In recent years, 3D scene graphs have become a critical tool in robotics and computer vision for enabling systems to understand both the geometric and semantic aspects of their surroundings. These data structures represent spatial and semantic relationships between objects in a three-dimensional environment, supporting tasks like navigation, object manipulation, and scene understanding. This paper presents a real-time pipeline for 3D scene graph generation that offers flexibility in image segmentation techniques while incorporating room classification that is based on a Random Forest model. Our work enables robots to dynamically update their understanding of complex and large-scale environments in real-time. We evaluate our approach systematically on a dataset and in a real-life experiment. The results demonstrate the capability of running our solution at over 10 Hz on an Nvidia Jetson AGX Orin SoC while also scaling favorably in larger environments. Our proposed room classification approach predicts classes with an average accuracy of 80%.

Abstract Wireless charging of electric vehicles can be achieved by installing a transmitter coil into the ground and a receiver coil at the underbody of a vehicle. In order to charge efficiently, accurate alignment of the charging components must be accomplished, which can be achieved with a camera-based positioning system. Due to an air gap between both charging components, foreign objects can interfere with the charging process and pose potential hazards to the environment. Various foreign object detection systems have been developed with the motivation to increase the safety of wireless charging. In this paper, we propose a foreign object detection technique that utilizes the integrated camera of an embedded positioning system. Due to operation in an outdoor environment, we cannot determine the types of objects that may occur in advance. Accordingly, our approach achieves object-type independence by learning the features of the charging surface, to then classify anomalous regions as foreign objects. To examine the capability of detecting foreign objects, we evaluate our approach by conducting experiments with images depicting known and unknown object types. For the experiments, we use an image dataset recorded by a positioning camera of an operating wireless charging station in an outdoor environment, which we published alongside our research. As a benchmark system, we employ YOLOv8 (Jocher et al. in Ultralytics YOLO, 2023), a state-of-the-art neural network that has been used in various contexts for foreign object detection. While we acknowledge the performance of YOLOv8 for known object types, our approach achieves up to 18% higher precision and 46% higher detection success for unknown objects.

Abstract Pose estimation is an important component of many real-world computer vision systems. Most existing pose estimation algorithms need a large number of point correspondences to accurately determine the pose of an object. Since the number of point correspondences depends on the object’s appearance, lighting and other external conditions, detecting many points may not be feasible. In many real-world applications, the movement of objects is limited, e.g. due to gravity. Hence, detecting objects with only three degrees of freedom is usually sufficient. This allows us to improve the accuracy of pose estimation by changing the underlying equations of the perspective-n-point problem to three variables instead of six. By using the simplified equations, our algorithm is more robust against detection errors with limited point correspondences. In this article, we study three scenarios where such constraints apply. The first one is about parking a vehicle on a specific spot. Here, a stationary camera is detecting the vehicle to assist the driver. The second scenario describes the perspective of a moving camera detecting objects in its environment. This scenario is common for driver assistance systems, autonomous cars or mobile robots. Third, we describe a camera observing objects from a birds-eye view, which occurs in industrial applications. In all three scenarios, observed objects can only move in the ground plane and rotate around the vertical axis. Hence, three degrees of freedom are sufficient to estimate the pose. Experiments with synthetic data and real-world photographs have shown that our algorithm outperforms state-of-the-art pose estimation algorithms. Depending on the scenario, our algorithm is able to achieve 50% better accuracy, while being equally fast.

A truly ubiquitous, planet-wide Internet of Things requires ultra-low-power, long-lasting sensor nodes at its core so that it can be practically utilized in real-world scenarios without prohibitively high maintenance efforts. Recent advances in energy harvesting and low-power electronics have provided a solid foundation for the design of such sensor nodes. However, the issue of reliable two-way communication among such devices is still an active research undertaking due to the high energy footprint of traditional wireless transceivers. Although approaches such as radio duty cycling have proved beneficial for reducing the overall energy consumption of wireless sensor nodes, they come with trade-offs such as increased communication latency and complex protocols.

Navigation is an essential task for social robots. However, certain rules must be followed to allow them to move without causing distraction or discomfort to people. Considering that the context surrounding robots and persons affects the expected behavior, this work defines a social area around a person that adapts to the real situation. In addition, the social context of a person is extended to identify groups of people, which the robot should take into account while navigating. With this understanding of the surrounding of the robot together with the ability to predict the trajectory of individuals as well as groups, the proposed solution not only effectively addresses collision avoidance while promoting socially acceptable behavior but also outperforms the majority of recent works in terms of accuracy. Furthermore, a dedicated policy is introduced to react to social navigation conflicts. The evaluation performed in a simulated environment shows that the computation of our proposed solution is at least 8 times faster than the best state-of-the-art approach while preserving comparable social conduct. Also, the results of realistic experiments performed using Gazebo and a real robot are reported.

