Transactions on Networks and Communications, Dec 30, 2016
In this paper, we proposed the Cognitive Improved Low Energy Adaptive Clustering Hierarchy (CogIL... more In this paper, we proposed the Cognitive Improved Low Energy Adaptive Clustering Hierarchy (CogILEACH) protocol that is the spectrum aware extension of the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. In CogILEACH, which selects a cluster head (CH) based on its ratio between the current residual energy and an initial energy, and multiplies by the root square of its number of neighbor nodes. The simulation results show that CogILEACH protocol improves the lifetime of the network compared to both the LEACH protocol and ILEACH protocol under two-level homogeneity.
Journal of information and communication convergence engineering, Dec 31, 2012
In this paper, we examine the problems of the low energy adaptive clustering hierarchy (LEACH) pr... more In this paper, we examine the problems of the low energy adaptive clustering hierarchy (LEACH) protocol and present ideas for improvement by selecting the cluster head node. The main problem with LEACH lies in the random selection of cluster heads. There exists a probability that the formed cluster heads are unbalanced and may remain in one part of the network, which makes some part of the network unreachable. In this paper, we present a new version of the LEACH protocol called the improved LEACH (ILEACH) protocol, which a cluster head is selected based on its ratio between the current energy level and an initial energy level, and multiplies by the root square of its number of neighbor nodes. The simulation results show that the proposed ILEACH increases the energy efficiency and network lifetime.
International Journal of Distributed Sensor Networks, Jul 1, 2015
Cognitive radio (CR) networks have been active area of research because of its ability to opportu... more Cognitive radio (CR) networks have been active area of research because of its ability to opportunistically share the spectrum. A cluster-based cooperative spectrum sensing (CCSS) has a tremendous impact on sensing reliability compared with cooperative spectrum sensing. The energy detection (ED) technique requires perfect knowledge of noise power. An eigenvalue-based spectrum sensing has mitigated the noise uncertainty problem. Sensing and reporting time slots are rigidly separated in the conventional ED and eigenvalue-based detection (EVD) schemes. In CCSS, more reporting time slots are required as the number of CR users (CRUs) increases. If the reporting time slots of other CRUs as sensing time slots with a superposition allocation, the more reliable channel sensing can be achieved. In this paper, we propose CCSS using EVD technique with a superposition approach scheme where the reporting time slot is properly utilized to sense the primary user's (PU's) signal more accurately by rescheduling the reporting time slot for CRUs and cluster heads (CHs). Simulation result shows that the proposed EVD scheme has better detection probability than the conventional CCSS using both ED and EVD techniques.
Journal of information and communication convergence engineering, Dec 31, 2012
This paper presents the implementation of an automatic road sign recognizer for an intelligent tr... more This paper presents the implementation of an automatic road sign recognizer for an intelligent transport system. In this system, lists of road signs are processed with actions such as line segmentation, single sign segmentation, and storing an artificial sign in the database. The process of taking the video stream and extracting the road sign and storing in the database is called the road sign recognition. This paper presents a study on recognizing traffic sign patterns using a segmentation technique for the efficiency and the speed of the system. The image is converted from one scale to another scale such as RGB to grayscale or grayscale to binary. The images are pre-processed with several image processing techniques, such as threshold techniques, Gaussian filters, Canny edge detection, and the contour technique.
AIMS Due to traditional endocrinological techniques, there is currently no shared work available,... more AIMS Due to traditional endocrinological techniques, there is currently no shared work available, and no therapeutic choices have been presented in type 2 diabetes (T2D), rheumatoid arthritis (RA), and tuberculosis (TB). The purpose of this research is to summarize the prospective molecular complications and potential therapeutic targets associated with T2D that have been connected to the development of TB and RA. MATERIALS AND METHODS We collected the transcriptomic data as GSE92724, GSE110999 and GSE 148036 for T2D, RA and TB patients. After collecting from NCBI, then GREIN were employed to process our datasets. STRING and Enrichr were used to construct protein-protein interaction (PPI), gene regulatory network (GRN), protein-drug-chemical, gene ontology and pathway network. Finally, Cytoscape and R studio were employed to visualize our proposed network. KEY FINDINGS We discovered a number of strong candidate hub proteins in significant pathways, namely RAB25, MAL2, SFN, MYO5B, and HLA-DQB1 out of 75 common genes. We also identified a number of TFs (JUN, TFAP2A, FOXC1, and GATA2); miRNA (mir-1-3p, mir-16-5p, and mir-34a5p); drugs (sulfasalazine, cholic acid, and nilflumic acid) and chemicals (Valproic acid, and Aflatoxin B1) may control DEGs in transcription as well as post- transcriptional expression levels. SIGNIFICANCE To summarize, our computational techniques discovered unique potential biomarkers that show how T2D, RA, and TB interacted, as well as pathways and gene regulators by which T2D may influence autoimmune inflammation and infectious diseases. In the future, more clinical and pharmacological research is needed to confirm the findings at the transcriptional and translational levels.
