scholarly journals Research on Task-Oriented Computation Offloading Decision in Space-Air-Ground Integrated Network

2021 ◽  
Vol 13 (5) ◽  
pp. 128
Author(s):  
Jun Liu ◽  
Xiaohui Lian ◽  
Chang Liu
Keyword(s):  
Energy Consumption ◽  
Decision Process ◽  
Learning Algorithm ◽  
Low Cost ◽  
Transition Process ◽  
Computation Offloading ◽  
Processing Efficiency ◽  
Integrated Network ◽  
Decision Scheme ◽  
Task Offloading

In Space–Air–Ground Integrated Networks (SAGIN), computation offloading technology is a new way to improve the processing efficiency of node tasks and improve the limitation of computing storage resources. To solve the problem of large delay and energy consumption cost of task computation offloading, which caused by the complex and variable network offloading environment and a large amount of offloading tasks, a computation offloading decision scheme based on Markov and Deep Q Networks (DQN) is proposed. First, we select the optimal offloading network based on the characteristics of the movement of the task offloading process in the network. Then, the task offloading process is transformed into a Markov state transition process to build a model of the computational offloading decision process. Finally, the delay and energy consumption weights are introduced into the DQN algorithm to update the computation offloading decision process, and the optimal offloading decision under the low cost is achieved according to the task attributes. The simulation results show that compared with the traditional Lyapunov-based offloading decision scheme and the classical Q-learning algorithm, the delay and energy consumption are respectively reduced by 68.33% and 11.21%, under equal weights when the offloading task volume exceeds 500 Mbit. Moreover, compared with offloading to edge nodes or backbone nodes of the network alone, the proposed mixed offloading model can satisfy more than 100 task requests with low energy consumption and low delay. It can be seen that the computation offloading decision proposed in this paper can effectively reduce the delay and energy consumption during the task computation offloading in the Space–Air–Ground Integrated Network environment, and can select the optimal offloading sites to execute the tasks according to the characteristics of the task itself.

scholarly journals Computation offloading through mobile vehicles in IoT-edge-cloud network

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Jun Long ◽  
Yueyi Luo ◽  
Xiaoyu Zhu ◽  
Entao Luo ◽  
Mingfeng Huang
Keyword(s):  
Reinforcement Learning ◽  
Energy Consumption ◽  
Low Cost ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Challenging Problem ◽  
Learning Technique ◽  
Cloud Network ◽  
The City ◽  
Task Offloading

AbstractWith the developing of Internet of Things (IoT) and mobile edge computing (MEC), more and more sensing devices are widely deployed in the smart city. These sensing devices generate various kinds of tasks, which need to be sent to cloud to process. Usually, the sensing devices do not equip with wireless modules, because it is neither economical nor energy saving. Thus, it is a challenging problem to find a way to offload tasks for sensing devices. However, many vehicles are moving around the city, which can communicate with sensing devices in an effective and low-cost way. In this paper, we propose a computation offloading scheme through mobile vehicles in IoT-edge-cloud network. The sensing devices generate tasks and transmit the tasks to vehicles, then the vehicles decide to compute the tasks in the local vehicle, MEC server or cloud center. The computation offloading decision is made based on the utility function of the energy consumption and transmission delay, and the deep reinforcement learning technique is adopted to make decisions. Our proposed method can make full use of the existing infrastructures to implement the task offloading of sensing devices, the experimental results show that our proposed solution can achieve the maximum reward and decrease delay.

scholarly journals Computation offloading through mobile vehicles in IoT-edge-cloud network

2020 ◽  
Author(s):  
Jun Long ◽  
Yueyi Luo ◽  
Xiaoyu Zhu ◽  
Entao Luo ◽  
Mingfeng Huang
Keyword(s):  
Reinforcement Learning ◽  
Energy Consumption ◽  
Low Cost ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Challenging Problem ◽  
Learning Technique ◽  
Cloud Network ◽  
The City ◽  
Task Offloading

Abstract With the developing of Internet of Things (IoT) and mobile edge computing (MEC), more and more sensing devices are widely deployed in the smart city. These sensing devices generate various kinds of tasks, which need to be sent to cloud to process. Usually, the sensing devices do not equip with wireless modules, because it is neither economical nor energy saving. Thus, it is a challenging problem to find a way to offload tasks for sensing devices. However, many vehicles are moving around the city, which can communicate with sensing devices in an effective and low-cost way. In this paper, we propose a computation offloading scheme through mobile vehicles in IoT-edge-cloud network. The sensing devices generate tasks and transmit the tasks to vehicles, then the vehicles decide to compute the tasks in the local vehicle, MEC server or cloud center. The computation offloading decision is made based on the utility function of the energy consumption and transmission delay, and the deep reinforcement learning technique is adopted to make decisions. Our proposed method can make full use of the existing infrastructures to implement the task offloading of sensing devices, the experimental results show that our proposed solution can achieve the maximum reward and decrease delay.

