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The phenomenon of phishing has now been a common threat, since many individuals and webpages have been observed to be attacked by phishers. The common purpose of phishing activities is to obtain user’s personal information for illegitimate usage. Considering the growing intensity of the issue, this study is aimed at developing a new hybrid rule-based solution by incorporating six different algorithm models that may efficiently detect and control the phishing issue. The study incorporates 37 features extracted from six different methods including the black listed method, lexical and host method, content method, identity method, identity similarity method, visual similarity method, and behavioral method. Furthermore, comparative analysis was undertaken between different machine learning and deep learning models which includes CART (decision trees), SVM (support vector machines), or KNN ( K -nearest neighbors) and deep learning models such as MLP (multilayer perceptron) and CNN (convolutional neural networks). Findings of the study indicated that the method was effective in analysing the URL stress through different viewpoints, leading towards the validity of the model. However, the highest accuracy level was obtained for deep learning with the given values of 97.945 for the CNN model and 93.216 for the MLP model, respectively. The study therefore concludes that the new hybrid solution must be implemented at a practical level to reduce phishing activities, due to its high efficiency and accuracy.

Mobile ad hoc networks (MANETs) are wireless networks, which consist of mobile nodes that communicate over wireless media. MANETs are typically characterized by high mobility and frequent link failures. Since routing protocols are crucial for the success of MANETs, multipath routing can be employed to reduce link failure so that alternate paths will be available. This paper aims to develop and evaluate the performance of a new multipath routing protocol based on the ad hoc on-demand distance vector (AODV) routing protocol. It is an on-demand multipath routing protocol named ad hoc on-demand distance vector-secure multipath routing (AODV-SMR). The on-demand multipath routing protocol was evaluated against three performance metrics: packet delivery ratio, average end-to-end delay and normalized routing load. The simulation results, obtained using an OMNET++ simulator, showed that the proposed protocol exhibits significant improvement in the packet delivery ratio. It also outperformed the ad hoc on-demand multipath distance vector (AOMDV) protocol in the average end-to-end delay, and outperformed AODV in the normalized routing load metric

This paper introduces an efficient method for communication resource use in dense wireless areas where all nodes must communicate with a common destination node. The proposed method groups nodes based on their \newt{distance from the destination} and creates a structured multi-hop configuration in which each group can relay its neighbor's data. \newt{The large number of active radio nodes and the common direction of communication toward a single destination are exploited to reuse the limited spectrum resources in spatially separated groups}. Spectrum allocation constraints among groups are then embedded in a joint routing and resource allocation framework to optimize the route and amount of resources allocated to each node. \newt{The solution to this problem uses coordination among the lower-layers of the wireless-network protocol stack to outperform conventional approaches where these layers are decoupled. Furthermore, the structure of this problem is exploited to obtain} a semi-distributed optimization algorithm based on the alternating direction method of multipliers (ADMM) where each node can optimize its resources independently based on local channel information.

The utilization of mobile learning continues to rise and has attracted many organizations, university environments and institutions of higher education all over the world. The cloud storage system consists of several defense issues since data security and privacy have become known as the foremost apprehension for the users. Uploading and storing specific data in the cloud is familiar and widespread, but securing the data is a complicated task. This paper proposes a cloud-based mobile learning system using a hybrid optimal elliptic curve cryptography (HOECC) algorithm comprising public and private keys for data encryption. The proposed approach utilizes an adaptive tunicate slime-mold (ATS) algorithm to generate optimal key value. Thus, the data uploaded in the cloud system are secured with high authentication, data integrity and confidentiality. The study investigation employed a survey consisting of 50 students and the questionnaire was sent to all fifty students. In addition to this, for obtaining secure data transmission in the cloud, various performance measures, namely the encryption time, decryption time and uploading/downloading time were evaluated. The results reveal that the time of both encryption and decryption is less in ATF approach when compared with other techniques.

In dense indoor areas, high numbers of people use their smartphones and tablets to share or download pictures, videos, or data. The heterogeneous network (HetNet) solves the problems caused by the explosion of data generated by smartphones and tablets. Heterogeneous networks use a mix of Relay, Femtocell, Pico, and Macro base stations to improve spectral efficiency per unit area. Operators wish to know how to upgrade existing networks and how to design new ones. This subject has become hot in the industry. In this paper, we presented the architecture of heterogeneous networks. The parameters affecting the heterogeneous networks topology plan are discussed. Moreover, a comparison of existing solutions that consider the problems of base station layout planning is presented. Finally, a road map is given to point out to the main future directions of researches on the topological design of dense area heterogeneous mobile networks.

