Massey Research Online - Browsing by Author "Alam F"

Browsing by Author "Alam F"

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A Machine Learning Approach to Enhance the Performance of D2D-Enabled Clustered Networks
(IEEE, 20/01/2021) Aslam S; Alam F; Hasan SF; Rashid MA
Clustering has been suggested as an effective technique to enhance the performance of multicasting networks. Typically, a cluster head is selected to broadcast the cached content to its cluster members utilizing Device-to-Device (D2D) communication. However, some users can attain better performance by being connected with the Evolved Node B (eNB) rather than being in the clusters. In this article, we apply machine learning algorithms, namely Support Vector Machine, Random Forest, and Deep Neural Network to identify the users that should be serviced by the eNB. We therefore propose a mixed-mode content distribution scheme where the cluster heads and eNB service the two segregated groups of users to improve the performance of existing clustering schemes. A D2D-enabled multicasting scenario has been set up to perform a comprehensive simulation study that demonstrates that by utilizing the mixed-mode scheme, the performance of individual users, as well as the whole network, improve significantly in terms of throughput, energy consumption, and fairness. This study also demonstrates the trade-off between eNB loading and performance improvement for various parameters.
A Novel Weighted Clustering Algorithm Supported by a Distributed Architecture for D2D Enabled Content-Centric Networks
(MDPI (Basel, Switzerland), 25/09/2020) Aslam S; Alam F; Hasan S; Rashid M
Next generation cellular systems need efficient content-distribution schemes. Content-sharing via Device-to-Device (D2D) clustered networks has emerged as a popular approach for alleviating the burden on the cellular network. In this article, we utilize Content-Centric Networking and Network Virtualization to propose a distributed architecture, that supports efficient content delivery. We propose to use clustering at the user level for content-distribution. A weighted multifactor clustering algorithm is proposed for grouping the D2D User Equipment (DUEs) sharing a common interest. The proposed algorithm is evaluated in terms of energy efficiency, area spectral efficiency, and throughput. The effect of the number of clusters on these performance parameters is also discussed. The proposed algorithm has been further modified to allow for a tradeoff between fairness and other performance parameters. A comprehensive simulation study demonstrates that the proposed clustering algorithm is more flexible and outperforms several classical and state-of-the-art algorithms.
Accurate Ultrasound Indoor Localization Using Spring-Relaxation Technique
(MDPI (Basel, Switzerland), 2021-06) Chew MT; Alam F; Legg M; Sen Gupta G
This paper reports on the development of an ultrasonic sensing-based active localization system. The system employs an ultrasonic array to transmit chirp signals and time-of-flight measurement for ranging. The position of the receiver is estimated iteratively using the spring-relaxation technique. A median and 90-percentile error of 12.4 and 21.7 mm, respectively, were obtained for measurements at 625 positions within a 1.2 × 1.2 m area testbed. The spring-relaxation technique outperforms the widely adopted linear least square-based lateration technique while using the same ranging data. The performance of the system is benchmarked against that of visible light positioning using the same platform setup. The reported results show the ultrasonic system to be more accurate when compared with the visible light positioning system, which achieved median and 90-percentile errors of 33.7 and 58.6 mm, respectively.
Analysis of Depth Cameras for Proximal Sensing of Grapes
(MDPI (Basel, Switzerland), 2022-06) Parr B; Legg M; Alam F
This work investigates the performance of five depth cameras in relation to their potential for grape yield estimation. The technologies used by these cameras include structured light (Kinect V1), active infrared stereoscopy (RealSense D415), time of flight (Kinect V2 and Kinect Azure), and LiDAR (Intel L515). To evaluate their suitability for grape yield estimation, a range of factors were investigated including their performance in and out of direct sunlight, their ability to accurately measure the shape of the grapes, and their potential to facilitate counting and sizing of individual berries. The depth cameras’ performance was benchmarked using high-resolution photogrammetry scans. All the cameras except the Kinect V1 were able to operate in direct sunlight. Indoors, the RealSense D415 camera provided the most accurate depth scans of grape bunches, with a 2 mm average depth error relative to photogrammetric scans. However, its performance was reduced in direct sunlight. The time of flight and LiDAR cameras provided depth scans of grapes that had about an 8 mm depth bias. Furthermore, the individual berries manifested in the scans as pointed shape distortions. This led to an underestimation of berry sizes when applying the RANSAC sphere fitting but may help with the detection of individual berries with more advanced algorithms. Applying an opaque coating to the surface of the grapes reduced the observed distance bias and shape distortion. This indicated that these are likely caused by the cameras’ transmitted light experiencing diffused scattering within the grapes. More work is needed to investigate if this distortion can be used for enhanced measurement of grape properties such as ripeness and berry size.
