An efficient data delivery mechanism for AUV-based Ad hoc UASNs
- Authors: Karmakar, Gour , Kamruzzaman, Joarder , Nowsheen, Nusrat
- Date: 2018
- Type: Text , Journal article
- Relation: Future Generation Computer Systems Vol. 86, no. (2018), p. 1193-1208
- Full Text: false
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- Description: Existing 3D Underwater Acoustic Sensor Networks (UASNs) are either fixed having nodes anchored with the seabed or a combination of Autonomous Underwater Vehicles (AUVs) and a fixed UASN where AUVs are controlled to move along paths for data collection. Existing data delivery protocols for such AUV equipped networks are designed in a way where AUVs act as message ferries. These UASNs are deployed for a specific service such as asset (e.g., oil pipes, shipwreck) monitoring and event detection. For a coordinated data collection, to deploy a network for any service like information discovery in an ad hoc manner, it requires a 3D UASN consisting of only AUVs and the movement of an AUV needs to be controlled by another AUV through commands. To our knowledge, no such data delivery protocol required for a 3D UASN comprising only AUVs exists in the current literature that can efficiently handle data collection and delivery. To address this research gap, in this paper, an AUV-based technique for ad hoc underwater network, namely AUV-based Data Delivery Protocol (ADDP), is introduced which ensures data delivery within a given time-constraint by controlling node (i.e., AUV) movement at each hop through commands of a node. The performance of the proposed protocol has also been evaluated and compared with existing relevant protocols in terms of packet delivery ratio, routing overhead and energy consumption considering various network scenarios and sizes. Results exhibit outstanding performance improvement achieved by the proposed protocol for all metrics. © 2017 Elsevier B.V.
Performance enhancement of intrusion detection system using bagging ensemble technique with feature selection
- Authors: Rashid, Md Mamunur , Kamruzzaman, Joarder , Ahmed, Mohiuddin , Islam, Nahina , Wibowo, Santoso , Gordon, Steven
- Date: 2020
- Type: Text , Conference paper
- Relation: 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020
- Full Text: false
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- Description: An intrusion detection system's (IDS) key role is to recognise anomalous activities from both inside and outside the network system. In literature, many machine learning techniques have been proposed to improve the performance of IDS. To create a good IDS, a single classifier might not be powerful enough. To overcome this bottleneck researchers focus on hybrid/ensemble techniques. Such methods are more complex and computation intensive, but they provide greater accuracy and lower false alarm rates (FAR). In this paper, we propose a bagging ensemble that improves the performance of IDS in terms of accuracy and FAR where the NSL-KDD dataset has been used to classify benign and abnormal traffic. We have also applied the information gain-based feature selection method to select highly relevant features for improving the accuracy of the proposed technique and achieved 84.93 % accuracy and 2.45 % FAR on the test dataset. © 2020 IEEE.
A Survey on Behavioral Pattern Mining from Sensor Data in Internet of Things
- 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.
A survey on context awareness in big data analytics for business applications
- Authors: Dinh, Loan , Karmakar, Gour , Kamruzzaman, Joarder
- Date: 2020
- Type: Text , Journal article
- Relation: Knowledge and Information Systems Vol. 62, no. 9 (2020), p. 3387-3415
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- Description: The concept of context awareness has been in existence since the 1990s. Though initially applied exclusively in computer science, over time it has increasingly been adopted by many different application domains such as business, health and military. Contexts change continuously because of objective reasons, such as economic situation, political matter and social issues. The adoption of big data analytics by businesses is facilitating such change at an even faster rate in much complicated ways. The potential benefits of embedding contextual information into an application are already evidenced by the improved outcomes of the existing context-aware methods in those applications. Since big data is growing very rapidly, context awareness in big data analytics has become more important and timely because of its proven efficiency in big data understanding and preparation, contributing to extracting the more and accurate value of big data. Many surveys have been published on context-based methods such as context modelling and reasoning, workflow adaptations, computational intelligence techniques and mobile ubiquitous systems. However, to our knowledge, no survey of context-aware methods on big data analytics for business applications supported by enterprise level software has been published to date. To bridge this research gap, in this paper first, we present a definition of context, its modelling and evaluation techniques, and highlight the importance of contextual information for big data analytics. Second, the works in three key business application areas that are context-aware and/or exploit big data analytics have been thoroughly reviewed. Finally, the paper concludes by highlighting a number of contemporary research challenges, including issues concerning modelling, managing and applying business contexts to big data analytics. © 2020, Springer-Verlag London Ltd., part of Springer Nature.
