Limitations Of Mobile Devices Research Articles

Research gaps based virtualization in mobile cloud computing

Recently, mobile computing is known as a fast-growing utilization of people's daily life. However, the main is the limited mobile devices’ resources such as processing capability, storage space and battery life. With the development of cloud computing, mobile devices’ resources are improved with the help of cloud services, which resulted an emerged technology named Mobile Cloud Computing (MCC). Although the MCC has several advantages for mobile users, it is also challenged by many critical issues like security and privacy of the mobile user's data that offloaded on the cloud’ servers and processed on the virtual machines (VMs). In virtualization, various investigations showed that malicious users are able to break down the cloud security methods by spreading their VMs in order to alter or violate the user sensitive data that executed on cloud’ VMs. This paper deeply analyzes the recent MCC based virtualization approaches and methods by criticizing them. We found out that no approach protects the data from being stolen while distributed VMs that deployed on different cloud servers exchanging data. Hence, the paper provides practical gaps related to virtualization in MCC and future perspectives.

Standards for the Gaming Industry [Standards

Reports on IEEE Standards development in the gaming industry. To address these issues while guaranteeing interoperability and compatibility for global gaming services, two recently established IEEE standard projects, IEEE P2861 and IEEE P2861.1, involve different stakeholders including game developers, game publishers, network vendors, and device vendors in the online gaming ecosystem. IEEE P2861, Standard for Mobile Gaming Performance Evaluation and Optimization, is designed to improve the experience of gamers by resolving tradeoffs between high demands from game applications and resource limitations of mobile devices. It aims at defining criteria and mechanisms to optimize the performance of game applications and devices, along with evaluation methods for game fluency and the optimization effect. This standards project also defines the classifications of gaming scenarios and interfaces between a game application and a device.

An energy-efficient task migration scheme based on genetic algorithms for mobile applications in CloneCloud

The limitations of mobile devices have attracted researchers to work out an energy-efficient mechanism to enhance user experience. The emerging mobile cloud computing (MCC) provides a new approach to solve this problem. Some parts of mobile applications, i.e., heavy computational tasks, are migrated to remote servers with powerful computational resources, which can improve the performance of mobile devices. This paper focuses on a popular MCC architecture, CloneCloud, and constructs a scheduling problem of task migration as a constrained stochastic shortest path problem in a directed acyclic graph. And then it designs a scheduling algorithm based on genetic algorithm to obtain the optimal task migrations. A user flexibly makes migration decisions through its own mobile device and migrates some tasks to the clone in CloneCloud without any change of application codes. Furthermore, this scheme facilitates mobile devices to collaboratively process computational applications. Real testbed experiments in Android smartphone demonstrate that the smartphone is able to save at most 59.42% energy within a time constraint by using the proposed task migration scheme.

Multi-Leader Multi-Follower Stackelberg Game in Mobile Blockchain Mining

The development of Blockchain-based mobile applications are impeded due to the resource limitations of mobile devices. Computation offloading can be a viable solution. In this paper, we consider a two-layer computation offloading paradigm including an edge computing service provider (ESP) and a cloud computing service provider (CSP). We formulate a multi-leader multi-follower Stackelberg game to address the computing resource management problem in such a network, by jointly maximizing the profits of each service provider (SP) and the payoffs of individual miners. We study two practical scenarios: a fixed-miner-number scenario for permissioned blockchains and a dynamic-miner-number scenario for permissionless blockchains. For the fixed-miner-number scenario, we discuss two different edge operation modes, i.e., the ESP is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">connected</i> (to the CSP) or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">standalone</i> , which form different miner subgames based on whether each miner's strategy set is mutually dependent. The existence and uniqueness of Stackelberg equilibrium (SE) in both modes are analyzed, according to which algorithms are proposed to achieve the corresponding SE(s). For the dynamic-miner-number scenario, we focus on the impact of population uncertainty and find that the uncertainty inflates the aggressiveness in the ESP resource purchasing. Numerical evaluations are presented to verify the proposed models.

Coordinated Self-Tuning Thermal Management Controller for Mobile Devices

Editor's notes: Thermal management is a critical factor in determining the performance limits of mobile devices due to their limited cooling capabilities. This article presents a coordinated self-tuning controller that reduces thermal violations significantly while providing higher performance. - Umit Y. Ogras, Arizona State University.

