Design and analysis of an efficient energy algorithm in wireless social sensor networks
- Xiong, Naixue, Zhang, Longzhen, Zhang, Wei, Vasilakos, Athanasios, Imran, Muhammad
- Authors: Xiong, Naixue , Zhang, Longzhen , Zhang, Wei , Vasilakos, Athanasios , Imran, Muhammad
- Date: 2017
- Type: Text , Journal article
- Relation: Sensors (Switzerland) Vol. 17, no. 10 (2017), p.
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- Description: Because mobile ad hoc networks have characteristics such as lack of center nodes, multi-hop routing and changeable topology, the existing checkpoint technologies for normal mobile networks cannot be applied well to mobile ad hoc networks. Considering the multi-frequency hierarchy structure of ad hoc networks, this paper proposes a hybrid checkpointing strategy which combines the techniques of synchronous checkpointing with asynchronous checkpointing, namely the checkpoints of mobile terminals in the same cluster remain synchronous, and the checkpoints in different clusters remain asynchronous. This strategy could not only avoid cascading rollback among the processes in the same cluster, but also avoid too many message transmissions among the processes in different clusters. What is more, it can reduce the communication delay. In order to assure the consistency of the global states, this paper discusses the correctness criteria of hybrid checkpointing, which includes the criteria of checkpoint taking, rollback recovery and indelibility. Based on the designed Intra-Cluster Checkpoint Dependence Graph and Inter-Cluster Checkpoint Dependence Graph, the elimination rules for different kinds of checkpoints are discussed, and the algorithms for the same cluster checkpoints, different cluster checkpoints, and rollback recovery are also given. Experimental results demonstrate the proposed hybrid checkpointing strategy is a preferable trade-off method, which not only synthetically takes all kinds of resource constraints of Ad hoc networks into account, but also outperforms the existing schemes in terms of the dependence to cluster heads, the recovery time compared to the pure synchronous, and the pure asynchronous checkpoint advantage. © 2017 by the authors. Licensee MDPI, Basel, Switzerland.
- Authors: Xiong, Naixue , Zhang, Longzhen , Zhang, Wei , Vasilakos, Athanasios , Imran, Muhammad
- Date: 2017
- Type: Text , Journal article
- Relation: Sensors (Switzerland) Vol. 17, no. 10 (2017), p.
- Full Text:
- Reviewed:
- Description: Because mobile ad hoc networks have characteristics such as lack of center nodes, multi-hop routing and changeable topology, the existing checkpoint technologies for normal mobile networks cannot be applied well to mobile ad hoc networks. Considering the multi-frequency hierarchy structure of ad hoc networks, this paper proposes a hybrid checkpointing strategy which combines the techniques of synchronous checkpointing with asynchronous checkpointing, namely the checkpoints of mobile terminals in the same cluster remain synchronous, and the checkpoints in different clusters remain asynchronous. This strategy could not only avoid cascading rollback among the processes in the same cluster, but also avoid too many message transmissions among the processes in different clusters. What is more, it can reduce the communication delay. In order to assure the consistency of the global states, this paper discusses the correctness criteria of hybrid checkpointing, which includes the criteria of checkpoint taking, rollback recovery and indelibility. Based on the designed Intra-Cluster Checkpoint Dependence Graph and Inter-Cluster Checkpoint Dependence Graph, the elimination rules for different kinds of checkpoints are discussed, and the algorithms for the same cluster checkpoints, different cluster checkpoints, and rollback recovery are also given. Experimental results demonstrate the proposed hybrid checkpointing strategy is a preferable trade-off method, which not only synthetically takes all kinds of resource constraints of Ad hoc networks into account, but also outperforms the existing schemes in terms of the dependence to cluster heads, the recovery time compared to the pure synchronous, and the pure asynchronous checkpoint advantage. © 2017 by the authors. Licensee MDPI, Basel, Switzerland.
Value-based caching in information-centric wireless body area networks
- Al-Turjman, Fadi, Imran, Muhammad, Vasilakos, Athanasios
- Authors: Al-Turjman, Fadi , Imran, Muhammad , Vasilakos, Athanasios
- Date: 2017
- Type: Text , Journal article
- Relation: Sensors (Switzerland) Vol. 17, no. 1 (2017), p.