How much I can rely on you : measuring trustworthiness of a twitter user
- Authors: Das, Rajkumar , Karmakar, Gour , Kamruzzaman, Joarder
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Dependable and Secure Computing Vol. 18, no. 2 (2021), p. 949-966
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- Description: Trustworthiness in an online environment is essential because individuals and organizations can easily be misled by false and malicious information receiving from untrustworthy users. Though existing methods assess users' trustworthiness by exploiting Twitter account properties, their efficacy is inadequate because of Twitter's restriction on profile and tweet size, the existence of missing or insufficient profiles, and ease to create fake accounts or relationships to pretend as trustworthy. In this paper, we present a holistic approach by exploiting ideas perceived from real-world organizations for trust estimation along with available Twitter information. Users' trustworthiness is determined by considering their credentials, recommendation from referees and the quality of the information in their Twitter accounts and tweets. We establish the feasibility of our approach analytically and further devise a multi-objective cost function for the A
Assessing transformer oil quality using deep convolutional networks
- Authors: Alam, Mohammad , Karmakar, Gour , Islam, Syed , Kamruzzaman, Joarder , Chetty, Madhu , Lim, Suryani , Appuhamillage, Gayan , Chattopadhyay, Gopi , Wilcox, Steve , Verheyen, Vincent
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 29th Australasian Universities Power Engineering Conference, AUPEC 2019
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- Description: Electrical power grids comprise a significantly large number of transformers that interconnect power generation, transmission and distribution. These transformers having different MVA ratings are critical assets that require proper maintenance to provide long and uninterrupted electrical service. The mineral oil, an essential component of any transformer, not only provides cooling but also acts as an insulating medium within the transformer. The quality and the key dissolved properties of insulating mineral oil for the transformer are critical with its proper and reliable operation. However, traditional chemical diagnostic methods are expensive and time-consuming. A transformer oil image analysis approach, based on the entropy value of oil, which is inexpensive, effective and quick. However, the inability of entropy to estimate the vital transformer oil properties such as equivalent age, Neutralization Number (NN), dissipation factor (tanδ) and power factor (PF); and many intuitively derived constants usage limit its estimation accuracy. To address this issue, in this paper, we introduce an innovative transformer oil analysis using two deep convolutional learning techniques such as Convolutional Neural Network (ConvNet) and Residual Neural Network (ResNet). These two deep neural networks are chosen for this project as they have superior performance in computer vision. After estimating the equivalent aging year of transformer oil from its image by our proposed method, NN, tanδ and PF are computed using that estimated age. Our deep learning based techniques can accurately predict the transformer oil equivalent age, leading to calculate NN, tanδ and PF more accurately. The root means square error of estimated equivalent age produced by entropy, ConvNet and ResNet based methods are 0.718, 0.122 and 0.065, respectively. ConvNet and ResNet based methods have reduced the error of the oil age estimation by 83% and 91%, respectively compared to that of the entropy method. Our proposed oil image analysis can calculate the equivalent age that is very close to the actual age for all images used in the experiment. © 2019 IEEE.
- Description: E1
Enhancing branch predictors using genetic algorithm
- Authors: Haque, Md Sarwar , Hassan, Md Rafiul , Sulaiman, Muhammad , Onoruoiza, Salami , Kamruzzaman, Joarder , Arifuzzaman, Md
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 8th International Conference on Modeling Simulation and Applied Optimization, ICMSAO 2019
- Full Text: false
- Reviewed:
- Description: Dynamic branch prediction is a hardware technique used to speculate the direction of control branches. Inaccurate prediction will make all speculative works useless while accurate prediction will significantly improve microprocessors performance. In this work, we have shown that Genetic Algorithm (GA) can be used to select (near) optimal parameters for branch predictors in most cases. The GA-enhanced predictors take time to find suitable parameters, but once the values of these parameters are determined, the GA-enhanced predictors take the same time to execute as the basic predictors with increased accuracy. © 2019 IEEE.