scholarly journals Computation offloading through mobile vehicles in IoT-edge-cloud network

2020 ◽  
Author(s):  
Jun Long ◽  
Yueyi Luo ◽  
Xiaoyu Zhu ◽  
Entao Luo ◽  
Mingfeng Huang
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Low Cost ◽  
Wireless Sensor ◽  
Computation Offloading ◽  
Challenging Problem ◽  
Learning Technique ◽  
Cloud Network ◽  
The City ◽  
Task Offloading

Abstract With the developing of Wireless Sensor Networks (WSNs), more and more sensing devices are widely deployed in the smart city. These sensing devices generate various kinds of tasks, which need to be sent to cloud to process. Usually, the sensing devices do not equip with wireless modules, because it is neither economical nor energy saving. Thus, it is a challenging problem to find a way to offload tasks for sensing devices. However, many vehicles are moving around the city, which can communicate with sensing devices in an effective and low-cost way. In this paper, we propose a computation offloading scheme through mobile vehicles in IoT-edge-cloud network. The sensing devices generate tasks and transmit the tasks to vehicles, then the vehicles decide to compute the tasks in the local vehicle, MEC server or cloud center. The computation offloading decision is made based on the utility function of the energy consumption and transmission delay, and the deep reinforcement learning technique is adopted to make decisions. Our proposed method can make full use of the existing infrastructures to implement the task offloading of sensing devices, the experimental results show that our proposed solution can achieve the maximum reward and decrease delay.

scholarly journals Task Offloading and Scheduling Strategy for Intelligent Prosthesis in Mobile Edge Computing Environment

2022 ◽  
Vol 2022 ◽  
pp. 1-13
Author(s):  
Ping Qi
Keyword(s):  
Deep Learning ◽  
Energy Consumption ◽  
Learning Algorithm ◽  
Problem Definition ◽  
Edge Computing ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Intent Recognition ◽  
Scheduling Strategy ◽  
Task Offloading

Traditional intent recognition algorithms of intelligent prosthesis often use deep learning technology. However, deep learning’s high accuracy comes at the expense of high computational and energy consumption requirements. Mobile edge computing is a viable solution to meet the high computation and real-time execution requirements of deep learning algorithm on mobile device. In this paper, we consider the computation offloading problem of multiple heterogeneous edge servers in intelligent prosthesis scenario. Firstly, we present the problem definition and the detail design of MEC-based task offloading model for deep neural network. Then, considering the mobility of amputees, the mobility-aware energy consumption model and latency model are proposed. By deploying the deep learning-based motion intent recognition algorithm on intelligent prosthesis in a real-world MEC environment, the effectiveness of the task offloading and scheduling strategy is demonstrated. The experimental results show that the proposed algorithms can always find the optimal task offloading and scheduling decision.

scholarly journals The Reinforcement Learning Method in Deciding on Task Offloading and Energy Consumption Management at the Edge of IoT Network

2021 ◽  
Author(s):  
Asghar Mohammadian ◽  
Houman Zarrabi ◽  
Sam Jabbehdari ◽  
Amir Masoud Rahmani
Keyword(s):  
Reinforcement Learning ◽  
Energy Consumption ◽  
Processing Speed ◽  
Learning Algorithm ◽  
Energy Resources ◽  
Operating Efficiency ◽  
Network Load ◽  
The Status ◽  
Time And Energy ◽  
Task Offloading

Abstract In this paper, using edge processing in IoT network ,a method to decide on task offloading to edge devices and improve management of consuming energy in network of edge devices is introduced. First, by defining the problem of maximizing utility and then by decomposing it based on the status of task offloading from end devices to smart gateway with the lowest battery consumption and the possible lowest use of the communication bandwidth, independent optimal models are obtained and then combining. Then the general problem of maximizing the usefulness and increasing the lifetime of the end devices with restrictions of processing time and energy resources will be obtained. Due to the unknown environment of the problem and how the end devices and the edge of the network, an iterative reinforcement learning algorithm is used to generate the optimal answer in order to maximize the utility gain.The results show the existence of processing overhead and network load with increasing number of devices. The proposed method, while improving energy consumption in existence of a small number of devices in end of edge, reduces latency and increases processing speed and maximizes system performance compared to the central cloud system. The operating efficiency of the whole system is improved by 36% and the energy consumption at the edge of the network is optimized by 12.5%. It should be noted that, with the addition of a large number of end devices, our method outperforms similar works.

