Resource-constrained Mobile Devices Research Articles

A Novel Two-Layered Reinforcement Learning for Task Offloading with Tradeoff between Physical Machine Utilization Rate and Delay

Mobile devices could augment their ability via cloud resources in mobile cloud computing environments. This paper developed a novel two-layered reinforcement learning (TLRL) algorithm to consider task offloading for resource-constrained mobile devices. As opposed to existing literature, the utilization rate of the physical machine and the delay for offloaded tasks are taken into account simultaneously by introducing a weighted reward. The high dimensionality of the state space and action space might affect the speed of convergence. Therefore, a novel reinforcement learning algorithm with a two-layered structure is presented to address this problem. First, k clusters of the physical machines are generated based on the k-nearest neighbors algorithm (k-NN). The first layer of TLRL is implemented by a deep reinforcement learning to determine the cluster to be assigned for the offloaded tasks. On this basis, the second layer intends to further specify a physical machine for task execution. Finally, simulation examples are carried out to verify that the proposed TLRL algorithm is able to speed up the optimal policy learning and can deal with the tradeoff between physical machine utilization rate and delay.

Light Weight Write Mechanism for Cloud Data

Outsourcing data to the cloud for computation and storage has been on the rise in recent years. In this paper we investigate the problem of supporting write operation on the outsourced data for clients using mobile devices. We consider the Ciphertext-Policy Attribute-based Encryption (CP-ABE) scheme as it is well suited to support access control in outsourced cloud environments. One shortcoming of CP-ABE is that users can modify the access policy specified by the data owner if write operations are incorporated in the scheme. We propose a protocol for collaborative processing of outsourced data that enables the authorized users to perform write operation without being able to alter the access policy specified by the data owner. Our scheme is accompanied with a light weight signature scheme and simple, inexpensive user revocation mechanism to make it suitable for processing on resource-constrained mobile devices. The implementation and detailed performance analysis of the scheme indicate the suitability of the proposed scheme for real mobile applications. Moreover, the security analysis demonstrates that the security properties of the system are not compromised.

LiteNet: Lightweight Neural Network for Detecting Arrhythmias at Resource-Constrained Mobile Devices.

By running applications and services closer to the user, edge processing provides many advantages, such as short response time and reduced network traffic. Deep-learning based algorithms provide significantly better performances than traditional algorithms in many fields but demand more resources, such as higher computational power and more memory. Hence, designing deep learning algorithms that are more suitable for resource-constrained mobile devices is vital. In this paper, we build a lightweight neural network, termed LiteNet which uses a deep learning algorithm design to diagnose arrhythmias, as an example to show how we design deep learning schemes for resource-constrained mobile devices. Compare to other deep learning models with an equivalent accuracy, LiteNet has several advantages. It requires less memory, incurs lower computational cost, and is more feasible for deployment on resource-constrained mobile devices. It can be trained faster than other neural network algorithms and requires less communication across different processing units during distributed training. It uses filters of heterogeneous size in a convolutional layer, which contributes to the generation of various feature maps. The algorithm was tested using the MIT-BIH electrocardiogram (ECG) arrhythmia database; the results showed that LiteNet outperforms comparable schemes in diagnosing arrhythmias, and in its feasibility for use at the mobile devices.

MWC: an efficient and secure multi-cloud storage approach to leverage augmentation of multi-cloud storage services on mobile devices using fog computing

To store the sensitive and large amount of information on mobile devices is not recommended as it can be lost or stolen and the storage capacity is limited. Even the services of the single-cloud storage provider are not preferred due to issues like limited free storage, vendor lock-in and data loss. To deal with these issues, multi-cloud storage services are preferred which provide a single platform for multiple cloud storage services. From recent studies, it has been observed that the use of existing multi-cloud storage systems is uncommon by mobile users because they have lots of issues when accessed through resource-constrained mobile devices as they are consuming a lot of resources like CPU, battery and data (Internet usage) of mobile devices. So, in this paper, an efficient and secure multi-cloud storage approach is developed using the concept of fog computing to leverage augmentation of multi-cloud storage services on resource-constrained mobile devices.