Autonomous Fingerprinting and Large Experimental Data Set for Visible Light Positioning
(MDPI (Basel, Switzerland), 8/05/2021) Glass T; Alam F; Legg M; Noble F
This paper presents an autonomous method of collecting data for Visible Light Positioning (VLP) and a comprehensive investigation of VLP using a large set of experimental data. Received Signal Strength (RSS) data are efficiently collected using a novel method that utilizes consumer grade Virtual Reality (VR) tracking for accurate ground truth recording. An investigation into the accuracy of the ground truth system showed median and 90th percentile errors of 4.24 and 7.35 mm, respectively. Co-locating a VR tracker with a photodiode-equipped VLP receiver on a mobile robotic platform allows fingerprinting on a scale and accuracy that has not been possible with traditional manual collection methods. RSS data at 7344 locations within a 6.3 × 6.9 m test space fitted with 11 VLP luminaires is collected and has been made available for researchers. The quality and the volume of the data allow for a robust study of Machine Learning (ML)- and channel model-based positioning utilizing visible light. Among the ML-based techniques, ridge regression is found to be the most accurate, outperforming Weighted k Nearest Neighbor, Multilayer Perceptron, and random forest, among others. Model-based positioning is more accurate than ML techniques when a small data set is available for calibration and training. However, if a large data set is available for training, ML-based positioning outperforms its model-based counterparts in terms of localization accuracy.
CapLoc: Capacitive Sensing Floor for Device-Free Localization and Fall Detection
(IEEE Xplore, 12/10/2020) Faulkner N; Parr B; Alam F; Legg M; Demidenko S
Passive indoor positioning, also known as Device-Free Localization (DFL), has applications such as occupancy sensing, human-computer interaction, fall detection, and many other location-based services in smart buildings. Vision-, infrared-, wireless-based DFL solutions have been widely explored in recent years. They are characterized by respective strengths and weaknesses in terms of the desired accuracy, feasibility in various real-world scenarios, etc. Passive positioning by tracking the footsteps on the floor has been put forward as one of the promising options. This article introduces CapLoc, a floor-based DFL solution that can localize a subject in real-time using capacitive sensing. Experimental results with three individuals walking 39 paths on the CapLoc show that it can detect and localize a single target's footsteps accurately with a median localization error of 0.026 m. The potential for fall detection is also shown with the outlines of various poses of the subject lying upon the floor.
Device-Free Localization Using Privacy-Preserving Infrared Signatures Acquired from Thermopiles and Machine Learning
(IEEE, 4/06/2021) Faulkner N; Alam F; Legg M; Demidenko S
The development of an accurate passive localization system utilizing thermopile sensing and artificial intelligence is discussed in this paper. Several machine learning techniques are explored to create robust angular and radius coordinate models for a localization target with respect to thermopile sensors. These models are leveraged to develop a reconfigurable passive localization system that can use a varying number of thermopiles without the need for retraining. The proposed robust system achieves high localization accuracy (with the median error between 0.13 m and 0.2 m) while being trained using a single human subject and tested against multiple other subjects. It is shown that the proposed system does not experience any significant performance deterioration when localizing a subject at different ambient temperatures or with different configurations of the thermopile sensors placement.
Estimation of the Rod Velocity in Wood using Multi-frequency Guided Wave Measurements
(Elsevier Ltd, 2023-01) Bakar AHA; Legg M; Konings D; Alam F
This study presents a new approach for measuring the acoustic “rod velocity” in wood using guided wave measurements. The approach fits the acoustic guided wave longitudinal L(0,1) wave mode dispersion curve, through experimental guided wave phase velocity measurements taken over a range of frequencies. The rod velocity is obtained by measuring the phase velocity of the fitted L(0,1) wave mode dispersion curve at zero frequency. This technique is used to obtain rod velocity measurements for cylindrical wood and aluminium samples. The same approach was also performed on resonance measurements at a wide range of harmonics. These rod velocities are then compared to acoustic velocities obtained using the traditional time of flight and resonance methods.
