Dominik Lieckfeldt

An Algorithm for Distributed Beacon Selection

This paper investigates wireless sensor networks where a small percentage of nodes are assumed to know their location a priori. These reference nodes enable absolute localization of other nodes in direct neighborhood. Having estimated their location, these nodes in turn provide their location to other nodes within transmission range. Therefore, location information spreads throughout the network. Consequently, in later state of the network, unknowns desiring to determine their location, or to improve it, will be able to choose from a large pool of nodes with known or estimated locations, which we refer to as beacons. We investigate a method to select a subset of beacons to minimize the error of localization. Regarding Cramer-Rao-Lower- Bound on localization error, the method proposed constitutes a significant improvement in comparison with the often used nearest-neighbors approach.

Exploiting RF-Scatter: Human Localization with Bistatic Passive UHF RFID-Systems

In ubiquitous computing, localization of users in indoor environments is a challenging issue. On the one hand, localization data needs to have fine granularity to provide reasonable input for intention recognition and task planning. On the other hand, effects like multi-path interference and signal scattering of RF propagation in indoor environments reduces the accuracy of traditional wireless localization techniques. However, we prove that such adversary effects can be characterized and utilized conversely to localize the source of RF-scatter with passive UHF RFID. Since measuring spatial correlation requires many spatially separated transmitter and receiver pairs, cost-effective and unobtrusively attachable passive RFID-tags are especially suitable for this purpose. The passive tags are spatially distributed in a manner such that it is possible to infer the spatial correlation of Received Signal Strength (RSS). The idea is to characterize the influence of user presence on RSS, and use such relationship for localization. Three localization algorithms are investigated which consist of a Maximum Likelihood Estimator (MLE), and two Linear Least Squares variants. Algorithms are applied to measurement data which we obtained in an indoor environment. The results evidences our idea of human localization in such bistatic RFID systems.

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.

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.

Distributed selection of references for localization in wireless sensor networks

The main purpose of wireless sensor networks is to provide information about an area of interest. In order to fulfill this task, physical parameters have to be measured by as many sensors as possible to improve the knowledge on the sensed area. In contrast, due to the resource-limited nature of sensor networks, the number of actively participating nodes should be kept to a minimum. This paper investigates the trade-off between the two conflicting requirements with special focus on localization of sensor nodes. A distributed algorithm to select subsets of sensor nodes for localization is analyzed regarding the accuracy of localization.

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%.

Characterizing the Influence of Human Presence on Bistatic Passive RFID-System

Using simple and cost-effective tags, passive RFID systems offer a promising aid for identifying and localizing objects and users in indoor environments. Although systems based on radio frequencies usually suffer from multi-path interference and signal scattering, we show that the characteristics of such interference and scattering can be analyzed with passive RFID. We present and analyze measurements of received signal strength (RSS) conducted in an indoor environment using a passive bistatic RFID-System. In order to characterize the influence of human presence on RSS, measurements were conducted for different user locations and orientations in an indoor deployment area. Finally, an analytical approximation of the relation between user location and RSS is presented in accordance to our measurement results.

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.

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.

Passive Tracking of Transceiver-Free Users with RFID

Providing location information while people move through a indoor environment is fundamental to smart environments. While many works address this problem, which is often referred to as tracking, the complex radio propagation and the need smart and unobtrusive localization system still represent challenges to current approaches.