Browsing by Author "Asyhari, A. Taufiq"

Now showing 1 - 9 of 9
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    A 3D-collaborative wireless network: towards resilient communication for rescuing flood victims
    (IEEE, 2018-04-02) Rahman, Md. Arafatur; Hasan, Md. Munirul; Asyhari, A. Taufiq; Alam Bhuiyan, Md. Zakirul
    Every year, floods result in huge damage and devastation both to lives and properties all over the world. Much of this devastation and its prolonged effects result from a lack of collaboration among the rescue agents as a consequence of the lack of reliable and resilient communication platform in the disrupted and damaged environments. In order to counteract this issue, this paper aims to propose a three-dimensional (3D)- collaborative wireless network utilizing air, water and ground based communication infrastructures to support rescue missions in flood-affected areas. Through simulated Search and Rescue(SAR) activities, the effectiveness of the proposed network model is validated and its superiority over the traditional SAR is demonstrated, particularly in the harsh flood environments. The model of the 3D-Collaborative wireless network is expected to significantly assist the rescuing teams in accomplishing their task more effectively in the corresponding disaster areas.
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    An Assessment on the Hidden Ecological Factors of the Incidence of Malaria
    (MDPI, 2016-06-09) Modus, B.; Asyhari, A. Taufiq; Konur, S.; Peng, Y.
    Confounding effects of climatic factors temporally contribute to the prevalence of malaria. In this study, we explore a new framework for assessment and identification of hidden ecological factors to the incidence of malaria. A statistical technique, partial least squares path modeling and exploratory factor analysis, is employed to identify hidden ecological factors. Three hidden factors are identified: Factor I is related to minimum temperature and relative humidity, Factor II is related to maximum temperature and solar radiation and Factor III is related to precipitation and wind speed, respectively. Factor I is identified as the most influential hidden ecological factor of malaria incidence in the study area, as evaluated by communality and Dillon-Goldstein’s indices.
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    Collab-SAR: a collaborative avalanche search-and-rescue missions exploiting hostile alpine networks
    (IEEE, 2018-06-27) Rachman, M. A.; Azad, S.; Asyhari, A. Taufiq; Bhuiyan, M. Z. A.; Anwar, K.
    Every year, Alpine experiences a considerable number of avalanches causing danger to visitor and saviors, where most of the existing techniques to mitigate the number of fatalities in such hostile environments are based on a non-collaborative approach and is time- and effort-inefficient. A recently completed European project on Smart collaboration between Humans and ground-aErial Robots for imProving rescuing activities in Alpine environments (SHERPA) has proposed a novel collaborative approach to improve the rescuing activities. To be an integral part of the SHERPA framework, this paper considers deployment of an air-ground collaborative wireless network (AGCWN) to support search and rescue (SAR) missions in hostile alpine environments. We propose a network infrastructure for such challenging environments by considering the available network components, hostility of the environments, scenarios, and requirements. The proposed infrastructure also considers two degrees of quality of service, in terms of high throughput and long coverage range, to enable timely delivery of videos and images of the long patrolled area, which is the key in any searching and rescuing mission. We also incorporate a probabilistic search technique, which is suitable for collaborative search assuming AGCWN infrastructure for sharing information. The effectiveness of the proposed infrastructure and collaborative search technique, referred to as Collab-SAR, is demonstrated via a series of computer simulations. The results confirm the effectiveness of the proposal.
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    Crowd Associated Network: Exploiting over Smart Garbage Management System
    (IEEE, 2017-07-14) Azad, S.; Rahman, A.; Asyhari, A. Taufiq; Pathan, A. K.
    Most existing non-real-time applications utilize infrastructure-based or semi-infrastructure-based network architectures. Such a network architecture demands a considerably high installment and maintenance cost. To alleviate the cost, in this article, we propose an efficient infrastructure-less network architecture named CrAN. In CrAN, a set of crowds play significant roles by completing the communication gaps among various associates in the network; hence the name. We show the usability of this proposed architecture to support non-real-time data transmission over an SGMS, where optimum solutions need to be discovered to minimize the management cost. Due to the complexity of the optimization problem, we approximate these optimum solutions using a GA. In the implementation of the GA, we apply new fitness functions to discover a feasible trade-off between distance and waste volume. We then compare the performance of the proposed fitness functions with that of an existing fitness function. The results favorably suggest the necessity of employing the proposed fitness functions to obtain near-optimum solutions.
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    IMPACT: Impersonation attack detection via edge computing using deep auto encoder and feature abstraction
    (IEEE, 2020-04-02) Lee, Seo Jin; Yoo, Paul D.; Asyhari, A. Taufiq; Jhi, Yoonchan; Chermak, Lounis; Yeun, Chan Yeob; Taha, Kamal
    An ever-increasing number of computing devices interconnected through wireless networks encapsulated in the cyber-physical-social systems and a significant amount of sensitive network data transmitted among them have raised security and privacy concerns. Intrusion detection system (IDS) is known as an effective defence mechanism and most recently machine learning (ML) methods are used for its development. However, Internet of Things (IoT) devices often have limited computational resources such as limited energy source, computational power and memory, thus, traditional ML-based IDS that require extensive computational resources are not suitable for running on such devices. This study thus is to design and develop a lightweight ML-based IDS tailored for the resource-constrained devices. Specifically, the study proposes a lightweight ML-based IDS model namely IMPACT (IMPersonation Attack deteCTion using deep auto-encoder and feature abstraction). This is based on deep feature learning with gradient-based linear Support Vector Machine (SVM) to deploy and run on resource-constrained devices by reducing the number of features through feature extraction and selection using a stacked autoencoder (SAE), mutual information (MI) and C4.8 wrapper. The IMPACT is trained on Aegean Wi-Fi Intrusion Dataset (AWID) to detect impersonation attack. Numerical results show that the proposed IMPACT achieved 98.22% accuracy with 97.64% detection rate and 1.20% false alarm rate and outperformed existing state-of-the-art benchmark models. Another key contribution of this study is the investigation of the features in AWID dataset for its usability for further development of IDS.
