Optimal energy-aware task scheduling for batteryless IoT devices

Abstract : Today's IoT devices rely on batteries, which offer stable energy storage but contain harmful chemicals. Having billions of IoT devices powered by batteries is not sustainable for the future. As an alternative, batteryless devices run on long-lived capacitors charged using energy harvesters. The small energy storage capacity of capacitors results in intermittent on-off behaviour. Traditional computing schedulers can not handle this intermittency, and in this paper we propose a first step towards an energy-aware task scheduler for constrained batteryless devices. We present a new energy-aware task scheduling algorithm that is able to optimally schedule application tasks to avoid power failures, and that will allow us to provide insights on the optimal look-ahead time for energy prediction. Our insights can be used as a basis for practical energy-aware scheduling and energy availability prediction algorithms. We formulate the scheduling problem as a Mixed Integer Linear Program. We evaluate its performance improvement when comparing it with state-of-the-art schedulers for batteryless IoT devices. Our results show that making the task scheduler energy aware avoids power failures and allows more tasks to successfully execute. Moreover, we conclude that a relatively short look-ahead energy prediction time of 8 future task executions is enough to achieve optimality.

 EXISTING SYSTEM :

 ? All existing task scheduling schemes employ energy harvesters as a source of power and use conventional sensor modules for the target application, consuming considerable energy during their operation. ? This section surveys existing task scheduling schemes for EH-IoT, with a particular focus on schemes that have the potential to support sensing modalities that harvest energy and sense simultaneously. ? Most of the existing task scheduling algorithms are complex and require significant amount of energy during their operation on the miniaturized and resource-constrained sensor node. ? There is no existing mechanism to manage the additional harvested energy in energy positive sensors, which may lead towards ENO of EH-IoT sensors.

 DISADVANTAGE :

 ? To address all these IoT-related battery problems, researchers have recently started investigating batteryless IoT devices and networks. ? To overcome this problem, InK considers a dynamic scheduler based on priorities and event-triggers (e.g., timers, energy level triggers, sensor value triggers), which are defined in advance by the programmer. ? To overcome this issue, and fully automate the problem of task selection, in this paper we propose an energy-aware task scheduler. ? However, we look at the optimal scheduling of application tasks on a constrained IoT device without batteries, which is a problem with significantly different constraints and requirements.

 PROPOSED SYSTEM :

 • It proposes that energy harvesting based sensing can be used in place of conventional power hungry activity sensors (such as accelerometers) to save the energy while attaining reasonable context detection accuracy. • It proposes a dynamic optimization model based on MDP to schedule the tasks taking into account their deadlines, energy consumption, and available harvested energy. • It also proposes a virtual energy harvesting sensing system to analyze the battery level which is helpful in deciding the sensing epoch. • They also propose an optimal start time for the execution of tasks depending upon their deadlines, energy consumption, stored energy and future harvested energy.

 ADVANTAGE :

 ? We have also presented an energyaware algorithm for batteryless LoRaWAN devices using energy harvesting, where we evaluate the performance of these constrained devices when allowing sleeping between tasks or letting them turn off. ? We provide some insights on how long in the future the behaviour of the batteryless device needs to be predicted in order to get the best performance and avoid power failures. ? We showed the potential of energyaware scheduling, by evaluating the maximum gain in performance when assuming perfect prediction of all future tasks and energy harvesting power. ? In this paper we have shown that energy-aware scheduling mechanisms are needed to improve the performance of successful application execution on batteryless devices.