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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. [ABSTRACT FROM AUTHOR]

In this paper, the newly emerging wireless powered communication network is studied. In doing so, the performance of the global controller (GC) is evaluated, which coordinates the wireless energy transmissions between two sensor nodes. Both the sensors have the same harvested energy for uplink (UL) transmission of information through time‐division‐multiple‐access. Afterwards, the information transmission time is optimised to maximise the common throughput of both the sensors with a total time constraint based on the user's UL channels along with the same harvested energy value. Further, due to the "doubly near‐far" phenomenon, a remote sensor from the GC, which has poor channel conditions than a nearer user, has to transmit more time in the UL for maximum common throughput. To overcome this problem, the energy exchange (EEx) model is proposed where both sensors first harvest the same amount of wireless energy and then exchange energy to nullify the different channel conditions between sensors and GC to send their independent information in the UL. Simulation results demonstrate the EEx Model's effectiveness over without energy exchange (WEEx) model in eliminating the doubly near‐far problem in wireless powered communication network but at the cost of maximum sum‐throughput. The maximum sum‐throughput of the proposed EEx model is 35% lower than the WEEx model. However, the average BER in the proposed EEx model is 74.6% lower than the WEEx model, which increases the reliability of the model. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) found application in many diverse fields, starting from environment monitoring to machine health monitoring. The sensor in WSNs senses information. Sensing and transmitting this information consume most of the energy. Also, this information requires proper processing before final usages. This paper deals with minimising the redundant information sensed by the sensors in WSNs to reduce the unnecessary energy consumption and prolong the network lifetime. The redundant information is expressed in terms of the overlapping sensing area of the working sensors set. A mathematical model is proposed to find the redundant information in terms of the overlapping area. A combined meta-heuristic approach is used to achieve the optimal coverage, and the effect of the overlapping area is considered in the objective function to reduce the amount of redundant information sensed by the working sensors set. Improved genetic algorithm (IGA) and Binary ant colony algorithm (BACA) are used as meta-heuristic tools to optimise the multi-objective function. The objective was to find the minimum number of sensors that cover a complete scenario with minimum overlapping sensing region. The results show that optimal coverage with the minimum working sensor set is achieved and then by incorporating the concept of overlapping area in the objective function, sensing of redundant information is further reduced. [ABSTRACT FROM AUTHOR]

Ensuring fast and efficient Intrusion Detection and Prevention (IDP) at international borders is crucial for maintaining security and safeguarding nations. In this study, we propose an innovative approach that harnesses the power of machine learning and Wireless Sensor Networks (WSNs) to achieve faster and more accurate IDP. Our novel Fuzzy fed Teaching Learning Based Optimisation regression algorithm (F-TLBO-ID) revolutionises the prediction of the required number of k -barriers for rapid IDP. To develop and validate our approach, we synthetically generated pertinent features using Monte-Carlo simulations. These features encompass essential parameters such as the concerned region's area, effective transmission range, effective sensing range, number of sensor nodes, and the fading parameter. Training the F-TLBO-ID algorithm with these features yielded exceptional results, accurately predicting the required number of k -barriers with an impressive correlation coefficient (R = 0.99), minimal Root Mean Square Error (RMSE = 11.32), and negligible bias (−3.66). To benchmark the performance of our F-TLBO-ID algorithm, we conducted comprehensive comparisons with fine-tuned benchmark algorithms, including AutoML, GPR, GRNN, RF, RNN, SVM, and ANN. Additionally, we evaluated the algorithm against 11 different variants of nature-inspired algorithms. Remarkably, our F-TLBO-ID algorithm outperformed all these methods in terms of accuracy, firmly establishing its superiority. Finally, we validated the performance of the F-TLBO-ID algorithm using publicly available datasets. The results were highly satisfactory, exhibiting a strong correlation coefficient (R = 0.84), acceptable RMSE (36.24), and minimal bias (−7.17). This study offers a robust and reliable algorithm to predict the required barriers for fast IDP, surpassing the accuracy of existing benchmark algorithms. By implementing our proposed algorithm, the efficiency of IDP systems at international borders can be significantly improved, ultimately enhancing security and facilitating smooth border operations. • Proposed a novel fuzzy fed TLBO regression algorithm for fast intrusion detection. • An experimental study to evaluate the performance of the proposed algorithm. • F-TLBO-ID outperforms benchmark algorithms (standalone, fine-tuned, and novel). • The proposed approach can solve the problem of real data scarcity. [ABSTRACT FROM AUTHOR]