Increasing the Efficiency of Code Offloading in n-tier Environments with Code Bubbling

Code offloading strives for increasing the energy efficiency and execution speed of mobile applications on resource-constrained mobile devices. First approaches only considered a code offloading between two (or three) tiers, executing code either locally on a mobile device or remotely on a powerful server. However, new execution environments comprise multiple tiers, containing highly distributed heterogeneous resources. To this end, we present our Code Bubbling Offload System (CoBOS) that targets n-tier environments containing highly distributed heterogeneous resources with different performance characteristics and cost implications. In such n-tier environments, it is very costly for a resource-constrained mobile device to gather a global view on available resources, why we propose the novel concept of code bubbling. Code bubbling moves code dynamically and adaptively toward more powerful and more distant tiers, enabling an efficient and scalable code offloading in n-tier environments. Each tier makes autonomous decisions to execute code in the tier or forward it further to the next tier. To support such a recursive escalation of code along autonomous tiers, CoBOS offloads self-contained offload requests that possess all information required for a decision-making and processing. Our real-world evaluation shows that CoBOS decreases execution time by 83% and energy consumption by 77% for code offloading in n-tier environments.

Lightweight attribute based encryption scheme for mobile cloud assisted cyber-physical systems

Protecting data in Cyber-Physical Systems (CPS) has been a longstanding challenge. The tradition Attribute Based Encryption (ABE) is known for its high computation load which creates a significant challenge for resource constrained mobile devices. In this paper, we propose a Lightweight Attribute Based Encryption Scheme (LABE) for mobile cloud-assisted CPS based on a proxy service architecture and a new ciphertext policy ABE. In particular, mobile devices will solely perform symmetric encryption by joining authentication and encryption proxy services encapsulated in RESTful. Encryption shall not need pairing and then ciphertext can be decrypted with one pairing. Security analysis shows that the proposed LABE is secure with fine grained access control and users revocation capability. The computation complexity shows that the proposed scheme imposes low overhead on mobile devices and performs very well in mobile cloud-assisted CPS.

An Energy-Aware Task Offloading Mechanism in Multiuser Mobile-Edge Cloud Computing

Mobile-edge cloud computing, an emerging and prospective computing paradigm, can facilitate the complex application execution on resource-constrained mobile devices by offloading computation-intensive tasks to the mobile-edge cloud server, which is usually deployed in close proximity to the wireless access point. However, in the multichannel wireless interference environment, the competition of mobile users for communication resources is not conducive to the energy efficiency of task offloading. Therefore, how to make the offloading decision for each mobile user and select its suitable channel become critical issues. In this paper, the problem of the offloading decision is formulated as a 0-1 nonlinear integer programming problem under the constraints of channel interference threshold and the time deadline. Through the classification and priority determination for the mobile devices, a reverse auction-based offloading method is proposed to solve this optimization problem for energy efficiency improvement. The proposed algorithm not only achieves the task offloading decision but also gives the facility of resource allocation. In the energy efficiency performance aspects, simulation results show the superiority of the proposed scheme.

Auction-Based Resource Allocation for Sharing Cloudlets in Mobile Cloud Computing

Driven by pervasive mobile devices and ubiquitous wireless communication networks, mobile cloud computing emerges as an appealing paradigm to accommodate demands for running power-hungry or computation-intensive applications over resource-constrained mobile devices. Cloudlets that move available resources closer to the network edge offer a promising architecture to support real-time applications, such as online gaming and speech recognition. To stimulate service provisioning by cloudlets, it is essential to design an incentive mechanism that charges mobile devices and rewards cloudlets. Although auction has been considered as a promising form for incentive, it is challenging to design an auction mechanism that holds certain desirable properties for the cloudlet scenario. In this paper, we propose an incentive-compatible auction mechanism (ICAM) for the resource trading between the mobile devices as service users (buyers) and cloudlets as service providers (sellers). ICAM can effectively allocate cloudlets to satisfy the service demands of mobile devices and determine the pricing. Both the theoretical analysis and the numerical results show that the ICAM guarantees desired properties with respect to individual rationality, budget balance and truthfulness (incentive compatibility) for both the buyers and the sellers, and computational efficiency.