The Internet of things (IoT) is a network of interconnected objects that are connected and contro... more The Internet of things (IoT) is a network of interconnected objects that are connected and controls autonomous machines in the world. The cognitive radio based Internet of things (CR-IoT) concept is a revolutionary technology for the future of IoT that mitigates the spectrum scarcity problem. However, each CR-IoT user does not obtain a better sensing gain, an enhanced sum rate, and a prolonged network lifetime in conventional CR-IoT networks under the existing energy harvesters due to both (i) underutilizing the reporting framework and (ii) without separating normal and abnormal (malicious) CR-IoT users. For these reasons, we proposed machine learning (e.g. logistic regression (LR), support vector machine (SVM) and k-nearest neighbors (k-NN)) based malicious user detection in energy harvested CR-IoT networks, where each CR-IoT user will be powered by finite capacity batteries and energy harvesters. The main contributions of this paper: First, we reviewed the technological attributes and platforms proposed in the current literature for the sensing, sum rate, and network lifetime with security threats; Second, the proposed classification algorithms using machine learning are divided into two groups between normal and abnormal (malicious) CR-IoT users; Third, this scheme is utilized the reporting framework by only normal CR-IoT users where each normal CR-IoT user is obtained a longer sensing time slot; Fourth, as a proof-of-concept, the performance of the proposed scheme is evaluated through numerical experiment; Finally, this proposed scheme is greatly achieved better sensing gain, enhanced sum rate, and prolonged network lifetime, in comparison to the existing conventional schemes.
Spectrum sensing plays a very important role in Cognitive Radio based Internet of Things (CR-IoT)... more Spectrum sensing plays a very important role in Cognitive Radio based Internet of Things (CR-IoT) networks for utilization of the licensed spectrum accurately. However, the performance of the conventional Energy Detector (ED) method is compromised in a noise-uncertain environment owing to interference constraints, i.e. the CR-IoT user interference with the licensed Primary User (PU) on the same licensed band. To overcome this drawback, we proposed an energy efficient Cooperative Spectrum Sensing (CSS) for a CR-IoT network with interference constraints using a novel ED method. In this method, each CR-IoT user is capable of spectrum sensing that makes both the local decision and the weight factor based on the sequential approach; we calculate the weight factor against each CR-IoT user based on the Kullback Leibler Divergence award score. After the local decision and the weight factor are made, each CR-IoT user transmits its measured both the local decisions, and the weight factor to a Fusion Center (FC), which is made a final decision about the PU activities based on the hard fusion rule. The simulation results demonstrates that the proposed ED method obtains an improved detection performance, an enhanced sum rate, a spectral efficiency, an energy efficiency,
Han'gug inteo'nes bangsong tongsin.TV haghoe nonmunji(Print), Aug 31, 2013
Cooperative spectrum sensing can improve sensing reliability, compared with single node spectrum ... more Cooperative spectrum sensing can improve sensing reliability, compared with single node spectrum sensing. In addition, Eigenvalue-based spectrum sensing has also drawn a great attention due to its performance improvement over the energy detection method in which the more smoothing factor, the better performance is achieved. However, the more smoothing factor in Eignevalue-based spectrum sensing requires the more sensing time. Furthermore, more reporting time in cooperative sensing will be required as the number of nodes increases. Subsequently, we in this paper propose an Eigenvalue and superposition-based spectrum sensing where the reporting time is utilized so as to increase the number of smoothing factors for autocorrelation calculation. Simulation result demonstrates that the proposed scheme has better detection probability in both local as well as global detection while requiring less sensing time as compared with conventional Eigenvalue-based detection scheme.
