• Journal of Information Processing Systems
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• v.17 no.4
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• pp.721-736
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• 2021

For the mobile edge computing (MEC) system supporting dense network, a joint allocation algorithm of computing and communication resources based on reinforcement learning is proposed. The energy consumption of task execution is defined as the maximum energy consumption of each user's task execution in the system. Considering the constraints of task unloading, power allocation, transmission rate and calculation resource allocation, the problem of joint task unloading and resource allocation is modeled as a problem of maximum task execution energy consumption minimization. As a mixed integer nonlinear programming problem, it is difficult to be directly solve by traditional optimization methods. This paper uses reinforcement learning algorithm to solve this problem. Then, the Markov decision-making process and the theoretical basis of reinforcement learning are introduced to provide a theoretical basis for the algorithm simulation experiment. Based on the algorithm of reinforcement learning and joint allocation of communication resources, the joint optimization of data task unloading and power control strategy is carried out for each terminal device, and the local computing model and task unloading model are built. The simulation results show that the total task computation cost of the proposed algorithm is 5%-10% less than that of the two comparison algorithms under the same task input. At the same time, the total task computation cost of the proposed algorithm is more than 5% less than that of the two new comparison algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.2
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• pp.102-113
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• 2014

Present communication and network environment have been moving rapidly toward wireless and mobile base from existing wired internet base. This change of trend influences greatly on business methods accordingly. Therefore many enterprises are trying hard to adopt mobile business in order to gain competitive edge of their products and they are in need of more effective and stable mobile solutions. However, the method of establishing optimal mobile computing environment and how to handle existing business process and use vast amount of database are still needed. Therefore this paper tries to realize a mobile middleware system as a mobile business establishment supporting tool that could link various computational resource on wired internet with wireless LAN, mobile phone network, and mobile devices. To accomplish that specific goal, this paper provides a powerful tool of mobile and wireless application data access that could expand the line of business already set up in general enterprises easily and rapidly into mobile environment. When this suggested solution is applied in the field of industry, it can economically change legacy business process into mobile environment without having to change existing logic and resources at all.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.168-169
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• 2017

모바일 디바이스는 제한된 프로세서 및 저장 공간, 배터리 수명의 문제로 인해 새로운 대안으로 모바일 엣지 컴퓨팅이 연구되고 있다. 그러나 모바일 디바이스는 제한된 자원을 활용하기 때문에 전력 공급에 제약이 따른다. 모바일 엣지 컴퓨팅의 자원으로 활용 되려면 전력 소비량에 대한 관리가 필요하다. 본 논문에서는 모바일 엣지 컴퓨팅 기반 모바일 디바이스의 전력 소비량 감소를 위한 작업 할당 알고리즘을 제안한다.

• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.113-119
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• 2019

The key features of 5G mobile communications recently commercialized can be represented by High Data Rate, Connection Density and Low Latency, of which the features most distinct from the existing 4G will be low Latency, which will be the foundation for various new service offerings. AR and self-driving technologies are being considered as services that utilize these features, and 5G Network Latency is also being discussed in related standards. However, it is true that the discussion of E2E Latency from a service perspective is much lacking. The final goal to achieve low Latency at 5G is to achieve 1ms of air interface based on RTD, which can be done through Ultra-reliable Low Latency Communications (URLLC) through Rel-16 in early 20 years, and further network parity through Mobile Edge Computing (MEC) is also being studied. In addition to 5G network-related factors, the overall 5G E2E Latency also includes link/equipment Latency on the path between the 5G network and the IDC server for service delivery, and the Processing Latency for service processing within the mobile app and server. Meanwhile, it is also necessary to study detailed service requirements by separating Latency for initial setup of service and Latency for continuous service. In this paper, the following three factors were reviewed for initial setup of service. First, the experiment and analysis presented the impact on Latency on the Latency in the case of 1 Data Lake Setup, 2 CRDX On/Off for efficient power, and finally 3H/O on Latency. Through this, we expect Low Latency to contribute to the service requirements and planning associated with Latency in the initial setup of the required services.

• Electronics and Telecommunications Trends
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• v.34 no.6
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• pp.133-144
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• 2019

Although deep learning-based visual image recognition technology has evolved rapidly, most of the commonly used methods focus solely on recognition accuracy. However, the demand for low latency and low power consuming image recognition with an acceptable accuracy is rising for practical applications in edge devices. For example, most Internet of Things (IoT) devices have a low computing power requiring more pragmatic use of these technologies; in addition, drones or smartphones have limited battery capacity again requiring practical applications that take this into consideration. Furthermore, some people do not prefer that central servers process their private images, as is required by high performance serverbased recognition technologies. To address these demands, the object and scene recognition technologies for mobile/embedded devices that enable optimized neural networks to operate in mobile and embedded environments are gaining attention. In this report, we briefly summarize the recent trends and issues of object and scene recognition technologies for mobile and embedded devices.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.1-10
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• 2021