Multi-user Boolean searchable encryption supporting fast ranking in mobile clouds

Mobile cloud computing has become an important technology for mobile services. It overcomes physical limitations of mobile devices towards flexible and scalable mobile services. When using mobile clouds, people usually upload and store their data in cloud servers, and access the data through mobile devices from anywhere. As the privacy of the sensitive data is the major concern of users, the data is often encrypted before storing in cloud servers. However, the flexibility using the data is thereby affected, such as a Boolean search over the encrypted data, or ranking search results. To address these problems, in this paper we first propose a novel multi-user Boolean keyword search scheme (MBKSS) to achieve a rapid Boolean query outcome, while eliminating query interactions between users and the data owner. To rank search results and protect the privacy of relevance scores between files and keywords, we design a new homomorphic cryptosystem with partial decryption, which could be used as a basis for constructing a fast ranking search protocol (FRSP). A comprehensive security analysis shows that the proposed MBKSS and FRSP can achieve secure Boolean search and ranking. The experimental results demonstrate that the proposed MBKSS and FRSP are efficient and suitable for mobile devices.

Computational intelligence intrusion detection techniques in mobile cloud computing environments: Review, taxonomy, and open research issues

With the increasing utilization of the Internet and its provided services, an increase in cyber-attacks to exploit the information occurs. A technology to store and maintain user's information that is mostly used for its simplicity and low-cost services is cloud computing (CC). Also, a new model of computing that is noteworthy today is mobile cloud computing (MCC) that is used to reduce the limitations of mobile devices by allowing them to offload certain computations to the remote cloud. The cloud environment may consist of critical or essential information of an organization; therefore, to prevent this environment from possible attacks a security solution is needed. An intrusion detection system (IDS) is a solution to these security issues. An IDS is a hardware or software device that can examine all inside and outside network activities and recognize doubtful patterns that may demonstrate a network attack and automatically alert the network (or system) administrator. Because of the ability of an IDS to detect known/unknown (inside/outside) attacks, it is an excellent choice for securing cloud computing. Various methods are used in an intrusion detection system to recognize attacks more accurately. Unlike survey papers presented so far, this paper aims to present a comprehensive survey of intrusion detection systems that use computational intelligence (CI) methods in a (mobile) cloud environment. We firstly provide an overview of CC and MCC paradigms and service models, also reviewing security threats in these contexts. Previous literature is critically surveyed, highlighting the advantages and limitations of previous work. Then we define a taxonomy for IDS and classify CI-based techniques into single and hybrid methods. Finally, we highlight open issues and future directions for research on this topic.

RETRACTED ARTICLE: Multi-user energy efficient secured framework with dynamic resource allocation policy for mobile edge network computing

With an exploding use of Mobiles, smartphones and IoT device, mobile edge computing (MEC) emerged as technological boon in computing paradigm. The device offloads the computationally intensive task as well as task relevant to storage to the MEC cloud server to meet the requirement of service delay, extend the battery lifespan of mobile, and resolve the problem of limited mobile device resources. With this reference we propose novel framework architecture with security to perform offloading of high storage and computationally intensive task to MEC server with minimum energy consumption and delay. For dynamic resource allocation, we employ two scheduling policy one at mobile side i.e. SJFP and other at MEC server side i.e. eSFFDRR. Before to offload task, first AES encryption technique is employed to secure input parameter, and then compressed this encrypted data to secure task data and utilize more bandwidth. Our experimental result depicts that for high storage and computationally intensive task our proposed framework can save 85–87% processing time, 70% of memory utilization with minimum energy consumption. The experimental result also proved that our proposed work improves the performance of computationally intensive mobile application with reduced consumption of mobile device resources like computation time, memory utilization, CPU usage and energy consumption.