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    L-CAQ: Joint link-oriented channel-availability and channel-quality based channel selection for mobile cognitive radio networks
    (Elsevier, 2018-03-27) Rahman, Md. Arafatur; Asyhari, A. Taufiq; Bhuiyan, Md. Zakirul A.; Salih, Q. M.; Zamli, Kamal Zuhairi Bin
    Channel availability probability (CAP) and channel quality (CQ) are two key metrics that can be used to efficiently design a channel selection strategy in cognitive radio networks. For static scenarios, i.e., where all the users are immobile, the CAP metric depends only on the primary users' activity whereas the CQ metric remains relatively constant. In contrast, for mobile scenarios, the values of both metrics fluctuate not only with time (time-variant) but also over different links between users (link-variant) due to the dynamic variation of primary- and secondary-users' relative positions. As an attempt to address this dynamic fluctuation, this paper proposes L-CAQ: a link-oriented channel-availability and channel-quality based channel selection strategy that aims to maximize the link throughput. The L-CAQ scheme considers accurate estimation of the aforementioned two channel selection metrics, which are governed by the mobility-induced non-stationary network topology, and endeavors to select a channel that jointly maximizes the CAP and CQ. The benefits of the proposed scheme are demonstrated through numerical simulation for mobile cognitive radio networks.
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    Orthogonal or superimposed pilots? a rate-efficient channel estimation strategy for stationary MIMO fading channels
    (IEEE, 2017-03-17) Asyhari, A. Taufiq; ten Brink, S.
    This paper considers channel estimation for multiple-input multiple-output (MIMO) channels and revisits two competing concepts of including training data into the transmit signal, namely orthogonal pilot (OP) that periodically transmits alternating pilot-data symbols, and superimposed pilot (SP) that overlays pilot-data symbols over time. We investigate rates achievable by both schemes when the channel undergoes time-selective bandlimited fading and analyze their behaviors with respect to the MIMO dimension and fading speed. By incorporating the multiple-antenna factors, we demonstrate that the widely-known trend, in which the OP is superior to the SP in the regimes of high signal-to-noise ratio (SNR) and slow-fading, and vice-versa, does not hold in general. As the number of transmit antennas (nt) increases, the range of operable fading speeds for the OP is significantly narrowed due to limited time resources for channel estimation and insufficient fading samples, which results in the SP being competitive in wider speed and SNR ranges. For a sufficiently small nt, we demonstrate thatas the fading variation becomes slower, the estimation quality for the SP can be superior to that for the OP. In this case, the SP outperforms the OP in the slow-fading regime due to full utilization of time for data transmission.
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    Towards a Predictive Analytics-Based Intelligent Malaria Outbreak Warning System
    (MDPI, 2017-08-17) Modu, B.; Polovina, N.; Lan, Y.; Konur, S.; Asyhari, A. Taufiq; Peng, Y.
    Malaria, as one of the most serious infectious diseases causing public health problems in the world, affects about two-thirds of the world population, with estimated resultant deaths close to a million annually. The effects of this disease are much more profound in third world countries, which have very limited medical resources. When an intense outbreak occurs, most of these countries cannot cope with the high number of patients due to the lack of medicine, equipment and hospital facilities. The prevention or reduction of the risk factor of this disease is very challenging, especially in third world countries, due to poverty and economic insatiability. Technology can offer alternative solutions by providing early detection mechanisms that help to control the spread of the disease and allow the management of treatment facilities in advance to ensure a more timely health service, which can save thousands of lives. In this study, we have deployed an intelligent malaria outbreak early warning system, which is a mobile application that predicts malaria outbreak based on climatic factors using machine learning algorithms. The system will help hospitals, healthcare providers, and health organizations take precautions in time and utilize their resources in case of emergency. To our best knowledge, the system developed in this paper is the first publicly available application. Since confounding effects of climatic factors have a greater influence on the incidence of malaria, we have also conducted extensive research on exploring a new ecosystem model for the assessment of hidden ecological factors and identified three confounding factors that significantly influence the malaria incidence. Additionally, we deploy a smart healthcare application; this paper also makes a significant contribution by identifying hidden ecological factors of malaria.
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    Transposition errors in diffusion-based mobile molecular communication
    (IEEE, 2017-06-06) Haselmayr, W.; Aejaz, S. M. H.; Asyhari, A. Taufiq; Springer, A.; Guo, W.
    In this work, we investigate diffusion-based molecular communication between two mobile nano-machines. We derive a closed-form expression for the first hitting time distribution, by characterizing the motion of the information particles and the nano-machines via Brownian motion. We validate the derived expression through a particle-based simulation. For the information transfer we consider single particles of different types, where transposition errors are the dominant source of errors. We derive an analytical expression for the expected bit error probability and evaluate the error performance for the static and the mobile case by means of computer simulations.