Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation J. Amutha, Sandeep Sharma, Sanjay Kumar Sharma; Reliability of clustering algorithm in wireless sensor networks using supervised machine learning classification approaches. AIP Conf. Proc. 7 May 2024; 2853 (1): 020263. https://doi.org/10.1063/5.0197752 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search

Wireless Sensor Networks (WSNs) is a promising technology with enormous applications in almost every walk of life. One of the crucial applications of WSNs is intrusion detection and surveillance at border areas and in the defence establishments. The border areas are stretched over hundreds to thousands of miles, hence, it is not possible to patrol the entire border region. As a result, an enemy may enter from any point absence of surveillance and cause the loss of lives or destroy the military establishments. WSNs can be a feasible solution for the problem of intrusion detection and surveillance at the border areas. Detection of an enemy at the border areas and nearby critical areas such as military cantonments is a time-sensitive task as a delay of a few seconds may have disastrous consequences. Therefore, it becomes imperative to design systems that can identify and detect the enemy as soon as it comes within the range of the deployed system. In this paper, we have proposed a deep learning architecture based on a fully connected feed-forward Artificial Neural Network (ANN) for the accurate prediction of the number of k-barriers for fast intrusion detection and prevention. We have trained and evaluated the feed-forward ANN model using four potential features, namely area of the circular region, sensing range of sensors, transmission range of sensors, and number of sensor for Gaussian and uniform sensor distribution. These features are extracted through Monte Carlo simulation. In doing so, we found that the model accurately predicts the number of k-barriers for both Gaussian and uniform sensor distribution with correlation coefficient (R = 0.78) and Root Mean Square Error (RMSE = 41.15) for the former and R = 0.79 and RMSE = 48.36 for the latter. Further, the proposed approach outperforms the other benchmark algorithms in terms of accuracy and computational time complexity.

Drastic advancement in computing technology and the dramatic increase in the usage of explainable machine learning algorithms provide a promising platform for developing robust intrusion detection algorithms. However, the development of these algorithms is constrained by their applicability over specific scenarios of Wireless Sensor Networks (WSNs). We introduced a hybrid framework by combining Probabilistic Principal Component Analysis (P2CA) and Generalised Additive Model (GAM), which is performing well for all the scenarios of WSNs. To demonstrate our framework's broad applicability, we evaluated its performance over three publicly available intrusion detection datasets (i.e., LT-FS-ID, AutoML-ID, and FF-ANN-ID), each from different scenarios. Our findings highlight that the presented framework can accurately predict the number of k-barriers for all three datasets. Furthermore, we conducted a comprehensive performance comparison between our proposed framework and benchmark algorithms, which revealed that our approach outperforms all of them. Additionally, we evaluated the framework's versatility by testing its performance on datasets unrelated to intrusion detection, specifically ALE datasets. Notably, our approach accurately predicted the response variable in these datasets and exceeded the performance of its primary algorithm, further demonstrating its robustness and adaptability. The implications of this research are substantial. By developing a robust intrusion detection framework that performs well across diverse WSN scenarios, we address a critical need for reliable network security in various domains, including industrial IoT, smart cities, and environmental monitoring. Our findings not only enhance the understanding of intrusion detection in WSNs but also pave the way for developing more sophisticated and adaptable systems to safeguard sensitive data and critical infrastructure.

The dramatic increase in the computational facilities integrated with the explainable machine learning algorithms allows us to do fast intrusion detection and prevention at border areas using Wireless Sensor Networks (WSNs). This study proposed a novel approach to accurately predict the number of barriers required for fast intrusion detection and prevention. To do so, we extracted four features through Monte Carlo simulation: area of the Region of Interest (RoI), sensing range of the sensors, transmission range of the sensor, and the number of sensors. We evaluated feature importance and feature sensitivity to measure the relevancy and riskiness of the selected features. We applied log transformation and feature scaling on the feature set and trained the tuned Support Vector Regression (SVR) model (i.e., LT-FS-SVR model). We found that the model accurately predicts the number of barriers with a correlation coefficient (R) = 0.98, Root Mean Square Error (RMSE) = 6.47, and bias = 12.35. For a fair evaluation, we compared the performance of the proposed approach with the benchmark algorithms, namely, Gaussian Process Regression (GPR), Generalised Regression Neural Network (GRNN), Artificial Neural Network (ANN), and Random Forest (RF). We found that the proposed model outperforms all the benchmark algorithms.