Approaches to Address the Operational Limitations of MANETs through Ad Hoc Mobile Cloud Computing Paradigm

Mobile Ad hoc Networks (MANETs) are getting essential to wireless communications because of the growing popularity of mobile devices. However, mobile devices face several challenges in their resources (eg., battery life, storage, and bandwidth) and communication (e.g., mobility and, security). Limited resources considerably impede the improvement of service qualities. MCC permits resources in cloud computing platforms to be used to overcome the dearth of native resources in mobile devices. However, this hinders a mobile user from taking part in a cloud computing service if a connection to the cloud computing platform is both unobtainable or too dear to afford. Therefore, an initial solution will be to use resources from nearby devices instantly. Such a paradigm is known as mobile ad hoc cloud computing where each mobile device can use the services and resources of its neighbor devices. This paper shortly explains the contributions done by us to overcome the three vital operational limitations of mobile devices namely connectivity, storage and, processing capability through the Mobile Ad Hoc Cloud Computing Paradigm. The potential promise of the proposed approaches is evaluated through simulations. Our proposals, taken together intend to increase the operational efficiency of MANETs.

QoS-Aware Cloudlet Load Balancing in Wireless Metropolitan Area Networks

With advances in wireless communication technology, more and more people depend heavily on portable mobile devices for business, entertainments and social interactions. This poses a great challenge of building a seamless application experience across different computing platforms. A key issue is the resource limitations of mobile devices due to their portable size, however this can be overcome by offloading computation-intensive tasks from the mobile devices to clusters of nearby computers called cloudlets through wireless access points. As increasing numbers of people access the Internet via mobile devices, it is reasonable to envision in the near future that cloudlet services will be available for the public through easily accessible public wireless metropolitan area networks (WMANs). However, the outdated notion of treating cloudlets as isolated data-centers-in-boxes must be discarded as there are clear benefits to connecting multiple cloudlets together to form a network. In this paper we investigate how to balance the workload among cloudlets in an WMAN to optimize mobile application performance. We first introduce a novel system model to capture the response time delays of offloaded tasks and formulate an optimization problem with the aim to minimize the maximum response time of all offloaded tasks. We then propose two algorithms for the problem: one is a fast heuristic, and another is a distributed genetic algorithm that is capable of delivering a more accurate solution compared with the first algorithm, but at the expense of a much longer running time. We finally evaluate the performance of the proposed algorithms in realistic simulation environments. The experimental results demonstrate the significant potential of the proposed algorithms in reducing the user task response time, maximizing user experience.

Analytical study of hardware-rooted security standards and their implementation techniques in mobile

Security of information in computers is of paramount importance. Considering the software security as inadequate, hardware rooted security standards were developed as Trusted Platform Module (TPM) 1.0 in 2003 and subsequently as TPM 2.0 in 2012. While trustworthy, these standards and their corresponding implementation in hardware as TPM chips were found to be inappropriate for mobile computing devices due to their small form factor, low computing resources, limited battery power and cost. Given these challenges, software derivative of TPM was devised for mobile devices as TPM Mobile. However, TPM Mobile was rarely implemented in real devices primarily due to lack of trust in its software nature. Another standard named as MTM also emerged as derivative of TPM but was never adopted widely due to physical limitations of the mobile devices that have been further constrained after introduction of Internet of Things. Subsequently, a software-cum-hardware combo implementation appeared in ARM-based mobile CPUs by the name of TrustZone as a trade-off between hardware and software. Although widely adopted ARM TrustZone has also been considered as inadequate vis-a-vis TPM standards. After conducting a comparative analysis of various security standards, this paper proposes mTPM, a comprehensive security standard. As such mTPM not only addresses prevalent information security requirements of mobile devices but also considers their physical constraints. mTPM primarily suggests an implementation of a security processor integrated within existing CPU, as stand-alone chip was considered infeasible for mobile devices. The detailed architectural model of mTPM has also been included as guidelines for uniformly secure implementation and standardization. In view of its advantages, mTPM is expected to find greater adoption and refinements over time.

Federated Learning via Over-the-Air Computation

The stringent requirements for low-latency and privacy of the emerging high-stake applications with intelligent devices such as drones and smart vehicles make the cloud computing inapplicable in these scenarios. Instead, edge machine learning becomes increasingly attractive for performing training and inference directly at network edges without sending data to a centralized data center. This stimulates a nascent field termed as federated learning for training a machine learning model on computation, storage, energy and bandwidth limited mobile devices in a distributed manner. To preserve data privacy and address the issues of unbalanced and non-IID data points across different devices, the federated averaging algorithm has been proposed for global model aggregation by computing the weighted average of locally updated model at each selected device. However, the limited communication bandwidth becomes the main bottleneck for aggregating the locally computed updates. We thus propose a novel over-the-air computation based approach for fast global model aggregation via exploring the superposition property of a wireless multiple-access channel. This is achieved by joint device selection and beamforming design, which is modeled as a sparse and low-rank optimization problem to support efficient algorithms design. To achieve this goal, we provide a difference-of-convex-functions (DC) representation for the sparse and low-rank function to enhance sparsity and accurately detect the fixed-rank constraint in the procedure of device selection. A DC algorithm is further developed to solve the resulting DC program with global convergence guarantees. The algorithmic advantages and admirable performance of the proposed methodologies are demonstrated through extensive numerical results.

