Visualizing Sensor Network Coverage with Location Uncertainty

We present an interactive visualization system for exploring the coverage in sensor networks with uncertain sensor locations. We consider a simple case of uncertainty where the location of each sensor is confined to a discrete number of points sampled uniformly at random from a region with a fixed radius. Employing techniques from topological data analysis, we model and visualize network coverage by quantifying the uncertainty defined on its simplicial complex representations. We demonstrate the capabilities and effectiveness of our tool via the exploration of randomly distributed sensor networks

Efficient Algorithms for Distributed Detection of Holes and Boundaries in Wireless Networks

We propose two novel algorithms for distributed and location-free boundary recognition in wireless sensor networks. Both approaches enable a node to decide autonomously whether it is a boundary node, based solely on connectivity information of a small neighborhood. This makes our algorithms highly applicable for dynamic networks where nodes can move or become inoperative. We compare our algorithms qualitatively and quantitatively with several previous approaches. In extensive simulations, we consider various models and scenarios. Although our algorithms use less information than most other approaches, they produce significantly better results. They are very robust against variations in node degree and do not rely on simplified assumptions of the communication model. Moreover, they are much easier to implement on real sensor nodes than most existing approaches.Comment: extended version of accepted submission to SEA 201

Deterministic boundary recongnition 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

Fine-grained boundary recognition in wireless ad hoc and sensor networks by topological methods

Location-free boundary recognition is crucial and critical for many fundamental network functionalities in wireless ad hoc and sensor networks. Previous designs, often coarse-grained, fail to accurately locate boundaries, especially when small holes exist. To address this issue, we propose a fine-grained boundary recognition approach using connectivity information only. This algorithm accurately discovers inner and outer boundary cycles without using location information. To the best of our knowledge, this is the first design being able to determinately locate all hole boundaries no matter how small the holes are. Also, this distributed algorithm does not rely on high node density. We formally prove the correctness of our design, and evaluate its effectiveness through extensive simulations. Categories and Subject Descriptor