Resources Of Mobile Devices Research Articles

Efficient offloading schemes using Markovian models: a literature review

The increasing demand for new mobile applications puts a heavy demand for more processing power and resources in smart mobile devices (SMD). Offloading is a promising solution for these issues which tries to move data, code, or computation from the SMDs to the remote or nearby resourceful servers. To increase the effectiveness of the offloading process and make better decisions, various stochastic offloading schemes are proposed in the literature which has adapted different stochastic models. Although offloading issues are vastly studied in the literature, there is a lack of comprehensive paper to focus on stochastic offloading solutions. This paper presents a meticulous review and classification of the stochastic offloading frameworks designed for different environments such as mobile cloud computing, mobile edge computing), and Fog computing. Following this, it first presents the required background concepts and key issues regarding the offloading problem and stochastic models. It then puts forward a taxonomy of the stochastic offloading approaches according to their applied stochastic models and highlights their architectures and contributions. In addition, in each category, a comparison of the stochastic offloading schemes is provided to illuminate their features. Finally, the concluding remarks and open research areas.

Energy efficient fuzzy-based DASH adaptation algorithm

With the expansive demand for video streaming over mobile networks, it is necessary to adopt schemes that balance the need for high video quality with the available network resources when streaming or downloading the video. Several approaches were proposed in the literature, including Dynamic Adaptive Streaming over HTTP (DASH). In this work, we consider an approach in which we place sufficient emphasis on the constrained battery resources in mobile devices when making decisions on the quality (or bitrate) of the video to be requested. This is done by using a fuzzy logic controller that enhances the performance of the Fuzzy-based DASH (FDASH) scheme. Simulation results show that our proposed approach conserves more energy than its predecessor while maintaining similar video quality and avoiding playback interruptions.

NOMA-Aided Mobile Edge Computing via User Cooperation

Exploiting the idle computation resources of mobile devices in mobile edge computing (MEC) system can achieve both channel diversity and computing diversity as mobile devices can offload their computation tasks to nearby mobile devices in addition to MEC server embedded access point (AP). In this paper, we propose a non-orthogonal multiple-access (NOMA)-aided cooperative computing scheme in a basic three-node MEC system consisting of a user, a helper, and an AP. In particular, we assume that the user can simultaneously offload data to the helper and the AP using NOMA, while the helper can locally compute data and offload data to the AP at the same time. We study two optimization problems, energy consumption minimization and offloading data maximization, by joint communication and computation resource allocation of the user and helper. We find the optimal solutions for the two non-convex problems by some proper mathematical methods. Simulation results are presented to demonstrate the effectiveness of the proposed schemes. Some useful insights are provided for practical designs.

Efficient Obfuscation for Encrypted Identity-Based Signatures in Wireless Body Area Networks

The rapid development of the Internet of Things has contributed to the emergence of novel applications in recent years, such as wireless body area networks (WBANs). The latter can potentially provide people with convenience and high-quality healthcare. However, how to protect the confidentiality and privacy of user data remains challenging. Although a number of schemes using cryptographic tools in WBANs have been proposed, they are not practical due to the low computing power and storage capabilities of the sensors. To remedy this shortcoming, in this article, we propose an efficient obfuscation for encrypted identity-based signatures in WBANs, which can securely and efficiently use the resources of mobile devices to sign and encrypt data collected by sensors. In particular, our proposed scheme employs the identity-based signature scheme in the IEEE P1363 standard. We prove the security of our scheme, and that it satisfies the virtual black box properties. Moreover, we implement our scheme using the Java pairing-based cryptography library on a laptop and an Android phone. The experimental results the utility of our scheme in WBANs.

