Pub Date : 2021-07-16DOI: 10.1108/IJPCC-10-2020-0179
Y. A. Rani, E. Reddy
Purpose Wireless sensor networks (WSN) have been widely adopted for various applications due to their properties of pervasive computing. It is necessary to prolong the WSN lifetime; it avails its benefit for a long time. WSN lifetime may vary according to the applications, and in most cases, it is considered as the time to the death of the first node in the module. Clustering has been one of the successful strategies for increasing the effectiveness of the network, as it selects the appropriate cluster head (CH) for communication. However, most clustering protocols are based on probabilistic schemes, which may create two CH for a single cluster group, leading to cause more energy consumption. Hence, it is necessary to build up a clustering strategy with the improved properties for the CH selection. The purpose of this paper is to provide better convergence for large simulation space and to use it for optimizing the communication path of WSN. Design/methodology/approach This paper plans to develop a new clustering protocol in WSN using fuzzy clustering and an improved meta-heuristic algorithm. The fuzzy clustering approach is adopted for performing the clustering of nodes with respective fuzzy centroid by using the input constraints such as signal-to-interference-plus-noise ratio (SINR), load and residual energy, between the CHs and nodes. After the cluster formation, the combined utility function is used to refine the CH selection. The CH is determined based on computing the combined utility function, in which the node attaining the maximum combined utility function is selected as the CH. After the clustering and CH formation, the optimal communication between the CH and the nodes is induced by a new meta-heuristic algorithm called Fitness updated Crow Search Algorithm (FU-CSA). This optimal communication is accomplished by concerning a multi-objective function with constraints with residual energy and the distance between the nodes. Finally, the simulation results show that the proposed technique enhances the network lifetime and energy efficiency when compared to the state-of-the-art techniques. Findings The proposed Fuzzy+FU-CSA algorithm has achieved low-cost function values of 48% to Fuzzy+Particle Swarm Optimization (PSO), 60% to Fuzzy+Grey Wolf Optimizer (GWO), 40% to Fuzzy+Whale Optimization Algorithm (WOA) and 25% to Fuzzy+CSA, respectively. Thus, the results prove that the proposed Fuzzy+FU-CSA has the optimal performance than the other algorithms, and thus provides a high network lifetime and energy. Originality/value For the efficient clustering and the CH selection, a combined utility function was developed by using the network parameters such as energy, load, SINR and distance. The fuzzy clustering uses the constraint inputs such as residual energy, load and SINR for clustering the nodes of WSN. This work had developed an FU-CSA algorithm for the selection of the optimal communication path for the WSN.
{"title":"An optimal communication in WSN enabled by fuzzy clustering and improved meta-heuristic model","authors":"Y. A. Rani, E. Reddy","doi":"10.1108/IJPCC-10-2020-0179","DOIUrl":"https://doi.org/10.1108/IJPCC-10-2020-0179","url":null,"abstract":"\u0000Purpose\u0000Wireless sensor networks (WSN) have been widely adopted for various applications due to their properties of pervasive computing. It is necessary to prolong the WSN lifetime; it avails its benefit for a long time. WSN lifetime may vary according to the applications, and in most cases, it is considered as the time to the death of the first node in the module. Clustering has been one of the successful strategies for increasing the effectiveness of the network, as it selects the appropriate cluster head (CH) for communication. However, most clustering protocols are based on probabilistic schemes, which may create two CH for a single cluster group, leading to cause more energy consumption. Hence, it is necessary to build up a clustering strategy with the improved properties for the CH selection. The purpose of this paper is to provide better convergence for large simulation space and to use it for optimizing the communication path of WSN.