Experimental Performance Analysis of a Scalable Distributed Hyperledger Fabric for a Large-Scale IoT Testbed
(MDPI (Basel, Switzerland), 2022-07) Pajooh HH; Rashid MA; Alam F; Demidenko S
Blockchain technology, with its decentralization characteristics, immutability, and traceability, is well-suited for facilitating secure storage, sharing, and management of data in decentralized Internet of Things (IoT) applications. Despite the increasing development of blockchain platforms, there is still no comprehensive approach for adopting blockchain technology in IoT systems. This is due to the blockchain’s limited capability to process substantial transaction requests from a massive number of IoT devices. Hyperledger Fabric (HLF) is a popular open-source permissioned blockchain platform hosted by the Linux Foundation. This article reports a comprehensive empirical study that measures HLF’s performance and identifies potential performance bottlenecks to better meet the requirements of blockchain-based IoT applications. The study considers the implementation of HLF on distributed large-scale IoT systems. First, a model for monitoring the performance of the HLF platform is presented. It addresses the overhead challenges while delivering more details on system performance and better scalability. Then, the proposed framework is implemented to evaluate the impact of varying network workloads on the performance of the blockchain platform in a large-scale distributed environment. In particular, the performance of the HLF is evaluated in terms of throughput, latency, network size, scalability, and the number of peers serviceable by the platform. The obtained experimental results indicate that the proposed framework can provide detailed real-time performance evaluation of blockchain systems for large-scale IoT applications.
Grape yield estimation with a smartphone’s colour and depth cameras using machine learning and computer vision techniques
(Elsevier, 2023-09-06) Parr B; Legg M; Alam F
A smartphone with both colour and time of flight depth cameras is used for automated grape yield estimation of Chardonnay grapes. A new technique is developed to automatically identify grape berries in the smartphone's depth maps. This utilises the distortion peaks in the depth map caused by diffused scattering of the light within each grape berry. This technique is then extended to allow unsupervised training of a YOLOv7 model for the detection of grape berries in the smartphone's colour images. A correlation coefficient (R2) of 0.946 was achieved when comparing the count of grape berries observed in RGB images to those accurately identified by YOLO. Additionally, an average precision score of 0.970 was attained. Two techniques are then presented to automatically estimate the size of the grape berries and generate 3D models of grape bunches using both colour and depth information.
How to measure light pollution - a systematic review of methods and applications
(Elsevier, 17/02/2023) Mander S; Alam F; Lovreglio R; Ooi M
Artificial light at night (ALAN) is ever-present in modern society and has revolutionised our lives. Along with its many benefits, ALAN can have adverse effects that are studied across many fields, including astronomy, epidemiology, and ecology. This paper collates multi-disciplinary knowledge on measuring light pollution through a systematic review of 140 articles. The measurement tools used in each context are comprehensively discussed, gaps are identified in current techniques, and benchmarking information is provided to aid future research. It is found that no single device meets all measurement objectives, and opportunities exist for further development, such as refining low-cost imaging tools. The paper highlights that each tool's limitations must be acknowledged to avoid incorrect conclusions. Thorough experimental reporting, including details on instrument location and aiming, is also essential for the replication of research. Collaboration is needed to develop unified measurement techniques, units and terminologies rather than maintaining disciplinary silos. This will enable light measurement to keep pace with lighting technologies and the growing effects of light pollution.
Hyperledger Fabric Blockchain for Securing the Edge Internet of Things
(MDPI (Basel, Switzerland), 7/01/2021) Pajooh HH; Rashid M; Alam F; Demidenko S
Providing security and privacy to the Internet of Things (IoT) networks while achieving it with minimum performance requirements is an open research challenge. Blockchain technology, as a distributed and decentralized ledger, is a potential solution to tackle the limitations of the current peer-to-peer IoT networks. This paper presents the development of an integrated IoT system implementing the permissioned blockchain Hyperledger Fabric (HLF) to secure the edge computing devices by employing a local authentication process. In addition, the proposed model provides traceability for the data generated by the IoT devices. The presented solution also addresses the IoT systems’ scalability challenges, the processing power and storage issues of the IoT edge devices in the blockchain network. A set of built-in queries is leveraged by smart-contracts technology to define the rules and conditions. The paper validates the performance of the proposed model with practical implementation by measuring performance metrics such as transaction throughput and latency, resource consumption, and network use. The results show that the proposed platform with the HLF implementation is promising for the security of resource-constrained IoT devices and is scalable for deployment in various IoT scenarios.
