Jakob Salzmann

sDLS - Distributed Least Squares localization for large Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have been of high interest during the past couple of years. One of the most important aspect of WSN research is location estimation. As a good solution of fine grained localization Reichenbach et al. introduced the Distributed Least Squares (DLS) algorithm, which splits the costly localization process in a complex precalculation and a simple postcalculation which is performed on constrained sensor nodes to finalize the localization by adding locale knowledge. This allows to perform an originally complex calculation with high precision on constrained nodes. Besides this advantage, DLS lacks in two harmful constraints concerning practical appliance. On the one hand the algorithm does not scale, i.e. calculation and communication increases with the number of beacon nodes or with network size, respectively. On the other hand DLS even does not work for large networks. An important assumption of DLS is that each blind node can communicate with each beacon node to receive the precalculation and to determine distances to beacon nodes. In this work we present an adaptation of DLS, concerning major changes, which enables DLS to be used in large WSNs for the first time. At the same time computational and communicational cost of each node becomes independent from network size, while precision is kept on the same high level.

4-MASCLE - Improved Coverage Aware Clustering with Self Healing Abilities

In large wireless sensor networks, randomly deployed nodes have to organize themselves as energy efficient as possible to avoid redundant sensor and transceiver operations. In addition to its energy awareness, the network has to guarantee complete functionality as long as possible. This paper presents an enhanced version of the clustering algorithm 2-MASCLE. The proposed 4 MASCLE algorithm combines advantages of temporal and spatial network fragmentation by introducing a network division in a four phase based cell system. The assisting ability of adjacent cells is exploited to apply self healing features to the network. We compare our developed algorithm to the basic algorithms and achieve a significant improve of network lifetime.

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.

2-MASCLE - A Coverage Aware Clustering Algorithm with Self Healing Abilities

This paper investigates organization problems of large wireless sensor networks. In spite of their random deployment, nodes have to organize themselves as energy efficient as possible to avoid redundant sensor and transceiver tasks. In addition to energy awareness, the network has to guarantee complete sensor coverage and connectivity as long as possible. This paper presents a novel clustering algorithm which combines the advantages of horizontal and vertical network fragmentation by introducing a network division in a dual phase cell system. The assisting ability of adjacent cells is exploited to switch-off half of the network cells and allows implementing a self healing algorithm. We compared our developed algorithm to former covering and clustering algorithms and achieved an increased network lifetime compared with them of approximately 80%.

GAF&Co: Connectivity aware topology management for sensor networks

In order to achieve energy conservation in WSNs, most topology management protocols use a subset of sensor nodes for global routing. Using fewer nodes results in a reduced connectivity of the network, which eventually increases the number of routing holes. Holes in networks often cause failures in message routing due to the local minimum problem. Therefore, traditional geographic routing protocols cannot be applied with such topology management protocols. In this paper, we propose a novel topology management protocol derived from the Geographical Adaptive Fidelity (GAF) protocol, called GAF with COnnectivity-awareness (GAF&Co). Instead of using virtual grids in GAF, our approach employs hierarchical hexagonal cells to avoid local minimums in WSNs. The purpose is to schedule redundant nodes into energy-saving mode, while maintaining the connectivity of a network for simple geographic routing protocols. Comparing to GAF, the number of cells as well as the overall energy consumption of a WSN also drops dramatically with the proposed protocol.

Tessellating Cell Shapes for Geographical Clustering

This paper investigates the energy-saving organization of sensor nodes in large wireless sensor networks. Due to a random deployment used in many application scenarios, much more nodes need to be deployed to achieve a complete sensor coverage than theoretically needed in case of an ideal deployment. Consequently, most of the deployed nodes are redundant and can be switched-off for a long time to save energy. A well-known principle to detect the redundancy of nodes is to divide sensor network into equally sized cells. Assuming a well chosen cell size, depending on transmission range and sensing range, it is possible to switch-off all nodes but one per cell. The idea was applied in the extended geographic adaptive fidelity algorithm (XGAF), which divides the network into virtual square cells. In the current work, we improve the idea of XGAF by using different tessellating cell shapes, namely triangles, pentagons and hexagons. Furthermore, we examine the cell shapes in terms of coverage, connectivity and average hop count.

Strategies to overcome border area effects of coarse grained localization

Localization of sensor nodes is one of the key issues in Wireless Sensor Networks. Next to the ability, to assign a phenomenon to a position, localization is a precondition for sensor network algorithms like geographic clustering and routing. A simple approach for coarse grained localization is Centroid Localization (CL), which was firstly presented by Bulusu et al. and assumes regularly arranged beacons. The localization accuracy was improved by various centroid-based algorithms, which use approximate distances to improve location estimation through weighting beacons in range, e.g. Weighted Centroid Localization (WCL). Nevertheless, all these approaches have in common an increased localization error near network borders. In this work, we investigate this error and present two strategies to reduce the localization error of border area nodes.

Hex-MASCLE – hexagon based clustering with self healing abilities

In large wireless sensor networks, low energy consumption is a major challenge. Hence, deployed nodes have to organize themselves as energy efficient as possible to avoid unnecessary sensor and transceiver operations. The energy conserving operations are limited by the task of the network, usually the network has to guarantee complete functionality during its lifetime. The contribution of this paper completes the functionality-aware and energy-efficient clustering algorithm family MASCLE by two innovative algorithms. As already given by the MASCLE-algorithms, the proposed Hex-MASCLE algorithms combine advantages of temporal and spatial network fragmentation. In contrast to previous approaches, the shapes of the basic cells are given by regular hexagons, similar to honeycombs. In the present work, two possible versions for hexagon-based clustering with self-healing abilities are proposed and evaluated. As result, the applying sensor network achieve a significant improve of network lifetime. Additionally, the algorithms are more fault-tolerant against localization errors.

Algorithm for Fast Statistical Timing Analysis

Problems of parameter variations are a main topic in current research and will gain importance in future technology generations due to the continuing scaling. Therefore, it requires appropriate timing analysis which is traditionally done with corner-case simulations. These are quite conservative and pessimistic approaches. In contrast, new statistical static timing analysis (SSTA) algorithms offer a more accurate prediction of the timing behavior of circuit designs. Further, correlations between various parameters and devices can be observed. Unfortunately, the SSTA algorithms mostly require high computational effort and accurate library characterization. This paper proposes an approach for a fast statistical static timing analysis (F-SSTA) with moderate requirements on computation time and library characterization. The approach considers the analysis of gates with multiple inputs. The simulation results show an average error of 5% compared to Monte-Carlo simulations but a significant speed improvement of around 20 times compared to a highly accurate SSTA algorithm.

sDLSne - Improved scalable Distributed Least Squares localization with minimized communication

Wireless Sensor Networks (WSNs) have been of high interest during the past couple of years. One of the most important aspects of WSN research is location estimation. As a good solution of fine grained localization Reichenbach et al. introduced the Distributed Least Squares (DLS) algorithm, which splits the costly localization process in a complex precalculation and a simple postcalculation which is performed on constrained sensor nodes to finalize the localization by adding local knowledge. This approach lacks for large WSNs, because cost of communication and computation theoretically increases with the network size. In practice the approach is even unusable for large WSNs. This restriction have been overcome by scalable DLS (sDLS), which enabled to use the idea of DLS in large WSNs for the first time. Although, sDLS outperforms DLS for large networks, cost of communication and computation is initially higher for small networks, caused by data updates. The approach, presented in this work, dramatically reduces cost of communication of sDLS. Additionally, a new approach of distance estimation is applied to original DLS. In contrast to earlier simulations, this leads to improved localization, which is used for fairer comparison.