Frank Reichenbach

Indoor Localization with Low Complexity in Wireless Sensor Networks

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.

Precise Positioning with a Low Complexity Algorithm in Ad hoc Wireless Sensor Networks

ABSTRACT Nodes in a sensor network are often randomly distributed. To assign measurements to locations, each node has to determine its own position. Due to limited resources of the nodes, resource-aware positioning algorithms are required. In this paper, we present the novel “Weighted Centroid Localization” algorithm (WCL). Compared to other approximative algorithms, WCL achieves a remarkable average positioning error below 6%. Moreover, the algorithm features simple implementation and scales well in large sensor networks. In contrast to exact algorithms using analytical equations and complex matrix operations, WCL requires only a very small memory footprint.

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.

Distributed obstacle localization in large wireless sensor networks

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.

Improved Precision of Coarse Grained Localization in Wireless Sensor Networks

In wireless sensor networks, the coarse grained localization is a method to compute the position of randomly distributed sensor nodes. Without optimizations, it provides low precision which heavily depends on the transmission range of base stations. In this paper, we propose novel optimizations of coarse grained localization with centroid determination (CGLCD) to determine the position of nodes more precisely. Our focus is to compute an optimal transmission range of all base stations and to reduce the total energy consumption. We present an analytic proof of a simple equation to determine the optimal transmission range in grid-aligned finite wireless sensor networks. Using this optimal transmission range, we reduced the positioning error about 80%. Thereby, nodes as well as base stations require lowest energy

Context-Aware Geographic Routing for Sensor Networks with Routing Holes

Modern sensor networks are deployed in various terrains of interest. As the complexity of their deployed areas is growing, existing geographic routing algorithms are facing challenges. Holes in networks often cause failures in message routing. Energy consumption, scalability, and routing efficiency are also key design challenges. In this paper, we propose a novel geographic routing algorithm called HOle-BYpassing routing with Context-AwareNess (HobyCan). Our approach locally sets up multiple detour paths to bypass almost all kinds of holes. Therefore, contours of holes are extended with multiple detour paths. According to various context information of a sensor network, such as the size of holes or the remaining energy of nodes, disjoint detour paths can be used alternatively to achieve optimal routing paths or load balance of the network. Simulation results demonstrate the performance of our algorithm, as well as the significance of context information as routing parameters.

Energy and Coverage Aware Routing Algorithm in Self Organized Sensor Networks

This paper investigates the energy problem in sensor networks. After random deployment, nodes have to observe a region and transmit their sensor data to a central station. By checking redundancies in coverage and transmission with our XGAF algorithm presented here it is possible to shutdown the majority of nodes into sleep mode. Computation reduction in sleeping nodes and reduced communication results in increased network lifetime. This paper presents a novel routing algorithm which takes into account information concerning coverage and energy and using the advantages of scale free networks. Additionally, the algorithm allows nodes to work self organized. Thence, communication with the central station is reduced.

Multi-core Technology -- Next Evolution Step in Safety Critical Systems for Industrial Applications?

Multi-core technology can provide valuable benefits for improving safety critical embedded systems. Examples range from multiple core architectures, introducing system redundancy, asymmetric multiprocessing allowing high software diversity, to hyper visors reducing system complexity. Can these benefits be taken for granted without considering the drawbacks and effects that come with them? The move to multi-core based architectures is already underway. Sooner, rather than later, we are forced to discover and resolve its issues for safety related applications. This paper is an attempt to evaluate the value of multi-core for safety critical systems on a broader level.

Increasing lifetime of wireless sensor networks with energy-aware role-changing

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”.

Controlling wireless sensor networks using SeNeTs and EnviSense

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.