Wireless sensor networks (WSNs) are power critical systems, because they are used in application areas without wired infrastructure. Each sensor node needs a dedicated power supply. Today's protocols and applications for WSNs are often power-aware. However, the state-of-charge (SoC) estimation of the energy storage component (e.g. rechargeable battery) influences the decisions of the power-aware software. A measurement error may cause wrong decisions which would reduce the lifetime of the network. This work presents the simulation results of the impact of an SoC measurement error on the network lifetime running a simple and an enhanced power-aware WSN application. A simulation environment written in SystemC-AMS is used, which enables the combined simulation of the sensor node's application software and its hardware. The simulation shows a reduction of the network lifetime of 3.13% caused by an erroneous measurement. However, the enhanced software is able to increase the network lifetime by 1.66%.

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.

Wireless sensor networks (WSNs) are resource-constrained devices. Especially energy is scarce. Recent advances with energy harvesting system (EHS) technology now offer new opportunities for long-lived WSNs. Several prototypes of EHS-enhanced WSN platforms have recently been designed in a low-power and power-efficient way. While the field starts to mature there are still a few lessons to be learned for building low-cost and robust systems. Especially, perpetual systems pose new constraints that have not yet been formalized in well-established metrics. Several studies exist, that try to tune systems and algorithms offline for being able to cope with different situations that might occur. The prediction of solar traces is a well-known example for EHS-enhanced WSNs. However, it is a cumbersome task trying to be prepared for all possible extreme conditions especially when prediction is needed. Therefore, we introduce RiverMote which is a low-cost highly efficient design with extended black-out sustainability (BOS) capabilities. We suggest to put special focus on BOS as a mechanism that can even react to hard-to-model events in a general way without the need to rely on error-prone prediction mechanisms. RiverMote is a low-cost WSN platform with integrated EHS. It outperforms state-of-the-art EHS technology efficiency for WSNs and supports BOS of more than three weeks.

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.

TinyOS2 (TOS2) is the state of the art operating system for wireless sensor network (WSN) programming. There are a number of testbeds and simulation tools for checking functional correctness and a few integrated development environments (IDEs) that support graphical user interface (GUI) and power profiling of WSN simulation as well. All these systems and environments are tailored towards design optimization of WSNs subject to functional correctness and energy conservation. Unfortunately, all these approaches lack self-contained support for energy harvesting systems (EHSs) which is the state of the art technique for tackling the energy conservation problem. Here, we present the design and implementation of a self-contained X-in-the-loop approach. TOSPIE2 is an Eclipse plug-in that integrates TOS2 installation and compilation support, power profiles and EHS efficiency in simulation and measurement.