Yong Yan¹, Yao Dai^{2, *}, Zhiqiang Zhou³, Wei Jiang⁴, Shaoyong Guo²

CMC-Computers, Materials & Continua, Vol.62, No.2, pp. 905-915, 2020, DOI:10.32604/cmc.2020.07425

Abstract Internet of Things (IoT) technology is rapidly evolving, but there is no trusted platform to protect user privacy, protect information between different IoT domains, and promote edge processing. Therefore, we integrate the blockchain technology into constructing trusted IoT platforms. However, the application of blockchain in IoT is hampered by the challenges posed by heavy computing processes. To solve the problem, we put forward a blockchain framework based on mobile edge computing, in which the blockchain mining tasks can be offloaded to nearby nodes or the edge computing service providers and the encrypted hashes of blocks can be cached in the… More >

Shen Li¹, Fang Liu^1,*, Jiayue Liang¹, Zhenhua Cai¹, Zhiyao Liang²

CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1377-1389, 2019, DOI:10.32604/cmc.2019.06402

Abstract With the rapid development of artificial intelligence, face recognition systems are widely used in daily lives. Face recognition applications often need to process large amounts of image data. Maintaining the accuracy and low latency is critical to face recognition systems. After analyzing the two-tier architecture “client-cloud” face recognition systems, it is found that these systems have high latency and network congestion when massive recognition requirements are needed to be responded, and it is very inconvenient and inefficient to deploy and manage relevant applications on the edge of the network. This paper proposes a flexible and efficient edge computing accelerated architecture.… More >

Yeting Guo¹, Fang Liu^2,*, Nong Xiao¹, Zhengguo Chen^1,3

CMC-Computers, Materials & Continua, Vol.61, No.2, pp. 777-792, 2019, DOI:10.32604/cmc.2019.06366

Abstract Edge computing attracts online service providers (SP) to offload services to edge computing micro datacenters that are close to end users. Such offloads reduce packet-loss rates, delays and delay jitter when responding to service requests. Simultaneously, edge computing resource providers (RP) are concerned with maximizing incomes by allocating limited resources to SPs. Most works on this topic make a simplified assumption that each SP has a fixed demand; however, in reality, SPs themselves may have multiple task-offloading alternatives. Thus, their demands could be flexibly changed, which could support finer-grained allocations and further improve the incomes for RPs. Here, we propose… More >

Bokyun Jo¹, Md. Jalil Piran^2,*, Daeho Lee³, Doug Young Suh^4,*

CMC-Computers, Materials & Continua, Vol.61, No.2, pp. 439-463, 2019, DOI:10.32604/cmc.2019.08194

Abstract In this paper, we investigate video quality enhancement using computation offloading to the mobile cloud computing (MCC) environment. Our objective is to reduce the computational complexity required to covert a low-resolution video to high-resolution video while minimizing computation at the mobile client and additional communication costs. To do so, we propose an energy-efficient computation offloading framework for video streaming services in a MCC over the fifth generation (5G) cellular networks. In the proposed framework, the mobile client offloads the computational burden for the video enhancement to the cloud, which renders the side information needed to enhance video without requiring much… More >

Yifei Wei^1,*, Zhaoying Wang¹, Da Guo¹, F. Richard Yu²

CMC-Computers, Materials & Continua, Vol.59, No.1, pp. 89-104, 2019, DOI:10.32604/cmc.2019.04836

Abstract To reduce the transmission latency and mitigate the backhaul burden of the centralized cloud-based network services, the mobile edge computing (MEC) has been drawing increased attention from both industry and academia recently. This paper focuses on mobile users’ computation offloading problem in wireless cellular networks with mobile edge computing for the purpose of optimizing the computation offloading decision making policy. Since wireless network states and computing requests have stochastic properties and the environment’s dynamics are unknown, we use the model-free reinforcement learning (RL) framework to formulate and tackle the computation offloading problem. Each mobile user learns through interactions with the… More >

Ying Li¹, Jianbo Li^1,*, Jianwei Chen¹, Minchao Lu¹, Caoyuan Li^2,3

CMC-Computers, Materials & Continua, Vol.57, No.3, pp. 571-587, 2018, DOI:10.32604/cmc.2018.02851

Abstract The explosive growth of mobile data demand is becoming an increasing burden on current cellular network. To address this issue, we propose a solution of opportunistic data offloading for alleviating overloaded cellular traffic. The principle behind it is to select a few important users as seeds for data sharing. The three critical steps are detailed as follows. We first explore individual interests of users by the construction of user profiles, on which an interest graph is built by Gaussian graphical modeling. We then apply the extreme value theory to threshold the encounter duration of user pairs. So, a contact graph… More >