Publications

The micro-ROS stack (micro.ros.org) integrates microcontrollers seamlessly with standard ROS 2 and brings all major ROS concepts such as nodes, publisher, subscriptions, parameters, and lifecycle onto deeply embedded systems. This enables accessing all software using the same ROS tools and APIs, regardless of the underlying computing hardware and operating system. Micro-ROS supports a broad spectrum of microcontroller families and the main open-source real-time operating systems like FreeRTOS, Zepyhr, or NuttX. It supports various microcontroller- or RTOS-specific build systems and provides ROS-CLI-based build tooling. Micro-ROS is an open-source project that has been under development at github.com/micro-ROS since 2018. It was initiated by the EU-funded innovation activity OFERA. During the the last two years, micro-ROS has been adopted by a relevant group of professional users inside the ROS community. In this chapter, we give a technical introduction to the micro-ROS stack including APIs and architecture, as well as the corresponding middleware Micro XRCE-DDS. Furthermore, tutorials for a simple application with an ESP32 microcontroller are provided together with a report on three use-cases from industrial and research applications.

The Functional Mock-up Interface (FMI) is a widely used industry standard for exchange and co-simulation of dynamic models as Functional Mock-up Units (FMU). It is supported by more than 100 modeling and simulation tools. In this chapter, we present two implementations of FMI that bridge the gap between these tools and the ROS and Gazebo community: First, the fmi_adapter package for running/simulating FMUs in ROS nodes, from https://github.com/boschresearch/fmi_adapter_ros2. Second, the gazebo-fmi package for integrating FMUs with Gazebo, from https://github.com/robotology/gazebo-fmi. After an introduction to the FMI standard, this chapter provides step-by-step, hands-on examples for both packages, followed by interface descriptions and selected implementation details. In addition to these tutorial-style sections, the chapter also provides comprehensive descriptions of two use-cases. First, it explains how the fmi_adapter enabled a convenient model-based control design workflow for a self-driving vehicle for industrial logistics. Second, it reports on the simulation of electrical actuators in Gazebo from a Modelica model.

Multi-modal robot programming with natural language and demonstration is a promising technique for efficient teaching of manipulation tasks in industrial environments. In particular, with modern dual-arm robots designed to quickly take over tasks at typical industrial workbenches, the direct teaching of task sequences hardly utilizes the robots' capabilities. We therefore propose a two-staged approach that combines natural language instructions and demonstration with simultaneous task allocation and motion scheduling based on constraint programming. Instead of providing a task description and demonstrations that are replayed to a large extent, the user describes tasks to be scheduled with all relevant constraints and demonstrates relevant locations relative to workpieces and other objects. With explicitly stated constraints on the partial ordering of tasks, the solver allocates the tasks to the robot arms and schedules them in time while avoiding self-collisions and reducing the makespan in our experiment by 33%. The linguistic concepts of naming and grouping enable systematic reuse of sub-task ensembles. The proposed approach is evaluated with four variants of a gluing use-case from furniture assembly in user studies with ten participants. In these user studies, we observed a speed-up for the task definition of more than 6 times compared to a textual specification of the planning problems using the Python-based planner API.

The ongoing proliferation of sensing technologies constitutes a huge potential for context-aware computing. It allows selecting relevant information about our physical environment from different sources and providers all over the globe. A fundamental challenge is how to provide efficient access to these immense amounts of distributed dynamic context information - particularly due to the mobility of devices and other entities. To enable such access to current and past position information about moving objects, we propose a family of protocols (CDR, GRTS) for efficiently tracking a moving object's trajectory at some remote database in real-time as well as a distributed indexing scheme (DTI) for optimized access to trajectory data that is partitioned in space to multiple database servers. For discovering context information that is relevant for the situation of an application, we propose a powerful formalism for describing context models in a concise manner and a tailored multidimensional data structure (SDC-Tree) for retrieving relevant context models out of potentially millions of descriptions.

The management of positions of mobile objects is an essential prerequisite for many context-aware systems such as advanced traffic management systems or personal assistance systems. In this paper, we present two approaches for the scalable tracking of mobile object trajectories and the efficient processing of continuous spatial range queries, respectively. We show in detail how both approaches utilize the basic concepts of accuracy relaxation and utilization of context information, such as movement predictions, to minimize the number of position updates, the size of trajectory data, and the number of energy-consuming position sensing operations.

