A framework for collaborative multi class heterogeneous wireless sensor networks
- Authors: Azad, Arman , Kamruzzaman, Joarder
- Date: 2008
- Type: Text , Conference paper
- Relation: Proceedings of the 2008 IEEE International Conference on Communications p. 1-6
- Full Text: false
- Reviewed:
- Description: For many applications, simultaneous sensing of a number of parameters is crucial that leads to the deployment of multiple classes of sensors having different initial energy, data generation rate and deployment density within the vicinity of a cluster as opposed to identical sensors assumed in the existing heterogeneous sensor networks. For data transmission to cluster head, such networks use single hop, multi hop and their hybrid as intra-cluster transmission policy which suffer highly from non-uniform energy usage among sensors, thereby reducing the lifetime drastically leaving considerable amount of energy in many nodes. In this paper, we propose a framework for multi-class heterogeneous sensor networks where incoming traffic is relayed towards cluster head in collaboration among multiple classes of sensors considering their heterogeneity. We also propose two transmission policies for this framework considering generic polygonal cluster and limited transmission range for individual sensors. Performance analysis shows substantial improvement of overall lifetime by the collaborative framework of multi-class sensors. Our proposed transmission policies further improve the lifetime over existing multi hop and hybrid communications through better distribution of energy usage among sensors.kam
A mapreduce based technique for mining behavioral patterns from sensor data
- 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.
A new resource distribution model for improved QoS in an integrated WiMAX/WiFi architecture
- Authors: Rabbani, Md , Kamruzzaman, Joarder , Gondal, Iqbal , Ahmad, Iftekhar
- Date: 2011
- Type: Text , Conference paper
- Full Text: false
- Reviewed:
A novel algorithm for mining behavioral patterns from wireless sensor networks
- Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
- Date: 2014
- Type: Text , Conference paper
- Relation: 2014 International Joint Conference on Neural Networks, IJCNN 2014; Beijing, China; 6th-11th July 2014 p. 1-7
- Full Text: false
- Reviewed:
- Description: Due to recent advances in wireless sensor networks (WSNs) and their ability to generate huge amount of data in the form of streams, knowledge discovery techniques have received a great deal of attention to extract useful knowledge regarding the underlying network. Traditionally sensor association rules measure occurrence frequency of patterns. However, these rules often generate a huge number of rules, most of which are non-informative or fail to reflect the true correlation among data objects. In this paper, we propose a new type of sensor behavioral pattern called associated sensor patterns that captures association-like co-occurrences and the strong temporal correlations implied by such co-occurrences in the sensor data. We also propose a novel tree structure called as associated sensor pattern tree (ASPT) and a mining algorithm, associated sensor pattern (ASP) which facilitates frequent pattern (FP) growth-based technique to generate all associated sensor patterns from WSN data with only one scan over the sensor database. Extensive performance study shows that our algorithm is very efficient in finding associated sensor patterns than the existing significant algorithms.
A novel vertical handover scheme for diminution in social network traffic
- Authors: Haider, Ammar , Gondal, Iqbal , Kamruzzaman, Joarder
- Date: 2012
- Type: Text , Conference paper
- Full Text:
- Reviewed:
A technique for parallel share-frequent sensor pattern mining from wireless sensor networks
- Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
- Date: 2014
- Type: Text , Conference paper
- Relation: 14th Annual International Conference on Computational Science, ICCS 2014; Cairns, Australia; 10th-12th June 2014; published in Procedia Computer Science p. 124-133
- Full Text:
- Reviewed:
- Description: WSNs generate huge amount of data in the form of streams and mining useful knowledge from these streams is a challenging task. Existing works generate sensor association rules using occurrence frequency of patterns with binary frequency (either absent or present) or support of a pattern as a criterion. However, considering the binary frequency or support of a pattern may not be a sufficient indicator for finding meaningful patterns from WSN data because it only reflects the number of epochs in the sensor data which contain that pattern. The share measure of sensorsets could discover useful knowledge about numerical values associated with sensor in a sensor database. Therefore, in this paper, we propose a new type of behavioral pattern called share-frequent sensor patterns by considering the non-binary frequency values of sensors in epochs. To discover share-frequent sensor patterns from sensor dataset, we propose a novel parallel technique. In this technique, we develop a novel tree structure, called parallel share-frequent sensor pattern tree (PShrFSP-tree) that is constructed at each local node independently, by capturing the database contents to generate the candidate patterns using a pattern growth technique with a single scan and then merges the locally generated candidate patterns at the final stage to generate global share-frequent sensor patterns. Comprehensive experimental results show that our proposed model is very efficient for mining share-frequent patterns from WSN data in terms of time and scalability.