Energy harvesting, batteryless devices can mitigate many shortcomings of battery-operated devices in the coming years, particularly in the area of the Internet of Things. Solar energy is an accessible and effective source of power for such devices. However, the uncontrollable nature of solar energy makes the task of designing and evaluating energy harvesting devices a challenging one. In this work, we propose PROGNOES, a tool that allows the user to forecast the harvestable solar energy by a particular solar cell at a specific time and location. To carry out the forecasts, various algorithms and models are used not only to provide the theoretical solar irradiation but also to provide the weather condition data corresponding to the time and location of the forecasting. PROG-NOES can generate forecasts in the form of IV curves, similar to those generated by physical solar cells. Such IV curves can then be emulated with suitable tools to provide energy traces to the energy harvesting devices at design time, therefore facilitating the task of designing robust, reliable energy harvesting, batteryless devices. In our experiments, we find that the energy forecasts carried out by PROGNOES can reach a mean absolute error of 7.16 mW.

The growing field of the Internet of Things relies at the bottom on components with very scarce computing resources that currently do not allow complex processing of sensed data. Any computation involving Fast Fourier Transforms (FFT), Wavelet Transforms (WT), or simple sines and cosines is considered impractical on low-end devices due to the lack of floating point and math libraries. This article presents new techniques that make it possible to use these functions also on severely constrained target platforms. Current literature abounds with schemes to compute sine and cosine functions, with focus on speed, hardware footprint, software size, target type, or precision. Even so, there is no practical exploration of the design space available for embedded devices with limited resources, in particular when only integer operations are possible. We select an efficient set of recursive sine and cosine generators and measure the frequency, amplitude, and phase error over a wide parameter range. We show that their simplicity allows them to be implemented on the most bare targets with good precision, reducing power consumption and size while being the fastest on integer-only processors. We also introduce specially tailored FFT and WT algorithms and show that they are usable in practice while having an extremely small code footprint, good precision, and high speed.

Low-Power wireless technology has allowed the free and flexible deployment of embedded systems in a variety of scenarios. As more and more systems get deployed and an increasing number of devices are introduced in scenarios with already existing infrastructures, it becomes increasingly important to understand system properties with limited information and intervention from the user. In this paper, we focus on understanding the positioning of wireless devices in an indoor environment with use of connectivity information and a physical map of the scenario as well as optional anchor devices to further increase accuracy. With limited information, we explore how the location of devices can be estimated through the use of force-directed graphs augmented with specific domain knowledge. By looking at different indoor testbeds and challenging simulated scenarios, we investigate to what extent we can infer the correct positioning. We discuss the assumptions that needed to be made, thus exploring to what extent our approach can be employed in practice. In conclusion, this paper shows that it is possible to position devices correctly with only limited user intervention, having the network identify the displacement of its own components and supporting further reasoning about system properties.

The Internet of Things and Cyber-Physical Systems rely on wireless interconnections. The importance of understanding the behaviour of wireless communication in practice has promoted many experimental studies, characterising signal propagation in specific environments. Despite the gathered knowledge, the perceived discrepancy between abstract models and actual network performance has supported the belief that system design and debugging can rely only on direct experience and trial-and-error. As a result, reasoning about wireless systems is nowadays a tedious, manual process. This work walks the playground between model and reality to make wireless networks understand their own behaviour in the environment where they operate. First, we introduce a practical method able to efficiently and accurately characterise wireless signal propagation in 3D environments with obstacles. We then exploit the model to make systems autonomously derive the impact of obstacles such as doors and windows on the link conditions. With an outline of the operational scenario and measurements from deployed devices, it is possible to effortlessly generate a situated, dynamic wireless map. We demonstrate our approach in an indoor testbed, showing its practicability and effectiveness. Our evaluation shows that our approach attributes up to 91% of the measurements to the corresponding obstacles correctly. We also show that different modelling techniques have an impact on such detection accuracy, promoting the use of 3D environment descriptions.

Stationary low-power wireless networks are able to continuously monitor large areas for long periods of time. If densely deployed, they can accurately detect intruders and localise them. However such density requires high installation and maintenance costs, and no identification of the intruders can be made. On the other side, mobile robots are able to precisely monitor areas and identify intruders when present in their field of view. Advances in multi-robot coordination allow them to cover large areas as patrolling guards. Nevertheless, they usually leave areas uncovered, and they can only work under battery-constrained time conditions. In this paper, we investigate the trade-offs in a practical hybrid surveillance system. A sparse low-power wireless network continuously monitors the environment and roughly estimates events locations, while robots resting on strategic positions wait for warning calls to survey specific areas and accurately observe the ongoing events. As a result, only a moderate accuracy is required from the stationary network, allowing a notable simplification with respect to similar localisation systems. At the same time the robotic team size can be decreased and its coordination simplified. The on-demand activation drastically saves the energy and wear required by the continuous patrolling. In this paper, we provide an experimental analysis of such trade-offs and we report on the lessons learned.

Queueing systems are used to estimate the attributes of waiting lines, for example the number of people in line or the waiting time. This information helps to reduce the time spent waiting by balancing people amongst multiple lines or aids in the decision making about opening a new line. However, current queueing systems are often mounted at fixed positions, require user participation, or need time consuming manual calibration after each layout change. In this work, we introduce CutiQueue, a flexible and portable queueing system that is battery powered, needs no manual calibration or user participation. CutiQueue is based on passive presence detection using the RSSI of low cost Bluetooth Low Energy transceivers. We analyze the performance of the system in experiments using a prototype implementation to estimate the number of people waiting as well as the length of the line. The experimental evaluation indicates an average accuracy of 97.96% when compared to counting people walking or standing in the waiting line using a laser barrier.