\u0000\u0000\u0000Design/methodology/approach\u0000This paper plans to develop a new clustering protocol in WSN using fuzzy clustering and an improved meta-heuristic algorithm. The fuzzy clustering approach is adopted for performing the clustering of nodes with respective fuzzy centroid by using the input constraints such as signal-to-interference-plus-noise ratio (SINR), load and residual energy, between the CHs and nodes. After the cluster formation, the combined utility function is used to refine the CH selection. The CH is determined based on computing the combined utility function, in which the node attaining the maximum combined utility function is selected as the CH. After the clustering and CH formation, the optimal communication between the CH and the nodes is induced by a new meta-heuristic algorithm called Fitness updated Crow Search Algorithm (FU-CSA). This optimal communication is accomplished by concerning a multi-objective function with constraints with residual energy and the distance between the nodes. Finally, the simulation results show that the proposed technique enhances the network lifetime and energy efficiency when compared to the state-of-the-art techniques.\u0000\u0000\u0000Findings\u0000The proposed Fuzzy+FU-CSA algorithm has achieved low-cost function values of 48% to Fuzzy+Particle Swarm Optimization (PSO), 60% to Fuzzy+Grey Wolf Optimizer (GWO), 40% to Fuzzy+Whale Optimization Algorithm (WOA) and 25% to Fuzzy+CSA, respectively. Thus, the results prove that the proposed Fuzzy+FU-CSA has the optimal performance than the other algorithms, and thus provides a high network lifetime and energy.\u0000\u0000\u0000Originality/value\u0000For the efficient clustering and the CH selection, a combined utility function was developed by using the network parameters such as energy, load, SINR and distance. The fuzzy clustering uses the constraint inputs such as residual energy, load and SINR for clustering the nodes of WSN. This work had developed an FU-CSA algorithm for the selection of the optimal communication path for the WSN.\u0000","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128354864","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 : 2021-07-16DOI: 10.1108/IJPCC-01-2021-0013
S. Chauhan, Richa Banerjee, Chinmay Chakraborty, Mohit Mittal, Atul Shiva, Vinayakumar Ravi
Purpose This study aims to investigate the shopping behaviour of consumers, mainly in fashion apparels, and intends to understand consumer buying patterns in Indian context. The study was designed to determine the level of consumer's sense of belonging towards apparel shopping by applying the concept of self-congruence. Design/methodology/approach The study used variance-based partial least squares structural equational modelling (PLS-SEM) on a cross-sectional study conducted on 569 consumers. The study was conducted by using questionnaire to collect the responses from the central zone of India. The results support most of the projected hypotheses. Findings The study focused on the shopping behaviour of consumer such as self-congruence, impulse buying, hedonic values and consumer satisfaction. The results of the study highlight the association of constructs and analysed the mediation relation of hedonic and impulse buying constructs. The results revealed a positive association among the constructs and also found a partial mediation effect in their relation with constructs. Research limitations/implications The findings are outcomes of an empirical study conducted in the fashion apparel industry of India based on the sample set of urban consumers. The study is restricted to the direct and indirect relationship of constructs. Further, research can examine by using moderating constructs like demographic factors (gender, age, income, etc.) and other shopping behaviours (like brand loyalty, brand love, brand attachment) for more clarity in results. Moreover, the study limited is with fashion apparel, whereas there are many categories in the fashion industry like accessories, perfumes, cosmetic products, footwear and also other products industry. Practical implications The study provided valuable inputs to the literature of marketing where self-congruence affects consumer shopping behaviour such as impulse buying, hedonic values and consumer satisfaction. The study proposes a practical approach that can help the marketing professionals and product developers to have a deep understanding about consumer shopping behaviour for facilitating consumer-oriented goods in the Indian fashion industry. Originality/value This is one of the first studies in the fashion industry to test the association of self-congruence with hedonic value and consumer satisfaction. This relation is not tested in context of fashion apparel. Additionally, this study also examined the mediating effect of hedonic value and impulse buying in relation with self-congruence and consumer satisfaction in the Indian context.