Skin cancer (SC) referred to as cutaneous carcinoma is a serious public health concern on a globa... more Skin cancer (SC) referred to as cutaneous carcinoma is a serious public health concern on a global scale particularly for those with fair skin. Ultraviolet (UV) radiation causes skin cancer, but the exact mechanism by which occurs and the most effective methods of intervention to prevent it are yet unknown. Using bioinformatic approaches several biomarkers have been identified to determine the severity of skin cancer. This study will use bioinformatics and pharmacology approaches to discover potential biomarkers of SC for early diagnosis, prevention of disease, and therapeutic target identification. This study compared gene expression and protein levels in UV-mediated cultured keratinocytes and adjacent normal skin tissue using RNA sequencing data from the NCBI-GEO database. Then we employed GO and signalling pathway database, selection of hub genes from protein-protein interaction (PPI) network, survival and expression profile, and gene regulatory network analysis to screen potential clinical biomarkers. In the study, we identified 32 shared differentially expressed genes (DEGs) including 19 upregulated genes and 13 downregulated genes by analyzing three different subsets of the GSE85443 dataset. Skin cancer development is related to control of several DEGs through cyclin-dependent protein serine/threonine kinase activity, cell cycle regulation and activation of the NIMA kinase pathways. The cytohubba plugin in Cytoscape identified twelve hub genes from PPI; among these three DEGs namely, AURKA, CDK4, and PLK1 were shown to be significantly associated with survival (p < 0.05) and highly expressed in SC tissues. Transcriptional, post-transcriptional, and protein-chemical also indicates the hub genes bound to several molecules. Further investigation and clinical experiments will be needed to evaluate the expression of these identified biomarkers regarding the prognosis of SC patients.
Concurrency and Computation: Practice and Experience, Oct 9, 2022
SummaryThe Internet of Things (IoT) concept increases the spectrum demands of mobile users in wir... more SummaryThe Internet of Things (IoT) concept increases the spectrum demands of mobile users in wireless communications because of the intensive and heterogeneous structure of IoT. Various devices are joining IoT networks every day, and spectrum scarcity may be a crucial issue for IoT environments in the near future. Cognitive radio (CR) is capable of sensing and detecting spectrum holes. With the aim of CR, more powerful IoT devices will be constructed in such crowded wireless environments. Also, dynamic and ad‐hoc CR networks have not a fixed base station. Therefore, CR capable IoT (CR‐based IoT) device approach with routing capabilities will be a solution for future IoT environments. In this study, spectrum aware Ad hoc on‐demand distance vector routing protocol is proposed for CR‐based IoT devices in IoT environments. For the performance analysis of the proposed method, various network scenarios with different idle probability have been performed and throughput and delay results for different offered loads have been analyzed.
Consequently, the research and development for the 5G systems have already been started. This cha... more Consequently, the research and development for the 5G systems have already been started. This chapter presents an overview of potential system network architecture and highlights a superallocation technique that could be employed in the 5G cognitive radio network (CRN). A superallocation scheme is proposed to enhance the sensing detection performance by rescheduling the sensing and reporting time slots in the 5G cognitive radio network with a cluster-based cooperative spectrum sensing (CCSS). In the 4G CCSS scheme, first, all secondary users (SUs) detect the primary user (PU) signal during a rigid sensing time slot to check the availability of the spectrum band. Second, during the SU reporting time slot, the sensing results from the SUs are reported to the corresponding cluster heads (CHs). Finally, during CH reporting time slots, the CHs forward their hard decision to a fusion center (FC) through the common control channels for the global decision. However, the reporting time slots for the SUs and CHs do not contribute to the detection performance. In this chapter, a superallocation scheme that merges the reporting time slots of SUs and CHs by rescheduling the reporting time slots as a nonfixed sensing time slot for SUs to detect the PU signal promptly and more accurately is proposed. In this regard, SUs in each cluster can obtain a nonfixed sensing time slot depending on their reporting time slot order. The effectiveness of the proposed chapter that can achieve better detection performance under-28 to-10 dB environments and thus reduce reporting overhead is shown through simulations.
The Internet of Things (IoT) based real-time health monitoring system has contributed towards a b... more The Internet of Things (IoT) based real-time health monitoring system has contributed towards a brilliant human welfare both in urban and rural areas. Many of such solutions are not well applicable in developing countries like Bangladesh due to lack of uninterrupted communication system. In this paper, we present an IoT-based real-time health monitoring system that can measure, monitor and report people's health condition online and offline from anywhere. Our proposed IoT based solution is capable to transmit the sensitive health information to medical centres and caregivers in real time. The proposed system has been designed with Arduino UNO, Nodemcu, and Global System for Mobile Communication (GSM) modules to measure body temperature, pulse rate, Oxygen saturation, room temperature, and air quality in a smart home setting. The system can also provide the patient's historical health records. Our implementation was tested on some test cases which works excellent with accuracy. The proposed system has high potentiality for the rural and urban areas in developing countries. INDEX TERMS Internet of Things, healthcare, rural and urban areas, monitoring system, architectures, networks.