Identity and Gender Recognition Using a Capacitive Sensing Floor and Neural Networks
(MDPI AG, 23/09/2022) Konings D; Alam F; Faulkner N; de Jong C
In recent publications, capacitive sensing floors have been shown to be able to localize individuals in an unobtrusive manner. This paper demonstrates that it might be possible to utilize the walking characteristics extracted from a capacitive floor to recognize subject and gender. Several neural network-based machine learning techniques are developed for recognizing the gender and identity of a target. These algorithms were trained and validated using a dataset constructed from the information captured from 23 subjects while walking, alone, on the sensing floor. A deep neural network comprising a Bi-directional Long Short-Term Memory (BLSTM) provided the most accurate identity performance, classifying individuals with an accuracy of 98.12% on the test data. On the other hand, a Convolutional Neural Network (CNN) was the most accurate for gender recognition, attaining an accuracy of 93.3%. The neural network-based algorithms are benchmarked against Support Vector Machine (SVM), which is a classifier used in many reported works for floor-based recognition tasks. The majority of the neural networks outperform SVM across all accuracy metrics.
IoT Big Data provenance scheme using blockchain on Hadoop ecosystem
(BioMed Central Ltd, 2021-12) Honar Pajooh H; Rashid MA; Alam F; Demidenko S
The diversity and sheer increase in the number of connected Internet of Things (IoT) devices have brought significant concerns associated with storing and protecting a large volume of IoT data. Storage volume requirements and computational costs are continuously rising in the conventional cloud-centric IoT structures. Besides, dependencies of the centralized server solution impose significant trust issues and make it vulnerable to security risks. In this paper, a layer-based distributed data storage design and implementation of a blockchain-enabled large-scale IoT system are proposed. It has been developed to mitigate the above-mentioned challenges by using the Hyperledger Fabric (HLF) platform for distributed ledger solutions. The need for a centralized server and a third-party auditor was eliminated by leveraging HLF peers performing transaction verifications and records audits in a big data system with the help of blockchain technology. The HLF blockchain facilitates storing the lightweight verification tags on the blockchain ledger. In contrast, the actual metadata are stored in the off-chain big data system to reduce the communication overheads and enhance data integrity. Additionally, a prototype has been implemented on embedded hardware showing the feasibility of deploying the proposed solution in IoT edge computing and big data ecosystems. Finally, experiments have been conducted to evaluate the performance of the proposed scheme in terms of its throughput, latency, communication, and computation costs. The obtained results have indicated the feasibility of the proposed solution to retrieve and store the provenance of large-scale IoT data within the Big Data ecosystem using the HLF blockchain. The experimental results show the throughput of about 600 transactions, 500 ms average response time, about 2–3% of the CPU consumption at the peer process and approximately 10–20% at the client node. The minimum latency remained below 1 s however, there is an increase in the maximum latency when the sending rate reached around 200 transactions per second (TPS).
Low-Cost CO Sensor Calibration Using One Dimensional Convolutional Neural Network
(MDPI AG, 11/01/2023) Ali S; Alam F; Arif K; Potgieter J-G
The advent of cost-effective sensors and the rise of the Internet of Things (IoT) presents the opportunity to monitor urban pollution at a high spatio-temporal resolution. However, these sensors suffer from poor accuracy that can be improved through calibration. In this paper, we propose to use One Dimensional Convolutional Neural Network (1DCNN) based calibration for low-cost carbon monoxide sensors and benchmark its performance against several Machine Learning (ML) based calibration techniques. We make use of three large data sets collected by research groups around the world from field-deployed low-cost sensors co-located with accurate reference sensors. Our investigation shows that 1DCNN performs consistently across all datasets. Gradient boosting regression, another ML technique that has not been widely explored for gas sensor calibration, also performs reasonably well. For all datasets, the introduction of temperature and relative humidity data improves the calibration accuracy. Cross-sensitivity to other pollutants can be exploited to improve the accuracy further. This suggests that low-cost sensors should be deployed as a suite or an array to measure covariate factors.
Multi-Layer Blockchain-Based Security Architecture for Internet of Things
(MDPI (Basel, Switzerland), 2021-02) Pajooh HH; Rashid M; Alam F; Demidenko S
The proliferation of smart devices in the Internet of Things (IoT) networks creates significant security challenges for the communications between such devices. Blockchain is a decentralized and distributed technology that can potentially tackle the security problems within the 5G-enabled IoT networks. This paper proposes a Multi layer Blockchain Security model to protect IoT networks while simplifying the implementation. The concept of clustering is utilized in order to facilitate the multi-layer architecture. The K-unknown clusters are defined within the IoT network by applying techniques that utillize a hybrid Evolutionary Computation Algorithm while using Simulated Annealing and Genetic Algorithms. The chosen cluster heads are responsible for local authentication and authorization. Local private blockchain implementation facilitates communications between the cluster heads and relevant base stations. Such a blockchain enhances credibility assurance and security while also providing a network authentication mechanism. The open-source Hyperledger Fabric Blockchain platform is deployed for the proposed model development. Base stations adopt a global blockchain approach to communicate with each other securely. The simulation results demonstrate that the proposed clustering algorithm performs well when compared to the earlier reported approaches. The proposed lightweight blockchain model is also shown to be better suited to balance network latency and throughput as compared to a traditional global blockchain.
