Abdulhameed Alelaiwi

A Survey on Sensor-Cloud: Architecture, Applications, and Approaches:

Nowadays, wireless sensor network (WSN) applications have been used in several important areas, such as healthcare, military, critical infrastructure monitoring, environment monitoring, and manufacturing. However, due to the limitations of WSNs in terms of memory, energy, computation, communication, and scalability, efficient management of the large number of WSNs data in these areas is an important issue to deal with. There is a need for a powerful and scalable high-performance computing and massive storage infrastructure for real-time processing and storing of the WSN data as well as analysis (online and offline) of the processed information under context using inherently complex models to extract events of interest. In this scenario, cloud computing is becoming a promising technology to provide a flexible stack of massive computing, storage, and software services in a scalable and virtualized manner at low cost. Therefore, in recent years, Sensor-Cloud infrastructure is becoming popular that can provide an open, flexible, and reconfigurable platform for several monitoring and controlling applications. In this paper, we present a comprehensive study of representative works on Sensor-Cloud infrastructure, which will provide general readers an overview of the Sensor-Cloud platform including its definition, architecture, and applications. The research challenges, existing solutions, and approaches as well as future research directions are also discussed in this paper.

Evaluating and Improving the Depth Accuracy of Kinect for Windows v2

Microsoft Kinect sensor has been widely used in many applications since the launch of its first version. Recently, Microsoft released a new version of Kinect sensor with improved hardware. However, the accuracy assessment of the sensor remains to be answered. In this paper, we measure the depth accuracy of the newly released Kinect v2 depth sensor, and obtain a cone model to illustrate its accuracy distribution. We then evaluate the variance of the captured depth values by depth entropy. In addition, we propose a trilateration method to improve the depth accuracy with multiple Kinects simultaneously. The experimental results are provided to ascertain the proposed model and method.

Secure Distributed Deduplication Systems with Improved Reliability

Data deduplication is a technique for eliminating duplicate copies of data, and has been widely used in cloud storage to reduce storage space and upload bandwidth. However, there is only one copy for each file stored in cloud even if such a file is owned by a huge number of users. As a result, deduplication system improves storage utilization while reducing reliability. Furthermore, the challenge of privacy for sensitive data also arises when they are outsourced by users to cloud. Aiming to address the above security challenges, this paper makes the first attempt to formalize the notion of distributed reliable deduplication system. We propose new distributed deduplication systems with higher reliability in which the data chunks are distributed across multiple cloud servers. The security requirements of data confidentiality and tag consistency are also achieved by introducing a deterministic secret sharing scheme in distributed storage systems, instead of using convergent encryption as in previous deduplication systems. Security analysis demonstrates that our deduplication systems are secure in terms of the definitions specified in the proposed security model. As a proof of concept, we implement the proposed systems and demonstrate that the incurred overhead is very limited in realistic environments.

Audio–Visual Emotion-Aware Cloud Gaming Framework

The promising potential and emerging applications of cloud gaming have drawn increasing interest from academia, industry, and the general public. However, providing a high-quality gaming experience in the cloud gaming framework is a challenging task because of the tradeoff between resource consumption and player emotion, which is affected by the game screen. We tackle this problem by leveraging emotion-aware screen effects in the cloud gaming framework and combining them with remote display technology. The first stage in the framework is the learning or training stage, which establishes a relationship between screen features and emotions using Gaussian mixture model-based classifiers. In the operating stage, a linear programming model provides appropriate screen changes based on the real-time user emotion obtained in the first stage. Our experiments demonstrate the effectiveness of the proposed framework. The results show that our proposed framework can provide a high quality gaming experience while generating an acceptable amount of workload for the cloud server in terms of resource consumption.

Enhancing SVM performance in intrusion detection using optimal feature subset selection based on genetic principal components

Intrusion detection is very serious issue in these days because the prevention of intrusions depends on detection. Therefore, accurate detection of intrusion is very essential to secure information in computer and network systems of any organization such as private, public, and government. Several intrusion detection approaches are available but the main problem is their performance, which can be enhanced by increasing the detection rates and reducing false positives. This issue of the existing techniques is the focus of research in this paper. The poor performance of such techniques is due to raw dataset which confuse the classifier and results inaccurate detection due to redundant features. The recent approaches used principal component analysis (PCA) for feature subset selection which is based on highest eigenvalues, but the features corresponding to the highest eigenvalues may not have the optimal sensitivity for the classifier due to ignoring many sensitive features. Instead of using traditional approach of selecting features with the highest eigenvalues such as PCA, this research applied a genetic algorithm to search the genetic principal components that offers a subset of features with optimal sensitivity and the highest discriminatory power. The support vector machine (SVM) is used for classification purpose. This research work used the knowledge discovery and data mining cup dataset for experimentation. The performance of this approach was analyzed and compared with existing approaches. The results show that proposed method enhances SVM performance in intrusion detection that outperforms the existing approaches and has the capability to minimize the number of features and maximize the detection rates.

