Data aggregation in resource-limited wireless communication environments — Differences between theory and praxis

Multimodal Wireless Sensor Networks generate huge amount of heterogeneous data, which has to be transmitted in a dynamically changing network infrastructure. Especially in the domain of wireless low-power applications, the energy-efficiency and the prioritisation of communication tasks is critical. Several research areas deal with these issues. They are optimising the respective hardware components as well as the protocols within the PHY, MAC or network layer. But for an optimised media transport in the topology also the data management and the task scheduling on the application layer are essential. Here, the key challenge is to minimise the data amount without decreasing the information quality. Related research work in the field of data aggregation and data fusion offer interesting techniques for an efficient data handling. In this paper, we discuss usual ways for data aggregation, including the adapted communication process. We critically analyse the benefits in theory and compare these conceptual advantages with measured real-world results. The evaluation was done in two steps. The first one is based on simulation scenarios of typical WSN/SANET applications. In a second step, we implement a demonstrator platform for a respective real-world environment. The test bed configuration is similar to the simulation scenario and provides comparable data. Based on the results and the respective analysis, we propose feasible methods for optimising data aggregation techniques. We clarify, that these improvements are essential for an efficient usage in resource-limited, multimodal sensor network environments.