Pub Date : 2021-08-25DOI: 10.1109/ICCITM53167.2021.9677877
Ali Al-Khawaja, S. Sadkhan
the subjective evaluation of electronic intelligence and warfare has been adopted as quantitive analysis by many organizational bodies to assess defense systems capabilities. One prominent case is the reliance of United States Army on subjective evaluations to drive its systems capabilities based on particular battlefield requirements due to the nonexistence of analytical instruments to meet such goals. Therefore, there is a pressing need to thoroughly review a comprehensive and effective approach for empirically evaluating the impact of intelligence and electronic warfare systems on combat effectiveness. In this direction, we discuss the main important capabilities of electronic intelligence and warfare systems that are necessary for ensuring fielded force mix and greater chance to success on the battlefield. However, the difficulty of the current battle is to process comprehensive electronic warfare (EW) information in a short period of time. Therefore, in this paper, the information system is proposed through the development of Electronic Warfare Intelligent Information System (EWIIS) which contracts electronic warfare processing, communications, radar, maps, warfare missions…etc. thus, aimed This system is at achieving the best performance in spite of the existence of antagonistic influences. Moreover, in this paper, we inclination discuss the design and implementation of a system case study.
{"title":"Intelligence and Electronic Warfare: Challenges and Future Trends","authors":"Ali Al-Khawaja, S. Sadkhan","doi":"10.1109/ICCITM53167.2021.9677877","DOIUrl":"https://doi.org/10.1109/ICCITM53167.2021.9677877","url":null,"abstract":"the subjective evaluation of electronic intelligence and warfare has been adopted as quantitive analysis by many organizational bodies to assess defense systems capabilities. One prominent case is the reliance of United States Army on subjective evaluations to drive its systems capabilities based on particular battlefield requirements due to the nonexistence of analytical instruments to meet such goals. Therefore, there is a pressing need to thoroughly review a comprehensive and effective approach for empirically evaluating the impact of intelligence and electronic warfare systems on combat effectiveness. In this direction, we discuss the main important capabilities of electronic intelligence and warfare systems that are necessary for ensuring fielded force mix and greater chance to success on the battlefield. However, the difficulty of the current battle is to process comprehensive electronic warfare (EW) information in a short period of time. Therefore, in this paper, the information system is proposed through the development of Electronic Warfare Intelligent Information System (EWIIS) which contracts electronic warfare processing, communications, radar, maps, warfare missions…etc. thus, aimed This system is at achieving the best performance in spite of the existence of antagonistic influences. Moreover, in this paper, we inclination discuss the design and implementation of a system case study.","PeriodicalId":406104,"journal":{"name":"2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117136110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-25DOI: 10.1109/ICCITM53167.2021.9677722
Huda H. Alsaabri, Saad S. Hreshee
an essential characteristic of chaotic systems is the difficulty in predicting the path of the chaotic time series and the size of the keyspace, which is very large compared to other encryption methods. This paper presents a new image encryption technique depending on a random bitstream generation method through a double Rabinovich hyperchaotic system. The proposed algorithm (Hyper Chaotic Rabinovich System (HCRS)) has been achieved in several ways. Each technique was examined in balanced and delay environments. Then an XOR operation will be done between the bits with the same index of the original image bits and the generated bits to obtain the encrypted image. The HCRS results showed that the balance in random bits between Zeros and Ones are equal in all proposed methods. The Pick Signal to Noise Ratio (PSNR) between the original and the encrypted images is (8.6262). The second significant result in this paper is that the keyspace is very large, more potent than 10ˆ288.