Moving objects databases (MOD) manage trajectory information of vehicles, animals, and other mobile objects. A crucial problem is how to efficiently track an object's trajectory in real-time, in particular if the trajectory data is sensed at the mobile object and thus has to be communicated over a wireless network.

We propose a family of tracking protocols that allow trading the communication cost and the amount of trajectory data stored at a MOD off against the spatial accuracy. With each of these protocols, the MOD manages a simplified trajectory that does not deviate by more than a certain accuracy bound from the actual movement. Moreover, the different protocols enable several trade-offs between computational costs, communication cost and the reduction of the trajectory data: Connection-Preserving Dead Reckoning (CDR) minimizes the communication cost using dead reckoning, a technique originally designed for tracking an object's current position. Generic Remote Trajectory Simplification (GRTS) further separates between tracking of the current position and simplification of the past trajectory and can be realized with different line simplification algorithms. For both protocols, we discuss how to bound the space consumption and computing time at the moving object and thereby present an effective compression technique to optimize the reduction performance of real-time line simplification in general.

Our evaluations with hundreds of real GPS traces show that a realization of GRTS with a simple simplification heuristic reaches 85 to 90% of the best possible reduction rate, given by retrospective offline simplification. A realization with the optimal line simplification algorithm by Imai and Iri even reaches more than 97% of the best possible reduction rate.

Differential drive mobile robots often use one or more caster wheels for balance. Caster wheels are appreciated for their ability to turn in any direction almost on the spot, allowing the robot to do the same and thereby greatly simplifying the motion planning and control. However, in aligning the caster wheels to the intended direction of motion they produce a so-called bore torque. As a result, additional motor torque is required to move the robot, which may in some cases exceed the motor capacity or compromise the motion planner's accuracy. Instead of taking a decoupled approach, where the navigation and disturbance rejection algorithms are separated, we propose to embed the caster wheel awareness into the motion planner. To do so, we present a caster-wheel-aware term that is compatible with MPC-based control methods, leveraging the existence of caster wheels in the motion planning stage. As a proof of concept, this term is combined with a model-predictive trajectory tracking controller. Since this method requires knowledge of the caster wheel angle and rolling speed, an observer that estimates these states is also presented. The efficacy of the approach is shown in experiments on an intralogistics robot and compared against a decoupled bore-torque reduction approach and a caster-wheel agnostic controller. Moreover, the experiments show that the presented caster wheel estimator performs sufficiently well and therefore avoids the need for additional sensors.

Robotic systems are typically subject to real-time constraints. Still, the ROS ecosystem-the most popular repository of open-source robotics software-exhibits little evidence of the use of real-time theory to bound or control worst-case response times. Hurdles to adoption are the amount of expertise required to correctly use real-time scheduling mechanisms and the inherent unpredictability of typical robotics workloads, which defy static provisioning. To overcome these hurdles, ROS-Llama, an automatic latency manager for ROS2, is proposed. Crucially, use of ROS-Llama requires only little effort and knowledge of realtime concepts. Relevant properties of ROS2 and essential requirements of the robotics domain are identified, and the conceptual and practical challenges in developing such a mostly automatic tool are discussed. Experiments on a mobile robot demonstrate the viability of the approach and show that ROS-Llama reduces the maximum observed latency under load compared to the default Linux scheduler. Finally, open problems in the underlying real-time analysis and major platform limitations in Linux and ROS2 that prevent further improvements are identified.

For most applications in mobile robotics, precise state estimation is essential. Typically, the state estimation is based on the fusion of data from different sensors. In practice, these sensors differ in their characteristics and measurements are available to the sensor fusion algorithm only with delay. Based on a brief survey of sensor fusion approaches that consider delayed and out-of-sequence availability of measurements, suitable approaches for applications in mobile robotics are identified. In a consumer robot use-case, experiments show that the estimation is biased if delayed availability of measurements is not considered appropriately. However, if delays are considered in the fusion process, the estimation bias is reduced to almost zero and in consequence, the estimation performance is distinctly improved. Two computational favorable approximative methods are described and provide almost the same accuracy as – theoretically optimal – brute-force filter recalculation at much lower and well-distributed computational costs.