scholarly journals A Cluster-Based Optimal Computation Offloading Decision Mechanism Using RL in the IIoT Field

2021 ◽  
Vol 12 (1) ◽  
pp. 384
Author(s):  
Seolwon Koo ◽  
Yujin Lim
Keyword(s):  
Energy Consumption ◽  
Job Shop ◽  
Learning Algorithm ◽  
D2d Communication ◽  
Computation Offloading ◽  
Task Completion ◽  
Delay Constraint ◽  
Learning Agent ◽  
Computational Capability ◽  
Cooperative Computing

In the Industrial Internet of Things (IIoT), various tasks are created dynamically because of the small quantity batch production. Hence, it is difficult to execute tasks only with devices that have limited battery lives and computation capabilities. To solve this problem, we adopted the mobile edge computing (MEC) paradigm. However, if there are numerous tasks to be processed on the MEC server (MECS), it may not be suitable to deal with all tasks in the server within a delay constraint owing to the limited computational capability and high network overhead. Therefore, among cooperative computing techniques, we focus on task offloading to nearby devices using device-to-device (D2D) communication. Consequently, we propose a method that determines the optimal offloading strategy in an MEC environment with D2D communication. We aim to minimize the energy consumption of the devices and task execution delay under certain delay constraints. To solve this problem, we adopt a Q-learning algorithm that is part of reinforcement learning (RL). However, if one learning agent determines whether to offload tasks from all devices, the computing complexity of that agent increases tremendously. Thus, we cluster the nearby devices that comprise the job shop, where each cluster’s head determines the optimal offloading strategy for the tasks that occur within its cluster. Simulation results show that the proposed algorithm outperforms the compared methods in terms of device energy consumption, task completion rate, task blocking rate, and throughput.

scholarly journals Deep Reinforcement Learning for Computation Offloading and Resource Allocation in Unmanned-Aerial-Vehicle Assisted Edge Computing

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6499
Author(s):  
Shuyang Li ◽  
Xiaohui Hu ◽  
Yongwen Du
Keyword(s):  
Resource Allocation ◽  
Reinforcement Learning ◽  
Energy Consumption ◽  
Unmanned Aerial Vehicle ◽  
Learning Algorithm ◽  
End Users ◽  
Edge Computing ◽  
Computation Offloading ◽  
Multiple Users ◽  
Aerial Vehicle

Computation offloading technology extends cloud computing to the edge of the access network close to users, bringing many benefits to terminal devices with limited battery and computational resources. Nevertheless, the existing computation offloading approaches are challenging to apply to specific scenarios, such as the dense distribution of end-users and the sparse distribution of network infrastructure. The technological revolution in the unmanned aerial vehicle (UAV) and chip industry has granted UAVs more computing resources and promoted the emergence of UAV-assisted mobile edge computing (MEC) technology, which could be applied to those scenarios. However, in the MEC system with multiple users and multiple servers, making reasonable offloading decisions and allocating system resources is still a severe challenge. This paper studies the offloading decision and resource allocation problem in the UAV-assisted MEC environment with multiple users and servers. To ensure the quality of service for end-users, we set the weighted total cost of delay, energy consumption, and the size of discarded tasks as our optimization objective. We further formulate the joint optimization problem as a Markov decision process and apply the soft actor–critic (SAC) deep reinforcement learning algorithm to optimize the offloading policy. Numerical simulation results show that the offloading policy optimized by our proposed SAC-based dynamic computing offloading (SACDCO) algorithm effectively reduces the delay, energy consumption, and size of discarded tasks for the UAV-assisted MEC system. Compared with the fixed local-UAV scheme in the specific simulation setting, our proposed approach reduces system delay and energy consumption by approximately 50% and 200%, respectively.

scholarly journals A Light Weight Multi-Objective Task Offloading Optimization for Vehicular Fog Computing