Efficient Server-Aided Secure Two-Party Computation in Heterogeneous Mobile Cloud Computing

With the ubiquity of mobile devices and rapid development of cloud computing, mobile cloud computing (MCC) has been considered as an essential computation setting to support complicated, scalable and flexible mobile applications by overcoming the physical limitations of mobile devices with the aid of cloud. In the MCC setting, since many mobile applications (e.g., map apps) interacting with cloud server and application server need to perform computation with the private data of users, it is important to realize secure computation for MCC. In this article, we propose an efficient server-aided secure two-party computation (2PC) protocol for MCC. This is the first work that considers collusion between a malicious garbled circuit evaluator and a semi-honest server while ensuring privacy and correctness. Also, it can guarantee fairness when collusion does not exist. The security analysis shows that our protocol can securely compute any function f(x, y) against different types of adversaries in the malicious model. Also, the experimental performance analysis shows that this work outperforms the previous works for at least 10 times with the same security level.

Enhancing the security in RSA and elliptic curve cryptography based on addition chain using simplified Swarm Optimization and Particle Swarm Optimization for mobile devices

Security is the major concern in mobile or portable devices because the internet community can do their work at any time at any place at anywhere. Today various cryptographic algorithms like RSA, Elliptic Curve Cryptography (ECC), etc., can be used to protect the information in mobile devices. But, they have some limitations viz., energy, battery power, processing speed, operating systems, screen size, resolution, memory size, etc. Providing security for limited power mobile devices is a challenging task. RSA and ECC are normally used in mobile devices. In RSA, both encryption and decryption are of the form xe mod n and in ECC, the scalar point k[P] where k is a scalar and P is a point in EC plays a vital role in performing encryption and decryption. The point arithmetic involved in ECC is a power starving process. To speed up the operations in both cryptographic algorithms, addition chains (AC) are normally used. If the encryption and decryption time get reduced, it ultimately reduces the power consumption. There are several AC algorithms exist in the literature. But, ACs are generated using Particle Swarm Optimization and Simplified Swarm Optimization are proposed in this paper and they are used in the said processes of RSA and ECC with two android and window emulators. The processing time, power consumption taken for encryption, decryption process and security of the said algorithms are also analysed.

Health App Recommendation System using Ensemble Multimodel Deep Learning

Nowadays, mobile devices and apps are meant to fulfill the needs of various people in society. But, mobile app Stores are facing major challenges in recommending proper apps for users. Recommending mobile apps for users according to personal preference and various mobile device limitations is therefore important. In this scenario, there is a huge need for developing recommender systems (RS) for the user’s community in enabling critical mobile apps such as Health based Apps. Recommendation Systems perform an extensive survey on the collection of user reviews, preferences and opinions to discover recommendations of suitable applications to the users' community. In this paper, we have designed an aspect-based recommendation framework by performing three tasks: such as identifying the mentions associated with item aspects in user reviews, extracting the sentiment related opinions using Latent Semantic Analysis of such aspects in the reviews, and perform the opinion mining from all of the aspects to generate enhanced recommendations with Ensemble Multimodel Deep Learning (EMDL). EMDL comprises of two state-of- the-art classifiers such as Deep Neural Networks (DNN) and Long Short Term Memory (LSTM). In contrast to the prior work, we conducted a series of experiments with several state-of-art deep learning models to extract useful recommendations. The achieved results show that classification with outperforms in all the aspects based on various evaluation metrics when compared to the rest of the models.