Spectrum sensing in a cognitive radio network involves detecting when a primary user vacates thei... more Spectrum sensing in a cognitive radio network involves detecting when a primary user vacates their licensed spectrum to enable secondary users to broadcast on the same band. Accurately sensing the absence of the primary user ensures maximum utilization of the licensed spectrum and is fundamental to building effective cognitive radio networks. Within that context, this thesis makes the following contributions: Firstly, for saving the cooperative bandwidth of the spectrum sensing process, we present an enhanced sum rate in the cluster based cognitive radio relay network (CCRRN) utilizing a reporting framework in the sequential approach. With such extended sensing intervals and amplified reporting, a better sensing performance can be obtained compared to a conventional non-sequential approach, therefore making it applicable for the future Internet of things (IoT). In addition, the sum rate of the primary network (PN) and CCRRN are also investigated for the utilization reporting framework in the sequential approach with a relay using the "n-out-of-k" rule. The simulation results show that the proposed sequential approach with a relay achieves a better sensing gain and an enhanced sum rate when compared with the conventional non-sequential approach with no relay under any condition. Secondly, state-of-the-art energy detection (ED) based spectrum sensing requires perfect knowledge of noise power and is vulnerable to noise uncertainty. An eigenvalue-based spectrum sensing approach performs well in such an uncertain environment, but does not mitigate the spectrum scarcity problem, which evolves with the future IoT rollout. For this reason, we propose a multi-user multiple-input and multiple-output (MU-MIMO) based cognitive radio scheme for the Internet of things (CR-IoT) with weighted-Eigenvalue detection (WEVD) for the analysis of sensing, system throughput, energy efficiency and expected lifetime. In this scheme, each CR-IoT user is being equipped with multiple-input and multiple-output (MIMO) antennas; we calculate the weighted Eigenvalue detection ratio, which is defined as the ratio between the difference of the maximum eigenvalue and minimum eigenvalue to the sum of the maximum eigenvalue and minimum eigenvalue. This mitigates against the spectrum scarcity problem, enhances system throughput, improves energy efficiency, prolongs expected lifetime and lowers error probability. Simulation results confirm the effectiveness of the proposed scheme; here the WEVD technique demonstrates a better detection gain, an enhanced system throughput, a lower energy consumption, prolonged expected lifetime and a lower error probability in comparison to the conventional scheme with Eigenvalue based detection (EVD) and ED techniques in a noise uncertainty environment. Thirdly, we address the issues of enhancing sensing gain, average throughput, energy consumption and network lifetime in a cognitive radio based Internet of things (CR-IoT) network under the non-sequential approach. As a solution, we propose a Dempster-Shafer theory based throughput analysis of an energy efficient spectrum sensing scheme for a heterogeneous CR-IoT network under the sequential approach, which utilizes both the signal-to-noise ratio (SNR) to evaluate the degree of reliability, and the reporting time slot to merge as a flexible sensing time slot in order to evaluate spectrum sensing more accurately. Before making a global decision based on both the Dempster-Shafer theory and the "n-out-of-k" rule at the fusion center, a flexible sensing time slot is applied to adapt its sensing data. Using the proposed Dempster-Shafer theory, evidence is aggregated during the reporting time slot and then a global decision is made at the fusion center. Simulation results show that the proposed approach improves sensing performance by 13% over previous approaches. In addition, it also improves overall throughput, reduces energy consumption, prolongs expected lifetime and reduces global error probability compared to the previous approaches under any condition. Finally, in a noise uncertain environment, the sensing performance of the conventional ED scheme is significantly degraded because of noise fluctuation, which is caused by the noise temperature, interference, and filtering. To mitigate this problem, we propose an analysis approach of the cooperative spectrum sensing and sum rate calculation for CR-IoT networks in noise uncertain environments using the Kullback–Leibler divergence (KLD) technique, excluding the deep fading CR-IoT users at a coordinator centre. The results obtained through simulations show that the proposed KLD scheme achieves a better sensing performance, an enhanced sum rate, a lower energy consumption, a longer network lifetime, a lower global error probability and a lower reporting overhead when compared to the conventional ED scheme in a noise uncertain environment
Transactions on Networks and Communications, Dec 30, 2016
In this paper, we proposed the Cognitive Improved Low Energy Adaptive Clustering Hierarchy (CogIL... more In this paper, we proposed the Cognitive Improved Low Energy Adaptive Clustering Hierarchy (CogILEACH) protocol that is the spectrum aware extension of the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. In CogILEACH, which selects a cluster head (CH) based on its ratio between the current residual energy and an initial energy, and multiplies by the root square of its number of neighbor nodes. The simulation results show that CogILEACH protocol improves the lifetime of the network compared to both the LEACH protocol and ILEACH protocol under two-level homogeneity.