2021 ◽  
Vol 17 (1) ◽  
pp. 1-10
Author(s):  
Sura Abdullah ◽  
Adnan Jabir
Keyword(s):  
Energy Consumption ◽  
Data Storage ◽  
Fog Computing ◽  
High Volume ◽  
Computation Offloading ◽  
Destination Node ◽  
Volume Data ◽  
Multi Objective ◽  
Minimum Path ◽  
Task Offloading

Most Internet of Vehicles (IoV) applications are delay-sensitive and require resources for data storage and tasks processing, which is very difficult to afford by vehicles. Such tasks are often offloaded to more powerful entities, like cloud and fog servers. Fog computing is decentralized infrastructure located between data source and cloud, supplies several benefits that make it a non-frivolous extension of the cloud. The high volume data which is generated by vehicles’ sensors and also the limited computation capabilities of vehicles have imposed several challenges on VANETs systems. Therefore, VANETs is integrated with fog computing to form a paradigm namely Vehicular Fog Computing (VFC) which provide low-latency services to mobile vehicles. Several studies have tackled the task offloading problem in the VFC field. However, recent studies have not carefully addressed the transmission path to the destination node and did not consider the energy consumption of vehicles. This paper aims to optimize the task offloading process in the VFC system in terms of latency and energy objectives under deadline constraint by adopting a Multi-Objective Evolutionary Algorithm (MOEA). Road Side Units (RSUs) x-Vehicles Mutli-Objective Computation offloading method (RxV-MOC) is proposed, where an elite of vehicles are utilized as fog nodes for tasks execution and all vehicles in the system are utilized for tasks transmission. The well-known Dijkstra's algorithm is adopted to find the minimum path between each two nodes. The simulation results show that the RxV-MOC has reduced significantly the energy consumption and latency for the VFC system in comparison with First-Fit algorithm, Best-Fit algorithm, and the MOC method.

scholarly journals Normative Aspects of Nudging in the International Sphere

AJIL Unbound ◽  
2021 ◽  
Vol 115 ◽  
pp. 263-267
Author(s):  
Doron Teichman ◽  
Eyal Zamir
Keyword(s):  
Energy Consumption ◽  
Organ Donation ◽  
Low Cost ◽  
National Level ◽  
The Past ◽  
The People ◽  
Posthumous Organ Donation ◽  
Regulatory Problems

The use of nudges—“low-cost, choice-preserving, behaviorally informed approaches to regulatory problems”—has become quite popular at the national level in the past decade or so. Examples include changing the default concerning employees’ saving for retirement in a bid to encourage such saving; altering the default about consent to posthumous organ donation to increase the supply of organs for transplantation; and informing people about other people's energy consumption to spur them to reduce theirs. Nudges are therefore used to promote the welfare of the people being nudged, and of society at large. However, the use of nudges has sparked a lively normative debate. When turning to the international arena, new arguments for and against nudges can be raised. This essay focuses on the normative aspects of using nudges in the international arena.

Sitsen: Passive sitting posture sensing based on wireless devices

2021 ◽  
Vol 17 (7) ◽  
pp. 155014772110248
Author(s):  
Miaoyu Li ◽  
Zhuohan Jiang ◽  
Yutong Liu ◽  
Shuheng Chen ◽  
Marcin Wozniak ◽  
...  
Keyword(s):  
Radio Frequency ◽  
Radio Frequency Identification ◽  
High Performance ◽  
Learning Algorithm ◽  
Low Cost ◽  
Recognition System ◽  
Sitting Posture ◽  
Average Accuracy ◽  
Phase Variations ◽  
Window Approach

Physical health diseases caused by wrong sitting postures are becoming increasingly serious and widespread, especially for sedentary students and workers. Existing video-based approaches and sensor-based approaches can achieve high accuracy, while they have limitations like breaching privacy and relying on specific sensor devices. In this work, we propose Sitsen, a non-contact wireless-based sitting posture recognition system, just using radio frequency signals alone, which neither compromises the privacy nor requires using various specific sensors. We demonstrate that Sitsen can successfully recognize five habitual sitting postures with just one lightweight and low-cost radio frequency identification tag. The intuition is that different postures induce different phase variations. Due to the received phase readings are corrupted by the environmental noise and hardware imperfection, we employ series of signal processing schemes to obtain clean phase readings. Using the sliding window approach to extract effective features of the measured phase sequences and employing an appropriate machine learning algorithm, Sitsen can achieve robust and high performance. Extensive experiments are conducted in an office with 10 volunteers. The result shows that our system can recognize different sitting postures with an average accuracy of 97.02%.

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