{"title":"A self-congruence and impulse buying effect on user's shopping behaviour over social networking sites: an empirical study","authors":"S. Chauhan, Richa Banerjee, Chinmay Chakraborty, Mohit Mittal, Atul Shiva, Vinayakumar Ravi","doi":"10.1108/IJPCC-01-2021-0013","DOIUrl":"https://doi.org/10.1108/IJPCC-01-2021-0013","url":null,"abstract":"\u0000Purpose\u0000This study aims to investigate the shopping behaviour of consumers, mainly in fashion apparels, and intends to understand consumer buying patterns in Indian context. The study was designed to determine the level of consumer's sense of belonging towards apparel shopping by applying the concept of self-congruence.\u0000\u0000\u0000Design/methodology/approach\u0000The study used variance-based partial least squares structural equational modelling (PLS-SEM) on a cross-sectional study conducted on 569 consumers. The study was conducted by using questionnaire to collect the responses from the central zone of India. The results support most of the projected hypotheses.\u0000\u0000\u0000Findings\u0000The study focused on the shopping behaviour of consumer such as self-congruence, impulse buying, hedonic values and consumer satisfaction. The results of the study highlight the association of constructs and analysed the mediation relation of hedonic and impulse buying constructs. The results revealed a positive association among the constructs and also found a partial mediation effect in their relation with constructs.\u0000\u0000\u0000Research limitations/implications\u0000The findings are outcomes of an empirical study conducted in the fashion apparel industry of India based on the sample set of urban consumers. The study is restricted to the direct and indirect relationship of constructs. Further, research can examine by using moderating constructs like demographic factors (gender, age, income, etc.) and other shopping behaviours (like brand loyalty, brand love, brand attachment) for more clarity in results. Moreover, the study limited is with fashion apparel, whereas there are many categories in the fashion industry like accessories, perfumes, cosmetic products, footwear and also other products industry.\u0000\u0000\u0000Practical implications\u0000The study provided valuable inputs to the literature of marketing where self-congruence affects consumer shopping behaviour such as impulse buying, hedonic values and consumer satisfaction. The study proposes a practical approach that can help the marketing professionals and product developers to have a deep understanding about consumer shopping behaviour for facilitating consumer-oriented goods in the Indian fashion industry.\u0000\u0000\u0000Originality/value\u0000This is one of the first studies in the fashion industry to test the association of self-congruence with hedonic value and consumer satisfaction. This relation is not tested in context of fashion apparel. Additionally, this study also examined the mediating effect of hedonic value and impulse buying in relation with self-congruence and consumer satisfaction in the Indian context.\u0000","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114311766","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 : 2021-07-14DOI: 10.1108/IJPCC-08-2020-0118
S. Garg, D. Mehrotra, Sujata Pandey, Harikesh Pandey
Purpose This paper aims to determine the network efficient topology for low power and lossy networks (LLNs) using routing protocol for LLN (RPL) with respect to the increase in network size and propose a novel approach to overcome the shortcomings of the existing models. Design/methodology/approach The authors have used Contiki OS/Cooja simulator to conduct experiments on primarily four topologies (star, bus/linear, ring/eclipse and random). They have implemented RPL protocol using Sky motes for each topology from 10, 20, 30 and up to 70 nodes. Consequently, after 24 h of experimentation, the readings have been noted and, alongside, a comprehensive comparative analysis has been performed based on the network density and metric parameters: packet delivery ratio (PDR), expected transmission (ETX) and power consumption. Further, a hybrid model is proposed where the additional factors of mobility, multiple sink and a combination of static and mobile nodes are introduced. The proposed model is then compared with the star model (all static nodes and star topology) and the dynamic model (all mobile nodes) to analyze the efficiency and network performance for different network sizes (28, 36, 38 and 44 nodes). The mobility is introduced using BonnMotion tool in Contiki OS. Findings Simulation results have shown that the star topology is most network efficient when compared with bus/linear, ring/eclipse and random topologies for low density and high scalable network. But when the same setup is compared with the proposed hybrid model, the proposed model shows a significant improvement and gives the best and efficient network performance with highest PDR (average improvement approximately 44.5%) and lowest ETX (average improvement approximately 49.5%) comparatively. Practical implications Also, these findings will benefit the deployment of smart devices used in advanced metering infrastructure, road side units and in various industrial applications such as traffic monitoring system, electronic toll collection and traffic analysis in the smart grid infrastructure. Originality/value The impact of topology is significant and detailed analysis is required to understand the impact of different topologies of the nodes in the network for the present and the future scenarios. As very few research studies have discussed this gap, this research paper is quintessential and shall open novel future potential direction. Also, the proposed approach of hybrid model with mobility has not been considered in the literature yet.
