Vibration spectrum imaging : A novel bearing fault classification approach
- Amar, Muhammad, Gondal, Iqbal, Wilson, Campbell
- Authors: Amar, Muhammad , Gondal, Iqbal , Wilson, Campbell
- Date: 2015
- Type: Text , Journal article
- Relation: IEEE Transactions on Industrial Electronics Vol. 62, no. 1 (2015), p. 494-502
- Full Text: false
- Reviewed:
- Description: Incipient fault detection in low signal-to-noise ratio (SNR) conditions requires robust features for accurate condition-based machine health monitoring. Accurate fault classification is positively linked to the quality of features of the faults. Therefore, there is a need to enhance the quality of the features before classification. This paper presents a novel vibration spectrum imaging (VSI) feature enhancement procedure for low SNR conditions. An artificial neural network (ANN) has been used as a fault classifier using these enhanced features of the faults. The normalized amplitudes of spectral contents of the quasi-stationary time vibration signals are transformed into spectral images. A 2-D averaging filter and binary image conversion, with appropriate threshold selection, are used to filter and enhance the images for the training and testing of the ANN classifier. The proposed novel VSI augments and provides the visual representation of the characteristic vibration spectral features in an image form. This provides enhanced spectral images for ANN training and thus leads to a highly robust fault classifier.
Sensor selection for tracking multiple groups of targets
- Armaghani, Farzaneh, Gondal, Iqbal, Kamruzzaman, Joarder, Green, David
- Authors: Armaghani, Farzaneh , Gondal, Iqbal , Kamruzzaman, Joarder , Green, David
- Date: 2014
- Type: Text , Journal article
- Relation: Journal of Network and Computer Applications Vol. 46, no. (2014), p. 36-47
- Full Text: false
- Reviewed:
- Description: Group target tracking is a challenge for sensor networks. It occurs where large numbers of closely spaced targets move together in different groups. In these applications, the sensor selection scheme plays a vital role in extending network lifetime while providing high tracking accuracy. Existing schemes cause an extreme imbalance between energy usages and tracking accuracy. They are capable of tracking only individual groups and without using prior knowledge about the groups. These problems make them impractical for group target tracking. With the aim of balancing the trade-off between lifetime and accuracy, we present a novel Multi-Sensor Group Tracking (MSGT) scheme. MSGT comprises the following steps to accomplish concurrent tracking of multiple groups: (1) Clustering to capture changes in the behavioural properties of groups, such as formation, merging, and splitting; (2) Sensor selection to activate the contributory sensors for the estimated group regions; and (3) Group tracking using the activated sensors. We develop a probabilistic decision-making strategy that triggers the clustering step adaptively with any detected change in group behavioural patterns. The sensor selection step coordinates periodic selection of leader and tracking sensor nodes in a distributed manner. We introduce cost metrics that include sensor′s energy parameters in the selection of active sensors that fully cover the group regions. The tracking step is a Bayesian modelling of the target groups which uses particle filtering algorithm to estimate the group locations. Simulation results show that our scheme achieves substantial improvements over existing approaches in terms of network lifetime and tracking accuracy.
Function similarity using family context
- Black, Paul, Gondal, Iqbal, Vamplew, Peter, Lakhotia, Arun
- Authors: Black, Paul , Gondal, Iqbal , Vamplew, Peter , Lakhotia, Arun
- Date: 2020
- Type: Text , Journal article
- Relation: Electronics Vol. 9, no. 7 (Jul 2020), p. 20
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- Description: Finding changed and similar functions between a pair of binaries is an important problem in malware attribution and for the identification of new malware capabilities. This paper presents a new technique called Function Similarity using Family Context (FSFC) for this problem. FSFC trains a Support Vector Machine (SVM) model using pairs of similar functions from two program variants. This method improves upon previous research called Cross Version Contextual Function Similarity (CVCFS) e epresenting a function using features extracted not just from the function itself, but also, from other functions with which it has a caller and callee relationship. We present the results of an initial experiment that shows that the use of additional features from the context of a function significantly decreases the false positive rate, obviating the need for a separate pass for cleaning false positives. The more surprising and unexpected finding is that the SVM model produced by FSFC can abstract function similarity features from one pair of program variants to find similar functions in an unrelated pair of program variants. If validated by a larger study, this new property leads to the possibility of creating generic similar function classifiers that can be packaged and distributed in reverse engineering tools such as IDA Pro and Ghidra.
- Description: This research was performed in the Internet Commerce Security Lab (ICSL), which is a joint venture with research partners Westpac, IBM, and Federation University Australia.
- Authors: Black, Paul , Gondal, Iqbal , Vamplew, Peter , Lakhotia, Arun
- Date: 2020
- Type: Text , Journal article
- Relation: Electronics Vol. 9, no. 7 (Jul 2020), p. 20
- Full Text:
- Reviewed:
- Description: Finding changed and similar functions between a pair of binaries is an important problem in malware attribution and for the identification of new malware capabilities. This paper presents a new technique called Function Similarity using Family Context (FSFC) for this problem. FSFC trains a Support Vector Machine (SVM) model using pairs of similar functions from two program variants. This method improves upon previous research called Cross Version Contextual Function Similarity (CVCFS) e epresenting a function using features extracted not just from the function itself, but also, from other functions with which it has a caller and callee relationship. We present the results of an initial experiment that shows that the use of additional features from the context of a function significantly decreases the false positive rate, obviating the need for a separate pass for cleaning false positives. The more surprising and unexpected finding is that the SVM model produced by FSFC can abstract function similarity features from one pair of program variants to find similar functions in an unrelated pair of program variants. If validated by a larger study, this new property leads to the possibility of creating generic similar function classifiers that can be packaged and distributed in reverse engineering tools such as IDA Pro and Ghidra.
- Description: This research was performed in the Internet Commerce Security Lab (ICSL), which is a joint venture with research partners Westpac, IBM, and Federation University Australia.
