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Cyberattacks detection in iot-based smart city applications using machine learning techniques

- Rashid, Md Mamunur, Kamruzzaman, Joarder, Hassan, Mohammad, Imam, Tassadduq, Gordon, Steven

  • Authors: Rashid, Md Mamunur , Kamruzzaman, Joarder , Hassan, Mohammad , Imam, Tassadduq , Gordon, Steven
  • Date: 2020
  • Type: Text , Journal article
  • Relation: International Journal of Environmental Research and Public Health Vol. 17, no. 24 (2020), p. 1-21
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  • Description: In recent years, the widespread deployment of the Internet of Things (IoT) applications has contributed to the development of smart cities. A smart city utilizes IoT-enabled technologies, communications and applications to maximize operational efficiency and enhance both the service providers’ quality of services and people’s wellbeing and quality of life. With the growth of smart city networks, however, comes the increased risk of cybersecurity threats and attacks. IoT devices within a smart city network are connected to sensors linked to large cloud servers and are exposed to malicious attacks and threats. Thus, it is important to devise approaches to prevent such attacks and protect IoT devices from failure. In this paper, we explore an attack and anomaly detection technique based on machine learning algorithms (LR, SVM, DT, RF, ANN and KNN) to defend against and mitigate IoT cybersecurity threats in a smart city. Contrary to existing works that have focused on single classifiers, we also explore ensemble methods such as bagging, boosting and stacking to enhance the performance of the detection system. Additionally, we consider an integration of feature selection, cross-validation and multi-class classification for the discussed domain, which has not been well considered in the existing literature. Experimental results with the recent attack dataset demonstrate that the proposed technique can effectively identify cyberattacks and the stacking ensemble model outperforms comparable models in terms of accuracy, precision, recall and F1-Score, implying the promise of stacking in this domain. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Adversarial training for deep learning-based cyberattack detection in IoT-based smart city applications

- Rashid, Md Mamunur, Kamruzzaman, Joarder, Mehedi Hassan, Mohammad, Imam, Tasadduq, Wibowo, Santoso, Gordon, Steven, Fortino, Giancarlo

  • Authors: Rashid, Md Mamunur , Kamruzzaman, Joarder , Mehedi Hassan, Mohammad , Imam, Tasadduq , Wibowo, Santoso , Gordon, Steven , Fortino, Giancarlo
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Computers and Security Vol. 120, no. (2022), p.
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  • Description: Intrusion Detection Systems (IDS) based on deep learning models can identify and mitigate cyberattacks in IoT applications in a resilient and systematic manner. These models, which support the IDS's decision, could be vulnerable to a cyberattack known as adversarial attack. In this type of attack, attackers create adversarial samples by introducing small perturbations to attack samples to trick a trained model into misclassifying them as benign applications. These attacks can cause substantial damage to IoT-based smart city models in terms of device malfunction, data leakage, operational outage and financial loss. To our knowledge, the impact of and defence against adversarial attacks on IDS models in relation to smart city applications have not been investigated yet. To address this research gap, in this work, we explore the effect of adversarial attacks on the deep learning and shallow machine learning models by using a recent IoT dataset and propose a method using adversarial retraining that can significantly improve IDS performance when confronting adversarial attacks. Simulation results demonstrate that the presence of adversarial samples deteriorates the detection accuracy significantly by above 70% while our proposed model can deliver detection accuracy above 99% against all types of attacks including adversarial attacks. This makes an IDS robust in protecting IoT-based smart city services. © 2022 Elsevier Ltd

ACSP-Tree: A tree structure for mining behavioral patterns from wireless sensor networks

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

  • 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
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  • 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.