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 (P 2 CA) 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. • Proposed a hybrid machine learning algorithm to predict the k barriers. • An experimental study to analyse the performance of the proposed algorithm. • Results demonstrate superior performance in comparison to other benchmark methods. • Presented approach can solve the problem of scenario-specific algorithms. [ABSTRACT FROM AUTHOR]

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. [Display omitted] • Proposed a deep learning-based architecture to predict the k barriers. • An experimental study to evaluate the performance of the proposed architecture. • Comparative results show the outperform performance concerning other benchmark algorithms. • Presented approach can solve the problem of unnecessary computing time and cost. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

[Display omitted] • Proposed three methods based on GPR to predict the k barrier coverage probability. • An experimental study to assess the performance of the proposed methods. • Comparative results show the outperform performance concerning other variants of SVR. • Presented methods can ameliorate the problem of unnecessary computing time and cost. Sensors in a Wireless Sensor Network (WSN) sense, process, and transmit information simultaneously. They mainly find applications in agriculture monitoring, environment monitoring, smart city development and defence. These applications demand high-end performance from the WSN. However, the performance of a WSN is highly vulnerable to various types of security threats. Any intrusion may reduce the performance of the WSN and result in fatal problems. Hence, fast intrusion detection and prevention is of great use. This paper aims towards fast detection and prevention of any intrusion using a machine learning approach based on Gaussian Process Regression (GPR) model. We have proposed three methods (S-GPR, C-GPR and GPR) based on feature scaling for accurate prediction of k -barrier coverage probability. We have selected the number of nodes, sensing range, Sensor to Intruder Velocity Ratio (SIVR), Mobile to Static Node Ratio (MSNR), angle of the intrusion path, and required k as the potential features. These features are extracted using an analytical approach. Simulation results demonstrate that the proposed method III accurately predicts the k -barrier coverage probability and outperforms the other two methods (I and II) with a correlation coefficient (R = 0.85) and Root Mean Square Error (RMSE = 0.095). Further, the proposed methods achieve a higher accuracy as compared to other benchmark schemes. [ABSTRACT FROM AUTHOR]

Node localisation plays a critical role in setting up Wireless Sensor Networks (WSNs). A sensor in WSNs senses, processes and transmits the sensed information simultaneously. Along with the sensed information, it is crucial to have the positional information associated with the information source. A promising method to localise these randomly deployed sensors is to use bio-inspired meta-heuristic algorithms. In this way, a node localisation problem is converted to an optimisation problem. Afterwards, the optimisation problem is solved for an optimal solution by minimising the errors. Various bio-inspired algorithms, including the conventional Cuckoo Search (CS) and modified CS algorithm, have already been explored. However, these algorithms demand a predetermined number of iterations to reach the optimal solution, even when not required. In this way, they unnecessarily exploit the limited resources of the sensors resulting in a slow search process. This paper proposes an Enhanced Cuckoo Search (ECS) algorithm to minimise the Average Localisation Error (ALE) and the time taken to localise an unknown node. In this algorithm, we have implemented an Early Stopping (ES) mechanism, which improves the search process significantly by exiting the search loop whenever the optimal solution is reached. Further, we have evaluated the ECS algorithm and compared it with the modified CS algorithm. While doing so, note that the proposed algorithm localised all the localisable nodes in the network with an ALE of 0.5–0.8 m. In addition, the proposed algorithm also shows an 80 % decrease in the average time taken to localise all the localisable nodes. Consequently, the performance of the proposed ECS algorithm makes it desirable to implement in practical scenarios for node localisation. [ABSTRACT FROM AUTHOR]

In order to solve the critical issues in Wireless Sensor Networks (WSNs), with concern for limited sensor lifetime, nature-inspired algorithms are emerging as a suitable method. Getting optimal network coverage is one of those challenging issues that need to be examined critically before any network setup. Optimal network coverage not only minimizes the consumption of limited energy of battery-driven sensors but also reduce the sensing of redundant information. In this paper, we focus on nature-inspired optimization algorithms concerning the optimal coverage in WSNs. In the first half of the paper, we have briefly discussed the taxonomy of the optimization algorithms along with the problem domains in WSNs. In the second half of the paper, we have compared the performance of two nature-inspired algorithms for getting optimal coverage in WSNs. The first one is a combined Improved Genetic Algorithm and Binary Ant Colony Algorithm (IGA-BACA), and the second one is Lion Optimization (LO). The simulation results confirm that LO gives better network coverage, and the convergence rate of LO is faster than that of IGA-BACA. Further, we observed that the optimal coverage is achieved at a lesser number of generations in LO as compared to IGA-BACA. This review will help researchers to explore the applications in this field as well as beyond this area. [ABSTRACT FROM AUTHOR]