{"title":"Network efficient topology for low power and lossy networks in smart corridor design using RPL","authors":"S. Garg, D. Mehrotra, Sujata Pandey, Harikesh Pandey","doi":"10.1108/IJPCC-08-2020-0118","DOIUrl":"https://doi.org/10.1108/IJPCC-08-2020-0118","url":null,"abstract":"\u0000Purpose\u0000This paper aims to determine the network efficient topology for low power and lossy networks (LLNs) using routing protocol for LLN (RPL) with respect to the increase in network size and propose a novel approach to overcome the shortcomings of the existing models.\u0000\u0000\u0000Design/methodology/approach\u0000The authors have used Contiki OS/Cooja simulator to conduct experiments on primarily four topologies (star, bus/linear, ring/eclipse and random). They have implemented RPL protocol using Sky motes for each topology from 10, 20, 30 and up to 70 nodes. Consequently, after 24 h of experimentation, the readings have been noted and, alongside, a comprehensive comparative analysis has been performed based on the network density and metric parameters: packet delivery ratio (PDR), expected transmission (ETX) and power consumption. Further, a hybrid model is proposed where the additional factors of mobility, multiple sink and a combination of static and mobile nodes are introduced. The proposed model is then compared with the star model (all static nodes and star topology) and the dynamic model (all mobile nodes) to analyze the efficiency and network performance for different network sizes (28, 36, 38 and 44 nodes). The mobility is introduced using BonnMotion tool in Contiki OS.\u0000\u0000\u0000Findings\u0000Simulation results have shown that the star topology is most network efficient when compared with bus/linear, ring/eclipse and random topologies for low density and high scalable network. But when the same setup is compared with the proposed hybrid model, the proposed model shows a significant improvement and gives the best and efficient network performance with highest PDR (average improvement approximately 44.5%) and lowest ETX (average improvement approximately 49.5%) comparatively.\u0000\u0000\u0000Practical implications\u0000Also, these findings will benefit the deployment of smart devices used in advanced metering infrastructure, road side units and in various industrial applications such as traffic monitoring system, electronic toll collection and traffic analysis in the smart grid infrastructure.\u0000\u0000\u0000Originality/value\u0000The impact of topology is significant and detailed analysis is required to understand the impact of different topologies of the nodes in the network for the present and the future scenarios. As very few research studies have discussed this gap, this research paper is quintessential and shall open novel future potential direction. Also, the proposed approach of hybrid model with mobility has not been considered in the literature yet.\u0000","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126630439","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 : 2021-06-17DOI: 10.1108/IJPCC-10-2020-0160
Venkatesh Chapala, Polaiah Bojja
Purpose Detecting cancer from the computed tomography (CT)images of lung nodules is very challenging for radiologists. Early detection of cancer helps to provide better treatment in advance and to enhance the recovery rate. Although a lot of research is being carried out to process clinical images, it still requires improvement to attain high reliability and accuracy. The main purpose of this paper is to achieve high accuracy in detecting and classifying the lung cancer and assisting the radiologists to detect cancer by using CT images. The CT images are collected from health-care centres and remote places through Internet of Things (IoT)-enabled platform and the image processing is carried out in the cloud servers. Design/methodology/approach IoT-based lung cancer detection is proposed to access the lung CT images from any remote place and to provide high accuracy in image processing. Here, the exact separation of lung nodule is performed by Otsu thresholding segmentation with the help of optimal characteristics and cuckoo search algorithm. The important features of the lung nodules are extracted by local binary pattern. From the extracted features, support vector machine (SVM) classifier is trained to recognize whether the lung nodule is malicious or non-malicious. Findings The proposed framework achieves 99.59% in accuracy, 99.31% in sensitivity and 71% in peak signal to noise ratio. The outcomes show that the proposed method has achieved high accuracy than other conventional methods in early detection of lung cancer. Practical implications The proposed algorithm is implemented and tested by using more than 500 images which are collected from public and private databases. The proposed research framework can be used to implement contextual diagnostic analysis. Originality/value The cancer nodules in CT images are precisely segmented by integrating the algorithms of cuckoo search and Otsu thresholding in order to classify malicious and non-malicious nodules.
