Leander B. Hörmann

Evaluation of component-aware dynamic voltage scaling for mobile devices and wireless sensor networks

Energy efficiency is very important for mobile devices and wireless sensor networks (WSNs), because the consumable energy is limited. Therefore, the operating time of such devices depends mainly on the capacity of the energy storage component and on the average power consumption of the device. The power consumption depends on the supply voltage and on the activated components of the hardware. This work presents the evaluation of component-aware dynamic voltage scaling (CADVS). This low power technique combines the power-down of unused components and the minimization of the supply voltage. Typically, each component of the hardware (microcontroller, transceiver, sensors) has its own supply voltage range. Therefore, the minimum allowed supply voltage depends on the activated components. However, the activated components and consequently the minimum allowed supply voltage varies over time. CADVS uses voltage converter to adjusts the supply voltage of the hardware to save as much energy as possible. This work presents the evaluation of six different voltage converters. It has been shown that CADVS can be used to save up to 38.7% of the energy compared to a constant voltage supply using the introduced scenario while achieving the same end-user performance.

A system for accurate characterization of wireless sensor networks with power states and energy harvesting system efficiency

Wireless sensor network (WSN) nodes have to cope with severe power supply constraints. Energy harvesting system (EHS) technology is used for prolonging network lifetime. Robust operation of such systems heavily relies on accurate models of EHS efficiency and node power dissipation for calculating sustainable operation modes. A nodes energy balance can be described with a power state model (PSM). While for battery operated WSNs PSM measurement errors and battery effects have to be considered, this paper widens the point of view to EHS properties. We analyze varying PSMs at runtime, EHS efficiency and measurement errors impacts on duty cycled WSNs. We show a measurement system setup and results from profiling a PSM for Mica2 nodes with an EHS. We explain important considerations for such systems with showing the variance of a nodes energy consumption depending on its supply and we profile EHS efficiency for deducing an energy storage level operating point. The results contribute to problems in the field of low power WSNs and especially EHS supported ones. Robustness of duty cycled WSNs heavily depends on measurement error bounds and is impacted by EHS efficiency. The reader is provided with a methodology for measuring and modeling robust WSNs.

Opportunistic Network Coding for Energy Conservation in Wireless Sensor Networks

Wireless sensor networks (WSN) continuously enhance processing capabilities and miniaturization. However, there exists a design gap to energy and bandwidth availability. Especially battery technology cannot keep pace with demands of novel versatile services. A common approach for conserving channel capacity and energy is optimizing power-aware routing and different kind of duty cycling (DC) and harvesting technology. While these optimizations are usually dealt with separately, we provide a novel framework with integrating and analyzing these different aspects in a practical TinyOS implementation at the same time. We implement the essential combination of energy harvesting aware routing (EHAR) together with radio and application DC and we add the novel approach of opportunistic network coding (ONC) for WSNs. We give detailed analysis of the applicability of application-level DC compared to low-power MAC and power save modes for state-of-the-art WSN and harvesting system hardware. We elaborate static and dynamic aspects of EHAR, scalable network coding (SNC) and ONC. Combining analytical models, comprehensive simulation and detailed highly accurate hardware power profiling measurement results, we demonstrate energy conservation from 13% to 50% when applying ONC and SNC.

Using a remote lab for teaching energy harvesting enhanced wireless sensor networks

Teaching wireless sensor networks (WSNs) only theoretically is not sufficient to understand the complex interaction of these networks. WSNs consist of sensor nodes which measure physical quantities of their environment, preprocess the measured data, and transmit it towards a base station in a multi-hop manner. WSNs are typically used in application areas without wired infrastructure and so they must be powered by batteries or energy harvesting systems. Due to the influence of different factors on the behavior, practical exercises can enhance the learning process because the students can perform their experimentation independently. This work presents the use of a remote lab for teaching energy harvesting enhanced WSNs. Students can learn the behavior of WSNs and the influences of energy harvesting. Furthermore, practical aspects of WSNs are shown by using a realistic application scenario. This work is part of the European project Remotelabs Access in Internet-based Performance-centered Learning Environment for Curriculum Support.