{"title":"Robust Image Encryption Based on Double Hyper Chaotic Rabinovich System","authors":"Huda H. Alsaabri, Saad S. Hreshee","doi":"10.1109/ICCITM53167.2021.9677722","DOIUrl":"https://doi.org/10.1109/ICCITM53167.2021.9677722","url":null,"abstract":"an essential characteristic of chaotic systems is the difficulty in predicting the path of the chaotic time series and the size of the keyspace, which is very large compared to other encryption methods. This paper presents a new image encryption technique depending on a random bitstream generation method through a double Rabinovich hyperchaotic system. The proposed algorithm (Hyper Chaotic Rabinovich System (HCRS)) has been achieved in several ways. Each technique was examined in balanced and delay environments. Then an XOR operation will be done between the bits with the same index of the original image bits and the generated bits to obtain the encrypted image. The HCRS results showed that the balance in random bits between Zeros and Ones are equal in all proposed methods. The Pick Signal to Noise Ratio (PSNR) between the original and the encrypted images is (8.6262). The second significant result in this paper is that the keyspace is very large, more potent than 10ˆ288.","PeriodicalId":406104,"journal":{"name":"2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121257943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-25DOI: 10.1109/ICCITM53167.2021.9677810
Eman A. Mansour, Emad A. Kuffi, Sadiq A. Mehdi
In this paper, the exact solution of Abel's integral equation is found by using new integral transform named “SEE (Sadiq-Emad-Eman) integral”. The capability of SEE integral transform in solving Abel's integral equation is demonstrated and proven by applying the transform on some Abel's practical applications, in which the efficiency and simplicity of the transform in finding the exact solution of these problems has been proven.
{"title":"Solving Abel's Integral Equation Using (Sadiq-Emad-Eman) Transform","authors":"Eman A. Mansour, Emad A. Kuffi, Sadiq A. Mehdi","doi":"10.1109/ICCITM53167.2021.9677810","DOIUrl":"https://doi.org/10.1109/ICCITM53167.2021.9677810","url":null,"abstract":"In this paper, the exact solution of Abel's integral equation is found by using new integral transform named “SEE (Sadiq-Emad-Eman) integral”. The capability of SEE integral transform in solving Abel's integral equation is demonstrated and proven by applying the transform on some Abel's practical applications, in which the efficiency and simplicity of the transform in finding the exact solution of these problems has been proven.","PeriodicalId":406104,"journal":{"name":"2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121981385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-25DOI: 10.1109/ICCITM53167.2021.9677825
Mohie M. Alqezweeni, V. Gorbachenko, D. A. Stenkin
The use of radial basis functions networks as physics-informed neural networks for solving direct and inverse boundary value problems is demonstrated. On the Levenberg-Marquardt basis optimization method, algorithms have been developed for solving partial differential equations. Comparison of the gradient descent method and the Levenberg-Marquardt method for solving the Poisson equation is given. To solve a direct boundary value problem describing processes in a piecewise homogeneous environment, an algorithm is proposed based on solving individual problems for each region with different properties of the environment associated with the conjugation conditions. It removes restrictions on the radial basis functions used. To solve the coefficient inverse problem of recovering the properties of the piecewise inhomogeneous medium, an algorithm based on parametric optimization is proposed. An algorithm uses two networks of radial basis functions. The first network approximates the solution to the direct problem. And another network approximates a function which describes the properties of the environment. Network learning is performed using an algorithm developed by the authors based on the Levenberg-Marquardt method. Expressions are obtained for the analytical calculation of the Jacobi matrix elements in the Levenberg-Marquardt method and the residual gradient vector elements. The application of the developed algorithms is demonstrated by the example of model direct boundary value problems and inverse coefficient boundary value problems for piecewise homogeneous media.
