A metaverse can bring many benefits (i.e., self-sustaining and proactive analytics (e.g., analysis before user requests)) to wireless applications; however, its deployment is very challenging due to simultaneous quality of service (QoS) and quality of physical experience (QoE) constraints. Furthermore, the computing and communication resources of end-nodes are limited. For instance, immersive experience devices (e.g., augmented reality (AR) headsets) in a metaverse have limited computing power and therefore, might not be able to perform rendering tasks. Consequently, this paper proposes a novel task offloading framework for metaverse-empowered wireless systems. Our formulated problem aims at minimizing the cost of task offloading in the metaverse while considering both QoS and QoE constraints by optimizing the task offloading, resource allocation, and transmit power allocation variables. For QoS, we consider latency and reliability, whereas for QoE, we consider both immersive experience and packet error rate. To optimize the formulated problem, we use a decomposition-based scheme that further uses modified block-successive upper-bound minimization (BSUM), convex optimization, and multi-agent reinforcement learning (MARL) for transmit power allocation, resource allocation, and task offloading, respectively. Our solution of using convex optimization-assisted MARL for joint resource allocation and task offloading significantly improves the performance of learning in terms of reward and attaining fast QoS as well as QoE. Furthermore, BSUM significantly improves transmit power allocation when used in conjunction with a convex optimizer and MARL. Other than that, we also use a dueling (i.e., it is a reinforcement learning architecture combining the dueling network structure with the double deep Q-learning network method for more stable and efficient Q-value learning) concept to further improve the performance of MARL. Our analyses show that convex optimization, BSUM, and dueling help in significantly improving the performance of MARL. Compared to traditional MARL, our proposal results in significant improvement in terms of reward and cost, as illustrated by the results.

Obesity, a global public health concern, is escalating rapidly, especially in the Middle East, with the United Arab Emirates (UAE) witnessing one of the highest prevalence rates among adults and children. This multifactorial health issue is influenced by genetic, environmental, behavioral, and social factors. However, the challenge lies in the lack of comprehensive and representative data that encapsulates the diversity and complexity of the population, particularly in the UAE. This research paper presents a novel AI-driven approach to address the obesity problem in the UAE. The study employs state-of-the-art data augmentation techniques to generate local data that are realistic, diverse, and representative of the UAE population and the global obesity situation. The data is derived from four global datasets, two related to obesity and two related to diabetes. The paper also identifies key features and factors that influence obesity in the UAE using machine learning and feature extraction methods. A predictive model is built and evaluated using the local data and the best-performing classifier, the random forest classifier. Our study’s random forest classifier achieved a 94.83% accuracy when trained only on the global dataset. However, when fine-tuned on datasets synthesized with different augmentation methods, it showed better results: SMOTE excelled with 98.09% accuracy, TabGAN was close behind at 97.02%, and VAE lagged at 70.99%. Therefore, domain adaptation using the synthesized data confirmed the robustness of the generated data, with SMOTE and TabGAN outperforming the original dataset with accuracies of 98.09% and 97.22%, respectively.

We investigate digital twin-assisted task offloading in unmanned-aerial-vehicle (UAV)–mobile edge computing (UAV-MEC) networks with energy harvesting. Digital twin technology leverages a real-time simulated environment to optimize UAV-MEC networks. Considering unpredictable mobile edge computing (MEC) environments and low-power Internet of Things (IoT) devices, we propose a digital twin-assisted task offloading scheme in UAV-MEC networks with energy harvesting. The goal is to minimize latency and maximize the number of associated IoT devices by optimizing UAV placement and IoT device association. The constraints on computing, caching, energy harvesting, latency, and maximum number of IoT devices an UAV can serve are considered. To solve the formulated problem, we employ a branch-and-bound algorithm to obtain optimal results. We also solve the optimization problem using the relaxed heuristic algorithm. In addition, we propose a difference of convex penalty-based algorithm to solve the problem with reduced computational complexity. This approach provide efficient alternatives to obtain near-optimal solution. Through extensive simulations, we demonstrate the effectiveness of the proposed algorithm and validate the benefits of leveraging digital twin technology in UAV-MEC networks with energy harvesting.

