Aggregation Scheme with Secure Hierarchical Clustering for Wireless Sensor Networks

Abstract

In a large-scale wireless sensor network, a topology is needed to gather state-based data from sensor network and efficiently aggregate the data given the requirements of balanced load, minimal energy consumption, and prolonged network lifetime. In this study, we proposed a ring-based hierarchical clustering scheme (RHC) consisting of four phases: predeployment, parent-child relationship building, deployment, and member join phases. Two node types are distributed throughout the network: cluster head nodes (type 1 node) and general sensor nodes (type 2 node). The type 1 node has better battery life, software capability, and hardware features than the type 2 node; therefore, the type 1 node is a better cluster head than type 2 node. Due to our IP naming rules and type 1 nodes as cluster heads, public key cryptography, such as RSA (Rivest, Shamir, Adleman), or ECC (Elliptic Curve Cryptosystem), is easily implanted to our system to strengthen our security. The sink node is the only certification authority in our system, but n level cluster heads can be extended to n level certification authorities if needed, where n is maximum number of level.

Structural damage detection using transmissibility together with hierarchical clustering analysis and similarity measure

“…In the last few decades, clustering techniques have experienced great progress with numerous outputs. [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40] As illustrated by Aldenderfer and Blashfield, 32 the application for clustering technique can be summarized with four principal goals: (1) development of a typology or classification, (2) investigation of useful conceptual schemes for grouping entities, (3) hypothesis generation through data exploration, and (4) hypothesis testing, or the attempt to determine if types defined through other procedures are in fact present in a data set. And basically, clustering analysis aims to partition the prepared data in accordance with some extracted features and find desired ones.…”

RSSI and LQI Data Clustering Techniques to Determine the Number of Nodes in Wireless Sensor Networks

“…In this paper, AHC method is utilized to explore if any hierarchical relationship inherently exists inside the RSSI and LQI data, hence allowing derivation of nodes' number. The Hierarchical clustering technique is now a widely used data analysis tool in many applications, such as data mining, statistics, machine learning, spatial database, biology, and marketing strategy [19][20][21]. In literature [22], Mirkin described the basic concept of AHC clustering method in detail.…”

The Combinations of Selected Parameters to Prolong the Network Lifetime for Cluster Head Selection in Wireless Sensor Network

“…The advantages of clustering is to reduce the communication overhead while transmitting the data [11]. Weighted clustering [12], hierarchical clustering [13] and dynamic clustering are some of the several clustering method that have been successfully implemented [14]. There are many ways to select the cluster head in clustering.…”