This work is motivated by schemes of robot-sensor network cooperation where sensor nodes—beacons—are used as landmarks for range-only (RO) simultaneous localization and mapping (SLAM). In most existing RO-SLAM techniques beacons are considered as passive devices ignoring the capabilities they are actually endowed with. This paper proposes a RO-SLAM scheme that distributes the measurements gathering and integration between the beacons surrounding the robot. It naturally integrates inter-beacon measurements, significantly improving map and robot estimations and speeding up beacon initialization. The proposed scheme is based on sparse extended information filter (SEIF) and it is proven that it preserves the constant time and sparsity properties of SEIF and thus, inherits its efficiency and scalability. As a result, our scheme has lower robot and map estimation errors, faster beacon initialization and lower computer requirements than existing methods. This paper experimentally validates and evaluates the proposed method for 3D SLAM using an octorotor.