A Cooperative Partial Computation Offloading Scheme for Mobile Edge Computing Enabled Internet of Things

Ning, Zhaolong; Dong, Peiran; Kong, Xiangjie; Xia, Feng

doi:10.1109/jiot.2018.2868616

Cited by 444 publications

(180 citation statements)

References 27 publications

Supporting

Mentioning

179

Contrasting

Unclassified

Order By: Relevance

“…Computation offloading design for MCC/MCE systems has been studied extensively in the literature, see recent surveys [14,15] and the references therein. Most existing works consider two main performance metrics for their designs, namely energy-efficiency [16][17][18][19] and delayefficiency [20][21][22][23]. Focusing on energy-efficiency, the authors of [16] develop partial offloading frameworks for multiuser MEC systems employing time division multiple access and frequencydivision multiple access.…”

Section: A Related Workmentioning

confidence: 99%

“…Considering computation offloading from the delay-efficiency point of view, an iterative heuristic algorithm to optimize the binary offloading decisions for minimization of the overall computation and transmission delay in a hierarchical fog-cloud system is proposed in [20]. The authors in [21] formulate the computation offloading and resource allocation problem as a studentproject-allocation game with the objective to maximize the ratio between the average offloaded data rate and the offloading cost at the users.…”

Section: A Related Workmentioning

confidence: 99%

“…However, references [7,9,[16][17][18][19][20][21][22]24] have not exploited DC for computation offloading.…”

Section: A Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Joint Data Compression and Computation Offloading in Hierarchical Fog-Cloud Systems

Nguyen

et al. 2020

IEEE Trans. Wireless Commun.

View full text Add to dashboard Cite

Data compression (DC) has the potential to significantly improve the computation offloading performance in hierarchical fog-cloud systems. However, it remains unknown how to optimally determine the compression ratio jointly with the computation offloading decisions and the resource allocation. This joint optimization problem is studied in the current paper where we aim to minimize the maximum weighted energy and service delay cost (WEDC) of all users. First, we consider a scenario where DC is performed only at the mobile users. We prove that the optimal offloading decisions have a threshold structure. Moreover, a novel three-step approach employing convexification techniques is developed to optimize the compression ratios and the resource allocation. Then, we address the more general design where DC is performed at both the mobile users and the fog server. We propose three efficient algorithms to overcome the strong coupling between the offloading decisions and the resource allocation. We show that the proposed optimal algorithm for DC at only the mobile users can reduce the WEDC by up to 65% compared to computation offloading strategies that do not leverage DC or use sub-optimal optimization approaches. Besides, the proposed algorithms for additional DC at the fog server can further reduce the WEDC.

show abstract

Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Joint Data Compression and Computation Offloading in Hierarchical Fog-Cloud Systems

Nguyen

et al. 2020

IEEE Trans. Wireless Commun.

View full text Add to dashboard Cite

show abstract

“…Since we prefer computing delay to be as low as possible, all available resources are assigned to a task when local computing. Assuming the computational capability of the device SU m i is f si which is fixed while computing . In the process of locally processing, the processing rate can be expanded to a variable considering the residual battery capacity of the mobile device and the deadline of the task.…”

Section: System Model and Offloading Problemmentioning

confidence: 99%

“…Assuming the computational capability of the device SU m i is f si which is fixed while computing. [32][33][34] In the process of locally processing, the processing rate can be expanded to a variable considering the residual battery capacity of the mobile device and the deadline of the task. Therefore, the local computing latency is…”

Section: Computation Modelmentioning

confidence: 99%

Edge computing and power control in NOMA‐enabled cognitive radio networks

Cheng

Chen

Liang

2019

Trans Emerging Tel Tech

View full text Add to dashboard Cite

Due to the limited computation resources of mobile devices in cognitive radio networks, the secondary users in the network can suffer from long executing time, which is not acceptable for latency‐sensitive and computation‐intensive tasks. To tackle this issue, this paper proposes to reduce the task computing latency for secondary networks by offloading the tasks to edge servers through leveraging mobile edge computing (MEC) that is emerging as a promising technology to augment the computation capacity of mobile devices. Specifically, under the conditions that the interference caused by secondary users is tolerable to primary user and within the available computation resources of the MEC server, the primary user and secondary users both can offload tasks to the MEC server through nonorthogonal multiple access. Thus, we jointly formulate the offloading decision and power control as an optimization problem, aiming at minimizing the overall computing latency for secondary networks. To overcome the computational complexity caused by the nonconvexity of the original problem, we transform the original problem to a solvable problem and decouple the transformed problem into the separate offloading decision and power control. An iterative algorithm is proposed based on block coordinate decent method to achieve the near‐optimal solution. Simulation results show that under the same parameters, such as the number of primary users, maximum transmit power, computational capability of the MEC server and the computational capability of the secondary users, the proposed NOMA‐enabled computation offloading scheme can effectively reduce the overall computing latency for the secondary network and improve the percentage of offloading secondary users than those of OMA‐enabled.

show abstract

Edge Computing and Artificial Intelligence‐Based Internet of Things for Industry 5.0: Framework, Challenges, Use Cases, and Research Directions

Ramanathan,

Ramamoorthy,

Grima

2024

Digital Twins in Industrial Production and Smart Manufacturing

View full text Add to dashboard Cite

A Cooperative Partial Computation Offloading Scheme for Mobile Edge Computing Enabled Internet of Things

Cited by 444 publications

References 27 publications

Joint Data Compression and Computation Offloading in Hierarchical Fog-Cloud Systems

Joint Data Compression and Computation Offloading in Hierarchical Fog-Cloud Systems

Edge computing and power control in NOMA‐enabled cognitive radio networks

Edge Computing and Artificial Intelligence‐Based Internet of Things for Industry 5.0: Framework, Challenges, Use Cases, and Research Directions

Contact Info

Product

Resources

About