Momentous increase in the popularity of explainable machine learning models coupled with the dramatic increase in the use of synthetic data facilitates us to develop a cost-efficient machine learning model for fast intrusion detection and prevention at frontier areas using Wireless Sensor Networks (WSNs). The performance of any explainable machine learning model is driven by its hyperparameters. Several approaches have been developed and implemented successfully for optimising or tuning these hyperparameters for skillful predictions. However, the major drawback of these techniques, including the manual selection of the optimal hyperparameters, is that they depend highly on the problem and demand application-specific expertise. In this paper, we introduced Automated Machine Learning (AutoML) model to automatically select the machine learning model (among support vector regression, Gaussian process regression, binary decision tree, bagging ensemble learning, boosting ensemble learning, kernel regression, and linear regression model) and to automate the hyperparameters optimisation for accurate prediction of numbers of k-barriers for fast intrusion detection and prevention using Bayesian optimisation. To do so, we extracted four synthetic predictors, namely, area of the region, sensing range of the sensor, transmission range of the sensor, and the number of sensors using Monte Carlo simulation. We used 80% of the datasets to train the models and the remaining 20% for testing the performance of the trained model. We found that the Gaussian process regression performs prodigiously and outperforms all the other considered explainable machine learning models with correlation coefficient (R = 1), root mean square error (RMSE = 0.007), and bias = - 0.006. Further, we also tested the AutoML performance on a publicly available intrusion dataset, and we observed a similar performance. This study will help the researchers accurately predict the required number of k-barriers for fast intrusion detection and prevention.

In wireless sensor networks (WSNs), energy efficiency is a significant design challenge that can be resolved by clustering and routing approaches. They are considered as Non-deterministic Polynomial (NP)-hard optimization problems, and the optimal or near-optimal solutions can be determined by using Swarm-Intelligence (SI) based algorithms. With this inspiration, this study focuses on two approaches, viz. the Hybrid Butterfly and Ant Colony optimization algorithm along with Static sink node (HBACS) and HBAC along with Mobile sink node (HBACM), which is a hybridization of Butterfly Optimization (BOA) and Ant Colony Optimization (ACO) algorithm. BOA determines the optimal cluster head, and ACO performs energy-efficient routing, thereby minimizing the energy consumption and maximizing the network's lifetime. Furthermore, in this study, mobility of the sink node is used to eliminate the multi-hop communication between cluster heads and sink nodes, hence addressing the hot-spot issue and further extending the network lifetime. The proposed HBACS and HBACM approaches are implemented in the NS2 simulator. The simulation findings reveal that the HBACS shows percentage improvement regarding residual energy by 24.23%, 41.98%, and 66.67%; an improved number of alive nodes by 28.19%, 37.81%, and 53.12%; and improved throughput by 8.11%, 14.29%, and 17.65% over CRWO, ERP, and IHSBEER algorithms respectively. Moreover, the HBACM approach performs better in LDN by 18.76%, 63.66%, and 66.28%; HDN by 8.35%, 56.26%, and 58.15%, and FDN by 7.77%, 52.76%, and 74.29% over HGWSFO, SFO, and GWO based approaches, respectively.

Wireless Sensor Networks (WSNs) have attracted various academic researchers, engineers, science, and technology communities. This attraction is due to their broad research areas such as energy efficiency, data communication, coverage, connectivity, load balancing, security, reliability, scalability, and network lifetime. Researchers are looking towards cost-effective approaches to improve the existing solutions that reveal novel schemes, methods, concepts, protocols, and algorithms in the desired domain. Generally, review studies provide complete, easy access or solution to these concepts. Considering this as a driving force and the impact of clustering on the deterioration of energy consumption in wireless sensor networks, this review focus on clustering methods based on different aspects. This study’s significant contribution is to provide a brief review in the field of clustering in wireless sensor networks based on three different categories, such as classical, optimization, and machine learning techniques. For each of these categories, various performance metrics and parameters are provided, and a comparative assessment of the corresponding aspects like cluster head selection, routing protocols, reliability, security, and unequal clustering are discussed. Various advantages, limitations, applications of each method, research gaps, challenges, and research directions are considered in this study, motivating the researchers to carry out further research by providing relevant information in cluster-based wireless sensor networks.

Border surveillance is indeed one of the most pertinent applications of wireless sensor networks, primarily used for security purposes such as intrusion detection in border regions or protected areas. In order to detect unauthorized access or penetration through the region of interest, sensor nodes are deployed to form barriers, that acts as the performance metric of wireless sensor networks. In this paper, a Distributed Border Surveillance (DBS) system incorporating shadowing effects is proposed for a wireless sensor network deployed in a rectangular region of interest. The DBS system evaluates the number of required barriers to monitor the given region and conserves energy. Besides, a log-normal shadowing model is considered, which incorporates the asymmetry in sensing range along with the stochastic nature of wireless channels. The performance of the proposed DBS system is analyzed based on the number of barriers obtained. Then, the impact of various network and system parameters such as the number of nodes, sensing range of nodes, height and width of the network region on the number of barriers obtained in a rectangular region are analyzed. The same approach is extended for a circular region of interest in terms of sensing range of nodes. The proposed system is implemented in NS-2.35 simulator, and it is found that the performance of the proposed DBS system is 75% better than the existing binary sensing range model-based DBS system.