Densification is one of the 5G networks solutions to increase data rate and network capacity. This technology aims for constructing a large number of small cells in a specific area which may include different wireless technologies (LTE, IEEE 802.11, WiMAX, etc.). Small cells provide shorter distance between the cell access point and the mobile node which will reduce the signal attenuation and noise. This could generate a better connection, which goes along with the expectations of high data rate requests for the year 2020. The downside of the densification is that frequent handoffs will be a challenge for the network nodes. A software-defined network SDN, on the other hand, is another promising solution for 5G networks which controls the network through centralization. Using these two solutions, the handoffs in the network are expected to be frequent and hence a more delay will be added. To reduce such delay, a distributed-mobility-management technique is used in SDN-based networks, where the mobile node takes the handoff decision. This paper has focused on the issue of the distributed-handoff delay and handover failure of mobile nodes in SDN-based IEEE 802.11 network. It proposes a hybrid clustering technique using K-mean and genetic algorithm to cluster the network. The clustering aims to reduce the scanning phase of the mobile node by minimizing the number of access points that need to be scanned for the handoff. The results show lower delays and a reduced number of handoff failure compared to the un-clustered network.

This paper focuses on the study of IoT network deployments, in both unlicensed and licensed bands, considering LoRaWAN and NB-IoT standards, respectively. The objective is to develop a comprehensive and detailed network planning and coverage dimensioning methodology for assessing key metrics related to the achieved throughput and capacity for specific requirements in order to identify tradeoffs and key issues that are related to the applicability of IoT access technologies for representative use case types. This paper will provide a concise overview of key characteristics of IoT representative IoT access network standards that are considered for being deployed in unlicensed and licensed bands and will present a methodology for modeling the characteristics of both access network technologies in order to assess their coverage and capacity considering different parameters.

The term “mobile learning” (or “m-learning”) refers to using handheld phones to learn and wireless computing as a learning tool and connectivity technology. This paper presents and explores the latest mobile platform for teaching and studying programming basics. The M-Learning tool was created using a platform-independent approach to target the largest available number of learners while reducing development and maintenance time and effort. Since the code is completely shared across mobile devices (iOS, Android, and Windows Phone), students can use any smartphone to access the app. To make the programme responsive, scalable, and dynamic, and to provide students with personalised guidance, the core application is based on an analysis design development implementation and assessment (ADDIE) model implemented in the Xamarin framework. The application’s key features are depicted in a prototype. An experiment is carried out on BS students at a university to evaluate the efficacy of the generated application. A usefulness questionnaire is administered to an experimental community in order to determine students’ expectations of the developed mobile application’s usability. The findings of the experiment show that the application is considerably more successful than conventional learning in developing students’ online knowledge assessment abilities, with an impact size of 1.96. The findings add to the existing mobile learning literature by defining usability assessment features and offering a basis for designing platform-independent m-learning applications. The current findings are explored in terms of their implications for study and teaching practice.

Seismic images are data collected by sending seismic waves to the earth subsurface, recording the reflection and providing subsurface structural information. Seismic attributes are quantities derived from seismic data and provide complementary information. Enhancing seismic images by fusing them with seismic attributes will improve the subsurface visualization and reduce the processing time. In seismic data interpretation, fusion techniques have been used to enhance the resolution and reduce the noise of a single seismic attribute. In this paper, we investigate the enhancement of 3D seismic images using image fusion techniques and neural networks to combine seismic attributes. The paper evaluates the feasibility of using image fusion models pretrained on specific image fusion tasks. These models achieved the best results on their respective tasks and are tested for seismic image fusion. The experiments showed that image fusion techniques are capable of combining up to three seismic attributes without distortion, future studies can increase the number. This is the first study conducted using pretrained models on other types of images for seismic image fusion and the results are promising.

