Alexander Born

A distributed linear least squares method for precise localization with low complexity in wireless sensor networks

Localizing sensor nodes is essential due to their random distribution after deployment. To reach a long network lifetime, which strongly depends on the limited energy resources of every node, applied algorithms must be developed with an awareness of computation and communication cost. In this paper we present a new localization method, which places a minimum computational requirement on the nodes but achieves very low localization errors of less than 1%. To achieve this, we split the complex least squares method into a less central precalculation and a simple, distributed subcalculation. This allows precalculating the complex part on high-performance nodes, e.g. base stations. Next, sensor nodes estimate their own positions by simple subcalculation, which does not exhaust the limited resources. We analyzed our method with three commonly used numerical techniques – normal equations, qr-factorization, and singular-value decomposition. Simulation results showed that we reduced the complexity on every node by more than 47% for normal equations. In addition, the proposed algorithm is robust with respect to high input errors and has low communication and memory requirements.

Splitting the linear least squares problem for precise localization in geosensor networks

Large amounts of cheap and easily deployable wireless sensors enable area-wide monitoring of both urban environments and inhospitable terrain. Due to the random deployment of these sensor nodes, one of the key issues is their position determination. Noisy distance measurements and the highly limited resources of every sensor node, due to tiny hardware and small battery capacity, demand the development of robust, energy aware, and precise localization algorithms. We believe this can be achieved by appropriately distributing the complex localization task between all participating nodes. Therefore, we use a linearization tool to linearize the arising non-linear system of equations into a linear form that can be solved by a distributed least squares method. It is shown in this paper that we can save with this new approach more than 47% of computation cost whilst maintaining a low network traffic. Additionally, we describe memory optimizations to process the complex matrix operations with only a few kilobyte of memory on the sensor node.

On distance estimation based on radio propagation models and outlier detection for indoor localization in Wireless Geosensor Networks

The determination of a precise position in Wireless Geosensor Networks (GSN) requires the use of e.g. distance measurements. These distance observations derived by Received Signal Strength (RSS) measurements are inherently inaccurate. Furthermore, in general, the distance observations using RSS do not take obstacles into account. In this work in progress paper we present a new approach and first simulations to correct erroneous RSS measurements affected by obstacles in indoor scenarios. This technique is combined with the known “Anomaly Correction in Localization” (ACL) algorithm where sensor measurements are used to improve the determined sensor node positions and to detect and to eliminate outliers. Therefore, the new “extended Anomaly Correction in Localization” algorithm (eACL) will be formulated.

A new localization and Accuracy Analyzer for Wireless Sensor Networks using defective observations

The development of energy aware and precise localization algorithms is a challenging task in the development of Wireless Sensor Networks (WSN). The achievable precision of the algorithms strongly depends on the accuracy of the measured observations (e.g. time of signal flight or received signal strength). In reality, observations are highly defective due to multiple effects (e.g. signal propagation). The geometrical conditions within the network can also affect the precision. It is therefore important to take the real characteristics of the observations into account as early as possible. This paper presents a new simulation tool to test geometrical conditions, different localization algorithms and measurements in WSNs either in the planing phase or after the localization process. Furthermore this tool enables the detection of outliers, to make accuracy statements and to analyze the determined position.

Combining scalability and resource awareness in Wireless Sensor Network localization

Wireless Sensor Networks (WSNs) have been shown to be most suitable for monitoring large and possibly inaccessible areas. To assign measured values to certain positions as well as for complex network algorithms, localization represents a required basic capability. By splitting a costly localization calculation into precalculation and postcalculation, Distributed Least Squares (DLS) has been introduced as an efficient approach of fine grained localization.