Malware variant identification using incremental clustering
- Black, Paul, Gondal, Iqbal, Bagirov, Adil, Moniruzzaman, Md
- Authors: Black, Paul , Gondal, Iqbal , Bagirov, Adil , Moniruzzaman, Md
- Date: 2021
- Type: Text , Journal article
- Relation: Electronics Vol. 10, no. 14 (2021), p.
- Relation: http://purl.org/au-research/grants/arc/DP190100580
- Full Text:
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- Authors: Black, Paul , Gondal, Iqbal , Bagirov, Adil , Moniruzzaman, Md
- Date: 2021
- Type: Text , Journal article
- Relation: Electronics Vol. 10, no. 14 (2021), p.
- Relation: http://purl.org/au-research/grants/arc/DP190100580
- Full Text:
- Reviewed:
Cross-compiler bipartite vulnerability search
- Authors: Black, Paul , Gondal, Iqbal
- Date: 2021
- Type: Text , Journal article
- Relation: Electronics (Switzerland) Vol. 10, no. 11 (2021), p.
- Full Text:
- Reviewed:
- Description: Open-source libraries are widely used in software development, and the functions from these libraries may contain security vulnerabilities that can provide gateways for attackers. This paper provides a function similarity technique to identify vulnerable functions in compiled programs and proposes a new technique called Cross-Compiler Bipartite Vulnerability Search (CCBVS). CCBVS uses a novel training process, and bipartite matching to filter SVM model false positives to improve the quality of similar function identification. This research uses debug symbols in programs compiled from open-source software products to generate the ground truth. This automatic extraction of ground truth allows experimentation with a wide range of programs. The results presented in the paper show that an SVM model trained on a wide variety of programs compiled for Windows and Linux, x86 and Intel 64 architectures can be used to predict function similarity and that the use of bipartite matching substantially improves the function similarity matching performance. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
- Authors: Black, Paul , Gondal, Iqbal
- Date: 2021
- Type: Text , Journal article
- Relation: Electronics (Switzerland) Vol. 10, no. 11 (2021), p.
- Full Text:
- Reviewed:
- Description: Open-source libraries are widely used in software development, and the functions from these libraries may contain security vulnerabilities that can provide gateways for attackers. This paper provides a function similarity technique to identify vulnerable functions in compiled programs and proposes a new technique called Cross-Compiler Bipartite Vulnerability Search (CCBVS). CCBVS uses a novel training process, and bipartite matching to filter SVM model false positives to improve the quality of similar function identification. This research uses debug symbols in programs compiled from open-source software products to generate the ground truth. This automatic extraction of ground truth allows experimentation with a wide range of programs. The results presented in the paper show that an SVM model trained on a wide variety of programs compiled for Windows and Linux, x86 and Intel 64 architectures can be used to predict function similarity and that the use of bipartite matching substantially improves the function similarity matching performance. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
An exploratory trial implementing a community-based child oral health promotion intervention for Australian families from refugee and migrant backgrounds : A protocol paper for Teeth Tales
- Gibbs, Lisa, Waters, Elizabeth, De Silva, Andrea, Riggs, Elisha, Moore, Laurence, Armit, Christine, Johnson, Britt, Morris, Michal, Calache, Hanny, Gussy, Mark, Young, Dana, Tadic, Maryanne, Christian, Bradley, Gondal, Iqbal, Watt, Richard, Pradel, Veronika, Truong, Mandy, Gold, Lisa
- Authors: Gibbs, Lisa , Waters, Elizabeth , De Silva, Andrea , Riggs, Elisha , Moore, Laurence , Armit, Christine , Johnson, Britt , Morris, Michal , Calache, Hanny , Gussy, Mark , Young, Dana , Tadic, Maryanne , Christian, Bradley , Gondal, Iqbal , Watt, Richard , Pradel, Veronika , Truong, Mandy , Gold, Lisa
- Date: 2014
- Type: Text , Journal article
- Relation: BMJ Open Vol. 4, no. 3 (2014), p. 1-14
- Full Text:
- Reviewed:
- Description: Introduction: Inequalities are evident in early childhood caries rates with the socially disadvantaged experiencing greater burden of disease. This study builds on formative qualitative research, conducted in the Moreland/Hume local government areas of Melbourne, Victoria 2006-2009, in response to community concerns for oral health of children from refugee and migrant backgrounds. Development of the community-based intervention described here extends the partnership approach to cogeneration of contemporary evidence with continued and meaningful involvement of investigators, community, cultural and government partners. This trial aims to establish a model for child oral health promotion for culturally diverse communities in Australia. Methods and analysis: This is an exploratory trial implementing a community-based child oral health promotion intervention for Australian families from refugee and migrant backgrounds. Families from an Iraqi, Lebanese or Pakistani background with children aged 1-4 years, residing in metropolitan Melbourne, were invited to participate in the trial by peer educators from their respective communities using snowball and purposive sampling techniques. Target sample size was 600. Moreland, a culturally diverse, inner-urban metropolitan area of Melbourne, was chosen as the intervention site. The intervention comprised peer educator led community oral health education sessions and reorienting of dental health and family services through cultural Competency Organisational Review (CORe). Ethics and dissemination: Ethics approval for this trial was granted by the University of Melbourne Human Research Ethics Committee and the Department of Education and Early Childhood Development Research Committee. Study progress and output will be disseminated via periodic newsletters, peer-reviewed research papers, reports, community seminars and at National and International conferences. Trial registration number: Australian New Zealand Clinical Trials Registry (ACTRN12611000532909).