Large-Scale User-Assisted Multi-Task Online Offloading for Latency Reduction in D2D-Enabled Heterogeneous Networks

Currently, the computing capability of smart mobile devices has been extremely improved. Exploiting computing resources of mobile devices to assist the network through offloading computation tasks will promisingly boost the fifth-generation (5G) and beyond heterogeneous networks. We investigate the user-assisted multi-task offloading scheme based on the mobile edge computing (MEC)-Cloud architecture to reduce the end-to-end computing latency. The offloading strategy, computing resource allocation and spectrum allocation are jointly optimized to minimize the computation latency while guaranteeing the energy available to the users. The formulated optimizing problem is a large-scale mixed-integer nonlinear optimizing problem which is hard to solve within a rational time. To overcome this problem, a low-complexity distributed framework based on the alternating direction method of multipliers algorithm is proposed to minimize the computing latency for all tasks. Compared with the existing schemes, the proposed scheme can reduce the computing latency and improve the performance efficiently. Simulation results illustrate the effectiveness of the proposed scheme in respect of latency reduction with different parameters.

A Lightweight Privacy-Preserving Fair Meeting Location Determination Scheme

Equipped with mobile devices, people relied on location-based services (LBSs) can expediently and reasonably organize their activities. But location information may disclose people’s sensitive information, such as interests and health status. Besides, the limited resources of mobile devices restrict the further development of LBSs. In this article, aiming at the fair meeting position determination service, we design a lightweight privacy-preserving solution. In our scheme, mobile users only need to submit service requests. A cloud server and a location services provider are responsible for service response, where the cloud server achieves most of the calculation, and the location services provider determines the fair meeting location based on the computational results of the cloud server and broadcasts it to mobile users. The proposed scheme adopts homomorphic encryptions and random permutation methods to preserve the location privacy of mobile users. The security analyses show that the proposed scheme is privacy preserving under our defined threat models. Besides, the presented solution only needs to calculate $n$ Euclidean distances, and hence, our scheme has linear computation and communication complexity.

Dynamic resource provisioning for workflow scheduling under uncertainty in edge computing environment

SummaryEdge computing, an extension of cloud computing, is introduced to provide sufficient computing and storage resources for mobile devices. Moreover, a series of computing tasks in a mobile device are set as structured computing processes and flows to achieve effective management by the workflow. However, the execution uncertainty caused by performance degradation, service failure, and new service additions remains a huge challenge to the user's service experience. In order to address the uncertainty, a software‐defined network (SDN)‐based edge computing framework and a dynamic resource provisioning (UARP) method are proposed in this paper. The UARP method is implemented in the proposed framework and addresses the uncertainty through the advantages of SDN. In addition, the nondominated sorting genetic algorithm‐III is employed to optimize two goals, that is, the energy consumption and the completion time, to obtain balanced scheduling strategies. The comparative experiments are performed and the results show that the UARP method is superior to other methods in addressing the uncertainty, while reducing energy consumption and shortening the completion time.

Resource-aware Feature Extraction in Mobile Edge Computing

Mobile image recognition services, which provide people with image recognition services through the cameras of mobile devices, are revolutionizing our lives. However, most existing cloud/edge-based approaches suffer from two major limitations, (i) Low recognition accuracy and high network bandwidth pressure, and (ii) Not easy to extract features based on currently available resources of mobile devices. In this paper, we propose a resource-aware feature extraction framework for mobile image recognition services. The proposed framework consists of discriminative feature extraction (DFE) and NestDFE algorithms. The DFE algorithm can generate an extractor E to extract discriminative features from the image data set on the edge server and images on mobile devices. Thus, the proposed framework can achieve higher recognition accuracy and require mobile devices to upload less feature data to the edge server. The NestDFE algorithm generates a single multi-capacity extractor that acts as a series of sub-extractors and enables mobile devices to dynamically select sub-extractors. Experimental results show that the proposed framework improves recognition accuracy by about 22.65% and reduces network traffic by about 76.09% compared with existing approaches.

A Power-Efficient Approach to Detect Mobile Threats on the Emergent Network Environment

Mobile and IoT devices are blooming, and their applications are prevailing worldwide. In the meantime, the Industry 4.0 trend converges industrial control systems with the internet environment, which makes it vulnerable. Mobile and IoT applications provide seamless connectivity to the emergent network environment for daily work. As mobile phones contain privacy information, malicious mobile applications could compromise mobile devices and cause financial losses. Moreover, attackers could launch DDoS attacks to exhaust a resource of mobile or IoT devices in the emergent network environment. Static malware analysis consumes less computing resources and power than dynamic analysis. This study proposes a power-efficient solution to identify mobile threats, which applies taint analysis to protect the emergent network environment. The experimental results show that the proposed approach could detect mobile malware efficiently.

