TY - JOUR
T1 - Wireless Powered Mobile Edge Computing Systems: Simultaneous Time Allocation and Offloading Policies
AU - Irshad, Amna
AU - Abbas, Ziaul Haq
AU - Ali, Zaiwar
AU - Abbas, Ghulam
AU - Baker, Thar
AU - Al-Jumeily, Dhiya
PY - 2021/4/18
Y1 - 2021/4/18
N2 - To improve the computational power and limited battery capacity of mobile devices (MDs), wireless powered mobile edge computing (MEC) systems are gaining much importance. In this paper, we consider a wireless powered MEC system composed of one MD and a hybrid access point (HAP) attached to MEC. Our objective is to achieve a joint time allocation and offloading policy simultaneously. We propose a cost function that considers both the energy consumption and the time delay of an MD. The proposed algorithm, joint time allocation and offload policy (JTAOP), is used to train a neural network for reducing the complexity of our algorithm that depends on the resolution of time and the number of components in a task. The numerical results are compared with three benchmark schemes, namely, total local computation, total offloading and partial offloading. Simulations show that the proposed algorithm performs better in producing the minimum cost and energy consumption as compared to the considered benchmark schemes.
AB - To improve the computational power and limited battery capacity of mobile devices (MDs), wireless powered mobile edge computing (MEC) systems are gaining much importance. In this paper, we consider a wireless powered MEC system composed of one MD and a hybrid access point (HAP) attached to MEC. Our objective is to achieve a joint time allocation and offloading policy simultaneously. We propose a cost function that considers both the energy consumption and the time delay of an MD. The proposed algorithm, joint time allocation and offload policy (JTAOP), is used to train a neural network for reducing the complexity of our algorithm that depends on the resolution of time and the number of components in a task. The numerical results are compared with three benchmark schemes, namely, total local computation, total offloading and partial offloading. Simulations show that the proposed algorithm performs better in producing the minimum cost and energy consumption as compared to the considered benchmark schemes.
U2 - 10.3390/electronics10080965
DO - 10.3390/electronics10080965
M3 - Article
SN - 2079-9292
VL - 10
JO - Electronics
JF - Electronics
IS - 8
M1 - 965
ER -