{"title":"Deep Radial Basis Function Networks","authors":"Mohie M. Alqezweeni, V. Gorbachenko, D. A. Stenkin","doi":"10.1109/ICCITM53167.2021.9677825","DOIUrl":"https://doi.org/10.1109/ICCITM53167.2021.9677825","url":null,"abstract":"The use of radial basis functions networks as physics-informed neural networks for solving direct and inverse boundary value problems is demonstrated. On the Levenberg-Marquardt basis optimization method, algorithms have been developed for solving partial differential equations. Comparison of the gradient descent method and the Levenberg-Marquardt method for solving the Poisson equation is given. To solve a direct boundary value problem describing processes in a piecewise homogeneous environment, an algorithm is proposed based on solving individual problems for each region with different properties of the environment associated with the conjugation conditions. It removes restrictions on the radial basis functions used. To solve the coefficient inverse problem of recovering the properties of the piecewise inhomogeneous medium, an algorithm based on parametric optimization is proposed. An algorithm uses two networks of radial basis functions. The first network approximates the solution to the direct problem. And another network approximates a function which describes the properties of the environment. Network learning is performed using an algorithm developed by the authors based on the Levenberg-Marquardt method. Expressions are obtained for the analytical calculation of the Jacobi matrix elements in the Levenberg-Marquardt method and the residual gradient vector elements. The application of the developed algorithms is demonstrated by the example of model direct boundary value problems and inverse coefficient boundary value problems for piecewise homogeneous media.","PeriodicalId":406104,"journal":{"name":"2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126391509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-25DOI: 10.1109/ICCITM53167.2021.9677878
Arqam M. Al-Nuimi, Ghassan J. Mohammed
Many studies had been presented to estimate face direction that relied on training images. A small portion of these studies relied on mathematical calculations with poor results. This paper proposed implementing of trigonometric functions to estimate face direction by using detected landmarks. These landmarks had been detected using the 3D model of (Mideapipe) method. Among there detected landmarks, several points are selected as predicted landmarks. The angles among extended lines from these selected landmarks have been measured to calculate the directions of the human head. The experimental results showed that proposed method achieved high performance with very low error ratio (0.055) for Yaw angle, (0.046) for Pith angle and (0.025) for Roll angle. The difference was approximately ±0.5 degree between real and predicted measures.
{"title":"Face Direction Estimation based on Mediapipe Landmarks","authors":"Arqam M. Al-Nuimi, Ghassan J. Mohammed","doi":"10.1109/ICCITM53167.2021.9677878","DOIUrl":"https://doi.org/10.1109/ICCITM53167.2021.9677878","url":null,"abstract":"Many studies had been presented to estimate face direction that relied on training images. A small portion of these studies relied on mathematical calculations with poor results. This paper proposed implementing of trigonometric functions to estimate face direction by using detected landmarks. These landmarks had been detected using the 3D model of (Mideapipe) method. Among there detected landmarks, several points are selected as predicted landmarks. The angles among extended lines from these selected landmarks have been measured to calculate the directions of the human head. The experimental results showed that proposed method achieved high performance with very low error ratio (0.055) for Yaw angle, (0.046) for Pith angle and (0.025) for Roll angle. The difference was approximately ±0.5 degree between real and predicted measures.","PeriodicalId":406104,"journal":{"name":"2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133966173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-25DOI: 10.1109/ICCITM53167.2021.9677685
Eman A. Mansour, Emad A. Kuffi, Sadiq A. Mehdi
In this paper, the exact solution of Abel's integral equation is found by using new integral transform named “SEE (Sadiq-Emad-Eman) integral”. The capability of SEE integral transform in solving Abel's integral equation is demonstrated and proven by applying the transform on some Abel's practical applications, in which the efficiency and simplicity of the transform in finding the exact solution of these problems has been proven.
{"title":"Solving Abel's Integral Equation Using (Sadiq-Emad-Eman) Transform","authors":"Eman A. Mansour, Emad A. Kuffi, Sadiq A. Mehdi","doi":"10.1109/ICCITM53167.2021.9677685","DOIUrl":"https://doi.org/10.1109/ICCITM53167.2021.9677685","url":null,"abstract":"In this paper, the exact solution of Abel's integral equation is found by using new integral transform named “SEE (Sadiq-Emad-Eman) integral”. The capability of SEE integral transform in solving Abel's integral equation is demonstrated and proven by applying the transform on some Abel's practical applications, in which the efficiency and simplicity of the transform in finding the exact solution of these problems has been proven.","PeriodicalId":406104,"journal":{"name":"2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131290445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-25DOI: 10.1109/ICCITM53167.2021.9677666
Loay Waadallh Saleem, H. Saieed
This paper concerned with the parameters estimation of modified Weibull distribution (MWD) depending on complete data. The least squares (L.S.) and maximum likelihood (M.L.) methods used for parameters estimation. The results applied on simulated samples with different sizes and different values for scale and shape parameters. The mean square error (MSE) criterion is used for the comparison between estimators in different cases. It is concluded that the sample size has a negative effect on the values of (MSE). This result gave an indicator to the researcher there is no need to use larger samples of sizes than or equal to (50). This indication built on the fact that the improvement in (MSE) is very small which can be ignored. On the other hand the increasing values on a scale parameter have a positive effect on (MSE).
