scholarly journals Energy-efficient offloading and resource allocation for mobile edge computing enabled mission-critical internet-of-things systems

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yaru Fu ◽  
Xiaolong Yang ◽  
Peng Yang ◽  
Angus K. Y. Wong ◽  
Zheng Shi ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Internet Of Things ◽  
Energy Saving ◽  
Energy Cost ◽  
Cost Minimization ◽  
Edge Computing ◽  
Mixed Integer ◽  
Mobile Edge Computing ◽  
Mission Critical ◽  
Iot Devices

AbstractThe energy cost minimization for mission-critical internet-of-things (IoT) in mobile edge computing (MEC) system is investigated in this work. Therein, short data packets are transmitted between the IoT devices and the access points (APs) to reduce transmission latency and prolong the battery life of the IoT devices. The effects of short-packet transmission on the radio resource allocation is explicitly revealed. We mathematically formulate the energy cost minimization problem as a mixed-integer non-linear programming (MINLP) problem, which is difficult to solve in an optimal way. More specifically, the difficulty is essentially derived from the coupling of the binary offloading variables and the resource management among all the IoT devices. For analytical tractability, we decouple the mixed-integer and non-convex optimization problem into two sub-problems, namely, the task offloading decision-making and the resource optimization problems, respectively. It is proved that the resource allocation problem for IoT devices under the fixed offloading strategy is convex. On this basis, an iterative algorithm is designed, whose performance is comparable to the best solution for exhaustive search, and aims to jointly optimize the offloading strategy and resource allocation. Simulation results verify the convergence performance and energy-saving function of the designed joint optimization algorithm. Compared with the extensive baselines under comprehensive parameter settings, the algorithm has better energy-saving effects.

scholarly journals Energy-Efficient Offloading and Resource Allocation for Mobile Edge Computing Enabled Mission-Critical Internet-of-Things Systems

2020 ◽  
Author(s):  
Yaru Fu ◽  
Xiaolong Yang ◽  
Peng Yang ◽  
Angus K. Y. Wong ◽  
Zheng Shi ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Internet Of Things ◽  
Energy Saving ◽  
Energy Cost ◽  
Cost Minimization ◽  
Edge Computing ◽  
Mixed Integer ◽  
Mobile Edge Computing ◽  
Mission Critical ◽  
Iot Devices

Abstract The energy cost minimization for mission-critical internet-of-things (IoT) in mobile edge computing (MEC) system is investigated in this work. Therein, short data packets are transmitted between the IoT devices and the access points (APs) to reduce transmission latency and prolong the battery life of the IoT devices. The effects of short-packet transmission on the radio resource allocation is explicitly revealed. We mathematically formulate the energy cost minimization problem as a mixed-integer non-linear programming (MINLP) problem, which is difficult to solve in an optimal way. More specifically, the difficulty is essentially derived from the coupling of the binary offloading variables and the resource management among all the IoT devices. For analytical tractability, we decouple the mixed-integer and non-convex optimization problem into two sub-problems, namely, the task offloading decision-making and the resource optimization problems, respectively. It is proved that the resource allocation problem for IoT devices under the fixed offloading strategy is convex. On this basis, an iterative algorithm is designed, whose performance is comparable to the best solution for exhaustive search, and aims to jointly optimize the offloading strategy and resource allocation. Simulation results verify the convergence performance and energy-saving function of the designed joint optimization algorithm. Compared with the extensive baselines under comprehensive parameter settings, the algorithm has better energy-saving effects.

scholarly journals Resource Allocation in Wireless-Powered Mobile Edge Computing Systems for Internet of Things Applications

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 206 ◽  
Author(s):  
Bingjie Liu ◽  
Haitao Xu ◽  
Xianwei Zhou
Keyword(s):  
Resource Allocation ◽  
Internet Of Things ◽  
Wireless Power Transfer ◽  
Edge Computing ◽  
Game Model ◽  
Mobile Edge Computing ◽  
Power Transfer ◽  
Wireless Devices ◽  
Wireless Power ◽  
Iot Applications

Wireless devices in Internet of Things (IoT) applications, such as wireless sensors and Radio Frequency Identifications (RFIDs), are faced with challenges of heavy computation tasks and limited energy, which can be solved by the importation of mobile edge computing (MEC) and wireless power transfer (WPT) techniques. As MEC can effectively enhance computation capability, and the wireless power transfer can ensure a sustainable supply of energy, it has drawn significant research interest in IoT applications. In this paper, we will study the resource allocation problem in the wireless-powered MEC system for IoT applications with one access point (AP) and many other wireless devices, and propose a Stackelberg dynamic game model to obtain the optimal allocated resource for the nodes in the IoT environment. The AP is a wireless power source that can charge wireless devices based on wireless power transfer techniques. The AP is also integrated with a MEC server that can carry out computation tasks that offload from wireless devices. The wireless devices can use the harvested energy to execute and offload computation tasks to the AP. Based on the proposed game model, the AP and wireless devices can control their optimal transmit power for energy transfer, and computation tasks offloading to the AP, respectively. The numerical simulation results show the correctness and effectiveness of the proposed model.

scholarly journals Mobile edge computing resource allocation: A joint Stackelberg game and matching strategy

2019 ◽  
Vol 15 (7) ◽  
pp. 155014771986155 ◽  
Author(s):  
Shaoyong Guo ◽  
Xing Hu ◽  
Gangsong Dong ◽  
Wencui Li ◽  
Xuesong Qiu
Keyword(s):  
Resource Allocation ◽  
Internet Of Things ◽  
Smart Home ◽  
Stackelberg Game ◽  
Edge Computing ◽  
Hierarchical Architecture ◽  
Smart Devices ◽  
Low Latency ◽  
Mobile Edge Computing ◽  
Computing Resource