The Internet of things (IoT) is the main advancement in data processing and communication technologies. In IoT, intelligent devices play an exciting role in wireless communication. Although, sensor nodes are low-cost devices for communication and data gathering. However, sensor nodes are more vulnerable to different security threats because these nodes have continuous access to the internet. Therefore, the multiparty security credential-based key generation mechanism provides effective security against several attacks. The key generation-based methods are implemented at sensor nodes, edge nodes, and also at server nodes for secure communication. The main challenging issue in a collaborative key generation scheme is the extensive multiplication. When the number of parties increased the multiplications are more complex. Thus, the computational cost of batch key and multiparty key-based schemes is high. This paper presents a Secure Multipart Key Distribution scheme (SMKD) that provides secure communication among the nodes by generating a multiparty secure key for communication. In this paper, we provide node authentication and session key generation mechanism among mobile nodes, head nodes, and trusted servers. We analyzed the achievements of the SMKD scheme against SPPDA, PPDAS, and PFDA schemes. Thus, the simulation environment is established by employing an NS 2. Simulation results prove that the performance of SMKD is better in terms of communication cost, computational cost, and energy consumption.

• Journal of KIISE:Databases
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• v.30 no.3
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• pp.296-309
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• 2003

One of commercial applications of mobile computing is ATIS(Advanced Traveler Information Systems) in ITS(Intelligent Transport Systems). In ATIS, a primary mobile computing task is to compute the shortest path from the current location to the destination. In this paper, we have studied the shortest path re-computation problem that arises in the DRGS(Dynamic Route Guidance System) in ATIS where the cost of topological digital road map is frequently updated as traffic condition changes dynamically. Previously suggested methods either re-compute the shortest path from scratch or re-compute the shortest path just between the two end nodes of the edge where the cost change occurs. However, these methods we trivial in that they do not intelligently utilize the previously computed shortest path information. In this paper, we propose an efficient approximate shortest path re-computation method based on the dynamic window scheme. The proposed method re-computes an approximate shortest path very quickly by utilizing the previously computed shortest path information. We first show the theoretical analysis of our methods and then present an in-depth experimental performance analysis by implementing it on grid graphs as well as a real digital road map.

• KIISE Transactions on Computing Practices
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• v.23 no.5
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• pp.328-333
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• 2017

Mobile game apps, which are popular in various mobile devices, tend to be power-hungry and rapidly drain the device's battery. Since a long battery lifetime is a key design requirement of mobile devices, reducing the power consumption of mobile game apps has become an important research topic. In this paper, we investigate the power consumption characteristics of popular mobile games with multiple threads, focusing on the inter-thread. From our power measurement study of popular mobile game apps, we observed that some of these apps have abnormally high-load threads that barely affect the user's gaming experience, despite the high energy consumption. In order to reduce the wasted power from these abnormal threads, we propose a novel technique that detects such abnormal threads during run time and reduces their power consumption without degrading user experience. Our experimental results on an Android smartphone show that the proposed technique can reduce the energy consumption of mobile game apps by up to 58% without any negative impact on the user's gaming experience.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.794-815
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• 2023

The rapid development of mobile communication not only has made the industry gradually diversified, but also has enhanced the service quality requirements of users. In this regard, it is imperative to consider jointly network slicing and mobile edge computing. The former mainly ensures the requirements of varied vertical services preferably, and the latter solves the conflict between the user's own energy and harsh latency. At present, the integration of the two faces many challenges and need to carry out at different levels. The main target of the paper is to minimize the energy consumption of the system, and introduce a multi-slice joint task offloading and resource allocation scheme for massive multiple input multiple output enabled heterogeneous networks. The problem is formulated by collaborative optimizing offloading ratios, user association, transmission power and resource slicing, while being limited by the dissimilar latency and rate of multi-slice. To solve it, assign the optimal problem to two sub-problems of offloading decision and resource allocation, then solve them separately by exploiting the alternative optimization technique and Karush-Kuhn-Tucker conditions. Finally, a novel slices task offloading and resource allocation algorithm is proposed to get the offloading and resource allocation strategies. Numerous simulation results manifest that the proposed scheme has certain feasibility and effectiveness, and its performance is better than the other baseline scheme.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.4
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• pp.77-82
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• 2024

Recent advancements in artificial intelligence (AI) technology have led to an increase in the implementation of AI applications in mobile environments. However, due to the limited resources in mobile devices compared to desktops and servers, there is growing interest in research aimed at efficiently executing AI workloads on mobile platforms. While most studies focus on offloading to edge or cloud solutions to mitigate computing resource constraints, research on the characteristics of file I/O related to storage access in mobile settings remains underexplored. This paper analyzes file I/O traces generated during the execution of deep learning applications in mobile environments and investigates how they differ from traditional mobile workloads. We anticipate that the findings of this study will be utilized to design future smartphone system software more efficiently, considering the file access characteristics of deep learning.