Abstract
Clustering organizes nodes into groups in order to enhance the connectivity and stability of cognitive radio sensor networks. Mainly depending on the channel availability, many existing spectrum-aware clustering algorithms may not achieve the most satisfactory clustering. Taking into account the various influence factors to establish the optimal clustering is a challenge to enhance the network performance. This paper proposes a novel spectrum-aware clustering algorithm based on weighted clustering metric to obtain the optimal clustering by solving an optimization model. The new weighted clustering metric, simultaneously evaluating temporal-spatial correlation, confidence level and residual energy, is used to elect clusterheads and ally member nodes. After clustering, the clusterheads sensing spectrum instead of all member nodes greatly reduces the energy consumption of spectrum sensing and increases the opportunity of data transmission. The performance comparison between the traditional spectrum-aware clustering algorithms and our proposed algorithm has been highlighted with the experiments.
Highlights
Tremendous growths of technologies and applications for wireless sensor networks (WSNs) have brought significantly increased demand for radio spectrum, but the traditional static spectrum allocation policies have led to the spectrum scarcity [1]–[3]
Equipped with the cognitive radio module, the cognitive sensor nodes (CSNs) can opportunistically access the spectrum bands licensed to the primary users (PUs) without interfering with them
The experimental scenario is set as follows: 3 PUs and 50 sensor nodes are randomly distributed in a square area with size 200m2, and their communication ranges are 100m and 50m, respectively
Summary
Tremendous growths of technologies and applications for wireless sensor networks (WSNs) have brought significantly increased demand for radio spectrum, but the traditional static spectrum allocation policies have led to the spectrum scarcity [1]–[3]. In [33], a distributed spectrum-aware clustering algorithm determined the optimal number of clusters by establishing a network-wide energy consumption model with respect to the residual energy. We propose a new spectrum-aware clustering algorithm based on weighted clustering metric involving temporal-spatial correlation, confidence level as well as residual energy. Different from the traditional spectrum-aware clustering criterion mainly involving the channel availability, we introduce a novel weighted clustering metric taking into account three important influence factors, i.e., temporal-spatial correlation, sensing confidence and residual energy. CLUSTERHEAD ELECTION The proposed evaluation criteria allow to clustering nodes involving various influence factors, which contributes to obtain the optimal spectrum-aware clustering We apply these evaluation criteria to define the following weighted clustering metric for node i.
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