Abstract
In wireless sensor networks, filter-based top-k query approaches are the state-of-the-art solutions and have been extensively researched in the literature, however, they are very sensitive to the network parameters, including the size of the network, dynamics of the sensors’ readings and declines in the overall range of all the readings. In this work, a random walk-based top-k query approach called RWTQ and a directed walk-based top-k query approach called DWTQ are proposed. At the beginning of a top-k query, one or several tokens are sent to the specific node(s) in the network by the base station. Then, each token walks in the network independently to record and process the readings in a random or directed way. A strategy of choosing the “right” way in DWTQ is carefully designed for the token(s) to arrive at the high-value regions as soon as possible. When designing the walking strategy for DWTQ, the spatial correlations of the readings are also considered. Theoretical analysis and simulation results indicate that RWTQ and DWTQ both are very robust against these parameters discussed previously. In addition, DWTQ outperforms TAG, FILA and EXTOK in transmission cost, energy consumption and network lifetime.
Highlights
Wireless sensor networks (WSNs) composed of a large number of wireless connected devices have been widely researched and applied in many fields
We present an in-depth analysis on filter-based top- query approaches and propose two novel top- query methods named RWTQ and DWTQ to overcome the shortcomings of filter-based top- query approaches
(3) We extend the RWTQ to DWTQ considering the spatial correlations between the readings of the nodes
Summary
Wireless sensor networks (WSNs) composed of a large number of wireless connected devices have been widely researched and applied in many fields. Top- queries, i.e., querying the topreadings with the corresponding nodes of a WSN, is a very common demand for the users and has been widely studied in the literature [1,2,3,4,5,6]. Aggregation-based query approaches perform well in defending against these parameters. For most of aggregation-based query approaches, the transmission cost is stable for a specific query. The filter-based top- query approaches outperforms aggregation-based top- query methods in transmission cost, energy consumption and network lifetime. The updating of the filters is very sensitive to the parameters discussed previously, especially the size of the network and the dynamics of the readings, which will be discussed in detail in Section 5.1 based on a simple model
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