A multi-hop heterogeneous cluster-based optimization algorithm for wireless sensor networks

Abstract

With the growth of different design goals and application requirements, wireless sensor networks (WSNs) have been increasingly popular for a wide variety of purposes, e.g., image formation of a target field, intrusion detection, surveillance and environmental monitoring. In this paper, a multi-hop heterogeneous cluster-based optimization algorithm (MHCOA) for WSNs is proposed. The motivation to MHCOA for WSNs is that several higher energy sensor nodes act as cluster heads, which are deployed artificially, while some low energy sensor nodes act as cluster members, which are deployed randomly. In order to realize monitoring task, we complete three major works in this paper. First, MHCOA calculates the number of cluster heads and communication radius of low energy sensor nodes; Second, it finishes monitoring task through data packets transmission with higher energy nodes acting as cluster heads in the form of multi-hop heterogeneous WSNs; Finally, simulation results show that compared to its peers in heterogeneous WSNs, MHCOA reduces the number of cluster heads, which saves the network average energy by up to 16.7 % and extends the network life by up to 38 %, while with less end-to-end delay.

A Review on Energy Efficient Clustering Protocol of Heterogeneous Wireless Sensor Network

“…But, node with different energy level is the best option for heterogeneous WSN. As different capabilities like processing and link bandwidth demand energy backup [1,23,29,42,48,53]. Objectives of adding heterogeneous nodes in WSN are listed as follows.…”

Key Feature Recognition Algorithm of Network Intrusion Signal Based on Neural Network and Support Vector Machine

“…It can be implemented in many structures, such as single-layer neural network, autocorrelation multi-layer perceptron, etc. The generalized Hebbian algorithm (GHA) is a common Symmetry 2019, 11,380 3 of 15 learning algorithm for multi-principal component extraction. It is used to train single-layer neural networks for principal component extraction.…”

“…The experimental results show that the algorithm has the advantages of high precision, low false positive rate and the recognition time of key features of R2L (it is a common way of network intrusion attack) data set is only 3.18 ms.Symmetry 2019, 11, 380 2 of 15 features and remove secondary features, which is often conducive to shortening the detection time and discovering the intrinsic features of a certain type of attack [10]. Feature extraction is not to explicitly remove some features from the input features, but to carry out linear or nonlinear transformation of the input features, extracting from them to replace the table components, using these components instead of the original input features, so as to achieve dimensionality reduction and feature space conversion [11][12][13].At present, there are few researches on intrusion detection from the perspective of feature extraction. Authors in [14] constructed a multi-layer hybrid intrusion detection model, using support vector machine and extreme learning machine to improve the efficiency of detection of known and unknown attacks; then proposed an improved k-means method, established a new small training data set representing the entire training data set, greatly shortened the training time of classifier, and improved the performance of the intrusion detection system.…”

“…Symmetry 2019, 11, 380 2 of 15 features and remove secondary features, which is often conducive to shortening the detection time and discovering the intrinsic features of a certain type of attack [10]. Feature extraction is not to explicitly remove some features from the input features, but to carry out linear or nonlinear transformation of the input features, extracting from them to replace the table components, using these components instead of the original input features, so as to achieve dimensionality reduction and feature space conversion [11][12][13].…”

Model-Checking Driven Design of QoS-Based Routing Protocol for Wireless Sensor Networks

“…Cheng et al [15] used the geographic opportunistic routing for QoS provisioning in WSNs and evaluated the protocol through NS-2 simulations and tests on the hardware nodes. Hu et al [16] proposed a multihop heterogeneous cluster-based optimization algorithm (MHCOA) for WSNs and also used NS-2 simulation to show the better performance of MHCOA in heterogeneous WSNs.…”