Mobile edge computing has attracted great interests in the popularity of fifth-generation (5G) networks and Internet of Things. It aims to supply low-latency and high-interaction services for delay-sensitive applications. Utilizing mobile edge computing with Smart Home, which is one of the most important fields of Internet of Things, is a method to satisfy users’ demand for higher computing power and storage capacity. However, due to limited computing resource, how to improve efficiency of resource allocation is a challenge. In this article, we propose a hierarchical architecture in Smart Home with mobile edge computing, providing low-latency services and promoting edge process for smart devices. Based on that, a Stackelberg Game is designed in order to allocate computing resource to devices efficiently. Then, one-to-many matching is established to handle resource allocation problems. It is proved that the allocation strategy can optimize the utility of mobile edge computing server and improve allocating efficiency. Simulation results show the effectiveness of the proposed strategy compared with schemes based on auction game, and present performance with different changing system parameters.

Frequency Resource Allocation and Interference Management in Mobile Edge Computing for an Internet of Things System

2019 ◽  
Vol 6 (3) ◽  
pp. 4910-4920 ◽  
Author(s):  
Woongsoo Na ◽  
Seonmin Jang ◽  
Yoonseong Lee ◽  
Laihyuk Park ◽  
Nhu-Ngoc Dao ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Internet Of Things ◽  
Interference Management ◽  
Edge Computing ◽  
Mobile Edge Computing

scholarly journals Joint Node Selection and Resource Allocation for Task Offloading in Scalable Vehicle-Assisted Multi-Access Edge Computing

Symmetry ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 58 ◽  
Author(s):  
Xuan-Qui Pham ◽  
Tien-Dung Nguyen ◽  
VanDung Nguyen ◽  
Eui-Nam Huh
Keyword(s):  
Resource Allocation ◽  
High Reliability ◽  
Resource Limitation ◽  
Optimization Techniques ◽  
Edge Computing ◽  
Mixed Integer ◽  
Node Selection ◽  
Computing Services ◽  
Iot Devices ◽  
Multi Access

The resource limitation of multi-access edge computing (MEC) is one of the major issues in order to provide low-latency high-reliability computing services for Internet of Things (IoT) devices. Moreover, with the steep rise of task requests from IoT devices, the requirement of computation tasks needs dynamic scalability while using the potential of offloading tasks to mobile volunteer nodes (MVNs). We, therefore, propose a scalable vehicle-assisted MEC (SVMEC) paradigm, which cannot only relieve the resource limitation of MEC but also enhance the scalability of computing services for IoT devices and reduce the cost of using computing resources. In the SVMEC paradigm, a MEC provider can execute its users’ tasks by choosing one of three ways: (i) Do itself on local MEC, (ii) offload to the remote cloud, and (iii) offload to the MVNs. We formulate the problem of joint node selection and resource allocation as a Mixed Integer Nonlinear Programming (MINLP) problem, whose major objective is to minimize the total computation overhead in terms of the weighted-sum of task completion time and monetary cost for using computing resources. In order to solve it, we adopt alternative optimization techniques by decomposing the original problem into two sub-problems: Resource allocation sub-problem and node selection sub-problem. Simulation results demonstrate that our proposed scheme outperforms the existing schemes in terms of the total computation overhead.

Service Provisioning for Multi-source IoT Applications in Mobile Edge Computing

2022 ◽  
Vol 18 (2) ◽  
pp. 1-25
Author(s):  
Jing Li ◽  
Weifa Liang ◽  
Zichuan Xu ◽  
Xiaohua Jia ◽  
Wanlei Zhou
Keyword(s):  
Minimization Problem ◽  
Optimization Problems ◽  
Cost Minimization ◽  
Edge Computing ◽  
Service Provisioning ◽  
Mobile Edge Computing ◽  
Iot Applications ◽  
Iot Devices ◽  
Cost Minimization Problem ◽  
The Cost

We are embracing an era of Internet of Things (IoT). The latency brought by unstable wireless networks caused by limited resources of IoT devices seriously impacts the quality of services of users, particularly the service delay they experienced. Mobile Edge Computing (MEC) technology provides promising solutions to delay-sensitive IoT applications, where cloudlets (edge servers) are co-located with wireless access points in the proximity of IoT devices. The service response latency for IoT applications can be significantly shortened due to that their data processing can be performed in a local MEC network. Meanwhile, most IoT applications usually impose Service Function Chain (SFC) enforcement on their data transmission, where each data packet from its source gateway of an IoT device to the destination (a cloudlet) of the IoT application must pass through each Virtual Network Function (VNF) in the SFC in an MEC network. However, little attention has been paid on such a service provisioning of multi-source IoT applications in an MEC network with SFC enforcement. In this article, we study service provisioning in an MEC network for multi-source IoT applications with SFC requirements and aiming at minimizing the cost of such service provisioning, where each IoT application has multiple data streams from different sources to be uploaded to a location (cloudlet) in the MEC network for aggregation, processing, and storage purposes. To this end, we first formulate two novel optimization problems: the cost minimization problem of service provisioning for a single multi-source IoT application, and the service provisioning problem for a set of multi-source IoT applications, respectively, and show that both problems are NP-hard. Second, we propose a service provisioning framework in the MEC network for multi-source IoT applications that consists of uploading stream data from multiple sources of the IoT application to the MEC network, data stream aggregation and routing through the VNF instance placement and sharing, and workload balancing among cloudlets. Third, we devise an efficient algorithm for the cost minimization problem built upon the proposed service provisioning framework, and further extend the solution for the service provisioning problem of a set of multi-source IoT applications. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms are promising.

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