SAMADroid: A Novel 3-Level Hybrid Malware Detection Model for Android Operating System

For the last few years, Android is known to be the most widely used operating system and this rapidly increasing popularity has attracted the malware developer’s attention. Android allows downloading and installation of apps from other unofficial market places. This gives malware developers an opportunity to put repackaged malicious applications in third-party app-stores and attack the Android devices. A large number of malware analysis and detection systems have been developed which uses static analysis, dynamic analysis, or hybrid analysis to keep Android devices secure from malware. However, the existing research clearly lags in detecting malware efficiently and accurately. For accurate malware detection, multilayer analysis is required which consumes large amount of hardware resources of resource constrained mobile devices. This research proposes an efficient and accurate solution to this problem, named SAMADroid, which is a novel 3-level hybrid malware detection model for Android operating systems. The research contribution includes multiple folds. First, many of the existing Android malware detection techniques are thoroughly investigated and categorized on the basis of their detection methods. Also, their benefits along with limitations are deduced. A novel 3-level hybrid malware detection model for Android operating systems is developed, that can provide high detection accuracy by combining the benefits of the three different levels: 1) Static and Dynamic Analysis; 2) Local and Remote Host; and 3) Machine Learning Intelligence. Experimental results show that SAMADroid achieves high malware detection accuracy by ensuring the efficiency in terms of power and storage consumption.

Efficiently Revocable and Searchable Attribute-Based Encryption Scheme for Mobile Cloud Storage

Attribute-based encryption (ABE) is suitable for mobile cloud storage to protect data confidentiality and realize fine-grained data access control. It is essential for ABE schemes to achieve attribute revocation as users’ attributes may be changed frequently. Keyword search over encrypted data also needs to be solved in the mobile cloud storage. In addition, computational efficiency is a consideration for the resource-constrained mobile device. Focusing on the above-mentioned problems, an efficiently revocable and searchable ABE (RSABE) scheme for the mobile cloud storage is proposed. In our scheme, the function of attribute revocation is efficiently achieved by delegating the update of secret key and ciphertext to the powerful cloud server. Keyword search is also supported, in which data owners and users can generate the keywords index and search trapdoor, respectively, without relying on always online trusted authority. Furthermore, an outsourced decryption technology is used to reduce the computational load of decryption on user side. Our RSABE scheme is proven to be semantically secure against selective ciphertext policy and chosen plaintext attack, and to be secure against chosen keyword attack in the random oracle model. Finally, performance evaluation demonstrates that our scheme is highly efficient.

Analysis of energy aware job offloading in mobile cloud

Mobile cloud computing is the combination of cloud computing and mobile computing, and provides rich computational resources to a mobile computer. In mobile cloud computing, computation offloading techniques are used to overcome the limitations of resource-constrained mobile devices. Offloading techniques perform some parts of a job of mobile devices in the cloud in behalf of mobile devices. If the cost of the operation in that part of the job in a mobile device is larger than the cost associated with offloading, the part is executed in the cloud. Traditional cost analysis models for deciding which parts of a job to execute in the cloud or a mobile device were estimated by using only the cost of offloading, which is composed of data transfer and response time required for the function call. In this paper, we propose a novel offloading cost analysis model based on the data synchronisation rate and the data exchange rate for the input of the function to improve the accuracy of offloading cost estimation. We confirm through experiments that the offloading technique with the proposed model can reduce the execution time of a job and consequentially improve the energy efficiency when compared to previous techniques.

