Duan Xue^1,2, Yan Guo^1,*, Ning Li¹, Xiaoxiang Song¹

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 609-631, 2023, DOI:10.32604/cmc.2023.035177

Abstract The traditional multi-access edge computing (MEC) capacity is overwhelmed by the increasing demand for vehicles, leading to acute degradation in task offloading performance. There is a tremendous number of resource-rich and idle mobile connected vehicles (CVs) in the traffic network, and vehicles are created as opportunistic ad-hoc edge clouds to alleviate the resource limitation of MEC by providing opportunistic computing services. On this basis, a novel scalable system framework is proposed in this paper for computation task offloading in opportunistic CV-assisted MEC. In this framework, opportunistic ad-hoc edge cloud and fixed edge cloud cooperate to form a novel hybrid cloud.… More >

Sanghyuck Nam¹, Suhwan Kwak¹, Jaehwan Lee², Sangoh Park^1,*

Computer Systems Science and Engineering, Vol.46, No.1, pp. 659-670, 2023, DOI:10.32604/csse.2023.034994

Abstract Autonomous platooning technology is regarded as one of the promising technologies for the future and the research is conducted actively. The autonomous platooning task generally requires highly complex computations so it is difficult to process only with the vehicle’s processing units. To solve this problem, there are many studies on task offloading technique which transfers complex tasks to their neighboring vehicles or computation nodes. However, the existing task offloading techniques which mainly use learning-based algorithms are difficult to respond to the real-time changing road environment due to their complexity. They are also challenging to process computation tasks within 100 ms which… More >

Ahmad Naseem Alvi¹, Muhammad Awais Javed^1,*, Mozaherul Hoque Abul Hasanat², Muhammad Badruddin Khan², Abdul Khader Jilani Saudagar², Mohammed Alkhathami²

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6321-6334, 2023, DOI:10.32604/cmc.2023.029913

Abstract Wireless nodes are one of the main components in different applications that are offered in a smart city. These wireless nodes are responsible to execute multiple tasks with different priority levels. As the wireless nodes have limited processing capacity, they offload their tasks to cloud servers if the number of tasks exceeds their task processing capacity. Executing these tasks from remotely placed cloud servers causes a significant delay which is not required in sensitive task applications. This execution delay is reduced by placing fog computing nodes near these application nodes. A fog node has limited processing capacity and is sometimes… More >

Alla Abbas Khadir¹, Seyed Amin Hosseini Seno^1,2,*, Baydaa Fadhil Dhahir^2,3, Rahmat Budiarto⁴

Computer Systems Science and Engineering, Vol.45, No.2, pp. 2223-2234, 2023, DOI:10.32604/csse.2023.032316

Abstract Fog computing became a traditional OffLad Destination (OLD) to compute the offloaded tasks of the Internet of Vehicles (IoV). Nevertheless, the limited computing resources of the fog node leads to re-offload these tasks to the neighboring fog nodes or the cloud. Thus, the IoV will incur additional offloading costs. In this paper, we propose a new offloading scheme by utilizing RoadSide Parked Vehicles (RSPV) as an alternative OLD for IoV. The idle computing resources of the RSPVs can compute large tasks with low offloading costs compared with fog nodes and the cloud. Finally, a performance evaluation of the proposed scheme… More >

Prohim Tam¹, Sa Math¹, Seokhoon Kim^1,2,*

Computer Systems Science and Engineering, Vol.45, No.2, pp. 2141-2154, 2023, DOI:10.32604/csse.2023.030984

Abstract Edge intelligence brings the deployment of applied deep learning (DL) models in edge computing systems to alleviate the core backbone network congestions. The setup of programmable software-defined networking (SDN) control and elastic virtual computing resources within network functions virtualization (NFV) are cooperative for enhancing the applicability of intelligent edge softwarization. To offer advancement for multi-dimensional model task offloading in edge networks with SDN/NFV-based control softwarization, this study proposes a DL mechanism to recommend the optimal edge node selection with primary features of congestion windows, link delays, and allocatable bandwidth capacities. Adaptive partial task offloading policy considered the DL-based recommendation to… More >