ACSP-Tree: A tree structure for mining behavioral patterns from wireless sensor networks
- Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
- Date: 2013
- Type: Text , Conference paper
- Relation: IEEE Conference on Local Computer Networks (LCN 2013) (21 October 2013 to 24 October 2013) p. 691-694
- Full Text: false
- Reviewed:
- Description: WSNs generates a large amount of data in the form of stream and mining knowledge from the stream of data can be extremely useful. Association rules mining, from the sensor data, has been studied in recent literature. However, sensor association rules mining often produces a huge number of rules, but most of them either are redundant or fail to reflect the true correlation relationship among data objects. In this paper, we address this problem and propose mining of a new type of sensor behavioral pattern called associated-correlated sensor patterns. The proposed behavioral patterns capture not only association-like co-occurrences but also the substantial temporal correlations implied by such co-occurrences in the sensor data. Here, we also use a prefix tree-based structure called associated-correlated sensor pattern-tree (ACSP-tree), which facilitates frequent pattern (FP) growth-based mining technique to generate all associated-correlated patterns from WSN data with only one scan over the sensor database. Extensive performance study shows that our approach is time and memory efficient in finding associated-correlated patterns than the existing most efficient algorithms.
Agile spectrum evacuation in cognitive radio networks
- Authors: Shahid, Mohammad , Kamruzzaman, Joarder
- Date: 2010
- Type: Text , Conference paper
- Relation: 2010 IEEE International Conference on Communications p. 1-6
- Full Text: false
- Reviewed:
- Description: One of the most important aspects of cognitive radio technology is to avoid interference on the primary system. Typically, the interference is avoided by sensing a particular spectrum band for the existence of primary transmitter while all secondary users are kept quiet. Hence, a periodic sensing method is used which incorporates alternate phases of sensing and transmission by all secondary users. In this paper, we introduce a new method of agile spectrum evacuation that allows any secondary user to continue using the band until the return of the primary user is detected through the formation of a set of users that exclusively engages in sensing primary user in a cooperative manner. The proposed method yields better interference protection and enhanced spectrum utilization.
An efficient data extraction framework for mining wireless sensor networks
- 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.
An efficient pose estimation for limited-resourced MAVs using sufficient statistics
- Authors: Senthooran, Ilankaikone , Barca, Jan , Kamruzzaman, Joarder , Murhsed, Manzur , Chung, Hoam
- Date: 2015
- Type: Text , Conference paper
- Relation: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015; Hamburg; Germany; 28th September-2nd October 2015 Vol. 2015, p. 3735-3740
- Full Text: false
- Reviewed:
- Description: We present a computationally efficient RGB-D based pose estimation solution for less computationally resourced MAVs, which are ideally suited as members in a swarm. Our approach applies the sufficient statistics derived for a least-squares problem to our problem context. RANSAC-based outlier detection in aligning corresponding feature points is a time consuming operation in visual pose estimation. The additive nature of the used sufficient statistics significantly reduces the computation time of the RANSAC procedure since the pose estimation in each test loop can be computed by reusing previously computed sufficient statistics. This eliminates the need for recomputing estimates from scratch each time. A simpler hypotheses testing method gave similar performance in terms of speed but less accurate than our proposed method. We further increase the efficiency by reducing the problem size to four dimensions using attitude data from an Attitude and Heading Reference System (AHRS). Using a real-world dataset, we show that our algorithm saves up to 94% of computation time for the RANSAC-based procedure in pose estimation while improving the accuracy.
An opportunistic message forwarding protocol for underwater acoustic sensor networks
- Authors: Nowsheen, Nusrat , Karmakar, Gour , Kamruzzaman, Joarder
- Date: 2013
- Type: Text , Conference paper
- Relation: 2013 19th Asia-Pacific Conference on Communications, APCC 2013; Denpasar; Indonesia; 29th-31st August 2013 p. 1-6
- Full Text: false
- Reviewed:
- Description: Designing message forwarding protocols for underwater acoustic sensor networks (UASNs) is challenging mainly due to high propagation delay, limited bandwidth and high packet loss. Most such protocols operate on the assumption that precise location of sensor nodes is known, which is difficult as GPS waves cannot propagate through water. Moreover, due to the error-prone nature of the acoustic link, message forwarding over multiple hops degrades end-to-end reliability, consumes significant energy and incurs longer delay. In this paper, we propose a location unaware message forwarding technique. It employs opportunistic routing where nodes use accumulate-and-forward paradigm to route data. The technique also exploits nodes' ability to overhear one another's transmission to select reliable route. Our opportunistic model uses independent and local forwarding decisions to select next hop forwarder on-the-fly based on its link transmission reliability and reachability to the gateway. Message ferrying approach is utilized to collect sensor data from gateway nodes of multiple UASNs at high data rate. Our simulation results exhibit its effectiveness and superiority compared with two well established message forwarding algorithms in underwater in terms of packet delivery ratio, routing overhead and energy consumption.
