GOM: New Genetic Optimizing Model for broadcasting tree in MANET
- Elaiwat, Said, Alazab, Ammar, Venkatraman, Sitalakshmi, Alazab, Mamoun
- Authors: Elaiwat, Said , Alazab, Ammar , Venkatraman, Sitalakshmi , Alazab, Mamoun
- Date: 2010
- Type: Text , Conference proceedings
- Full Text:
- Description: Data broadcasting in a mobile ad-hoc network (MANET) is the main method of information dissemination in many applications, in particular for sending critical information to all hosts. Finding an optimal broadcast tree in such networks is a challenging task due to the broadcast storm problem. The aim of this work is to propose a new genetic model using a fitness function with the primary goal of finding an optimal broadcast tree. Our new method, called Genetic Optimisation Model (GOM) alleviates the broadcast storm problem to a great extent as the experimental simulations result in efficient broadcast tree with minimal flood and minimal hops. The result of this model also shows that it has the ability to give different optimal solutions according to the nature of the network. © 2010 IEEE.
- Authors: Elaiwat, Said , Alazab, Ammar , Venkatraman, Sitalakshmi , Alazab, Mamoun
- Date: 2010
- Type: Text , Conference proceedings
- Full Text:
- Description: Data broadcasting in a mobile ad-hoc network (MANET) is the main method of information dissemination in many applications, in particular for sending critical information to all hosts. Finding an optimal broadcast tree in such networks is a challenging task due to the broadcast storm problem. The aim of this work is to propose a new genetic model using a fitness function with the primary goal of finding an optimal broadcast tree. Our new method, called Genetic Optimisation Model (GOM) alleviates the broadcast storm problem to a great extent as the experimental simulations result in efficient broadcast tree with minimal flood and minimal hops. The result of this model also shows that it has the ability to give different optimal solutions according to the nature of the network. © 2010 IEEE.
A new hybrid method combining genetic algorithm and coordinate search method
- Authors: Long, Qiang , Huang, Junjian
- Date: 2012
- Type: Text , Conference proceedings
- Full Text:
- Description: This paper proposed a new hybrid method combining genetic algorithm(GA) and coordinate search method (CSM). Genetic algorithm is good at global exploration but bad at accuracy and local search. Whereas, coordinate search method is good at local exploitation, and its accuracy is reliable when searching in a local area. Thus we combine those two methods in this paper to design a hybrid method called genetic algorithm with coordinate search (GACS). Experimental tests shows that this method are good at both global search and local accuracy. © 2012 IEEE.
- Description: 2003010808
- Authors: Long, Qiang , Huang, Junjian
- Date: 2012
- Type: Text , Conference proceedings
- Full Text:
- Description: This paper proposed a new hybrid method combining genetic algorithm(GA) and coordinate search method (CSM). Genetic algorithm is good at global exploration but bad at accuracy and local search. Whereas, coordinate search method is good at local exploitation, and its accuracy is reliable when searching in a local area. Thus we combine those two methods in this paper to design a hybrid method called genetic algorithm with coordinate search (GACS). Experimental tests shows that this method are good at both global search and local accuracy. © 2012 IEEE.
- Description: 2003010808
Prediction of drillability of rocks with strength properties using a hybrid GA-ANN technique
- Khandelwal, Manoj, Armaghani, Danial
- Authors: Khandelwal, Manoj , Armaghani, Danial
- Date: 2016
- Type: Text , Journal article
- Relation: Geotechnical and Geological Engineering Vol. 34, no. 2 (2016), p. 605-620
- Full Text:
- Reviewed:
- Description: The purpose of this paper is to provide a proper, practical and convenient drilling rate index (DRI) prediction model based on rock material properties. In order to obtain this purpose, 47 DRI tests were used. In addition, the relevant strength properties i.e. uniaxial compressive strength and Brazilian tensile strength were also used and selected as input parameters to predict DRI. Examined simple regression analysis showed that the relationships between the DRI and predictors are statistically meaningful but not good enough for DRI estimation in practice. Moreover, multiple regression, artificial neural network (ANN) and hybrid genetic algorithm (GA)-ANN models were constructed to estimate DRI. Several performance indices i.e. coefficient of determination (R2), root mean square error and variance account for were used for evaluation of performance prediction the proposed methods. Based on these results and the use of simple ranking procedure, the best models were chosen. It was found that the hybrid GA-ANN technique can performed better in predicting DRI compared to other developed models. This is because of the fact that the proposed hybrid model can update the biases and weights of the network connection to train by ANN.