Wireless sensor networks (WSNs) are growing rapidly in various fields of commerce, medicine, industrial, agriculture, research, meteorology, etc. that eases complicated tasks. The most active and recent research areas in wireless sensor networks are deployment strategies, energy efficiency and coverage. Besides energy harvesting, network lifetime of the sensors can be increased by decreasing the consumption of energy. This becomes the most challenging areas of utilizing wireless sensor network in practical applications. Deployment in WSNs directly influence the performance of the networks. The usage of sensor nodes in large quantity in the random deployment improves concerns in reliability and scalability. Coverage in wireless sensor networks measures how long the physical space is monitored by the sensors. Barrier coverage is an issue in wireless sensor networks, which is used for security application aims in intruder detection of the protected area. Several ongoing research work focuses on energy efficiency and coverage in wireless sensor networks and numerous schemes, algorithms, methods and architectures have been proposed. Still, there is no comprehensive solution applicable universally. Hence,this work provides with a state-of-the-art of the classification of wireless sensor networks based on different dimensions, such as, types of sensors, deployment strategies, sensing models, coverage and energy efficiency.

Wormhole attack, a resource depletion attack being considered as one of the severe attack in Wireless Sensor Networks (WSN). It can interfere routing process at any time, drain the battery power of sensor nodes and also can disable the whole system. Hence, this work deals with a technique to identify and prevent wormhole attack using Adhoc On-demand Multipath Distance Vector (AOMDV) protocol. The main purpose of using AOMDV protocol is route discovery, multipath routing, network load handling, increases reliability and avoid the risks of congestion. The proposed technique is implemented in NS2.35 simulator and various performance parameters are evaluated.

An All-IP network is probably getting highly feasible since all applications and services in the telecommunication are already getting IP enabled. Internet Protocol version 6 or IPv6 is a later version of IP suite as it is designed to handle the increasing number of Internet applications. Security has become a major concern for the IP next generation network architecture and is considered as one of the most fundamental requirements for business continuity and service delivery. Several initiatives have been made by researchers to integrate secure IPv4/IPv6 address translation between IPv4 and IPv6 networks. But, not much progress has been reported in the recent past. Hence, in this research, an Integrated Secure Architecture for IPv4/IPv6 Address Translation between IPv4 and IPv6 Networks, with an IPv4/IPv6-Enabled Gateway Translator (IP46EGT), has been proposed to achieve MAC-level, VPN-IPSec, and Certificate level security. Network performance is evaluated and the generated results are tabulated and graphically presented.

Next Generation Network (NGN) is an integration of heterogeneous network environment, achieves the vision of next generation services for the delivery of voice, data and video. Researchers have proposed different protocols and architectures for NGN to access heterogeneous networks. Accessing to the devices connected through the NGN with seamless mobility is an important area of research in NGN. Several efforts have been carried out by researchers to integrate MPLS technology into IP networks to provide seamless mobility. But, not much progress has been made so far. Hence, in this paper, a novel framework to integrate IP in MPLS is proposed to achieve seamless mobility with end to end quality of service. The performance of the proposed framework has been tested by establishing a test bed in a simulated environment.

Next Generation Networks (NGN) is a strategy to achieve the vision of next-generation services for the delivery of quad play data, voice and video anywhere and anytime virtually across any access technology. It integrates the services of 2G/3G/4G Networks including IPv4 and IPv6 and incorporates the services of traditional networks into a single service platform with the usage of Multi-Protocol Label Switching (MPLS). Much work has been reported by researchers to integrate MPLS technology into IP networks. But, not much progress has been made so far. In this paper, a novel integrated IP-MPLS Architecture is proposed, which provides IP-integration, end-to-end quality of service, security, scalability, resiliency, and management enhancements for deploying data, voice, and video services. Test bed has been established for testing the performance of the proposed architecture.

Next Generation Network (NGN) is envisioned to be an internetworking environment of heterogeneous networks to offer next generation services anywhere, anytime. It integrates the services of 2G/3G/4G networks by incorporating IPv4/IPv6 into a single service platform with Multi-Protocol Label Switching (MPLS). Several efforts have been carried out by researchers to integrate MPLS technology into IP networks and to access the mobile nodes connected through the NGN to provide service continuity with guaranteed quality of service. But, not much progress has been reported in recent past. Hence, in this paper, an integrated next generation IP-MPLS hybrid architecture for seamless mobility has been proposed to achieve seamless mobility with end to end quality of service. The performance of the proposed architecture has been tested by establishing a test bed in a simulated environment. Key-Words: NGN, IP-MPLS, IPv4 / IPv6, VoIP, Seamless Mobility, Quality of Service