Platform Proposal for Control Systems Practices with Smart Mobile Devices

This research study is an attempt to investigate and recommend the applications and usefulness of social media as a knowledge sharing platform using smart mobile devices and students’ academic performances. Research literature reveals that further examination of this idea is a must as it is increasingly becoming an integral part and a current mandate for digital-era learners. This study is exploratory based on learners’ data which includes students from foundation, junior and senior courses at a university. The objective was to cognize learners’ perceptions on collaborative learning while using applications of social media knowledge platform and smart mobile devices. Structural Model approach is followed to validate the data and undertake the required analysis. The resulting analysis shows that a significantly positive relation exists between the available platforms of knowledge applications using smart devices and its substantial impact on students’ overall performances. In addition, it could be observed that generally students have positive and valuable perception on the use of social media knowledge platform and smart mobile devices in collaborative learning settings. Moreover, such settings encourage students-students’ interactions, studentsteachers’ interactions, and the growing trends of students to online experts’ interactions. Furthermore, it was noticed that these interactions improve students’ engagement with such platforms using smart devices, which consequently has significant positive impact on students’ academic performances. The findings propose the inculcation in students the positive role of the use of social media knowledge platforms using smart devices so to encourage collaborative learning, to enhance active learning, to be more dynamic & engaging, to broaden their domain-knowledge, and to promote academic researchoriented learning, which are all crucial for graduate employability.

As smart mobile devices are widely used, mobile threats are more serious, so security in mobile becomes more and more important. However, the performance of these devices are not powerful enough to use the same security algorithms as PCs. Public key cryptosystems such as RSA need big primes to enhance the security, however, generating a big prime takes substantial time even on a PC. In this paper, we study two prime generation algorithms for smart mobile devices. First, we analyze a previous prime generation algorithm using a GCD test, named PGCD-MR, and show it sometimes performs inferior to the traditional TD-MR test. Second, we propose a new GCD test, named m-bit GCD-MR, for fast prime generation in both PCs and smart mobile devices. We compare the running times of PGCD-MR, m-bit GCD-MR, and TD-MR combinations on PCs and Samsung Galaxy Tab 10.1. The experimental results show our running time analysis is accurate (only 2% error) and m-bit GCD-MR test is the fastest among three prime generation algorithms. More exactly, m-bit GCD-MR test is about 20% faster than the TD-MR combination.

Many studies have revealed that homework (e.g., relaxation exercises) are crucial for remote therapeutic interventions. In this context, to monitor whether patients actually perform their homework and to check whether they perform it in the right way constitute complex tasks. So far, therapeutic interventions have not been properly supported by IT systems and, hence, the opportunities provided by mobile assistance have been neglected. For example, a smart mobile device may notify a patient about an assigned homework or motivate him to accomplish it in time. Moreover, the patient might be further assisted through a video providing detailed instructions. In turn, the smart mobile device could inform the therapist of the homework outcome. In practice, a proper support of the various types of homework is challenging, even when using modern IT systems. To remedy this drawback, we propose an approach integrating mobile services with process management technology in order to enable the complex coordination tasks that become necessary in connection with homework. For example, a process might enable remote monitoring of homework, giving therapists the opportunity of timely adjustments. In addition, the approach allows involving researchers by providing them with valuable data (e.g., heart rate) gathered during and after homework. This paper presents an approach for creating processes that run on smart mobile devices and enable flexible remote therapeutic intervention support. Such mobile approach significantly enhances therapy assistance on one hand and mobile homework-related scenarios on the other.

This article is part of a PhD research and investigates the impact of rapid technological evolution, focusing on the adoption of smart mobile devices (SMD), such as smartphones, tablets and laptops, and applications (app) in the personal lives of higher education professors in the area of Information Science. In addition, gaps are identified in existing research, emphasizing the need to understand how SMD is used in personal activities for personal knowledge management (PKM) practices and infocommunication. Specific objectives include identifying the most commonly used SMDs and apps, the frequency of such use, and how they are used for those practices. The methodology is qualitative, triangulating the integrative review with a questionnaire (N-10). The results point to interest in device ecologies for PKM and Info-communication, with communication apps (text and voice) and e-mail being the most important for teachers. It is hoped that the results of this study can contribute to the advancement of knowledge about the use of SMD by teachers and to the development of a generic descriptive model of infocommunicational behaviour and PKM that can be applied in other contexts.

