Optimal task scheduling policy in energy harvesting wireless sensor networks

Abstract

Ambient energy harvesting for Wireless Sensor Networks (WSNs) is being pitched as a promising solution for longlasting deployments in various WSN applications. However, the sensor nodes most often do not have enough energy to handle application, network and house-keeping tasks because amount of energy harvested highly varies spatially and temporally. Moreover the ambient source cannot be assumed to be continuously available. When harvested energy is in excess, it is desirable that the nodes take up higher loads. The nodes should switch to highly energy efficient schemes when the energy is not sufficient. Hence harvesting-aware scheduling of tasks is required. The two most important challenges for harvesting-aware scheduling are (a) to determine the amount of energy to be expended in a time slot, and (b) to utilize this energy for execution of tasks maximally. To increase energy utilization for task execution, we decompose application level tasks into subtasks, some of which can be executed concurrently. In this article, we propose a dynamic optimization model, based on Markov Decision Process (MDP) that takes into account priorities and deadlines of the tasks, and stored and harvested energy to derive an optimal scheduling policy. Since the complexity of the MDP is intractable in realtime, we propose a greedy scheduling policy. We compare its performance with the optimal policy.

Energy Allocation for LoRaWAN Nodes with Multi-Source Energy Harvesting

“…Energy allocation for multi-task systems can be seen as a task scheduling problem, where tasks are constrained by both their energy consumption and QoS requirements instead of their deadline and/or period. Most energy-aware task scheduling policies [ 28 , 29 , 30 , 31 , 32 , 33 ] target real-time systems, for which the objective is to ensure that all tasks meet their deadline requirements instead of allocating energy to different tasks. DEOS [ 34 ] takes a different approach by considering energy as a schedulable resource to dynamically schedule tasks depending on their energy consumption and the available energy.…”

Deployment Schemes in WSNs

“…Tiago Semprebomy et al in 2015 [10] blocking upkeep untrustworthy or challenging to reach situations. Making the system adaptable to disappointment and natural changes, excess arrangement procedures typically considered in this situation.…”

Optimal data collection in wireless sensor networks with correlated energy harvesting

“…We discuss some more recent contributions. In [36], Rao et Al. use the framework of Markov decision processes to determine the optimal task scheduling for a sensor node.…”