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The determination of a precise position in geosensor networks requires the use of measurements which are inherently inaccurate while minimizing the required computations. The imprecise positions produced using these inaccurate measurements mean that available methods for measurement of distances or angles are unsuitable for use in most applications. In this paper we present a new approach, the Anomaly Correction in Localization (ACL) algorithm, whereby classical trilateration is combined with the measurements of physical parameters at the sensor nodes to improve the precision of the localization. Simulation results show that for localization using triangulation of distance measurements with a standard deviation of 10% then the improvement in precision of the estimated location when using ACL is up to 30%. For a standard deviation in the measurements of 5% then an improvement in positioning precision of ca. 12% was achieved.

The localization process in wireless sensor networks must produce precise positions at minimal requirements. It is hard to comply with these conditions, due to the strong resource limitation of each sensor node. Although numerous algorithms have already been presented, so far there exist neither an extensive simulative comparison of them nor a qualified parameter to indicate their efficiency. Thus, this paper introduces a selected number of the most important algorithms for sensor networks. These algorithms are compared with each other considering achieved localization error and consumed energy. Then, we propose an adequate parameter to quantify their efficiency - the "Power Error Product" (PEP).

Autonomous localization of nodes in wireless sensor networks is essential to minimize the complex self organization task consequently enhancing the overall network lifetime. Recently, precise indoor localization is impeded by multi path propagation of signals due to reflections at walls or objects. In this paper we partly overcome some of these problems by methods like frequency diversity and averaging multiple measured data. Received radio signal strength (RSS) in combination with weighted centroid localization, featuring low communication overhead and a low complexity of O(n), is our basis of a localization on the energy constrained sensor nodes. We first analyze the RSS-characteristics on a specific sensor node platform in different rooms. Next, we describe methods to improve these characteristics to reach best localization results at minimized complexity. Finally, in a practice indoor localization we achieve a small localization error of only 14% for 69% of all test-points that was enhanced to at least 8% in average by simple optimizations. For that, no hardware modifications as well as time consuming RSSI-maps or complex signal propagation models are required.

Obstacles are but pleasing for many aspects of large real-world sensor networks. Among other things, the presence of obstacles distort the sensor node localization process and might lead to costly routing because of unnecessary detours and/or dead ends. In order to relieve these problems, this paper proposes a distributed obstacle localization algorithm, called DOLfor short, in which the sensor nodes interact with each other mostly locally. The proposed algorithm is very resource and communication efficient in that all sensor nodes send only a small number of additional messages. Finally, the sensor network fine tunes the employed routing algorithm.

Energy aware and robust self-organization is a challenging task in large, randomly deployed wireless sensor networks. In this paper, we achieve such a self-organization by introducing a hierarchical network structure and additionally roles that represent basic network functionalities like packet forwarding or data aggregation. These roles are exchanged between the participating nodes considering specific constraints. We are focusing on a long network lifetime, which strongly depends on the limited energy resources of each node. Therefore, the complex roles are released by nodes with critical battery levels and are assigned to nodes with more energy capacity left. With this approach, we achieve a uniform energy distribution over the whole network. Finally, we extend the overall lifetime of the network by 40% at continuous capability at all time. We demonstrate the proper function and the efficiency of the postulated protocol and we show its benefits by simulating an applicable “Forest Fire Scenario”.

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.

Autonomous localization of nodes in wireless sensor networks is essential to minimize the complex self-organization task and to enhance network lifetime. Known techniques such as distance estimation based on received signal strength are often inaccurate and produce outliers. We propose a new method to measure a distance using the minimal transmission power between a transmitting node and a receiving node. The determined distance is very precise and has a low variance. It is therefore suitable for localization which is exemplary demonstrated for the approximate "weighted centroid localization" algorithm

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.

We present SeNeTs and EnviSense. SeNeTs is a powerful software environment for test and validation of sensor network applications. It administrates large-scale sensor networks during execution without affecting communication among sensor nodes. Moreover, SeNeTs allows efficient debugging of sensor network applications with sophisticated update mechanisms. EnviSense is a graphical frontend to easily configure and administrate sensor networks using SeNeTs. Both software systems are independent on the hardware platform and also able to run stand-alone.

One of the key issues in large wireless sensor networks is a precise position determination of every node with minimal hardware requirements and lowest energy consumption. Exact localization techniques, which are prone to input errors, are resource-intensive and not feasible on simple sensor nodes without adaptations. The key mechanism is to effectively split the complex computation and distribute the communication load across all nodes. Therefore, we present the "distributed least squares" (DLS)-algorithm. DLS is a resource-aware localization method that achieves 47% computation savings and 86% energy savings compared to the reference method, a "fully distributed multilateration" on every sensor node.