Packet loss rate (PLR) is a crucial and popular link quality metric for wireless sensor networks (WSNs). In this paper, we investigate how to estimate PLR of an IEEE 802.15.4 link from the information that is easily obtained from radio chip. Specifically, we aim to establish a generalized model that connects PLR to link quality indicator (LQI), a physical layer link quality measure, and packet length under diverse environmental conditions. To this aim, an extensive experimental study considering various environmental factors and packet lengths is conducted, from which rich observations are made on the spatio-temporal characteristics of the dependency of PLR on LQI and packet length. Based on the observations, we propose a packet loss rate model as a function of LQI and packet length, that is applicable in all experimented scenarios. Besides, a comparison with a literature LQI-only based PLR model shows that our proposed model has higher accuracy for various packet lengths. Finally, we provide the implications of the empirical study and the guidelines for real-world WSN applications to construct and adapt the proposed PLR model in different environments.

Accurate detection of the channel quality degradation is crucial for applying effective remedial actions to ensure the reliability of IEEE 802.15.4 links. Without knowing the channel quality is degraded, remedial actions may lead to more packet losses, e.g., increasing transmission power may cause even more interference. In this work, we aim to detect the channel quality degradation that turns a good link into a bad one, based on the received signal strength of radio links. The detection should be accurate and robust to diverse channel characteristics and dynamic environmental changes. To achieve this, we propose RADIUS, a lightweight approach that lays its foundation on a thresholding technique based on Bayesian decision theory and combines it with techniques for adapting to environmental changes. Extensive evaluation of RADIUS on a testbed shows that the employed Bayes thresholding technique outperforms two relevant state-of-the-art thresholding techniques by providing a higher accuracy consistently for all links across the network. Besides, RADIUS is able to keep a low error rate of detection (5.78% on average) in a 72-hour experiment, adapting to environmental changes. Furthermore, we developed an exemplary application of RADIUS to show how an existing transmission power tuning scheme can benefit from using RADIUS as an accurate and robust trigger for taking remedial actions.

Modeling and prediction of Packet Loss Rate (PLR) of wireless links using hardware information is essential for the design of higher-layer protocols in Wireless Sensor Networks. While many previous studies revealed the spatio-temporal variation of various link quality metrics, how environment impacts on the mapping between PLR and hardware indicators still remains unclear. Without a comprehensive understanding of such environmental impact, the acquired empirical PLR models are severely limited to specific scenarios. In this paper, we present the results of indoor and outdoor experimental campaigns focusing on the impact of various environmental factors (e.g., obstacles, human activities, climate conditions) on the dependency between the link PLR, signal to noise ratio (SNR) and packet length. Rich observations are made on the spatio-temporal characteristics of the PLR-SNR relationship and our analysis shows that link PLR can be modeled, in all experimented scenarios, as an exponential function of SNR and packet length with two model parameters that may vary over space and time. Besides, implications of the observations are summarized, providing guidelines to construct and adapt PLR models in different environments.

A wide range of mobile devices, e.g., wireless sensor nodes, RFID tags and smartphones, gradually coalesces into the Internet of Things (IoT). Debugging and optimizing such systems is challenging and necessary to realize the foreseen vision. In this paper, we describe our endeavor to build and adapt a testbed enabling the analysis of different scenarios involving mobile elements. Instead of creating a versatile but expansive testbed based on free-ranging robots, we developed a set of specialized components based on model trains. Our goal was to save costs while providing repeatable and accurate experiments where the movement of the tested devices is not provided by battery powered platforms. %This endeavor did not come without trouble and more realistic requirements changed our planned path. The contribution of this paper is to describe the iterative design and realization process for each testbed component and to quantify the trade-offs between different approaches. We discuss how our solutions evolved due to the constraints of the real experiments that were carried out on the various instances of the testbed, with a particular focus on the experience we acquired in the process and the lessons we have learned along the way.

The wide availability of GPS-enabled devices has fueled the development of location-based applications. Among the most popular are navigation applications that guide users along their way. Due to the limited availability and precision of GPS in typical indoor environments, most navigation applications are limited to outdoor scenarios. To mitigate this, researchers and practitioners have been developing alternative systems to capture the position of a mobile device indoors. Using these systems, they clearly show that it is possible to provide precise location information at a reasonable cost. However, past applications in this domain have often focused exclusively on indoor scenarios.In this paper, we describe an approach to integrate the indoor and outdoor world to provide a seamless hybrid navigation experience. The approach builds upon our past work on indoor localization and integrates with our existing infrastructure for outdoor navigation that has been developed in the GAMBAS European research project. To evaluate the approach, we present data gathered from a real-world deployment as part of the SIMON European research project.