The advent of smart cities and IoT resulted in smart applications like smart traffic, energy management systems, smart parking systems, waste management systems, and public safety systems, which are rapidly expanding. These smart systems significantly address everyday life issues and are very helpful in mitigating the risks involved in establishing smart cities. Smart and environment-friendly transportation can be achieved by reducing traffic jams and parking problems. Many researchers have attempted to automate parking space allocation using cutting-edge technologies such as WSN, cloud computing, and fog computing. This paper addresses the abovementioned issues and introduces a provenance-based smart parking system for reducing traffic jams and parking space availability. The proposed article depicts multilevel fog nodes, namely upper-level fog and lower-level fog nodes, for efficient data storage, transfer, utilization, and real-time availability of resources in parking areas. The provenance component of the system is used to assist users in knowing about the parking areas. Simulation is carried out in iFogSim and evaluated in terms of network usage and latency. The proposed system outperforms others by providing efficient network usage and less latency with limited resources. Experimental results indicate that the proposed provenance-based model outperforms latency reduction and fog environment network usage. The proposed model also implements upper-level and lower-level fog nodes that minimize downtime and enhance the reliability of the fog network.

Abstract A smart plug can transform the typical electrical appliance into a smart multi-functional device, which can communicate over the Internet. It has the ability to report the energy consumption pattern of the attached appliance which offer the further analysis. Inside the home, smart plugs can be utilized to recognize daily life activities and behavior. These are the key elements to provide human-centered applications including healthcare services, power consumption footprints, and household appliance identification. In this research, we propose a novel framework ApplianceNet that is based on energy consumption patterns of home appliances attached to smart plugs. Our framework can process the collected univariate time-series data intelligently and classifies them using a multi-layer, feed-forward neural network. The performance of this approach is evaluated on publicly available real homes collected dataset. The experimental results have shown the ApplianceNet as an effective and practical solution for recognizing daily life activities and behavior. We measure the performance in terms of precision, recall, and F1-score, and the obtained score is 87%, 88%, 88%, respectively, which is 11% higher than the existing method in terms of F1-score. Furthermore, our scheme is simple and easy to adopt in the existing home infrastructure.

Software-as-a-Service (SaaS) has received significant attention from software providers and users as a software delivery model. Most of the existing companies are transferring their business into a SaaS model. This intensely competitive environment has imposed many challenges for SaaS developers and vendors. SaaS development is a very complex process and SaaS success depends on its architecture design and development. This paper provides a better understanding of SaaS applications architecture phase during the SaaS development process. It focuses mainly on an empirical investigation of key factors of SaaS Architecture phase identified from the systematic literature review. A quantitative survey was developed and conducted to identify key architecture factors for an improved and successful SaaS application. A developed survey was used to test the proposed hypothesis presented in this study. Empirical investigation's results provide evidence that vendors and developers must consider key architecture factors for SaaS development process to stand in the current competitive environment. These key factors include customization, scalability, MTA (Multi-Tenancy Architecture), security, integration, and fault tolerance and recovery management. The main contribution of this paper is to investigate empirically the influence of identified key factors of the architecture on SaaS applications success.

In this paper, we analyze pilot contamination (PC) attacks on a multi-cell massive multiple-input multiple-output (MIMO) network with correlated pilots. We obtain correlated pilots using a user capacity-achieving pilot sequence design. This design relies on an algorithm which designs correlated pilot sequences based on signal-to-interference-plus-noise ratio (SINR) requirements for all the legitimate users. The pilot design is capable of achieving the SINR requirements for all users even in the presence of PC. However, this design has some intrinsic limitations and vulnerabilities, such as a known pilot sequence and the non-zero cross-correlation among different pilot sequences. We reveal that such vulnerabilities may be exploited by an active attacker to increase PC in the network. Motivated by this, we analyze the correlated pilot design for vulnerabilities that can be exploited by an active attacker. Based on this analysis, we develop an effective active attack strategy in the massive MIMO network with correlated pilot sequences. Our examinations reveal that the user capacity region of the network is significantly reduced in the presence of the active attack. Importantly, the SINR requirements for the worst-affected users may not be satisfied even with an infinite number of antennas at the base station.