Mobile cloud computing (MCC) allows smart mobile devices (SMD) to access the cloud resources in order to offload data from smartphones and to acquire computational services for application processing. A distinctive factor in accessing cloud resources is the communication link. However, the communication links between SMD and cloud resources are weak, which allows intruders to perform malicious activities by exploiting their vulnerabilities. This makes security a key challenge in the MCC environment. Several intrusion detection and response systems (IDRSs) are adapted to address the exploitation of vulnerabilities that affect smartphones, communication links between cloud resources and smartphones, as well as cloud resources. In this article, we discuss the cloud-based IDRS in the context of SMD and cloud resources in the MCC infrastructure. The stringent security requirements are provided as open issues along with possible solutions. The article aims at providing motivations for researchers, academicians, security administrators, and cloud service providers to discover mechanisms, frameworks, standards, and protocols to address the challenges faced by cloud-based IDRS for SMD.

Recently, application scenario of crowdsourcing IoT has covered to e-healthcare service, smart home, smart city, internet of vehicles due to the proliferation of smart devices such as smart mobile devices, smart wearable device, smart medical devices and smart furniture, etc. Patient's data collected by the smart devices send to the various remote medical servers. A group of medical professionals remote access patient data stored at the medical server database. Smart home users want to remote real-time access information of smart devices at home. All these operations need via wireless remote communication, which is suffering from various kinds of threat and attacks. Hence, there are a large number of multi-factor remote authentication and key agreement schemes designed for the application of crowdsourcing IoT. However, in most existing related multi-factor schemes, all factors for identity authentication only act as a parameter for encrypting the local secret key. In this paper, we propose a new secure remote multi-factor authentication scheme that includes three factors: 1) user identity; 2) password; and 3) user biometrics, which are authenticated by the remote server, act as a part of the secret key and participate in the key agreement process. We choose the chaotic map since it has a smaller key size and lower computational overhead, and then achieve remote multi-factor authentication and key agreement by artfully employ it to zero-knowledge technology and the fuzzy extractor technology. Our scheme is more secure and robust since the user revealing nothing sensitive information, and the adversary cannot impersonate any user even if he gets the server's master key. We have done security proof for our proposed scheme using the Random-Or-Real(ROR) model, Burrows-Abadi-Needham (BAN) logic, and ProVerif 2.00 to show that the presented scheme is secure. Also, we give an additional security analysis for other various attacks. Finally, according to the test and simulation result, the proposed scheme is very suitable for the power-constrained smart devices, and in the next generation 5G communication environment, its applicability and usability will be greatly enhanced.

Falls are a major health concern leading cause of fatal and non-fatal injuries for neurological disorders. Balance dysfunction is one of the common factors to determine fall risk in neurological patients. Preventative measures may help to reduce the incidence and severity of falls for detecting balance function and fall risk factors. However, the objective measures for balance require expensive equipment with the assessment of clinical expertise. A main gap remains in the evaluation method to objectively characterize the balance functions in individuals with high risk of falling. With the development of wearable and mobile devices, recent advances in smart mobile devices may provide a potential opportunity to manage the gap in the detailed quantification of balance impairments. The purpose of this study is to identify whether the biomechanical data measured by the mobile device is reliable to characterize the posture stability in various balance test conditions. A total of 39 children with Down syndrome completed four balance-testing tasks under altered base of support and vision. Simultaneous biomechanical measurements were gathered from the iPod and force plate analysis system during functional balance testing. The force plate and mobile system provided similar patterns of stability across groups. Correlation ([Formula: see text] between two systems for path length, 95% ellipse area, peak-to-peak, standard deviation and mean ranged from 0.60 to 0.99. We expect that the smart mobile device can provide reliable and accurate information to quantify the postural stability in individuals with elderly people or neurological disorders. The objectivity, portability and easy use of such mobile device make it ideal to apply in clinical environments for detecting balance functions and reducing the risk of falls in Down syndrome or other neurological patients.