Cloud-Based Cyber-Physical Intrusion Detection for Vehicles Using Deep Learning

Detection of cyber attacks against vehicles is of growing interest. As vehicles typically afford limited processing resources, proposed solutions are rule-based or lightweight machine learning techniques. We argue that this limitation can be lifted with computational offloading commonly used for resource-constrained mobile devices. The increased processing resources available in this manner allow access to more advanced techniques. Using as case study a small four-wheel robotic land vehicle, we demonstrate the practicality and benefits of offloading the continuous task of intrusion detection that is based on deep learning. This approach achieves high accuracy much more consistently than with standard machine learning techniques and is not limited to a single type of attack or the in-vehicle CAN bus as previous work. As input, it uses data captured in real-time that relate to both cyber and physical processes, which it feeds as time series data to a neural network architecture. We use both a deep multilayer perceptron and recurrent neural network architecture, with the latter benefitting from a long-short term memory hidden layer, which proves very useful for learning the temporal context of different attacks. We employ denial of service, command injection and malware as examples of cyber attacks that are meaningful for a robotic vehicle. The practicality of computation offloading depends on the resources afforded onboard and remotely, and the reliability of the communication means between them. Using detection latency as the criterion, we have developed a mathematical model to determine when computation offloading is beneficial given parameters related to the operation of the network and the processing demands of the deep learning model. The more reliable the network and the greater the processing demands, the greater the reduction in detection latency achieved through offloading.

Analysis of energy aware job offloading in mobile cloud

Mobile cloud computing is the combination of cloud computing and mobile computing, and provides rich computational resources to a mobile computer. In mobile cloud computing, computation offloading techniques are used to overcome the limitations of resource-constrained mobile devices. Offloading techniques perform some parts of a job of mobile devices in the cloud in behalf of mobile devices. If the cost of the operation in that part of the job in a mobile device is larger than the cost associated with offloading, the part is executed in the cloud. Traditional cost analysis models for deciding which parts of a job to execute in the cloud or a mobile device were estimated by using only the cost of offloading, which is composed of data transfer and response time required for the function call. In this paper, we propose a novel offloading cost analysis model based on the data synchronisation rate and the data exchange rate for the input of the function to improve the accuracy of offloading cost estimation. We confirm through experiments that the offloading technique with the proposed model can reduce the execution time of a job and consequentially improve the energy efficiency when compared to previous techniques.

An input-centric performance model for computational offloading of mobile applications

Computational offloading frameworks are a widely-researched technology for optimising mobile applications through the use of cloud resources. Existing frameworks fail to fully account for the effect of input data characteristics on application behaviour. Comprehensive timing models exist in the literature, but feature information requirements and performance overheads that preclude use on mobile devices. In this paper, we propose a conceptual model for an input-centric view of application performance. Our proposed model simplifies the existing count-and-weights and pipeline timing models to significantly reduce their information and processing requirements, facilitating use on resource-constrained mobile devices. Our proposed model also utilises symbolic execution techniques to account for the effects of application input data characteristics. Validation with both synthetic and real device datasets demonstrates that our model provides an extremely accurate approximation of the count-and-weights model. Results demonstrate the predictive power of our model for linear execution paths with no loops or recursion. Further work with improved symbolic execution techniques may look to expand application of our proposed model to real-world use cases. The proposed input-centric approach provides a promising foundation for incorporating a deeper level of application-specific knowledge into computational offloading framework cost models, with the potential to contribute to higher-quality offloading decisions.

Design and implementation of an efficient multi cloud storage approach for resource constrained mobile devices

In todays scenario of Big Data, to reduce data management and maintenance cost, many companies and users starts shifting their data to cloud. To store or access data from remote storage (cloud storage) by using a desktop device as PC is not a problem but as the demand of smart mobile devices is increases day by day as compared to desktop devices if user want to store or retrieve data from cloud storage using these mobile devices, it will consume a lot of resources of mobile device. Which is the main issue as mobile devices have limited resources as battery, CPU, memory, etc. To deal with the free limited storage issue, a lot of multi cloud storage systems are available but none of them is capable to save resources of mobile devices as battery, CPU and data (Internet) usage. So there is a need of an efficient multi cloud storage approach that will consume fewer resources of mobile devices. In this paper, we are proposing design and implementation of an efficient multi cloud storage approach for resource constrained mobile devices that will consume fewer resources of mobile devices and give better results in terms of battery consumption, CPU usage and data usage.

