Name
Abstract | ||
|---|---|---|
Aggregation functions are intended to save energy and capacity in Wireless Sensor Networks (WSNs), by avoiding unnecessary transmissions. Aggregation functions take benefits from spatial and/or temporal correlations to forecast or to compress the real data which are collected. Although several works have focused on data aggregation in WSNs, there is a lack of a formal unified framework that can compare several aggregation functions suitable for a given network topology, a given application and a target accuracy. We address this question in this paper by proposing a Markov Decision Process (MDP) that can help to evaluate the performances of aggregation functions. The performances are expressed using two new proposed metrics, which can assess the energy and capacity saving of aggregation functions. As illustrative examples, we use our MDP to evaluate and analyse the performances of basic aggregation functions (e.g. average) and more complex ones (time series, polynomial functions). |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2810379.2810384 | PE-WASUN@MSWiM |
Field | DocType | Citations |
Data mining,Polynomial,Computer science,Markov decision process,Network topology,Wireless sensor network,Data aggregator,Distributed computing | Conference | 0 |
PageRank | References | Authors |
0.34 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jin Cui | 1 | 4 | 2.46 |
| Khaled Boussetta | 2 | 193 | 27.71 |
| Fabrice Valois | 3 | 187 | 28.07 |