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- Description: We propose a resilient cache replacement approach based on a Value of sensed Information (VoI) policy. To resolve and fetch content when the origin is not available due to isolated in-network nodes (fragmentation) and harsh operational conditions, we exploit a content caching approach. Our approach depends on four functional parameters in sensory Wireless Body Area Networks (WBANs). These four parameters are: age of data based on periodic request, popularity of on-demand requests, communication interference cost, and the duration for which the sensor node is required to operate in active mode to capture the sensed readings. These parameters are considered together to assign a value to the cached data to retain the most valuable information in the cache for prolonged time periods. The higher the value, the longer the duration for which the data will be retained in the cache. This caching strategy provides significant availability for most valuable and difficult to retrieve data in the WBANs. Extensive simulations are performed to compare the proposed scheme against other significant caching schemes in the literature while varying critical aspects in WBANs (e.g., data popularity, cache size, publisher load, connectivity-degree, and severe probabilities of node failures). These simulation results indicate that the proposed VoI-based approach is a valid tool for the retrieval of cached content in disruptive and challenging scenarios, such as the one experienced in WBANs, since it allows the retrieval of content for a long period even while experiencing severe in-network node failures. © 2017 by the authors; licensee MDPI, Basel, Switzerland.
- Authors: Al-Turjman, Fadi , Imran, Muhammad , Vasilakos, Athanasios
- Date: 2017
- Type: Text , Journal article
- Relation: Sensors (Switzerland) Vol. 17, no. 1 (2017), p.
- Full Text:
- Reviewed:
- Description: We propose a resilient cache replacement approach based on a Value of sensed Information (VoI) policy. To resolve and fetch content when the origin is not available due to isolated in-network nodes (fragmentation) and harsh operational conditions, we exploit a content caching approach. Our approach depends on four functional parameters in sensory Wireless Body Area Networks (WBANs). These four parameters are: age of data based on periodic request, popularity of on-demand requests, communication interference cost, and the duration for which the sensor node is required to operate in active mode to capture the sensed readings. These parameters are considered together to assign a value to the cached data to retain the most valuable information in the cache for prolonged time periods. The higher the value, the longer the duration for which the data will be retained in the cache. This caching strategy provides significant availability for most valuable and difficult to retrieve data in the WBANs. Extensive simulations are performed to compare the proposed scheme against other significant caching schemes in the literature while varying critical aspects in WBANs (e.g., data popularity, cache size, publisher load, connectivity-degree, and severe probabilities of node failures). These simulation results indicate that the proposed VoI-based approach is a valid tool for the retrieval of cached content in disruptive and challenging scenarios, such as the one experienced in WBANs, since it allows the retrieval of content for a long period even while experiencing severe in-network node failures. © 2017 by the authors; licensee MDPI, Basel, Switzerland.
On connectivity of wireless sensor networks with directional antennas
- Wang, Qiu, Dai, Hong-Ning, Zheng, Zibin, Imran, Muhammad, Vasilakos, Athanasios
- Authors: Wang, Qiu , Dai, Hong-Ning , Zheng, Zibin , Imran, Muhammad , Vasilakos, Athanasios
- Date: 2017
- Type: Text , Journal article
- Relation: Sensors (Switzerland) Vol. 17, no. 1 (2017), p.
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- Description: In this paper, we investigate the network connectivity of wireless sensor networks with directional antennas. In particular, we establish a general framework to analyze the network connectivity while considering various antenna models and the channel randomness. Since existing directional antenna models have their pros and cons in the accuracy of reflecting realistic antennas and the computational complexity, we propose a new analytical directional antenna model called the iris model to balance the accuracy against the complexity. We conduct extensive simulations to evaluate the analytical framework. Our results show that our proposed analytical model on the network connectivity is accurate, and our iris antenna model can provide a better approximation to realistic directional antennas than other existing antenna models. © 2017 by the authors; licensee MDPI, Basel, Switzerland.
- Authors: Wang, Qiu , Dai, Hong-Ning , Zheng, Zibin , Imran, Muhammad , Vasilakos, Athanasios
- Date: 2017
- Type: Text , Journal article
- Relation: Sensors (Switzerland) Vol. 17, no. 1 (2017), p.