Regularly frequent patterns mining from sensor data stream

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

  • Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2013
  • Type: Text , Conference paper
  • Relation: International Conference on Neural Information Processing (ICONIP 2013) p. 417-424
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  • Description: Mining interesting and useful knowledge from the huge amount of data gathered in wireless sensor networks is a challenging task. Works reported in literature use support metric-based sensor association rule which employs the occurrence frequency of patterns as criteria. Such criteria may not be appropriate for finding significant patterns. Moreover, temporal regularity in occurrence behavior should be considered as another important measure for assessing the importance of patterns in WSNs. Frequent sensor patterns that occur after regular intervals is called regularly frequent sensor patterns. Even though mining regularly frequent sensor patterns from sensor data stream is extremely important in many real-time applications, no such algorithm has been proposed yet. In this paper, we propose a novel tree structure called Regularly Frequent Sensor Pattern-tree (RSP-tree) and an efficient mining approach for finding regularly frequent sensor patterns from WSNs. Extensive performance analyses show that our technique is time and memory efficient in finding regularly frequent sensor patterns.

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
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  • 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.

Mining associated sensor patterns for data stream of wireless sensor networks

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

  • Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2013
  • Type: Text , Conference proceedings
  • Relation: 8th ACM International Workshop on Performance Monitoring, Measurement, and Evaluation of Heterogeneous Wireless and Wired Networks, PM2HW2N 2013, Barcelona; Spain; 3rd-8th November 2013 p. 91-98
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  • Description: WSNs generate a large amount of data in the form of data stream; and mining these streams to extract useful knowledge is a highly challenging task. Existing works proposed in literature use sensor association rules measured in terms of 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. Additionally mining associated sensor patterns from sensor stream data, which is vital for real-time applications, has not been addressed yet in literature. In this paper, we address these problems and propose a new type of sensor behavioral pattern called associated sensor patterns which capture simultaneously association-like co-occurrence as well as substantial temporal correlations implied by such co-occurrences in sensor data. We propose a novel tree structure, called associated sensor pattern stream tree (ASPS-tree) and a new technique, called associated sensor pattern mining of data stream (ASPMS), using sliding window-based associated sensor pattern mining for WSNs. By capturing the useful knowledge of the data stream into an ASPS-tree, our ASPMS algorithm can mine associated sensor patterns in the current window with frequent pattern (FP)-growth like pattern-growth method. Extensive experimental analyses show that our technique is very efficient in discovering associated sensor patterns over sensor data stream.

A novel algorithm for mining behavioral patterns from wireless sensor networks

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

  • 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
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  • 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.

Share-frequent sensor patterns mining from wireless sensor network data

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

  • Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2015
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Parallel and Distributed Systems Vol. 26, no. 12 (2015), p. 3471-3484
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  • Description: Mining interesting knowledge from the huge amount of data gathered from WSNs is a challenge. Works reported in literature use support metric-based sensor association rules which employ the occurrence frequency of patterns as criteria. However, consideration of the binary frequency of a pattern is not a sufficient indicator for finding meaningful patterns because it only reflects the number of epochs which contain that pattern in the dataset. The share measure of sensorsets could discover useful knowledge about trigger values associated with a sensor. Here, we propose a new type of behavioral pattern called share-frequent sensor patterns (SFSPs) by considering the non-binary frequency values of sensors in epochs. SFSPs can find a correlation among a set of sensors and hence can improve the performance of WSNs in a resource management process. In this paper, a share-frequent sensor pattern tree (ShrFSP-Tree) has been proposed to facilitate a pattern growth mining technique to discover SFSPs from WSN data. We also present a parallel and distributed method where the ShrFSP-Tree is enhanced into PShrFSP-Tree and its performance is investigated for both homogeneous and heterogeneous systems. Results show that our method is time and memory efficient in finding SFSPs than the existing most efficient algorithms.

