In this paper, we present a distributed sensor scheduling scheme based on Euclidean distance (DSS-ED) for Internet of Things (IoT) local networks, with the aim of extending the network lifetime while maximizing the utilization of the limited network capacity of the IoT local network to support various IoT applications. For this purpose, the DSS-ED adapts the state of individual sensor devices by comprehensively considering the characteristics of various variables. In the DSS-ED, each sensor device first calculates the Euclidian distance between the measured variables and their ideal values, then notifies its neighbors of the calculated results. Afterward, the sensor device adaptively determines its own state by comparing its Euclidian distance with those of its neighbors. An experimental simulation is conducted to evaluate the performance of the DSS-ED. The results show that the DSS-ED obtains better performance than the lightweight redundancy-aware topology control protocol (LRTCP) in terms of throughput and energy consumption.