- Full Text:
- Reviewed:
- Description: In this paper, we investigate the network connectivity of wireless sensor networks with directional antennas. In particular, we establish a general framework to analyze the network connectivity while considering various antenna models and the channel randomness. Since existing directional antenna models have their pros and cons in the accuracy of reflecting realistic antennas and the computational complexity, we propose a new analytical directional antenna model called the iris model to balance the accuracy against the complexity. We conduct extensive simulations to evaluate the analytical framework. Our results show that our proposed analytical model on the network connectivity is accurate, and our iris antenna model can provide a better approximation to realistic directional antennas than other existing antenna models. © 2017 by the authors; licensee MDPI, Basel, Switzerland.
A quantitative risk assessment model involving frequency and threat degree under line-of-business services for infrastructure of emerging sensor networks
- Jing, Xu, Hu, Hanwen, Yang, Huijun, Au, Man, Li, Shuqin, Xiong, Naixue, Imran, Muhammad, Vasilakos, Athanasios
- Authors: Jing, Xu , Hu, Hanwen , Yang, Huijun , Au, Man , Li, Shuqin , Xiong, Naixue , Imran, Muhammad , Vasilakos, Athanasios
- Date: 2017
- Type: Text , Journal article
- Relation: Sensors (Switzerland) Vol. 17, no. 3 (2017), p.
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- Description: The prospect of Line-of-Business Services (LoBSs) for infrastructure of Emerging Sensor Networks (ESNs) is exciting. Access control remains a top challenge in this scenario as the service provider’s server contains a lot of valuable resources. LoBSs’ users are very diverse as they may come from a wide range of locations with vastly different characteristics. Cost of joining could be low and in many cases, intruders are eligible users conducting malicious actions. As a result, user access should be adjusted dynamically. Assessing LoBSs’ risk dynamically based on both frequency and threat degree of malicious operations is therefore necessary. In this paper, we proposed a Quantitative Risk Assessment Model (QRAM) involving frequency and threat degree based on value at risk. To quantify the threat degree as an elementary intrusion effort, we amend the influence coefficient of risk indexes in the network security situation assessment model. To quantify threat frequency as intrusion trace effort, we make use of multiple behavior information fusion. Under the influence of intrusion trace, we adapt the historical simulation method of value at risk to dynamically access LoBSs’ risk. Simulation based on existing data is used to select appropriate parameters for QRAM. Our simulation results show that the duration influence on elementary intrusion effort is reasonable when the normalized parameter is 1000. Likewise, the time window of intrusion trace and the weight between objective risk and subjective risk can be set to 10 s and 0.5, respectively. While our focus is to develop QRAM for assessing the risk of LoBSs for infrastructure of ESNs dynamically involving frequency and threat degree, we believe it is also appropriate for other scenarios in cloud computing. © 2017 by the authors. Licensee MDPI, Basel, Switzerland.
- Authors: Jing, Xu , Hu, Hanwen , Yang, Huijun , Au, Man , Li, Shuqin , Xiong, Naixue , Imran, Muhammad , Vasilakos, Athanasios
- Date: 2017
- Type: Text , Journal article
- Relation: Sensors (Switzerland) Vol. 17, no. 3 (2017), p.
- Full Text:
- Reviewed:
- Description: The prospect of Line-of-Business Services (LoBSs) for infrastructure of Emerging Sensor Networks (ESNs) is exciting. Access control remains a top challenge in this scenario as the service provider’s server contains a lot of valuable resources. LoBSs’ users are very diverse as they may come from a wide range of locations with vastly different characteristics. Cost of joining could be low and in many cases, intruders are eligible users conducting malicious actions. As a result, user access should be adjusted dynamically. Assessing LoBSs’ risk dynamically based on both frequency and threat degree of malicious operations is therefore necessary. In this paper, we proposed a Quantitative Risk Assessment Model (QRAM) involving frequency and threat degree based on value at risk. To quantify the threat degree as an elementary intrusion effort, we amend the influence coefficient of risk indexes in the network security situation assessment model. To quantify threat frequency as intrusion trace effort, we make use of multiple behavior information fusion. Under the influence of intrusion trace, we adapt the historical simulation method of value at risk to dynamically access LoBSs’ risk. Simulation based on existing data is used to select appropriate parameters for QRAM. Our simulation results show that the duration influence on elementary intrusion effort is reasonable when the normalized parameter is 1000. Likewise, the time window of intrusion trace and the weight between objective risk and subjective risk can be set to 10 s and 0.5, respectively. While our focus is to develop QRAM for assessing the risk of LoBSs for infrastructure of ESNs dynamically involving frequency and threat degree, we believe it is also appropriate for other scenarios in cloud computing. © 2017 by the authors. Licensee MDPI, Basel, Switzerland.