Modern lightweight dual-arm robots bring the physical capabilities to quickly take over tasks at typical industrial workplaces designed for workers. Low setup times - including the instructing/specifying of new tasks - are crucial to stay competitive. We propose a constraint programming approach to simultaneous task allocation and motion scheduling for such industrial manipulation and assembly tasks. Our approach covers the robot as well as connected machines. The key concept are Ordered Visiting Constraints, a descriptive and extensible model to specify such tasks with their spatiotemporal requirements and combinatorial or ordering constraints. Our solver integrates such task models and robot motion models into constraint optimization problems and solves them efficiently using various heuristics to produce makespan-optimized robot programs. For large manipulation tasks with 200 objects, our solver implemented using Google's Operations Research tools requires less than a minute to compute usable plans. The proposed task model is robot-independent and can easily be deployed to other robotic platforms. This portability is validated through several simulation-based experiments.

This paper presents a new planar wheel model with bore friction, a control strategy to avoid locking conditions of floor vehicles with caster wheels, and the new FMIAdapter software package, which connects the Functional Mock-up Interface (FMI) standard with the Robot Operating System (ROS). It is demonstrated how this technology enables a convenient model-based control design workflow. The approach is applied to the ActiveShuttle, a self-driving vehicle (SDV) for industrial logistics. After modeling the wheel friction characteristics of the ActiveShuttle, a feed forward controller to avoid high friction torques at the caster wheels in critical operation scenarios is designed and validated by model-in-the-loop simulations. The control function is exported as Functional Mock-up Unit (FMU) for co-simulation. With help of the FMI-Adapter package, the FMU is integrated as ROS node into the service-oriented robot control architecture, enhancing the existing motion controller. The functionality and performance is tested and successfully verified on the ActiveShuttle Dev Kit prototype.

Preprogramming of tasks still plays an important role in complex robotic systems despite the advances in automated planning and symbolic learning. Often, it is desired that end-users implement further tasks to adapt the robotic application to their needs. These user-defined tasks have to meet safety and integrity constraints for protecting the robotic platform and its users. We introduce a verifiable task specification language (vTSL) that enables to automatically prove that a task specification satisfies a set of predefined or task-specific constraints. We illustrate our approach using an example of a self-driving vehicle for intra-logistics and report experiences with two commercial applications.

Real-time properties such as reaction times play a safety-critical role for service robot applications. Current robotic software frameworks and middlewares, however, abstract from the underlying executing platform and execution management of the operating system thereby hiding relevant timing information. In other domains, such as automotive, model-based timing analysis is used early in the development process to analyze timing properties of the system. These approaches are not yet common in the service robotics domain. In this paper, we extend a model-driven development approach for robotic systems and enhance the system-configuration step to include a novel performance view that enables model-based timing analysis for robotic applications. Our evaluation shows that the performance analysis reasonably represents the real robot's run-time performance.

Scaling heterogeneous information systems (HIS) to thousands of sources poses particular challenges to source discovery. It requires a powerful formalism for describing the contents of the sources in a concise manner and for formulating compatible queries as well as a suitable structure for indexing and retrieving the source descriptions efficiently.

We propose an extended logic-based description formalism for large-scale HIS with structured sources and a shared ontology. The formalism refines existing approaches that describe the sources by constraints on the attribute value ranges in several ways: It allows for complex, nested descriptions based on defined classes. It supports alternative descriptions to express that a source may be discovered by different combinations of constraints. Finally, it allows to adjust between positive matching, similar to keyword-based discovery, and negative matching, as used in existing logic-based approaches.

We further propose the SDC-Tree for indexing such source descriptions. To allow for efficient discovery, the SDC-Tree features multidimensional indexing capabilities for the different attributes and the IS-A hierarchy of the shared ontology, but also incorporates the existence or absence of constraints. For this purpose, it supports three different types of node split operations which exploit the expressiveness of the description formalism. Therefore, we also propose a generic split algorithm which can be used with arbitrary ontologies.

Moving objects databases (MODs) have been proposed for managing trajectory data, an important kind of information for pervasive applications. To save storage capacity, a MOD generally stores simplified trajectories only. A simplified trajectory approximates the actual trajectory of the mobile object according to a certain accuracy bound.

In order to minimize the costs of communicating position information between mobile object and MOD, the trajectory simplification should be performed by the mobile object. To assure that the MOD always has a valid simplified trajectory of the remote object, we propose the generic remote trajectory simplification protocol (GRTS) allowing for computing and managing a simplified trajectory in such a system in real-time.

We show how to combine GRTS with existing line simplification algorithms for computing the simplified trajectory and analyze trade-offs between the different algorithms. Our evaluations show that GRTS outperforms the two existing approaches by a factor of two and more in terms of reduction efficiency. Moreover, on average, the reduction efficiency of GRTS is only 12% worse compared to optimal offline simplification.