Mining associated patterns from wireless sensor networks

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

  • Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2015
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Computers Vol. 64, no. 7 (2015), p. 1998-2011
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  • Description: Mining of sensor data for useful knowledge extraction is a very challenging task. Existing works generate sensor association rules using occurrence frequency of patterns to extract the knowledge. These techniques often generate huge number of rules, most of which are non-informative or fail to reflect true correlation among sensor data. In this paper, we propose a new type of behavioral pattern called associated sensor patterns which capture association-like co-occurrences as well as temporal correlations which are linked with such co-occurrences. To capture such patterns a compact tree structure, called associated sensor pattern tree (ASP-tree) and a mining algorithm (ASP) are proposed which use pattern growth-based approach to generate all associated patterns with only one scan over dataset. Moreover, when data stream flows through, old information may lose significance for the current time. To capture significance of recent data, ASP-tree is further enhanced to SWASP-tree by adopting sliding observation window and updating the tree structure accordingly. Finally, window size is made dynamically adaptive to ensure efficient resource usage. Different characteristics of the proposed techniques and their computational complexity are presented. Experimental results show that our approach is very efficient in discovering associated sensor patterns and outperforms existing techniques.

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
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  • 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 technique for parallel share-frequent sensor pattern mining from wireless sensor networks

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

  • 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
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  • 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.

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
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  • 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.

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
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  • 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.

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
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  • 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.

Applications of machine learning and deep learning in antenna design, optimization, and selection : a review

- Sarker, Nayan, Podder, Prajoy, Mondal, M., Shafin, Sakib, Kamruzzaman, Joarder

  • Authors: Sarker, Nayan , Podder, Prajoy , Mondal, M. , Shafin, Sakib , Kamruzzaman, Joarder
  • Date: 2023
  • Type: Text , Journal article , Review
  • Relation: IEEE Access Vol. 11, no. (2023), p. 103890-103915
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  • Description: This review paper provides an overview of the latest developments in artificial intelligence (AI)-based antenna design and optimization for wireless communications. Machine learning (ML) and deep learning (DL) algorithms are applied to antenna engineering to improve the efficiency of the design and optimization processes. The review discusses the use of electromagnetic (EM) simulators such as computer simulation technology (CST) and high-frequency structure simulator (HFSS) for ML and DL-based antenna design, which also covers reinforcement learning (RL)-bases approaches. Various antenna optimization methods including parallel optimization, single and multi-objective optimization, variable fidelity optimization, multilayer ML-assisted optimization, and surrogate-based optimization are discussed. The review also covers the AI-based antenna selection approaches for wireless applications. To support the automation of antenna engineering, the data generation technique with computational electromagnetics software is described and some useful datasets are reported. The review concludes that ML/DL can enhance antenna behavior prediction, reduce the number of simulations, improve computer efficiency, and speed up the antenna design process. © 2013 IEEE.

Search and tracking algorithms for swarms of robots: A survey

- Senanayake, Madhubhashi, Senthooran, Ilankaikaone, Barca, Jan, Chung, Hoam, Kamruzzaman, Joarder, Murshed, Manzur

  • Authors: Senanayake, Madhubhashi , Senthooran, Ilankaikaone , Barca, Jan , Chung, Hoam , Kamruzzaman, Joarder , Murshed, Manzur
  • Date: 2016
  • Type: Text , Journal article
  • Relation: Robotics and Autonomous Systems Vol. 75, no. Part B (2016), p. 422-434
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  • Description: Target search and tracking is a classical but difficult problem in many research domains, including computer vision, wireless sensor networks and robotics. We review the seminal works that addressed this problem in the area of swarm robotics, which is the application of swarm intelligence principles to the control of multi-robot systems. Robustness, scalability and flexibility, as well as distributed sensing, make swarm robotic systems well suited for the problem of target search and tracking in real-world applications. We classify the works we review according to the variations and aspects of the search and tracking problems they addressed. As this is a particularly application-driven research area, the adopted taxonomy makes this review serve as a quick reference guide to our readers in identifying related works and approaches according to their problem at hand. By no means is this an exhaustive review, but an overview for researchers who are new to the swarm robotics field, to help them easily start off their research. © 2015 Elsevier B.V.