- Authors: Gibbs, Lisa , Waters, Elizabeth , De Silva, Andrea , Riggs, Elisha , Moore, Laurence , Armit, Christine , Johnson, Britt , Morris, Michal , Calache, Hanny , Gussy, Mark , Young, Dana , Tadic, Maryanne , Christian, Bradley , Gondal, Iqbal , Watt, Richard , Pradel, Veronika , Truong, Mandy , Gold, Lisa
- Date: 2014
- Type: Text , Journal article
- Relation: BMJ Open Vol. 4, no. 3 (2014), p. 1-14
- Full Text:
- Reviewed:
- Description: Introduction: Inequalities are evident in early childhood caries rates with the socially disadvantaged experiencing greater burden of disease. This study builds on formative qualitative research, conducted in the Moreland/Hume local government areas of Melbourne, Victoria 2006-2009, in response to community concerns for oral health of children from refugee and migrant backgrounds. Development of the community-based intervention described here extends the partnership approach to cogeneration of contemporary evidence with continued and meaningful involvement of investigators, community, cultural and government partners. This trial aims to establish a model for child oral health promotion for culturally diverse communities in Australia. Methods and analysis: This is an exploratory trial implementing a community-based child oral health promotion intervention for Australian families from refugee and migrant backgrounds. Families from an Iraqi, Lebanese or Pakistani background with children aged 1-4 years, residing in metropolitan Melbourne, were invited to participate in the trial by peer educators from their respective communities using snowball and purposive sampling techniques. Target sample size was 600. Moreland, a culturally diverse, inner-urban metropolitan area of Melbourne, was chosen as the intervention site. The intervention comprised peer educator led community oral health education sessions and reorienting of dental health and family services through cultural Competency Organisational Review (CORe). Ethics and dissemination: Ethics approval for this trial was granted by the University of Melbourne Human Research Ethics Committee and the Department of Education and Early Childhood Development Research Committee. Study progress and output will be disseminated via periodic newsletters, peer-reviewed research papers, reports, community seminars and at National and International conferences. Trial registration number: Australian New Zealand Clinical Trials Registry (ACTRN12611000532909).
- Gibbs, Lisa, De Silva, Andrea, Christian, Bradley, Gold, Lisa, Gussy, Mark, Moore, Laurence, Calache, Hanny, Young, Dana, Riggs, Elisha, Tadic, Maryanne, Watt, Richard, Gondal, Iqbal, Waters, Elizabeth
- Authors: Gibbs, Lisa , De Silva, Andrea , Christian, Bradley , Gold, Lisa , Gussy, Mark , Moore, Laurence , Calache, Hanny , Young, Dana , Riggs, Elisha , Tadic, Maryanne , Watt, Richard , Gondal, Iqbal , Waters, Elizabeth
- Date: 2016
- Type: Text , Journal article
- Relation: Community Dental Health Vol. 33, no. 2 (2016), p. 100-106
- Full Text: false
- Reviewed:
- Description: Early Childhood Caries (ECC) is the most common, preventable disease of childhood. It can affect children’s health and wellbeing and children from migrant families may be at greater risk of developing ECC. Objective: To describe ECC in children from migrant families, and explore possible influences. Basic research design: Cross-sectional analysis of caries data collected as baseline data for an oral health promotion study. Participants: The analysis sample included 630 1-4 year-old children clustered within 481 Iraqi, Lebanese and Pakistani families in Melbourne, Australia. Method: Child participants received a community-based visual dental examination. Parents completed a self-administered questionnaire on demographics, ethnicity, and oral health knowledge, behaviour and attitudes. Main outcome measure: Child caries experience. Bivariate associations between oral health behaviours and ethnicity were tested for significance using chi-square. Multivariate logistic regression analyses were performed to identify associations with ECC, adjusting for demographic variables and accounting for clustering by family. Results: Overall, 34% of children in the sample experienced caries (both non-cavitated and cavitated). For all caries lesions, parent’ length of residence in Australia, consumption of sweet drinks and parental education remained as independent predictors of child caries experience. Adding sugar to drinks was an additional risk factor for cavitation. Ethnicity was associated with some individual oral health behaviours suggesting cultural influences on health, however the relationship was not independent of other predictors. Conclusion: Culturally competent oral health promotion interventions should aim to support migrant families with young children, and focus on reducing sweet drink consumption. © BASCD 2016.
Teeth Tales : A community-based child oral health promotion trial with migrant families in Australia
- Gibbs, Lisa, Waters, Elizabeth, Christian, Bradley, Gold, Lisa, Young, Dana, De Silva, Andrea, Calache, Hanny, Gussy, Mark, Watt, Richard, Riggs, Elisha, Tadic, Maryanne, Hall, Martin, Gondal, Iqbal, Pradel, Veronika, Moore, Laurence
- Authors: Gibbs, Lisa , Waters, Elizabeth , Christian, Bradley , Gold, Lisa , Young, Dana , De Silva, Andrea , Calache, Hanny , Gussy, Mark , Watt, Richard , Riggs, Elisha , Tadic, Maryanne , Hall, Martin , Gondal, Iqbal , Pradel, Veronika , Moore, Laurence
- Date: 2015
- Type: Text , Journal article
- Relation: BMJ Open Vol. 5, no. 6 (2015), p. 1-13
- Relation: http://purl.org/au-research/grants/arc/LP100100223
- Full Text:
- Reviewed:
- Description: Objectives: The Teeth Tales trial aimed to establish a model for child oral health promotion for culturally diverse communities in Australia. Design: An exploratory trial implementing a communitybased child oral health promotion intervention for Australian families from migrant backgrounds. Mixed method, longitudinal evaluation. Setting: The intervention was based in Moreland, a culturally diverse locality in Melbourne, Australia. Participants: Families with 1-4-year-old children, self-identified as being from Iraqi, Lebanese or Pakistani backgrounds residing in Melbourne. Participants residing close to the intervention site were allocated to intervention. Intervention: The intervention was conducted over 5 months and comprised community oral health education sessions led by peer educators and follow-up health messages. Outcome measures: This paper reports on the intervention impacts, process evaluation and descriptive analysis of health, knowledge and behavioural changes 18 months after baseline data collection. Results: Significant differences in the Debris Index (OR=0.44 (0.22 to 0.88)) and the Modified Gingival Index (OR=0.34 (0.19 to 0.61)) indicated increased tooth brushing and/or improved toothbrushing technique in the intervention group. An increased proportion of intervention parents, compared to those in the comparison group reported that they had been shown how to brush their child's teeth (OR=2.65 (1.49 to 4.69)). Process evaluation results highlighted the problems with recruitment and retention of the study sample (275 complete case families). The child dental screening encouraged involvement in the study, as did linking attendance with other community/cultural activities. Conclusions: The Teeth Tales intervention was promising in terms of improving oral hygiene and parent knowledge of tooth brushing technique. Adaptations to delivery of the intervention are required to increase uptake and likely impact. A future cluster randomised controlled trial would provide strongest evidence of effectiveness if appropriate to the community, cultural and economic context.
