TY - JOUR
T1 - Collaborative Computation Offloading and Resource Allocation in Multi-UAV-Assisted IoT Networks
T2 - A Deep Reinforcement Learning Approach
AU - Seid, Abegaz Mohammed
AU - Boateng, Gordon Owusu
AU - Anokye, Stephen
AU - Kwantwi, Thomas
AU - Sun, Guolin
AU - Liu, Guisong
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2021/8/1
Y1 - 2021/8/1
N2 - In the fifth-generation (5G) wireless networks, Edge-Internet-of-Things (EIoT) devices are envisioned to generate huge amounts of data. Due to the limitation of computation capacity and battery life of devices, all tasks cannot be processed by these devices. However, mobile-edge computing (MEC) is a very promising solution enabling offloading of tasks to nearby MEC servers to improve quality of service. Also, during emergency situations in areas where network failure exists, unmanned aerial vehicles (UAVs) can be deployed to restore the network by acting as Aerial Base Stations and computational nodes for the edge network. In this article, we consider a central network controller who trains observations and broadcasts the trained data to a multi-UAV cluster network. Each UAV cluster head acts as an agent and autonomously allocates resources to EIoT devices in a decentralized fashion. We propose model-free deep reinforcement learning (DRL)-based collaborative computation offloading and resource allocation (CCORA-DRL) scheme in an aerial to ground (A2G) network for emergency situations, which can control the continuous action space. Each agent learns efficient computation offloading policies independently in the network and checks the statuses of the UAVs through Jain's Fairness index. The objective is minimizing task execution delay and energy consumption and acquiring an efficient solution by adaptive learning from the dynamic A2G network. Simulation results reveal that our scheme through deep deterministic policy gradient, effectively learns the optimal policy, outperforming A3C, deep $Q$ -network and greedy-based offloading for local computation in stochastic dynamic environments.
AB - In the fifth-generation (5G) wireless networks, Edge-Internet-of-Things (EIoT) devices are envisioned to generate huge amounts of data. Due to the limitation of computation capacity and battery life of devices, all tasks cannot be processed by these devices. However, mobile-edge computing (MEC) is a very promising solution enabling offloading of tasks to nearby MEC servers to improve quality of service. Also, during emergency situations in areas where network failure exists, unmanned aerial vehicles (UAVs) can be deployed to restore the network by acting as Aerial Base Stations and computational nodes for the edge network. In this article, we consider a central network controller who trains observations and broadcasts the trained data to a multi-UAV cluster network. Each UAV cluster head acts as an agent and autonomously allocates resources to EIoT devices in a decentralized fashion. We propose model-free deep reinforcement learning (DRL)-based collaborative computation offloading and resource allocation (CCORA-DRL) scheme in an aerial to ground (A2G) network for emergency situations, which can control the continuous action space. Each agent learns efficient computation offloading policies independently in the network and checks the statuses of the UAVs through Jain's Fairness index. The objective is minimizing task execution delay and energy consumption and acquiring an efficient solution by adaptive learning from the dynamic A2G network. Simulation results reveal that our scheme through deep deterministic policy gradient, effectively learns the optimal policy, outperforming A3C, deep $Q$ -network and greedy-based offloading for local computation in stochastic dynamic environments.
KW - Collaborative computation offloading
KW - deep reinforcement learning (DRL)
KW - Edge Internet of Things (EIoT)
KW - IoT network
KW - multi-UAV network
KW - resource allocation
UR - http://www.scopus.com/inward/record.url?scp=85102274239&partnerID=8YFLogxK
U2 - 10.1109/JIOT.2021.3063188
DO - 10.1109/JIOT.2021.3063188
M3 - Article
AN - SCOPUS:85102274239
SN - 2327-4662
VL - 8
SP - 12203
EP - 12218
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 15
M1 - 9366889
ER -