Secure authentication for remote patient monitoring withwireless medical sensor networks
- Hayajneh, Taier, Mohd, Bassam, Imran, Muhammad, Almashaqbeh, Ghada, Vasilakos, Athanasios
- Authors: Hayajneh, Taier , Mohd, Bassam , Imran, Muhammad , Almashaqbeh, Ghada , Vasilakos, Athanasios
- Date: 2016
- Type: Text , Journal article
- Relation: Sensors (Switzerland) Vol. 16, no. 4 (2016), p.
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- Description: There is broad consensus that remote health monitoring will benefit all stakeholders in the healthcare system and that it has the potential to save billions of dollars. Among the major concerns that are preventing the patients from widely adopting this technology are data privacy and security. Wireless Medical Sensor Networks (MSNs) are the building blocks for remote health monitoring systems. This paper helps to identify the most challenging security issues in the existing authentication protocols for remote patient monitoring and presents a lightweight public-key-based authentication protocol for MSNs. In MSNs, the nodes are classified into sensors that report measurements about the human body and actuators that receive commands from the medical staff and perform actions. Authenticating these commands is a critical security issue, as any alteration may lead to serious consequences. The proposed protocol is based on the Rabin authentication algorithm, which is modified in this paper to improve its signature signing process, making it suitable for delay-sensitive MSN applications. To prove the efficiency of the Rabin algorithm, we implemented the algorithm with different hardware settings using Tmote Sky motes and also programmed the algorithm on an FPGA to evaluate its design and performance. Furthermore, the proposed protocol is implemented and tested using the MIRACL (Multiprecision Integer and Rational Arithmetic C/C++) library. The results show that secure, direct, instant and authenticated commands can be delivered from the medical staff to the MSN nodes. © 2016 by the authors; licensee MDPI, Basel, Switzerland.
- Authors: Hayajneh, Taier , Mohd, Bassam , Imran, Muhammad , Almashaqbeh, Ghada , Vasilakos, Athanasios
- Date: 2016
- Type: Text , Journal article
- Relation: Sensors (Switzerland) Vol. 16, no. 4 (2016), p.
- Full Text:
- Reviewed:
- Description: There is broad consensus that remote health monitoring will benefit all stakeholders in the healthcare system and that it has the potential to save billions of dollars. Among the major concerns that are preventing the patients from widely adopting this technology are data privacy and security. Wireless Medical Sensor Networks (MSNs) are the building blocks for remote health monitoring systems. This paper helps to identify the most challenging security issues in the existing authentication protocols for remote patient monitoring and presents a lightweight public-key-based authentication protocol for MSNs. In MSNs, the nodes are classified into sensors that report measurements about the human body and actuators that receive commands from the medical staff and perform actions. Authenticating these commands is a critical security issue, as any alteration may lead to serious consequences. The proposed protocol is based on the Rabin authentication algorithm, which is modified in this paper to improve its signature signing process, making it suitable for delay-sensitive MSN applications. To prove the efficiency of the Rabin algorithm, we implemented the algorithm with different hardware settings using Tmote Sky motes and also programmed the algorithm on an FPGA to evaluate its design and performance. Furthermore, the proposed protocol is implemented and tested using the MIRACL (Multiprecision Integer and Rational Arithmetic C/C++) library. The results show that secure, direct, instant and authenticated commands can be delivered from the medical staff to the MSN nodes. © 2016 by the authors; licensee MDPI, Basel, Switzerland.
Mobile crowd sensing for traffic prediction in internet of vehicles
- Wan, Jiafu, Liu, Jianqi, Shao, Zehui, Vasilakos, Athanasios, Imran, Muhammad, Zhou, Keliang
- Authors: Wan, Jiafu , Liu, Jianqi , Shao, Zehui , Vasilakos, Athanasios , Imran, Muhammad , Zhou, Keliang
- Date: 2016
- Type: Text , Journal article
- Relation: Sensors (Switzerland) Vol. 16, no. 1 (2016), p.
