Suvasini Panigrahi - Academia.edu (original) (raw)

Papers by Suvasini Panigrahi

ECS Transactions

Cloud computing is growing tremendously for its on-demand services, a massive pool of distributed... more Cloud computing is growing tremendously for its on-demand services, a massive pool of distributed resources, rapid provisioning of resources, and many more. It empowers many organizations/customers to build on-demand applications without investing large capital in creating hardware infrastructure. These organizations encounter numerous challenges towards obtaining full-pledged services from the cloud service providers (CSPs). One such challenge is identifying and deciding upon a suitable CSP that can fulfill the quality of service (QoS) requirements of these organizations. Moreover, the services offered by the CSPs are interrelated and beneficial, and non-beneficial. As a result, it makes it difficult for organizations to suitably evaluate the services rendered by the CSPs. Therefore, multi-attribute decision-making (MADM) algorithms are applied in the literature to overcome the above challenge of uncertainty. In this paper, we survey applications of such algorithms from the perspec...

Lecture notes in networks and systems, 2022

Cyber Security in Parallel and Distributed Computing, 2019

2018 International Conference on Recent Innovations in Electrical, Electronics & Communication Engineering (ICRIEECE), 2018

In this research, we have carried out a systematic study in automobile insurance fraud detection.... more In this research, we have carried out a systematic study in automobile insurance fraud detection. The fraudster, their main types and subtypes of known insurance frauds has been defined. We have categorized, compared, and summarized from almost all published technical and review articles in this domain within the last 10 years. A novel scheme has been proposed that uses a Particle Swarm Optimization (PSO) based feature selection method for extracting irrelevant and redundant features in automobile insurance dataset. As the dataset is highly skewed in nature, we have devised a Quarter Sphere Support Vector Machine (QS-SVM) based under sampling approach for data balancing. Thereafter, we have employed Decision Tree (DT) and Logistic Regression (LR) for classification purpose on the balanced data. The effectiveness of our proposed methodology is evaluated experimentally using a real world automobile insurance fraud dataset taken from literature.

In this paper, we have suggested a deep learning model aimed at effective detection of malicious ... more In this paper, we have suggested a deep learning model aimed at effective detection of malicious transactions in a database system. This method focuses on exploiting the user normal behavior, data dependencies, and data sensitivity of a transaction to predict intrusions. Currently, we have used different kinds of neural networks according to their strengths of predicting the intrusion according to the type of data such as sequential or featured data. For experimental evaluation, we have used a recurrent neural network for sequence data and feed-forward with back propagation for other attributes, together creating a hybrid deep learning model which works effectively to predict the database intrusions.

International Journal of Information Technology, 2021

In recent years, cloud computing is becoming an attractive research topic for its emerging issues... more In recent years, cloud computing is becoming an attractive research topic for its emerging issues and challenges. Not only in research but also the enterprises are rapidly adopting cloud computing because of its numerous profitable services. Cloud computing provides a variety of quality of services (QoSs) and allows its users to access these services in the form of infrastructure, platform and software on a subscription basis. However, due to its flexible nature and huge benefits, the demand for cloud computing is rising day by day. As a circumstance, many cloud service providers (CSPs) have been providing services in the cloud market. Therefore, it becomes significantly cumbersome for cloud users to select an appropriate CSP, especially considering various QoS criteria. This paper presents a hybrid multi-criteria decision-making (H-MCDM) algorithm to find a solution by considering different conflicting QoS criteria. The proposed algorithm takes advantage of two well-known MCDM algorithms, namely analytic network process (ANP) and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), to select the best CSP or alternative. Here, ANP is used to categorize the criteria into subnets and finds the local rank of the CSPs in each subnet, followed by VIKOR, to find the global rank of the CSPs. H-MCDM considers both beneficial and non-beneficial criteria and finds the CSP that holds the maximum and minimum values of these criteria, respectively. We demonstrate the performance of H-MCDM using a real-life test case (case study) and compare the results to show the efficacy. Finally, we perform a sensitivity analysis to show the robustness and stability of our algorithm.

2019 Global Conference for Advancement in Technology (GCAT)

In this paper we have shown a comparison of ensemble classifiers which are used for detecting mob... more In this paper we have shown a comparison of ensemble classifiers which are used for detecting mobile telecommunication fraud. k-means clustering has been used to label the data and then the ensemble techniques Boosting and Bagging techniques have been used for classification. Four relevant features are extracted from the reality-mining dataset which are used for constructing the user profile. The results shows how an ensemble technique improves the performance of the classifier and a comparative analysis has been done between the two ensemble methods by calculating their accuracy.