Space-partitioned Moving Objects Databases (SP-MODs) allow for the scalable, distributed management of large sets of mobile objects' trajectories by partitioning the trajectory data to a network of database servers.

Processing a spatio-temporal query q therefore requires efficiently routing q to the servers storing the affected trajectory segments. With a coordinate-based query - like a spatio-temporal range query - the relevant servers are directly determined by the queried range. However, with trajectory-based queries - like retrieving the distance covered by a certain object during a given time interval - the relevant servers depend on actual movement of the queried object. Therefore, efficient routing mechanisms for trajectory-based queries are an important challenge in SP-MODs.

In this paper, we present the Distributed Trajectory Index (DTI) that allows for such efficient query routing by creating an overlay network for each trajectory. We further present an enhanced index called DTI+S. It accelerates the processing of queries on aggregates of dynamic attributes, like the maximum speed during a time interval, by augmenting DTI with summaries of trajectory segments. Our simulations with a network of 1000 database servers show that DTI+S can reduce the overall processing time by more than 98%.

Moving objects databases (MODs) store objects' trajectories by spatiotemporal polylines that approximate the actual movements given by sequences of sensed positions. Determining such a polyline with as few vertices as possible under the constraint that it does not deviate by more than a certain accuracy bound epsilon from the sensed positions is an algorithmic problem known as trajectory reduction.

A specific challenge is online trajectory reduction, i.e. continuous reduction with position sensing in realtime. This particularly is required for moving objects with embedded position sensors whose movements are tracked and stored by a remote MOD.

In this paper, we present Connection-preserving Dead Reckoning (CDR), a new approach for online trajectory reduction. It outperforms the existing approaches by 30 to 50 CDR requires the moving objects to temporally store some of the previously sensed positions. Although the storage consumption of CDR generally is small, it is not bounded. We therefore further present CDRM whose storage allocation and execution time per position fix can be adjusted and limited. Even with very limited storage allocations of less than 1 kB CDRM outperforms the existing approach by 20 to 40

Many location-based systems rely on fine-grained tracking of mobile objects that determine their own locations with sensing devices like GPS receivers. For these objects, energy is a very valuable and limited resource. A distance-based reporting protocol can be employed to reduce the energy they consume by sending position updates. However, the energy required for position sensing has not been considered in the past.

In this paper, we study how the resulting energy consumption from both sensing and update operations can be reduced for distance- based reporting. We show that significant savings are achieved by sending position updates earlier than actually required. For uniform movement, we derive the minimal power consumption analytically. Subsequently, two novel online heuristics are proposed that control the sending of position updates at runtime. Their effectiveness is validated by extensive simulations.

This paper provides a perspective on the literature and current challenges in Multi-Agent Systems for interoperable robot navigation in industry. The focus is on the multi-agent decision stack for Autonomous Mobile Robots operating in mixed environments with humans, manually driven vehicles, and legacy Automated Guided Vehicles. We provide typical characteristics of such Multi-Agent Systems observed today and how these are expected to change on the short term due to the new standard VDA5050 and the interoperability framework OpenRMF. We present recent changes in fleet management standards and the role of open middleware frameworks like ROS2 reaching industrial-grade quality. Approaches to increase the robustness and performance of multi-robot navigation systems for transportation are discussed, and research opportunities are derived.

High-level deliberation in robotic systems has to handle three different but closely interwoven aspects simultaneously: intended tasks, contingencies, and system-level errors. To reduce the complexity, we propose the system modes concept to abstract runtime state information and reconfiguration of the software components of the underlying layers by a model-based approach. The proposed concept introduces a notion of hierarchically composed, virtual subsystems as well as a notion of modes that determine their configuration. It features an inference engine to deduce the modes of the subsystems from the components and top-down reconfiguration mechanisms. Repetitive and fine-grained communication between high-level deliberation and underlying software components can thereby be reduced, decreasing unwanted coupling between system parts.

Robots are networks of a variety of computing devices, such as powerful computing platforms but also tiny microcontrollers. The Robot Operating System (ROS) is the dominant framework for powerful computing devices. While ROS version 2 adds important features like quality of service and security, it cannot be directly applied to microcontrollers because of its large memory footprint. The micro-ROS project has ported the ROS 2 API to microcontrollers. However, the standard ROS 2 concepts are not enough for real-time performance: In the ROS 2 release “Foxy”, the standard ROS 2 Executor, which is the central component responsible for handling timers and incoming message data, is neither real-time capable nor deterministic. Domain-specific requirements of mobile robots, like sense-plan-act control loops, cannot be addressed with the standard ROS 2 Executor. In this paper, we present an advanced Executor for the ROS 2 C API which provides deterministic scheduling and supports domain-specific requirements. A proof-of-concept is demonstrated on a 32-bit microcontroller.

