Data MULEs: modeling a three-tier architecture for sparse sensor networks

TLDR

The architecture can substantially reduce sensor power consumption by limiting transmissions to short ranges. The study aims to present and analyze a three‑tier architecture for collecting sensor data in sparse networks and to develop a simple analytical model to understand performance scaling. The model uses mobile units (MULEs) that collect data via short‑range pickup, buffer it, and deliver it to wired access points, assuming a two‑dimensional random walk mobility pattern and evaluating data success rate and buffer requirements. Simulation and modeling results offer guidelines for deploying such MULE‑based sensor networks.

Abstract

This paper presents and analyzes an architecture to collect sensor data in sparse sensor networks. Our approach exploits the presence of mobile entities (called MULEs) present in the environment. MULEs pick up data from the sensors when in close range, buffer it, and drop off the data to wired access points. This can lead to substantial power savings at the sensors as they only have to transmit over a short range. This paper focuses on a simple analytical model for understanding performance as system parameters are scaled. Our model assumes two-dimensional random walk for mobility and incorporates key system variables such as number of MULEs, sensors and access points. The performance metrics observed are the data success rate (the fraction of generated data that reaches the access points) and the required buffer capacities on the sensors and the MULEs. The modeling along with simulation results can be used for further analysis and provide certain guidelines for deployment of such systems.