Green underwater wireless communications using hybrid optical-acoustic technologies
- Islam, Kazi, Ahmad, Iftekhar, Habibi, Daryoush, Zahed, M., Kamruzzaman, Joarder
- Authors: Islam, Kazi , Ahmad, Iftekhar , Habibi, Daryoush , Zahed, M. , Kamruzzaman, Joarder
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 85109-85123
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- Description: Underwater wireless communication is a rapidly growing field, especially with the recent emergence of technologies such as autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs). To support the high-bandwidth applications using these technologies, underwater optics has attracted significant attention, alongside its complementary technology - underwater acoustics. In this paper, we propose a hybrid opto-acoustic underwater wireless communication model that reduces network power consumption and supports high-data rate underwater applications by selecting appropriate communication links in response to varying traffic loads and dynamic weather conditions. Underwater optics offers high data rates and consumes less power. However, due to the severe absorption of light in the medium, the communication range is short in underwater optics. Conversely, acoustics suffers from low data rate and high power consumption, but provides longer communication ranges. Since most underwater equipment relies on battery power, energy-efficient communication is critical for reliable underwater communications. In this work, we derive analytical models for both underwater acoustics and optics, and calculate the required transmit power for reliable communications in various underwater communication environments. We then formulate an optimization problem that minimizes the network power consumption for carrying data from underwater nodes to surface sinks under varying traffic loads and weather conditions. The proposed optimization model can be solved offline periodically, hence the additional computational complexity to find the optimum solution for larger networks is not a limiting factor for practical applications. Our results indicate that the proposed technique yields up to 35% power savings compared to existing opto-acoustic solutions. © 2013 IEEE.
- Authors: Islam, Kazi , Ahmad, Iftekhar , Habibi, Daryoush , Zahed, M. , Kamruzzaman, Joarder
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 85109-85123
- Full Text:
- Reviewed:
- Description: Underwater wireless communication is a rapidly growing field, especially with the recent emergence of technologies such as autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs). To support the high-bandwidth applications using these technologies, underwater optics has attracted significant attention, alongside its complementary technology - underwater acoustics. In this paper, we propose a hybrid opto-acoustic underwater wireless communication model that reduces network power consumption and supports high-data rate underwater applications by selecting appropriate communication links in response to varying traffic loads and dynamic weather conditions. Underwater optics offers high data rates and consumes less power. However, due to the severe absorption of light in the medium, the communication range is short in underwater optics. Conversely, acoustics suffers from low data rate and high power consumption, but provides longer communication ranges. Since most underwater equipment relies on battery power, energy-efficient communication is critical for reliable underwater communications. In this work, we derive analytical models for both underwater acoustics and optics, and calculate the required transmit power for reliable communications in various underwater communication environments. We then formulate an optimization problem that minimizes the network power consumption for carrying data from underwater nodes to surface sinks under varying traffic loads and weather conditions. The proposed optimization model can be solved offline periodically, hence the additional computational complexity to find the optimum solution for larger networks is not a limiting factor for practical applications. Our results indicate that the proposed technique yields up to 35% power savings compared to existing opto-acoustic solutions. © 2013 IEEE.
A Survey on Behavioral Pattern Mining from Sensor Data in Internet of Things
- Rashid, Md Mamunur, Kamruzzaman, Joarder, Hassan, Mohammad, Shahriar Shafin, Sakib, Bhuiyan, Md Zakirul
- Authors: Rashid, Md Mamunur , Kamruzzaman, Joarder , Hassan, Mohammad , Shahriar Shafin, Sakib , Bhuiyan, Md Zakirul
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 33318-33341
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- Description: The deployment of large-scale wireless sensor networks (WSNs) for the Internet of Things (IoT) applications is increasing day-by-day, especially with the emergence of smart city services. The sensor data streams generated from these applications are largely dynamic, heterogeneous, and often geographically distributed over large areas. For high-value use in business, industry and services, these data streams must be mined to extract insightful knowledge, such as about monitoring (e.g., discovering certain behaviors over a deployed area) or network diagnostics (e.g., predicting faulty sensor nodes). However, due to the inherent constraints of sensor networks and application requirements, traditional data mining techniques cannot be directly used to mine IoT data streams efficiently and accurately in real-time. In the last decade, a number of works have been reported in the literature proposing behavioral pattern mining algorithms for sensor networks. This paper presents the technical challenges that need to be considered for mining sensor data. It then provides a thorough review of the mining techniques proposed in the recent literature to mine behavioral patterns from sensor data in IoT, and their characteristics and differences are highlighted and compared. We also propose a behavioral pattern mining framework for IoT and discuss possible future research directions in this area. © 2013 IEEE.