{"title":"IoT based lung cancer detection using machine learning and cuckoo search optimization","authors":"Venkatesh Chapala, Polaiah Bojja","doi":"10.1108/IJPCC-10-2020-0160","DOIUrl":"https://doi.org/10.1108/IJPCC-10-2020-0160","url":null,"abstract":"\u0000Purpose\u0000Detecting cancer from the computed tomography (CT)images of lung nodules is very challenging for radiologists. Early detection of cancer helps to provide better treatment in advance and to enhance the recovery rate. Although a lot of research is being carried out to process clinical images, it still requires improvement to attain high reliability and accuracy. The main purpose of this paper is to achieve high accuracy in detecting and classifying the lung cancer and assisting the radiologists to detect cancer by using CT images. The CT images are collected from health-care centres and remote places through Internet of Things (IoT)-enabled platform and the image processing is carried out in the cloud servers.\u0000\u0000\u0000Design/methodology/approach\u0000IoT-based lung cancer detection is proposed to access the lung CT images from any remote place and to provide high accuracy in image processing. Here, the exact separation of lung nodule is performed by Otsu thresholding segmentation with the help of optimal characteristics and cuckoo search algorithm. The important features of the lung nodules are extracted by local binary pattern. From the extracted features, support vector machine (SVM) classifier is trained to recognize whether the lung nodule is malicious or non-malicious.\u0000\u0000\u0000Findings\u0000The proposed framework achieves 99.59% in accuracy, 99.31% in sensitivity and 71% in peak signal to noise ratio. The outcomes show that the proposed method has achieved high accuracy than other conventional methods in early detection of lung cancer.\u0000\u0000\u0000Practical implications\u0000The proposed algorithm is implemented and tested by using more than 500 images which are collected from public and private databases. The proposed research framework can be used to implement contextual diagnostic analysis.\u0000\u0000\u0000Originality/value\u0000The cancer nodules in CT images are precisely segmented by integrating the algorithms of cuckoo search and Otsu thresholding in order to classify malicious and non-malicious nodules.\u0000","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134134118","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 : 2021-06-17DOI: 10.1108/IJPCC-02-2021-0056
Lalitha Bhavani Konkyana, Sudhakar V. Alapati
Purpose This paper aims to state the configuration of the proposed antenna which is competent to many networks such as LTE and X band applications. The experimental study encountered the significance of the proposed antenna. Design/methodology/approach A novel compact Kuznets curve with parabola-shaped quad-band notched antenna is demonstrated in this paper. The presented prototype is ascertained on a composite material composed of woven fiberglass cloth with an epoxy resin binder. The resulting ultra-wideband antenna ranges 3.1–3.54 GHz, 5.17–5.51 GHz, 5.74–6.43 GHz and 6.79–7.60 GHz. To avoid the frequency bands which cause UWB interference,the projected antenna has been incorporated with slotted patch. The proposed antenna design is attained in four steps. The simple circular patch antenna model with defected ground plane is subjected to stepwise progression by including parabola-shaped slot and U shaped slot on the patch to attain four notched bands. Findings This projected antenna possesses an optimal bond among simulated and measured outcomes,which is more suitable for the quad notched band applications. Substrate analysis is done by varying substrate material, and notch behavior is presented. The proposed method’s optimum performance in metrics such as return loss, voltage standing wave ratio and radiation pattern varies its frequency range from 2.56 to 7.6 GHz. Originality/value The antenna adaptation of the defected ground plane has achieved through the quad notched band with operating frequency ranges 2.56 to 7.6 GHz and with eliminated frequency ranges 3.55–5.16 GHz, 5.52–5.73 GHz, 6.44–6.78 GHz and 7.66–10.6 GHz.
{"title":"Kuznets curve with parabola-shaped ultra-wideband antenna with defected ground plane for communications","authors":"Lalitha Bhavani Konkyana, Sudhakar V. Alapati","doi":"10.1108/IJPCC-02-2021-0056","DOIUrl":"https://doi.org/10.1108/IJPCC-02-2021-0056","url":null,"abstract":"\u0000Purpose\u0000This paper aims to state the configuration of the proposed antenna which is competent to many networks such as LTE and X band applications. The experimental study encountered the significance of the proposed antenna.\u0000\u0000\u0000Design/methodology/approach\u0000A novel compact Kuznets curve with parabola-shaped quad-band notched antenna is demonstrated in this paper. The presented prototype is ascertained on a composite material composed of woven fiberglass cloth with an epoxy resin binder. The resulting ultra-wideband antenna ranges 3.1–3.54 GHz, 5.17–5.51 GHz, 5.74–6.43 GHz and 6.79–7.60 GHz. To avoid the frequency bands which cause UWB interference,the projected antenna has been incorporated with slotted patch. The proposed antenna design is attained in four steps. The simple circular patch antenna model with defected ground plane is subjected to stepwise progression by including parabola-shaped slot and U shaped slot on the patch to attain four notched bands.\u0000\u0000\u0000Findings\u0000This projected antenna possesses an optimal bond among simulated and measured outcomes,which is more suitable for the quad notched band applications. Substrate analysis is done by varying substrate material, and notch behavior is presented. The proposed method’s optimum performance in metrics such as return loss, voltage standing wave ratio and radiation pattern varies its frequency range from 2.56 to 7.6 GHz.\u0000\u0000\u0000Originality/value\u0000The antenna adaptation of the defected ground plane has achieved through the quad notched band with operating frequency ranges 2.56 to 7.6 GHz and with eliminated frequency ranges 3.55–5.16 GHz, 5.52–5.73 GHz, 6.44–6.78 GHz and 7.66–10.6 GHz.\u0000","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130601205","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 : 2021-06-16DOI: 10.1108/ijpcc-01-2021-0020
