A computing resource scheduling strategy of massive IoT devices in the mobile edge computing environment

Abstract

Aiming at the problem of scheduling computing resources for massive Internet of Things (IoT) devices, this paper proposes a scheduling strategy model based on mobile edge computing for massive IoT computing resources. First, the application scenarios are defined, the task offloading model and queue model are constructed. Then, task urgency and BS energy are considered to determine the optimization goal. Next, wolf colony algorithm is used to improve pheromone calculation so that the ant colony algorithm converges faster and is not easy to fall into local optimum when adjusting the computing resources of IoT devices, and then realizes the scheduling strategy of massive IoT devices. Finally, the experimental verification and comparative analysis of our proposed method are carried out. Experimental results show that proposed method is superior to the method based on Game Theory (GT) and the method based on Sub-Optimal Policy (SOP). Besides, the proposed method can offload more tasks under the same conditions. The average energy consumption of proposed method is lower in 60-240 GHz frequency band. Moreover, it appears to increase significantly in 60-120 GHz frequency band, and tends to be stable in 120-240 GHz frequency band.

Multipath Routing Implementation in SD-IoT Network Using OpenFlow-based Routing Metrics

“…The results of the path discovery from h1 to h2 using the DFS and the Dijkstra multipath algorithm, which are carried out five times, are presented in Tables 2 and 3. Based on five trials using the multipath DFS algorithm, a path was chosen with the smallest cost value in the 5th experiment with paths [13,6,2,3,11,20] and a cost of 1206.88. The alternative path was path [13,6,2,8,3,11,20] with a cost value of 1440.84, and path [13,6,2,9,3,11,20] with a cost value of 1444.02.…”

“…Based on five trials using the multipath DFS algorithm, a path was chosen with the smallest cost value in the 5th experiment with paths [13,6,2,3,11,20] and a cost of 1206.88. The alternative path was path [13,6,2,8,3,11,20] with a cost value of 1440.84, and path [13,6,2,9,3,11,20] with a cost value of 1444.02. Meanwhile, from five trials using the Dijkstra multipath algorithm, there was a path with the smallest cost value in the 4th experiment with path [13,6,2,3,11,20] and a cost value of 1263.05.…”

“…The alternative path was path [13,6,2,8,3,11,20] with a cost value of 1440.84, and path [13,6,2,9,3,11,20] with a cost value of 1444.02. Meanwhile, from five trials using the Dijkstra multipath algorithm, there was a path with the smallest cost value in the 4th experiment with path [13,6,2,3,11,20] and a cost value of 1263.05. Other alternative paths were on the path [13,6,2,8,3,11,20] with a cost value of 1498.42 and path [13,5,1,2,3,11,20] with a cost value of 1521.09.…”

“…IoT integrates sensors, embedded computing, and communication technologies to expand internet connectivity in sharing data, remote control, or sensing [1], [2]. However, IoT has limitations in processing the formation of the delivery path due to its complexity and limited computing resources, thus slowing down the path formation and affecting the data transmission process [3].…”