A quick-response framework for multi-user computation offloading in mobile cloud computing

The execution of much sophisticated applications on the resource-constrained mobile device will lead to the fast exhaustion of the battery of mobile device. Therefore, mobile cloud computing (MCC) is regarded as an energy-effective approach by offloading tasks from mobile device to the resource-enough cloud, which cannot only save energy for mobile devices but also prolong the operation time of battery. However, it still remains a challenging issue to coordinate task offloading among mobile devices and get offloading results quickly at the same time. In this paper, we propose an agent-based MCC framework to enable the device to receive offloading results faster by making offloading decision on the agent. Moreover, to get an offloading strategy, we formulate the problem of maximizing energy savings among multiple users under the completion time and bandwidth constraints. To solve the optimization problem, we propose a Dynamic Programming After Filtering (DPAF) algorithm. In the algorithm, firstly, the original offloading problem is transformed to the classic 0–1 Knapsack problem by the filtering process on the agent. Furthermore, we adopt dynamic programming algorithm to find an optimal offloading strategy. Simulation results show that the framework can more quickly get response from agent than other schemes and the DPAF algorithm outperforms other solutions in energy saving.

ExpSOS: Secure and Verifiable Outsourcing of Exponentiation Operations for Mobile Cloud Computing

Discrete exponential operation, such as modular exponentiation and scalar multiplication on elliptic curves, is a basic operation of many public-key cryptosystems. However, the exponential operations are considered prohibitively expensive for resource-constrained mobile devices. In this paper, we address the problem of secure outsourcing of exponentiation operations to one single untrusted server. Our proposed secure outsourcing scheme for general exponential (ExpSOS) only requires a very limited number of modular multiplications at local mobile environment, and thus it can achieve significant computational performance gain. ExpSOS also provides a secure verification scheme with probability approximately 1 to ensure that the mobile end users can always receive valid results. The comprehensive analysis as well as the simulation results in real mobile device demonstrates that our proposed ExpSOS can significantly improve the existing schemes in efficiency, security, and result verifiability. We apply ExpSOS to securely outsource several cryptographic protocols to show that ExpSOS can be widely applied to many computation-intensive applications and achieve significant performance improvement.

Secure attribute-based data sharing for resource-limited users in cloud computing

Data sharing becomes an exceptionally attractive service supplied by cloud computing platforms because of its convenience and economy. As a potential technique for realizing fine-grained data sharing, attribute-based encryption (ABE) has drawn wide attentions. However, most of the existing ABE solutions suffer from the disadvantages of high computation overhead and weak data security, which has severely impeded resource-constrained mobile devices to customize the service. The problem of simultaneously achieving fine-grainedness, high-efficiency on the data owner's side, and standard data confidentiality of cloud data sharing actually still remains unresolved. This paper addresses this challenging issue by proposing a new attribute-based data sharing scheme suitable for resource-limited mobile users in cloud computing. The proposed scheme eliminates a majority of the computation task by adding system public parameters besides moving partial encryption computation offline. In addition, a public ciphertext test phase is performed before the decryption phase, which eliminates most of computation overhead due to illegitimate ciphertexts. For the sake of data security, a Chameleon hash function is used to generate an immediate ciphertext, which will be blinded by the offline ciphertexts to obtain the final online ciphertexts. The proposed scheme is proven secure against adaptively chosen-ciphertext attacks, which is widely recognized as a standard security notion. Extensive performance analysis indicates that the proposed scheme is secure and efficient.

Data security in mobile cloud computing paradigm: a survey, taxonomy and open research issues

The incessant spurt of research activities to augment capabilities of resource-constrained mobile devices by leveraging heterogeneous cloud resources has created a new research impetus called mobile cloud computing. However, this rapid relocation to the cloud has fueled security and privacy concerns as users' data leave owner's protection sphere and enter the cloud. Significant efforts have been devoted by academia and research community to study and build secure frameworks in cloud environment, but there exists a research gap for comprehensive study of security frameworks in mobile cloud computing environment. Therefore, we aim to conduct a comprehensive survey to analyze various cryptographic, biometric and multifactor lightweight solutions for data security in mobile cloud. This survey highlights the current security issues in mobile cloud environment and infrastructure, investigates various data security frameworks and provides a taxonomy of the state-of-the-art data security frameworks and deep insight into open research issues for ensuring security and privacy of data in mobile cloud computing platform.

Mobile Data Analytics

Mobile data analytics (MDA) is a growing area among IT professionals, entrepreneurs, and academics. MDA deals with data analytics, especially big data analytics, on resource-constrained mobile devices. This special issue discusses the most recent advances in analytics for mobile devices.