called "Fog computing". The later will obviously lead to improvements in quality of service (QoS) via processing and responding quickly and efficiently to varieties of data processing requests. Therefore, understanding Fog computing architecture and its role in improving QoS is a paramount research topic. In this research, we are proposing a Fog computing architecture and framework to improve QoS for IoT applications. Proposed system supports cooperation among Fog nodes in a given location, in order to permit data processing in a shared mode, hence satisfies QoS and serves largest number of service requests. The proposed framework could have the potential in achieving sustainable network paradigm and highlights significant benefits of Fog computing into the computing ecosystem.

The initial monitoring of patients with headache is an essential part of ongoing patient safety. Usually, patients are asked to fill in traditional paper-based diaries or outcome measures (e.g., HIT-6 and MIDAS) on a regular basis to measure the impact of headache on a patient's life. However, within publicly funded health care systems such as the UK's National Health Service (NHS), long term monitoring in neurology clinics appears not to be possible for all patients with chronic headache due to the continued decline in funding over the past decade. Nowadays, there is scope to improve patient monitoring and safety in the headache clinic by employing mobile health (mhealth) technologies. The M-health application represents an intelligent solution and holds potential to allow specialists to monitor a larger number of patients than would be possible within the current service model. Mobile applications could replace traditional paper-based diaries and outcome measures and provide several advantages including improved monitoring of historical responses to therapies, improved recording of side effects and can be adapted to improve communication between patients and clinicians. We therefore developed a mobile application-based system to allow remote monitoring of patients with chronic headache.

People have always feared aging, and the increasing rate of dementia disease caused this fear to twofold. Dementia is irreversible, unstoppable and has no known cure. According to Alzheimer's Disease International 2015 and World Alzheimer Report 2015, the estimated financial cost for healthcare services of Alzheimer's Disease is $1 Trillion in 2018. This paper discusses the importance of investigating Alzheimer's Disease using machine learning, the need to use both behavioural and biological markers data, and a computational method to rank Alzheimer's Disease risk factors by importance using different machine learning models on Alzheimer's Disease clinical assessment data from ADNI. The dataset contains Alzheimer's Disease risk factors data related to medical history, family dementia history, demographical, and some lifestyle data for 1635 subjects. There are 387 normal control, 87 significant memory concerns, 289 early mild cognitive impairment, 539 late mild cognitive impairment and 333 Alzheimer's Disease subjects. We deployed different machine learning models on the dataset to rank the importance of the variables (risk factors). The results show that some risk factors in subjects genetically, demography and lifestyle are more important than some medical history risk factors. Having APOE4, education level, age, weight, family dementia history, and type of work rank as more influential among Alzheimer's Disease subjects.

International audience

Abstract: This paper discusses the blend of cognitive computing with the Internet-of-Things that should result into developing cognitive things. Today’s things are confined into a data-supplier role, which deprives them from being the technology of choice for smart applications development. Cognitive computing is about reasoning, learning, explaining, acting, etc. In this paper, cognitive things’ features include functional and non-functional restrictions along with a 3 stage operation cycle that takes into account these restrictions during reasoning, adaptation, and learning. Some implementation details about cognitive things are included in this paper based on a water pipe case-study.

Despite the smooth weaving of the Internet-of-Things into people's daily lives, many challenges, such as diversity and multiplicity of things' development technologies and communication standards, and users' reluctance due to privacy invasion, are slowing down this weaving. This paper tackles the challenge of things' passive nature that has confined them into a data-supplier role. Empowering things with additional capabilities would make them proactive so, that, they can for instance, reach out to peers exposing collaborative attitude and (un)form dynamic communities when necessary. In this paper, this empowerment takes shape through thing agentification that relies on norms (specialized into business and social) to regulate the operations of things and commitments to ensure thing compliance with these norms. No-compliance would lead to sanctions over things, which should affect their credibility and reputation. A proof-of-concept and missing-child case study technically illustrate thing agentification.

Abstract Service composition is a key concept of Service‐Oriented Architecture that allows for combining loosely coupled services that are offered and operated by different service providers. Such environments are expected to dynamically respond to changes that may occur at runtime, including changes in the environment and individual services themselves. Therefore, it is crucial to monitor these loosely coupled services throughout their lifetime. In this paper, we present a novel framework for monitoring services at runtime and ensuring that services behave as they have promised. In particular, we focus on monitoring non‐functional properties that are specified within an agreed security contract. The novelty of our work is based on the way in which monitoring information can be combined from multiple dynamic services to automate the monitoring of business processes and proactively report compliance violations. The framework enables monitoring of both atomic and composite services and provides a user friendly interface for specifying the monitoring policy. We provide an information service case study using a real composite service to demonstrate how we achieve compliance monitoring. The transformation of security policy into monitoring rules, which is done automatically, makes our framework more flexible and accurate than existing techniques.

In the twenty-first century, globalisation made corporate boundaries invisible and difficult to manage. This new macroeconomic transformation caused by globalisation introduced new challenges for critical infrastructure management. By replacing manual tasks with automated decision making and sophisticated technology, no doubt we feel much more secure than half a century ago. As the technological advancement takes root, so does the maturity of security threats. It is common that today’s critical infrastructures are operated by non-computer experts, e.g. nurses in health care, soldiers in military or firefighters in emergency services. In such challenging applications, protecting against insider attacks is often neither feasible nor economically possible, but these threats can be managed using suitable risk management strategies. Security technologies, e.g. firewalls, help protect data assets and computer systems against unauthorised entry. However, one area which is often largely ignored is the human factor of system security. Through social engineering techniques, malicious attackers are able to breach organisational security via people interactions. This paper presents a security awareness training framework, which can be used to train operators of critical infrastructure, on various social engineering security threats such as spear phishing, baiting, pretexting, among others.