Journal of information and communication convergence engineering, Dec 31, 2012
In this paper, we examine the problems of the low energy adaptive clustering hierarchy (LEACH) pr... more In this paper, we examine the problems of the low energy adaptive clustering hierarchy (LEACH) protocol and present ideas for improvement by selecting the cluster head node. The main problem with LEACH lies in the random selection of cluster heads. There exists a probability that the formed cluster heads are unbalanced and may remain in one part of the network, which makes some part of the network unreachable. In this paper, we present a new version of the LEACH protocol called the improved LEACH (ILEACH) protocol, which a cluster head is selected based on its ratio between the current energy level and an initial energy level, and multiplies by the root square of its number of neighbor nodes. The simulation results show that the proposed ILEACH increases the energy efficiency and network lifetime.
International Journal of Distributed Sensor Networks, Jul 1, 2015
Cognitive radio (CR) networks have been active area of research because of its ability to opportu... more Cognitive radio (CR) networks have been active area of research because of its ability to opportunistically share the spectrum. A cluster-based cooperative spectrum sensing (CCSS) has a tremendous impact on sensing reliability compared with cooperative spectrum sensing. The energy detection (ED) technique requires perfect knowledge of noise power. An eigenvalue-based spectrum sensing has mitigated the noise uncertainty problem. Sensing and reporting time slots are rigidly separated in the conventional ED and eigenvalue-based detection (EVD) schemes. In CCSS, more reporting time slots are required as the number of CR users (CRUs) increases. If the reporting time slots of other CRUs as sensing time slots with a superposition allocation, the more reliable channel sensing can be achieved. In this paper, we propose CCSS using EVD technique with a superposition approach scheme where the reporting time slot is properly utilized to sense the primary user's (PU's) signal more accurately by rescheduling the reporting time slot for CRUs and cluster heads (CHs). Simulation result shows that the proposed EVD scheme has better detection probability than the conventional CCSS using both ED and EVD techniques.
Journal of information and communication convergence engineering, Dec 31, 2012
This paper presents the implementation of an automatic road sign recognizer for an intelligent tr... more This paper presents the implementation of an automatic road sign recognizer for an intelligent transport system. In this system, lists of road signs are processed with actions such as line segmentation, single sign segmentation, and storing an artificial sign in the database. The process of taking the video stream and extracting the road sign and storing in the database is called the road sign recognition. This paper presents a study on recognizing traffic sign patterns using a segmentation technique for the efficiency and the speed of the system. The image is converted from one scale to another scale such as RGB to grayscale or grayscale to binary. The images are pre-processed with several image processing techniques, such as threshold techniques, Gaussian filters, Canny edge detection, and the contour technique.
AIMS Due to traditional endocrinological techniques, there is currently no shared work available,... more AIMS Due to traditional endocrinological techniques, there is currently no shared work available, and no therapeutic choices have been presented in type 2 diabetes (T2D), rheumatoid arthritis (RA), and tuberculosis (TB). The purpose of this research is to summarize the prospective molecular complications and potential therapeutic targets associated with T2D that have been connected to the development of TB and RA. MATERIALS AND METHODS We collected the transcriptomic data as GSE92724, GSE110999 and GSE 148036 for T2D, RA and TB patients. After collecting from NCBI, then GREIN were employed to process our datasets. STRING and Enrichr were used to construct protein-protein interaction (PPI), gene regulatory network (GRN), protein-drug-chemical, gene ontology and pathway network. Finally, Cytoscape and R studio were employed to visualize our proposed network. KEY FINDINGS We discovered a number of strong candidate hub proteins in significant pathways, namely RAB25, MAL2, SFN, MYO5B, and HLA-DQB1 out of 75 common genes. We also identified a number of TFs (JUN, TFAP2A, FOXC1, and GATA2); miRNA (mir-1-3p, mir-16-5p, and mir-34a5p); drugs (sulfasalazine, cholic acid, and nilflumic acid) and chemicals (Valproic acid, and Aflatoxin B1) may control DEGs in transcription as well as post- transcriptional expression levels. SIGNIFICANCE To summarize, our computational techniques discovered unique potential biomarkers that show how T2D, RA, and TB interacted, as well as pathways and gene regulators by which T2D may influence autoimmune inflammation and infectious diseases. In the future, more clinical and pharmacological research is needed to confirm the findings at the transcriptional and translational levels.
