BI-Filtered Forwarding: A Quasi-Optimal Routing Algorithm for Query Delivery in Wireless Sensor Networks

Zhang, Junhu; Zhu, Xiujuan; Peng, Hui

doi:10.21307/ijssis-2017-576

Cited by 1 publication

(1 citation statement)

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“…[12,13] or infrastructurefree (i.e., unstructured) [13][14][15]. The latter can be walk-based [13][14][15], random walk-based [16], flooding-based [17,18], and gradient-based [17]. In this sense, unstructured-and gradient-based schemes are the best option in case of typethreshold function computation due to the low overhead of unstructured mechanisms and the information gain concept of gradient-based strategies for query dissemination.…”

Section: Related Workmentioning

confidence: 99%

In-Network Filtering Schemes for Type-Threshold Function Computation in Wireless Sensor Networks

Riva

Finochietto

2014

International Journal of Distributed Sensor Networks

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Data collection in wireless sensor networks (WSNs) can become extremely expensive in terms of power consumption if all measurements have to be fetched. However, since multiple applications do not require data from all nodes but to compute a function over a smaller data set, much of the available data on the network can be considered irrelevant and not worthy of spending energy. In this context, in-network filtering schemes can be used to forward only relevant data towards a sink node for processing purposes. In this work, we propose and evaluate two schemes that can drive this filtering process. Both of them are based on the integration of metaheuristics and learning algorithms inspired by nature. In particular, we consider the computation of the maximum function as case study for these schemes. We investigate the trade-off between communications costs, which are directly associated with power consumption, and error costs due to fetching not all relevant data. We show by simulation that communication costs can be significantly reduced with respect to traditional schemes while keeping the computation error bounded.

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