- Description: E1
Significance level of a big data query by exploiting business processes and strategies
- Authors: Dinh, Loan , Karmakar, Gour , Kamruzzaman, Joarder , Stranieri, Andrew
- Date: 2018
- Type: Text , Conference paper
- Relation: 13th Joint International Baltic Conference on Databases and Information Systems Forum and Doctoral Consortium, Baltic-DB and IS Forum-DC 2018 Vol. 2158, p. 63-73
- Full Text: false
- Reviewed:
- Description: Querying data is one of the most frequent activities in business organisations. The tasks involving queries for big data collection, extraction and analysis have never been easy, because to obtain the high quality responses, the expected outcome from these tasks need to be more accurate and highly relevant to a business organisation. The emergence of big data era has further complicated the task. The enormous volume of data from diverse sources and the variety of queries impose a big challenge on business organisations on how to extract deep insight from big data within acceptable time. Determining significance levels of queries based on their relevance to business organisations is able to deal with such challenge. To address this issue, up to our knowledge, there exists only one approach in the literature to calculate the significance level of a query. However, in this approach, only business processes are considered by manually selecting weights for core and non-core business processes. As the significance level of a query must express the importance of that query to a business organisation, it has to be calculated based on the consideration of business strategic direction, which requires the consideration of both business processes and strategies. This paper proposes an approach for the first time where the significance level of a query is determined by exploiting process contributions and strategy priorities. The results produced by our proposed approach using a business case study show the queries that are associated with more important business processes and higher priority strategies have higher significance levels. This vindicates the application of the significance level in a query to dynamically scale the semantic information use in capturing the appropriate level of deep insight and relevant information required for a business organisation. Copyright © 2018 for this paper by the papers' authors.
A novel OFDM format and a machine learning based dimming control for lifi
- Authors: Nowrin, Itisha , Mondal, M. , Islam, Rashed , Kamruzzaman, Joarder
- Date: 2021
- Type: Text , Journal article
- Relation: Electronics (Switzerland) Vol. 10, no. 17 (2021), p.
- Full Text:
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- Description: This paper proposes a new hybrid orthogonal frequency division multiplexing (OFDM) form termed as DC‐biased pulse amplitude modulated optical OFDM (DPO‐OFDM) by combining the ideas of the existing DC‐biased optical OFDM (DCO‐OFDM) and pulse amplitude modulated discrete multitone (PAM‐DMT). The analysis indicates that the required DC‐bias for DPO‐OFDM-based light fidelity (LiFi) depends on the dimming level and the components of the DPO‐OFDM. The bit error rate (BER) performance and dimming flexibility of the DPO‐OFDM and existing OFDM schemes are evaluated using MATLAB tools. The results show that the proposed DPO‐OFDM is power efficient and has a wide dimming range. Furthermore, a switching algorithm is introduced for LiFi, where the individual components of the hybrid OFDM are switched according to a target dimming level. Next, machine learning algorithms are used for the first time to find the appropriate proportions of the hybrid OFDM components. It is shown that polynomial regression of degree 4 can reliably predict the constellation size of the DCO‐OFDM component of DPO‐OFDM for a given constellation size of PAM‐DMT. With the component switching and the machine learning algorithms, DPO‐OFDM‐based LiFi is power efficient at a wide dimming range. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Generative malware outbreak detection
- Authors: Park, Sean , Gondal, Iqbal , Kamruzzaman, Joarder , Oliver, Jon
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 2019 IEEE International Conference on Industrial Technology, ICIT 2019 Vol. 2019-February, p. 1149-1154
- Full Text: false
- Reviewed:
- Description: Recently several deep learning approaches have been attempted to detect malware binaries using convolutional neural networks and stacked deep autoencoders. Although they have shown respectable performance on a large corpus of dataset, practical defense systems require precise detection during the malware outbreaks where only a handful of samples are available. This paper demonstrates the effectiveness of the latent representations obtained through the adversarial autoencoder for malware outbreak detection. Using instruction sequence distribution mapped to a semantic latent vector, the model provides a highly effective neural signature that helps detecting variants of a previously identified malware within a campaign mutated with minor functional upgrade, function shuffling, or slightly modified obfuscations. The method demonstrates how adversarial autoencoder can turn a multiclass classification task into a clustering problem when the sample set size is limited and the distribution is biased. The model performance is evaluated on OS X malware dataset against traditional machine learning models. © 2019 IEEE.