Smart Health Solution Integrating IoT and Cloud: A Case Study of Voice Pathology Monitoring

The integration of the IoT and cloud technology is very important to have a better solution for an uninterrupted, secured, seamless, and ubiquitous framework. The complementary nature of the IoT and the could in terms of storage, processing, accessibility, security, service sharing, and components makes the convergence suitable for many applications. The advancement of mobile technologies adds a degree of flexibility to this solution. The health industry is one of the venues that can benefit from IoT–Cloud technology, because of the scarcity of specialized doctors and the physical movement restrictions of patients, among other factors. In this article, as a case study, we discuss the feasibility of and propose a solution for voice pathology monitoring of people using IoT–cloud. More specifically, a voice pathology detection system is proposed inside the monitoring framework using a local binary pattern on a Mel-spectrum representation of the voice signal, and an extreme learning machine classifier to detect the pathology. The proposed monitoring framework can achieve high accuracy of detection, and it is easy to use.

How Close are We to Realizing a Pragmatic VANET Solution? A Meta-Survey

Vehicular Ad-hoc Networks (VANETs) are seen as the key enabling technology of Intelligent Transportation Systems (ITS). In addition to safety, VANETs also provide a cost-effective platform for numerous comfort and entertainment applications. A pragmatic solution of VANETs requires synergistic efforts in multidisciplinary areas of communication standards, routings, security and trust. Furthermore, a realistic VANET simulator is required for performance evaluation. There have been many research efforts in these areas, and consequently, a number of surveys have been published on various aspects. In this article, we first explain the key characteristics of VANETs, then provide a meta-survey of research works. We take a tutorial approach to introducing VANETs and gradually discuss intricate details. Extensive listings of existing surveys and research projects have been provided to assess development efforts. The article is useful for researchers to look at the big picture and channel their efforts in an effective way.

Toward end-to-end biomet rics-based security for IoT infrastructure

The IoT is the next generation of innovation in the smart world, which has the potential to improve safety, security, and the quality of our daily lives through pervasive communication and computation. Currently, we have observed that a plethora of interconnected smartphones, devices, and sensors are deployed for providing personalized services (e.g., social media, smart home, health monitoring) at any time from anywhere. The personalized services offered by IoT, although enhancing the quality of our lives, have serious challenges of securing networks and data in transit, as every day a myriad of devices and services are connected to the IoT. However, existing security solutions, such as two-factor authentication based on passwords along with second-level protection may not be efficient and reliable for providing end-to-end secure communication solutions among different devices and services connected to the IoT. To this end, this article proposes an end-to-end secure IoT-based solution using biometrics and pairing-based cryptography. Because of the uniqueness of ones biometric traits (e.g., face, fingerprint, palm, iris, voice, heartbeat), a biometric-based security solution is less vulnerable to security breaches for IoT systems or infrastructure. We present a biometric- based IoT infrastructure comprising four layers, and for each layer, we discuss possible security challenges along with the corresponding countermeasures. Finally, we provide a case study of face-based biometric recognition, where sensors or smartphones capture a face image and securely transmit it to the IoT platform to provide end-to end security.

A Performance Evaluation of Machine Learning-Based Streaming Spam Tweets Detection

The popularity of Twitter attracts more and more spammers. Spammers send unwanted tweets to Twitter users to promote websites or services, which are harmful to normal users. In order to stop spammers, researchers have proposed a number of mechanisms. The focus of recent works is on the application of machine learning techniques into Twitter spam detection. However, tweets are retrieved in a streaming way, and Twitter provides the Streaming API for developers and researchers to access public tweets in real time. There lacks a performance evaluation of existing machine learning-based streaming spam detection methods. In this paper, we bridged the gap by carrying out a performance evaluation, which was from three different aspects of data, feature, and model. A big ground-truth of over 600 million public tweets was created by using a commercial URL-based security tool. For real-time spam detection, we further extracted 12 lightweight features for tweet representation. Spam detection was then transformed to a binary classification problem in the feature space and can be solved by conventional machine learning algorithms. We evaluated the impact of different factors to the spam detection performance, which included spam to nonspam ratio, feature discretization, training data size, data sampling, time-related data, and machine learning algorithms. The results show the streaming spam tweet detection is still a big challenge and a robust detection technique should take into account the three aspects of data, feature, and model.

A two-stage approach for task and resource management in multimedia cloud environment

In recent years, multimedia cloud computing is becoming a promising technology that can effectively process multimedia services and provide quality of service (QoS) provisioning for multimedia applications from anywhere, at any time and on any device at lower costs. However, there are two major challenges exist in this emerging computing paradigm: one is task management, which maps multimedia tasks to virtual machines, and the other is resource management, which maps virtual machines (VMs) to physical servers. In this study, we aim at providing an efficient solution that jointly addresses these challenges. In particular, a queuing based approach for task management and a heuristic algorithm for resource management are proposed. By adopting allocation deadline in each VM request, both task manager and VM allocator receive better chances to optimize the cost while satisfying the constraints on the quality of multimedia service. Various simulations were conducted to validate the efficiency of the proposed task and resource management approaches. The results showed that the proposed solutions provided better performance as compared to the existing state-of-the-art approaches.