U. Raut, L. Vishwamitra
Purpose Software-define vehicular networks (SDVN) assure the direct programmability for controlling the vehicles with improved accuracy and flexibility. In this research, the resource allocation strategy is focused on which the seek-and-destroy algorithm is implemented in the controller in such a way that an effective allocation of the resources is done based on the multi-objective function. Design/methodology/approach The purpose of this study is focuses on the resource allocation algorithm for the SDVN with the security analysis to analyse the effect of the attacks in the network. The genuine nodes in the network are granted access to the communication in the network, for which the factors such as trust, throughput, delay and packet delivery ratio are used and the algorithm used is Seek-and-Destroy optimization. Moreover, the optimal resource allocation is done using the same optimization in such a way that the network lifetime is extended. Findings The security analysis is undergoing in the research using the simulation of the attackers such as selective forwarding attacks, replay attacks, Sybil attacks and wormhole attacks that reveal that the replay attacks and the Sybil attacks are dangerous attacks and in future, there is a requirement for the security model, which ensures the protection against these attacks such that the network lifetime is extended for a prolonged communication. The achievement of the proposed method in the absence of the attacks is 84.8513% for the remaining nodal energy, 95.0535% for packet delivery ratio (PDR), 279.258 ms for transmission delay and 28.9572 kbps for throughput. Originality/value The seek-and-destroy algorithm is one of the swarm intelligence-based optimization designed based on the characteristics of the scroungers and defenders, which is completely novel in the area of optimizations. The diversification and intensification of the algorithm are perfectly balanced, leading to good convergence rates.
{"title":"Seek-and-destroy algorithm for optimal resource allocation and security analysis in software-defined vehicular networks","authors":"U. Raut, L. Vishwamitra","doi":"10.1108/ijpcc-01-2021-0020","DOIUrl":"https://doi.org/10.1108/ijpcc-01-2021-0020","url":null,"abstract":"\u0000Purpose\u0000Software-define vehicular networks (SDVN) assure the direct programmability for controlling the vehicles with improved accuracy and flexibility. In this research, the resource allocation strategy is focused on which the seek-and-destroy algorithm is implemented in the controller in such a way that an effective allocation of the resources is done based on the multi-objective function.\u0000\u0000\u0000Design/methodology/approach\u0000The purpose of this study is focuses on the resource allocation algorithm for the SDVN with the security analysis to analyse the effect of the attacks in the network. The genuine nodes in the network are granted access to the communication in the network, for which the factors such as trust, throughput, delay and packet delivery ratio are used and the algorithm used is Seek-and-Destroy optimization. Moreover, the optimal resource allocation is done using the same optimization in such a way that the network lifetime is extended.\u0000\u0000\u0000Findings\u0000The security analysis is undergoing in the research using the simulation of the attackers such as selective forwarding attacks, replay attacks, Sybil attacks and wormhole attacks that reveal that the replay attacks and the Sybil attacks are dangerous attacks and in future, there is a requirement for the security model, which ensures the protection against these attacks such that the network lifetime is extended for a prolonged communication. The achievement of the proposed method in the absence of the attacks is 84.8513% for the remaining nodal energy, 95.0535% for packet delivery ratio (PDR), 279.258 ms for transmission delay and 28.9572 kbps for throughput.\u0000\u0000\u0000Originality/value\u0000The seek-and-destroy algorithm is one of the swarm intelligence-based optimization designed based on the characteristics of the scroungers and defenders, which is completely novel in the area of optimizations. The diversification and intensification of the algorithm are perfectly balanced, leading to good convergence rates.\u0000","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116363237","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 : 2021-06-03DOI: 10.1108/IJPCC-10-2020-0162
Mohan V. Pawar, Anuradha Jagadeesan
Purpose This study aims to present a novel system for detection and prevention of black hole and wormhole attacks in wireless sensor network (WSN) based on deep learning model. Here, different phases are included such as assigning the nodes, data collection, detecting black hole and wormhole attacks and preventing black hole and wormhole attacks by optimal path communication. Initially, a set of nodes is assumed for carrying out the communication in WSN. Further, the black hole attacks are detected by the Bait process, and wormhole attacks are detected by the round trip time (RTT) validation process. The data collection procedure is done with the Bait and RTT validation process with attribute information. The gathered data attributes are given for the training in which long short-term memory (LSTM) is used that includes the attack details. This is used for attack detection process. Once they are detected, those attacks are removed from the network using the optimal path selection