• KIPS Transactions on Computer and Communication Systems
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• v.7 no.2
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• pp.27-32
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• 2018

One of the crucial issues in mobile edge cloud computing environments is to monitor mobile devices. Due to the inherit properties of mobile devices, they are prone to unstable behavior that leads to failures. In order to satisfy the service level agreement (SLA), the mobile edge cloud administrators should take appropriate measures through a monitoring scheme. In this paper, we propose a monitoring scheme of mobile devices based on artificial intelligence in mobile edge cloud computing environments. The proposed monitoring scheme is able to measure faults of mobile devices based on previous and current monitoring information. To this end, we adapt the hidden markov chain model, one of the artificial intelligence technologies, to monitor mobile devices. We validate our monitoring scheme based on the hidden markov chain model. The proposed monitoring scheme can also be used in general cloud computing environments to monitor virtual machines.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.11
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• pp.3916-3936
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• 2014

Smartphone applications like games, image processing, e-commerce and social networking are gaining exponential growth, with the ubiquity of cellular services. This demands increased computational power and storage from mobile devices with a sufficiently high bandwidth for mobile internet service. But mobile nodes are highly constrained in the processing and storage, along with the battery power, which further restrains their dependability. Adopting the unlimited storage and computing power offered by cloud servers, it is possible to overcome and turn these issues into a favorable opportunity for the growth of mobile cloud computing. As the mobile internet data traffic is predicted to grow at the rate of around 65 percent yearly, even advanced services like 3G and 4G for mobile communication will fail to accommodate such exponential growth of data. On the other hand, developers extend popular applications with high end graphics leading to smart phones, manufactured with multicore processors and graphics processing units making them unaffordable. Therefore, to address the need of resource constrained mobile nodes and bandwidth constrained cellular networks, the computations can be migrated to resourceful servers connected to cloud. The server now acts as a bridge that should enable the participating mobile nodes to offload their computations through Wi-Fi directly to the virtualized server. Our proposed model enables an on-demand service offloading with a decision support system that identifies the capabilities of the client's hardware and software resources in judging the requirements for offloading. Further, the node's location, context and security capabilities are estimated to facilitate adaptive migration.

• International Journal of Advanced Culture Technology
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• v.5 no.2
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• pp.90-97
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• 2017

The Internet of Things(IoT) market is expected to grow from 15.5billion to 75.4 billion by 2015-2025. As the number of IoT devices increases, the amount of data that is sent to the cloud is increasing. Today's Cloud Computing models are not suited to handle the vast amount of data generated by IoT devices. In this paper, we propose a Cooperation Coordinator System that reduces server load and improved real-time processing capability under specific circumstances by using Band-Cloud. The cooperation coordinator system dynamically forms the cloud when cooperation is needed between mobile devices located near. It is called Band-Cloud. Band-Cloud provides services entrusted by Central Cloud. This paper describes the proposed system and shows the cooperation process using the Android-based mobile devices and Wi-Fi Direct technology. Such a system can be applied to cases where real-time processing is required in a narrow area such as a hospital ward or a school classroom.

• The Journal of Korea Robotics Society
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• v.17 no.2
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• pp.118-123
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• 2022

We implemented a real-time cloud robotics application by offloading robot navigation engine over to 5G Mobile Edge Computing (MEC) sever. We also ran a fleet management system (FMS) in the server and controlled the movements of multiple robots at the same time. The mobile robots under the test were connected to the server through 5G SA network. Public 5G network, which is already commercialized, has been temporarily modified to support this validation by the network operator. Robot engines are containerized based on micro-service architecture and have been deployed using Kubernetes - a container orchestration tool. We successfully demonstrated that mobile robots are able to avoid obstacles in real-time when the engines are remotely running in 5G MEC server. Test results are compared with 5G Public Cloud and 4G (LTE) Public Cloud as well.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.12
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• pp.5529-5552
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• 2016

Mobile cloud computing (MCC) has revolutionized the way in which the services can be obtained from the cloud service providers. Manifold increase in the number of mobile devices and subscribers in MCC has further enhanced the need of an efficient and robust authentication solution. Earlier, the subscribers could get cloud-computing services from the cloud service providers only after having consulted the trusted third party. Recently, Tsai and Lo has proposed a multi-server authenticated key agreement solution for MCC based on bilinear pairing, to eliminate the trusted third party for mutual authentication. The scheme has been novel as far as the minimization of trusted party involvement in authenticating the user and service provider, is concerned. However, the Tsai and Lo scheme has been found vulnerable to server spoofing attack (misrepresentation attack), de-synchronization attack and denial-of-service attack, which renders the scheme unsuitable for practical deployment in different wireless mobile access networks. Therefore, we have proposed an improved model based on bilinear pairing, countering the identified threats posed to Tsai and Lo scheme. Besides, the proposed work also demonstrates performance evaluation and formal security analysis.

