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.
Highlights
Wireless sensor networks (WSNs) is widely deployed in the smart city
In [28], the authors aimed at task offloading and service orchestration based on software defined network technology, the tasks were offloaded to mobile edge computing (MEC) server or cloud server according to the required resource and allowed latency
The tasks can be processed in the vehicle, MEC server or cloud center
Summary
Wireless sensor networks (WSNs) is widely deployed in the smart city. There exists a great amount of sensing devices in the urban environment, the sensing devices in WSNs can sense the environmental data [1,2,3,4]. In [28], the authors aimed at task offloading and service orchestration based on software defined network technology, the tasks were offloaded to MEC server or cloud server according to the required resource and allowed latency. In [29], the authors proposed a task offloading and resource allocation algorithm based on software defined network technology in ultra-dense network, the battery capacity of the devices was considered as an impact factor of task offloading decision. None of these works consider the offloading costs of sensing devices, it is better to design a cost-effective way to solve the computation offloading problem for sensing devices
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: EURASIP Journal on Wireless Communications and Networking
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.