Pub Date : 2023-12-30DOI: 10.22247/ijcna/2023/223685
Abdullah A. Al-Atawi
{"title":"Extending the Energy Efficiency of Nodes in an Internet of Things (IoT) System via Robust Clustering Techniques","authors":"Abdullah A. Al-Atawi","doi":"10.22247/ijcna/2023/223685","DOIUrl":"https://doi.org/10.22247/ijcna/2023/223685","url":null,"abstract":"","PeriodicalId":36485,"journal":{"name":"International Journal of Computer Networks and Applications","volume":" 60","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139137468","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 : 2023-12-29DOI: 10.22247/ijcna/2023/223695
Mohammad Sirajuddin, B. Sateesh Kumar
{"title":"Secure Power Aware Hybrid Routing Strategy for Large-Scale Wireless Sensor Networks","authors":"Mohammad Sirajuddin, B. Sateesh Kumar","doi":"10.22247/ijcna/2023/223695","DOIUrl":"https://doi.org/10.22247/ijcna/2023/223695","url":null,"abstract":"","PeriodicalId":36485,"journal":{"name":"International Journal of Computer Networks and Applications","volume":" 47","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139142410","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 : 2023-12-28DOI: 10.22247/ijcna/2023/223694
M. Kayalvizhi, S. Geetha
{"title":"Efficacy Artificial Bee Colony Optimization-Based Gaussian AOMDV (EABCO-GAOMDV) Routing Protocol for Seamless Traffic Rerouting in Stochastic Vehicular Ad Hoc Network","authors":"M. Kayalvizhi, S. Geetha","doi":"10.22247/ijcna/2023/223694","DOIUrl":"https://doi.org/10.22247/ijcna/2023/223694","url":null,"abstract":"","PeriodicalId":36485,"journal":{"name":"International Journal of Computer Networks and Applications","volume":"61 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139150420","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 : 2023-12-27DOI: 10.22247/ijcna/2023/223691
Mohamed A. Ryan, Sayed Nouh, Aly M. El-Semary
{"title":"A Survey of Current Detection and Prevention Techniques for Black Hole Attack in AODV of MANET","authors":"Mohamed A. Ryan, Sayed Nouh, Aly M. El-Semary","doi":"10.22247/ijcna/2023/223691","DOIUrl":"https://doi.org/10.22247/ijcna/2023/223691","url":null,"abstract":"","PeriodicalId":36485,"journal":{"name":"International Journal of Computer Networks and Applications","volume":"2 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139153632","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 : 2023-10-01DOI: 10.22247/ijcna/2023/223417
Simon Niyonsaba, Karim Konate, Moussa Moindze Soidridine
– Today, Unmanned Aerial Vehicles (UAV), also known as drones, are increasingly used by organizations, businesses and governments in a variety of military and civilian applications, including reconnaissance, border surveillance, port security, transportation, public safety surveillance, agriculture, scientific research, rescue and more. However, drone cybersecurity has become a major concern due to the growing risk of cyberattacks aimed at compromising the confidentiality, integrity and availability of drone systems. These cyberattacks can have serious consequences, such as disclosure or theft of sensitive data, loss of drones, disruption of drone performance, etc. In the existing literature, little work has been devoted to the cybersecurity of UAV systems. To fill this gap, a taxonomy of cyberattacks in UAV is proposed focusing on the three main categories, namely interception attacks against confidentiality, modification or fabrication attacks against integrity and disruption attacks against data availability. Next, a survey of defense techniques that can be used to protect UAV systems is carried out. Finally, a discussion is held on technologies for improving drone cybersecurity, such as Blockchain and Machine Learning, as well as the challenges and future direction of research.
