Showing items 1 - 20 of 95

Your selections:

A patient agent to manage blockchains for remote patient monitoring

- Uddin, Ashraf, Stranieri, Andrew, Gondal, Iqbal, Balasubramanian, Venki

  • Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
  • Date: 2018
  • Type: Text , Conference proceedings , Conference Paper
  • Relation: 7th International Conference on Global Telehealth, GT 2018; Colombo, Sri Lanka; 10th-11th October 2018; published in Studies in Health Technology and Informatics Vol. 254, p. 105-115
  • Full Text: false
  • Reviewed:
  • Description: Continuous monitoring of patient's physiological signs has the potential to augment traditional medical practice, particularly in developing countries that have a shortage of healthcare professionals. However, continuously streamed data presents additional security, storage and retrieval challenges and further inhibits initiatives to integrate data to form electronic health record systems. Blockchain technologies enable data to be stored securely and inexpensively without recourse to a trusted authority. Blockchain technologies also promise to provide architectures for electronic health records that do not require huge government expenditure that challenge developing nations. However, Blockchain deployment, particularly with streamed data challenges existing Blockchain algorithms that take too long to place data in a block, and have no mechanism to determine whether every data point in every stream should be stored in such a secure way. This article presents an architecture that involves a Patient Agent, coordinating the insertion of continuous data streams into Blockchains to form an electronic health record.
  • Description: Studies in Health Technology and Informatics

Continuous patient monitoring with a patient centric agent : A block architecture

- Uddin, Ashraf, Stranieri, Andrew, Gondal, Iqbal, Balasubramanian, Venki

  • Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
  • Date: 2018
  • Type: Text , Journal article
  • Relation: IEEE Access Vol. 6, no. (2018), p. 32700-32726
  • Full Text:
  • Reviewed:
  • Description: The Internet of Things (IoT) has facilitated services without human intervention for a wide range of applications, including continuous remote patient monitoring (RPM). However, the complexity of RPM architectures, the size of data sets generated and limited power capacity of devices make RPM challenging. In this paper, we propose a tier-based End to End architecture for continuous patient monitoring that has a patient centric agent (PCA) as its center piece. The PCA manages a blockchain component to preserve privacy when data streaming from body area sensors needs to be stored securely. The PCA based architecture includes a lightweight communication protocol to enforce security of data through different segments of a continuous, real time patient monitoring architecture. The architecture includes the insertion of data into a personal blockchain to facilitate data sharing amongst healthcare professionals and integration into electronic health records while ensuring privacy is maintained. The blockchain is customized for RPM with modifications that include having the PCA select a Miner to reduce computational effort, enabling the PCA to manage multiple blockchains for the same patient, and the modification of each block with a prefix tree to minimize energy consumption and incorporate secure transaction payments. Simulation results demonstrate that security and privacy can be enhanced in RPM with the PCA based End to End architecture.

A Cultural Competence Organizational Review for community health services : Insights from a participatory approach

- Truong, Mandy, Gibbs, Lisa, Pradel, Veronika, Morris, Michal, Gwatirisa, Pauline, Tadic, Maryanne, De Silva, Andrea, Hall, Martin, Young, Dana, Riggs, Elisha, Calache, Hanny, Gussy, Mark, Watt, Richard, Gondal, Iqbal, Waters, Elizabeth