Optimization of Task Offloading Strategy for Mobile Edge Computing Based on Multi-Agent Deep Reinforcement Learning

Combined with wireless power transfer (WPT) technology, mobile edge computing can provide continuous energy supply and computing resources for mobile devices, and improve their battery life and business application scenarios. This article first designs the mobile edge computing (MEC) model of mobile devices with random mobility and hybrid access point (HAP) with data transmission and energy transmission. On this basis, the selection of target server and the amount of data offloading are taken as the learning objectives, and the task offloading strategy based on multi-agent deep reinforcement learning is constructed. Then combined with MADDPG algorithm and SAC algorithm, the problems of multi-agent environment instability and the difficulty of convergence are solved. The final experimental results show that the improved algorithm based on MADDPG and SAC has good stability and convergence. Compared with other algorithms, it has achieved good results in energy consumption, delay and task failure rate.

A Novel Hybrid Cryptosystem for Secure Streaming of High Efficiency H.265 Compressed Videos in IoT Multimedia Applications

In this modernistic age of innovative technologies like big data processing, cloud computing, and Internet of things, the utilization of multimedia information is growing daily. In contrast to other forms of multimedia, videos are extensively utilized and streamed over the Internet and communication networks in numerous Internet of Multimedia Things (IoMT) applications. Consequently, there is an immense necessity to achieve secure video transmission over modern communication networks due to the third-party exploitation and falsification of transmitted and stored digital multimedia data. The present methods for secure communication of multimedia content between clouds and mobile devices have constraints in terms of processing load, memory support, data size, and battery power. These methods are not the optimum solutions for large-sized multimedia content and are not appropriate for the restricted resources of mobile devices and clouds. The High-Efficiency Video Coding (HEVC) is the latest and modern video codec standard introduced for efficiently storing and streaming of high-resolution videos with suitable size and higher quality. In this paper, a novel hybrid cryptosystem combining DNA (Deoxyribonucleic Acid) sequences, Arnold chaotic map, and Mandelbrot sets is suggested for secure streaming of compressed HEVC streams. Firstly, the high-resolution videos are encoded using the H.265/HEVC codec to achieve efficient compression performance. Subsequently, the suggested Arnold chaotic map ciphering process is employed individually on three channels (Y, U, and V) of the compressed HEVC frame. Then, the DNA encoding sequences are established on the primary encrypted frames resulted from the previous chaotic ciphering process. After that, a modified Mandelbrot set-based conditional shift process is presented to effectively introduce confusion features on the Y, U, and V channels of the resulted ciphered frames. Massive simulation results and security analysis are performed to substantiate that the suggested HEVC cryptosystem reveals astonishing robustness and security accomplishment in contrast to the literature cryptosystems.

CFRO: Cloudlet Federation for Resource Optimization

A Cloud computing paradigm augments the limited resources of mobile devices resulting in increased distance, limited Internet bandwidth, and seamless connectivity challenges between a remote cloud and mobile devices. Cloudlet computing based solutions are widely used to address these challenges by bringing the computational facility closer to the user. The ever growing number of mobile devices, Internet of Things (IoT) sensors and Information Communication Technology (ICT) infrastructure used for smart cities demand more resources. The existing cloudlet based solutions are unable to manage the ever-increasing demand for power, storage, and computational resources, and therefore forward the resource extensive tasks to a remote cloud, limiting cloudlet computing benefits. We present the Cloudlet Federation for Resource Optimization (CFRO), a federated cloudlet model for resource optimization to address these resource scarcity challenges. The proposed model exerts the features of scalability, resource collaboration, and robustness. The underlying scheme for resource optimization has been modeled as a Nested Multi Objective Resource Optimization Problem (NMOROP) and a novel algorithm has been proposed to solve it. The detailed analysis and comparative results show that the proposed model offers improved performance and more resource elasticity as compared to the conventional cloudlet model.