Fingerprinting-based indoor localization systems rely on stable signal distribution characteristics of fixed signal sources for location estimation. However, indoor environments are not static and changes in the environment can lead to displacement of some signal sources, potentially causing a drop in the localization performance. It is therefore necessary to regularly monitor the signal sources and manually recalibrate any whose signal distribution has changed. The effort for calibrating these systems is typically high, especially for large indoor environments. This paper proposes an approach for autonomously detecting the displacement of a signal source using only measurements collected by active users of the system. The proposed approach is demonstrated to reliably detect displaced signal sources as well as multiple simultaneous displacements of up to half of the deployed signals sources. It is further shown that the same measurements can be used to autonomously recalibrate the (WLAN- or Bluetooth-based) indoor localization system, achieving localization performance comparable to manual calibration.

The design and deployment of networked embedded systems is challenging. In particular, the environment in which the system operates has a severe impact on the final performance. Existing tools trade generality for specificity with arbitrary setups, e.g., in simulation, or specific configurations, e.g., in public testbeds. As a result, the peculiar effect of the target deployment scenario on the system performance can only be experienced by hand. Optimising and adapting the system to the operational environment becomes then a trial and error, impractical challenge. We introduce MOTION, a hybrid modelling technique able to characterise indoor wireless communication in the target environment from measurements and descriptions, e.g., maps, of the deployment area. By exploiting all available knowledge, our model is able to increase the achieved accuracy by up to 35% in our primary evaluation scenario with respect to conventional approaches. The computed models can then support traditional design and analysis tools in investigating the impact of arbitrary displacements of devices in the target environment. E.g., by controlling the relocation of a single node based on the information provided by the model, we were able to reduce the latency of a typical application by up to 21.4%. Ultimately, our work allows situated network optimisation of wireless systems where devices can be freely moved in the environment and the corresponding impact on the network performance can be estimated beforehand.

Cyber-Physical Systems are immersed in an environment in which they are expected to operate reliably. The specificity of the target scenario has, however, a unique impact on the system behaviour that is hard to foresee. Therefore, the deployment of these systems often results in a trial and error task. To address this challenge, we have realised a system design framework based on the ability of modelling the target environment through the fusion of in situ measurements and descriptions, e.g., maps, of the scenario. We demonstrate the ability of our solution to accurately perform the situated analysis of the spatial and temporal characteristics of wireless communication in an indoor testbed.

Radio signals can be used to detect the presence of a person (target) in an environment by analysing the fluctuations in the Received Signal Strength Indicator (RSSI).The velocity of the target can be estimated by examining the sequence of disturbances in consecutive radio links over a period of time.This requires knowledge of the deployment of the radio transceivers and the time when the target crosses the Line of Sight (LoS) of each radio link.However, it is not trivial to precisely estimate the exact time of the link crossing due to the broad range of RSSI fluctuations generated as the target approaches the link.In this paper, we evaluate and compare 15 techniques for estimating the velocity of the target and propose enhancements to some of the techniques.In our experiments the techniques perform with an average accuracy in the range between 13.02% and 96.18%, which corresponds to an average error of 0.05m/s for a moving target.

Fingerprinting-based indoor localization systems tend to achieve higher accuracy compared to other approaches such as signal propagation modeling. However, they also tend to have a higher effort/cost for deployment and maintenance. Changes in the configuration of the indoor space like moving of furniture, or defective signal sources can cause the signal characteristics in the environment to change significantly, and thereby render the fingerprint radio map (used for training the system) outdated. This leads to a drop in localization performance of the system over time. In this paper, we propose an approach to using the system infrastructure for periodically detecting changes in the signal characteristics and autonomously recalibrating the fingerprint radio map. We demonstrate that we can reliably detect changes in signal characteristics stemming from the dampening of a signal source (e.g. induced by moving of furniture) and recalibrate the localization system with an accuracy of 83% to 93% of the optimum localization performance achievable through manual system recalibration.

A key challenge for rapidly growing cities of today is to provide effective public transport services to satisfy the increasing demands for urban mobility. Toward this goal, the Internet of Things (IoT) has great potential to overcome existing deficiencies of public transport systems given its ability to embed smart technology into real-life urban contexts. In this paper, we show how this paradigm can be applied to the public transport domain and present the Urban Bus Navigator (UBN), an IoT enabled navigation system for urban bus riders. UBN provides two novel information services for bus users: 1) micro-navigation and 2) crowd-aware route recommendation. Micro-navigation refers to fine-grained contextual guidance of passengers along a bus journey by recognizing boarded bus vehicles and tracking the passenger's journey progress. Crowd-aware route recommendation collects and predicts crowd levels on bus journeys to suggest better and less crowded routes to bus riders. We present the technical system behind the UBN and report on results from an in-the-wild study in Madrid, Spain, that indicates removed barriers for public transport usage and a positive impact on how people feel about bus journeys.