  • Authors: Truong, Mandy , Gibbs, Lisa , Pradel, Veronika , Morris, Michal , Gwatirisa, Pauline , Tadic, Maryanne , De Silva, Andrea , Hall, Martin , Young, Dana , Riggs, Elisha , Calache, Hanny , Gussy, Mark , Watt, Richard , Gondal, Iqbal , Waters, Elizabeth
  • Date: 2017
  • Type: Text , Journal article
  • Relation: Health Promotion Practice Vol. 18, no. 3 (2017), p. 466-475
  • Full Text: false
  • Reviewed:
  • Description: Cultural competence is an important aspect of health service access and delivery in health promotion and community health. Although a number of frameworks and tools are available to assist health service organizations improve their services to diverse communities, there are few published studies describing organizational cultural competence assessments and the extent to which these tools facilitate cultural competence. This article addresses this gap by describing the development of a cultural competence assessment, intervention, and evaluation tool called the Cultural Competence Organizational Review (CORe) and its implementation in three community sector organizations. Baseline and follow-up staff surveys and document audits were conducted at each participating organization. Process data and organizational documentation were used to evaluate and monitor the experience of CORe within the organizations. Results at follow-up indicated an overall positive trend in organizational cultural competence at each organization in terms of both policy and practice. Organizations that are able to embed actions to improve organizational cultural competence within broader organizational plans increase the likelihood of sustainable changes to policies, procedures, and practice within the organization. The benefits and lessons learned from the implementation of CORe are discussed. © 2017, Society for Public Health Education.

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: false
  • 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. © 2016

Dependable large scale behavioral patterns mining from sensor data using Hadoop platform

- Rashid, Md. Mamunur, Gondal, Iqbal, Kamruzzaman, Joarder

  • Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2017
  • Type: Text , Journal article
  • Relation: Information Sciences Vol. 379, no. (2017), p. 128-145
  • Full Text: false
  • Reviewed:
  • Description: Wireless sensor networks (WSNs) will be an integral part of the future Internet of Things (loT) environment and generate large volumes of data. However, these data would only be of benefit if useful knowledge can be mined from them. A data mining framework for WSNs includes data extraction, storage and mining techniques, and must be efficient and dependable. In this paper, we propose a new type of behavioral pattern mining technique from sensor data called regularly frequent sensor patterns (RFSPs). RFSPs can identify a set of temporally correlated sensors which can reveal significant knowledge from the monitored data. A distributed data extraction model to prepare the data required for mining RFSPs is proposed, as the distributed scheme ensures higher availability through greater redundancy. The tree structure for RFSP is compact requires less memory and can be constructed using only a single scan through the dataset, and the mining technique is efficient with low runtime. Current mining techniques in the literature on sensor data employ a single memory-based sequential approach and hence are not efficient. Moreover, usage of the. MapReduce model for the distributed solution has not been explored extensively. Since MapReduce is becoming the de facto model for computation on large data, we also propose a parallel implementation of the RFSP mining algorithm, called RFSP on Hadoop (RFSP-H), which uses a MapReduce-based framework to gain further efficiency. Experiments conducted to evaluate the compactness and performance of the data extraction model, RFSP-tree and RFSP-H mining show improved results. (C) 2016 Elsevier Inc. All rights reserved.

Dynamic content distribution for decentralized sharing in tourist spots using demand and supply

- Kamruzzaman, Joarder, Karmakar, Gour, Gondal, Iqbal, Kaisar, Shahriar

  • Authors: Kamruzzaman, Joarder , Karmakar, Gour , Gondal, Iqbal , Kaisar, Shahriar
  • Date: 2017
  • Type: Text , Conference proceedings
  • Relation: 13th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2017; Valencia, Spain; 26th-30th June 2016 p. 2121-2126
  • Full Text: false
  • Reviewed:
  • Description: Decentralized content sharing (DCS) is emerging as an important platform for sharing contents among smart mobile device users, where devices form an ad-hoc network and communicate opportunistically. Existing DCS approaches for tourist spot like scenarios achieve low delivery success rate and high latency as they do not focus on dynamic demand for contents which usually vary considerably with the number of visitors present or occurrence of some influencing events. The amount of available supply also changes because of the nodes leaving the area. Only way to improve content delivery service is to distribute the contents in strategic positions based on dynamic demand and supply. In this paper, we propose a dynamic content distribution (DCD) method considering dynamic demand and supply for contents in tourist spots. Simulation results validate the improvement of the proposed approach. © 2017 IEEE.
  • Description: 2017 13th International Wireless Communications and Mobile Computing Conference, IWCMC 2017