- Description: E1
Passive detection of splicing and copy-move attacks in image forgery
- Authors: Islam, Mohammad , Kamruzzaman, Joarder , Karmakar, Gour , Murshed, Manzur , Kahandawa, Gayan
- Date: 2018
- Type: Text , Conference proceedings , Conference paper
- Relation: 25th International Conference on Neural Information Processing, ICONIP 2018; Siem Reap, Cambodia; 13th-16th December 2018; published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11304 LNCS, p. 555-567
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- Description: Internet of Things (IoT) image sensors for surveillance and monitoring, digital cameras, smart phones and social media generate huge volume of digital images every day. Image splicing and copy-move attacks are the most common types of image forgery that can be done very easily using modern photo editing software. Recently, digital forensics has drawn much attention to detect such tampering on images. In this paper, we introduce a novel feature extraction technique, namely Sum of Relevant Inter-Cell Values (SRIV) using which we propose a passive (blind) image forgery detection method based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP). First, the input image is divided into non-overlapping blocks and 2D block DCT is applied to capture the changes of a tampered image in the frequency domain. Then LBP operator is applied to enhance the local changes among the neighbouring DCT coefficients, magnifying the changes in high frequency components resulting from splicing and copy-move attacks. The resulting LBP image is again divided into non-overlapping blocks. Finally, SRIV is applied on the LBP image blocks to extract features which are then fed into a Support Vector Machine (SVM) classifier to identify forged images from authentic ones. Extensive experiment on four well-known benchmark datasets of tampered images reveal the superiority of our method over recent state-of-the-art methods.
Detecting splicing and copy-move attacks in color images
- Authors: Islam, Mohammad , Karmakar, Gour , Kamruzzaman, Joarder , Murshed, Manzur , Kahandawa, Gayan , Parvin, Nahida
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018; Canberra, Australia; 10th-13th December 2018 p. 1-7
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- Description: Image sensors are generating limitless digital images every day. Image forgery like splicing and copy-move are very common type of attacks that are easy to execute using sophisticated photo editing tools. As a result, digital forensics has attracted much attention to identify such tampering on digital images. In this paper, a passive (blind) image tampering identification method based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP) has been proposed. First, the chroma components of an image is divided into fixed sized non-overlapping blocks and 2D block DCT is applied to identify the changes due to forgery in local frequency distribution of the image. Then a texture descriptor, LBP is applied on the magnitude component of the 2D-DCT array to enhance the artifacts introduced by the tampering operation. The resulting LBP image is again divided into non-overlapping blocks. Finally, summations of corresponding inter-cell values of all the LBP blocks are computed and arranged as a feature vector. These features are fed into a Support Vector Machine (SVM) with Radial Basis Function (RBF) as kernel to distinguish forged images from authentic ones. The proposed method has been experimented extensively on three publicly available well-known image splicing and copy-move detection benchmark datasets of color images. Results demonstrate the superiority of the proposed method over recently proposed state-of-the-art approaches in terms of well accepted performance metrics such as accuracy, area under ROC curve and others.
- Description: 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018
One-shot malware outbreak detection using spatio-temporal isomorphic dynamic features
- Authors: Park, Sean , Gondal, Iqbal , Kamruzzaman, Joarder , Zhang, Leo
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 18th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/13th IEEE International Conference on Big Data Science and Engineering, TrustCom/BigDataSE 2019 p. 751-756
- Full Text: false
- Reviewed:
- Description: Fingerprinting the malware by its behavioural signature has been an attractive approach for malware detection due to the homogeneity of dynamic execution patterns across different variants of similar families. Although previous researches show reasonably good performance in dynamic detection using machine learning techniques on a large corpus of training set, decisions must be undertaken based upon a scarce number of observable samples in many practical defence scenarios. This paper demonstrates the effectiveness of generative adversarial autoencoder for dynamic malware detection under outbreak situations where in most cases a single sample is available for training the machine learning algorithm to detect similar samples that are in the wild. © 2019 IEEE.
