6533b851fe1ef96bd12a8f66
RESEARCH PRODUCT
A Spatial-Temporal Correlation Approach for Data Reduction in Cluster-Based Sensor Networks
Charith PereraGaby Bou TayehAbdallah MakhoulJacques Demerjiansubject
Signal Processing (eess.SP)FOS: Computer and information sciencesAdaptive samplingGeneral Computer ScienceComputer sciencespatial-temporal correlationReal-time computing02 engineering and technologyData loss[INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE]data reconstructionQA76Computer Science - Networking and Internet Architecture[INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing[INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR]FOS: Electrical engineering electronic engineering information engineering0202 electrical engineering electronic engineering information engineeringGeneral Materials ScienceElectrical Engineering and Systems Science - Signal ProcessingNetworking and Internet Architecture (cs.NI)General EngineeringSampling (statistics)020206 networking & telecommunicationsReconstruction algorithmDissipation[INFO.INFO-MO]Computer Science [cs]/Modeling and SimulationWireless sensor networks[INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA]data reduction020201 artificial intelligence & image processing[INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET]lcsh:Electrical engineering. Electronics. Nuclear engineering[INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC]lcsh:TK1-9971Wireless sensor networkData reductiondescription
International audience; In a resource-constrained Wireless Sensor Networks (WSNs), the optimization of the sampling and the transmission rates of each individual node is a crucial issue. A high volume of redundant data transmitted through the network will result in collisions, data loss, and energy dissipation. This paper proposes a novel data reduction scheme, that exploits the spatial-temporal correlation among sensor data in order to determine the optimal sampling strategy for the deployed sensor nodes. This strategy reduces the overall sampling/transmission rates while preserving the quality of the data. Moreover, a back-end reconstruction algorithm is deployed on the workstation (Sink). This algorithm can reproduce the data that have not been sampled by finding the spatial and temporal correlation among the reported data set, and filling the non-sampled parts with predictions. We have used real sensor data of a network that was deployed at the Grand-St-Bernard pass located between Switzerland and Italy. We tested our approach using the previously mentioned data-set and compared it to a recent adaptive sampling based data reduction approach. The obtained results show that our proposed method consumes up to 60$ less energy and can handle non-stationary data more effectively.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2019-01-01 |