In large-scale indoor facilities such as airports, train stations, factories, and hospitals, the locations of mobile smart devices provide important information for grasping the activity state of people and the utilization state of things. Consequently, many indoor localization systems for people and things have been proposed. However, such systems require a large amount of localization equipment or advanced measurements of environmental physical characteristics, and thus they strongly depend on the infrastructure for localization. In this paper, we propose SmartFinder as a solution with extremely low dependence on the infrastructure. This localization system applies our previously proposed Cloud-based Self-Organizing Localization to mobile smart devices, such as smartphones carried by people and BLE devices attached to things. SmartFinder can track the location of many mobile smart devices with only three anchor nodes and in real time.

With the rapid spread and development of the wireless network, secure access control has caught significant attention. The existing Wi-Fi network system access control methods, mainly based on the static password, the MAC address or the username/password, are prone to spoofing attacks, such as malicious users forge the authentication information to access the network and realize the penetration attack of Intranet. In this paper, we propose a new secure access control model based on Geo-fence to solve the above problems, further strengthening the security of the wireless network system. Our novel solution facilitates 1) providing convenient wireless network service in the trusted zone without authentication 2) isolating cyber-attacks outside the security zone 3) monitoring the access smart mobile device real time without affecting users' using the wireless network. Furthermore, we present the consideration of the smart mobile device heterogeneity in our model. Through using three different smart mobile devices to experiment, the analytical and experimental results demonstrate that the difference among the devices have a great impact on the device localization and the final access control effect.

The various services using smart mobile devices including smartphones have increased and personal smart mobile devices have utilized for the business use in many cases. There are the technologies for using smart mobile devices safely by separating the personal area and the business area such as TEE. This paper describes a MSS(Mobile Security Solution) system which is designed based on the TEE system architecture of Global Platform. The Secure Domain of the MSS System allows the secure preservation of sensitive data and the secure execution of sensitive functions. In this paper, we describe the architecture of the MSS System and the procedure for providing a secure message service by using the Secure Domain of the MSS system.

Smart mobile devices (SMDs) are integral for running advanced applications that demand significant computing resources and quick response time, e.g., immersive gaming and advanced image editing. However, SMDs often face constraints in computational capacity and battery duration, restricting their ability to process these tasks instantaneously. Cloud computing can circumvent these limitations by computation offloading, but cloud data centers (CDCs) are often deployed at long distances from users, which results in longer computational latency. To address the latency issue, the incorporation of small base stations (SBSs) in the vicinity of the user provides services with high bandwidth and low latency. The primary challenge lies in balancing the economics of the system consisting of different SMDs, SBSs, and a CDC, i.e., minimizing cost while still meeting the latency requirements of applications. In this work, a cost-minimized computation offloading framework is formulated and solved by a two-stage optimization algorithm named Lévy flight and Simulated Annealing-based Grey wolf optimizer (LSAG). The optimal edge selection strategy is defined in the first stage for dealing with the case of several available SBSs. The second stage coordinates task scheduling and optimizes the allocation of resources among SMDs, SBSs, and CDC. LSAG integrates the extended search property of Lévy flight and the individual selection strategy of simulated annealing in the grey wolf optimizer, which reduces the risk of falling into local optima and finds the global optimum. Experimental results of executing real-life tasks show that LSAG outperforms its state-of-the-art peers in terms of cost and speed of convergence.