{"title":"Parameters Estimation for Modified Weibull Distribution Using Some Methods of Estimators with Simmulation","authors":"Loay Waadallh Saleem, H. Saieed","doi":"10.1109/ICCITM53167.2021.9677666","DOIUrl":"https://doi.org/10.1109/ICCITM53167.2021.9677666","url":null,"abstract":"This paper concerned with the parameters estimation of modified Weibull distribution (MWD) depending on complete data. The least squares (L.S.) and maximum likelihood (M.L.) methods used for parameters estimation. The results applied on simulated samples with different sizes and different values for scale and shape parameters. The mean square error (MSE) criterion is used for the comparison between estimators in different cases. It is concluded that the sample size has a negative effect on the values of (MSE). This result gave an indicator to the researcher there is no need to use larger samples of sizes than or equal to (50). This indication built on the fact that the improvement in (MSE) is very small which can be ignored. On the other hand the increasing values on a scale parameter have a positive effect on (MSE).","PeriodicalId":406104,"journal":{"name":"2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129525452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-25DOI: 10.1109/ICCITM53167.2021.9677769
H. M. Ahmed, M. Kashmola
Data generation systems and architectures seek to create new and valuable data with specific sizes and characteristics which are similar to the original data that fit the mechanism of the application used. GANs (generative adversarial network architectures) are a type of generative modeling that uses deep learning methods like convolutional neural networks to generate data, particularly digital images which are used in scientific applications. The goal of this paper is to design and build three architectures of Generative adversarial network (based on convolutional neural networks) to generate three types of digital images of skin diseases by defining two supervised models, the generator model that is trained to generate new digital images and the discrimination model that classifies digital images as real or fake from the field. An intelligent architecture for training the generative model was created, where the two models are trained together in a zero-sum field. Each digital image that was generated differs in its accuracy, size, and dimensions. After applying all the architectures, obtaining and comparing digital images, it turns out that the fewer dimensions of the resulting digital images, the faster the generation processes and the lower the memory costs, but their accuracy is low. The importance of building architecture lies in increasing the images of skin diseases to be used in the data set to perform classification operations for these diseases, as digital images were generated in different sizes, which are 64×64, 128×128, and 512×512, and the new images were obtained with accuracy commensurate with the requirements of this paper. The generated digital images with dimensions 128×128 gave the best results in the accuracy of the resulted images. At the same time, they do not consume much memory, which leads to their processing speed.
{"title":"Generating digital images of skin diseases based on deep learning","authors":"H. M. Ahmed, M. Kashmola","doi":"10.1109/ICCITM53167.2021.9677769","DOIUrl":"https://doi.org/10.1109/ICCITM53167.2021.9677769","url":null,"abstract":"Data generation systems and architectures seek to create new and valuable data with specific sizes and characteristics which are similar to the original data that fit the mechanism of the application used. GANs (generative adversarial network architectures) are a type of generative modeling that uses deep learning methods like convolutional neural networks to generate data, particularly digital images which are used in scientific applications. The goal of this paper is to design and build three architectures of Generative adversarial network (based on convolutional neural networks) to generate three types of digital images of skin diseases by defining two supervised models, the generator model that is trained to generate new digital images and the discrimination model that classifies digital images as real or fake from the field. An intelligent architecture for training the generative model was created, where the two models are trained together in a zero-sum field. Each digital image that was generated differs in its accuracy, size, and dimensions. After applying all the architectures, obtaining and comparing digital images, it turns out that the fewer dimensions of the resulting digital images, the faster the generation processes and the lower the memory costs, but their accuracy is low. The importance of building architecture lies in increasing the images of skin diseases to be used in the data set to perform classification operations for these diseases, as digital images were generated in different sizes, which are 64×64, 128×128, and 512×512, and the new images were obtained with accuracy commensurate with the requirements of this paper. The generated digital images with dimensions 128×128 gave the best results in the accuracy of the resulted images. At the same time, they do not consume much memory, which leads to their processing speed.","PeriodicalId":406104,"journal":{"name":"2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115979223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-25DOI: 10.1109/ICCITM53167.2021.9677849
D. Abdullah, Rana Abdul-Ghafoor Mohammed
The rapid and tremendous growth of data and its diverse resources has become a hallmark of our age, many applications in various fields demand to discover patterns and relationships in real-time to gain awareness of the situation, improve workflows and enhance the bottom line to provide the best insights and make the most appropriate decisions. This requires tools and techniques different from the traditional one by its capability for processing and analyzing these data with its all heterogeneity, complexity, randomness in addition to its huge volume with low latency. Dealing with Big data in real-time poses many challenges in terms of collecting, processing, analyzing the data, and choosing the appropriate framework and architecture to implement the application. The paper reviews different fields of application that require real-time analytics for Big Data with its different approaches and the frameworks used as well as the challenges that may pose for implementing these applications. This review provides a guide for researchers in the future to find out the appropriate study in a specific field of application and the Big Data tools used.