- Description: E1
Green underwater wireless communications using hybrid optical-acoustic technologies
- 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.
Periodic associated sensor patterns mining from wireless sensor networks
- Authors: Rashid, Mamunur , Kamruzzaman, Joarder , Gondal, Iqbal , Hassan, Rafiul
- Date: 2017
- Type: Text , Conference proceedings
- Relation: Proceedings of the 24th International Conference on Neural Information Processing (ICONIP 2017); Guangzhou, China; 14/11/2017-18/11/2017 p. 247-255
- Full Text: false
- Reviewed:
- Description: Mining interesting knowledge from the massive amount of data gathered in wireless sensor networks is a challenging task. Works reported in literature all-confidence measure based associated sensor patterns can captures association-like co-occurrences and the strong temporal correlations implied by such co-occurrences in the sensor data. However, when the user given all-confidence threshold is low, a huge amount of patterns are generated and mining these patterns may not be space and time efficient. Temporal periodicity of pattern appearance can be regarded as an important criterion for measuring the interestingness of associated patterns in WSNs. Associated sensor patterns that occur after regular intervals is called periodic associated sensor patterns. Even though mining periodic associated sensor patterns from sensor data stream is extremely important in many real-time applications, no such algorithm has been proposed yet. In this paper, we propose a compact tree structure called Periodic Associated Sensor Pattern-tree (PASP-tree) and an efficient mining approach for finding periodic associated sensor patterns (PASPs) from WSNs. Extensive performance analyses show that our technique is time and memory efficient in finding periodic associated sensor patterns.
A novel ensemble of hybrid intrusion detection system for detecting internet of things attacks
- Authors: Khraisat, Ansam , Gondal, Iqbal , Vamplew, Peter , Kamruzzaman, Joarder , Alazab, Ammar
- Date: 2019
- Type: Text , Journal article
- Relation: Electronics (Switzerland) Vol. 8, no. 11 (2019), p.
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- Description: The Internet of Things (IoT) has been rapidly evolving towards making a greater impact on everyday life to large industrial systems. Unfortunately, this has attracted the attention of cybercriminals who made IoT a target of malicious activities, opening the door to a possible attack to the end nodes. Due to the large number and diverse types of IoT devices, it is a challenging task to protect the IoT infrastructure using a traditional intrusion detection system. To protect IoT devices, a novel ensemble Hybrid Intrusion Detection System (HIDS) is proposed by combining a C5 classifier and One Class Support Vector Machine classifier. HIDS combines the advantages of Signature Intrusion Detection System (SIDS) and Anomaly-based Intrusion Detection System (AIDS). The aim of this framework is to detect both the well-known intrusions and zero-day attacks with high detection accuracy and low false-alarm rates. The proposed HIDS is evaluated using the Bot-IoT dataset, which includes legitimate IoT network traffic and several types of attacks. Experiments show that the proposed hybrid IDS provide higher detection rate and lower false positive rate compared to the SIDS and AIDS techniques. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.
A dynamic content distribution scheme for decentralized sharing in tourist hotspots
- Authors: Kaisar, Shahriar , Kamruzzaman, Joarder , Karmakar, Gour
- Date: 2019
- Type: Text , Journal article
- Relation: Journal of Network and Computer Applications Vol. 129, no. (2019), p. 9-24
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- Description: Decentralized content sharing (DCS) is emerging as a suitable platform for smart mobile device users to generate and share contents seamlessly without the requirement of a centralized server. This feature is particularly important for places that lack Internet coverage such as tourist attractions where users can form an ad-hoc network and communicate opportunistically to share contents. Existing DCS approaches when applied for such type of places suffer from low delivery success rate and high latency. Although a handful of recent approaches have specifically targeted improvement of content delivery service in tourist spot like scenario, these and other DCS approaches do not focus on contents’ demand and supply which vary considerably due to visitor in-and-out flow and occurrence of influencing events. This is further compounded by the lack of any content distribution (replication) scheme. The content delivery service will be improved if contents can be proactively distributed in strategic positions based on dynamic demand and supply and medium access contention. In this paper, we propose a dynamic content distribution scheme (DCDS) considering these practical issues for sharing contents in tourist attractions. Simulation results show that the proposed approach significantly improves (7
State estimation within ied based smart grid using kalman estimates
- Authors: Rashed, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder , Islam, Syed
- Date: 2021
- Type: Text , Journal article
- Relation: Electronics (Switzerland) Vol. 10, no. 15 (2021), p.