Many applications of wireless sensor networks (WSNs) need to balance multiple yet often conflicting performance requirements such as high energy efficiency, high throughput, low delay and low loss. Finding appropriate WSN parameter configuration to achieve the best trade-off requires in depth understanding of the joint effect of key parameters residing at different layers on the performance. In this paper, we present an extensive experimental study on the data delivery performance of aWSN link, where 4 major performance metrics, namely energy, throughput, delay and loss, were measured over 6 months under around 50 thousand parameter configurations of 7 key stack parameters. Different from existing work, rich observations are made out of the extensive measurement data, with the focus on the joint effect of these parameters on the performance. Specifically, for each of the four performance metrics, a set of guidelines is derived for parameter optimization. In addition, we propose empirical models for each performance metric to quantify the joint effects, which enable finding optimal settings for parameters such as payload size or retransmissions, in consideration of link quality and other parameter settings, to achieve better performance trade-offs. To demonstrate the potential of this work, the obtained joint parameter optimization results are applied to an example. The outcome is compared with those achieved by following representative single-parameter tuning guidelines from the literature. The comparison reveals that by considering the joint effect of multi-layer parameters together, a WSN application can obtain a much improved performance trade-off.

Enabling the automated recognition and sharing of a user's context is a primary motivation for many pervasive computing applications. In the past, a significant amount of research has been focusing on the aspect of effective and efficient recognition. Yet, when context is shared with others, the resulting disclosure of personal information can have undesirable privacy implications. A common solution to this problem is the manual creation of an application-specific privacy policy that defines which information may be shared with whom. However, as the number of applications increases, such a manual approach becomes increasingly cumbersome and over time, it is likely to lead to incomplete or even inconsistent policies. In this paper, we discuss how a privacy policy can be derived automatically by analyzing the user's sharing behaviour when using online collaboration tools. Our approach retrieves shared content and the associated sharing settings, detects context types and automatically derives a privacy policy that reflects the user's past sharing behaviour. To validate our approach, we have implemented it as an extensible software library for the Android platform and we have developed plug-ins for two popular collaboration tools, namely Google Calendar and Facebook.

Intelligent environments rely on context information for providing services to their users. Among various existing platforms for context recognition, smart phones are one of the most widely used. However, despite numerous advantages, smart phones exhibit limited energy resources. To mitigate this there exist approaches for energy efficient context recognition for smart phones but these approaches are usually fine-tuned for specific types of context. As a result their applicability to other context types is limited. In this paper, we present an energy efficient context recognition system for smart phones which provides generalized applicability of four generic energy efficiency techniques which have been described in the literature and applied to location sensing using mobile phones. These four techniques as termed by authors include Suppression, Substitution, Adaptation and Piggybacking. Our system provides their generalized applicability by modelling context recognition applications using a state machine abstraction. Consequently the resulting applications are structured as combinations of states and transitions. To aid rapid prototyping our system is equipped with an off-line development tool which allows the creation and (code) generation of state machines using a graphical editor. We evaluate our system by creating a test application. Using a precise hardware set up, we perform energy measurements to demonstrate the energy savings of these different techniques.

Fingerprinting-based indoor localization involves building a signal strength radio map. This map is usually built manually by a person holding the mapping device, which results in orientation-dependent fingerprints due to signal attenuation by the human body. To offset this distortion, fingerprints are typically collected for multiple orientations, but this requires a high effort for large localization areas. In this paper, we propose an approach to reduce the mapping effort by modeling the WLAN signal attenuation caused by the human body. By applying the model to the captured signal to compensate for the attenuation, it is possible to generate an orientation-independent fingerprint. We demonstrate that our model is location and person independent and its output is comparable with manually created radio maps. By using the model, the WLAN scanning effort can be reduced by 75% to 87.5% (depending on the number of orientations).

Online collaboration tools such as Google+, Face-book or Dropbox have become an important and ubiquitous mediator of many human interactions. In the virtual world, they enable secure interaction by controlling access to shared resources. Yet relying on them to support synchronous direct interactions, such as face-to-face meetings, might be suboptimal as they require reliable online connectivity and even then often introduce delays. A much more efficient way of co-located resource sharing is the use of local communications, such as ad-hoc WiFi. Yet setting up the necessary encryption and authentication mechanisms is often cumbersome. In this paper, we present PIKE, a key exchange protocol that minimizes this configuration effort. PIKE piggybacks the exchange of keys on top of an existing service infrastructure. To support encryption or authentication without Internet connection, PIKE relies on triggers for upcoming personal interactions and exchanges keys before they take place. To evaluate PIKE, we present two example applications and we perform an experimental as well as an analytical analysis of its characteristics. The evaluation indicates that PIKE is broadly applicable, scales well enough to support larger events and provides a level of security that is (at least) comparable to the one provided by the underlying service.

The use of unmanned aerial systems (UAS) is growing in research and civil applications. Advancing technology offers huge potential which is just starting being used. We present a method to have a helicopter UAS interacting with the environment physically. This basic technology, which includes high precision control and a vision system can be adapted for different in situ measurement tasks. Applications cover a wide range from placement of sensors or direct measurements to sampling. While UAS systems get more complex, we describe a way of interacting with a them by means of an interactive control framework, which can also include other systems and sensors.