Due to unavoidable environmental factors, wireless sensor networks are facing numerous tribulations regarding network coverage. These arose due to the uncouth deployment of the sensor nodes in the wireless coverage area that ultimately degrades the performance and confines the coverage range. In order to enhance the network coverage range, an instance (node) redeployment-based Bodacious-instance Coverage Mechanism (BiCM) is proposed. The proposed mechanism creates new instance positions in the coverage area. It operates in two stages; in the first stage, it locates the intended instance position through the Dissimilitude Enhancement Scheme (DES) and moves the instance to a new position, while the second stage is called the depuration, when the moving distance between the initial and intended instance positions is sagaciously reduced. Further, the variations of various parameters of BiCM such as loudness, pulse emission rate, maximum frequency, grid points, and sensing radius have been explored, and the optimized parameters are identified. The performance metric has been meticulously analyzed through simulation results and is compared with the state-of-the-art Fruit Fly Optimization Algorithm (FOA) and, one step above, the tuned BiCM algorithm in terms of mean coverage rate, computation time, and standard deviation. The coverage range curve for various numbers of iterations and sensor nodes is also presented for the tuned Bodacious-instance Coverage Mechanism (tuned BiCM), BiCM, and FOA. The performance metrics generated by the simulation have vouched for the effectiveness of tuned BiCM as it achieved more coverage range than BiCM and FOA.

Smart devices are becoming more universally adopted and can be used to track and model user activity and monitor for abnormalities. Deviations from what is expected may indicate that a fall is imminent or that an injury has been sustained. Healthcare practitioners can use descriptive models of human kinematics as a tool to monitor patient recovery. This work extends previous work which generated descriptive nonlinear symbolic models of human kinematics with genetic programming. Previously, linear models were developed and compared to the nonlinear models. Although the linear models fit the data well, they were significantly worse than the nonlinear models. In this phase of the project, ensembles of nonlinear models were created to more accurately fit and classify data. Different model selection strategies for the ensembles were investigated. As one would expect, ensembles of models were significantly better than a single model classifier. It was also observed that, although more models in the ensemble yielded better results, only 2 models were required to obtain significantly better results. It was also observed that a random model selection strategy for the ensembles produced competitive results when compared to a more rigorous model selection strategy.

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.

Smart devices are becoming an extension of ourselves that contain sensitive information and are often targeted for theft. The development of an intelligent and reliable means of user identification and authentication is critical. Not only can the development of user models performing tasks be used for user and task identification, but systems can also notify individuals if there is a potential health concern. The construction of an idealized model of human locomotion may give medical care providers a better understanding of individual differences and guide therapy and treatment. Data was gathered from a smartwatch worn by six subjects performing five different tasks and Genetic Programming was used to perform symbolic regression - a model free, nonlinear type of regression analysis. Symbolic regression was applied to smartwatch data and a collection of nonlinear closed form symbolic mathematical models were generated. Not only did these models fit the data well, but they provided insight into the underlying system. With only 5 seconds of unseen data, the models could classify which subjects were performing which task with 83.9% accuracy when chance was only 3.33%.

Automatic understanding of videos is one of the complex problems in machine learning and computer vision. An important area in the field of video analysis is human action recognition (HAR). Though a large number of HAR systems have already been developed, there is plenty of daily life actions that are difficult to recognize, due to several reasons, such as recording on different devices, poor video quality and similarities among actions. Development in the field of deep learning, especially in convolutional neural networks (CNN), has provided us with methods that are well-suited for the tasks of image and video recognition. This work implements a CNN-based hierarchical recognition approach to recognize 20 most difficult-to-recognize actions from the Kinetics dataset. Experimental results have shown that the application of our method significantly improves the quality of recognition for these actions.

Abstract A physical activity recommendation system promotes active lifestyles for users. Real‐world reasoning and recommendation systems face the issues of data and knowledge integration, knowledge acquisition, and accurate recommendation generation. The knowledge‐based reasoning and recommendation framework (KRF) proposed here, which accurately generates reliable recommendations and educational facts for users, could solve those issues. The KRF methodology focuses on integrating data with knowledge, rule‐based reasoning, and conflict resolution. The integration issue is resolved using a semi‐automatic mapping approach in which rule conditions are mapped to data schema. The rule‐based reasoning methodology uses explicit rules with a maximum‐specificity conflict resolution strategy to ensure the generation of appropriate and correct recommendations. The data used during the reasoning process are generated in real time from users' physical activities and personal profiles in order to personalize recommendations. The proposed KRF is part of a wellness and health care platform, Mining Minds, and has been tested in the Mining Minds integrated environment using a sedentary user behaviour scenario. To evaluate the KRF methodology, a stand‐alone, open‐source application (Version 1.0) was released and tested using a dataset of 10 volunteers with 40 different types of sedentary behaviours. The KRF performance was measured using average execution time and recommendation accuracy.

