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The performance of the 5G system in the heavy region is closely related to the distance between the base station of the network as the mm-Wave frequency employed suffers from deep signal fade due to rain. For this reason, the knowledge of spatial variability of small scale rain profiles is of key importance. This work investigates such spatial features of the rain profile based on 16 months of measured rain gauge networks data in UTM, Johor, Malaysia. The spatial pattern and trend were carefully processed and analyzed according to the rain events extracted from 4 rain gauges within the UTM campus. The investigation work only focuses on the high rain rate profile with a threshold of above 50 mm/h. The preliminary results indicate the Gaussian profile seems to represent the small scale rain cell; however, the validity of the results should be further investigated with respect to the complete rain cell profile in this particular region. Nevertheless, this preliminary work gave radio engineers at least an early insight into the spatial variability of rain and their impact on the design of the 5G system in heavy rain regions.

Due to their diverse range of applications, Wireless Sensor Networks (WSNs) have been the center of attention for research since last few decades. Due to their inherent characteristics, mostly WSNs are deployed in hostile environments where human intervention is mostly infeasible. A sensor node failure is a common phenomenon in WSNs and node failure leads to partitioning of the networks resulting in loss of inter-node connectivity. Loss of connectivity can compromise the operation of the whole sensor network. Therefore, rapid recovery of partitioned network is crucial for persistence of the operation of the sensor network. In the literature, a plethora of different approaches regarding the restoration of inter-node connectivity are present. However, these approaches have not focused on the efficient use of energy, coverage-aware mechanisms, and connectivity restoration in an integrated manner. In this paper we compare two representative approaches for connectivity restoration named C3R (Coverage Conscious Connectivity Restoration) and DEENR (Distributed Energy Efficient Node Relocation Algorithm). Both of these approaches are thoroughly evaluated with respect to multiple performance metrics by using extensive simulations in NS-2. Simulation results conclude that DEENR prove to outperform C3R with respect to multiple performance metrics.

We are living in a world where everything is automated and linked online. The internet of things, image processing, and machine learning are evolving day by day. Many systems have been completely changed due to this evolve to achieve more accurate results. The attendance system is a typicalexample of this transition, starting from the traditional signature on a paper sheet to face recognition. This paper proposes a method of developing a comprehensive embedded class attendance systemusing facial recognition with controlling the door access. The system is based on Raspberry Pi thatruns Raspbian (Linux) Operating System installed on micro SD card. The Raspberry Pi Camera, as well as a 5-inch screen, are connected to the Raspberry Pi. By facing the camera, the camera will capture the image then pass it to the Raspberry Pi which is programmed to handle the face recognition by implementing the Local Binary Patterns algorithm LBPs. If the student’s input image matches withthetrained dataset image the prototype door will open using Servo Motor, then the attendance results will be stored in the MySQL database. The database is connected to Attendance Management Syste(AMS) web server, which makes the attendance results reachable to any online connected web browser.The system has 95{\% accuracy with the dataset of 11 person images.

Hadoop is an open-source software framework for distributed computing that is widely used to develop large-scale data processing applications, such as big data applications. Hadoop application programs are normally run on in-house or cloud computing platforms. Recently, a hybrid cloud composed of in-house and remote cloud computing platforms has been found to be capable of sustaining a certain level of application performance. In this paper, we discuss the performance of a Hadoop application program running on such hybrid clouds. We will begin by presenting the performance model used to estimate the execution time of a Hadoop application program running on a hybrid cloud. Then, we will show the results of experiments conducted on hybrid cloud test beds. These experimental results revealed that the performance levels of the Hadoop application programs running on the hybrid cloud were application type dependent, and that performance improvements could be expected by using a remote cloud computing platform in conjunction with in-house computing platforms for certain types of applications. Furthermore, the results showed that our performance model captured the performance trend of the application programs on the hybrid cloud. However, room for improvement still exists in the performance model, particularly for the shuffle phase.

In the era of cloud computing, users encounter the challenging task of effectively composing and running their applications on the cloud. In an attempt to understand user behavior in constructing applications and interacting with typical cloud infrastructures, we analyzed a large utilization dataset of Google cluster. In the present paper, we consider user behaviorin composing applications from the perspective of topology, maximum requested computational resources, and workload type. We model user dynamic behavior around the user's session view. Mass-Count disparity metrics are used to investigate the characteristics of underlying statistical models and to characterize users into distinct groups according to their composition and behavioral classes and patterns. The present study reveals interesting insight into the heterogeneous structure of the Google cloud workload.

The development of incident area network (IAN) is to provide temporary wireless service primarily in emergency situations. The coverage areas of interest include both outdoor and indoor scenarios. In a high rise or multi floor building, staircases are commonly used by public safety personnel to move about different floors. As such, a good understanding of wireless signal wave attenuation along the staircase is crucial in order to support the planning of a reliable wireless IAN. This paper presents comparison of different established empirical indoor propagation models when applied to the staircase environment. Measured path losses in two dog-leg staircases are compared to path losses calculated from three different models at 900 MHz and 1800 MHz that are near to public safety communication bands. This paper attempts to demonstrate the accurateness of the standard models investigated and recommend the best model for application in the dog-leg staircase configuration setting.

