Using Network Density as a New Parameter to Estimate Distance

A wireless sensor network consists of a large quantity of small, low-cost sensor nodes that are limited in terms of memory, available energy and processing capacity. Generally, these sensor nodes are distributed in space to obtain physical parameters such as temperature, humidity, vibration or light conditions, and transmit the measured values to a central entity. The measurements are tagged with the corresponding location of the nodes in the network and the time of sampling, to enable a view on the value distribution in space and time later on. Positioning of wireless sensor nodes without dedicated hardware is an open research question. Especially in the domain of embedded networked sensors, many applications rely on spatial information to relate collected data to the location of its origin. As a first step towards localization, an estimation of the distance between two nodes is often carried out to determine their positions. So far, the majority of approaches therefore explore physical properties of radio signals such as the strength of a received signal or its trip time. However, this is problematic since either the complexity on the software or on the hardware side is not adequate for embedded systems, or the approaches lack the required accuracy. In this paper we present the WDNI algorithm (Weighted Density of Node Intersection) to determine the distance between two nodes, relying solely on the investigation of local node densities. To evaluate the accuracy of this algorithm, we ran extensive simulations and experimented with different testbed setups using real sensor nodes, and finally compared WDNI to a range-free distance estimation algorithm based the analysis of RSSI values.