Optimal edge gateway deployment in internet of things based on simulated annealing with adaptive external penalty
Abstract In the large scale Internet of things, edge gateway (EG) deployment is used to find the minimal number of gateways required in the network and their optimal locations under the design constraints to meet different service requirements, which is one significant issue for improving network pe...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-10-01
|
| Series: | IET Communications |
| Online Access: | https://doi.org/10.1049/cmu2.12244 |
| Summary: | Abstract In the large scale Internet of things, edge gateway (EG) deployment is used to find the minimal number of gateways required in the network and their optimal locations under the design constraints to meet different service requirements, which is one significant issue for improving network performances. We formulate the EG deployment problem as a k‐median problem with some constraints, which is known as a NP‐hard problem. For that, we propose a new heuristic based on simulated annealing with external penalty function (SA‐AEP) for its solution. The external penalty function is used to transform the multi‐constrained optimization problem into a single constraint one. Also the complexity of proposed heuristic algorithm is analyzed. In addition, in order to evaluate the proposed algorithm, we compare it with an adaptive variable‐length particle swarm optimization algorithm with varying lengths. Simulation results show that the proposed SA‐AEP algorithm has much better performances on network cost and efficiency than other algorithms when the higher service rate is required. |
|---|---|
| ISSN: | 1751-8628 1751-8636 |