An efficient pose estimation for limited-resourced MAVs using sufficient statistics

- Senthooran, Ilankaikone, Barca, Jan, Kamruzzaman, Joarder, Murhsed, Manzur, Chung, Hoam

  • 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
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  • 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 efficient RANSAC hypothesis evaluation using sufficient statistics for RGB-D pose estimation

- Senthooran, Ilankalkone, Murshed, Manzur, Barca, Jan, Kamruzzaman, Joarder, Chung, Hoam

  • Authors: Senthooran, Ilankalkone , Murshed, Manzur , Barca, Jan , Kamruzzaman, Joarder , Chung, Hoam
  • Date: 2019
  • Type: Text , Journal article
  • Relation: Autonomous Robots Vol. 43, no. 5 (2019), p. 1257-1270
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  • Description: Achieving autonomous flight in GPS-denied environments begins with pose estimation in three-dimensional space, and this is much more challenging in an MAV in a swarm robotic system due to limited computational resources. In vision-based pose estimation, outlier detection is the most time-consuming step. This usually involves a RANSAC procedure using the reprojection-error method for hypothesis evaluation. Realignment-based hypothesis evaluation method is observed to be more accurate, but the considerably slower speed makes it unsuitable for robots with limited resources. We use sufficient statistics of least-squares minimisation to speed up this process. The additive nature of these sufficient statistics makes it possible to compute pose estimates in each evaluation by reusing previously computed statistics. Thus estimates need not be calculated from scratch each time. The proposed method is tested on standard RANSAC, Preemptive RANSAC and R-RANSAC using benchmark datasets. The results show that the use of sufficient statistics speeds up the outlier detection process with realignment hypothesis evaluation for all RANSAC variants, achieving an execution speed of up to 6.72 times.

Weighted soft decision for cooperative sensing in cognitive radio networks

- Shahid, Mohammad, Kamruzzaman, Joarder

  • Authors: Shahid, Mohammad , Kamruzzaman, Joarder
  • Date: 2008
  • Type: Text , Conference paper
  • Relation: 2008 16th International Conference on Networks (ICON) p. 1-6
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  • Description: Enhancing the current services or deploying new services operating in RF spectrum requires more licensed spectrum which may not be provided by the regulatory bodies because of spectrum scarcity. On the contrary, recent studies suggest that many portions of the licensed spectrum remains unused or underused for significant period of time raising the issue of spectrum access without license in an opportunistic manner. Among all the spectrum accessing techniques, sensing based methods are considered optimal for their simplicity and cost effectiveness. In this paper, we introduce a new cooperative spectrum sensing technique which considers the spatial variation of secondary (unlicensed) users and each user's contribution is weighted by a factor that depends on received power and path loss. Compared to existing techniques, the proposed one increases the sensing ability and spectrum utilization, and offers greater robustness to noise uncertainty. Moreover, this cooperative technique uses very simple energy detector as its building block thereby reduces the cost and operational complexity.

Interference protection in cognitive radio networks

- Shahid, Mohammad, Kamruzzaman, Joarder

  • Authors: Shahid, Mohammad , Kamruzzaman, Joarder
  • Date: 2010
  • Type: Text , Conference paper
  • Relation: 2010 IEEE 71st Vehicular Technology Conference p. 1-5
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  • Description: Protection of interference on the primary system is the key requirement for deployment of a cognitive radio system. Typically, spectrum sensing is interleaved in the transmission process of a secondary user to detect the return of primary user for quick evacuation, resulting in frequent stopping of transmission. In this paper, we introduce a novel method of agile spectrum evacuation that ensures continuous transmission by the secondary user until the primary user actually returns. The sensing process goes on simultaneously by a dedicated set of users working in a cooperative manner. The proposed method is evaluated under fading and shadowing conditions and better interference protection and improved utilization of RF spectrum is obtained.