International Journal of Business Intelligence and Data Mining

2016 International Conference on Information Technology (ICIT), 2016

This article reports a novel scheme called minimal mean distant scalar leader selection inspired ... more This article reports a novel scheme called minimal mean distant scalar leader selection inspired by event driven camera actuation for redundant data minimization. The proposed algorithm aims at actuating minimum number of cameras while maximizing the event area coverage and reducing the resulting traffic overhead. This is accomplished by effective selection of scalar leaders in each sub-region of the monitored region and the chosen scalar leaders operate as informants for event information communication to their respective camera sensors. The less camera actuation with enhanced coverage ratio, minimized redundancy ratio and reduced energy expenditure obtained from the experimentation reveal the effectiveness of the proposed algorithm as compared to three other recent approaches.

2020 International Conference on Computer Science, Engineering and Applications (ICCSEA), 2020

Intrusion detection is a very effective mechanism to deal with threats and challenges in database... more Intrusion detection is a very effective mechanism to deal with threats and challenges in database security. The rapid development of the usage of the Internet, E-commerce and cashless transactions has raised the need for high-security mechanism such as Smart Intrusion detection system as traditional detection system not always handle and compete with newly intelligent attacks. Thus, the application of machine learning/ data mining comes to play to identify such unusual attacks in an efficient manner. In this paper, we propose a database intrusion detection system based on the concept of outlier detection which is a derived concept of data mining. We deploy Bat algorithm, a swarm intelligence technique in order to build the intrusion detection model. The performance of the approach is then measured by feeding with an biometric dataset and achieved promising results.

International Journal of Business Intelligence and Data Mining, 2022

2017 3rd International Conference on Computational Intelligence and Networks (CINE), 2017

Database security has become a prime concern in today's internet world due to the escalation ... more Database security has become a prime concern in today's internet world due to the escalation of various web applications and information systems. Ensuring the security of the back-end databases is highly essential for maintaining the confidentiality and integrity of the stored sensitive information. In this paper, a Density-based clustering technique, namely, OPTICS, has been applied for constructing the normal profile of users. Each incoming transaction either lies within a cluster or is found to deviate from the clusters based on its Local Outlier Factor value. The transactions observed as outliers are further verified by employing various supervised machine learning techniques individually – Naïve Bayes, Decision Tree, Rule Induction, k-Nearest Neighbor and Radial Basis Function Network. The effectiveness of our system is demonstrated by carrying out extensive experimentations and comparative analysis using stochastic models.

International Journal of Applied Metaheuristic Computing, 2021

Very large amounts of time and effort have been invested by the research community working on dat... more Very large amounts of time and effort have been invested by the research community working on database security to achieve high assurance of security and privacy. An important component of a secure database system is intrusion detection system which has the ability to successfully detect anomalous behavior caused by applications and users. However, modeling the normal behavior of a large number of users in a huge organization is quite infeasible and inefficient. The main purpose of this research investigation is thus to model the behavior of roles instead of users by applying adaptive resonance theory neural network. The observed behavior which deviates from any of the established role profiles is treated as malicious. The proposed model has the advantage of identifying insider threat and is applicable for large organizations as it is based on role profiling instead of user profiling. The proposed system is capable of detecting intrusion with high accuracy along with minimized false...

Advances in Intelligent Systems and Computing, 2020

With the rapid development of World Wide Web and E-commerce, concern of security is a very sensit... more With the rapid development of World Wide Web and E-commerce, concern of security is a very sensitive issue in this modern era of information and communication technology. A lot of financial and brain effort has been invested in this problem and still requires serious attention due to the increasing threats. Database centered Intrusion Detection is a prominent field in this research circumference. Concept of outlier analysis in data mining can automate this intrusion detection process with higher accuracy. In this research, we present the role of soft outlier analysis in Database-centered Intrusion Detection while comparing its performance with its counterpart hard outlier analysis which ultimately enhances its productivity by improving the accuracy and reducing the false positive costs.