process. Here, the optimal shortest path is determined by the improvement in the whale optimization algorithm (WOA) that is called as fitness rate-based whale optimization algorithm (FR-WOA). This shortest path communication is carried out based on the multi-objective function using energy, distance, delay and packet delivery ratio as constraints. Design/methodology/approach This paper implements a detection and prevention of attacks model based on FR-WOA algorithm for the prevention of attacks in the WSNs. With this, this paper aims to accomplish the desired optimization of multi-objective functions. Findings From the analysis, it is found that the accuracy of the optimized LSTM is better than conventional LSTM. The energy consumption of the proposed FR-WOA with 35 nodes is 7.14% superior to WOA and FireFly, 5.7% superior to grey wolf optimization and 10.3% superior to particle swarm optimization. Originality/value This paper develops the FR-WOA with optimized LSTM detecting and preventing black hole and wormhole attacks from WSN. To the best of the authors’ knowledge, this is the first work that uses FR-WOA with optimized LSTM detecting and preventing black hole and wormhole attacks from WSN.
{"title":"Detection and prevention of black-hole and wormhole attacks in wireless sensor network using optimized LSTM","authors":"Mohan V. Pawar, Anuradha Jagadeesan","doi":"10.1108/IJPCC-10-2020-0162","DOIUrl":"https://doi.org/10.1108/IJPCC-10-2020-0162","url":null,"abstract":"Purpose\u0000This study aims to present a novel system for detection and prevention of black hole and wormhole attacks in wireless sensor network (WSN) based on deep learning model. Here, different phases are included such as assigning the nodes, data collection, detecting black hole and wormhole attacks and preventing black hole and wormhole attacks by optimal path communication. Initially, a set of nodes is assumed for carrying out the communication in WSN. Further, the black hole attacks are detected by the Bait process, and wormhole attacks are detected by the round trip time (RTT) validation process. The data collection procedure is done with the Bait and RTT validation process with attribute information. The gathered data attributes are given for the training in which long short-term memory (LSTM) is used that includes the attack details. This is used for attack detection process. Once they are detected, those attacks are removed from the network using the optimal path selection process. Here, the optimal shortest path is determined by the improvement in the whale optimization algorithm (WOA) that is called as fitness rate-based whale optimization algorithm (FR-WOA). This shortest path communication is carried out based on the multi-objective function using energy, distance, delay and packet delivery ratio as constraints.\u0000\u0000\u0000Design/methodology/approach\u0000This paper implements a detection and prevention of attacks model based on FR-WOA algorithm for the prevention of attacks in the WSNs. With this, this paper aims to accomplish the desired optimization of multi-objective functions.\u0000\u0000\u0000Findings\u0000From the analysis, it is found that the accuracy of the optimized LSTM is better than conventional LSTM. The energy consumption of the proposed FR-WOA with 35 nodes is 7.14% superior to WOA and FireFly, 5.7% superior to grey wolf optimization and 10.3% superior to particle swarm optimization.\u0000\u0000\u0000Originality/value\u0000This paper develops the FR-WOA with optimized LSTM detecting and preventing black hole and wormhole attacks from WSN. To the best of the authors’ knowledge, this is the first work that uses FR-WOA with optimized LSTM detecting and preventing black hole and wormhole attacks from WSN.","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121327992","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 : 2021-06-02DOI: 10.1108/IJPCC-12-2020-0213
Subramonian Krishna Sarma
Purpose The cloud is a network of servers to share computing resources to run applications and data storage that offers services in various flavours, namely, infrastructure as a service, platform as a service and software as a service. The containers in the cloud are defined as “standalone and self-contained units that package software and its dependencies together”. Similar to virtual machines, the virtualization method facilitates the resource on a specific server that could be used by numerous appliances. Design/methodology/approach This study introduces a new Dragon Levy updated squirrel algorithm (DLU-SA) for container aware application scheduling. Furthermore, the solution of optimal resource allocation is attained via defining the objective function that considers certain criteria such as “total network distance (TND), system failure (SF), balanced cluster use (BC) and threshold distance (TD)”. Eventually, the supremacy of the presented model is confirmed over existing models in terms of cost and statistical analysis. Findings On observing the outcomes, the total cost of an adopted model for Experimentation 1 has attained a lesser cost value, and it was 0.97%, 10.45% and 10.37% superior to traditional velocity updated grey wolf (VU-GWO), squirrel search algorithm (SSA) and dragonfly algorithm (DA) models, respectively, for mean case scenario. Especially, under best case scenario, the implemented model has revealed a minimal cost value of 761.95, whereas, the compared models such as whale random update assisted lion algorithm, VU-GWO, SSA and DA has revealed higher cost value of 761.98, 779.46, 766.62 and 766.51, respectively. Thus, the enhancement of the developed model has been validated over the existing works. Originality/value This paper proposes a new DLU-SA for container aware application scheduling. This is the first work that uses the DLU-SA model for optimal container resource allocation by taking into consideration of certain constraints such as TND, SF, BC and TD.
