Wireless sensor networks (WSN) are composed of hardware constrained and battery-powered devices that communicate wirelessly. WSN find more and more applications, but their deployment is limited among others by the range and the throughput of the communication technology used. Several technologies are available nowadays, with various performances, cost and coverage. One solution to overcome the deployment limitations and in some cases extend the coverage would be to dynamically select the technology based on the data requirements, environment, geographic location, etc. Thus we need multitechnologies WSN devices and efficient algorithms to select the best available technology in an autonomous and local way. This issue is known as Network Interface Selection (NIS). Multi-Attribute Decision Making (MADM) methods are an efficient tool to tackle NIS. Among MADM methods is Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). However, TOPSIS suffers from a rank reversal issue, which may alter the ranking quality. Furthermore, TOPSIS method is computationally heavy, which might increase the energy consumption of the constrained devices and the latency of the network. In this paper, we introduce a lightweight TOPSIS-based method tailored for NIS in WSN, allowing more reliable communications. Experimental results obtained on real hardware, i.e., Pycom FiPy modules, show an improvement in computation time of 38% while maintaining a selection similar to TOPSIS in 82% of runs.