Edge computing and power control in NOMA‐enabled cognitive radio networks

AbstractDue to the limited computation resources of mobile devices in cognitive radio networks, the secondary users in the network can suffer from long executing time, which is not acceptable for latency‐sensitive and computation‐intensive tasks. To tackle this issue, this paper proposes to reduce the task computing latency for secondary networks by offloading the tasks to edge servers through leveraging mobile edge computing (MEC) that is emerging as a promising technology to augment the computation capacity of mobile devices. Specifically, under the conditions that the interference caused by secondary users is tolerable to primary user and within the available computation resources of the MEC server, the primary user and secondary users both can offload tasks to the MEC server through nonorthogonal multiple access. Thus, we jointly formulate the offloading decision and power control as an optimization problem, aiming at minimizing the overall computing latency for secondary networks. To overcome the computational complexity caused by the nonconvexity of the original problem, we transform the original problem to a solvable problem and decouple the transformed problem into the separate offloading decision and power control. An iterative algorithm is proposed based on block coordinate decent method to achieve the near‐optimal solution. Simulation results show that under the same parameters, such as the number of primary users, maximum transmit power, computational capability of the MEC server and the computational capability of the secondary users, the proposed NOMA‐enabled computation offloading scheme can effectively reduce the overall computing latency for the secondary network and improve the percentage of offloading secondary users than those of OMA‐enabled.

Dynamical Service Deployment and Replacement in Resource-Constrained Edges

With the rapid development of mobile computing technology, more and more complex tasks are now able to be fulfilled on users’ mobile devices with an increasing number of novel services. However, the development of mobile computing is limited by the latency brought by unstable wireless network and the computation failure caused by the constrained resources of mobile devices. Therefore, people turn to establish a service provisioning system based on mobile edge computing (MEC) model to solve this problem. With the help of services deployed on edge servers, the latency can be reduced and the computation can be offloaded. Though the edge servers have more available resources than mobile devices, they are still resource-constrained, so they must carefully choose the services for deployment. In this paper, we focus on improving performance of the service provisioning system by deploying and replacing services on edge servers. Firstly, we design and implement a prototype of service provisioning system that simulates the behaviors between users and servers. Secondly, we propose an approach to deploy services on edge servers before the launching of these servers, and propose an approach to replace services on edge servers dynamically. Finally, we conduct a series of experiments to evaluate the performance of our approaches. The result shows that our approach can improve the performance of service provisioning systems.

Resource allocation for edge computing over fibre‐wireless access networks

Edge Computing (EC) has been proposed as a promising approach to fulfil the requirements of high bandwidth and ultra-latency of mobile applications. However, existing researches on resource allocation only consider the computing resource of mobile devices and EC servers, while ignored the constraint of the networking resources once spreading applications among multiple EC servers via wireless and wired networks. Fibre-Wireless access networks (FiWi) combine the huge bandwidth of optical fibre networks and flexible access of wireless networks to address the above issue and bridge the coexistence of multiple EC servers. They propose a Virtualisation-based Architecture converging EC over FiWi (VAECFW) to centralise control and allocate networking and computing resources for serving requested services. In addition, they study the problem of resource allocation of EC over FiWi and propose two algorithms Revenue-based Virtual Network Embedding (R-VNE) and Balanced Central Processing Unit Resource Allocation with Virtual Network Embedding (BCRA-VNE). Simulation experiments show that the services acceptance ratio is increased about 35% under their proposed architecture, and the average service requests bandwidth utilisation of R-VNE is increasing from 50 to 66%, and the BCRA-VNE is from 37 to 48%. The two algorithms not only achieve higher revenue but also get better profit rate.

Efficient Training Management for Mobile Crowd-Machine Learning: A Deep Reinforcement Learning Approach

In this letter, we consider the concept of mobile crowd-machine learning (MCML) for a federated learning model. The MCML enables mobile devices in a mobile network to collaboratively train neural network models required by a server while keeping data on the mobile devices. The MCML thus addresses data privacy issues of traditional machine learning. However, the mobile devices are constrained by energy, CPU, and wireless bandwidth. Thus, to minimize the energy consumption, training time, and communication cost, the server needs to determine proper amounts of data and energy that the mobile devices use for training. However, under the dynamics and uncertainty of the mobile environment, it is challenging for the server to determine the optimal decisions on mobile device resource management. In this letter, we propose to adopt a deep $Q$ -learning algorithm that allows the server to learn and find optimal decisions without any a priori knowledge of network dynamics. Simulation results show that the proposed algorithm outperforms the static algorithms in terms of energy consumption and training latency.