Improving authorship attribution in twitter through topic-based sampling

- Pan, Luoxi, Gondal, Iqbal, Layton, Robert

  • Authors: Pan, Luoxi , Gondal, Iqbal , Layton, Robert
  • Date: 2017
  • Type: Text , Conference proceedings
  • Relation: 30th Australasian Joint Conference on Artificial Intelligence, AI 2017 : Advances in Artificial Intelligence; Melbourne, Australia; 19th-20th August 2017; published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10400 LNAI, p. 250-261
  • Full Text: false
  • Reviewed:
  • Description: Aliases are used as a means of anonymity on the Internet in environments such as IRC (internet relay chat), forums and micro-blogging websites such as Twitter. While there are genuine reasons for the use of aliases, such as journalists operating in politically oppressive countries, they are increasingly being used by cybercriminals and extremist organisations. In recent years, we have seen increased research on authorship attribution of Twitter messages, including authorship analysis of aliases. Previous studies have shown that anti-aliasing of randomly generated sub-aliases yields high accuracies when linking the sub-aliases, but become much less accurate when topic-based sub-aliases are used. N-gram methods have previously been demonstrated to perform better than other methods in this situation. This paper investigates the effect of topic-based sampling on authorship attribution accuracy for the popular micro-blogging website Twitter. Features are extracted using character n-grams, which accurately capture differences in authorship style. These features are analysed using support vector machines using a one-versus-all classifier. The predictive performance of the algorithm is then evaluated using two different sampling methodologies - authors that were sampled through a context-sensitive topic-based search and authors that were sampled randomly. Topic-based sampling of authors is found to produce more accurate authorship predictions. This paper presents several theories as to why this might be the case. © Springer International Publishing AG 2017.

Optimization based clustering algorithms for authorship analysis of phishing emails

- Seifollahi, Sattar, Bagirov, Adil, Layton, Robert, Gondal, Iqbal

  • Authors: Seifollahi, Sattar , Bagirov, Adil , Layton, Robert , Gondal, Iqbal
  • Date: 2017
  • Type: Text , Journal article
  • Relation: Neural Processing Letters Vol. 46, no. 2 (2017), p. 411-425
  • Relation: http://purl.org/au-research/grants/arc/DP140103213
  • Full Text: false
  • Reviewed:
  • Description: Phishing has given attackers power to masquerade as legitimate users of organizations, such as banks, to scam money and private information from victims. Phishing is so widespread that combating the phishing attacks could overwhelm the victim organization. It is important to group the phishing attacks to formulate effective defence mechanism. In this paper, we use clustering methods to analyze and characterize phishing emails and perform their relative attribution. Emails are first tokenized to a bag-of-word space and, then, transformed to a numeric vector space using frequencies of words in documents. Wordnet vocabulary is used to take effects of similar words into account and to reduce sparsity. The word similarity measure is combined with the term frequencies to introduce a novel text transformation into numeric features. To improve the accuracy, we apply inverse document frequency weighting, which gives higher weights to features used by fewer authors. The k-means and recently introduced three optimization based algorithms: MS-MGKM, INCA and DCClust are applied for clustering purposes. The optimization based algorithms indicate the existence of well separated clusters in the phishing emails dataset. © 2017, Springer Science+Business Media New York.

A data mining approach for machine fault diagnosis based on associated frequency patterns