API based discrimination of ransomware and benign cryptographic programs
- Authors: Black, Paul , Sohail, Ammar , Gondal, Iqbal , Kamruzzaman, Joarder , Vamplew, Peter , Watters, Paul
- Date: 2020
- Type: Text , Conference paper
- Relation: 27th International Conference on Neural Information Processing, ICONIP 2020, Bangkok, 18 to 22 November 2020, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12533 LNCS, p. 177-188
- Full Text: false
- Reviewed:
- Description: Ransomware is a widespread class of malware that encrypts files in a victim’s computer and extorts victims into paying a fee to regain access to their data. Previous research has proposed methods for ransomware detection using machine learning techniques. However, this research has not examined the precision of ransomware detection. While existing techniques show an overall high accuracy in detecting novel ransomware samples, previous research does not investigate the discrimination of novel ransomware from benign cryptographic programs. This is a critical, practical limitation of current research; machine learning based techniques would be limited in their practical benefit if they generated too many false positives (at best) or deleted/quarantined critical data (at worst). We examine the ability of machine learning techniques based on Application Programming Interface (API) profile features to discriminate novel ransomware from benign-cryptographic programs. This research provides a ransomware detection technique that provides improved detection accuracy and precision compared to other API profile based ransomware detection techniques while using significantly simpler features than previous dynamic ransomware detection research. © 2020, Springer Nature Switzerland AG.
Assessing reliability of smart grid against cyberattacks using stability index
- Authors: Rashed, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder , Islam, Syed
- Date: 2021
- Type: Text , Conference paper
- Relation: 31st Australasian Universities Power Engineering Conference, AUPEC 2021, Virtual, Online 26 to 30 September 2021, Proceedings of 2021 31st Australasian Universities Power Engineering Conference, AUPEC 2021
- Full Text: false
- Reviewed:
- Description: The degradation of stability index within smart grid leads to incorrect power generation and poor load balancing. The remote data dependency of the central energy management system (CEMS) causes communication delay that further leads to poor synchronization within the system. This becomes worse in the presence of cyber-attacks such as stealth or false data injection attack (FDIA). We used dynamic estimation to obtain state data after the inception of false data attack and analyzed its impact on the stability index of the smart grid. A lookup table was constructed based on the fluctuations within the voltage estimates of IEEE-Bus system. An index number was assigned to output estimates at the bus that highlights the level of severity within the grid. We used IEEE-57 Bus using MATLAB to capture and plot the results related to voltage estimates, latency, and inception time delay. The results demonstrate a clear relationship between stability index and state estimates especially when the system is under the influence of a cyber-attack. © 2021 IEEE.
Assessing transformer oil quality using deep convolutional networks
- Authors: Alam, Mohammad , Karmakar, Gour , Islam, Syed , Kamruzzaman, Joarder , Chetty, Madhu , Lim, Suryani , Appuhamillage, Gayan , Chattopadhyay, Gopi , Wilcox, Steve , Verheyen, Vincent
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 29th Australasian Universities Power Engineering Conference, AUPEC 2019
- Full Text:
- Reviewed:
- Description: Electrical power grids comprise a significantly large number of transformers that interconnect power generation, transmission and distribution. These transformers having different MVA ratings are critical assets that require proper maintenance to provide long and uninterrupted electrical service. The mineral oil, an essential component of any transformer, not only provides cooling but also acts as an insulating medium within the transformer. The quality and the key dissolved properties of insulating mineral oil for the transformer are critical with its proper and reliable operation. However, traditional chemical diagnostic methods are expensive and time-consuming. A transformer oil image analysis approach, based on the entropy value of oil, which is inexpensive, effective and quick. However, the inability of entropy to estimate the vital transformer oil properties such as equivalent age, Neutralization Number (NN), dissipation factor (tanδ) and power factor (PF); and many intuitively derived constants usage limit its estimation accuracy. To address this issue, in this paper, we introduce an innovative transformer oil analysis using two deep convolutional learning techniques such as Convolutional Neural Network (ConvNet) and Residual Neural Network (ResNet). These two deep neural networks are chosen for this project as they have superior performance in computer vision. After estimating the equivalent aging year of transformer oil from its image by our proposed method, NN, tanδ and PF are computed using that estimated age. Our deep learning based techniques can accurately predict the transformer oil equivalent age, leading to calculate NN, tanδ and PF more accurately. The root means square error of estimated equivalent age produced by entropy, ConvNet and ResNet based methods are 0.718, 0.122 and 0.065, respectively. ConvNet and ResNet based methods have reduced the error of the oil age estimation by 83% and 91%, respectively compared to that of the entropy method. Our proposed oil image analysis can calculate the equivalent age that is very close to the actual age for all images used in the experiment. © 2019 IEEE.