{"title":"A Survey on Cybersecurity in Unmanned Aerial Vehicles: Cyberattacks, Defense Techniques and Future Research Directions","authors":"Simon Niyonsaba, Karim Konate, Moussa Moindze Soidridine","doi":"10.22247/ijcna/2023/223417","DOIUrl":"https://doi.org/10.22247/ijcna/2023/223417","url":null,"abstract":"– Today, Unmanned Aerial Vehicles (UAV), also known as drones, are increasingly used by organizations, businesses and governments in a variety of military and civilian applications, including reconnaissance, border surveillance, port security, transportation, public safety surveillance, agriculture, scientific research, rescue and more. However, drone cybersecurity has become a major concern due to the growing risk of cyberattacks aimed at compromising the confidentiality, integrity and availability of drone systems. These cyberattacks can have serious consequences, such as disclosure or theft of sensitive data, loss of drones, disruption of drone performance, etc. In the existing literature, little work has been devoted to the cybersecurity of UAV systems. To fill this gap, a taxonomy of cyberattacks in UAV is proposed focusing on the three main categories, namely interception attacks against confidentiality, modification or fabrication attacks against integrity and disruption attacks against data availability. Next, a survey of defense techniques that can be used to protect UAV systems is carried out. Finally, a discussion is held on technologies for improving drone cybersecurity, such as Blockchain and Machine Learning, as well as the challenges and future direction of research.","PeriodicalId":36485,"journal":{"name":"International Journal of Computer Networks and Applications","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136129574","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 : 2023-10-01DOI: 10.22247/ijcna/2023/223428
V. Veerakumaran, Aruchamy Rajini
– The research focuses on enhancing the performance of Mobility Enabled Wireless Sensor Networks (ME-WSNs) through the introduction of a novel routing protocol named Sophisticated Eagle Search Optimization-Based Gaussian Ad Hoc On-demand Distance Vector (SESO-GAODV). ME-WSNs pose unique challenges due to their dynamic and rapidly changing network topologies. To address these challenges, SESO-GAODV leverages the intelligent optimization techniques of Sophisticated Eagle Search Optimization and the dynamic route discovery capabilities of Gaussian Ad Hoc On-demand Distance Vector (GAODV). The proposed protocol undergoes extensive evaluations and comparisons with other existing routing protocols. Through comprehensive performance analysis, SESO-GAODV demonstrates superior results, including reduced delay, increased throughput, minimized packet loss, and lower energy consumption. The protocol's adaptability to changing network conditions and efficient handling of node mobility contribute to its energy-efficient nature, making it a promising solution for enhancing data transmission efficiency and reliability in ME-WSNs. SESO-GAODV's ability to optimize energy consumption ensures a prolonged network lifetime, facilitating seamless communication and optimized network performance in dynamic and challenging environments.
{"title":"Performance Enhancement of Mobility-Enabled Wireless Sensor Network Using Sophisticated Eagle Search Optimization-Based Gaussian Ad Hoc On-Demand Distance Vector (SESO-GAODV) Routing Protocol","authors":"V. Veerakumaran, Aruchamy Rajini","doi":"10.22247/ijcna/2023/223428","DOIUrl":"https://doi.org/10.22247/ijcna/2023/223428","url":null,"abstract":"– The research focuses on enhancing the performance of Mobility Enabled Wireless Sensor Networks (ME-WSNs) through the introduction of a novel routing protocol named Sophisticated Eagle Search Optimization-Based Gaussian Ad Hoc On-demand Distance Vector (SESO-GAODV). ME-WSNs pose unique challenges due to their dynamic and rapidly changing network topologies. To address these challenges, SESO-GAODV leverages the intelligent optimization techniques of Sophisticated Eagle Search Optimization and the dynamic route discovery capabilities of Gaussian Ad Hoc On-demand Distance Vector (GAODV). The proposed protocol undergoes extensive evaluations and comparisons with other existing routing protocols. Through comprehensive performance analysis, SESO-GAODV demonstrates superior results, including reduced delay, increased throughput, minimized packet loss, and lower energy consumption. The protocol's adaptability to changing network conditions and efficient handling of node mobility contribute to its energy-efficient nature, making it a promising solution for enhancing data transmission efficiency and reliability in ME-WSNs. SESO-GAODV's ability to optimize energy consumption ensures a prolonged network lifetime, facilitating seamless communication and optimized network performance in dynamic and challenging environments.","PeriodicalId":36485,"journal":{"name":"International Journal of Computer Networks and Applications","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136129328","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 : 2023-10-01DOI: 10.22247/ijcna/2023/223425
Prabu B., G. Jagatheeshkumar
– Service discovery is one of the most difficult aspects of MANETs. The primary concern is the assignment of the optimal service to the service requester. This work intends to address this issue by proposing a clustered trustworthy service discovery scheme. The Cluster Head (CH) node selection and recycling, 𝑺𝑬𝑹𝑽 𝑨𝑫 , request, response and service ranking are the crucial phases of this work. The CH node is chosen by considering the trust parameters like mobility, energy and number of neighbors. The selected CH node calculates the level of trust for each of its member nodes by employing trust criteria such as energy consumption, packet forwarding ratio, and node behavior. The node responsible for requesting services delivers the 𝑺𝑬𝑹𝑽 𝑹𝒆𝒒 packet to the CH node, which thereafter searches its local memory for the corresponding service. Finally, the matching services are evaluated based on the distance of the service, the level of trust and the workload of the service provider. As significant metrics are considered for recommending service, the service requester is assured with reliable and faster service provisioning, which is proven by the experimental results.