The Internet of things (IoT) is a network of interconnected objects that are connected and contro... more The Internet of things (IoT) is a network of interconnected objects that are connected and controls autonomous machines in the world. The cognitive radio based Internet of things (CR-IoT) concept is a revolutionary technology for the future of IoT that mitigates the spectrum scarcity problem. However, each CR-IoT user does not obtain a better sensing gain, an enhanced sum rate, and a prolonged network lifetime in conventional CR-IoT networks under the existing energy harvesters due to both (i) underutilizing the reporting framework and (ii) without separating normal and abnormal (malicious) CR-IoT users. For these reasons, we proposed machine learning (e.g. logistic regression (LR), support vector machine (SVM) and k-nearest neighbors (k-NN)) based malicious user detection in energy harvested CR-IoT networks, where each CR-IoT user will be powered by finite capacity batteries and energy harvesters. The main contributions of this paper: First, we reviewed the technological attributes and platforms proposed in the current literature for the sensing, sum rate, and network lifetime with security threats; Second, the proposed classification algorithms using machine learning are divided into two groups between normal and abnormal (malicious) CR-IoT users; Third, this scheme is utilized the reporting framework by only normal CR-IoT users where each normal CR-IoT user is obtained a longer sensing time slot; Fourth, as a proof-of-concept, the performance of the proposed scheme is evaluated through numerical experiment; Finally, this proposed scheme is greatly achieved better sensing gain, enhanced sum rate, and prolonged network lifetime, in comparison to the existing conventional schemes.
Spectrum sensing plays a very important role in Cognitive Radio based Internet of Things (CR-IoT)... more Spectrum sensing plays a very important role in Cognitive Radio based Internet of Things (CR-IoT) networks for utilization of the licensed spectrum accurately. However, the performance of the conventional Energy Detector (ED) method is compromised in a noise-uncertain environment owing to interference constraints, i.e. the CR-IoT user interference with the licensed Primary User (PU) on the same licensed band. To overcome this drawback, we proposed an energy efficient Cooperative Spectrum Sensing (CSS) for a CR-IoT network with interference constraints using a novel ED method. In this method, each CR-IoT user is capable of spectrum sensing that makes both the local decision and the weight factor based on the sequential approach; we calculate the weight factor against each CR-IoT user based on the Kullback Leibler Divergence award score. After the local decision and the weight factor are made, each CR-IoT user transmits its measured both the local decisions, and the weight factor to a Fusion Center (FC), which is made a final decision about the PU activities based on the hard fusion rule. The simulation results demonstrates that the proposed ED method obtains an improved detection performance, an enhanced sum rate, a spectral efficiency, an energy efficiency,
Han'gug inteo'nes bangsong tongsin.TV haghoe nonmunji(Print), Aug 31, 2013
Cooperative spectrum sensing can improve sensing reliability, compared with single node spectrum ... more Cooperative spectrum sensing can improve sensing reliability, compared with single node spectrum sensing. In addition, Eigenvalue-based spectrum sensing has also drawn a great attention due to its performance improvement over the energy detection method in which the more smoothing factor, the better performance is achieved. However, the more smoothing factor in Eignevalue-based spectrum sensing requires the more sensing time. Furthermore, more reporting time in cooperative sensing will be required as the number of nodes increases. Subsequently, we in this paper propose an Eigenvalue and superposition-based spectrum sensing where the reporting time is utilized so as to increase the number of smoothing factors for autocorrelation calculation. Simulation result demonstrates that the proposed scheme has better detection probability in both local as well as global detection while requiring less sensing time as compared with conventional Eigenvalue-based detection scheme.