Teeth Tales : A community-based child oral health promotion trial with migrant families in Australia
- Authors: Gibbs, Lisa , Waters, Elizabeth , Christian, Bradley , Gold, Lisa , Young, Dana , De Silva, Andrea , Calache, Hanny , Gussy, Mark , Watt, Richard , Riggs, Elisha , Tadic, Maryanne , Hall, Martin , Gondal, Iqbal , Pradel, Veronika , Moore, Laurence
- Date: 2015
- Type: Text , Journal article
- Relation: BMJ Open Vol. 5, no. 6 (2015), p. 1-13
- Relation: http://purl.org/au-research/grants/arc/LP100100223
- Full Text:
- Reviewed:
- Description: Objectives: The Teeth Tales trial aimed to establish a model for child oral health promotion for culturally diverse communities in Australia. Design: An exploratory trial implementing a communitybased child oral health promotion intervention for Australian families from migrant backgrounds. Mixed method, longitudinal evaluation. Setting: The intervention was based in Moreland, a culturally diverse locality in Melbourne, Australia. Participants: Families with 1-4-year-old children, self-identified as being from Iraqi, Lebanese or Pakistani backgrounds residing in Melbourne. Participants residing close to the intervention site were allocated to intervention. Intervention: The intervention was conducted over 5 months and comprised community oral health education sessions led by peer educators and follow-up health messages. Outcome measures: This paper reports on the intervention impacts, process evaluation and descriptive analysis of health, knowledge and behavioural changes 18 months after baseline data collection. Results: Significant differences in the Debris Index (OR=0.44 (0.22 to 0.88)) and the Modified Gingival Index (OR=0.34 (0.19 to 0.61)) indicated increased tooth brushing and/or improved toothbrushing technique in the intervention group. An increased proportion of intervention parents, compared to those in the comparison group reported that they had been shown how to brush their child's teeth (OR=2.65 (1.49 to 4.69)). Process evaluation results highlighted the problems with recruitment and retention of the study sample (275 complete case families). The child dental screening encouraged involvement in the study, as did linking attendance with other community/cultural activities. Conclusions: The Teeth Tales intervention was promising in terms of improving oral hygiene and parent knowledge of tooth brushing technique. Adaptations to delivery of the intervention are required to increase uptake and likely impact. A future cluster randomised controlled trial would provide strongest evidence of effectiveness if appropriate to the community, cultural and economic context.
Optimally parameterized wavelet packet transform for machine residual life prediction
- Gondal, Iqbal, Yaqub, Muhammad, Kamruzzaman, Joarder
- Authors: Gondal, Iqbal , Yaqub, Muhammad , Kamruzzaman, Joarder
- Date: 2011
- Type: Text , Conference paper , Journal article
- Relation: Australian Acoustical SocietyConference 2011: Breaking New Ground, Acoustics 2011; Gold Coast, Australia; 2nd-4th November 2011; p.1-8
- Full Text:
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- Description: One of the prevalent issues in condition based maintenance (CBM) is to predict the residual life of the equipment. This paper propos-es a novel framework to predict the remnant life of the equipment, called Residual life prediction based on optimally parameterized Wavelet transform and Mute-step Support vector regression (RWMS). In optimally parameterized wavelet transform, a generalized criterion is proposed to select the wavelet decomposition level which works for all the applications and decomposition nodes are selected by characterizing their dominancy level based upon relative fault signature-signal energy contents. The prediction model is based on multi-step support vector regression (MSVR) and prediction accuracy is improved in comparison with the techniques based on support vector regression (SVR). Performance of RWMS is evaluated in terms of Root Means Square Error (RMSE), studies show that proposed algorithm predicts the residual life of the equipment accurately.
- Authors: Gondal, Iqbal , Yaqub, Muhammad , Kamruzzaman, Joarder
- Date: 2011
- Type: Text , Conference paper , Journal article
- Relation: Australian Acoustical SocietyConference 2011: Breaking New Ground, Acoustics 2011; Gold Coast, Australia; 2nd-4th November 2011; p.1-8
- Full Text:
- Reviewed:
- Description: One of the prevalent issues in condition based maintenance (CBM) is to predict the residual life of the equipment. This paper propos-es a novel framework to predict the remnant life of the equipment, called Residual life prediction based on optimally parameterized Wavelet transform and Mute-step Support vector regression (RWMS). In optimally parameterized wavelet transform, a generalized criterion is proposed to select the wavelet decomposition level which works for all the applications and decomposition nodes are selected by characterizing their dominancy level based upon relative fault signature-signal energy contents. The prediction model is based on multi-step support vector regression (MSVR) and prediction accuracy is improved in comparison with the techniques based on support vector regression (SVR). Performance of RWMS is evaluated in terms of Root Means Square Error (RMSE), studies show that proposed algorithm predicts the residual life of the equipment accurately.