One of the key characteristics of pervasive computing applications is their ability to adapt to their environment. One possible approach to simplify the development of such adaptive applications is to use middleware to automate adaptation decisions. However, in many application scenarios, adaptation decisions can have critical security implications. Thus, when designing middleware to support adaptation it is necessary to take security constraints into account. In previous work, we presented generic role assignment as a middleware abstraction to support adaptation. In contrast to other approaches, role assignment enables adaptation within a single and across multiple smart environments in a flexible, application-overarching manner. In this paper, we extend the concept to support security constraints. Furthermore, we describe the necessary modifications to our existing role assignment system. Finally, we evaluate our approach showing resulting benefits and limitations. In comparison with existing approaches, the proposed approach is more flexible and does not introduce a single point of trust.

Existing context recognition applications for personal mobile devices are usually fine-tuned to recognize the set of characteristics required to support a particular user task. Outside of a laboratory environment, however, users are often involved in multiple tasks at a time which requires the simultaneous execution of several applications. Yet, due to the energy constraints of most personal mobile devices this approach is inherently limited in scale. In this paper, we show how this problem can be avoided by applying a component-based approach to application development and execution. We present a component system that enables developers to build individual applications in isolation without compromising runtime efficiency. When applications are executed simultaneously, the component system applies a novel technique called configuration folding to automatically remove redundancies. Our experimental evaluation of configuration folding shows that it can save up to 48 percent of energy when applied to a music and a speech detection application, thus, amortizing energy costs after a few seconds of execution.

Indoor localization based on signal strength fingerprinting has received significant attention from the community. This method is attractive because it does not require complex hardware beyond a simple radio transmitter. However, its main limitation is the inaccuracy caused by the variability of the signal strength. When applied to the localization of people, the signal variability can be attributed to three main sources: environmental dynamics (movement of people or objects), movement of transceiver (changes in the position and/or orientation of the transceivers) and body effects (distortion of the wireless signal due to body absorption). Our work focuses on the impact of the last two sources and provides two important contributions. First, we present an analysis to quantify the effects of antenna disorientation and transmitter misplacement. For the RFID system used in our work, these effects can decrease the localization accuracy by up to 50%. Motivated by these results, we identify parts of the human body where tags are less affected by unintentional movements. Second, we describe how multiple transmitters can be used to overcome the absorption effects of the human body. Our results indicate that four transmitters provide a reasonable trade-off between accuracy and hardware cost. We validate our findings through an extensive set of measurements gathered in a home environment. Our tests indicate that by following the guidelines proposed in this paper, the localization accuracy can improve from around 20% up to 88%.

Asynchronous low-power listening techniques reduce the energy footprint of radio communication by enforcing link layer duty cycling. At the same time, these techniques make broadcast traffic significantly more expensive than unicast traffic. Because broadcast is a key network primitive and is widely used in various protocols, recently several techniques have been proposed to reduce the amount of broadcast activity by merging broadcasts from different protocols. In this paper we focus on collection protocols and investigate the more extreme approach of eliminating broadcast completely. To this end, we design, implement and, evaluate a Broadcast-Free Collection Protocol, BFC. We derive first-order models to quantify the costs of broadcasts, and evaluate the performance of BFC on a public testbed. Compared to the Collection Tree Protocol, the de facto standard for data collection, BFC achieves double-digit percentage improvements on the duty cycles. The specific benefits to individual nodes depend on the relative cost of unicast activity; we show that the nodes that benefit the most are the sink's neighbors, which are crucial for network lifetime extension. Eliminating broadcast also brings several other advantages, including extra flexibility with link layer calibrations and energy savings in the presence of poor connectivity.

A major characteristic of pervasive computing applications is their ability to adapt themselves to changing execution environments and physical contexts. In this article, we analyze different kinds of adaptations and introduce a multidimensional classification for them. On this basis, we propose a novel approach for peer-to-peer-based pervasive computing that provides support for the identified classes and integrates them in a multilevel architecture. We give a comprehensive overview of this architecture and its current realization in the Peer-to-Peer Pervasive Computing (3PC) project, discussing what adaptation is realized on each level, how the levels interact with each other, and how the overall system benefits from the integrated treatment of adaptation.

Logging and tracing are important methods to gain insight into the behavior of sensor network applications. Existing generic solutions are often limited to nodes with a direct serial connection and do not provide the required efficiency for network-wide logging. Instead, this is often realized by application-specific subsystems developed for custom logging statements. In this paper, we present TinyLTS - a generic and efficient Logging and Tracing System for TinyOS. TinyLTS consists of a compiler extension that separates dynamic from static information at compile time, a declarative solution for inserting logging statements, an extensible framework for flexible storing and transmitting of logging data and a frontend for recombining dynamic and static information. Our system provides concise yet expressive programming abstractions for the developer combined with efficiency comparable to custom solutions.

Due to the fact that distant objects are often less relevant to an application than objects in the proximity, middleware systems for pervasive computing typically exploit locality to improve efficiency. To do this, they configure the environment by introducing logical boundaries that reduce the number of interacting devices. Yet, in cases where applications require the interaction with distant objects, the boundaries become an artificial barrier that must be overcome by supplemental mechanisms. In this paper, we show how this problem can be avoided by using role assignment as a generic mechanism for environment configuration. To do this, we first derive the requirements for configuring a pervasive computing environment. We discuss how these requirements can be met by means of role assignment. To evaluate the approach, we present a prototypical implementation which we use to quantify the resulting overheads. The results indicate that role assignment enables a more flexible definition of boundaries at a low cost.