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- Description: The advances in wireless communication techniques, mobile cloud computing, automotive and intelligent terminal technology are driving the evolution of vehicle ad hoc networks into the Internet of Vehicles (IoV) paradigm. This leads to a change in the vehicle routing problem from a calculation based on static data towards real-time traffic prediction. In this paper, we first address the taxonomy of cloud-assisted IoV from the viewpoint of the service relationship between cloud computing and IoV. Then, we review the traditional traffic prediction approached used by both Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V) communications. On this basis, we propose a mobile crowd sensing technology to support the creation of dynamic route choices for drivers wishing to avoid congestion. Experiments were carried out to verify the proposed approaches. Finally, we discuss the outlook of reliable traffic prediction. © 2016 by the authors, licensee MDPI, Basel, Switzerland.
- Authors: Wan, Jiafu , Liu, Jianqi , Shao, Zehui , Vasilakos, Athanasios , Imran, Muhammad , Zhou, Keliang
- Date: 2016
- Type: Text , Journal article
- Relation: Sensors (Switzerland) Vol. 16, no. 1 (2016), p.
- Full Text:
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- Description: The advances in wireless communication techniques, mobile cloud computing, automotive and intelligent terminal technology are driving the evolution of vehicle ad hoc networks into the Internet of Vehicles (IoV) paradigm. This leads to a change in the vehicle routing problem from a calculation based on static data towards real-time traffic prediction. In this paper, we first address the taxonomy of cloud-assisted IoV from the viewpoint of the service relationship between cloud computing and IoV. Then, we review the traditional traffic prediction approached used by both Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V) communications. On this basis, we propose a mobile crowd sensing technology to support the creation of dynamic route choices for drivers wishing to avoid congestion. Experiments were carried out to verify the proposed approaches. Finally, we discuss the outlook of reliable traffic prediction. © 2016 by the authors, licensee MDPI, Basel, Switzerland.
Efficient data gathering in 3D linear underwater wireless sensor networks using sink mobility
- Akbar, Mariam, Javaid, Nadeem, Khan, Ayesha, Imran, Muhammad, Shoaib, Muhammad, Vasilakos, Athanasios
- Authors: Akbar, Mariam , Javaid, Nadeem , Khan, Ayesha , Imran, Muhammad , Shoaib, Muhammad , Vasilakos, Athanasios
- Date: 2016
- Type: Text , Journal article
- Relation: Sensors (Switzerland) Vol. 16, no. 3 (2016), p.
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- Description: Due to the unpleasant and unpredictable underwater environment, designing an energy-efficient routing protocol for underwater wireless sensor networks (UWSNs) demands more accuracy and extra computations. In the proposed scheme, we introduce a mobile sink (MS), i.e., an autonomous underwater vehicle (AUV), and also courier nodes (CNs), to minimize the energy consumption of nodes. MS and CNs stop at specific stops for data gathering; later on, CNs forward the received data to the MS for further transmission. By the mobility of CNs and MS, the overall energy consumption of nodes is minimized. We perform simulations to investigate the performance of the proposed scheme and compare it to preexisting techniques. Simulation results are compared in terms of network lifetime, throughput, path loss, transmission loss and packet drop ratio. The results show that the proposed technique performs better in terms of network lifetime, throughput, path loss and scalability. © 2016 by the authors; licensee MDPI, Basel, Switzerland.
- Authors: Akbar, Mariam , Javaid, Nadeem , Khan, Ayesha , Imran, Muhammad , Shoaib, Muhammad , Vasilakos, Athanasios
- Date: 2016
- Type: Text , Journal article
- Relation: Sensors (Switzerland) Vol. 16, no. 3 (2016), p.
- Full Text:
- Reviewed:
- Description: Due to the unpleasant and unpredictable underwater environment, designing an energy-efficient routing protocol for underwater wireless sensor networks (UWSNs) demands more accuracy and extra computations. In the proposed scheme, we introduce a mobile sink (MS), i.e., an autonomous underwater vehicle (AUV), and also courier nodes (CNs), to minimize the energy consumption of nodes. MS and CNs stop at specific stops for data gathering; later on, CNs forward the received data to the MS for further transmission. By the mobility of CNs and MS, the overall energy consumption of nodes is minimized. We perform simulations to investigate the performance of the proposed scheme and compare it to preexisting techniques. Simulation results are compared in terms of network lifetime, throughput, path loss, transmission loss and packet drop ratio. The results show that the proposed technique performs better in terms of network lifetime, throughput, path loss and scalability. © 2016 by the authors; licensee MDPI, Basel, Switzerland.