- Description: The purpose of this paper is to provide a proper, practical and convenient drilling rate index (DRI) prediction model based on rock material properties. In order to obtain this purpose, 47 DRI tests were used. In addition, the relevant strength properties i.e. uniaxial compressive strength and Brazilian tensile strength were also used and selected as input parameters to predict DRI. Examined simple regression analysis showed that the relationships between the DRI and predictors are statistically meaningful but not good enough for DRI estimation in practice. Moreover, multiple regression, artificial neural network (ANN) and hybrid genetic algorithm (GA)-ANN models were constructed to estimate DRI. Several performance indices i.e. coefficient of determination (R2), root mean square error and variance account for were used for evaluation of performance prediction the proposed methods. Based on these results and the use of simple ranking procedure, the best models were chosen. It was found that the hybrid GA-ANN technique can performed better in predicting DRI compared to other developed models. This is because of the fact that the proposed hybrid model can update the biases and weights of the network connection to train by ANN. © 2015 Springer International Publishing Switzerland
- Authors: Khandelwal, Manoj , Armaghani, Danial
- Date: 2016
- Type: Text , Journal article
- Relation: Geotechnical and Geological Engineering Vol. 34, no. 2 (2016), p. 605-620
- Full Text:
- Reviewed:
- Description: The purpose of this paper is to provide a proper, practical and convenient drilling rate index (DRI) prediction model based on rock material properties. In order to obtain this purpose, 47 DRI tests were used. In addition, the relevant strength properties i.e. uniaxial compressive strength and Brazilian tensile strength were also used and selected as input parameters to predict DRI. Examined simple regression analysis showed that the relationships between the DRI and predictors are statistically meaningful but not good enough for DRI estimation in practice. Moreover, multiple regression, artificial neural network (ANN) and hybrid genetic algorithm (GA)-ANN models were constructed to estimate DRI. Several performance indices i.e. coefficient of determination (R2), root mean square error and variance account for were used for evaluation of performance prediction the proposed methods. Based on these results and the use of simple ranking procedure, the best models were chosen. It was found that the hybrid GA-ANN technique can performed better in predicting DRI compared to other developed models. This is because of the fact that the proposed hybrid model can update the biases and weights of the network connection to train by ANN.
- Description: The purpose of this paper is to provide a proper, practical and convenient drilling rate index (DRI) prediction model based on rock material properties. In order to obtain this purpose, 47 DRI tests were used. In addition, the relevant strength properties i.e. uniaxial compressive strength and Brazilian tensile strength were also used and selected as input parameters to predict DRI. Examined simple regression analysis showed that the relationships between the DRI and predictors are statistically meaningful but not good enough for DRI estimation in practice. Moreover, multiple regression, artificial neural network (ANN) and hybrid genetic algorithm (GA)-ANN models were constructed to estimate DRI. Several performance indices i.e. coefficient of determination (R2), root mean square error and variance account for were used for evaluation of performance prediction the proposed methods. Based on these results and the use of simple ranking procedure, the best models were chosen. It was found that the hybrid GA-ANN technique can performed better in predicting DRI compared to other developed models. This is because of the fact that the proposed hybrid model can update the biases and weights of the network connection to train by ANN. © 2015 Springer International Publishing Switzerland
Market model for clustered microgrids optimisation including distribution network operations
- Batool, Munira, Islam, Syed, Shahnia, Farhad
- Authors: Batool, Munira , Islam, Syed , Shahnia, Farhad
- Date: 2019
- Type: Text , Journal article
- Relation: IET Generation, Transmission and Distribution Vol. 13, no. 22 (2019), p. 5139-5150
- Full Text:
- Reviewed:
- Description: This paper proposes a market model for the purpose of optimisation of clustered but sparse microgrids (MGs). The MGs are connected with the market by distribution networks for the sake of energy balance and to overcome emergency situations. The developed market structure enables the integration of virtual power plants (VPPs) in energy requirement of MGs. The MGs, internal service providers (ISPs), VPPs and distribution network operator (DNO) are present as distinct entities with individual objective of minimum operational cost. Each MG is assumed to be present with a commitment to service its own loads prior to export. Thus an optimisation problem is formulated with the core objective of minimum cost of operation, reduced network loss and least DNO charges. The formulated problem is solved by using heuristic optimization technique of Genetic Algorithm. Case studies are carried out on a distribution system with multiple MGs, ISP and VPPs which illustrates the effectiveness of the proposed market optimisation strategy. The key objective of the proposed market model is to coordinate the operation of MGs with the requirements of the market with the help of the DNO, without decreasing the economic efficiency for the MGs nor the distribution network. © The Institution of Engineering and Technology 2019.