There is a significant development in computer-aided detection (CADe) and computer-aided diagnostic (CADx) systems in recent years. This development coincides with the evolution of computing power and the growth of data. The CAD systems support detections and diagnosis of significant diseases, including cancer. Breast cancer is one of the most prevalent cancers influencing women and causing death around the world. Early detection of breast cancer has a significant effect on treatment. The typical CAD system goes through various steps, including image segmentation, feature extraction, and image classification. Image segmentation plays an important role in CAD systems and simplifies further processing. This review explores popular mammogram segmentation techniques. A mammogram is medical imaging which uses a low-dose x-ray system to see inner tissues of the breast. There are many segmentation techniques used to segment medical images. These techniques can be categorized into five main categories: region-based methods, boundary-based methods, atlas-based methods, model-based methods, and deep learning. A ground truth image is needed to measure the performance of the segmentation algorithm. Different performance measurements were used to evaluate the segmentation process, including accuracy, precision, recall, F1 score, Hausdorff Distance, Jaccard, and Dice Index. The research in mammogram segmentation has yielded promising results, but there is room for improvements.

The development of the Internet of Things (IoT) expands to an ultra-large-scale, which provides numerous services across different domains and environments. The use of middleware eases application development by providing the necessary functional capability. This paper presents a new form of middleware for controlling smart devices installed in an intelligent environment. This new form of middleware functioned seamlessly with any manufacturer API or bespoke controller program. It acts as an all-encompassing top layer of middleware in an intelligent environment control system capable of handling numerous different types of devices simultaneously. This protected de-synchronization of data stored in clone devices. It showed that in this middleware, the clone devices were regularly synchronized with their original master such as locally stored representations were continuously updated with the known true state values.

Widespread development of system software, the process of learning, and the excellence in profession of teaching are the formidable challenges faced by the learning behavior prediction system. The learning styles of teachers have different kinds of content designs to enhance their learning. In this learning environment, teachers can work together with the students, but the learning materials are designed by the teachers. The cognitive style deals with mental activities such as learning, remembering, thinking, and the usage of language. Therefore, being motivated by the problems mentioned above, this paper proposes the concept of adaptive optimization-based neural network (AONN). The learning behavior and browsing behavior features are extracted and incorporated into the input of artificial neural network (ANN). Hence, in this paper, the neural network weights are optimized with the use of grey wolf optimizer (GWO) algorithm. The output operation of e-learning with teaching equipment is chosen based on the cognitive style predicted by AONN. In experimental section, the measures of accuracy, sensitivity, specificity, time (sec), and memory (bytes) are carried out. Each of the measure is compared with the proposed AONN and existing fuzzy logic methodologies. Ultimately, the proposed AONN method produces higher accuracy, specificity, and sensitivity results. The results demonstrate that the algorithm proposed in this study can automatically learn network structures competitively, unlike those achieved for neural networks through standard approaches.

Femto Access Points (FAP) and Device-to-Device (D2D) communications have recently been considered as potential candidates for 5G network densification and cell-edge performance. Mobility and handover management is a major issue in heterogeneous networks (HetNet) offering different access technologies. Recently, many Fuzzy and Multi-Attribute Decision Making (MADM) handover decision algorithms have been proposed to ensure Quality of Service (QoS), reduce the number of handovers and handover blocking probability of mobile users. However, network discovery is still an issue as it increases the total handover delay and drains the battery of the user equipment (UE). In addition, the UE may undergo high interference with other co-channel users after the handover is executed, thus limiting the overall network performance. Currently, the emerging Software Defined Networking (SDN) has been proposed in which one centralized controller can assist in the handover discovery, handover decision, and co-channel interference coordination. SDN-based handover algorithms ensure QoS, Quality of Experience (QoE), reduce delay and interference. In this paper, we will integrate the Fuzzy logic into the SDN to assist in FAPs and D2D discovery, and the decision of candidate networks based on the networks’ QoS parameters. Then, the UE will make the final handover decision by selecting the best network based on the predicted QoE using TOPSIS and AHP algorithms. Frequency reuse and appropriate power control are also applied in order to increase network capacity and reduce interference. Performance results show that the proposed SDN-based Fuzzy MADM handover scheme reduces unnecessary handovers, blocking probability and total handover delay. In addition, throughput is increased as the number of users increases.

Nodes in a multi-hop wireless network rely on each other to maintain network connectivity. A primary design objective of these networks is to eliminate or minimize the unavailability of network at some point. However, the movement of nodes creates service disruption and delay as path failure detection and re-establishment consume considerable amount of time. Existing solutions for link classification and prediction operate on discrete values resulting in performance degradation. This problem can best be modelled using fuzzy logic. This work presents a new fuzzy logic based link status classification mechanism for the use with multipath routing protocols in multi-hop wireless networks. The new Enhanced Adhoc On-demand Multipath Distance Vector (eAOMDV) uses fuzzy logic to classify the link status as active, about-to-break or broken. On the basis of fuzzy input parameters RSSI, velocity, distance and bit error rate, the fuzzy based classification mechanism predicts in advance that a particular link, on the active path between the sender and receiver, is about-to-break and helps the reactive multipath routing protocol to start the rerouting operation, thus minimizing the service disruption time, route discovery frequency, end-to-end delay and the packet loss.

