Wireless energy transfer has recently emerged as a promising alternative to realize the vision of perpetual embedded sensing. However, this technology transforms the notion of energy from merely a node’s local commodity to, similarly to data, a deployment-wide shareable resource. The challenges of managing a shareable energy resource are much more complicated and radically different from the research of the past decade: Besides energy-efficient operation of individual devices, we also need to optimize networkwide energy distribution. To counteract these challenges, we propose an energy stack , a layered software model for energy management in future transiently powered embedded networks. An initial specification of the energy stack, which is based on the historically successful layered approach for data networking, consists of three layers: (i) the transfer layer, which deals with the physical transfer of energy; (ii) the scheduling layer, which optimizes energy distribution over a single hop; and (iii) the network layer, creates a global view of the energy in the network for optimizing its networkwide distribution. As a contribution, we define the interfacing APIs between these layers, delineate their responsibilities, identify corresponding challenges, and provide a first implementation of the energy stack. Our evaluation, using both experimental deployments and high-level simulations, establishes the feasibility of a layered solution to energy management under transient power.

Automatic emotion recognition is an interdisciplinary research field which deals with the algorithmic detection of human affect, e.g. anger or sadness, from a variety of sources, such as speech or facial gestures. Apart from the obvious usage for industry applications in human-robot interaction, acquiring the emotional state of a person automatically also is of great potential for the health domain, especially in psychology and psychiatry. Here, evaluation of human emotion is often done using oral feedback or questionnaires during doctor-patient sessions. However, this can be perceived as intrusive by the patient. Furthermore, the evaluation can only be done in a noncontinuous manner, e.g. once a week during therapy sessions. In contrast, using automatic emotion detection, the affect state of a person can be evaluated in a continuous non-intrusive manner, for example to detect early on-sets of depression. An additional benefit of automatic emotion recognition is the objectivity of such an approach, which is not influenced by the perception of the patient and the doctor. To reach the goal of objectivity, it is important, that the source of the emotion is not easily manipulable, e.g. as in the speech modality. To circumvent this caveat, novel approaches in emotion detection research the potential of using physiological measures, such as galvanic skin sensors or pulse meters. In this paper we outline a way of detecting emotion from brain waves, i.e., EEG data. While EEG allows for a continuous, real-time automatic emotion recognition, it furthermore has the charm of measuring the affect close to the point of emergence: the brain. Using EEG data for emotion detection is nevertheless a challenging task: Which features, EEG channel locations and frequency bands are best suited for is an issue of ongoing research. In this paper we evaluate the use of state of the art feature extraction, feature selection and classification algorithms for EEG emotion classification using data from the de facto standard dataset, DEAP. Moreover, we present results that help choose methods to enhance classification performance while simultaneously reducing computational complexity.

We propose incremental checkpointing techniques enabling transiently powered devices to retain computational state across multiple activation cycles. As opposed to the existing approaches, which checkpoint complete program state, the proposed techniques keep track of modified RAM locations to incrementally update the retained state in secondary memory, significantly reducing checkpointing overhead both in terms of time and energy.

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.

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.

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.

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.

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.

In this work, we present RatMote, a new wireless sensor node for subterranean animal habitat monitoring. RatMote has been developed for project RatPack, which aims at creating a new method for behavioral research on rats in their natural environment using wireless sensor nodes.Recent development in microcontroller architecture allowed us to design a sensor node which calculates up to 22 times more operations per mAh than the widely used TelosB node. This significant performance and efficiency increase allows us to perform computationally demanding algorithms inside the node, needed for vocalization analysis, localization, and mapping.

We present TinyWifi, a nesC code base extending TinyOS to support Linux powered network nodes. It enables developers to build arbitrary TinyOS applications and protocols and execute them directly on Linux by compiling for the new TinyWifi platform. Using TinyWifi as a TinyOS platform, we expand the applicability and means of evaluation of wireless protocols originally designed for sensornets towards inherently similar Linux driven ad hoc and mesh networks.

In this paper, we present SimBetAge, an extension to Sim-Bet taking into account the gradual aging of connections in social networks which thereby increases the performance by an order of magnitude, especially in evolving network structures. For this purpose, we redefine similarity and betweenness t

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.

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.