Social-connectivity-aware vertical handover for heterogeneous wireless networks
- Haider, Ammar, Gondal, Iqbal, Kamruzzaman, Joarder
- Authors: Haider, Ammar , Gondal, Iqbal , Kamruzzaman, Joarder
- Date: 2013
- Type: Text , Journal article
- Relation: Journal Of Network And Computer Applications Vol. 36, no. 4 (2013), p. 1131-1139
- Full Text: false
- Reviewed:
- Description: Vertical handover mechanism for a WLAN-cellular heterogeneous network could be made efficient with the use of context aware admission control strategy. Existing admission control methods aim to provide satisfactory quality of service, but rely solely on the availability of wireless resources in the target network. We propose that the admission control in WLAN should make use of social connectivity context of users in its coverage area to classify local and global traffic. In this paper, we introduce a novel Social-Connectivity-aware Vertical Handover (SCVH) scheme, which performs admission control using connectivity graph data from the online social networking services. A higher importance of visiting node for users resident in WLAN, advocates a higher priority for granting admission. We employ graph-theoretic concept of centrality to calculate the social importance of potential handing-over nodes. By giving handover precedence to higher-centrality nodes, we achieve an optimal allocation of wireless resources in addition to improved quality of service. The proposed handover strategy offers an additional advantage of reducing global social network traffic.
Decentralized content sharing among tourists in visiting hotspots
- Kaisar, Shahriar, Kamruzzaman, Joarder, Karmakar, Gour, Gondal, Iqbal
- Authors: Kaisar, Shahriar , Kamruzzaman, Joarder , Karmakar, Gour , Gondal, Iqbal
- Date: 2017
- Type: Text , Journal article
- Relation: Journal of Network and Computer Applications Vol. 79, no. (2017), p. 25-40
- Full Text:
- Reviewed:
- Description: Content sharing with smart mobile devices using decentralized approach enables users to share contents without the use of any fixed infrastructure, and thereby offers a free-of-cost platform that does not add to Internet traffic which, in its current state, is approaching bottleneck in its capacity. Most of the existing decentralized approaches in the literature consider spatio-temporal regularity in human movement patterns and pre-existing social relationship for the sharing scheme to work. However, such predictable movement patterns and social relationship information are not available in places like tourist spots where people visit only for a short period of time and usually meet strangers. No works exist in literature that deals with content sharing in such environment. In this work, we propose a content sharing approach for such environments. The group formation mechanism is based on users' interest score and stay probability in the individual region of interest (ROI) as well as on the availability and delivery probabilities of contents in the group. The administrator of each group is selected by taking into account its probability of stay in the ROI, connectivity with other nodes, its trustworthiness and computing and energy resources to serve the group. We have also adopted an incentive mechanism as encouragement that awards nodes for sharing and forwarding contents. We have used network simulator NS3 to perform extensive simulation on a popular tourist spot in Australia which facilitates a number of activities. The proposed approach shows promising results in sharing contents among tourists, measured in terms of content hit, delivery success rate and latency.
- Description: Content sharing with smart mobile devices using decentralized approach enables users to share contents without the use of any fixed infrastructure, and thereby offers a free-of-cost platform that does not add to Internet traffic which, in its current state, is approaching bottleneck in its capacity. Most of the existing decentralized approaches in the literature consider spatio-temporal regularity in human movement patterns and pre-existing social relationship for the sharing scheme to work. However, such predictable movement patterns and social relationship information are not available in places like tourist spots where people visit only for a short period of time and usually meet strangers. No works exist in literature that deals with content sharing in such environment. In this work, we propose a content sharing approach for such environments. The group formation mechanism is based on users' interest score and stay probability in the individual region of interest (ROI) as well as on the availability and delivery probabilities of contents in the group. The administrator of each group is selected by taking into account its probability of stay in the ROI, connectivity with other nodes, its trustworthiness and computing and energy resources to serve the group. We have also adopted an incentive mechanism as encouragement that awards nodes for sharing and forwarding contents. We have used network simulator NS3 to perform extensive simulation on a popular tourist spot in Australia which facilitates a number of activities. The proposed approach shows promising results in sharing contents among tourists, measured in terms of content hit, delivery success rate and latency. © 2016
- Authors: Kaisar, Shahriar , Kamruzzaman, Joarder , Karmakar, Gour , Gondal, Iqbal
- Date: 2017
- Type: Text , Journal article
- Relation: Journal of Network and Computer Applications Vol. 79, no. (2017), p. 25-40
- Full Text:
- Reviewed:
- Description: Content sharing with smart mobile devices using decentralized approach enables users to share contents without the use of any fixed infrastructure, and thereby offers a free-of-cost platform that does not add to Internet traffic which, in its current state, is approaching bottleneck in its capacity. Most of the existing decentralized approaches in the literature consider spatio-temporal regularity in human movement patterns and pre-existing social relationship for the sharing scheme to work. However, such predictable movement patterns and social relationship information are not available in places like tourist spots where people visit only for a short period of time and usually meet strangers. No works exist in literature that deals with content sharing in such environment. In this work, we propose a content sharing approach for such environments. The group formation mechanism is based on users' interest score and stay probability in the individual region of interest (ROI) as well as on the availability and delivery probabilities of contents in the group. The administrator of each group is selected by taking into account its probability of stay in the ROI, connectivity with other nodes, its trustworthiness and computing and energy resources to serve the group. We have also adopted an incentive mechanism as encouragement that awards nodes for sharing and forwarding contents. We have used network simulator NS3 to perform extensive simulation on a popular tourist spot in Australia which facilitates a number of activities. The proposed approach shows promising results in sharing contents among tourists, measured in terms of content hit, delivery success rate and latency.
