The paper studies the case of a sensor node which is operating with the power generated by an environmental source. We present our model of an energy driven scheduling scenario that is characterized by the capacity of the node's energy storage, the deadlines and the power dissipation of the tasks to be performed. Since the execution of these tasks requires a certain amount of energy as well as time, we show that the complexity of finding useful scheduling strategies is significantly increased compared to conventional real-time scheduling. We state online scheduling algorithms that jointly account for constraints arising from both the energy and time domain. In order to demonstrate the benefits of our algorithms, we compare them by means of simulation with the classical Earliest Deadline First Algorithm. Wireless sensor networks have been the subject of intensive research over the past several years. As for many other battery-operated embedded systems, a sensor's operating time is a crucial design parameter. As electronic systems continue to shrink, however, less energy is storable on-board. Research continues to develop higher energy-density batteries and supercapacitors, but the amount of energy available still severely limits the system's lifespan. Recently, energy harvesting has emerged as viable option to power sensor nodes: If nodes are equipped with energy transducers like e.g. solar cells, the generated energy may increase the autonomy of the nodes significantly. In [6] , technologies have been discussed how a sensor node may extract energy from its physical environment. Moreover, several prototypes (e.g. [2, 3] ) have been presented which demonstrate both feasibility and usefulness of sensors nodes which are powered by solar or vibrational energy.
A scheduling algorithm that relies on the battery capacity of the sensors is presented by Moser et al. REF .
LAZY SCHEDULING FOR ENERGY HARVESTING SENSOR NODES
DIPES
Computer Science