User profiling is the process of constructing a normal profile by accumulating the past calling b... more User profiling is the process of constructing a normal profile by accumulating the past calling behavior of a user. The technique of clustering focusses on outcome of a structure or an intrinsic grouping in unlabeled data collection. In this paper, our main intention is on building appropriate user profile by applying generalized possibilistic fuzzy c-means (GPFCM) clustering technique. All the call features required to build a user profile is collected from the call detail record of the individual users. The behavioral profile modeling of users is prepared by implementing the clustering on two relevant calling features from the reality-mining dataset. The labels are not present in the dataset and thus we have applied clustering which is an unsupervised approach. Before applying the clustering algorithm, a proper cluster validity analysis has to be done for finding the best cluster value and then the cluster analysis is done using some performance parameters.

Intelligent Computing and Communication, 2020

A novel hybrid data balancing method based on both undersampling and oversampling with ensemble t... more A novel hybrid data balancing method based on both undersampling and oversampling with ensemble technique has been presented in this paper for efficiently detecting the auto insurance frauds. Initially, the skewness from the original imbalance dataset is removed by excluding outliers from the majority class samples using Box and Whisker plot and synthetic samples are generated from the minority class samples by using synthetic minority oversampling (SMOTE) technique. We employed three supervised classifiers, namely, support vector machine, multilayer perceptron, and K-nearest neighbors for classification purpose. The final classification results are obtained by aggregating the results obtained from these classifiers using the majority voting ensemble technique. Our model has been experimentally evaluated with a real-world automobile insurance dataset.

2019 Global Conference for Advancement in Technology (GCAT), 2019

In this paper, we have removed the class imbalance problem using SMOTE and used Decision Template... more In this paper, we have removed the class imbalance problem using SMOTE and used Decision Templates ensemble technique for efficiently detecting the auto insurance fraud. We employed three supervised classifiers namely, Support Vector Machine, Multilayer Perceptron and K-nearest Neighbors for classification purpose. The final classification results are obtained by aggregating the results obtained from the classifiers using Decision Templates technique. Our model has been experimentally evaluated with a real –world automobile insurance dataset.

In current scenario, Wireless Multimedia Sensor Networks have gained much popularity in many sphe... more In current scenario, Wireless Multimedia Sensor Networks have gained much popularity in many spheres of life. Both scalar sensors and camera sensors are present in case of Wireless Multimedia Sensor Networks. Camera sensors have two basic parameters. They are depth of field and field of view. Depth of field is the distance at which a camera can capture accurate image of an object. Field of view is the angle at which a camera can capture accurate image of an object. When any kind of event takes place in a monitored region, it is initially detected by scalar sensors. Then the scalars inform their respective camera(s) regarding the occurrence of event. However, the problem is that when event occurs, sensing of event takes place in the exact event region and also up to some extent outside the event region. Due to this, the cameras present at the region outside the event boundary are activated unnecessarily, since their depths of fields do not cover the exact event region. In our paper, ...

Advances in Intelligent Systems and Computing, 2019

In the current automation world, every organizations starting from education to industry and also... more In the current automation world, every organizations starting from education to industry and also in research organization are maintaining database and several security breaches are found in these databases. Traditional database security mechanism cannot handle the malicious access toward database. Although, various researches have been done in database intrusion detection, but most of the researches are limited in efficiency and accuracy. Inspired by human cognitive system, we present a database intrusion detection system using Adaptive Resonance Theory which is accompanied with some data mining techniques for preprocessing data. The proposed model can learn easily and cope up with the dynamic environment which entitles the system to detect both known and unknown patterns accurately with low false positive cost. The calculated simulation result shows that the database intrusion detection based on Adaptive Resonance Theory can accelerate the detection process with higher accuracy as compared to Self Organizing Map and Radial basis functional neural network.

2016 International Conference on Information Technology (ICIT), 2016

This article reports a novel algorithm inspired byscalar premier determination considering reduce... more This article reports a novel algorithm inspired byscalar premier determination considering reduced cameraactivation while contributing enhanced coverage of the occurringevent zone. The proposed scheme chooses the scalar premiers in ahexagonal fashion, which operate as representatives of the scalarswithin whose sensing ranges the scalar premiers are located. Theselection of the scalar premiers is accomplished in such a mannerthat on any occasion of prevailing event in the monitored region, the scalar premiers appraise their respective camera sensorsregarding the event occurrence. The scalar premiers execute asthe messengers for event information communication to theircorresponding cameras, thereby, averting the redundant datatransmission by all the scalars. Further, the numerical resultsestablished from the experimental inspection and comparativeperformance estimates in terms of minimal camera activation, reduced redundancy ratio as well as maximized coverage ratiocorroborate the sup...