Pulmonary nodules detection play a significant role in the early detection and treatment of lung cancer. False positive reduction is the one of the major parts of pulmonary nodules detection systems. In this study a novel method aimed at recognizing real pulmonary nodule among a large group of candidates was proposed. The method consists of three steps: appropriate receptive field selection, feature extraction and a strategy for high level feature fusion and classification. The dataset consists of 888 patient's chest volume low dose computer tomography (LDCT) scans, selected from publicly available LIDC-IDRI dataset. This dataset was marked by LUNA16 challenge organizers resulting in 1186 nodules. Trivial data augmentation and dropout were applied in order to avoid overfitting. Our method achieved high competition performance metric (CPM) of 0.735 and sensitivities of 78.8% and 83.9% at 1 and 4 false positives per scan, respectively. This study is also accompanied by detailed descriptions and results overview in comparison with the state of the art solutions.

Bone suppression in lung radiographs is an important task, as it improves the results on other related tasks, such as nodule detection or pathologies classification. In this paper, we propose two architectures that suppress bones in radiographs by treating them as noise. In the proposed methods, we create end-to-end learning frameworks that minimize noise in the images while maintaining sharpness and detail in them. Our results show that our proposed noise-cancellation scheme is robust and does not introduce artifacts into the images.

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.

A novel framework for software defined Wireless Sensor Networks (SDWSNs) is presented that draws on Software Defined Networking (SDN) concepts and capabilities to enhance control, management, and security, whilst reducing device complexity. These inherent complexities pose significant challenges toward the advancement of ubiquitous sensing and sensory data access through Sensing-as-a- Service (S2aaS) model. Therefore, it is advantageous to utilize SDN to decouple the control and the data forwarding planes and incorporate greater control over dynamic virtualization and approaches to improve the quality of experience. Enhanced algorithms can be applied on improved knowledge of the network conditions that is attainable when SDN is employed. We run simulations based on sensor flow model and provide a comprehensive analysis of the SDWSN framework, architecture and implementation constraints.

Smartphones have ubiquitously integrated into our home and work environments. It is now a common practice for people to store their sensitive and confidential information on their phones. This has made it extremely important to authenticate legitimate users of a phone and block imposters. In this paper, we demonstrate that the motion dynamics of smartphones, captured using their built in accelerometers, can be used for accurate user identification. We call this mechanism gait fingerprinting. To this end, we first collected the acceleration data from multiple users as they walked with a smartphone placed freely in their pants pockets. Next, we studied the application of different feature extraction, feature selection and classification techniques from the machine learning literature on these data. Through extensive experimentation, demonstrated is that simple time domain features extracted from these data, which are further optimized using stepwise linear discrimination analysis, can be used to train artificial neural networks to identify legitimate user and block imposter with an average accuracy of 95%.

Database auditing is a prerequisite in the process of database forensics. Log files of different types and purposes are used in correlating evidence related to forensic investigation. In this paper, a new framework is proposed to explore and implement auditing features and DBMS-specific built-in utilities to aid in carrying out database forensics. The new framework is implemented in three phases, where ideal forensic auditing settings are suggested, techniques and approaches to conduct forensics are evaluated, and finally database forensic tools are investigated and evaluated. The research findings serve as guidelines toward focusing on database forensics. There is a crucial need to fill in the gap where forensic tools are few and not database specific.

