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Optimizing Resources Allocation for Fog Computing-Based Internet of Things Networks - 2019

Optimizing Resources Allocation for Fog Computing-Based Internet of Things Networks

Research Area:  Fog Computing


In the wireless Internet of Things (IoT) networks with resource-constrained devices, fog computing has been introduced to deal with the computation-intensive applications at the edges of the networks. While fog computing decreases the computation delay and fronthaul traffic data, it also brings the severe challenge on complex resource allocation of the available computation and communication resources under the stringent quality of service (QoS) requirements. In this paper, we investigate the problem of tasks scheduling and heterogeneous resource allocation for multiple devices in the wireless IoT networks. The IoT devices that collect a massive amount of data need to make proper offloading decision to transfer the data to the fog computing nodes (FNs). Moreover, to support a massive number of device connections and transfer a huge amount of data with low latency and limited resource, we consider the deployment of non-orthogonal multiple access (NOMA) in IoT networks, which enables multiple IoT devices to simultaneously transmit data to the same FN at the same time, frequency, and code domain. We jointly optimize the allocation of resource blocks and transmit power of multiple IoT devices, subject to the respective QoS requirements. Furthermore, the optimization problem is formulated as a mixed-integer nonlinear programming problem to minimize the system energy consumption. Since it is an NP-hard problem, we introduce an improved genetic algorithm (IGA) to solve it. The simulation results show that the proposed scheme achieves good performance in throughput, delay, outage probability, and energy consumption.


Author(s) Name:  Xi Li; Yiming Liu; Hong Ji; Heli Zhang; Victor C. M. Leung

Journal name:  IEEE Access

Conferrence name:  

Publisher name:  IEEE

DOI:  10.1109/ACCESS.2019.2917557

Volume Information:  Volume: 7, Page(s): 64907 - 64922