- Full Text:
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- Description: State Estimation is a traditional and reliable technique within power distribution and control systems. It is used for building a topology of the power grid network based on state measurements and current operational state of different nodes & buses. The protection of sensors and measurement units such as Intelligent Electronic Devices (IED) in Central Energy Management System (CEMS) against False Data Injection Attacks (FDIAs) is a big concern to grid operators. These are special kind of cyber-attacks that are directed towards the state & measurement data in such a way that mislead the CEMS into making incorrect decisions and create generation load imbalance. These are known to bypass the traditional bad data detection systems within central estimators. This paper presents the use of an additional novel state estimator based on Kalman filter along with traditional Distributed State Estimation (DSE) which is based on Weighted Least Square (WLS). Kalman filter is a feedback control mechanism that constantly updates itself based on state prediction and state correction technique and shows improvement in the estimates. The additional estimator output is compared with the results of DSE in order to identify anomalies and injection of false data. We evaluated our methodology by simulating proposed technique using MATPOWER over IEEE-14, IEEE-30, IEEE-118, IEEE-300 bus. The results clearly demonstrate the superiority of the proposed method over traditional state estimation. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Sliding window-based regularly frequent patterns mining over sensor data streams
- Authors: Rashid, Md Mamunur , Kamruzzaman, Joarder , Wasimi, Saleh
- Date: 2019
- Type: Text , Conference paper
- Relation: 2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2019, Melbourne, 9 December 2019 through 11 December 2019
- Full Text: false
- Reviewed:
- Description: WSNs generate a large amount of data in the form of data stream; and mining these streams with well used support metric-based sensor association rule mechanism can result in extracting interesting knowledge. Support metric-based sensor association use occurrence frequency of pattern as criteria, but the occurrence frequency of a pattern may not be an appropriate criterion for finding significant patterns. However, temporal regularity in occurrence behavior can be considered as another important measure for assessing the importance of patterns in WSNs. A frequent pattern that occurs after regular intervals in WSNs called as regularly frequent sensor patterns. Even though mining regularly frequent sensor patterns from sensor data stream is extremely required in real-time applications, no such algorithm has been proposed yet. Therefore, in this paper we propose a novel tree structure, called regular frequent sensor pattern stream tree (RFSPS-tree) and a new technique, called regularly frequent sensor pattern mining of data stream (RFSPMS), using sliding window-based regularly frequent sensor pattern mining for WSNs. By capturing the useful knowledge of the data stream into an RFSPS-tree, our RFSPMS algorithm can mine associated sensor patterns in the current window with frequent pattern (FP)-growth like pattern-growth method. Extensive experimental analyses show that our technique is very efficient in discovering regularly frequent sensor patterns over sensor data stream. © 2019 IEEE.
Friendly jammer against an adaptive eavesdropper in a relay-aided network
- Authors: Giti, J. E. , Sakzad, A. , Srinivasan, B. , Kamruzzaman, Joarder , Gaire, R.
- Date: 2020
- Type: Text , Conference paper
- Relation: 16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020; Limassol, Cyprus; 15-19 June 2020, International Wireless Communications and Mobile Computing, IWCMC 2020 p. 1707-1712
- Full Text: false
- Reviewed:
- Description: In this paper, we consider the problem of information theoretic security for a single-input single-output (SISO) relay-aided network in the presence of an adaptive eavesdropper. We assess the impact of deceptive friendly jammers on the secrecy of communication in this network when countering adaptive eavesdroppers. Specifically, we derive the secrecy capacity and secrecy outage probability of the network and compare the results in the absence and presence of a deceptive friendly jammer. Our results show that the secrecy capacity of the network increases while the achievable secrecy outage probability decreases significantly in the presence of friendly jammer to nullify the effect of the adversary. Numerical results, obtained through computer simulations, under different scenarios of varying jamming power and average main channel gain to average eavesdropper channel gain ratio demonstrate the effectiveness of friendly jammer in providing physical layer security. © 2020 IEEE.