The adaptive educational systems within e-learning platforms are built in response to the fact that the learning process is different for each and every learner. In order to provide adaptive e-learning services and study materials that are tailor-made for adaptive learning, this type of educational approach seeks to combine the ability to comprehend and detect a person’s specific needs in the context of learning with the expertise required to use appropriate learning pedagogy and enhance the learning process. Thus, it is critical to create accurate student profiles and models based upon analysis of their affective states, knowledge level, and their individual personality traits and skills. The acquired data can then be efficiently used and exploited to develop an adaptive learning environment. Once acquired, these learner models can be used in two ways. The first is to inform the pedagogy proposed by the experts and designers of the adaptive educational system. The second is to give the system dynamic self-learning capabilities from the behaviors exhibited by the teachers and students to create the appropriate pedagogy and automatically adjust the e-learning environments to suit the pedagogies. In this respect, artificial intelligence techniques may be useful for several reasons, including their ability to develop and imitate human reasoning and decision-making processes (learning-teaching model) and minimize the sources of uncertainty to achieve an effective learning-teaching context. These learning capabilities ensure both learner and system improvement over the lifelong learning mechanism. In this paper, we present a survey of raised and related topics to the field of artificial intelligence techniques employed for adaptive educational systems within e-learning, their advantages and disadvantages, and a discussion of the importance of using those techniques to achieve more intelligent and adaptive e-learning environments.

Nodes’ clustering in wireless networks is one of the solutions that used to improve network performance. This paper discusses the clustering in wireless networks. Then it presents a novel clustering algorithm named Pragmatic Genetic Algorithm (PGA). It combines two of the well known artificial intelligence techniques: K-means and Genetic algorithm. The proposed algorithm aims at minimizing the execution time of the clustering, especially in time-sensitive wireless networks applications. The performance of PGA has been compared with the classical clustering algorithms, namely, K-means and KGA. The experiments have been conducted using synthetic and real data from public repositories. PGA obtained excellent results in execution and stable accuracy even when the number of nodes was increased.

Nowadays, the popularity of smart phones creates huge capacity requirements for networks during mass events where thousands of people coexist in specific areas. At such events, large numbers of people use their smartphones to share pictures and download information. This behavior creates traffic profiles that differ from those typically observed in legacy -less populated- networks where lower uplink traffic volumes are generated. Thus, novel network planning and radio resource management mechanisms have to be considered for such dense network conditions, one of which is macrocell offloading. In this context, reducing the cell size has always been the best way to increase the network capacity of LTE. Femtocells are used to enable offloading data-traffic from macrocell network to increase the capacity. In this paper, to achieve efficient user offloading from macro to femtocells, we propose an offloading algorithm based on a perceived rate threshold in combination with uplink power control for hybrid femtocells also considering resource block partitioning. The proposed algorithm enhances the network capacity so that more mobile users can satisfy their minimum Quality of Service (QoS) requirements, thus the overall performance of dense cellular networks increases. The proposed offloading mechanism is assessed in terms of the achieved total throughput and outage probability figures. Simulation results demonstrate the potential increase of the number of supported users per macrocell in joint macro and hybrid femtocell deployments.