In conjunction with the rising trend towards consumption of resource-hungry multimedia content over wireless medium, efficient radio resource allocation (eRRA) represents an ongoing challenge to network operators. This paper explores the application of multicasting in light of an eRRA that considers the perceptual quality aspect of wireless video transmission such that transmission energy is minimized. A multiuser orthogonal frequency-division multiplexing (OFDM) environment is considered. The resource allocation scheme presented is based on the genetic algorithm (GA) and offers fairness through the balance of average perceptual quality and minimal total energy among all subscribers in multicasting groups. In order to assist with this balance, a utility function is introduced as a fitness function for the GA. The allocation scheme relies on a well-established video quality model (VQM) for assessment of user-perceived quality. Simulation results show that the allocation scheme helps to preserve the energy requirement at low levels despite the proportionate rise in the number of subscribers. Moreover, perceived quality is well-secured and maintained at high levels, with a consistent increase of the utility value. Reflecting on today's consumption patterns of popular video content, adoption of the presented scheme in multicasting would help lessen the carbon footprint emitted by wireless communications, while consumers' quality of experience (QoE) is maintained.

This paper suggests a power and bandwidth efficient resource allocation approach for mobile networks based on consumer's perception of 3D video. We simulate a mobile 3D video transmission through which we identify the correlation between receiver's signal-to-noise ratio and perceived quality. Our QoE-based approach is shown to save up to 33.13% in power and grant up to 50% more data bits for transmission, compared to QoS-based techniques.

Cloud computing is widely used as a computing platform in business and academic communities. Performance is an important issue when a user runs an application in the cloud. The user may want to estimate the application execution time before running the application in order to guarantee the application performance or choose a suitable cloud for the user application. However, the application performance in the cloud sometime fluctuates, which makes the estimation of the application performance difficult. In this paper, we present the experimental results obtained in investigating the fluctuation of the application execution time in the cloud. We investigated the performance fluctuation of Hadoop jobs in both a public cloud and a private cloud over three months. The obtained experimental results indicate that the fluctuation is smaller than expected, except for some cases for irregular jobs with significantly long execution times. Moreover, the application performance is independent of the time of day and the day of the week. Finally, we also discuss the reasons for the existence of these irregular jobs through a detailed analysis of job execution traces.

Resource allocation is an active direction of research that is drawing interest within academic and technological circles. Resource allocation imposes numerous challenges. This is especially true for Inter-Clouds, a recent paradigm for horizontal expansion and integration of disparate and heterogeneous cloud platforms. In an attempt to realize an efficient resource management system, this work-in-progress paper proposes and describes a new multi-layered management framework to address the tasks of virtualized resource control, dynamic resource provisioning, life-cycle management and resource exchange within Inter-Cloud environments.

Resource management in cloud platforms becomes an increasingly complex and daunting task surrounded by various challenges of stringent QoS requirements, service availability guaranteeing and escalating overhead of the infrastructure that resulted from operation costs and ecological impact. On the other hand, virtualization adds a greater flexibility to the resource managers in addressing such challenges. However, at the same time, it imposes a further challenge of added management complexity. Recently, we have proposed a resource management model for cloud platforms, which utilizes a new resource mapping formulation and relays on a hybrid virtualization framework in an attempt to realize a resource manager that intelligently adapts the available cloud resources to satisfy the conflicting objectives of the running applications and underlying infrastructures' requirements. Moreover, we have proposed state of the art Binary-Real coded Genetic Algorithm (BRGA), which has been applied successfully to a wide spectrum of global and constrained optimization problems from the known benchmark suites. In this paper, we aim to proceed by proposing a mathematical model and a modified version of BRGA to validate our model. In addition, we aim to evaluate the feasibility, effectiveness and scalability of our approach through simulation experiments.

Genetic algorithms (GAs) are vital members within the family biologically inspired algorithms. It has been proven that the performance of GAs is largely affected by the type of encoding schemes used to encode optimization problems. Binary and real encoding schemes are the most popular ones. However, it is still controversial to decide the superiority of one of them for GAs performance. Therefore, we have recently proposed binary-real coded GA (BRGA) that has the ability to use both encoding schemes at the same time. BRGA relays on a parameterized hybrid scheme to share the computational power and coordinate the cooperation between binary coded GA (BGA) and real coded GA (RGA). In this paper, we aim to evaluate the performance of BRGA systematically by utilizing CEC'2005 benchmark of 25 problems and adopting a robust experimental analysis approach. The quality and time performance of BRGA against the benchmark suite and in comparison with original component algorithms (BGA and RGA) is reported discussed and analyzed. Moreover, the performance of BRGA is compared with other Evolutionary Algorithms (EAs) from the literature.

3-Dimensional Television (3D-TV) is an emerging area of consumer entertainment, which gives its viewers an added dimension of experience. At the moment 3DTV is going through a series of standardization activities, especially to finalize a representation technique. Extra information that needs to be transmitted in 3D-TV systems has brought about new problems that need to be solved. Models have been introduced to explain the distortion characteristics in traditional 2D video, when they are transmitted over existing wireless networks. However, the effect of transmission errors caused by bandwidth limited wireless channels on 3D video is not yet fully known. In this paper we present a performance evaluation of 3D-TV transmission over an error prone WiMAX broadband wireless access network. Experiments are performed on two popular types of representation methods of 3D video, namely color plus depth and left and right representation. Based on these experiments, important conclusions regarding 3D-TV transmission over WiMAX networks are presented.

Industrial automation processes has lead to an increasing need for more precise parts. A stamping die is a special and one of a kind precision tool that cuts and forms sheet metal into desired shape. Springback, wrinkling and tearing are some of the common defects in stamped parts. The aim of this research work is to develop and apply an effective approach to understand the formability and prevent potential defects in sheet metal for a particular automotive component. The defect on springback is the main focus of interest in this study and the first stage, as discussed in this paper, is to show experimentally that springback is proportionally related to the die radius of the tool geometry. The second phase of the work is to carry out finite element analysis using a simulation tool to analyze further the springback effect in the automotive part. Comparison is then made between the experimental results of springback and the part design formability.