To increase the flexibility in industrial manufacturing, two-arm robots that may quickly take over typical human assembly tasks have been developed. One important issue for the easy and quick preparation for new assembly tasks is integrated task and motion planning for such robots. We propose a CSP-based planning approach based on a general constraint model for assembly tasks. Our model combines both, the space and collision-free motion aspects as well as a general graph-based approach to represent the assembly goal and the possible assembly actions, in one formalism. We explain the implementation of our model in the constraint programming language MiniZinc and present several optimizations to reduce the computing time of common solvers significantly.

Position information of moving objects plays a vital role in many pervasive applications. Therefore, moving objects databases (MODs), which can manage trajectory data of a number objects, are used in many pervasive systems. A crucial problem with MODs is how to efficiently track a remote object's trajectory in real-time, i.e. how to continuously report the sensed trajectory data to the MOD with minimal effort. For this purpose, we present a prototypical implementation of the Generic Remote Trajectory Simplification (GRTS) protocol, which optimizes storage consumption, processing, and communication costs. Our prototypical system includes a fully functional MOD as well as map-based mobile applications for subnotebooks and smartphones to illustrate the functioning of GRTS.

Position information of moving as well as stationary objects is generally subject to uncertainties due to inherent measuring errors of positioning technologies, explicit tolerances of position update protocols, and approximations by interpolation algorithms. There exist a variety of approaches for specifying these uncertainties by mathematical uncertainty models such as tolerance regions or the Dilution of Precision (DOP) values of GPS. In this paper we propose a principled generic uncertainty model that integrates the different approaches and derive a comprehensive query interface for processing spatial queries on uncertain position information of different sources based on this model. Finally, we show how to implement our approach with prevalent existing uncertainty models.

Context-aware applications rely on models of the physical world. Within the Nexus project, we envision a World Wide Space which provides the conceptual and technological framework for integrating and sharing such context models in an open, global platform of context providers. In our ongoing research we tackle important challenges in such a platform including distributed processing of streamed context data, situation recognition by distributed reasoning, efficient management of context data histories, and quality of context information. In this paper we discuss our approach to cope with these challenges and present an extended Nexus architecture.

Dieser Beitrag beschreibt die Verteilung kontextbezogener Informationen mittels eines erweiterten Instant-Messaging-Dienstes. Dieser Dienst ermöglicht das Senden von Nachrichten an alle Teilnehmer, die einen bestimmten Kontext besitzen und sich z.B. an einem bestimmten Ort aufhalten oder bestimmte Interessen besitzen. Als Basis dient das Extensible Messaging and Presence Protokoll (XMPP) sowie ein XMPP-basierter Instant-Messaging-Dienst. Wir beschreiben Protokoll- und Architekturerweiterungen für die Integration von Kontextinformationen in das XMPP-Protokoll und die Server-Infrastruktur. Der erweiterte Dienst unterstützt insbesondere die Server-seitige Filterung von Nachrichten aufgrund von Kontextinformationen und ermöglicht dadurch die effiziente Nachrichtenverteilung.

Context-awareness refers to the idea that applications adapt to their context of use including, for example, location, nearby devices and user habits. In the last years, billions of sensors have been deployed all over the globe, which allow creating comprehensive context models of our physical environment. The availability of such models constitutes a huge potential for context-aware computing as it allows selecting relevant context information from different providers all over the globe. However, such sharing of context information poses a number of challenges. A fundamental problem is how to provide efficient access to the immense amounts of distributed dynamic context information - particularly due to the mobility of devices and other entities.

To enable efficient access to current and past position information about moving objects, we propose a family of trajectory tracking protocols (CDR, GRTS) as well as a distributed indexing scheme (DTI) for trajectories. Given a certain accuracy bound, CDR and GRTS optimize the storage consumption and communication cost for tracking a moving object's trajectory in real-time at some remote database and allow for various trade-offs between computational costs, reduction efficiency, and communication. DTI enables efficient access to trajectory information that is partitioned in space and stored by different servers for scalability reasons. In addition, an extended scheme DTI+S is presented, which optimizes the processing of aggregate queries.

