Alexander Kröller

Deterministic boundary recognition and topology extraction for large sensor networks

We present a new framework for the crucial challenge of self-organization of a large sensor network. The basic scenario can be described as follows: Given a large swarm of immobile sensor nodes that have been scattered in a polygonal region, such as a street network. Nodes have no knowledge of size or shape of the environment or the position of other nodes. Moreover, they have no way of measuring coordinates, geometric distances to other nodes, or their direction. Their only way of interacting with other nodes is to send or to receive messages from any node that is within communication range. The objective is to develop algorithms and protocols that allow self-organization of the swarm into large-scale structures that reflect the structure of the street network, setting the stage for global routing, tracking and guiding algorithms.Our algorithms work in two stages: boundary recognition and topology extraction. All steps are strictly deterministic, yield fast distributed algorithms, and make no assumption on the distribution of nodes in the environment, other than sufficient density.

Neighborhood-Based Topology Recognition in Sensor Networks

We consider a crucial aspect of self-organization of a sensor network consisting of a large set of simple sensor nodes with no location hardware and only very limited communication range. After having been distributed randomly in a given two-dimensional region, the nodes are required to develop a sense for the environment, based on a limited amount of local communication. We describe algorithmic approaches for determining the structure of boundary nodes of the region, and the topology of the region. We also develop methods for determining the outside boundary, the distance to the closest boundary for each point, the Voronoi diagram of the different boundaries, and the geometric thickness of the network. Our methods rely on a number of natural assumptions that are present in densely distributed sets of nodes, and make use of a combination of stochastics, topology, and geometry. Evaluation requires only a limited number of simple local computations.

SpyGlass: a wireless sensor network visualizer

In this paper we present a modular and extensible visualization framework for wireless sensor networks. These networks have typically no means of visualizing their internal state, sensor readings or computational results. Visualization is therefore a key issue to develop and operate these networks. Data emitted by individual sensor nodes is collected by gateway software running on a machine in the sensor network. It is then passed on via TCP/IP to the visualization software on a potentially remote machine. Visualization plug-ins can register to different data types, and visualize the information using a flexible multi-layer mechanism that renders the information on a canvas. Developers can easily adapt existing or develop new custom tailored plug-ins for their specific visualization needs and applications.

Flexible experimentation in wireless sensor networks

Virtual testbeds model them by seamlessly integrating physical, simulated, and emulated sensor nodes and radios in real time.

Shawn: The fast, highly customizable sensor network simulator

Shawn is a discrete event simulator for sensor networks. Due to its high customizability, it is extremely fast but can be tuned to any accuracy that is required by the simulation or application.

Wiselib: a generic algorithm library for heterogeneous sensor networks

One unfortunate consequence of the success story of wireless sensor networks (WSNs) in separate research communities is an ever-growing gap between theory and practice. Even though there is a increasing number of algorithmic methods for WSNs, the vast majority has never been tried in practice; conversely, many practical challenges are still awaiting efficient algorithmic solutions. The main cause for this discrepancy is the fact that programming sensor nodes still happens at a very technical level. We remedy the situation by introducing Wiselib, our algorithm library that allows for simple implementations of algorithms onto a large variety of hardware and software. This is achieved by employing advanced C++ techniques such as templates and inline functions, allowing to write generic code that is resolved and bound at compile time, resulting in virtually no memory or computation overhead at run time. The Wiselib runs on different host operating systems, such as Contiki, iSense OS, and ScatterWeb. Furthermore, it runs on virtual nodes simulated by Shawn. For any algorithm, the Wiselib provides data structures that suit the specific properties of the target platform. Algorithm code does not contain any platform-specific specializations, allowing a single implementation to run natively on heterogeneous networks. In this paper, we describe the building blocks of the Wiselib, and analyze the overhead. We demonstrate the effectiveness of our approach by showing how routing algorithms can be implemented. We also report on results from experiments with real sensor-node hardware.

Exact solutions and bounds for general art gallery problems

The classical Art Gallery Problem asks for the minimum number of guards that achieve visibility coverage of a given polygon. This problem is known to be NP-hard, even for very restricted and discrete special cases. For the case of vertex guards and simple orthogonal polygons, Cuoto et al. have recently developed an exact method that is based on a set-cover approach. For the general problem (in which both the set of possible guard positions and the point set to be guarded are uncountable), neither constant-factor approximation algorithms nor exact solution methods are known. We present a primal-dual algorithm based on linear programming that provides lower bounds on the necessary number of guards in every step and—in case of convergence and integrality—ends with an optimal solution. We describe our implementation and give experimental results for an assortment of polygons, including nonorthogonal polygons with holes.

Virtualising testbeds to support large-scale reconfigurable experimental facilities

Experimentally driven research for wireless sensor networks is invaluable to provide benchmarking and comparison of new ideas. An increasingly common tool in support of this is a testbed composed of real hardware devices which increases the realism of evaluation. However, due to hardware costs the size and heterogeneity of these testbeds is usually limited. In addition, a testbed typically has a relatively static configuration in terms of its network topology and its software support infrastructure, which limits the utility of that testbed to specific case-studies. We propose a novel approach that can be used to (i) interconnect a large number of small testbeds to provide a federated testbed of very large size, (ii) support the interconnection of heterogeneous hardware into a single testbed, and (iii) virtualise the physical testbed topology and thus minimise the need to relocate devices. We present the most important design issues of our approach and evaluate its performance. Our results indicate that testbed virtualisation can be achieved with high efficiency and without hindering the realism of experiments.

RDF provisioning for the Internet of Things

We present the platform-independent Wiselib RDF Provider for embedded IoT devices such as sensor nodes. It enables the devices to act as semantic data providers. They can describe themselves, including their services, sensors, and capabilities, by means of RDF documents. Used in a protocol stack that provides Internet connectivity (6LowPAN) and a service layer (CoAP), a sensor can autoconfigure itself, connect to the Internet, and provide Linked Data without manual intervention. We introduce Streaming HDT, a lightweight serialization format for RDF documents that allows for transmitting compressed documents with minimal effort for the encoding; this is tailored for typical IoT applications where the embedded devices are often senders and seldom receivers of complete documents.

Using and operating wireless sensor network testbeds with WISEBED

Current surveys and forecast predict that the number of wireless devices is going to increase tremendously. These wireless devices can be computers of all kinds, notebooks, netbooks, Smartphones and sensor nodes that evolve into real-world scenarios forming a “Real-World-Internet” in the future. In our work we focus on the Future Internet with small battery driven devices forming the “Internet of Things”. In recent networking research, testbeds gain more and more attention, especially in the context of Future Internet and wireless sensor networks (WSNs). This development stems from the fact that simulations and even emulations are not considered sufficient for the deployment of new technologies as they often lack realism. Experimental research on testbeds is a promising alternative that can help to close the gap. The deployment of testbeds is challenging and user and operator requirements need to be considered carefully. Therefore, the goal is to design an architecture that allows operators of WSN testbeds to offer numerous users access to their testbeds in a standardized flexible way that matches these requirements. In this paper we first identify some of the requirements, then introduce the architecture and general concepts of our WISEBED approach and show how this architecture meets the requirements of both groups. We give an overview of existing WISEBED-compatible WSN testbeds that can be used for experimentation today. Main focus in this paper compared to previous work is to address the perspective of both users and operators on how to experiment or respectively operate a WSN testbed based on WISEBED technology.