This paper introduces iFringe, a mobile application that attempts to incorporate the resource heavy fringe analysis algorithms into the smart mobile devices platform. This first step taken towards mobility in the optical processing field aims to become a catalyst for modernization of various aspects of the field as well as to diversify developments to other applications. Predominantly, the motivation of this work stems from the vastly indifferent human interactive method of mobile devices, which enable images displayed on its touch screen to be manipulated in ways that could enhance the fringe analysis experience. Furthermore, given its hardware compatibility to the conventional fringe projection system, these mobile devices could potentially serve as a much more compact replacement. However, one imperative weakness that mobile devices pose is its limited computing ability. Therefore, to examine the feasibility of incorporating the fringe analysis algorithms into a mobile platform, we have implemented two fundamental fringe analysis techniques, namely the Fourier transform fringe analysis method and the phase-shifting technique. Formulas and processing procedures such as discrete Fourier transform (DFT) and quality-guided phase unwrapping, were included in accordance to their original algorithms to test their performance and usability on a smart mobile device. Details of the implementation and the performance results will also be presented in this paper to demonstrate the practicality of these algorithms on the smart mobile device platform.

Mobile cloud computing (MCC) as an emerging and prospective computing paradigm, can significantly enhance computation capability and save energy of smart mobile devices (SMDs) by offloading computation-intensive tasks from resource-constrained SMDs onto the resource-rich cloud. However, how to achieve energy-efficient computation offloading under the hard constraint for application completion time remains a challenge issue. To address such a challenge, in this paper, we provide an energy-efficient dynamic offloading and resource scheduling (eDors) policy to reduce energy consumption and shorten application completion time. We first formulate the eDors problem into the energy-efficiency cost (EEC) minimization problem while satisfying the task-dependency requirements and the completion time deadline constraint. To solve the optimization problem, we then propose a distributed eDors algorithm consisting of three subalgorithms of computation offloading selection, clock frequency control and transmission power allocation. More importantly, we find that the computation offloading selection depends on not only the computing workload of a task, but also the maximum completion time of its immediate predecessors and the clock frequency and transmission power of the mobile device. Finally, our experimental results in a real testbed demonstrate that the eDors algorithm can effectively reduce the EEC by optimally adjusting the CPU clock frequency of SMDs based on the dynamic voltage and frequency scaling (DVFS) technique in local computing, and adapting the transmission power for the wireless channel conditions in cloud computing.

We present a technique for detecting floor changes in an indoor environment and improving pedestrian indoor localization and navigation. Our technique relies on barometric pressure sensors commonly available on smartphones and tablets. We developed an algorithm running on smart mobile devices that can be integrated in any indoor localization system to improve accuracy and support 3D navigation. The main novelty of our technique is that it can work in any type of environment, without any prior knowledge of the building layout, it does not require calibration and it is adaptive. Experimental results show that our method can accurately detect floor changes in any condition without requiring any additional work.

In the scenario of Industry 4.0, mobile smart devices (SDs) on production lines have to process massive amounts of data. These computing tasks sometimes far exceed the computing capability of SDs and require lots of energy and time to process. How to effectively reduce energy consumption and latency is necessary to be solved. To this end, we first propose an SDN (Software Defined Network)-based MEC (Mobile Edge Computing) system. In the MEC system, SDs can offload computation tasks to edge servers to decrease the processing latency and avoid the waste of energy. At the same time, taking advantage of SDNs programmability, scalability, and isolation of the control plane and the data plane, an SDN controller can manage edge devices within the MEC system. Secondly, based on a stochastic game, we study the computation offloading and resource allocation problems in the MEC system and establish a stochastic game-based computation offloading model. Furthermore, we prove that the multi-user stochastic game in this system can achieve Nash Equilibrium. We further consider each SD as an independent agent and design a stochastic game-based resource allocation algorithm with prioritized experience replays (SGRA-PER) to minimize energy consumption and processing latency with Multi-Agent Reinforcement Learning. Experiment results demonstrate that the proposed SGRA-PER is superior to MADDPG, Q-Mix, and MAPPO algorithms, which can significantly reduce the processing delay and energy consumption with dynamic resource allocation. Moreover, SGRA-PER can still keep a higher performance under the increase of SDs, which can be applied in a large-scale MEC system.