For discovering context information that is relevant for the situation of an application, we propose a powerful formalism for describing context models in a concise manner and a corresponding index structure (SDC-Tree). The formalism considerably extends existing approaches for describing information sources by constraints and permits to adjust between different semantics for matching descriptions against corresponding queries. The SDC-Tree enables to discover relevant context models out of potentially millions of descriptions efficiently using multidimensional indexing capabilities.

For context-aware computing, position is an important aspect. Usually the mobile objects fix position on their own. A central instance called location service manages the position data. Transmitting latest position data to the location service is called position update. The location service uses the position data to answer queries on the positions of mobile objects.

Often this implies that the mobile objects regularly fix and update position. Therefore they spend a lot of energy, which decreased the availability time of mobile devices. Existing approaches to save energy only reduce the number of position updates. In order to minimize the energy consumption the number of position fixes and updates has to be adapted to the actual demand of the queries.

First of all the query interface of the location service is analyzed in this work. The query interface is enhanced so that an inquiring application can specify its actual demand precisely. This implies research on continuous queries as well as on temporal and spatial tolerance.

Afterwards it is shown how the number of position fixes can be minimized for a given query and tolerance. Starting from that, the energy consumption is minimized. Several approaches are developed for processing queries efficiently. Normally the queries have to be processed by the location service and the inquired mobile objects together.

Detailed simulations fortify the theoretical results. An all new approach to process range queries is put to test with promising results.

The final result of this work is that there are a lot of possibilities to save energy in location management, thus increasing the availability time of mobile devices.

The Robot Operating System (ROS) is a popular robotics middleware framework. In the last years, it underwent a redesign and reimplementation under the name ROS 2. It now features QoS-configurable communication and a flexible layered architecture. Micro-ROS is a variant developed specifically for resource-constrained microcontrollers (MCU). Such MCUs are commonly used in robotics for sensors and actuators, for time-critical control functions, and for safety. While the execution management of ROS 2 has been addressed by an Executor concept, its lack of real-time capabilities make it unsuitable for industrial use. Neither defining an execution order nor the assignment of scheduling parameters to tasks is possible, despite the fact that advanced real-time scheduling algorithms are well-known and available in modern RTOS's. For example, the NuttX RTOS supports a variant of the reservation-based scheduling which is very attractive for industrial applications: It allows to assign execution time budgets to software components so that a system integrator can thereby guarantee the real-time requirements of the entire system. This paper presents for the first time a ROS 2 Executor design which enables the real-time scheduling capabilities of the operating system. In particular, we successfully demonstrate the budget-based scheduling of the NuttX RTOS with a micro-ROS application on an STM32 microcontroller.

We present a P2P overlay network protocol based on Voronoi diagram and Delaunay triangulation. The proposed algorithms of node join and leave include the resolution of conflicts between distributed operations, by which concurrent networking is provided. The topology updating operations are performed having minimized maintenance cost measured in the number of messages. The proposed approach provides scalability by guaranteeing that for a large network the cost of a single join operation is constant. For proving this result, we evaluated the network maintenance cost by using an event-driven simulator.

In ubiquitous computing - the third era of the electronic data processing - energy efficient algorithms play an important role, since the corresponding computer systems have limited energy supplies. Many tasks are mastered through cooperation in ubiquitous computer systems. The computers communicate via wireless networks with each other. The network interface of such a computer consumes a lot of energy. Energy can be saved by deactivating this interface, but then the computer becomes unreachable for others.

Today there are a lot of energy efficient algorithms for media access control and routing. These algorithms save energy by deactivating the network interface during regular intervals and try to compensate the unreachability.

In this work energy efficient algorithms, which use knowledge on the applications and services of a computer, are examined. Using this knowledge potentially most of all energy can be saved. At the same time negative effects of the deactivation of the network interfaces can be minimized. Here, especially variants or extensions of SANDMAN, an algorithm for energy efficient service discovery, are examined. SANDMAN divides a wireless network in clusters and uses a clusterhead per cluster as service directory. In the approach discussed here, not only the service discovery but also the use of the service occurs via the clusterhead. The real service is hidden behind the clusterhead.

In the first part of this work advantages and disadvantages of this approach are examined. One result among others is how services and computers have to look like to be suitable for this approach. After the discussion of some existing energy efficient algorithms, LATE RISER, a concrete algorithm according to the described approach, is developed and its implementation is described. Data of the analysis of LATE RISER through simulation are presented and discussed.

The conclusion is that using the described approach a lot of energy can be saved, but it can only be used under suitable circumstances.