- Rashid, Md. Mamunur, Amar, Muhammad, Gondal, Iqbal, Kamruzzaman, Joarder

  • Authors: Rashid, Md. Mamunur , Amar, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2016
  • Type: Text , Journal article
  • Relation: Applied Intelligence Vol. 45, no. 3 (2016), p. 638-651
  • Full Text: false
  • Reviewed:
  • Description: Bearings play a crucial role in rotational machines and their failure is one of the foremost causes of breakdowns in rotary machinery. Their functionality is directly relevant to the operational performance, service life and efficiency of these machines. Therefore, bearing fault identification is very significant. The accuracy of fault or anomaly detection by the current techniques is not adequate. We propose a data mining-based framework for fault identification and anomaly detection from machine vibration data. In this framework, to capture the useful knowledge from the vibration data stream (VDS), we first pre-process the data using Fast Fourier Transform (FFT) to extract the frequency signature and then build a compact tree called SAFP-tree (sliding window associated frequency pattern tree), and propose a mining algorithm called SAFP. Our SAFP algorithm can mine associated frequency patterns (i.e., fault frequency signatures) in the current window of VDS and use them to identify faults in the bearing data. Finally, SAFP is further enhanced to SAFP-AD for anomaly detection by determining the normal behavior measure (NBM) from the extracted frequency patterns. The results show that our technique is very efficient in identifying faults and detecting anomalies over VDS and can be used for remote machine health diagnosis. © 2016, Springer Science+Business Media New York.

Action-02MCF : A robust space-time correlation filter for action recognition in clutter and adverse lighting conditions

- lhaq, Anwaar, Yin, Xiaoxia, Zhang, Yunchan, Gondal, Iqbal

  • Authors: lhaq, Anwaar , Yin, Xiaoxia , Zhang, Yunchan , Gondal, Iqbal
  • Date: 2016
  • Type: Text , Conference paper
  • Relation: 17th International Conference, ACIVS 2016 p. 465-476
  • Full Text: false
  • Reviewed:
  • Description: Human actions are spatio-temporal visual events and recognizing human actions in different conditions is still a challenging computer vision problem. In this paper, we introduce a robust feature based space-time correlation filter, called Action-02MCF (0’zero-aliasing’ 2M’ Maximum Margin’) for recognizing human actions in video sequences. This filter combines (i) the sparsity of spatio-temporal feature space, (ii) generalization of maximum margin criteria, (iii) enhanced aliasing free localization performance of correlation filtering using (iv) rich context of maximally stable space-time interest points into a single classifier. Its rich multi-objective function provides robustness, generalization and recognition as a single package. Action-02MCF can simultaneously localize and classify actions of interest even in clutter and adverse imaging conditions. We evaluate the performance of our proposed filter for challenging human action datasets. Experimental results verify the performance potential of our action-filter compared to other correlation filtering based action recognition approaches.

Action-02MCF : A robust space-time correlation filter for action recognition in clutter and adverse lighting conditions

- Ulhaq, Anwaar, Yin, Xiaoxia, Zhang, Yunchan, Gondal, Iqbal

  • Authors: Ulhaq, Anwaar , Yin, Xiaoxia , Zhang, Yunchan , Gondal, Iqbal
  • Date: 2016
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 17th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2016; Lecce, Italy; 24th-27th October 2016; published in Advanced Conepts for Intelligent Vision Systems (Lecture Notes in Computer Science series) Vol. 10016 LNCS, p. 465-476
  • Full Text: false
  • Reviewed:
  • Description: Human actions are spatio-temporal visual events and recognizing human actions in different conditions is still a challenging computer vision problem. In this paper, we introduce a robust feature based space-time correlation filter, called Action-02MCF (0’zero-aliasing’ 2M’ Maximum Margin’) for recognizing human actions in video sequences. This filter combines (i) the sparsity of spatio-temporal feature space, (ii) generalization of maximum margin criteria, (iii) enhanced aliasing free localization performance of correlation filtering using (iv) rich context of maximally stable space-time interest points into a single classifier. Its rich multi-objective function provides robustness, generalization and recognition as a single package. Action-02MCF can simultaneously localize and classify actions of interest even in clutter and adverse imaging conditions. We evaluate the performance of our proposed filter for challenging human action datasets. Experimental results verify the performance potential of our action-filter compared to other correlation filtering based action recognition approaches. © Springer International Publishing AG 2016.
  • Description: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