ECS Transactions

Cloud computing is growing tremendously for its on-demand services, a massive pool of distributed... more Cloud computing is growing tremendously for its on-demand services, a massive pool of distributed resources, rapid provisioning of resources, and many more. It empowers many organizations/customers to build on-demand applications without investing large capital in creating hardware infrastructure. These organizations encounter numerous challenges towards obtaining full-pledged services from the cloud service providers (CSPs). One such challenge is identifying and deciding upon a suitable CSP that can fulfill the quality of service (QoS) requirements of these organizations. Moreover, the services offered by the CSPs are interrelated and beneficial, and non-beneficial. As a result, it makes it difficult for organizations to suitably evaluate the services rendered by the CSPs. Therefore, multi-attribute decision-making (MADM) algorithms are applied in the literature to overcome the above challenge of uncertainty. In this paper, we survey applications of such algorithms from the perspec...

Lecture notes in networks and systems, 2022

Cyber Security in Parallel and Distributed Computing, 2019

2018 International Conference on Recent Innovations in Electrical, Electronics & Communication Engineering (ICRIEECE), 2018

In this research, we have carried out a systematic study in automobile insurance fraud detection.... more In this research, we have carried out a systematic study in automobile insurance fraud detection. The fraudster, their main types and subtypes of known insurance frauds has been defined. We have categorized, compared, and summarized from almost all published technical and review articles in this domain within the last 10 years. A novel scheme has been proposed that uses a Particle Swarm Optimization (PSO) based feature selection method for extracting irrelevant and redundant features in automobile insurance dataset. As the dataset is highly skewed in nature, we have devised a Quarter Sphere Support Vector Machine (QS-SVM) based under sampling approach for data balancing. Thereafter, we have employed Decision Tree (DT) and Logistic Regression (LR) for classification purpose on the balanced data. The effectiveness of our proposed methodology is evaluated experimentally using a real world automobile insurance fraud dataset taken from literature.

In this paper, we have suggested a deep learning model aimed at effective detection of malicious ... more In this paper, we have suggested a deep learning model aimed at effective detection of malicious transactions in a database system. This method focuses on exploiting the user normal behavior, data dependencies, and data sensitivity of a transaction to predict intrusions. Currently, we have used different kinds of neural networks according to their strengths of predicting the intrusion according to the type of data such as sequential or featured data. For experimental evaluation, we have used a recurrent neural network for sequence data and feed-forward with back propagation for other attributes, together creating a hybrid deep learning model which works effectively to predict the database intrusions.

International Journal of Information Technology, 2021

In recent years, cloud computing is becoming an attractive research topic for its emerging issues... more In recent years, cloud computing is becoming an attractive research topic for its emerging issues and challenges. Not only in research but also the enterprises are rapidly adopting cloud computing because of its numerous profitable services. Cloud computing provides a variety of quality of services (QoSs) and allows its users to access these services in the form of infrastructure, platform and software on a subscription basis. However, due to its flexible nature and huge benefits, the demand for cloud computing is rising day by day. As a circumstance, many cloud service providers (CSPs) have been providing services in the cloud market. Therefore, it becomes significantly cumbersome for cloud users to select an appropriate CSP, especially considering various QoS criteria. This paper presents a hybrid multi-criteria decision-making (H-MCDM) algorithm to find a solution by considering different conflicting QoS criteria. The proposed algorithm takes advantage of two well-known MCDM algorithms, namely analytic network process (ANP) and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), to select the best CSP or alternative. Here, ANP is used to categorize the criteria into subnets and finds the local rank of the CSPs in each subnet, followed by VIKOR, to find the global rank of the CSPs. H-MCDM considers both beneficial and non-beneficial criteria and finds the CSP that holds the maximum and minimum values of these criteria, respectively. We demonstrate the performance of H-MCDM using a real-life test case (case study) and compare the results to show the efficacy. Finally, we perform a sensitivity analysis to show the robustness and stability of our algorithm.

2019 Global Conference for Advancement in Technology (GCAT)

In this paper we have shown a comparison of ensemble classifiers which are used for detecting mob... more In this paper we have shown a comparison of ensemble classifiers which are used for detecting mobile telecommunication fraud. k-means clustering has been used to label the data and then the ensemble techniques Boosting and Bagging techniques have been used for classification. Four relevant features are extracted from the reality-mining dataset which are used for constructing the user profile. The results shows how an ensemble technique improves the performance of the classifier and a comparative analysis has been done between the two ensemble methods by calculating their accuracy.