Sophisticated educational technologies are evolving rapidly, and online courses are becoming more easily available, generating interest in innovating lightweight data-driven adaptive approaches that foster responsive teaching and improving the overall learning experience. However, in most existing adaptive educational systems, the black-box modeling of learner and instructional models based on the views of a few designers or experts tended to drive the adaptation of learning content. However, different sources of uncertainty could affect these views, including how accurately the proposed adaptive educational methods actually assess student responses and the corresponding uncertainties associated with how students receive and comprehend the resulting instruction. E-learning environments contain high levels of linguistic uncertainties, whereby students can interpret and act on the same terms, words, or methods (e.g., course difficulty, length of study time, or preferred learning style) in various ways according to varying levels of motivation, pre-knowledge, cognition, and future plans. Thus, one adaptive instructional model does not fit the needs of all students. Basing the instruction model on determining learners’ interactions within the learning environment in interpretable and easily read white-box models is crucial for adapting the model to students’ needs and understanding how learning is realized. This paper presents a new zSlices-based type-2 fuzzy-logic-based system that can learn students’ preferred knowledge delivery needs based on their characteristics and current levels of knowledge to generate an adaptive learning environment. We have evaluated the proposed system’s efficiency through various large-scale, real-world experiments involving 1871 students from King Abdulaziz University. These experiments demonstrate the proposed zSlices type-2 fuzzy-logic-based system’s capability for handling linguistic uncertainties to produce better performance, particularly in terms of enhanced student performance and improved success rates compared with interval type-2 fuzzy logic, type-1 fuzzy systems, adaptive, instructor-led systems, and non-adaptive systems.

Technological advancements within the educational sector and online learning promoted portable data-based adaptive techniques to influence the developments within transformative learning and enhancing the learning experience. However, many common adaptive educational systems tend to focus on adopting learning content that revolves around pre-black box learner modelling and teaching models that depend on the ideas of a few experts. Such views might be characterized by various sources of uncertainty about the learner response evaluation with adaptive educational system, linked to learner reception of instruction. High linguistic uncertainty levels in e-learning settings result in different user interpretations and responses to the same techniques, words, or terms according to their plans, cognition, pre-knowledge, and motivation levels. Hence, adaptive teaching models must be targeted to individual learners’ needs. Thus, developing a teaching model based on the knowledge of how learners interact with the learning environment in readable and interpretable white box models is critical in the guidance of the adaptation approach for learners’ needs as well as understanding the way learning is achieved. This paper presents a novel interval type-2 fuzzy logic-based system which is capable of identifying learners’ preferred learning strategies and knowledge delivery needs that revolves around characteristics of learners and the existing knowledge level in generating an adaptive learning environment. We have conducted a large scale evaluation of the proposed system via real-word experiments on 1458 students within a massively crowded e-learning platform. Such evaluations have shown the proposed interval type-2 fuzzy logic system’s capability of handling the encountered uncertainties which enabled to achieve superior performance with regard to better completion and success rates as well as enhanced learning compared to the non-adaptive systems, adaptive system versions led by the teacher, and type-1-based fuzzy based counterparts.

Due to the highly mobile nature of VANET, especially on highways, a reliable and fast penetration of emergency messages is required so that in-time decisions can be performed. A broadcast routing protocol can perform flooding in sparse network but it will suffer from high control overhead, higher delay and lower packet delivery ratio in dense environment. Since, a significant number of VANET messages including neighbour discovery, safety, destination discovery, location and service advertisements is broadcast therefore, the area of broadcast routing is important and needs careful design considerations. In this article, we propose ZoomOut Broadcast Routing Protocol for driver safety information dissemination in VANET. In ZBRP, 1-hop neighbour discovery messages are used in an intelligent way based on the speed and inter-vehicle distance of 1-hop neighbours to select a front and a behind vehicle. A neighbour from the front area is called front relative while the neighbour from behind area is called behind relative. During the processing of multi-hop safety messages, only a front or a behind relative rebroadcasts a safety message whereas nonrelatives drop it. ZBRP is compared with G-AODV, PGB and DV-CAST through ns-2 simulations. The results show that overall ZBRP performs better than the stated protocols. Keywords—Information dissemination; CAM; DENM; Intelligent beaconing; Intelligent transportation system; Broadcast suppression

In recent years, our homes have been equipped with smarter and more energy-efficient electric appliances, such as smart fridges, washing machines, TVs, etc. However, it seems that cookers seem to have been left aside during this trend although, for example, in U.K., electric cookers consume up to 20% of the evening peak electricity consumption. In addition, over half of the accidental house fires are due to cooking and cooking appliances. One of the reasons for the lack of smart energy-efficient electric cookers is the complexity of performing energy-efficient control for the various cooking techniques. This paper presents a fuzzy logic-based system, which can be cheaply retrofitted in existing electric cookers to convert them to semiautonomous, energy efficient, and safe smart electric cookers. The proposed system can control the cooker heating plate to allow the semiautonomous safe operation of the most common cooking techniques including boiling, stir/shallow-frying, deep-frying, and warming. In addition, the developed system can identify when human intervention is necessary and when dangerous situations happen or are imminent. We will present several real-world experiments, which were performed in the University of Essex intelligent apartment (iSpace) with various users where the proposed system operated a cooker semiautonomously in various cooking modes, and it was shown that when compared with the human manual operation, the proposed system realized an average energy saving of 21.42%, 34.43%, and 20.29% for the boiling, stir/shallow-frying, and deep-frying cooking techniques, respectively. In addition, the realized smart cooker has shown unique safety features not present in the existing commercial cookers.