{"title":"Real-Time Big Data Analytics Perspective on Applications, Frameworks and Challenges","authors":"D. Abdullah, Rana Abdul-Ghafoor Mohammed","doi":"10.1109/ICCITM53167.2021.9677849","DOIUrl":"https://doi.org/10.1109/ICCITM53167.2021.9677849","url":null,"abstract":"The rapid and tremendous growth of data and its diverse resources has become a hallmark of our age, many applications in various fields demand to discover patterns and relationships in real-time to gain awareness of the situation, improve workflows and enhance the bottom line to provide the best insights and make the most appropriate decisions. This requires tools and techniques different from the traditional one by its capability for processing and analyzing these data with its all heterogeneity, complexity, randomness in addition to its huge volume with low latency. Dealing with Big data in real-time poses many challenges in terms of collecting, processing, analyzing the data, and choosing the appropriate framework and architecture to implement the application. The paper reviews different fields of application that require real-time analytics for Big Data with its different approaches and the frameworks used as well as the challenges that may pose for implementing these applications. This review provides a guide for researchers in the future to find out the appropriate study in a specific field of application and the Big Data tools used.","PeriodicalId":406104,"journal":{"name":"2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128295534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-25DOI: 10.1109/ICCITM53167.2021.9677837
Yasameen Sajid Razooqi, M. Al-Asfoor
One of the most critical issues in Wireless Sensor Networks (WSNs) is energy consumption, because of the battery limitation in each sensor. Most energy is consumed on communication phase between the sensor nodes in WSNs, therefore, an accurate routing model is required for managing the communication in WSN. In this paper, a nature inspired flat routing for wireless sensor networks has been introduced with the aim of optimising the energy consumption by improving the routing process. Ant Colony Optimisation algorithm (ACO) has been adopted to facilitate the routing decisions which have to be taken by every node. Furthermore, ACO-based Flat Routing Protocol has been compared with two other algorithms, namely: Random Walk Algorithm and Energy Based Routing in addition to the traditional ACO used in routing. Experimental results have shown a promising improvement in energy consumption, system stability and better lifetime with ACO than the other algorithms. Also, the model has shown better transmission balance with ACO even with extreme scenarios of multi-hop flat routing.
{"title":"Energy Aware Nature inspired Routing for Wireless Sensor Networks","authors":"Yasameen Sajid Razooqi, M. Al-Asfoor","doi":"10.1109/ICCITM53167.2021.9677837","DOIUrl":"https://doi.org/10.1109/ICCITM53167.2021.9677837","url":null,"abstract":"One of the most critical issues in Wireless Sensor Networks (WSNs) is energy consumption, because of the battery limitation in each sensor. Most energy is consumed on communication phase between the sensor nodes in WSNs, therefore, an accurate routing model is required for managing the communication in WSN. In this paper, a nature inspired flat routing for wireless sensor networks has been introduced with the aim of optimising the energy consumption by improving the routing process. Ant Colony Optimisation algorithm (ACO) has been adopted to facilitate the routing decisions which have to be taken by every node. Furthermore, ACO-based Flat Routing Protocol has been compared with two other algorithms, namely: Random Walk Algorithm and Energy Based Routing in addition to the traditional ACO used in routing. Experimental results have shown a promising improvement in energy consumption, system stability and better lifetime with ACO than the other algorithms. Also, the model has shown better transmission balance with ACO even with extreme scenarios of multi-hop flat routing.","PeriodicalId":406104,"journal":{"name":"2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125846754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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