{"title":"Metaheuristic based auto-scaling for microservices in cloud environment: a new container-aware application scheduling","authors":"Subramonian Krishna Sarma","doi":"10.1108/IJPCC-12-2020-0213","DOIUrl":"https://doi.org/10.1108/IJPCC-12-2020-0213","url":null,"abstract":"\u0000Purpose\u0000The cloud is a network of servers to share computing resources to run applications and data storage that offers services in various flavours, namely, infrastructure as a service, platform as a service and software as a service. The containers in the cloud are defined as “standalone and self-contained units that package software and its dependencies together”. Similar to virtual machines, the virtualization method facilitates the resource on a specific server that could be used by numerous appliances.\u0000\u0000\u0000Design/methodology/approach\u0000This study introduces a new Dragon Levy updated squirrel algorithm (DLU-SA) for container aware application scheduling. Furthermore, the solution of optimal resource allocation is attained via defining the objective function that considers certain criteria such as “total network distance (TND), system failure (SF), balanced cluster use (BC) and threshold distance (TD)”. Eventually, the supremacy of the presented model is confirmed over existing models in terms of cost and statistical analysis.\u0000\u0000\u0000Findings\u0000On observing the outcomes, the total cost of an adopted model for Experimentation 1 has attained a lesser cost value, and it was 0.97%, 10.45% and 10.37% superior to traditional velocity updated grey wolf (VU-GWO), squirrel search algorithm (SSA) and dragonfly algorithm (DA) models, respectively, for mean case scenario. Especially, under best case scenario, the implemented model has revealed a minimal cost value of 761.95, whereas, the compared models such as whale random update assisted lion algorithm, VU-GWO, SSA and DA has revealed higher cost value of 761.98, 779.46, 766.62 and 766.51, respectively. Thus, the enhancement of the developed model has been validated over the existing works.\u0000\u0000\u0000Originality/value\u0000This paper proposes a new DLU-SA for container aware application scheduling. This is the first work that uses the DLU-SA model for optimal container resource allocation by taking into consideration of certain constraints such as TND, SF, BC and TD.\u0000","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128800676","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 : 2021-05-19DOI: 10.1108/IJPCC-07-2020-0089
Mithun B. Patil, Rekha Patil
{"title":"A network controlled vertical handoff mechanism for heterogeneous wireless network using optimized support vector neural network","authors":"Mithun B. Patil, Rekha Patil","doi":"10.1108/IJPCC-07-2020-0089","DOIUrl":"https://doi.org/10.1108/IJPCC-07-2020-0089","url":null,"abstract":"","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128884956","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 : 2021-05-17DOI: 10.1108/IJPCC-10-2020-0159
Hamidreza Nasiriasayesh, A. Yari, E. Nazemi
{"title":"Adaptive IWD-based algorithm for deployment of business processes into cloud federations","authors":"Hamidreza Nasiriasayesh, A. Yari, E. Nazemi","doi":"10.1108/IJPCC-10-2020-0159","DOIUrl":"https://doi.org/10.1108/IJPCC-10-2020-0159","url":null,"abstract":"","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123952994","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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