- Authors: Rashid, Md Mamunur , Kamruzzaman, Joarder , Hassan, Mohammad , Shahriar Shafin, Sakib , Bhuiyan, Md Zakirul
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 33318-33341
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- Description: The deployment of large-scale wireless sensor networks (WSNs) for the Internet of Things (IoT) applications is increasing day-by-day, especially with the emergence of smart city services. The sensor data streams generated from these applications are largely dynamic, heterogeneous, and often geographically distributed over large areas. For high-value use in business, industry and services, these data streams must be mined to extract insightful knowledge, such as about monitoring (e.g., discovering certain behaviors over a deployed area) or network diagnostics (e.g., predicting faulty sensor nodes). However, due to the inherent constraints of sensor networks and application requirements, traditional data mining techniques cannot be directly used to mine IoT data streams efficiently and accurately in real-time. In the last decade, a number of works have been reported in the literature proposing behavioral pattern mining algorithms for sensor networks. This paper presents the technical challenges that need to be considered for mining sensor data. It then provides a thorough review of the mining techniques proposed in the recent literature to mine behavioral patterns from sensor data in IoT, and their characteristics and differences are highlighted and compared. We also propose a behavioral pattern mining framework for IoT and discuss possible future research directions in this area. © 2013 IEEE.
Low-power wide-area networks : design goals, architecture, suitability to use cases and research challenges
- Buurman, Ben, Kamruzzaman, Joarder, Karmakar, Gour, Islam, Syed
- Authors: Buurman, Ben , Kamruzzaman, Joarder , Karmakar, Gour , Islam, Syed
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 17179-17220
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- Description: Previous survey articles on Low-Powered Wide-Area Networks (LPWANs) lack a systematic analysis of the design goals of LPWAN and the design decisions adopted by various commercially available and emerging LPWAN technologies, and no study has analysed how their design decisions impact their ability to meet design goals. Assessing a technology's ability to meet design goals is essential in determining suitable technologies for a given application. To address these gaps, we have analysed six prominent design goals and identified the design decisions used to meet each goal in the eight LPWAN technologies, ranging from technical consideration to business model, and determined which specific technique in a design decision will help meet each goal to the greatest extent. System architecture and specifications are presented for those LPWAN solutions, and their ability to meet each design goal is evaluated. We outline seventeen use cases across twelve domains that require large low power network infrastructure and prioritise each design goal's importance to those applications as Low, Moderate, or High. Using these priorities and each technology's suitability for meeting design goals, we suggest appropriate LPWAN technologies for each use case. Finally, a number of research challenges are presented for current and future technologies. © 2013 IEEE.
- Authors: Buurman, Ben , Kamruzzaman, Joarder , Karmakar, Gour , Islam, Syed
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 17179-17220
- Full Text:
- Reviewed:
- Description: Previous survey articles on Low-Powered Wide-Area Networks (LPWANs) lack a systematic analysis of the design goals of LPWAN and the design decisions adopted by various commercially available and emerging LPWAN technologies, and no study has analysed how their design decisions impact their ability to meet design goals. Assessing a technology's ability to meet design goals is essential in determining suitable technologies for a given application. To address these gaps, we have analysed six prominent design goals and identified the design decisions used to meet each goal in the eight LPWAN technologies, ranging from technical consideration to business model, and determined which specific technique in a design decision will help meet each goal to the greatest extent. System architecture and specifications are presented for those LPWAN solutions, and their ability to meet each design goal is evaluated. We outline seventeen use cases across twelve domains that require large low power network infrastructure and prioritise each design goal's importance to those applications as Low, Moderate, or High. Using these priorities and each technology's suitability for meeting design goals, we suggest appropriate LPWAN technologies for each use case. Finally, a number of research challenges are presented for current and future technologies. © 2013 IEEE.
Robust malware defense in industrial IoT applications using machine learning with selective adversarial samples
- Khoda, Mahbub, Imam, Tasadduq, Kamruzzaman, Joarder, Gondal, Iqbal, Rahman, Ashfaqur
- Authors: Khoda, Mahbub , Imam, Tasadduq , Kamruzzaman, Joarder , Gondal, Iqbal , Rahman, Ashfaqur
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Transactions on Industry Applications Vol.56, no 4. (2020), p. 4415-4424
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- Description: Industrial Internet of Things (IIoT) deploys edge devices to act as intermediaries between sensors and actuators and application servers or cloud services. Machine learning models have been widely used to thwart malware attacks in such edge devices. However, these models are vulnerable to adversarial attacks where attackers craft adversarial samples by introducing small perturbations to malware samples to fool a classifier to misclassify them as benign applications. Literature on deep learning networks proposes adversarial retraining as a defense mechanism where adversarial samples are combined with legitimate samples to retrain the classifier. However, existing works select such adversarial samples in a random fashion which degrades the classifier's performance. This work proposes two novel approaches for selecting adversarial samples to retrain a classifier. One, based on the distance from malware cluster center, and the other, based on a probability measure derived from a kernel based learning (KBL). Our experiments show that both of our sample selection methods outperform the random selection method and the KBL selection method improves detection accuracy by 6%. Also, while existing works focus on deep neural networks with respect to adversarial retraining, we additionally assess the impact of such adversarial samples on other classifiers and our proposed selective adversarial retraining approaches show similar performance improvement for these classifiers as well. The outcomes from the study can assist in designing robust security systems for IIoT applications.