An efficient data extraction framework for mining wireless sensor networks

- Rashid, Md. Mamunur, Gondal, Iqbal, Kamruzzaman, Joarder

  • Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2016
  • Type: Text , Conference paper
  • Relation: 23rd International Conference, ICONIP 2016; Kyoto, Japan; 16th-21st October 2016; published in Neural Information Processing, Part III (Lecture Notes in Computer Science series) Vol. 9949, p. 491-498
  • Full Text:
  • Reviewed:
  • Description: Behavioral patterns for sensors have received a great deal of attention recently due to their usefulness in capturing the temporal relations between sensors in wireless sensor networks. To discover these patterns, we need to collect the behavioral data that represents the sensor's activities over time from the sensor database that attached with a well-equipped central node called sink for further analysis. However, given the limited resources of sensor nodes, an effective data collection method is required for collecting the behavioral data efficiently. In this paper, we introduce a new framework for behavioral patterns called associated-correlated sensor patterns and also propose a MapReduce based new paradigm for extract data from the wireless sensor network by distributed away. Extensive performance study shows that the proposed method is capable to reduce the data size almost 50% compared to the centralized model.

Carry me if you can : A utility based forwarding scheme for content sharing in tourist destinations

- Kaisar, Shahriar, Kamruzzaman, Joarder, Karmakar, Gour, Gondal, Iqbal

  • Authors: Kaisar, Shahriar , Kamruzzaman, Joarder , Karmakar, Gour , Gondal, Iqbal
  • Date: 2016
  • Type: Text , Conference proceedings
  • Relation: 22nd Asia-Pacific Conference on Communications, APCC 2016; Yogyakarta, Indonesia; 25th-27th August 2016 p. 261-267
  • Full Text:
  • Reviewed:
  • Description: Message forwarding is an integral part of the decentralized content sharing process as the content delivery success highly depends on it. Existing literature employs spatio-temporal regularity of human movement pattern and pre-existing social relationship to take message forwarding decisions. However, such approaches are ineffectual in environments where those information are unavailable such as a tourist spot or camping site. In this study, we explore the message forwarding techniques in such environments considering the information that are readily available and can be gathered on the fly. We propose a utility based forwarding scheme to select the appropriate forwarder node based on co-location stay time, connectivity and available resources. A higher co-location stay time reflects that the forwarder and the destination node is likely to have more opportunistic contacts, while the connectivity and available resource ensure that the selected forwarder has sufficient neighbours and resources to carry the message forward. Simulation results suggest that the proposed approach attains high hit and success rate and low latency for successful content delivery, which is comparable to those proposed for work-place type scenarios with regular movement pattern and pre-existing relationships. © 2016 IEEE.

Child oral health in migrant families : A cross-sectional study of caries in 1-4 year old children from migrant backgrounds residing in Melbourne, Australia

- 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.

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:
  • 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).

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
  • 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.

A mapreduce based technique for mining behavioral patterns from sensor data

- Rashid, Md. Mamunur, Gondal, Iqbal, Kamruzzaman, Joarder

  • Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2015
  • Type: Text , Conference paper
  • Relation: 22nd International Conference on Neural Information Processing, ICONIP 2015; Istanbul, Turkey; 9th-12th November 2015 Vol. 9492, p. 145-153
  • Full Text: false
  • Reviewed:
  • Description: WSNs generate a large amount of data in the form of streams, and temporal regularity in occurrence behavior is considered as an important measure for assessing the importance of patterns in WSN data. A frequent sensor pattern that occurs after regular intervals in WSNs is called regularly frequent sensor patterns (RFSPs). Existing RFSPs techniques assume that the data structure of the mining task is small enough to fit in the main memory of a processor. However, given the emergence of the Internet of Things (IoT), WSNs in future will generate huge volume of data, which means such an assumption does not hold any longer. To overcome this, a distributed solution using MapReduce model has not yet been explored extensively. Since MapReduce is becoming the de-facto model for computation on large data, an efficient RFSPs mining algorithm on this model is likely to provide a highly effective solution. In this work, we propose a regularly frequent sensor patterns mining algorithm called RFSP-H which uses MapReduce based framework. Extensive performance analyses show that our technique is significantly time efficient in finding regularly frequent sensor patterns. © Springer International Publishing Switzerland 2015.