Cycle-accurate emulation of sensor networks allows a detailed analysis of platform target code for development and evaluation. However, the high overhead incurred by providing the necessary fidelity limits the size of the emulated networks considerably. The use of multiple cores provided by modern hardware can significantly improve the speed of emulation but requires synchronization algorithms to preserve causality. Based on the well-known multithreaded event-driven emulator Avrora, we investigate a number of synchronization methods including an algorithm that does not require any locks to improve the performance. We show that both the speed and the scalability can be significantly improved without sacrificing correctness. Additionally, we evaluate the impact of modern CPU technologies such as simultaneous multithreading on emulation performance.

This paper and video are devoted to the last experiments and demonstration of the AWARE project (European Commission, FP6) carried out in Utrera, near Seville (Spain) May, 2009. The project has developed and validated in field experiments a platform providing the functionalities required for the cooperation of aerial robots with ground sensor-actuator wireless networks, including static and mobile nodes carried by people and vehicles. The project demonstrated the self-deployment, self-configuration and self-repairing of the network by using autonomous helicopters that transported and deployed sensor nodes and loads. These features are highly relevant in natural and urban environments without pre-existing infrastructure or where the infrastructure has been damaged or destroyed. Two validation scenarios have been considered: Disaster Management/Civil Security and Filming.

Boundary recognition is an important and challenging issue in wireless sensor networks when no coordinates or distances are available. The distinction between inner and boundary nodes of the network can provide valuable knowledge to a broad spectrum of algorithms. This article tackles the challenge of providing a scalable and range-free solution for boundary recognition that does not require a high node density. We explain the challenges of accurately defining the boundary of a wireless sensor network with and without node positions and provide a new definition of network boundary in the discrete domain. Our solution for boundary recognition approximates the boundary of the sensor network by determining the majority of inner nodes using geometric constructions, which guarantee that for a given d , a node lies inside of the construction for a d -quasi unit disk graph model of the wireless sensor network. Moreover, such geometric constructions make it possible to compute a guaranteed distance from a node to the boundary. We present a fully distributed algorithm for boundary recognition based on these concepts and perform a detailed complexity analysis. We provide a thorough evaluation of our approach and show that it is applicable to dense as well as sparse deployments.

The availability of secret keys is a precondition for the use of many security solutions and protocols. However, securely assigning such keys to nodes is a challenging task in the context of wireless sensor networks. In this paper we present a novel solution for a secure key assignment in wireless sensor networks that can be used during the initial configuration of nodes or for an ad-hoc key assignment by mobile nodes. The idea is to transmit the key information over a side channel using a controllable light source as the sender and the light sensors available on wireless sensor nodes as receivers. We demonstrate that our solution fulfills the relevant security requirements while at the same time being cost effective and easy to use.

Wireless sensor networks play an essential role in many pervasive computing scenarios as providers of context data. However, interacting with sensor nodes and selecting specific nodes for an interaction is difficult due to the constraints of the sensor node hardware. In this paper, we present and discuss three different approaches for this node interaction problem based on gestures, on light signals and on information provided by the sensor nodes using their LEDs. We demonstrate in our evaluation that these mechanisms solve the interaction problem for a variety of scenarios and effectively support the user in the interaction process. At the same time, our solutions set only low requirements on the wireless sensor node hardware.

Most work in sensor networks tries to maximize network lifetime. However, for many applications the required lifetime is known in advance. Therefore, application quality should rather be maximized for that given time. Levels , the approach presented in this article, is a programming abstraction for energy-aware sensor network applications that helps to meet such a user-defined lifetime goal by deactivating optional functionality. With this programming abstraction, the application developer defines so-called energy levels . Functionality in energy levels is deactivated if the required lifetime cannot be met otherwise. The runtime system uses data about the energy consumption of different levels to compute an optimal level assignment that maximizes each node's quality for the time remaining. As described in this paper, Levels includes a completely distributed coordination algorithm that balances energy level assignments and keeps the application quality of the network roughly constant over time. In this approach, each node computes its schedule based on those of its neighbors. As the evaluation shows, applications using Levels can accurately meet given lifetime goals with only small fluctuations in application quality. In addition, the runtime overhead both for computation and for communication is negligible.

Boundary recognition is an important and challenging issue in wireless sensor networks when no coordinates or distances are available. The distinction between inner and boundary nodes of the network can provide valuable knowledge to a broad spectrum of algorithms. This paper tackles the challenge of providing a scalable and range-free solution for boundary recognition that does not require a high node density. Our solution approximates the boundary of the sensor network by determining the inner nodes using geometric constructions that guarantee that, for a given d, a node lies inside of the construction for a d-quasi unit disk graph model of the wireless sensor network. Moreover, such geometric constructions make it possible to compute a guaranteed distance from a node to the boundary. We provide a thorough evaluation of our approach and show that it is applicable to dense as well as sparse deployments.