Skin cancer (SC) referred to as cutaneous carcinoma is a serious public health concern on a globa... more Skin cancer (SC) referred to as cutaneous carcinoma is a serious public health concern on a global scale particularly for those with fair skin. Ultraviolet (UV) radiation causes skin cancer, but the exact mechanism by which occurs and the most effective methods of intervention to prevent it are yet unknown. Using bioinformatic approaches several biomarkers have been identified to determine the severity of skin cancer. This study will use bioinformatics and pharmacology approaches to discover potential biomarkers of SC for early diagnosis, prevention of disease, and therapeutic target identification. This study compared gene expression and protein levels in UV-mediated cultured keratinocytes and adjacent normal skin tissue using RNA sequencing data from the NCBI-GEO database. Then we employed GO and signalling pathway database, selection of hub genes from protein-protein interaction (PPI) network, survival and expression profile, and gene regulatory network analysis to screen potential clinical biomarkers. In the study, we identified 32 shared differentially expressed genes (DEGs) including 19 upregulated genes and 13 downregulated genes by analyzing three different subsets of the GSE85443 dataset. Skin cancer development is related to control of several DEGs through cyclin-dependent protein serine/threonine kinase activity, cell cycle regulation and activation of the NIMA kinase pathways. The cytohubba plugin in Cytoscape identified twelve hub genes from PPI; among these three DEGs namely, AURKA, CDK4, and PLK1 were shown to be significantly associated with survival (p < 0.05) and highly expressed in SC tissues. Transcriptional, post-transcriptional, and protein-chemical also indicates the hub genes bound to several molecules. Further investigation and clinical experiments will be needed to evaluate the expression of these identified biomarkers regarding the prognosis of SC patients.
Concurrency and Computation: Practice and Experience, Oct 9, 2022
SummaryThe Internet of Things (IoT) concept increases the spectrum demands of mobile users in wir... more SummaryThe Internet of Things (IoT) concept increases the spectrum demands of mobile users in wireless communications because of the intensive and heterogeneous structure of IoT. Various devices are joining IoT networks every day, and spectrum scarcity may be a crucial issue for IoT environments in the near future. Cognitive radio (CR) is capable of sensing and detecting spectrum holes. With the aim of CR, more powerful IoT devices will be constructed in such crowded wireless environments. Also, dynamic and ad‐hoc CR networks have not a fixed base station. Therefore, CR capable IoT (CR‐based IoT) device approach with routing capabilities will be a solution for future IoT environments. In this study, spectrum aware Ad hoc on‐demand distance vector routing protocol is proposed for CR‐based IoT devices in IoT environments. For the performance analysis of the proposed method, various network scenarios with different idle probability have been performed and throughput and delay results for different offered loads have been analyzed.
Consequently, the research and development for the 5G systems have already been started. This cha... more Consequently, the research and development for the 5G systems have already been started. This chapter presents an overview of potential system network architecture and highlights a superallocation technique that could be employed in the 5G cognitive radio network (CRN). A superallocation scheme is proposed to enhance the sensing detection performance by rescheduling the sensing and reporting time slots in the 5G cognitive radio network with a cluster-based cooperative spectrum sensing (CCSS). In the 4G CCSS scheme, first, all secondary users (SUs) detect the primary user (PU) signal during a rigid sensing time slot to check the availability of the spectrum band. Second, during the SU reporting time slot, the sensing results from the SUs are reported to the corresponding cluster heads (CHs). Finally, during CH reporting time slots, the CHs forward their hard decision to a fusion center (FC) through the common control channels for the global decision. However, the reporting time slots for the SUs and CHs do not contribute to the detection performance. In this chapter, a superallocation scheme that merges the reporting time slots of SUs and CHs by rescheduling the reporting time slots as a nonfixed sensing time slot for SUs to detect the PU signal promptly and more accurately is proposed. In this regard, SUs in each cluster can obtain a nonfixed sensing time slot depending on their reporting time slot order. The effectiveness of the proposed chapter that can achieve better detection performance under-28 to-10 dB environments and thus reduce reporting overhead is shown through simulations.
The Internet of Things (IoT) based real-time health monitoring system has contributed towards a b... more The Internet of Things (IoT) based real-time health monitoring system has contributed towards a brilliant human welfare both in urban and rural areas. Many of such solutions are not well applicable in developing countries like Bangladesh due to lack of uninterrupted communication system. In this paper, we present an IoT-based real-time health monitoring system that can measure, monitor and report people's health condition online and offline from anywhere. Our proposed IoT based solution is capable to transmit the sensitive health information to medical centres and caregivers in real time. The proposed system has been designed with Arduino UNO, Nodemcu, and Global System for Mobile Communication (GSM) modules to measure body temperature, pulse rate, Oxygen saturation, room temperature, and air quality in a smart home setting. The system can also provide the patient's historical health records. Our implementation was tested on some test cases which works excellent with accuracy. The proposed system has high potentiality for the rural and urban areas in developing countries. INDEX TERMS Internet of Things, healthcare, rural and urban areas, monitoring system, architectures, networks.
