Real-time scheduling for energy harvesting sensor nodes
Real-Time Systems
A Novel Energy Efficient Wireless Sensor MAC Protocol
NCM '08 Proceedings of the 2008 Fourth International Conference on Networked Computing and Advanced Information Management - Volume 01
Proceedings of the 16th ACM/IEEE international symposium on Low power electronics and design
Resource-Aware Scheduling of Distributed Ontological Reasoning Tasks in Wireless Sensor Networks
SUTC '10 Proceedings of the 2010 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing
GREENCOMP '10 Proceedings of the International Conference on Green Computing
Performance Evaluation of Real-Time Scheduling Heuristics for Energy Harvesting Systems
GREENCOM-CPSCOM '10 Proceedings of the 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing
Constrained Relay Node Placement in Energy Harvesting Wireless Sensor Networks
MASS '11 Proceedings of the 2011 IEEE Eighth International Conference on Mobile Ad-Hoc and Sensor Systems
ECODA: enhanced congestion detection and avoidance for multiple class of traffic in sensor networks
IEEE Transactions on Consumer Electronics
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One of the most challenging issues for nowadays Wireless Sensor Networks (WSNs) is represented by the capability of self-powering the network sensor nodes by means of suitable Energy Harvesting (EH) techniques. However, the nature of such energy captured from the environment is often irregular and unpredictable and therefore some intelligence is required to efficiently use it for information processing at the sensor level. In particular in this work the authors address the problem of task scheduling in processors located in WSN nodes powered by EH sources. The authors' objective consists in employing a conservative scheduling paradigm in order to achieve a more efficient management of energy resources. To prove such a claim, the recently advanced Lazy Scheduling Algorithm (LSA) has been taken as reference and integrated with the automatic ability of foreseeing at runtime the task energy starving, i.e. the impossibility of finalizing a task due to the lack of power. The resulting technique, namely Energy Aware Lazy Scheduling Algorithm (EA-LSA), has then been tested in comparison with the original one and a relevant performance improvement has been registered in terms of number of executable tasks.