The majority of contemporary 5G networks employ HetNets which improve data rate and the number of serving users over previous cell architecture. HetNets are comprised of different small-cell types such as femtocells and microcells. The tradeoff of these smaller cells is that they do not meet QoS requirements for flexibility and low latency because the increased frequency of handoffs increases network delay. This paper compares the state- of-the-art mobility management approaches that use SDN and (or) NFV concepts in 5G cellular networks. These approaches are evaluated in terms of scalability, signaling cost, probability of handoff failures, and system load balance.

Ambient Intelligence (AmI) is a multidisciplinary paradigm, which positively alters the relationship between humans and technology. Concerning home environments, the functions of AmI vision include home automation, communication, entertainment, working and learning. In the area of communication, AmI still needs better mechanisms for human-computer communication. A natural human-computer interaction necessitates having systems capable of modelling words and computing with them. For this purpose, the paradigm of Computing With Words (CWWs) can be employed to mimic human-like communication in Ambient Intelligent Environments (AIEs). This paper demonstrates the extendibility of Linear General Type-2 (LGT2) Fuzzy Logic based CWWs Framework to create an advanced real-world application, which integrates a semi-autonomous, safe and energy efficient electric hob. The motivation of this work is twofold: 1) there is a need to develop transparent human-computer communication rather than embedding obtrusive tablets and computing equipment throughout our surroundings, and 2) one of the most hazardous and energy consuming household devices, the electric hob, does not have competent levels of intelligence and energy efficiency. The proposed Ambient Intelligent Food Preparation System (AIFPS) can increase user comfort, facilitate food preparation, minimize energy consumption and be a useful tool for the elderly and people with major disabilities including vision impairment. The results of real-world experiments with various lay users in the intelligent flat (iSpace) show the success of AIFPS in providing up to 55.43% improved natural interaction (compared to Interval Type-2 based CWWs Framework) while achieving semi-autonomous, safe and energy efficient cooking that can save energy between 11.5% and 35.2%.

Coordinated tethering over a white-space spectrum is investigated herein to increase mobile broadband spectrum efficiency in densely populated areas. This paper proposes an algorithm for operator-controlled tethering over white spaces. The proposed approach does not add to the existing infrastructure but instead allows the individual nodes to act as “hotspots” and to tether data to and from other nodes. The proposed algorithm iteratively clusters the nodes into hotspots and slaves and allocates resources to maximize spectrum utility. The proposed method's dynamic characteristics allow cellular systems to hierarchically evolve in dense areas as necessary. A signaling framework for node-to-node and base-station-to-node communication that enables such operator-controlled tethering is also presented. Simulation results show that given a fixed amount of network resources, the proposed algorithm can significantly increase the number of supported users.

E-learning platforms facilitate the interaction between students and instructors while mitigating temporal or spatial constraints. Nevertheless, such platforms require measuring the degree of students' engagement with the delivered course content and teaching style. Such information is highly valuable for evaluating the quality of the teaching and altering the teaching delivery style in massively crowded online learning platforms. When the number of learners is high, it is essential to attain overall engagement and feedback, yet doing so is highly challenging due to the high levels of uncertainties related to students and the learning context. To handle these uncertainties more robustly, we present a method based on type-2 fuzzy logic utilizing visual RGB-D features, including head pose direction and facial expressions captured from Kinect v2, a low-cost but robust 3D camera, to measure the engagement degree of students in both remote and on-site education. This system augments another self-learning type-2 fuzzy logic system that helps teachers with recommendations of how to adaptively vary their teaching methods to suit the level of students and enhance their instruction delivery. This proposed dynamic e-learning environment integrates both on-site and distance students as well as teachers who instruct both groups of students. The rules are learned from the students' and teachers' learning/teaching behaviors, and the system is continuously updated to give the teacher the ability to adapt the delivery approach to varied learners' engagement levels. The efficiency of the proposed system has been tested through various real-world experiments in the University of Essex iClassroom among a group of thirty students and six teachers. These experiments demonstrate the capabilities--compared to type-1 fuzzy systems and non-adaptive systems--of the proposed interval type-2 fuzzy logic-based system to handle the uncertainties and improve average learners' motivations to engage during learning.