Spectrum sensing in a cognitive radio network involves detecting when a primary user vacates thei... more Spectrum sensing in a cognitive radio network involves detecting when a primary user vacates their licensed spectrum to enable secondary users to broadcast on the same band. Accurately sensing the absence of the primary user ensures maximum utilization of the licensed spectrum and is fundamental to building effective cognitive radio networks. Within that context, this thesis makes the following contributions: Firstly, for saving the cooperative bandwidth of the spectrum sensing process, we present an enhanced sum rate in the cluster based cognitive radio relay network (CCRRN) utilizing a reporting framework in the sequential approach. With such extended sensing intervals and amplified reporting, a better sensing performance can be obtained compared to a conventional non-sequential approach, therefore making it applicable for the future Internet of things (IoT). In addition, the sum rate of the primary network (PN) and CCRRN are also investigated for the utilization reporting framework in the sequential approach with a relay using the "n-out-of-k" rule. The simulation results show that the proposed sequential approach with a relay achieves a better sensing gain and an enhanced sum rate when compared with the conventional non-sequential approach with no relay under any condition. Secondly, state-of-the-art energy detection (ED) based spectrum sensing requires perfect knowledge of noise power and is vulnerable to noise uncertainty. An eigenvalue-based spectrum sensing approach performs well in such an uncertain environment, but does not mitigate the spectrum scarcity problem, which evolves with the future IoT rollout. For this reason, we propose a multi-user multiple-input and multiple-output (MU-MIMO) based cognitive radio scheme for the Internet of things (CR-IoT) with weighted-Eigenvalue detection (WEVD) for the analysis of sensing, system throughput, energy efficiency and expected lifetime. In this scheme, each CR-IoT user is being equipped with multiple-input and multiple-output (MIMO) antennas; we calculate the weighted Eigenvalue detection ratio, which is defined as the ratio between the difference of the maximum eigenvalue and minimum eigenvalue to the sum of the maximum eigenvalue and minimum eigenvalue. This mitigates against the spectrum scarcity problem, enhances system throughput, improves energy efficiency, prolongs expected lifetime and lowers error probability. Simulation results confirm the effectiveness of the proposed scheme; here the WEVD technique demonstrates a better detection gain, an enhanced system throughput, a lower energy consumption, prolonged expected lifetime and a lower error probability in comparison to the conventional scheme with Eigenvalue based detection (EVD) and ED techniques in a noise uncertainty environment. Thirdly, we address the issues of enhancing sensing gain, average throughput, energy consumption and network lifetime in a cognitive radio based Internet of things (CR-IoT) network under the non-sequential approach. As a solution, we propose a Dempster-Shafer theory based throughput analysis of an energy efficient spectrum sensing scheme for a heterogeneous CR-IoT network under the sequential approach, which utilizes both the signal-to-noise ratio (SNR) to evaluate the degree of reliability, and the reporting time slot to merge as a flexible sensing time slot in order to evaluate spectrum sensing more accurately. Before making a global decision based on both the Dempster-Shafer theory and the "n-out-of-k" rule at the fusion center, a flexible sensing time slot is applied to adapt its sensing data. Using the proposed Dempster-Shafer theory, evidence is aggregated during the reporting time slot and then a global decision is made at the fusion center. Simulation results show that the proposed approach improves sensing performance by 13% over previous approaches. In addition, it also improves overall throughput, reduces energy consumption, prolongs expected lifetime and reduces global error probability compared to the previous approaches under any condition. Finally, in a noise uncertain environment, the sensing performance of the conventional ED scheme is significantly degraded because of noise fluctuation, which is caused by the noise temperature, interference, and filtering. To mitigate this problem, we propose an analysis approach of the cooperative spectrum sensing and sum rate calculation for CR-IoT networks in noise uncertain environments using the Kullback–Leibler divergence (KLD) technique, excluding the deep fading CR-IoT users at a coordinator centre. The results obtained through simulations show that the proposed KLD scheme achieves a better sensing performance, an enhanced sum rate, a lower energy consumption, a longer network lifetime, a lower global error probability and a lower reporting overhead when compared to the conventional ED scheme in a noise uncertain environment
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Papers by Sipon Miah