- Authors: Khoda, Mahbub , Imam, Tasadduq , Kamruzzaman, Joarder , Gondal, Iqbal , Rahman, Ashfaqur
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Transactions on Industry Applications Vol.56, no 4. (2020), p. 4415-4424
- Full Text:
- Reviewed:
- Description: Industrial Internet of Things (IIoT) deploys edge devices to act as intermediaries between sensors and actuators and application servers or cloud services. Machine learning models have been widely used to thwart malware attacks in such edge devices. However, these models are vulnerable to adversarial attacks where attackers craft adversarial samples by introducing small perturbations to malware samples to fool a classifier to misclassify them as benign applications. Literature on deep learning networks proposes adversarial retraining as a defense mechanism where adversarial samples are combined with legitimate samples to retrain the classifier. However, existing works select such adversarial samples in a random fashion which degrades the classifier's performance. This work proposes two novel approaches for selecting adversarial samples to retrain a classifier. One, based on the distance from malware cluster center, and the other, based on a probability measure derived from a kernel based learning (KBL). Our experiments show that both of our sample selection methods outperform the random selection method and the KBL selection method improves detection accuracy by 6%. Also, while existing works focus on deep neural networks with respect to adversarial retraining, we additionally assess the impact of such adversarial samples on other classifiers and our proposed selective adversarial retraining approaches show similar performance improvement for these classifiers as well. The outcomes from the study can assist in designing robust security systems for IIoT applications.
A comprehensive spectrum trading scheme based on market competition, reputation and buyer specific requirements
- Hassan, Md Rakib, Karmakar, Gour, Kamruzzaman, Joarder, Srinivasan, Bala
- Authors: Hassan, Md Rakib , Karmakar, Gour , Kamruzzaman, Joarder , Srinivasan, Bala
- Date: 2015
- Type: Text , Journal article
- Relation: Computer Networks Vol. 84, no. (2015), p. 17-31
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- Description: In the exclusive-use model of spectrum trading, cognitive radio devices or secondary users can buy spectrum resources from licensed users or primary users for a short or long period of time. Considering such spectrum access, a trading model is introduced where a buyer can select a set of candidate sellers based on their reputation and their offers in fulfilling its requirements, namely, offered signal quality, contract duration, coverage and bandwidth. Similarly, a seller can assess a buyer as a potential trading partner considering the buyer's reliability, which the seller can derive from the buyer's reputation and financial profile. In our scheme, seller reputation or buyer reliability can be either obtained from a reputation brokerage service, if one exists, or calculated using our model. Since in a competitive market, the price of a seller depends on that of other sellers, game theory is used to model the competition among multiple sellers. An optimization technique is used by a buyer to select the best seller(s) and optimize purchase to maximize its utility. This may result in buying from multiple sellers of certain amount of bandwidth from each, depending on price and meeting requirements and budget constraints. Stability of the model is analyzed and performance evaluation shows that it benefits sellers and buyers in terms of profit and throughput, respectively. © 2015 Elsevier B.V. All rights reserved.
- Authors: Hassan, Md Rakib , Karmakar, Gour , Kamruzzaman, Joarder , Srinivasan, Bala
- Date: 2015
- Type: Text , Journal article
- Relation: Computer Networks Vol. 84, no. (2015), p. 17-31
- Full Text:
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- Description: In the exclusive-use model of spectrum trading, cognitive radio devices or secondary users can buy spectrum resources from licensed users or primary users for a short or long period of time. Considering such spectrum access, a trading model is introduced where a buyer can select a set of candidate sellers based on their reputation and their offers in fulfilling its requirements, namely, offered signal quality, contract duration, coverage and bandwidth. Similarly, a seller can assess a buyer as a potential trading partner considering the buyer's reliability, which the seller can derive from the buyer's reputation and financial profile. In our scheme, seller reputation or buyer reliability can be either obtained from a reputation brokerage service, if one exists, or calculated using our model. Since in a competitive market, the price of a seller depends on that of other sellers, game theory is used to model the competition among multiple sellers. An optimization technique is used by a buyer to select the best seller(s) and optimize purchase to maximize its utility. This may result in buying from multiple sellers of certain amount of bandwidth from each, depending on price and meeting requirements and budget constraints. Stability of the model is analyzed and performance evaluation shows that it benefits sellers and buyers in terms of profit and throughput, respectively. © 2015 Elsevier B.V. All rights reserved.
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