scholarly journals An Efficient Forwarding Capability Evaluation Method for Opportunistic Offloading in Mobile Edge Computing

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Qian Wang ◽  
Zhipeng Gao ◽  
Kun Niu ◽  
Yang Yang ◽  
Xuesong Qiu
Keyword(s):  
Evaluation Method ◽  
Edge Computing ◽  
Second Step ◽  
Delivery Ratio ◽  
Mobile Edge Computing ◽  
Traffic Data ◽  
Data Forwarding ◽  
Data Redundancy ◽  
Computation Process ◽  
Capability Evaluation

Opportunistic offloading can be utilized to offload computing tasks and traffic data in Mobile Edge Computing (MEC). To improve the ratio of successful data offloading and reduce unnecessary data redundancy in opportunistic forwarding process, some methods of evaluating a device’s forwarding capability are proposed. However, most of these methods do not consider the temporal impact from device mobility and the efficiency influence from the capability computation process. To settle these problems, we proposed a Transient-cluster-based Capability Evaluation Method (TCEM) to evaluate a device’s data forwarding capability. The TCEM can be divided into two steps. The first step aims to reduce computational complexity by evaluating a device’s possibility of contacting the destination within a time constraint based on the transient cluster generated by our proposed Transient Cluster Detection Method (TCDM). The second step is to calculate a device’s probability of directly and indirectly forwarding data to the destination. The probability as a metric of evaluating a device’s forwarding capability can be used in different data forwarding strategies. Simulation results demonstrate that the TCEM-based data forwarding strategy outperforms other data forwarding strategies from the aspect of the proportion of the data delivery ratio to the data redundancy.

scholarly journals Bandwidth-Aware Traffic Sensing in Vehicular Networks with Mobile Edge Computing

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3547 ◽  
Author(s):  
Kong Ye ◽  
Penglin Dai ◽  
Xiao Wu ◽  
Yan Ding ◽  
Huanlai Xing ◽  
...  
Keyword(s):  
Data Collection ◽  
Vehicular Networks ◽  
Estimation Error ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Traffic Data ◽  
Norm Minimization ◽  
Traffic Data Collection ◽  
Dynamic Mobility ◽  
Traffic Systems

Traffic sensing is one of the promising applications to guarantee safe and efficient traffic systems in vehicular networks. However, due to the unique characteristics of vehicular networks, such as limited wireless bandwidth and dynamic mobility of vehicles, traffic sensing always faces high estimation error based on collected traffic data with missing elements and over-high communication cost between terminal users and central server. Hence, this paper investigates the traffic sensing system in vehicular networks with mobile edge computing (MEC), where each MEC server enables traffic data collection and recovery in its local server. On this basis, we formulate the bandwidth-constrained traffic sensing (BCTS) problem, aiming at minimizing the estimation error based on the collected traffic data. To tackle the BCTS problem, we first propose the bandwidth-aware data collection (BDC) algorithm to select the optimal uploaded traffic data by evaluating the priority of each road segment covered by the MEC server. Then, we propose the convex-based data recovery (CDR) algorithm to minimize estimation error by transforming the BCTS into an l 2 -norm minimization problem. Last but not the least, we implement the simulation model and conduct performance evaluation. The comprehensive simulation results verify the superiority of the proposed algorithm.

scholarly journals Influence of Crowd Participation Features on Mobile Edge Computing

2018 ◽  
Vol 10 (10) ◽  
pp. 94
Author(s):  
Peiyan Yuan ◽  
Xiaoxiao Pang ◽  
Xiaoyan Zhao
Keyword(s):  
Research Area ◽  
End Users ◽  
Edge Computing ◽  
Traffic Load ◽  
Mobile Edge Computing ◽  
Data Forwarding ◽  
Backbone Networks ◽  
Content Caching ◽  
Communication Paradigm

Mobile edge computing is a new communication paradigm, which stores content close to the end users, so as to reduce the backhaul delay and alleviate the traffic load of the backbone networks. Crowd participation is one of the most striking features of this technology, and it enables numerous interesting applications. The dynamics of crowd participation offer unprecedented opportunities for both content caching and data forwarding. In this paper, we investigate the influence of the dynamics of crowd participation, from the perspective of opportunistic caching and forwarding, and discuss how we can exploit such opportunities to allocate content and select relays efficiently. Some existing issues in this emerging research area are also discussed.

Load Balancing for Energy-Harvesting Mobile Edge Computing

2021 ◽  
Vol E104.A (1) ◽  
pp. 336-342
Author(s):  
Ping ZHAO ◽  
Jiawei TAO ◽  
Abdul RAUF ◽  
Fengde JIA ◽  
Longting XU
Keyword(s):  
Energy Harvesting ◽  
Load Balancing ◽  
Edge Computing ◽  
Mobile Edge Computing

Computation Offloading Game in Multiple UAV-Enabled Mobile Edge Computing Networks

2020 ◽  
Author(s):  
Yanling Ren ◽  
Zhibin Xie ◽  
Zhenfeng Ding ◽  
xiyuan sun ◽  
Jie Xia ◽  
...  
Keyword(s):  
Edge Computing ◽  
Computation Offloading ◽  
Mobile Edge Computing

Communication-efficient Offloading for Mobile Edge Computing in 5G Heterogeneous Networks

2020 ◽  
pp. 1-1
Author(s):  
Ping Zhou ◽  
Ke Shen ◽  
Neeraj Kumar ◽  
Yin Zhang ◽  
Mohammad Mehedi Hassan ◽  
...  
Keyword(s):  
Heterogeneous Networks ◽  
Edge Computing ◽  
Mobile Edge Computing

scholarly journals Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2628
Author(s):  
Mengxing Huang ◽  
Qianhao Zhai ◽  
Yinjie Chen ◽  
Siling Feng ◽  
Feng Shu
Keyword(s):  
Optimization Algorithm ◽  
Edge Computing ◽  
Solution Set ◽  
Whale Optimization Algorithm ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Multi Objective ◽  
Whale Optimization ◽  
Artificial Immune System Algorithm

Computation offloading is one of the most important problems in edge computing. Devices can transmit computation tasks to servers to be executed through computation offloading. However, not all the computation tasks can be offloaded to servers with the limitation of network conditions. Therefore, it is very important to decide quickly how many tasks should be executed on servers and how many should be executed locally. Only computation tasks that are properly offloaded can improve the Quality of Service (QoS). Some existing methods only focus on a single objection, and of the others some have high computational complexity. There still have no method that could balance the targets and complexity for universal application. In this study, a Multi-Objective Whale Optimization Algorithm (MOWOA) based on time and energy consumption is proposed to solve the optimal offloading mechanism of computation offloading in mobile edge computing. It is the first time that MOWOA has been applied in this area. For improving the quality of the solution set, crowding degrees are introduced and all solutions are sorted by crowding degrees. Additionally, an improved MOWOA (MOWOA2) by using the gravity reference point method is proposed to obtain better diversity of the solution set. Compared with some typical approaches, such as the Grid-Based Evolutionary Algorithm (GrEA), Cluster-Gradient-based Artificial Immune System Algorithm (CGbAIS), Non-dominated Sorting Genetic Algorithm III (NSGA-III), etc., the MOWOA2 performs better in terms of the quality of the final solutions.

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