Wireless networks have resource limitations; in a dense area, cellular spectrum resources are insufficient and affect the system performance. A Long Term Evolution (LTE) network aims to serve heterogeneous users with different QoS requirements. Traditional approaches need new infrastructure and degrade performance of delay sensitive applications, which may result in users with minimum rate requirements being in high blocking probability. To utilize wireless resources efficiently, users want to access the same medium to connect with the same multicast group and be overhauled at the same time. In this paper, a new technique is proposed for operator-controlled called the QoS-Aware Tethering in a Heterogeneous Wireless Network using LTE and TV White Spaces (QTHN) to improve QoS for Constant Bit Rate (CBR) and Best Effort (BE) users. The proposed QTHN converts the whole dense wireless network into hexagonal clusters via two layer network communication. In a cluster, one node is selected as a cluster head and all other nodes act as slaves. Within a cluster, a cluster head acts as an access point known as a Hotspot (H), which is further connected to the Base-station (BS). The proposed QTHN aims to improve QoS within heterogeneous wireless network using LTE and unused White Spaces in a wireless dense area. Simulation results show that the proposed QTHN reduced the numbers of blocked users and improved network utility.

Ambient Intelligence (AmI) is an emerging vision that aims to realize intelligent environments which are sensitive and responsive to the users' needs and behaviors. This paper presents an insight on the benefits that type-2 Fuzzy Logic Systems (FLSs) can offer towards the efforts to realize Ambient Intelligent Environments (AIEs). We will introduce research results from the Scaleup project showing different type-2 FLSs based applications in AIEs. Such applications include intelligent machine vision systems, blending real and virtual realities over dispersed geographical areas and allowing natural communication between the AIE and humans.

Recent years have witnessed rapid growth in medical applications based on wearable devices and sensors, which is attributed mainly due to the progress in wireless technologies and antennas. As the mobility and compactness of medical devices are becoming more demanding, wearable wireless sensors and antennas become an important candidate with their compact size, light weight, and ease of integration into clothes for Body Area Networks (BANs). This paper presents a state-of-the-art survey of the recent advances in wearable and epidermal antennas for medical applications, with emphasis on how flexible and light materials and textiles have a major effect on their design. While miniaturization and protecting the body against electromagnetic waves is of utter importance, the use of Electromagnetic Band Gap (EBG) structures is also emphasised. Some of the design challenges of wearable and epidermal antennas are highlighted as well.

Recently, Terahertz technologies, due to the extensive research and tremendous progress achieved in the field of Terahertz radiation, promise countless potential applications in the fields of security and defense. A particular interest is the development of new technologies that enable the identification of concealed prohibited drugs. This paper surveys the existing research which investigates the utilization of Terahertz spectroscopy and imaging in the detection and identification of illicit drugs. It sheds light on the terahertz time domain spectroscopy which is used to obtain the Terahertz fingerprint spectra of drugs-of-abuse. It also focuses on various Terahertz imaging techniques that are used to probe the presence of illegal drugs by penetrating packaging and concealment materials. Furthermore, this paper addresses current challenges and future research areas for the security application of Terahertz technologies.

Tethering has been recently proposed as an efficient solution for the increase in number of mobile users and the requested bandwidth in cellular networks. The idea is to group the mobile nodes into clusters, each containing slaves served by a hotspot. To save licensed spectrum, the hotspot–slave link can use other frequency channels like the newly vacated TV white space (TVWS) bands. A recent work has proposed an iterative clustering algorithm for cellular networks. Despite its computational complexity and signaling overhead, this algorithm did not handle the nodes׳ mobility inside the cell neither the change in their required data rate. In this work, we propose a new dynamic protocol for clustering the nodes taking into account the possible changes occurring in a cellular network. Specifically, the Dynamic Clustering Protocol (DCP) adapts the network configuration with the variable mobiles׳ requirements and the different network events. This will reduce the needed time and signaling and offers better service quality for the clustered users. After presenting the various network events, the handover scenarios and signaling for the Dynamic Clustering Protocol, the performance of the proposed protocol is studied. This was accomplished by modeling different network scenarios and computing the required number of handovers as a function of user mobility, available network resources and data rate requirements for a given clustered nodes configuration.