• ETRI Journal
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• v.37 no.2
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• pp.348-358
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• 2015

Mobile devices have been widespread and become very popular with connectivity to the Internet, and a lot of desktop PC applications are now aggressively ported to them. Unfortunately, mobile devices are often vulnerable to malicious attacks due to their common usage and connectivity to the Internet. Therefore, the demands on the development of mobile security systems increase in accordance with advances in mobile computing. However, it is very hard to run a security program on a mobile device all of the time due the device's limited computational power and battery life. To overcome these problems, we propose a novel mobile security scheme that migrates heavy computations on mobile devices to cloud servers. An efficient data transmission scheme for reducing data traffic between devices and servers over networks is introduced. We have evaluated the proposed scheme with a mobile device in a cloud environment, whereby it achieved a maximum speedup of 13.4 compared to a traditional algorithm.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.3
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• pp.127-135
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• 2015

Context-aware mobile cloud computing is a new promising paradigm that allows to improve user experience by analyzing contextual information such as user location, time of the day, neighboring devices and current activity. In this paper we provide performance study of context-aware mobile cloud computing system with Volare middleware. Volare monitors the resources and context of the device. and dynamically adapts cloud service requests accordingly, at discovery time or at runtime. This approach allows for more resource-efficient and reliable cloud service discovery, as well as significant cost savings at runtime. We also have studied the performance of context-aware mobile cloud computing for different quality of service (QoS) adaptation policies. Our simulations results show that when battery level is low and CPU usage is high and user cannot maintain the initial QoS, service cost is decreased according to current adaptation policy. In conclusion, the current adaptation policy suggested in this paper may improve user experience by providing a dynamically adapted service cost according to a situation.

• Journal of Internet Computing and Services
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• v.18 no.5
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• pp.79-85
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• 2017

This paper proposes the use of mobile ad-hoc cloud for reducing the process time of panoramic image generation in mobile smart devices. In order to effectively assign tasks relevant to panoramic image generation to the mobile ad-hoc cloud, a method for image acquisition and sorting and an algorithm for task distribution and offloading decision making are proposed. The proposed methods are applied to Android OS based smart devices, and their effects on panoramic image generation are analyzed.

• Journal of the Korea Society of Computer and Information
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• v.19 no.2
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• pp.67-75
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• 2014

In this paper, we proposed offloading delay method which determines effectively offloading timing by measuring of handoff delay and offloading time at mobile node side in mobile environment. The propose method measures each of handoff delay and offloading time and making decision of proper offloading timing on mobile node side. Therefore, it is possible to support cloud computing without changing previous implemented cloud computing structure for fixed node in a mobile environment. We compare the energy consumption of server and node to analyze efficiency of proposed method by using existing method of energy consumption measurement. Simulation results shows the reducing energy consumption more than previous method and operation time similar to previous method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.3254-3272
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• 2017

With the development of wireless access technologies and the popularity of mobile intelligent terminals, cloud computing is expected to expand to mobile environments. Attribute-based encryption, widely applied in cloud computing, incurs massive computational cost during the encryption and decryption phases. The computational cost grows with the complexity of the access policy. This disadvantage becomes more serious for mobile devices because they have limited resources. To address this problem, we present an efficient verifiable outsourced scheme based on the bilinear group of prime order. The scheme is called the verifiable outsourced computation ciphertext-policy attribute-based encryption scheme (VOC-CP-ABE), and it provides a way to outsource intensive computing tasks during encryption and decryption phases to CSP without revealing the private information and leaves only marginal computation to the user. At the same time, the outsourced computation can be verified by two hash functions. Then, the formal security proofs of its (selective) CPA security and verifiability are provided. Finally, we discuss the performance of the proposed scheme with comparisons to several related works.