Impact of traditional and embedded image denoising on CNN-based deep learning
- Kaur, Roopdeep, Karmakar, Gour, Imran, Muhammad
- Authors: Kaur, Roopdeep , Karmakar, Gour , Imran, Muhammad
- Date: 2023
- Type: Text , Journal article
- Relation: Applied sciences Vol. 13, no. 20 (2023), p.
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- Description: In digital image processing, filtering noise is an important step for reconstructing a high-quality image for further processing such as object segmentation, object detection, and object recognition. Various image-denoising approaches, including median, Gaussian, and bilateral filters, are available in the literature. Since convolutional neural networks (CNN) are able to directly learn complex patterns and features from data, they have become a popular choice for image-denoising tasks. As a result of their ability to learn and adapt to various denoising scenarios, CNNs are powerful tools for image denoising. Some deep learning techniques such as CNN incorporate denoising strategies directly into the CNN model layers. A primary limitation of these methods is their necessity to resize images to a consistent size. This resizing can result in a loss of vital image details, which might compromise CNN’s effectiveness. Because of this issue, we utilize a traditional denoising method as a preliminary step for noise reduction before applying CNN. To our knowledge, a comparative performance study of CNN using traditional and embedded denoising against a baseline approach (without denoising) is yet to be performed. To analyze the impact of denoising on the CNN performance, in this paper, firstly, we filter the noise from the images using traditional means of denoising method before their use in the CNN model. Secondly, we embed a denoising layer in the CNN model. To validate the performance of image denoising, we performed extensive experiments for both traffic sign and object recognition datasets. To decide whether denoising will be adopted and to decide on the type of filter to be used, we also present an approach exploiting the peak-signal-to-noise-ratio (PSNRs) distribution of images. Both CNN accuracy and PSNRs distribution are used to evaluate the effectiveness of the denoising approaches. As expected, the results vary with the type of filter, impact, and dataset used in both traditional and embedded denoising approaches. However, traditional denoising shows better accuracy, while embedded denoising shows lower computational time for most of the cases. Overall, this comparative study gives insights into whether denoising will be adopted in various CNN-based image analyses, including autonomous driving, animal detection, and facial recognition.
- Authors: Kaur, Roopdeep , Karmakar, Gour , Imran, Muhammad
- Date: 2023
- Type: Text , Journal article
- Relation: Applied sciences Vol. 13, no. 20 (2023), p.
- Full Text:
- Reviewed:
- Description: In digital image processing, filtering noise is an important step for reconstructing a high-quality image for further processing such as object segmentation, object detection, and object recognition. Various image-denoising approaches, including median, Gaussian, and bilateral filters, are available in the literature. Since convolutional neural networks (CNN) are able to directly learn complex patterns and features from data, they have become a popular choice for image-denoising tasks. As a result of their ability to learn and adapt to various denoising scenarios, CNNs are powerful tools for image denoising. Some deep learning techniques such as CNN incorporate denoising strategies directly into the CNN model layers. A primary limitation of these methods is their necessity to resize images to a consistent size. This resizing can result in a loss of vital image details, which might compromise CNN’s effectiveness. Because of this issue, we utilize a traditional denoising method as a preliminary step for noise reduction before applying CNN. To our knowledge, a comparative performance study of CNN using traditional and embedded denoising against a baseline approach (without denoising) is yet to be performed. To analyze the impact of denoising on the CNN performance, in this paper, firstly, we filter the noise from the images using traditional means of denoising method before their use in the CNN model. Secondly, we embed a denoising layer in the CNN model. To validate the performance of image denoising, we performed extensive experiments for both traffic sign and object recognition datasets. To decide whether denoising will be adopted and to decide on the type of filter to be used, we also present an approach exploiting the peak-signal-to-noise-ratio (PSNRs) distribution of images. Both CNN accuracy and PSNRs distribution are used to evaluate the effectiveness of the denoising approaches. As expected, the results vary with the type of filter, impact, and dataset used in both traditional and embedded denoising approaches. However, traditional denoising shows better accuracy, while embedded denoising shows lower computational time for most of the cases. Overall, this comparative study gives insights into whether denoising will be adopted in various CNN-based image analyses, including autonomous driving, animal detection, and facial recognition.
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