The lifetime requirements on wireless sensor networks often require the redundant deployment of sensor nodes with appropriate management mechanisms based on node clustering. Yet, existing clustering approaches do not take the primary task of sensor networks into account: performing relevant measurements. They usually form 'arbitrary' clusters, e.g., using connectivity information, and thus, the resulting measurements are often of only limited use to the applications. This problem can be avoided by considering application-specific semantics. For indoor applications, the notion of a room provides a natural unit of clustering since walls are constructed deliberately to ensure locality. This paper shows that it is feasible to automatically create clusters that reflect boundaries between rooms by analyzing the measurements of inexpensive, broadly available sensors. The paper first analyzes the applicability of statistical clustering methods and based on this analysis, it proposes and evaluates a lightweight approach to determine clusters in real deployments.

Virtual memory has been successfully used in different domains to extend the amount of memory available to applications. We have adapted this mechanism to sensor networks, where, traditionally, RAM is a severely constrained resource. In this paper we show that the overhead of virtual memory can be significantly reduced with compile-time optimizations to make it usable in practice, even with the resource limitations present in sensor networks.

The performance of distributed algorithms in mobile ad hoc networks is strongly influenced by the connectivity of the network. In cases where the connectivity is low, network partitioning occurs. The mobility and the density of network nodes as well as the communication technology are fundamental properties that have a large impact on partitioning. A detailed characterization of this behavior helps to improve the performance of distributed algorithms. In this paper we introduce a set of metrics that characterize partitioning in mobile ad hoc networks. Based on an extensive simulation study we show the impact of node mobility, density and transmission range on the proposed metrics for a wide range of network scenarios.

In this paper we present Levels, a programming abstraction for energy-aware sensor network applications. Unlike most previous work it does not try to maximize network lifetime but rather helps to meet user-defined lifetime goals while maximizing application quality. Levels is targeted to applications where there is no redundancy and no node should fail early.

Data Centric Storage (DCS) is a powerful storage paradigm for wireless ad hoc networks. In mobile ad hoc networks (MANETs), however, the mobility and varying density of nodes may significantly impact the efficiency of data access and the level of data consistency for existing DCS mechanisms. In this paper, we propose an efficient resilience mechanism for data centric storage that supports DCS in mobile environments. We introduce a novel indirection strategy that enables us to distinguish the storage of data at dedicated server nodes from the storage of additional information to locate these servers. Our approach places server location information dynamically in strategic parts of the network based on its current topology. Combining our server location advertisement with any geographic routing protocol, we provide a robust data update and query processing technique for data centric storage in MANETs. We show analytically and by means of experimental evaluations that, despite the additional indirection during packet forwarding, our approach provides superior storage and retrieval performance than the original DCS algorithm even for large amounts of dynamic data.

In the area of wireless sensor networks, cross-layer interactions are often preferred to strictly layered architectures. However, architectural properties such as modularity and the reusability of components suffer from such optimizations. In this paper we present TinyXXL that provides programming abstractions for data exchange, a form of cross-layer interaction with a large potential for optimizations. Our approach decouples components providing and using data, and it allows for automatic optimizations of applications composed of reusable components. Its runtime representation is efficient regarding memory consumption and processing overhead

Mobility plays a major role in mobile ad hoc networks (MANETs) since it stresses networking tasks such as routing on one hand but aids to increase the network capacity and to overcome network partitioning on the other hand. To benefit from node mobility, a new class of MANET protocols and applications are designed to be delay-tolerant and mobility-aided. For delay-tolerant mobility-aided networking mobility on a large time-scale is a key feature. So far, in MANETs, the mobility is investigated on a short time-scale. That is why we present novel mobility metrics that quantify large time-scale mobility. Our approach is based on the pair-wise contacts between mobile nodes. We present a detailed statistical study of our novel metrics using the widely used random waypoint mobility model as an example. For the random waypoint model we introduce an analytical model, which allows protocol developers to analytically compute some of the designed metrics. In order to provide an easy access to these metrics in a network simulator, we provide a framework for ns-2.

The proliferation of mobile devices has led to the creation of hoarding algorithms that attempt to mitigate the problems related with disconnected operation or with the operation in areas where bandwidth is either scarce or very expensive. Traditional hoarding approaches use probability access tables to determine what information needs to be sent to the mobile device, but fail to take the structured nature of data into account. In this paper, we present an enhanced hoarding approach for semistructured information that relies on the analysis of graphs to determine the information that needs to be hoarded. We show by means of experimental evaluations on Web pages that our approach outperforms other hoarding algorithms that treat information as the combination of unrelated items.

No abstract available.

The fact that, most of the time, the integration of different catalogs implies the generation, maintenance and adaptation of a global catalog that forwards the appropriate queries to the corresponding local catalogs for evaluation, requires us to perform query rewriting in order to provide the user with an answer. We provide some insight on the adaptive evaluation techniques for querying XML-based electronic catalogs. This technique eliminates the need to perform an explicit query rewriting when performing a query upon a global catalog, while still obtaining accurate answers, as we show in our experiments.

No abstract available.