Designing of efficient energy harvesting systems for autonomous WSNs using a tier model

Energy harvesting systems (EHSs) are the key to perpetual operation of electronic devices in application areas with bad infrastructure or mobility. Wireless sensor networks (WSNs) are often used in such areas. Normal WSN nodes are powered by batteries. Therefore, the lifetime is limited and the batteries have to be replaced manually after a certain period of time. This problem can be solved by EHSs. They exploit energy sources of the environment and store the harvested energy in energy buffers. The EHS supplies the electronic device and ensures a continuous operation. WSNs can benefit from these developments, because the lifetime can be enhanced dramatically. However, the EHS have to be adapted to the requirements of the application area and of the supplied device. This enhances the overall efficiency of EHS. To be able to do that, the fundamental mode of operation of an EHS has to be well-understood. We introduce a novel tier model for EHSs. It structures the EHS into tiers with special functions. This enhances the design process of an EHS, because tiers can be adapted to each other and the overall efficiency of can be increased. The tier model is applied to RiverMote, a WSN node for in-river water level monitoring. Each node is supplied by solar cells and the energy is stored in double layer capacitors (DLCs). The hardware of the EHS of RiverMote is divided into the tiers of the model. These tiers are adapted to each other carefully. Although no maximum power point tracker has been implemented, it has been shown that the available power of the solar cell is greater than 80 % of the maximum power point if the energy level of the DLCs is between 42 % and 100 %. This result was only possible by a careful design and an adaption of the tiers.

Towards an on-site characterization of energy harvesting devices for wireless sensor networks

Wireless sensor networks (WSNs) suffer from the lack of wired infrastructure. Each node needs its own power supply, e.g. batteries or energy harvesting systems (EHSs). Typically, EHSs can extend the lifetime of a sensor node or even enable perpetual operation. Due to the high variation of harvestable energy of the environment, the design of the EHSs has to be done very carefully. The design process can be enhanced by using simulation of the WSN including energy harvesting. However, a realistic simulation needs accurate data of the harvestable energy of the environment. This paper presents the concept of an on-site characterization instrument for different types of energy harvesting devices. These instruments can be connected like a WSN.

Labor-oriented online master degree program

This contribution reports on a new European project, Remote-labs Access in Internet-based Performance-Centered Learning Environment for Curriculum Support (RIPLECS), in which an official inter-institutional European master degree program in Information and Communication System (ICS) is created. The program is conducted online across five European institutions and is oriented to labor market needs for qualified graduates, with special focus on realizing real-world experiments in each subject remotely. The network architecture of RIPLECS platform enables world-wide distribution of learning resources by utilizing multiple Web servers at several European universities, within a single network topology. The paper discusses the project development stages, implementation and the expected outcomes.

HANS: Harvesting aware networking service for energy management in wireless sensor networks

Large scale deployments of small, wireless, networked, embedded systems demand for cost reduction in development and maintenance. Most often, this translates into the need for reliable methods for energy conservation as it is the case for wireless sensor networks (WSNs). Our work considers energy harvesting system (EHS)-enhanced WSN technology which is the state-of-the-art technology for perpetual systems supplied from ambient environmental energy. Therefore several aspects have been considered in literature so far: EHS design, energy prediction modeling, harvesting aware media access control (MAC) and routing and finally power management. This paper postulates that identifying and optimizing these aspects on their own does not necessarily lead to feasible solutions. We take a cross-layer perspective and provide a networking protocol for implicit or explicit EHS policy negotiation under the constraint of two prototypical communication patterns. The methods presented show how to combine energy aware routing or conservation from network coding with EHS power management policies under the constraint of state-of-the-art MAC. Evaluation of different end-user communication patterns lets the reader interpret the results for an application at hand.

Implementing autonomous network coding for wireless sensor network applications

Wireless sensor network (WSN) motes are resource constrained devices. Especially, bandwidth and energy are scarce resources. Therefore, lots of effort is put into the optimization of low-power networking protocols. While network control overhead is an issue for many to most of such protocols, we present an approach that is virtually overhead-free. We introduce the implementation of an autonomous network coding implementation that can be implemented for existing applications and added over existing routing mechanisms with no need to change the application or networking protocols. We discuss and carefully evaluate the approach for applicability and gain according to the most prominent optimization metrics for WSN networking protocols. Especially, we consider the comparison of autonomous network coding to state-of-the-art collection protocols and multipath routing. The completely autonomously acting network coding plug-in implementation is profiled to conserve up to 29 % of the messages that need to be sent locally without the need for centralized network control.

Measuring the state-of-charge — Analysis and impact on wireless sensor networks

Wireless sensor networks (WSNs) are typically used in application areas without wired infrastructure or mobility. Therefore, each sensor node needs its own energy supply unit. Sustainable WSNs are powered by energy harvesting systems (EHSs). These systems harvest and buffer the energy from the environment into rechargeable batteries or double layer capacitors. Due to the fact that the connectivity of the WSN depends on every single sensor node, it is important to know the state-of-charge (SoC) of the buffer to determine the remaining operating time. Therefore, each node has to measure its SoC, calculate the remaining operating time and populate this information through the network. The measurement is usually done by an integrated analog-to-digital converter (ADC) of the microcontroller. Each ADC has a specified error that has to be taken into account at the calculations of the remaining operating time. This work presents the error analysis of the SoC measurement using different energy storage components.