- Description: E1
CAM : Congestion avoidance and mitigation in wireless sensor networks
- Authors: Bhuiyan, Mohammad , Gondal, Iqbal , Kamruzzaman, Joarder
- Date: 2010
- Type: Text , Conference paper
- Relation: Vehicular Technology Conference (VTC 2010-Spring), 2010 IEEE 71st
- Full Text: false
- Reviewed:
Churn prediction in telecom industry using machine learning ensembles with class balancing
- Authors: Chowdhury, Abdullahi , Kaisar, Shahriar , Rashid, Md Mamunur , Shafin, Sakib , Kamruzzaman, Joarder
- Date: 2021
- Type: Text , Conference paper
- Relation: 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2021, Brisbane, 8-10 December 2021
- Full Text: false
- Reviewed:
- Description: Telecommunication service providers are going through a very competitive and challenging time to retain existing customers by offering new and attractive services (e.g., unlimited local and international calls, high-speed internet, new phones). It is therefore imperative to analyse and predict customer churn behaviour more accurately. One of the major challenges to analyse churn data and build better prediction model is the imbalance nature of the data. Customer behaviour for churn and non-churn scenarios may contain resembling features. Using a single classifier or simple oversampling method to handle data imbalance often struggles to identify the minority (churn) class data. To overcome the issue, we introduce a model that uses sophisticated oversampling technique in conjunction with ensemble methods, namely Random Forest, Gradient Boost, Extreme Gradient Boost, and AdaBoost. The hyperparameters of the baseline ensemble methods and the oversampling methods were tuned in several ways to investigate their impact on prediction performances. Using a widely used publicly available customer churn dataset, prediction performance of the proposed model was evaluated in term of various metrics, namely, accuracy, precision, recall, F-1 score, AUC under ROC curve. Our model outperformed the existing models and significantly reduced both false positive and false negative prediction. © IEEE 2022.
CODAR: Congestion and delay aware routing to detect time critical events
- Authors: Bhuiyan, Mohammad , Gondal, Iqbal , Kamruzzaman, Joarder
- Date: 2011
- Type: Text , Conference paper
- Full Text: false
- Reviewed:
- Description: Reliability and timeliness are two essential requirements of successful detection of critical events in Wireless Sensor Networks (WSNs). The base station (BS) is particularly interested about reliable and timely collection of data sent by the nodes close to the ongoing event, and at that time, the data sent by other nodes have little importance. In this paper, we propose Congestion and Delay Aware Routing (CODAR) protocol that tries to route data in congestion and delay aware manners. If congestion occurs, it also mitigates congestion by utilizing an accurate data-rate adjustment. Each node collects control information from neighbours and works in a distributed manner. CODAR also puts emphasis on successful collection of these control information which eventually provides desirable performance. Experimental results show that CODAR is capable of avoiding and mitigating congestion effectively, and performs better than similar known techniques in terms of reliable and timely event detection.
Coexistence mechanism for industrial automation network
- Authors: Yaqub, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder
- Date: 2010
- Type: Text , Conference paper
- Relation: 12th IEEE International Conference on High Performance Computing and Communications
- Full Text: false
- Reviewed:
- Description: Increase in the number of coexisting networks in license free Industrial, Scientific and Medical (ISM) band causes interferences for industrial automation, e.g., shop floors of manufacturing facilities. In order to ensure the reliability for automation networks, interference avoidance schemes are required. This paper proposes a novel Predefined Hopping Pattern (PHP) technique for frequency hopping in ISM band, which mitigates self-interferences and static interferers as well. This technique generates optimized frequency hopping sequences which ensure sufficient frequency diversity and frequency offset among the coexisting Bluetooth piconets and exploits transmission experiences for a particular frequency in eliminating interference. Simulation studies have shown that PHP has better collision avoidance rate than well known adaptive frequency hopping (AFH) and adaptive frequency rolling (AFR) schemes.
Condition monitoring through mining fault frequency from machine vibration data
- 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
- 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.