Modeling multiuser spectrum allocation for cognitive radio networks
- Authors: Bin Shahid, Mohammad , Kamruzzaman, Joarder , Hassan, Md Rafiul
- Date: 2016
- Type: Text , Journal article
- Relation: Computers & Electrical Engineering Vol. 52, no. (2016), p. 266-283
- Full Text: false
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- Description: Spectrum allocation scheme in cognitive radio networks (CRNs) becomes complex when multiple CR users concomitantly need to be allocated new and suitable bands once the primary user returns. Most existing schemes focus on the gain of individual users, ignoring the effect of an allocation on other users and rely on the 'periodic sensing and transmission' cycle which reduces spectrum utilization. This paper introduces a scheme that exploits collaboration among users to detect PU's return which relieves active CR users from the sensing task, and thereby improves spectrum utilization. It defines a Capacity of Service (CoS) metric based on the optimal sensing parameters which measures the suitability of a band for each contending user and takes into consideration the impact of allocating a particular band on other band seeking users. The proposed scheme significantly improves capacity of service, reduces interference loss and collision, and hence, enhances dynamic spectrum access capabilities. (C) 2015 Elsevier Ltd. All rights reserved.
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
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
- Reviewed:
- 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.
Share-frequent sensor patterns mining from wireless sensor network data
- Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
- Date: 2015
- Type: Text , Journal article
- Relation: IEEE Transactions on Parallel and Distributed Systems Vol. 26, no. 12 (2015), p. 3471-3484
- Full Text: false
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- Description: Mining interesting knowledge from the huge amount of data gathered from WSNs is a challenge. Works reported in literature use support metric-based sensor association rules which employ the occurrence frequency of patterns as criteria. However, consideration of the binary frequency of a pattern is not a sufficient indicator for finding meaningful patterns because it only reflects the number of epochs which contain that pattern in the dataset. The share measure of sensorsets could discover useful knowledge about trigger values associated with a sensor. Here, we propose a new type of behavioral pattern called share-frequent sensor patterns (SFSPs) by considering the non-binary frequency values of sensors in epochs. SFSPs can find a correlation among a set of sensors and hence can improve the performance of WSNs in a resource management process. In this paper, a share-frequent sensor pattern tree (ShrFSP-Tree) has been proposed to facilitate a pattern growth mining technique to discover SFSPs from WSN data. We also present a parallel and distributed method where the ShrFSP-Tree is enhanced into PShrFSP-Tree and its performance is investigated for both homogeneous and heterogeneous systems. Results show that our method is time and memory efficient in finding SFSPs than the existing most efficient algorithms.
Mining associated patterns from wireless sensor networks
- Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
- Date: 2015
- Type: Text , Journal article
- Relation: IEEE Transactions on Computers Vol. 64, no. 7 (2015), p. 1998-2011
- Full Text: false
- Reviewed:
- Description: Mining of sensor data for useful knowledge extraction is a very challenging task. Existing works generate sensor association rules using occurrence frequency of patterns to extract the knowledge. These techniques often generate huge number of rules, most of which are non-informative or fail to reflect true correlation among sensor data. In this paper, we propose a new type of behavioral pattern called associated sensor patterns which capture association-like co-occurrences as well as temporal correlations which are linked with such co-occurrences. To capture such patterns a compact tree structure, called associated sensor pattern tree (ASP-tree) and a mining algorithm (ASP) are proposed which use pattern growth-based approach to generate all associated patterns with only one scan over dataset. Moreover, when data stream flows through, old information may lose significance for the current time. To capture significance of recent data, ASP-tree is further enhanced to SWASP-tree by adopting sliding observation window and updating the tree structure accordingly. Finally, window size is made dynamically adaptive to ensure efficient resource usage. Different characteristics of the proposed techniques and their computational complexity are presented. Experimental results show that our approach is very efficient in discovering associated sensor patterns and outperforms existing techniques.
Abrasion modeling of multiple-point defect dynamics for machine condition monitoring
- Authors: Yaqub, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder , Loparo, Kenneth
- Date: 2013
- Type: Text , Journal article
- Relation: IEEE Transactions on Reliability Vol. 62, no. 1 (2013), p. 171-182
- Full Text: false
- Reviewed:
- Description: Multiple-point defects and abraded surfaces in rotary machinery induce complex vibration signatures, and have a tendency to mislead defect diagnosis models. A challenging problem in machine defect diagnosis is to model and study defect signature dynamics in the case of multiple-point defects and surface abrasion. In this study, a multiple-point defect model (MPDM) that characterizes the dynamics of n-point bearing defects is proposed. MPDM is further extended to model degradation in a rotating machine as a special case of multiple-point defects. Analytical and experimental results for multiple-point defects and abrasions show that the location of the fundamental defect frequency shifts depending upon the relative location of the defects and width of the abrasive region. This variation in the defect frequency results in a degradation of the defect detection accuracy of the defect diagnostic model. Based on envelope detection analysis, a modification in existing defect diagnostic models is recommended to nullify the impact of multiple-point defects, and general abrasion in machine components.
An adaptive self-configuration scheme for severity invariant machine fault diagnosis
- Authors: Yaqub, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder
- Date: 2013
- Type: Text , Journal article
- Relation: IEEE Transactions on Reliability Vol. 62, no. 1 (2013), p. 116-126
- Full Text: false
- Reviewed:
- Description: Vibration signals, used for abnormality detection in machine health monitoring (MHM), exhibit significant variation with varying fault severity. This signal variation causes overlap among the features characterizing different types of faults, which results in severe performance degradation of the fault diagnostic model. In this paper, a wavelet based adaptive training set and feature selection (WATF) self-configuration scheme is presented, which selects the optimum wavelet decomposition level, and employs adaptive selection of the training set and features. Optimal wavelet decomposition level selection is such that the maximum fault signature-signal energy bands are achieved. The severity variant features, which could cause detrimental class overlap for MHM, are avoided using adaptive selection of the training set and features based on the location of a test data in feature space. WATF uses Support Vector Machines (SVM) to build the fault diagnostic model, and its performance and robustness has been tested with data having different severity levels. Comparative studies of WATF with eight existing fault diagnosis schemes show that, for publicly available data sets, WATF achieves higher fault detection accuracy, even when training and testing data sets belong to different severity levels.
Self static interference mitigation scheme for coexisting wireless networks
- Authors: Yaqub, Muhammad , Haider, Ammar , Gondal, Iqbal , Kamruzzaman, Joarder
- Date: 2014
- Type: Text , Journal article
- Relation: Computers and Electrical Engineering Vol. 40, no. 2 (2014), p. 307-318
- Full Text: false
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- Description: High density of coexisting networks in the Industrial, Scientific and Medical (ISM) band leads to static and self interferences among different communication entities. The inevitability of these interferences demands for interference avoidance schemes to ensure reliability of network operations. This paper proposes a novel Diversified Adaptive Frequency Rolling (DAFR) technique for frequency hopping in Bluetooth piconets. DAFR employs intelligent hopping procedures in order to mitigate self interferences, weeds out the static interferer efficiently and ensures sufficient frequency diversity. We compare the performance of our proposed technique with the widely used existing frequency hopping techniques, namely, Adaptive Frequency Hopping (AFH) and Adaptive Frequency Rolling (AFR). Simulation studies validate the significant improvement in goodput and hopping diversity of our scheme compared to other schemes and demonstrate its potential benefit in real world deployment.