- Description: Content sharing with smart mobile devices using decentralized approach enables users to share contents without the use of any fixed infrastructure, and thereby offers a free-of-cost platform that does not add to Internet traffic which, in its current state, is approaching bottleneck in its capacity. Most of the existing decentralized approaches in the literature consider spatio-temporal regularity in human movement patterns and pre-existing social relationship for the sharing scheme to work. However, such predictable movement patterns and social relationship information are not available in places like tourist spots where people visit only for a short period of time and usually meet strangers. No works exist in literature that deals with content sharing in such environment. In this work, we propose a content sharing approach for such environments. The group formation mechanism is based on users' interest score and stay probability in the individual region of interest (ROI) as well as on the availability and delivery probabilities of contents in the group. The administrator of each group is selected by taking into account its probability of stay in the ROI, connectivity with other nodes, its trustworthiness and computing and energy resources to serve the group. We have also adopted an incentive mechanism as encouragement that awards nodes for sharing and forwarding contents. We have used network simulator NS3 to perform extensive simulation on a popular tourist spot in Australia which facilitates a number of activities. The proposed approach shows promising results in sharing contents among tourists, measured in terms of content hit, delivery success rate and latency. © 2016
A smart priority-based traffic control system for emergency vehicles
- Karmakar, Gour, Chowdhury, Abdullahi, Kamruzzaman, Joarder, Gondal, Iqbal
- Authors: Karmakar, Gour , Chowdhury, Abdullahi , Kamruzzaman, Joarder , Gondal, Iqbal
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Sensors Journal Vol. 21, no. 14 (2021), p. 15849-15858
- Full Text: false
- Reviewed:
- Description: Unwanted events on roads, such as incidents and increased traffic jams, can cause human lives and economic loss. For efficient incident management, it is essential to send Emergency Vehicles (EVs) to the incident place as quickly as possible. To reduce incidence clearance time, several approaches exist to provide a clear pathway to EVs mainly fitted with RFID sensors in the urban areas. However, they neither assign priority to the EVs based on the type and severity of an incident nor consider the effect on other on-road traffic. To address this issue, in this paper, we introduce an Emergency Vehicle Priority System (EVPS) by determining the priority level of an EV based on the type and the severity of an incident, and estimating the number of necessary signal interventions while considering the impact of those interventions on the traffic in the roads surrounding the EV's travel path. We present how EVPS determines the priority code and a new algorithm to estimate the number of green signal interventions to attain the quickest incident response while concomitantly reducing impact on others. A simulation model is developed in Simulation of Urban Mobility (SUMO) using the real traffic data of Melbourne, Australia, captured by various sensors. Results show that our system recommends appropriate number of intervention that can reduce emergency response time significantly. © 2001-2012 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
- 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.
- 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.
Malware detection in edge devices with fuzzy oversampling and dynamic class weighting
- Khoda, Mahbub, Kamruzzaman, Joarder, Gondal, Iqbal, Imam, Tasadduq, Rahman, Ashfaqur
- Authors: Khoda, Mahbub , Kamruzzaman, Joarder , Gondal, Iqbal , Imam, Tasadduq , Rahman, Ashfaqur
- Date: 2021
- Type: Text , Journal article
- Relation: Applied Soft Computing Vol. 112, no. (2021), p.
- Full Text: false
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- Description: In Internet-of-things (IoT) domain, edge devices are used increasingly for data accumulation, preprocessing, and analytics. Intelligent integration of edge devices with Artificial Intelligence (AI) facilitates real-time analysis and decision making. However, these devices simultaneously provide additional attack opportunities for malware developers, potentially leading to information and financial loss. Machine learning approaches can detect such attacks but their performance degrades when benign samples substantially outnumber malware samples in training data. Existing approaches for such imbalanced data assume samples represented as continuous features and thus can generate invalid samples when malware applications are represented by binary features. We propose a novel malware oversampling technique that addresses this issue. Further, we propose two approaches for malware detection. Our first approach uses fuzzy set theory, while the second approach dynamically assigns higher priority to malware samples using a novel loss function. Combining our oversampling technique with these approaches, the proposed approach attains over 9% improvement over competing methods in terms of F1_score. Our approaches can, therefore, result in enhanced privacy and security in edge computing services. © 2021 Elsevier B.V.
Hybrid intrusion detection system based on the stacking ensemble of C5 decision tree classifier and one class support vector machine
- Khraisat, Ansam, Gondal, Iqbal, Vamplew, Peter, Kamruzzaman, Joarder, Alazab, Ammar
- Authors: Khraisat, Ansam , Gondal, Iqbal , Vamplew, Peter , Kamruzzaman, Joarder , Alazab, Ammar
- Date: 2020
- Type: Text , Journal article
- Relation: Electronics (Switzerland) Vol. 9, no. 1 (2020), p.
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- Description: Cyberttacks are becoming increasingly sophisticated, necessitating the efficient intrusion detection mechanisms to monitor computer resources and generate reports on anomalous or suspicious activities. Many Intrusion Detection Systems (IDSs) use a single classifier for identifying intrusions. Single classifier IDSs are unable to achieve high accuracy and low false alarm rates due to polymorphic, metamorphic, and zero-day behaviors of malware. In this paper, a Hybrid IDS (HIDS) is proposed by combining the C5 decision tree classifier and One Class Support Vector Machine (OC-SVM). HIDS combines the strengths of SIDS) and Anomaly-based Intrusion Detection System (AIDS). The SIDS was developed based on the C5.0 Decision tree classifier and AIDS was developed based on the one-class Support Vector Machine (SVM). This framework aims to identify 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 benchmark datasets, namely, Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) and Australian Defence Force Academy (ADFA) datasets. Studies show that the performance of HIDS is enhanced, compared to SIDS and AIDS in terms of detection rate and low false-alarm rates. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
- Authors: Khraisat, Ansam , Gondal, Iqbal , Vamplew, Peter , Kamruzzaman, Joarder , Alazab, Ammar
- Date: 2020
- Type: Text , Journal article
- Relation: Electronics (Switzerland) Vol. 9, no. 1 (2020), p.