Occluded Grape Cluster Detection and Vine Canopy Visualisation Using an Ultrasonic Phased Array
(MDPI (Basel, Switzerland), 20/03/2021) Parr B; Legg M; Bradley S; Alam F
Grape yield estimation has traditionally been performed using manual techniques. However, these tend to be labour intensive and can be inaccurate. Computer vision techniques have therefore been developed for automated grape yield estimation. However, errors occur when grapes are occluded by leaves, other bunches, etc. Synthetic aperture radar has been investigated to allow imaging through leaves to detect occluded grapes. However, such equipment can be expensive. This paper investigates the potential for using ultrasound to image through leaves and identify occluded grapes. A highly directional low frequency ultrasonic array composed of ultrasonic air-coupled transducers and microphones is used to image grapes through leaves. A fan is used to help differentiate between ultrasonic reflections from grapes and leaves. Improved resolution and detail are achieved with chirp excitation waveforms and near-field focusing of the array. The overestimation in grape volume estimation using ultrasound reduced from 222% to 112% compared to the 3D scan obtained using photogrammetry or from 56% to 2.5% compared to a convex hull of this 3D scan. This also has the added benefit of producing more accurate canopy volume estimations which are important for common precision viticulture management processes such as variable rate applications.
SpringLoc: A device-free localization technique for indoor positioning and tracking using adaptive RSSI spring relaxation
(Institute of Electrical and Electronics Engineers (IEEE), 5/05/2019) Konings D; Alam F; Noble F; Lai E
Device-free localization (DFL) algorithms using the received signal strength indicator (RSSI) metrics have become a popular research focus in recent years as they allow for location-based service using commercial-off-the-shelf (COTS) wireless equipment. However, most existing DFL approaches have limited applicability in realistic smart home environments as they typically require extensive offline calibration, large node densities, or use technology that is not readily available in commercial smart homes. In this paper, we introduce SpringLoc and a DFL algorithm that relies on simple parameter tuning and does not require offline measurements. It localizes and tracks an entity using an adaptive spring relaxation approach. The anchor points of the artificial springs are placed in regions containing the links that are affected by the entity. The affected links are determined by comparing the kernel-based histogram distance of successive RSSI values. SpringLoc is benchmarked against existing algorithms in two diverse and realistic environments, showing significant improvement over the state-of-the-art, especially in situations with low-node deployment density.
Static Hand Gesture Recognition Using Capacitive Sensing and Machine Learning
(MDPI AG, 24/03/2023) Noble F; Xu M; Alam F
Automated hand gesture recognition is a key enabler of Human-to-Machine Interfaces (HMIs) and smart living. This paper reports the development and testing of a static hand gesture recognition system using capacitive sensing. Our system consists of a 6×18 array of capacitive sensors that captured five gestures-Palm, Fist, Middle, OK, and Index-of five participants to create a dataset of gesture images. The dataset was used to train Decision Tree, Naïve Bayes, Multi-Layer Perceptron (MLP) neural network, and Convolutional Neural Network (CNN) classifiers. Each classifier was trained five times; each time, the classifier was trained using four different participants' gestures and tested with one different participant's gestures. The MLP classifier performed the best, achieving an average accuracy of 96.87% and an average F1 score of 92.16%. This demonstrates that the proposed system can accurately recognize hand gestures and that capacitive sensing is a viable method for implementing a non-contact, static hand gesture recognition system.
The effects of dispersion on time-of-flight acoustic velocity measurements in a wooden rod
(Elsevier BV, 2023-03) Bakar AHA; Legg M; Konings D; Alam F
The stiffness of wood can be estimated from the acoustic velocity in the longitudinal direction. Studies have reported that stiffness measurements obtained using time-of-flight acoustic velocity measurements are overestimated compared to those obtained using the acoustic resonance and bending test methods. More research is needed to understand what is causing this phenomenon. In this work, amplitude threshold time-of-flight, resonance, and guided wave measurements are performed on wooden and aluminium rods. Using guided wave theory, it is shown through simulations and experimental results that dispersion causes an overestimation of time-of-flight measurements. This overestimation was able to be mitigated using dispersion compensation. However, other guided wave techniques could potentially be used to obtain improved measurements.

Browsing by Author "Alam F"

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