International Journal of Business Intelligence and Data Mining

2016 International Conference on Information Technology (ICIT), 2016

This article reports a novel scheme called minimal mean distant scalar leader selection inspired ... more This article reports a novel scheme called minimal mean distant scalar leader selection inspired by event driven camera actuation for redundant data minimization. The proposed algorithm aims at actuating minimum number of cameras while maximizing the event area coverage and reducing the resulting traffic overhead. This is accomplished by effective selection of scalar leaders in each sub-region of the monitored region and the chosen scalar leaders operate as informants for event information communication to their respective camera sensors. The less camera actuation with enhanced coverage ratio, minimized redundancy ratio and reduced energy expenditure obtained from the experimentation reveal the effectiveness of the proposed algorithm as compared to three other recent approaches.

2020 International Conference on Computer Science, Engineering and Applications (ICCSEA), 2020

Intrusion detection is a very effective mechanism to deal with threats and challenges in database... more Intrusion detection is a very effective mechanism to deal with threats and challenges in database security. The rapid development of the usage of the Internet, E-commerce and cashless transactions has raised the need for high-security mechanism such as Smart Intrusion detection system as traditional detection system not always handle and compete with newly intelligent attacks. Thus, the application of machine learning/ data mining comes to play to identify such unusual attacks in an efficient manner. In this paper, we propose a database intrusion detection system based on the concept of outlier detection which is a derived concept of data mining. We deploy Bat algorithm, a swarm intelligence technique in order to build the intrusion detection model. The performance of the approach is then measured by feeding with an biometric dataset and achieved promising results.

International Journal of Business Intelligence and Data Mining, 2022

2017 3rd International Conference on Computational Intelligence and Networks (CINE), 2017

Database security has become a prime concern in today's internet world due to the escalation ... more Database security has become a prime concern in today's internet world due to the escalation of various web applications and information systems. Ensuring the security of the back-end databases is highly essential for maintaining the confidentiality and integrity of the stored sensitive information. In this paper, a Density-based clustering technique, namely, OPTICS, has been applied for constructing the normal profile of users. Each incoming transaction either lies within a cluster or is found to deviate from the clusters based on its Local Outlier Factor value. The transactions observed as outliers are further verified by employing various supervised machine learning techniques individually – Naïve Bayes, Decision Tree, Rule Induction, k-Nearest Neighbor and Radial Basis Function Network. The effectiveness of our system is demonstrated by carrying out extensive experimentations and comparative analysis using stochastic models.

International Journal of Applied Metaheuristic Computing, 2021

Very large amounts of time and effort have been invested by the research community working on dat... more Very large amounts of time and effort have been invested by the research community working on database security to achieve high assurance of security and privacy. An important component of a secure database system is intrusion detection system which has the ability to successfully detect anomalous behavior caused by applications and users. However, modeling the normal behavior of a large number of users in a huge organization is quite infeasible and inefficient. The main purpose of this research investigation is thus to model the behavior of roles instead of users by applying adaptive resonance theory neural network. The observed behavior which deviates from any of the established role profiles is treated as malicious. The proposed model has the advantage of identifying insider threat and is applicable for large organizations as it is based on role profiling instead of user profiling. The proposed system is capable of detecting intrusion with high accuracy along with minimized false...

Advances in Intelligent Systems and Computing, 2020

With the rapid development of World Wide Web and E-commerce, concern of security is a very sensit... more With the rapid development of World Wide Web and E-commerce, concern of security is a very sensitive issue in this modern era of information and communication technology. A lot of financial and brain effort has been invested in this problem and still requires serious attention due to the increasing threats. Database centered Intrusion Detection is a prominent field in this research circumference. Concept of outlier analysis in data mining can automate this intrusion detection process with higher accuracy. In this research, we present the role of soft outlier analysis in Database-centered Intrusion Detection while comparing its performance with its counterpart hard outlier analysis which ultimately enhances its productivity by improving the accuracy and reducing the false positive costs.