- Authors: Batool, Munira , Islam, Syed , Shahnia, Farhad
- Date: 2019
- Type: Text , Journal article
- Relation: IET Generation, Transmission and Distribution Vol. 13, no. 22 (2019), p. 5139-5150
- Full Text:
- Reviewed:
- Description: This paper proposes a market model for the purpose of optimisation of clustered but sparse microgrids (MGs). The MGs are connected with the market by distribution networks for the sake of energy balance and to overcome emergency situations. The developed market structure enables the integration of virtual power plants (VPPs) in energy requirement of MGs. The MGs, internal service providers (ISPs), VPPs and distribution network operator (DNO) are present as distinct entities with individual objective of minimum operational cost. Each MG is assumed to be present with a commitment to service its own loads prior to export. Thus an optimisation problem is formulated with the core objective of minimum cost of operation, reduced network loss and least DNO charges. The formulated problem is solved by using heuristic optimization technique of Genetic Algorithm. Case studies are carried out on a distribution system with multiple MGs, ISP and VPPs which illustrates the effectiveness of the proposed market optimisation strategy. The key objective of the proposed market model is to coordinate the operation of MGs with the requirements of the market with the help of the DNO, without decreasing the economic efficiency for the MGs nor the distribution network. © The Institution of Engineering and Technology 2019.
Bio-inspired network security for 5G-enabled IoT applications
- Saleem, Kashif, Alabduljabbar, Ghadah, Alrowais, Nouf, Al-Muhtadi, Jalal, Imran, Muhammad, Rodrigues, Joel
- Authors: Saleem, Kashif , Alabduljabbar, Ghadah , Alrowais, Nouf , Al-Muhtadi, Jalal , Imran, Muhammad , Rodrigues, Joel
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE access Vol. 8, no. (2020), p. 1-1
- Full Text:
- Reviewed:
- Description: Every IPv6-enabled device connected and communicating over the Internet forms the Internet of things (IoT) that is prevalent in society and is used in daily life. This IoT platform will quickly grow to be populated with billions or more objects by making every electrical appliance, car, and even items of furniture smart and connected. The 5th generation (5G) and beyond networks will further boost these IoT systems. The massive utilization of these systems over gigabits per second generates numerous issues. Owing to the huge complexity in large-scale deployment of IoT, data privacy and security are the most prominent challenges, especially for critical applications such as Industry 4.0, e-healthcare, and military. Threat agents persistently strive to find new vulnerabilities and exploit them. Therefore, including promising security measures to support the running systems, not to harm or collapse them, is essential. Nature-inspired algorithms have the capability to provide autonomous and sustainable defense and healing mechanisms. This paper first surveys the 5G network layer security for IoT applications and lists the network layer security vulnerabilities and requirements in wireless sensor networks, IoT, and 5G-enabled IoT. Second, a detailed literature review is conducted with the current network layer security methods and the bio-inspired techniques for IoT applications exchanging data packets over 5G. Finally, the bio-inspired algorithms are analyzed in the context of providing a secure network layer for IoT applications connected over 5G and beyond networks.
- Authors: Saleem, Kashif , Alabduljabbar, Ghadah , Alrowais, Nouf , Al-Muhtadi, Jalal , Imran, Muhammad , Rodrigues, Joel
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE access Vol. 8, no. (2020), p. 1-1
- Full Text:
- Reviewed:
- Description: Every IPv6-enabled device connected and communicating over the Internet forms the Internet of things (IoT) that is prevalent in society and is used in daily life. This IoT platform will quickly grow to be populated with billions or more objects by making every electrical appliance, car, and even items of furniture smart and connected. The 5th generation (5G) and beyond networks will further boost these IoT systems. The massive utilization of these systems over gigabits per second generates numerous issues. Owing to the huge complexity in large-scale deployment of IoT, data privacy and security are the most prominent challenges, especially for critical applications such as Industry 4.0, e-healthcare, and military. Threat agents persistently strive to find new vulnerabilities and exploit them. Therefore, including promising security measures to support the running systems, not to harm or collapse them, is essential. Nature-inspired algorithms have the capability to provide autonomous and sustainable defense and healing mechanisms. This paper first surveys the 5G network layer security for IoT applications and lists the network layer security vulnerabilities and requirements in wireless sensor networks, IoT, and 5G-enabled IoT. Second, a detailed literature review is conducted with the current network layer security methods and the bio-inspired techniques for IoT applications exchanging data packets over 5G. Finally, the bio-inspired algorithms are analyzed in the context of providing a secure network layer for IoT applications connected over 5G and beyond networks.
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