Complex anomaly for enhanced machine independent condition monitoring

- Amar, Muhammad, Gondal, Iqbal, Wilson, Campbell

  • Authors: Amar, Muhammad , Gondal, Iqbal , Wilson, Campbell
  • Date: 2015
  • Type: Text , Conference proceedings
  • Relation: 9th International Conference on Open Source Systems and Technologies, ICOSST 2015; Lahore, Pakistan; 17th-19th December 2015
  • Full Text: false
  • Description: Safety in machine applications requires tracking machine health during the time of operations. Anomaly detection techniques are used to model normal behavior of the machines and raise an alarm if any anomaly is observed. But traditional anomaly detection techniques do not identify type and severity of aberrance in terms of amplitude, pattern or both. Once the anomalous behavior is observed then fault detection techniques are applied to diagnose faults. For machine independent condition monitoring (MICM) a range of features transforms are needed for autonomous learning of the fault classifiers for different parameters to identify variety of fault types which requires huge amount of time. In this paper a novel complex anomaly plan (CAP) representation has been proposed with amplitude anomalies on real and pattern anomalies on imaginary axis. To plot amplitude and pattern anomalies in the CAP, normal state vibrations frequency features are used to train Gaussian models for each of the frequency. The dynamic location of the anomaly plotted in the CAP gives a measure of the intensity of the anomaly, where real and imaginary axis components help the fault classifier to make an appropriate selection of the transform and thus enhances the efficiency of MICM framework. © 2015 IEEE.
  • Description: ICOSST 2015 - 2015 International Conference on Open Source Systems and Technologies, Proceedings

Condition monitoring through mining fault frequency from machine vibration data

- Rashid, Md. Mamunur, Gondal, Iqbal, Kamruzzaman, Joarder

  • Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2015
  • Type: Text , Conference paper
  • Relation: International Joint Conference on Neural Networks, IJCNN 2015; Killarney; Ireland; 12th-17th July 2015 p. 1-8
  • Full Text: false
  • Reviewed:
  • Description: In machine health monitoring, fault frequency identification of potential bearing faults is very important and necessary when it comes to reliable operation of a given system. In this paper, we proposed a data mining based scheme for fault frequency identification from the bearing data. In this scheme, we propose a compact tree called SAP-tree (sliding window associated frequency pattern tree) which is built upon the analysis of frequency domain characteristics of machine vibration data. Using this tree we devised a sliding window-based associated frequency pattern mining technique, called SAP algorithm, that mines for the frequencies relevant to machine fault. Our SAP algorithm can mine associated frequency patterns in the current window with frequent pattern (FP)-growth like pattern-growth method and used these patterns to identify the fault frequency. Extensive experimental analyses show that our technique is very efficient in identifying fault frequency over vibration data stream.

Content exchange among mobile tourists using users' interest and place-centric activities

- Kaisar, Shahriar, Kamruzzaman, Joarder, Karmakar, Gour, Gondal, Iqbal

  • Authors: Kaisar, Shahriar , Kamruzzaman, Joarder , Karmakar, Gour , Gondal, Iqbal
  • Date: 2015
  • Type: Text , Conference paper
  • Relation: 2015 10th International Conference on Information, Communications and Signal Processing (Icics); Singapore, Singapore; 2nd-4th December 2015 p. 1-5
  • Full Text: false
  • Reviewed:
  • Description: In this work we investigate decentralized content exchange among tourists who are mostly strangers, depicts irregular movement patterns and most likely not to have any prior social relationship or difficult to establish any in a tourist spot. We incorporate user's interest, trustworthy online recommendations, and place-centric information to facilitate content exchange in such tourist destinations. The proposed administrator selection policy considers stay probability in activities, connectivity among nodes and their available resources. We have done extensive simulation using network simulator NS3 on a popular tourist spot in Australia that provides a number of activities. Our proposed approach shows promising results in exchanging contents among users measured in terms of content hit and delivery success rate as well as latency. The success rate is comparable to those reported in the literature for cases where social relationship exist and nodes follow regular predictable movement patterns.