User profiling is the process of constructing a normal profile by accumulating the past calling b... more User profiling is the process of constructing a normal profile by accumulating the past calling behavior of a user. The technique of clustering focusses on outcome of a structure or an intrinsic grouping in unlabeled data collection. In this paper, our main intention is on building appropriate user profile by applying generalized possibilistic fuzzy c-means (GPFCM) clustering technique. All the call features required to build a user profile is collected from the call detail record of the individual users. The behavioral profile modeling of users is prepared by implementing the clustering on two relevant calling features from the reality-mining dataset. The labels are not present in the dataset and thus we have applied clustering which is an unsupervised approach. Before applying the clustering algorithm, a proper cluster validity analysis has to be done for finding the best cluster value and then the cluster analysis is done using some performance parameters.

Intelligent Computing and Communication, 2020

A novel hybrid data balancing method based on both undersampling and oversampling with ensemble t... more A novel hybrid data balancing method based on both undersampling and oversampling with ensemble technique has been presented in this paper for efficiently detecting the auto insurance frauds. Initially, the skewness from the original imbalance dataset is removed by excluding outliers from the majority class samples using Box and Whisker plot and synthetic samples are generated from the minority class samples by using synthetic minority oversampling (SMOTE) technique. We employed three supervised classifiers, namely, support vector machine, multilayer perceptron, and K-nearest neighbors for classification purpose. The final classification results are obtained by aggregating the results obtained from these classifiers using the majority voting ensemble technique. Our model has been experimentally evaluated with a real-world automobile insurance dataset.

2019 Global Conference for Advancement in Technology (GCAT), 2019

In this paper, we have removed the class imbalance problem using SMOTE and used Decision Template... more In this paper, we have removed the class imbalance problem using SMOTE and used Decision Templates ensemble technique for efficiently detecting the auto insurance fraud. We employed three supervised classifiers namely, Support Vector Machine, Multilayer Perceptron and K-nearest Neighbors for classification purpose. The final classification results are obtained by aggregating the results obtained from the classifiers using Decision Templates technique. Our model has been experimentally evaluated with a real –world automobile insurance dataset.

In current scenario, Wireless Multimedia Sensor Networks have gained much popularity in many sphe... more In current scenario, Wireless Multimedia Sensor Networks have gained much popularity in many spheres of life. Both scalar sensors and camera sensors are present in case of Wireless Multimedia Sensor Networks. Camera sensors have two basic parameters. They are depth of field and field of view. Depth of field is the distance at which a camera can capture accurate image of an object. Field of view is the angle at which a camera can capture accurate image of an object. When any kind of event takes place in a monitored region, it is initially detected by scalar sensors. Then the scalars inform their respective camera(s) regarding the occurrence of event. However, the problem is that when event occurs, sensing of event takes place in the exact event region and also up to some extent outside the event region. Due to this, the cameras present at the region outside the event boundary are activated unnecessarily, since their depths of fields do not cover the exact event region. In our paper, ...

Advances in Intelligent Systems and Computing, 2019

In the current automation world, every organizations starting from education to industry and also... more In the current automation world, every organizations starting from education to industry and also in research organization are maintaining database and several security breaches are found in these databases. Traditional database security mechanism cannot handle the malicious access toward database. Although, various researches have been done in database intrusion detection, but most of the researches are limited in efficiency and accuracy. Inspired by human cognitive system, we present a database intrusion detection system using Adaptive Resonance Theory which is accompanied with some data mining techniques for preprocessing data. The proposed model can learn easily and cope up with the dynamic environment which entitles the system to detect both known and unknown patterns accurately with low false positive cost. The calculated simulation result shows that the database intrusion detection based on Adaptive Resonance Theory can accelerate the detection process with higher accuracy as compared to Self Organizing Map and Radial basis functional neural network.

2016 International Conference on Information Technology (ICIT), 2016

This article reports a novel algorithm inspired byscalar premier determination considering reduce... more This article reports a novel algorithm inspired byscalar premier determination considering reduced cameraactivation while contributing enhanced coverage of the occurringevent zone. The proposed scheme chooses the scalar premiers in ahexagonal fashion, which operate as representatives of the scalarswithin whose sensing ranges the scalar premiers are located. Theselection of the scalar premiers is accomplished in such a mannerthat on any occasion of prevailing event in the monitored region, the scalar premiers appraise their respective camera sensorsregarding the event occurrence. The scalar premiers execute asthe messengers for event information communication to theircorresponding cameras, thereby, averting the redundant datatransmission by all the scalars. Further, the numerical resultsestablished from the experimental inspection and comparativeperformance estimates in terms of minimal camera activation, reduced redundancy ratio as well as maximized coverage ratiocorroborate the sup...