Skyline-Based Aggregator Node Selection in Wireless Sensor Networks

Abstract

In order to achieve the equal usage of limited resources in the wireless sensor networks (WSNs), we must aggregate the sensor data before passing it to the base station. In WSNs, the aggregator nodes perform a data aggregation process. Careful selection of the aggregator nodes in the data aggregation process results in reducing large amounts of communication traffic in the WSNs. However, network conditions change frequently due to sharing of resources, computation load, and congestion on network nodes and links, which makes the selection of the aggregator nodes difficult. In this paper, we study an aggregator node selection method in the WSNs. We formulate the selection process as a top-k query problem, where we efficiently solve the problem by using a modified Sort-Filter-Skyline (SFS) algorithm. The main idea of our approach is to immediately perform a skyline query on the sensor nodes in the WSNs, which enables to extract a set of sensor nodes that are potential candidates to become an aggregator node. The experiments show that our method is several times faster compared to the existing approaches.

An Efficient Index Building Algorithm for Selection of Aggregator Nodes in Wireless Sensor Networks

“…PANEL is a cluster-based data aggregation protocol that uses the geographical position information of the sensor nodes in order to select an aggregator node. Nasridinov et al [4,18] proposed an efficient aggregator node selection method. Similar to our method, the authors formulate the selection process of aggregator node as a topquery problem and solve it by using a modified SortFilter-Skyline (SFS) algorithm.…”

Collaborative Filtering Based Aggregator Node Selection in Wireless Sensor Networks

“…In [2], the authors investigated optimal aggregator node selection in WSNs. The selection process is formulated as a top-k query problem, which is solved by adapting a modified Soft-Filter-Skyline (SFS) algorithm.…”

“…Cosine similarity is the most widely used in collaborative filtering. The two users are thought as two vectors, while the similarity between two vectors is calculated by their cosine angle [2]:…”

HI-Sky: Hash Index-Based Skyline Query Processing

“…Generally, the skyline query can be used in many real-world applications, such as retail [2], road networks [3], and Web services [4]. Among these, it is commonly known that retail applications deal with dynamic datasets.…”