Xiaojuan Wang¹, Zikui Lu^1,*, Siyuan Sun², Jingyue Wang¹, Luona Song³, Merveille Nicolas⁴

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2055-2071, 2023, DOI:10.32604/cmc.2023.031750

Abstract With the development of the Industrial Internet of Things (IIoT), end devices (EDs) are equipped with more functions to capture information. Therefore, a large amount of data is generated at the edge of the network and needs to be processed. However, no matter whether these computing tasks are offloaded to traditional central clusters or mobile edge computing (MEC) devices, the data is short of security and may be changed during transmission. In view of this challenge, this paper proposes a trusted task offloading optimization scheme that can offer low latency and high bandwidth services for IIoT with data security. Blockchain… More >

Abednego Acheampong¹, Yiwen Zhang^1,*, Xiaolong Xu², Daniel Appiah Kumah²

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 35-88, 2023, DOI:10.32604/cmes.2022.021394

Abstract Task offloading is an important concept for edge computing and the Internet of Things (IoT) because computationintensive tasks must be offloaded to more resource-powerful remote devices. Task offloading has several advantages, including increased battery life, lower latency, and better application performance. A task offloading method determines whether sections of the full application should be run locally or offloaded for execution remotely. The offloading choice problem is influenced by several factors, including application properties, network conditions, hardware features, and mobility, influencing the offloading system’s operational environment. This study provides a thorough examination of current task offloading and resource allocation in edge… More >

Mohamed H. Mousa^1,2,*, Mohamed K. Hussein²

CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.2, pp. 413-434, 2022, DOI:10.32604/cmes.2022.020639

Abstract Mobile-Edge Computing (MEC) displaces cloud services as closely as possible to the end user. This enables the edge servers to execute the offloaded tasks that are requested by the users, which in turn decreases the energy consumption and the turnaround time delay. However, as a result of a hostile environment or in catastrophic zones with no network, it could be difficult to deploy such edge servers. Unmanned Aerial Vehicles (UAVs) can be employed in such scenarios. The edge servers mounted on these UAVs assist with task offloading. For the majority of IoT applications, the execution times of tasks are often… More >

V. R. Balaji^1,*, T. Kalavathi², J. Vellingiri³, N. Rajkumar⁴, Venkat Prasad Padhy⁵

Computer Systems Science and Engineering, Vol.42, No.1, pp. 289-301, 2022, DOI:10.32604/csse.2022.020971

Abstract Data offloading at the network with less time and reduced energy consumption are highly important for every technology. Smart applications process the data very quickly with less power consumption. As technology grows towards 5G communication architecture, identifying a solution for QoS in 5G through energy-efficient computing is important. In this proposed model, we perform data offloading at 5G using the fuzzification concept. Mobile IoT devices create tasks in the network and are offloaded in the cloud or mobile edge nodes based on energy consumption. Two base stations, small (SB) and macro (MB) stations, are initialized and the first tasks randomly… More >

Ao Xiong¹, Meng Chen^1,*, Shaoyong Guo¹, Yongjie Li², Yujing Zhao², Qinghai Ou³, Chuan Liu⁴, Siwen Xu⁵, Xiangang Liu⁶

Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1641-1654, 2022, DOI:10.32604/iasc.2022.018881

Abstract To solve the problem of energy consumption optimization of edge servers in the process of edge task unloading, we propose a task unloading algorithm based on reinforcement learning in this paper. The algorithm observes and analyzes the current environment state, selects the deployment location of edge tasks according to current states, and realizes the edge task unloading oriented to energy consumption optimization. To achieve the above goals, we first construct a network energy consumption model including servers’ energy consumption and link transmission energy consumption, which improves the accuracy of network energy consumption evaluation. Because of the complexity and variability of… More >