Computation offloading for mobile sign language video encoding

Mobile sign language video encoding is a kind of application that has high computation complexity. When using this application on battery powered mobile devices, there is conflict between computation complexity of encoder and energy limitation of mobile devices. In this paper, we propose a novel computation offloading method for mobile sign language video encoding. The method utilises sufficient cloud resources for Regions Of Interest (ROI) based sign language video encoding, which is the most computation-intensive part of the whole computation offloading system. With the method, a mobile device only consumes a small portion of energy to perform other parts of the offloading system. The theoretical analysis and experimental results both demonstrate that the proposed method can reduce the processing time and energy consumption of mobile devices.

Mobility-Aware Energy Optimization in Hosts Selection for Computation Offloading in Multi-Access Edge Computing

Multi-access edge computing (MEC) has been proposed as an approach capable of addressing latency and bandwidth issues in application computation offloading to extend the capabilities beyond the computational and storage limitations of mobile devices. However, there is a critical challenge in MEC to maintain the service continuity between the offloaded user application that is running on the MEC host and the mobile device when a user is moving from radio node to radio node. Furthermore, energy consumption of application computation offloading is an important consideration for MEC service providers in terms of operational costs. Therefore, we formulate the MEC host selection and user application migration problem as a shortest path problem of network energy minimization. We simulate the problem in a hierarchical MEC network deployment environment. We also propose the metric, computational intensity (CI), that can be used by MEC service providers to address the MEC host selection problem. Our results show that with the increment of CI, the selection of MEC hosts tends to move toward level 3 (central deployment) due to energy efficiency and then return to the deployment at level 1 (radio node level) due to latency constraint of the user application. We show that with high accuracy in predicting the user mobility and the available resources in the MEC network, latency- and mobility-aware MEC host selection and user application migration can be pre-calculated to improve response time and energy efficiency.

Cache Replacement Strategy With Limited Service Capacity in Heterogeneous Networks

Recently, cellular networks face a huge challenge to satisfy large increment of user demands for higher-speed and lower-latency communication service. One promising solution is to apply the cache technology in edge cache networks to reduce redundant data transmission. The cache technology is effective, but the cache capacity is limited. Researchers have proposed various cache strategies to efficiently utilize the limited cache capacity. However, most existing solutions have not taken into account the service capacity limitation of mobile devices. In this paper, we propose a cache replacement strategy for heterogeneous networks considering the limitation of service capacity and user mobility. In the cache replacement strategy, we utilize the user characteristics, such as user mobility and file popularity, to estimate the user demands, and then define the system cost. We formulate the cache strategy design as a mixed integer linear programming problem to minimize the system cost, and use Lagrangian relaxation and hierarchical primal-dual decomposition method to solve this problem. Numerical results show that the proposed cache strategy can significantly reduce the system cost and increase the cache hit ratio compared to the cache strategy that does not consider the limitation of user service capacity.

Dynamic Power–Latency Tradeoff for Mobile Edge Computation Offloading in NOMA-Based Networks

Mobile edge computing (MEC) has been recognized as an emerging technology that allows users to send the computation-intensive tasks to the MEC server deployed at the macro base station. This process overcomes the limitations of mobile devices (MDs), instead of sending the data to a cloud server which is far away from MDs. In addition, MEC results in decreasing the latency of cloud computing and improves the quality of service. In this article, an MEC scenario in the 5G networks is considered, in which several users request for computation service from the MEC server in the cell. We assume that users can access the radio spectrum by the nonorthogonal multiple access protocol and employ the queuing theory in the user side. The main goal is to minimize the total power consumption for computing by users with the stability condition of the buffer queue to investigate the power–latency tradeoff, which the modeling of the system leads to a conditional stochastic optimization problem. In order to obtain an optimum solution, we employ the Lyapunov optimization method along with successive convex approximation. Extensive simulations are conducted to illustrate the advantages of the proposed algorithm in terms of power–latency tradeoff of the joint optimization of communication and computing resources and the superior performance over other benchmark schemes.

Design and Development of an Advanced Authentication Protocol for Mobile Applications using NFC Technology

In this paper, we proposed a new Authentication Protocol for Mobile Applications using NFC technology (AP for MAN). The proposed protocol minimizes the required time to complete the authentication process between the shared entities with a high level of privacy. According to the main security measures, the proposed protocol is evaluated. The current paper presents a new idea for preventing denial of service attack and preserves the limited mobile device capability. The proposed protocol is checked using BAN logic and established that it has no redundancy, the mutual authentication property between the shared parties is verified. The implementation of the proposed protocol shows that it works as designed and it is practical.