Please use this identifier to cite or link to this item: https://doi.org/10.1186/s13638-015-0490-3
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dc.titleEnergy-aware task allocation for energy harvesting sensor networks
dc.contributor.authorEdalat, N
dc.contributor.authorMotani, M
dc.date.accessioned2020-09-14T08:10:21Z
dc.date.available2020-09-14T08:10:21Z
dc.date.issued2016
dc.identifier.citationEdalat, N, Motani, M (2016). Energy-aware task allocation for energy harvesting sensor networks. Eurasip Journal on Wireless Communications and Networking 2016 (1) : 1-14. ScholarBank@NUS Repository. https://doi.org/10.1186/s13638-015-0490-3
dc.identifier.issn1687-1472
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/176113
dc.description.abstractAmbient energy harvesting is a solution to mitigate the typical finite energy supply of sensor nodes in wireless sensor networks (WSNs). On the one hand, the uncertainty of energy availability in energy harvesting systems makes network protocol design challenging. On the other hand, the fact that energy is continuously replenished opens up avenues for protocol design based on prediction of future energy arrivals. One of the key application scenarios for sensor networks is task allocation, in which a central entity allocates tasks to a set of geographically distributed sensor nodes to accomplish an overall objective. In this work, we consider a scenario in which the sensor nodes are equipped with devices capable of harvesting ambient energy, e.g., solar panels to harvest the Sun’s energy, and focus on energy-aware strategies for adaptive task allocation. We decompose the static task allocation problem into two phases: scheduling of the task graph and task mapping to the appropriate sensor nodes. The solution objectives are to minimize the makespan and maximize the fairness in energy-driven task mapping (i.e., energy-balancing), while satisfying the task precedence constraints and energy harvesting causality constraints. We employ a novel energy prediction model which incorporates seasonal changes in solar energy harvesting as well as sudden weather changes. In case of an error in available energy prediction, a dynamic adaptation phase runs to avoid violation of the task allocation objectives. The performance of our proposed algorithms, in terms of energy-balancing and scheduling length, is evaluated through simulation and compared with other approaches, including a genetic algorithm as a baseline. We achieve more balanced residual energy levels across the network while attaining a near optimum scheduling length. By utilizing the dynamic adaptation phase, for certain runs of simulation, the missing ratio, which is the percentage of times in which the task allocation fails due to a temporal shortage of energy availability, is dramatically decreased. © 2015, Edalat and Motani.
dc.sourceUnpaywall 20200831
dc.subjectEnergy balance
dc.subjectEnergy harvesting
dc.subjectForecasting
dc.subjectGenetic algorithms
dc.subjectMapping
dc.subjectNetwork protocols
dc.subjectPower management
dc.subjectScheduling
dc.subjectSensor nodes
dc.subjectSolar energy
dc.subjectWireless sensor networks
dc.subjectAdaptive task allocations
dc.subjectApplication scenario
dc.subjectEnergy harvesting sensors
dc.subjectEnergy harvesting systems
dc.subjectPrecedence constraints
dc.subjectSolar-powered
dc.subjectTask allocation algorithm
dc.subjectWireless sensor network (WSNs)
dc.subjectPower management (telecommunication)
dc.typeArticle
dc.contributor.departmentDEPT OF ELECTRICAL & COMPUTER ENGG
dc.description.doi10.1186/s13638-015-0490-3
dc.description.sourcetitleEurasip Journal on Wireless Communications and Networking
dc.description.volume2016
dc.description.issue1
dc.description.page1-14
dc.published.statePublished
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