The optimal 5G base station location of the wireless sensor network considering timely reliability

To ensure the timely reliability of the data packets transmitted in the intelligent Internet of Things, many 5 G base stations must be established as relay nodes. Thus, how to meet the transmission requirements with the minimum building cost has become an urgent problem. The existing studies abstract this problem into a network design problem with relays (NDPR). However, these studies only focus on whether all target areas can be covered, and ignore timely reliability. Secondly, they use the number of hops to measure the transmission delay and ignore the actual transmission process. Therefore, to solve the above problems, we study the 5 G base station optimization location model considering timely reliability. Firstly, combining the definition of network reliability and the law of large numbers, we establish the timely reliability model, and transform it into a mixed integer linear programming (MILP) model. Secondly, considering the transmission process, we propose a novel NDPR model to optimize the base station location and propose a partial optimization algorithm to solve the large-scale case. Besides, taking the smart grid as an example, the validity of the proposed method is verified. Finally, the sensitivity analysis is carried out to provide corresponding suggestions for reducing the building cost.

Automated summary

To ensure the reliability of data transmission in the intelligent Internet of Things, establishing numerous 5G base stations as relay nodes is necessary. However, determining the optimal locations with minimum building cost is a pressing issue. Existing studies abstract this into a network design problem with relays (NDPR), but focus only on coverage and overlook timely reliability. This research presents a 5G base station optimization location model considering timely reliability, establishing a mixed integer linear programming (MILP) model and proposing a novel NDPR model to optimize base station locations. The proposed method is verified using the smart grid as an example, and sensitivity analysis is conducted to provide cost-reducing suggestions.