“Off the beaten map”: Navigating with digital maps on moorland

Resources made available through the digital map app change, but do not replace, the skills of “ordinary wayfinding.” Looking at the challenges of wayfinding with new mobile devices helps inform the development of digital mapping tools for navigating through difficult terrain. With this background in mind, in this paper we consider how the contemporary navigational resources of mobile devices with GPS, and the resources of countryside landscape features, are brought together in visiting a tourist site. We analyse video data from groups walking across unfamiliar moorland terrain, following a guide and map app which takes them on a tour of a remote Roman marching camp in the Brecon Beacons National Park, Wales. Following an ethnomethodological and conversation analytic approach, we examine three instances of navigational work for paired walkers as they traverse the moorland. The three fragments are of: an orientational struggle to establish where to go next; a routine check to select a path; and the discovery of a feature mentioned in the guide. Across the three episodes we explicate how our walkers make sense of the guide and map in relation to investigating the moorland surface. We examine how their ambulatory and undulatory practices on the moorland are tied to their wayfinding practices. While we analyse wayfinding talk, we also attend to the mobile practices of stopping and pausing as part of practical navigational reasoning.

Adaptive Security for Self-Protection of Mobile Computing Devices

Mobile computing has emerged as a pervasive technology that empowers its users with portable computation and context-aware communication. Smart systems and infrastructures can exploit portable and context-aware computing technologies to provide any time, any place digitized services on the go. Despite the offered benefits, such as portability, context-sensitivity, and high connectivity, mobile computing also faces some critical challenges. These challenges include resource poverty as well as data security and privacy that need to be addressed to increase the pervasiveness of mobile systems. We propose to provide a self-protection mechanism for mobile devices against the unforeseen security threats that can attack the critical resources of mobile devices. We have unified the concepts of autonomic computing and computer security to develop a framework that enables adaptive security to dynamically configure the security measures of a mobile device. We have developed a framework - an android-based prototype - that supports automation and user decision to protect the critical hardware and software resources of a device. Evaluation results demonstrate (i) framework’s accuracy for runtime detection and minimization of threats, and (ii) framework’s efficiency for device’s resource utilization.

MDR Model: Contributions to the Motivation and Sociocultural Integration

The aim of this article is to present the results of improvement in the motivation and sociocultural integration of ethnic minority children at risk of social exclusion (Romani children) after the implementation of the MDR model in the classroom. This model is based on the creation of digital educational resources for mobile devices adapted to the special characteristics of the sample we have worked with. They are aimed at facilitating the teaching-learning process for the overall target population. After implementing the digital educational resources provided by the MDR model, a quantitative, quasi-experimental method was used to collect data related to motivation and sociocultural integration through two questionnaires: before the implementation phase (pre-phase), and after the use of these resources (post-phase). The results show that both the motivation and the sociocultural integration of these children have increased as a consequence of the use of the MDR model in mobile devices.

Mobile terminal trajectory recognition based on improved LSTM model

Mobile terminal hand trajectory recognition is challenged by spatio-temporal variations, complex background, light variations as well as the limited computing resources for mobile devices. The authors propose a hand trajectory recognition method based on improved Long-Short Term Memory network (LSTM) model. First, for the sake of eliminating the effects of complex background and light, the SSD hand detector and multi-color space are combined to segment gesture. To eliminate the influence of trajectory points' overlap, the sparseness of the trajectory points is used to determine the start and end of the trajectory; then, to maintain the feature difference while decreasing the feature extraction time, they extract the trajectory feature sequence based on the central point direction angle; Finally, a new loss function (M-Softmax loss) is proposed in LSTM training process to maximise the inter-class variations and minimise intra-class variations. At the same time, the adaptive training iteration number is proposed to ensure the convergence of the model and reduce the training time cost. The experimental results illustrate that the improved model learned features are more compact, and the model is easier to deal with external data changes. Authors’ proposed method has great significance in practical applications.