Interference and secrecy analysis based on randomly spacial model in clustered WSNs

Abstract

Abstract Clustering and layering are widely employed in large‐scale wireless sensor networks (WSNs) to alleviate data implosion, reduce transmission delay and improve energy efficiency. It is vital to conduct performance analysis in clustered WSNs for better defining and designing networks. Here, a general analytical framework based on a randomly spacial model is proposed to conduct inter‐cluster interference analysis and physical‐layer security analysis for clustered WSNs. It is assumed that two adjacent cluster heads exchange confidential massage with a passive eavesdropper and the non‐head nodes in clusters could be selected as cooperative jamming (CJ) nodes to disturb the eavesdropper. All mentioned nodes are randomly located in their specific areas. Under the above settings, three scenarios, namely interference, eavesdropping and CJ are considered, and a stochastic geometry tool based on kinematic measure is employed for analysis. Comparing to existing stochastic geometry methods, the proposed analytical framework can handle arbitrarily shaped, disjoint and tiered networks. The results obtained from extensive simulations have verified the rationality of the framework and demonstrated the impact of different parameters on the performance metrics of interest. The comparison with PPP also shows the advantages of this proposed model.