{"title":"Delay Aware Clustered Service Discovery Scheme Based on Trust for Mobile Ad Hoc Networks (MANET)","authors":"Prabu B., G. Jagatheeshkumar","doi":"10.22247/ijcna/2023/223425","DOIUrl":"https://doi.org/10.22247/ijcna/2023/223425","url":null,"abstract":"– Service discovery is one of the most difficult aspects of MANETs. The primary concern is the assignment of the optimal service to the service requester. This work intends to address this issue by proposing a clustered trustworthy service discovery scheme. The Cluster Head (CH) node selection and recycling, 𝑺𝑬𝑹𝑽 𝑨𝑫 , request, response and service ranking are the crucial phases of this work. The CH node is chosen by considering the trust parameters like mobility, energy and number of neighbors. The selected CH node calculates the level of trust for each of its member nodes by employing trust criteria such as energy consumption, packet forwarding ratio, and node behavior. The node responsible for requesting services delivers the 𝑺𝑬𝑹𝑽 𝑹𝒆𝒒 packet to the CH node, which thereafter searches its local memory for the corresponding service. Finally, the matching services are evaluated based on the distance of the service, the level of trust and the workload of the service provider. As significant metrics are considered for recommending service, the service requester is assured with reliable and faster service provisioning, which is proven by the experimental results.","PeriodicalId":36485,"journal":{"name":"International Journal of Computer Networks and Applications","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136129566","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 : 2023-10-01DOI: 10.22247/ijcna/2023/223421
Nyashadzashe Tamuka, Khulumani Sibanda
– CR (cognitive radio) technology has become an attractive field of research owing to the increased demand for spectrum resources. One of the duties of this technology is spectrum sensing which involves the opportunistic identification of vacant frequency bands for occupation by unlicensed users. Various traditional and state of art Machine-Learning algorithms have been proposed for sensing these vacant frequency bands. However, the common drawbacks of the proposed traditional techniques are degraded performance at low signal-to-noise ratios (SNR) as well as the requirement for prior information about the licensed user signal characteristics. More so, several Machine-Learning / Deep Learning techniques depend on simulated, supervised, and static (batch) spectrum datasets with synthesized features, which is not the case with real-world networks. Hence, this study aims to optimize real-time and dynamic spectrum sensing in wireless networks by establishing and evaluating a K-means-LSTM novice model (artifact) that is robust to low SNR and doesn’t require a supervised spectrum dataset. Firstly, the unsupervised spectrum dataset was collected by an RTL-SDR dongle and labelled by the K-means algorithm in MATLAB. The labelled spectrum dataset was utilized for training the LSTM algorithm. The resultant LSTM model’s performance was evaluated and compared to other commonly used spectrum detection models. Findings revealed that the proposed model established from the K-Means and LSTM algorithms yielded a Pd (detection probability) of 94%, Pfa (false-alarm probability) of 71%, and an accuracy of 97% at low SNR such as -20 dB, a performance which was superior to other models' performance. Using our proposed model, it is possible to optimize real-time spectrum sensing at low SNR without a prior supervised spectrum dataset.
{"title":"Performance Evaluation of the K-Means-LSTM Hybrid Model for Optimization of Spectrum Sensing in Cognitive Radio Networks","authors":"Nyashadzashe Tamuka, Khulumani Sibanda","doi":"10.22247/ijcna/2023/223421","DOIUrl":"https://doi.org/10.22247/ijcna/2023/223421","url":null,"abstract":"– CR (cognitive radio) technology has become an attractive field of research owing to the increased demand for spectrum resources. One of the duties of this technology is spectrum sensing which involves the opportunistic identification of vacant frequency bands for occupation by unlicensed users. Various traditional and state of art Machine-Learning algorithms have been proposed for sensing these vacant frequency bands. However, the common drawbacks of the proposed traditional techniques are degraded performance at low signal-to-noise ratios (SNR) as well as the requirement for prior information about the licensed user signal characteristics. More so, several Machine-Learning / Deep Learning techniques depend on simulated, supervised, and static (batch) spectrum datasets with synthesized features, which is not the case with real-world networks. Hence, this study aims to optimize real-time and dynamic spectrum sensing in wireless networks by establishing and evaluating a K-means-LSTM novice model (artifact) that is robust to low SNR and doesn’t require a supervised spectrum dataset. Firstly, the unsupervised spectrum dataset was collected by an RTL-SDR dongle and labelled by the K-means algorithm in MATLAB. The labelled spectrum dataset was utilized for training the LSTM algorithm. The resultant LSTM model’s performance was evaluated and compared to other commonly used spectrum detection models. Findings revealed that the proposed model established from the K-Means and LSTM algorithms yielded a Pd (detection probability) of 94%, Pfa (false-alarm probability) of 71%, and an accuracy of 97% at low SNR such as -20 dB, a performance which was superior to other models' performance. Using our proposed model, it is possible to optimize real-time spectrum sensing at low SNR without a prior supervised spectrum dataset.","PeriodicalId":36485,"journal":{"name":"International Journal of Computer Networks and Applications","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136152368","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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