The use of mobile sensing systems (MSS), via smartphone, in various application domains such as (medical and healthcare) are growing rapidly. However, there is a very limited research and development effort put toward exploiting these smartphone sensing technologies to promote human beings wellness and active lifestyle. This paper introduces “Alert Me”, a smartphone application that quantifies the sedentary behavior (e.g., prolonged sitting) and aims at reducing it. It generates the timely personalized messages by suggesting short breaks to promote active lifestyle. We utilize the accelerometer sensor of smartphone to observe the sedentary behavior of the user under free-living conditions. Alert Me computes the features over accelerometer data and performs the classification to generate the alerts by using computation power of smartphone. It facilitates the users to create a personal profile and manage alerts according to their own choice. We develop an initial working prototype to evaluate the applicability of our approach in a real-world scenario to avoid prolonged uninterrupted periods of sedentary time.

The recent advancements in cellular communication domain have resulted in the emergence of Machine-to-Machine applications, in support of the wide range and coverage provision, low costs, and high mobility. 5G network standards represent a promising technology to support the future of Machine-to-Machine data traffic. In recent years, Human-Type-Communication traffic has seen exponential growth over cellular networks, which resulted in increasing the capacity and higher data rates. These networks are expected to face challenges such as explosion of the data traffic due to the future of smart devices data traffic with various Quality of Service requirements. This paper proposes a novel data traffic aggregation model and algorithm along with a new 5G network slicing based on classification and measuring the data traffic to satisfy Quality of Service for smart systems in a smart city environment. In our proposal, 5G radio resources are efficiently utilized as the smallest unit of a physical resource block in a relay node by aggregating the data traffic of several Machine-to-Machine devices as separate slices based on Quality of Service for each application. OPNET is used to assess the performance of the proposed model. The simulated 5G data traffic classes include file transfer protocol, voice over IP, and video users.

With the emergence of smartphones and the widespread use of social media services, distribution of multimedia files over the Internet, and using mobile phones, has increased exponentially over the past few years. A significant number of cybercrimes pertain to illicit possession, modification, and distribution of multimedia files. The use of smartphones for this purpose makes these mobile phones rich sources of evidence. Therefore, it is crucial for forensic examiners to have the capability of recovering, analyzing, and authenticating the source of multimedia contents stored on these devices. This paper focuses on the analysis of multimedia files created on the most popular smartphones in order to ascertain the source and examine whether the files are original or edited through these devices. The popular smartphones brands analyzed in this paper include iPhone 5, iPhone 6, Blackberry Z10, Samsung Galaxy Note 3, Nokia Lumia 930, and Lenovo A536. Experimental results on all these brands are also presented.

During the last 10 years, experts in critical infrastructure security have been increasingly directing their focus and attention to the security of control structures such as Supervisory Control and Data Acquisition (SCADA) systems in the light of the move toward Internet-connected architectures. However, this more open architecture has resulted in an increasing level of risk being faced by these systems, especially as they became offered as services and utilised via Service Oriented Architectures (SOA). For example, the SOA-based SCADA architecture proposed by the AESOP project concentrated on facilitating the integration of SCADA systems with distributed services on the application layer of a cloud network. However, whilst each service specified various security goals, such as authorisation and authentication, the current AESOP model does not attempt to encompass all the necessary security requirements and features of the integrated services. This paper presents a concept for an innovative integrated cloud platform to reinforce the integrity and security of SOA-based SCADA systems that will apply in the context of Critical Infrastructures to identify the core requirements, components and features of these types of system. The paper uses the SmartGrid to highlight the applicability and importance of the proposed platform in a real world scenario.

The dependability of runtime composition of Cloud-Based Services is affected by many issues including scalability and reliability, which are commonly proposed to be solved by the use of adaptive software solutions. Usually, however, the use of various software paradigms to achieve such adaptability requires that the design of the formal operational model to be implicitly linked with the end implementation. Such rigid reliance upon a static model thereby constrains the adaptability and flexibility of the composition. This paper contends that in order for adaptable Cloud-Based Services to be produced, a formal model should be specified and enacted independently from the runtime implementation. The paper shows by use of situation calculus and software representation techniques for service composition, such as an Intention Description Language, how these issues can be addressed for assured runtime adaptable, deliberative systems. In conclusion, by use of a representative example and a case study, the methodology proposed is evaluated to highlight the advantages discussed, and to identify problems still to be resolved.

The rise of virtualisation and cloud computing is one of the most significant features of computing in the last 10 years. However, despite its popularity, there are still a number of technical barriers that prevent it from becoming the truly ubiquitous service it has the potential to be. Central to this are the issues of data security and the lack of trust that users have in relying on cloud services to provide the foundation of their IT infrastructure. This is a highly complex issue, which covers multiple inter-related factors such as platform integrity, robust service guarantees, data and network security , and many others that have yet to be overcome in a meaningful way. This paper presents a concept for an innovative integrated platform to reinforce the integrity and security of cloud services and we apply this in the context of Critical Infrastructures to identify the core requirements, components and features of this infrastructure.