Improving the impact of power efficiency in mobile cloud applications using cloudlet model

© 2020 John Wiley & Sons, Ltd. The applications and services of Information and Communication Technologies are becoming a very essential part of our daily life. In addition, the spread of advanced technologies including the cloud and mobile cloud computing (MCC), wireless communication, and smart devices made it easy to access the internet and utilize unlimited number of services. For example, we use mobile applications to carry out critical tasks in hospitals, education, finance, and many others. This wide useful usage makes the smart devices an essential component of our daily life. The limited processing capacity and battery lifetime of mobile devices are considered main challenges. This challenge is increased when executing intensive applications. The MCC is believed to overcome these limitations. There are many models in MCC and one efficient model is the cloudlet-based computing. In this model, the mobile devices users communicate with the cloudlets using cheaper efficient technologies, and offload the job requests to be executed on the cloudlet rather than on the enterprise cloud or on the device itself. In this article, we investigated the cloudlet-based MCC architecture, and more specifically, the cooperative cloudlets model. In this model, the applications that require intensive computations such as image processing are offloaded from the mobile device to the nearest cloudlet. If the task cannot be accomplished at this cloudlet, the cloudlets cooperate with each other to accomplish the user request and send the results back to the user. To demonstrate the efficiency of this cooperative cloudlet-based MCC model, we conducted real experiments that execute selected applications such as: object code recognition, and array sorting to measure the delay and power consumption of the cloudlet-based system. Moreover, suitable cloud/mobile cloud simulators such as CloudSim and MCCSim will be used to perform simulation experiments and obtain time and power results

Greener and Smarter Phones for Future Cities: Characterizing the Impact of GPS Signal Strength on Power Consumption

Smart cities appear as the next stage of urbanization aiming to not only exploit physical and digital infrastructure for urban development but also the intellectual and social capital as its core ingredient for urbanization. Smart cities harness the power of data from sensors in order to understand and manage city systems. The most important of these sensing devices are smartphones as they provide the most important means to connect the smart city systems with its citizens, allowing personalization n and cocreation. The battery lifetime of smartphones is one of the most important parameters in achieving good user experience for the device. Therefore, the management and the optimization of handheld device applications in relation to their power consumption are an important area of research. This paper investigates the relationship between the energy consumption of a localization application and the strength of the global positioning system (GPS) signal. This is an important focus, because location-based applications are among the top power-hungry applications. We conduct experiments on two android location-based applications, one developed by us, and the other one, off the shelf. We use the results from the measurements of the two applications to derive a mathematical model that describes the power consumption in smartphones in terms of SNR and the time to first fix. The results from this study show that higher SNR values of GPS signals do consume less energy, while low GPS signals causing faster battery drain (38% as compared with 13%). To the best of our knowledge, this is the first study that provides a quantitative understanding of how the poor strength (SNR) of satellite signals will cause relatively higher power drain from a smartphone\u27s battery

IoT Privacy and Security: Challenges and Solutions

Privacy and security are among the significant challenges of the Internet of Things (IoT). Improper device updates, lack of efficient and robust security protocols, user unawareness, and famous active device monitoring are among the challenges that IoT is facing. In this work, we are exploring the background of IoT systems and security measures, and identifying (a) different security and privacy issues, (b) approaches used to secure the components of IoT-based environments and systems, (c) existing security solutions, and (d) the best privacy models necessary and suitable for different layers of IoT driven applications. In this work, we proposed a new IoT layered model: generic and stretched with the privacy and security components and layers identification. The proposed cloud/edge supported IoT system is implemented and evaluated. The lower layer represented by the IoT nodes generated from the Amazon Web Service (AWS) as Virtual Machines. The middle layer (edge) implemented as a Raspberry Pi 4 hardware kit with support of the Greengrass Edge Environment in AWS. We used the cloud-enabled IoT environment in AWS to implement the top layer (the cloud). The security protocols and critical management sessions were between each of these layers to ensure the privacy of the users’ information. We implemented security certificates to allow data transfer between the layers of the proposed cloud/edge enabled IoT model. Not only is the proposed system model eliminating possible security vulnerabilities, but it also can be used along with the best security techniques to countermeasure the cybersecurity threats facing each one of the layers; cloud, edge, and IoT

Tasks and User Performance Improvement for UUM Online Payment Using Key Stroke Level Model

Online payment is one of the components in postgraduate website in University Utara Malaysia (UUM). Not a lot of Student prefers to use this task, this research will focus a weakness points in the current payment model interface and strength points in proposed new online payment model by using Keystroke-Level Model (KLM) technique and improve weakness points in the current payment model interface. The study will be guided by a research question which was formulated as Follows. What is the efficiency problem of online payment that effect user to use the system? .How can the recommended online payment Model achieve efficiency of system and user aim? What is the user performance of current online payment Model to achieve the tasks? The population for this study will be the (undergraduate and postgraduate) students and staff in the University Utara Malaysia (UUM). The quantitative research approach was used since the researcher aimed to explore the important of (KLM) technique to enhance the current online payment model, and increases the acceptance level of the system

Decrypting SSL/TLS traffic for hidden threats detection

The paper presents an analysis of the main mechanisms of decryption of SSL/TLS traffic. Methods and technologies for detecting malicious activity in encrypted traffic that are used by leading companies are also considered. Also, the approach for intercepting and decrypting traffic transmitted over SSL/TLS is developed, tested and proposed. The developed approach has been automated and can be used for remote listening of the network, which will allow to decrypt transmitted data in a mode close to real time.Comment: 4 pages, 1 table, 1 figur

Experimental Comparison of Simulation Tools for Efficient Cloud and Mobile Cloud Computing Applications

Cloud computing provides a convenient and on-demand access to virtually unlimited computing resources. Mobile cloud computing (MCC) is an emerging technology that integrates cloud computing technology with mobile devices. MCC provides access to cloud services for mobile devices. With the growing popularity of cloud computing, researchers in this area need to conduct real experiments in their studies. Setting up and running these experiments in real cloud environments are costly. However, modeling and simulation tools are suitable solutions that often provide good alternatives for emulating cloud computing environments. Several simulation tools have been developed especially for cloud computing. In this paper, we present the most powerful simulation tools in this research area. These include CloudSim, CloudAnalyst, CloudReports, CloudExp, GreenCloud, and iCanCloud. Also, we perform experiments for some of these tools to show their capabilities