- Full Text:
- Reviewed:
- Description: Cyberttacks are becoming increasingly sophisticated, necessitating the efficient intrusion detection mechanisms to monitor computer resources and generate reports on anomalous or suspicious activities. Many Intrusion Detection Systems (IDSs) use a single classifier for identifying intrusions. Single classifier IDSs are unable to achieve high accuracy and low false alarm rates due to polymorphic, metamorphic, and zero-day behaviors of malware. In this paper, a Hybrid IDS (HIDS) is proposed by combining the C5 decision tree classifier and One Class Support Vector Machine (OC-SVM). HIDS combines the strengths of SIDS) and Anomaly-based Intrusion Detection System (AIDS). The SIDS was developed based on the C5.0 Decision tree classifier and AIDS was developed based on the one-class Support Vector Machine (SVM). This framework aims to identify 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 benchmark datasets, namely, Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) and Australian Defence Force Academy (ADFA) datasets. Studies show that the performance of HIDS is enhanced, compared to SIDS and AIDS in terms of detection rate and low false-alarm rates. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
A novel ensemble of hybrid intrusion detection system for detecting internet of things attacks
- Khraisat, Ansam, Gondal, Iqbal, Vamplew, Peter, Kamruzzaman, Joarder, Alazab, Ammar
- 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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- Reviewed:
- 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.
- 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.
- Full Text:
- Reviewed:
- 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.
The role of love stories in Romance Scams : A qualitative analysis of fraudulent profiles
- Kopp, Christian, Layton, Robert, Sillitoe, Jim, Gondal, Iqbal
- Authors: Kopp, Christian , Layton, Robert , Sillitoe, Jim , Gondal, Iqbal
- Date: 2016
- Type: Text , Journal article
- Relation: International Journal of Cyber Criminology Vol. 9, no. 2 (2016), p. 205-216
- Full Text:
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- Description: The Online Romance Scam is a very successful scam which causes considerable financial and emotional damage to its victims. In this paper, we provide a perspective that might be helpful to explain the success of this scam. In a similar way to the "The Nigerian letter", we propose that the scam techniques appeal to strong emotions, which are clearly involved in Romantic relationships. We also assume that the same success factors found in normal relationships contribute to the success of the romance scam. In an exploratory study using a qualitative analysis of fraudulent profiles from an international dating website, we examined this assumption. The findings show that personal affinities related to personal romantic imaginations, which are described by personal love stories, play an important role in the success of a romance scam. © 2016 International Journal of Cyber Criminology (IJCC).
- Authors: Kopp, Christian , Layton, Robert , Sillitoe, Jim , Gondal, Iqbal
- Date: 2016
- Type: Text , Journal article
- Relation: International Journal of Cyber Criminology Vol. 9, no. 2 (2016), p. 205-216
- Full Text:
- Reviewed:
- Description: The Online Romance Scam is a very successful scam which causes considerable financial and emotional damage to its victims. In this paper, we provide a perspective that might be helpful to explain the success of this scam. In a similar way to the "The Nigerian letter", we propose that the scam techniques appeal to strong emotions, which are clearly involved in Romantic relationships. We also assume that the same success factors found in normal relationships contribute to the success of the romance scam. In an exploratory study using a qualitative analysis of fraudulent profiles from an international dating website, we examined this assumption. The findings show that personal affinities related to personal romantic imaginations, which are described by personal love stories, play an important role in the success of a romance scam. © 2016 International Journal of Cyber Criminology (IJCC).
“I am your perfect online partner" analysis of dating profiles used in cybercrime
- Kopp, Christian, Sillitoe, James, Gondal, Iqbal
- Authors: Kopp, Christian , Sillitoe, James , Gondal, Iqbal
- Date: 2017
- Type: Text , Journal article
- Relation: Asia Pacific Journal of Advanced Business and Social Studies Vol. 3, no. 2 (2017), p. 207-217
- Full Text:
- Reviewed:
- Description: Internet Online Dating has become an influential mainstream social practice facilitating the finding of a partner. Unscrupulous operators have identified its potential and started to use this platform for identity theft in form of so called Online Romance Scams. Quickly, this cybercrime has become very successful and thus, an increasing threat in the social networking environment. So far, very little is known about its structure and the reason for its success, and this needs to be known in order to be able to fight it efficiently. This research tries to contribute to this knowledge, and argues that scammers use so-called ‘Love Stories’, which represent personal affinities related to romantic relationships, to their benefit when tailoring common narratives as part of fraudulent online profiles to attract their victims. We look at these different types of ‘Personal Love Stories’ and discuss how they can be used in this type of scam, followed by a qualitative analysis of fraudulent profiles from three different international websites to examine this assumption
- Authors: Kopp, Christian , Sillitoe, James , Gondal, Iqbal
- Date: 2017
- Type: Text , Journal article
- Relation: Asia Pacific Journal of Advanced Business and Social Studies Vol. 3, no. 2 (2017), p. 207-217
- Full Text:
- Reviewed:
- Description: Internet Online Dating has become an influential mainstream social practice facilitating the finding of a partner. Unscrupulous operators have identified its potential and started to use this platform for identity theft in form of so called Online Romance Scams. Quickly, this cybercrime has become very successful and thus, an increasing threat in the social networking environment. So far, very little is known about its structure and the reason for its success, and this needs to be known in order to be able to fight it efficiently. This research tries to contribute to this knowledge, and argues that scammers use so-called ‘Love Stories’, which represent personal affinities related to romantic relationships, to their benefit when tailoring common narratives as part of fraudulent online profiles to attract their victims. We look at these different types of ‘Personal Love Stories’ and discuss how they can be used in this type of scam, followed by a qualitative analysis of fraudulent profiles from three different international websites to examine this assumption
Wake-up timer and binary exponential backoff for ZigBee-based wireless sensor network for flexible movement control system of a self-lifting scaffold
- Liang, Hua, Yang, Guangxiang, Xu, Ye, Gondal, Iqbal, Wu, Chao
- Authors: Liang, Hua , Yang, Guangxiang , Xu, Ye , Gondal, Iqbal , Wu, Chao
- Date: 2016
- Type: Text , Journal article
- Relation: International Journal of Distributed Sensor Networks Vol. 12, no. 9 (2016), p. 1-12
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- Description: Synchronous movement of attached self-lifting scaffolds is traditionally monitored with wired sensors in high-rise building construction, which limits their flexibility of movements. A ZigBee-based wireless sensor system has been suggested in this article to prove the effectiveness of wireless sensor networks in actual implementation. Two optoelectronic sensors are integrated into a ZigBee node for measuring the displacement of attached self-lifting scaffolds. The proposed wireless sensor network combines an end device and a coordinator to allow easy replacement of sensors as compared to a wired network. A wake-up timer algorithm is proposed to reduce the transmitting power during continuous wireless data communication in the wireless sensor network. Furthermore, a variant binary exponential backoff transmission algorithm for data loss avoidance is proposed. The variant binary exponential backoff algorithm reduces packet collisions during simultaneous access by increasing the randomizing moments at nodes attempting to access the wireless channels. The performance of three of the proposed modules - a cable sensor, a 315-MHz sensor, and a ZigBee sensor - is evaluated in terms of packet delivery ratio and the end-to-end delay of a ZigBee-based wireless sensor network. The experimental results show that the proposed variant binary exponential backoff transmission algorithm achieves a higher packet delivery ratio at the cost of higher delays. The average cost of the developed ZigBee-based wireless sensor network decreased by 24% compared with the cable sensor. The power consumption of ZigBee is approximately 53.75% of the 315-MHz sensor. The average current consumption is reduced by approximately 1.5 mA with the wake-up timer algorithm at the same sampling rate. © The Author(s) 2016.
