Jó Ágila Bitsch Link

FootPath: Accurate map-based indoor navigation using smartphones

We present FootPath, a self-contained, map-based indoor navigation system. Using only the accelerometer and the compass readily available in modern smartphones we accurately localize a user on her route, and provide her with turn-by-turn instructions to her destination. To compensate for inaccuracies in step detection and heading estimation, we match the detected steps onto the expected route using sequence alignment algorithms from the field of bioinformatics. As our solution integrates well with OpenStreetMap, it allows painless and cost-efficient collaborative deployment, without the need for additional infrastructure.

Bursty traffic over bursty links

Accurate estimation of link quality is the key to enable efficient routing in wireless sensor networks. Current link estimators focus mainly on identifying long-term stable links for routing. They leave out a potentially large set of intermediate links offering significant routing progress. Fine-grained analysis of link qualities reveals that such intermediate links are bursty, i.e., stable in the short term. In this paper, we use short-term estimation of wireless links to accurately identify short-term stable periods of transmission on bursty links. Our approach allows a routing protocol to forward packets over bursty links if they offer better routing progress than long-term stable links. We integrate a Short Term Link Estimator and its associated routing strategy with a standard routing protocol for sensor networks. Our evaluation reveals an average of 19% and a maximum of 42% reduction in the overall transmissions when routing over long-range bursty links. Our approach is not tied to any specific routing protocol and integrates seamlessly with existing routing protocols and link estimators.

GeoDTN: Geographic Routing in Disruption Tolerant Networks

In this paper we present a disruption tolerant routing algorithm based on geographic location information, which improves upon the hop count compared to the current state of the art by up to a factor of three in large scale human networks. Leveraging only the history of geographic movement patterns in the two-hop neighborhood, our algorithm is able to perform well in the absence of knowledge of social interaction between nodes and without detailed future schedule information. Representing previously visited locations as probability distributions encoded in an efficient vector, we formalize a heuristic for efficiently forwarding messages in disruption tolerant networks, implement a framework for comparing our approach with the state of the art, and evaluate key metrics, such as hop count and delivery rate, as well as energy consumption and battery depletion fairness on real world data. We are able to outperform the state of the art in human mobility based networks considerably in terms of energy usage per node, thereby extending data network availability further into areas devoid of otherwise necessary communication infrastructure.

SimBetAge: utilizing temporal changes in social networks for pocket switched networks

In this paper, we present SimBetAge, a delay and disruption tolerant routing protocol for highly dynamic socially structured mobile networks. We exploit the lightweight and ego-centric scheme of SimBet routing while at the same time taking the strength and the gradual aging of social relations into account and thereby increase the performance by one order of magnitude, especially in evolving network structures. We explore the model of similarity and betweenness over weighted graphs, and present a simulation on realistic traces from previous experiments, comparing our approach to the original SimBet, Epidemic Routing and Prophet.

Perfect difference sets for neighbor discovery: energy efficient and fair

We present an energy efficient neighbor discovery framework that enables Linux and TinyOS based systems to discover and connect to neighbors via IEEE 802.11 and IEEE 802.15.4, which are only available sporadically. Using quorum schemes, we schedule on and off times of the wireless transmitters, to guarantee mutual discovery with minimum power given a specific latency requirement. Neighbor discovery is fundamental to intermittently connected networks, such as disruption and delay tolerant networks and optimizing it, can lead to significant overall energy savings. Using perfect difference sets, our results indicate that we reduce the latency by up to 10 times at a duty cycle of 2% compared to the state of the art. We further define and characterize our neighbor discovery scheme with respect to fairness for asymmetric energy scenarios. Using these results, we allow energy-harvesting applications to adjust neighbor discovery based on their current energy requirements as a well defined trade-off.

BurrowView - seeing the world through the eyes of rats

For a long time, life sciences were restricted to look at animal habitats only post-factum. Pervasive computing puts us in the novel position to gain live views. In this paper we present BurrowView, an application that tracks the movement of rats in their natural habitat and reconstructs the underground tunnel system. To make reliable statements, special consideration has been taken with regard to the information quality. Our system is able to reconstruct paths up to a resolution of 20 cm, the length of a rat without its tail.

On the Applicability of Computer Vision based Gaze Tracking in Mobile Scenarios

Gaze tracking is a common technique to study user interaction but is also increasingly used as input modality. In this regard, computer vision based systems provide a promising low-cost realization of gaze tracking on mobile devices. This paper complements related work focusing on algorithmic designs by conducting two users studies aiming to i) independently evaluate EyeTab as promising gaze tracking approach and ii) by providing the first independent use case driven evaluation of its applicability in mobile scenarios. Our evaluation elucidates the current state of mobile computer vision based gaze tracking and aims to pave the way for improved algorithms. In this regard, we aim to further foster the development by releasing our source data as reference database open to the public.

Indoor navigation on wheels (and on foot) using smartphones

While indoor navigation in unfamiliar surroundings is challenging for pedestrians, it is even more so for persons bound to a wheelchair. Additionally, the necessary Wi-Fi infrastructure for fine grained RF fingerprinting-based indoor positioning is often unavailable. We propose a system completely self-contained within current smartphones, that allows people, wheelchair bound and others, to find their way in a building.

KleeNet: automatic bug hunting in sensor network applications

We present KleeNet, a Klee based bug hunting tool for sensor network applications before deployment. KleeNet automatically tests code for all possible inputs, ensures memory safety, and integrates well into TinyOS based application development life cycle, making it easy for developers to test their applications.

Hidden Markov model-based 3D path-matching using raytracing-generated Wi-Fi models

We propose an efficient approach to probabilistic 3D indoor path-matching and localization based on Wi-Fi-signal measurements using Hidden Markov Model-based (HMM) algorithms. Given a 3D model of the building, we derive high-resolution emission probabilities and transition probabilities from raytracing-generated Wi-Fi signal propagations. Therefore we use both the generated signal-strength values and the geometric information of the 3D model. Based on the emission and transition probabilities and a sequence of Wi-Fi signal measurements provided by the client, the HMM-based algorithm computes the most probable path through the building.