In this paper a framework for energy management in energy harvesting embedded systems is presented. As a possible example scenario, we focus on wireless sensor nodes which are powered by solar cells. We demonstrate that classical power management solutions have to be reconceived and/or new problems arise if perpetual operation of the system is required. In particular, we provide a set of algorithms and methods for different application scenarios, including real-time scheduling, application rate control as well as reward maximization. The goal is to optimize the performance of the application subject to given energy constraints. Our methods optimize the system performance which allows the usage of, e.g., smaller solar cells and smaller batteries. Our theoretical results are supported by simulations using long-term measurements of solar energy in an outdoor environment. Furthermore, to demonstrate the practical relevance of our approaches, we measured the implementation overhead of our algorithms on real sensor nodes.

Energy harvesting (also known as energy scavenging) is the process of generating electrical energy from environmental energy sources. There exists a variety of different energy sources such as solar energy, kinetic energy, or thermal energy. In recent years, this term has been frequently applied in the context of small autonomous devices such as wireless sensor nodes. In this article, a framework for energy management in energy harvesting embedded systems is presented. As a possible scenario, we focus on wireless sensor nodes that are powered by solar cells. We demonstrate that classical power management solutions have to be reconceived and/or new problems arise if perpetual operation of the system is required. In particular, we provide a set of algorithms and methods for various application scenarios, including real-time scheduling, application rate control, as well as reward maximization. The goal is to optimize the performance of the application subject to given energy constraints. Our methods optimize the system performance which, for example, allows the usage of smaller solar cells and smaller batteries. Furthermore, we show how to dimension important system parameters like the minimum battery capacity or a sufficient prediction horizon. Our theoretical results are supported by simulations using long-term measurements of solar energy in an outdoor environment. In contrast to previous works, we present a formal framework which is able to capture the performance, the parameters, and the energy model of various energy harvesting systems. We combine different viewpoints, include corresponding simulation results, and provide a thorough discussion of implementation aspects.

Recently, there has been a substantial interest in the design of systems that receive their energy from regenerative sources such as solar cells. In contrast to approaches that minimize the power consumption subject to performance constraints, we are concerned with optimizing the performance of an application while respecting the limited and time-varying amount of available power. In this paper, we address power management of, e.g., wireless sensor nodes which receive their energy from solar cells. Based on a prediction of the future available energy, we adapt parameters of the application in order to maximize the utility in a long-term perspective. The paper presents a formal model of the corresponding optimization problem including constraints concerning buffer sizes, timing, and rates. Instead of solving the optimization problem online which may be prohibitively complex in terms of running time and energy consumption, we apply multiparametric programming to precompute the application parameters offline for different environmental conditions and system states. In order to guarantee sustainable operation, we propose a hierarchical software design which comprises a worst-case prediction of the incoming energy. As a further contribution, we suggest a new method for approximate multiparametric linear programming which substantially lowers the computational demand and memory requirement of the embedded software. Our approaches are evaluated using long-term measurements of solar energy in an outdoor environment.

Power management has been a critical issue in the design of embedded systems due to the limited power supply. To prolong the lifetime, energy minimization has been studied under performance constraints in the past decade. The emerging embedded systems with the capability to harvest energy from the environment have recently triggered the revision of power management to improve the quality of service dynamically. As the available power/energy of an electronic device changes over time and is limited by many environmental factors, the system has to decide when to change to which service level to provide better quality of service without wasting the harvested energy. In this paper, we explore how to maximize the quality of service, in terms of system rewards, of a periodic application with discrete levels in an energy harvesting system. To decide service levels in a time horizon, this paper presents algorithms to derive optimal solutions if the future harvested energy is known. In addition, we present efficient algorithms to derive near-optimal solutions approximately. Our work is supported by simulation results which are based on long-term measurements of the power generated by real solar cells.

Solar harvesting circuits have been recently proposed to increase the autonomy of embedded systems. One key design challenge is how to optimize the efficiency of solar energy collection under non stationary light conditions. This paper proposes a scavenger that exploits miniaturized photovoltaic modules to perform automatic maximum power point tracking at a minimum energy cost. The system adjusts dynamically to the light intensity variations and its measured power consumption is less than 1 mW. Experimental results show increments of global efficiency up to 80%, diverging from ideal situation by less than 10%, and demonstrate the flexibility and the robustness of our approach.

This paper is concerned with solar driven sensors deployed in an outdoor environment. We present feedback controllers which adapt parameters of the application such that a maximal utility is obtained while respecting the time-varying amount of available energy. We show that already simple applications lead to complex optimization problems, involving unacceptable running times and energy consumptions for resource constrained nodes. In addition, naive designs are highly susceptible to energy prediction errors. We address both issues by proposing a hierarchical control approach which both reduces complexity and increases robustness towards prediction uncertainty. As a key component of this hierarchical approach, we propose a new worst-case energy prediction algorithm which guarantees sustainable operation. All methods are evaluated using long-term measurements of solar energy in an outdoor setting. Furthermore, we measured the implementation overhead on a real sensor node.

Renewable energies can enable embedded systems to be functional indefinitely. In particular for small autonomous sensors, energy harvesting techniques have attracted much interest. This paper considers systems which provide services periodically with adjustable quality evaluated in terms of rewards. The reward garnered for one service is monotonically increasing and strictly concave with respect to the energy consumption of the service. There exist two major constraints which arise due to the burstiness of common energy sources: (1) The harvested energy is temporarily low and the service must be lowered or suspended. (2) During bursts, the harvested energy exceeds the battery capacity. To resolve these issues, we propose algorithms to derive optimal solutions which maximize the overall reward. Furthermore, we determine the minimum battery capacity necessary to optimally exploit a given power source. By applying real data recorded for photovoltaic cells as the harvested energy, simulations illuminate the merits of our algorithms.

Recently, there has been a substantial interest in the design of systems that receive their energy from regenerative sources such as solar cells. In contrast to approaches that attempt to minimize the power consumption we are concerned with adapting parameters of the application such that a maximal utility is obtained while respecting the limited and time-varying amount of available energy. Instead of solving the optimization problem on-line which may be prohibitively complex in terms of running time and energy consumption, we propose a parameterized specification and the computation of a corresponding optimal on-line controller. The efficiency of the new approach is demonstrated by experimental results and measurements on a sensor node

This paper investigates real-time scheduling in a system whose energy reservoir is replenished by an environmental power source. The execution of tasks is deemed primarily energy-driven, i.e., a task may only respect its deadline if its energy demand can be satisfied early enough. Hence, a useful scheduling policy should account for properties of the energy source, capacity of the energy storage as well as power dissipation of the single tasks. We show that conventional scheduling algorithms (like e.g. EDF) are not suitable for this scenario. Based on this motivation, we state and prove optimal scheduling algorithms that jointly handle constraints from both energy and time domain. Furthermore, an offline schedulability test for a set of periodic or even bursty tasks is presented. Finally, we validate the proposed theory by means of simulation and compare our algorithms with the classical earliest deadline first algorithm

This paper analyzes the impact of beamforming antennas on the topological connectivity of multihop wireless networks. As a metric for the connectivity of the network, we use the percentage P(path) of nodes that are connected via a multihop path. We show that simple randomized beamforming - i.e., each node adjusts its main beam into a randomly chosen direction for transmission and reception - significantly improves P(path) compared to networks with omnidirectional antennas employing the same power and sensitivity. The study is performed using accurate, analytical antenna models for uniform linear and circular antenna arrays. Already small arrays with four antenna elements give high gains of P(path). These gains are achieved although the nodes' average number of neighbors does not necessarily increase.