- Authors: Liang, Hua , Yang, Guangxiang , Xu, Ye , Gondal, Iqbal , Wu, Chao
- Date: 2016
- Type: Text , Journal article
- Relation: International Journal of Distributed Sensor Networks Vol. 12, no. 9 (2016), p. 1-12
- Full Text:
- Reviewed:
- Description: Synchronous movement of attached self-lifting scaffolds is traditionally monitored with wired sensors in high-rise building construction, which limits their flexibility of movements. A ZigBee-based wireless sensor system has been suggested in this article to prove the effectiveness of wireless sensor networks in actual implementation. Two optoelectronic sensors are integrated into a ZigBee node for measuring the displacement of attached self-lifting scaffolds. The proposed wireless sensor network combines an end device and a coordinator to allow easy replacement of sensors as compared to a wired network. A wake-up timer algorithm is proposed to reduce the transmitting power during continuous wireless data communication in the wireless sensor network. Furthermore, a variant binary exponential backoff transmission algorithm for data loss avoidance is proposed. The variant binary exponential backoff algorithm reduces packet collisions during simultaneous access by increasing the randomizing moments at nodes attempting to access the wireless channels. The performance of three of the proposed modules - a cable sensor, a 315-MHz sensor, and a ZigBee sensor - is evaluated in terms of packet delivery ratio and the end-to-end delay of a ZigBee-based wireless sensor network. The experimental results show that the proposed variant binary exponential backoff transmission algorithm achieves a higher packet delivery ratio at the cost of higher delays. The average cost of the developed ZigBee-based wireless sensor network decreased by 24% compared with the cable sensor. The power consumption of ZigBee is approximately 53.75% of the 315-MHz sensor. The average current consumption is reduced by approximately 1.5 mA with the wake-up timer algorithm at the same sampling rate. © The Author(s) 2016.
Dynamic resource allocation for improved QoS in WiMAX/WiFi integration
- Rabbani, Md, Kamruzzaman, Joarder, Gondal, Iqbal, Ahmad, Iftekhar, Hassan, Md Rafiul
- Authors: Rabbani, Md , Kamruzzaman, Joarder , Gondal, Iqbal , Ahmad, Iftekhar , Hassan, Md Rafiul
- Date: 2011
- Type: Text , Journal article
- Relation: Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2011 (Studies in Computational Intelligence series) Vol. 368, no. 2011 (2011), p. 141-156
- Full Text: false
- Reviewed:
- Description: Wireless access technology has come a long way in its relatively short but remarkable lifetime, which has so far been led by WiFi technology. WiFi enjoys a high penetration in the market.Most of the electronic gadgets such as laptop, notepad, mobile set, etc., boast the provision ofWiFi. Currently most WiFi hotspots are connected to the Internet via wired connections (e.g., Ethernet), and the deployment cost of wired connection is high. On the other hand, since WiMAX can provide a high coverage area and transmission bandwidth, it is very suitable for the backbone networks of WiFi. WiMAX can also provide the better QoS needed for many 4G applications. WiMAX devices, however, are not as common as WiFi devices and it is also expensive to deploy aWiMAX-only infrastructure. An integrated WiMAX/WiFi architecture (using WiMAX as backhaul connection for WiFi) can support 4G applications with QoS assurance and mobility, and provide high-speed broadband services in rural, regional and urban areas while reducing the backhaul cost. WiMAX and WiFi have different MAC mechanisms to handle QoS. WiMAX MAC architecture is connection-oriented providing the platform for strong QoS control. In contrast,WiFi MAC is not connection-oriented, hence can provide only best effort services. Delivering improved QoS in an integrated WiMAX/WiFi architecture poses a serious technological challenge. The paper depicts a converged architecture of WiMAX and WiFi, and then proposes an adaptive resource distribution model for the access points. The resource distribution model ultimately allocates more time slots to those connections that need more instantaneous resources to meet QoS requirements. A dynamic splitting technique is also presented that divides the total transmission period into downlink and uplink transmission by taking the minimum data rate requirements of the connections into account. This ultimately improves the utilization of the available resources, and the QoS of the connections. Simulation results show that the proposed schemes significantly outperform the other existing resource sharing schemes, in terms of maintaining QoS of different traffic classes in an integratedWiMAX/WiFi architecture