Personalized healthcare envisions providing customized treatment and management plans to individuals at their doorstep. Key factors to ensure personalized healthcare is to involve with the individual in their daily life activities and process the gathered information to provide recommendations. We identified the mostly exposed domains for gathering chronic disease patients information that includes: clinical, social media, and daily life activities. Clinical data is related to the health-care of the patients while social media, sensory, and wearables data is related to the wellness data of the patients. A framework is required to monitor the health and wellness information of the patients for health and wellness analytics provisioning to the physicians for better decision making. We propose Personalized, Ubiquitous Life-care Decision Support System (PULSE); a state of the art decision support system that helps physicians and patients in life-style management of chronic disease patients such as Diabetes. The proposed approach not only utilizes clinical information but also personalized information by correlation to find hidden information using big data health analytic for improvement of life-care. PULSE provides health analytics by utilizing and processing clinical information of the patient. In the same way, it provides wellness analytics to the patients by using their social, activities, emotions and daily life information. The co-relation between clinical and personalized analytics is performed for better recommendations to the patients. This eventually results in improved life-care and healthy living of the individuals.

The rise of social media in couple of years has changed the general perspective of networking, socialization, and personalization. Use of data from social networks for different purposes, such as election prediction, sentimental analysis, marketing, communication, business, and education, is increasing day by day. Precise extraction of valuable information from short text messages posted on social media (Twitter) is a collaborative task. In this paper, we analyze tweets to classify data and sentiments from Twitter more precisely. The information from tweets are extracted using keyword based knowledge extraction. Moreover, the extracted knowledge is further enhanced using domain specific seed based enrichment technique. The proposed methodology facilitates the extraction of keywords, entities, synonyms, and parts of speech from tweets which are then used for tweets classification and sentimental analysis. The proposed system is tested on a collection of 40,000 tweets. The proposed methodology has performed better than the existing system in terms of tweets classification and sentiment analysis. By applying the Knowledge Enhancer and Synonym Binder module on the extracted information we have achieved increase in information gain in a range of 0.1% to 55%. The increase in information gain has enabled our proposed system to better summarize the twitter data for user sentiments regarding a keyword from a particular category.

To provide robust healthcare services and personalized recommendations details relating to a patient's daily life activities, profile information, and personal experience is of vital importance. This paper focuses on improvement in general health status of elderly patients through the use of an innovative service which align dietary intake with activity information. Personalized healthcare services based on the patient's activities of daily living and their shared experience, are provided as outputs. A knowledge driven approach has been used where all the daily life activities, social interactions, and profile information are modeled in an ontology. The semantic context is exploited that enables fine-grained situation analysis for recommendation of personalized services and decision support. Preliminary experimental results for the dynamic nature of the systems and its corresponding personalized recommendations have been found to be encouraging.

This paper presents a Secured Wireless Sensor Network-integrated Cloud computing for u-Life Care (SC3). SC3 monitors human health, activities, and shares information among doctors, care-givers, clinics, and pharmacies in the Cloud, so that users can have better care with low cost. SC3 incorporates various technologies with novel ideas including; sensor networks, Cloud computing security, and activities recognition.

Knowledge constantly grows in scientific discourse and is revised over time by domain experts. The body of knowledge will get structured and refined as the communities of practice concerned with the field of knowledge develop a deeper understanding of issues. The knowledge model, as a result evolves to a new state to accommodate the new knowledge. Keeping trail of these changes in semantically rich and formally sound mechanism, has pragmatic advantages for providing the undo and redo facility and recover to a previous state of the knowledge body (i.e. ontology). In this research, we have developed and tested comprehensive methodological framework for change tracer. The ontology changes are captured and then stored in change history log (CHL) in conformance to change history ontology (CHO). The CHL is later used for reverting ontology to a previous consistent state and visualization of change effects on ontology. The system is compared with ChangesTab of Protege, a comprehensive evaluation of the accuracy of roll-back and roll-forward algorithm has been conducted over documentation ontology. The system is also tested over a standard dataset of OMV and high accuracy results are observed for both roll-back and roll-forward algorithms.

Distributed repositories, containing digital documents are usually maintained and managed independently according to organization/users own requirements. Same information (i.e. documents containing same information) in different repositories, may be represented differently, makes it hard to retrieve desired information. Keyword based information retrieval technique helps in improving recall of user query result, but having low precision. To improve precision, we adopt semantic information retrieval technique. We are proposing architecture for semantic based information retrieval, in which plain text is read semantically and the extracted metadata is stored and later used for semantic search.