Recent advances in Wi-Fi sensing have ushered in a plethora of pervasive applications in home surveillance, remote healthcare, road safety, and home entertainment, among others. Most of the existing works are limited to the activity classification of a single human subject at a given time. Conversely, a more realistic scenario is to achieve simultaneous, multi-subject activity classification. The first key challenge in that context is that the number of classes grows exponentially with the number of subjects and activities. Moreover, it is known that Wi-Fi sensing systems struggle to adapt to new environments and subjects. To address both issues, we propose SiMWiSense, the first framework for simultaneous multi-subject activity classification based on Wi-Fi that generalizes to multiple environments and subjects. We address the scalability issue by using the Channel State Information (CSI) computed from the device positioned closest to the subject. We experimentally prove this intuition by confirming that the best accuracy is experienced when the CSI computed by the transceiver positioned closest to the subject is used for classification. To address the generalization issue, we develop a brand-new few-shot learning algorithm named Feature Reusable Embedding Learning (FREL). Through an extensive data collection campaign in 3 different environments and 3 subjects performing 20 different activities simultaneously, we demonstrate that SiMWiSense achieves classification accuracy of up to 97%, while FREL improves the accuracy by 85% in comparison to a traditional Convolutional Neural Network (CNN) and up to 20% when compared to the state-of-the-art few-shot embedding learning (FSEL), by using only 15 seconds of additional data for each class. For reproducibility purposes, we share our 1 TB dataset and code repository: https://github.com/kfoysalhaque/SiMWiSense
@inproceedings{10195411,author={Haque, Khandaker Foysal and Zhang, Milin and Restuccia, Francesco},abbr={WoWMoM},booktitle={2023 IEEE 24th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)},title={SiMWiSense: Simultaneous Multi-Subject Activity Classification Through Wi-Fi Signals},year={2023},volume={},number={},html={https://ieeexplore.ieee.org/document/10195411},pages={46-55},pdf={SiMWiSense.pdf},bibtex_show={true},selected={true},doi={10.1109/WoWMoM57956.2023.00019}}
WiNTECH
Wi-BFI: Extracting the IEEE 802.11 Beamforming Feedback Information from Commercial Wi-Fi Devices
Haque, Khandaker Foysal, Meneghello, Francesca, and Restuccia, Francesco
In Proceedings of the 17th ACM Workshop on Wireless Network Testbeds, Experimental Evaluation & Characterization 2023
Recently, researchers have shown that the beamforming feedback angles (BFAs) used for Wi-Fi multiple-input multiple-output (MIMO) operations can be effectively leveraged as a proxy of the channel frequency response (CFR) for different purposes. Examples are passive human activity recognition and device fingerprinting. However, even though the BFAs report frames are sent in clear text, there is not yet a unified open-source tool to extract and decode the BFAs from the frames. To fill this gap, we developed Wi-BFI, the first tool that allows retrieving Wi-Fi BFAs and reconstructing the beamforming feedback information (BFI) – a compressed representation of the CFR – from the BFAs frames captured over the air. The tool supports BFAs extraction within both IEEE 802.11ac and 802.11ax networks operating on radio channels with 160/80/40/20 MHz bandwidth. Both multi-user and single-user MIMO feedback can be decoded through Wi-BFI. The tool supports real-time and offline extraction and storage of BFAs and BFI. The real-time mode also includes a visual representation of the channel state that continuously updates based on the collected data. Wi-BFI code is open source and the tool is also available as a pip package1.
@inproceedings{10.1145/3615453.3616514,author={Haque, Khandaker Foysal and Meneghello, Francesca and Restuccia, Francesco},title={Wi-BFI: Extracting the IEEE 802.11 Beamforming Feedback Information from Commercial Wi-Fi Devices},abbr={WiNTECH},year={2023},isbn={9798400703409},publisher={Association for Computing Machinery},address={New York, NY, USA},url={https://doi.org/10.1145/3615453.3616514},doi={10.1145/3615453.3616514},booktitle={Proceedings of the 17th ACM Workshop on Wireless Network Testbeds, Experimental Evaluation \& Characterization},pages={104–111},numpages={8},pdf={Wi-BFI.pdf},bibtex_show={true},selected={true},html={https://dl.acm.org/doi/10.1145/3615453.3616514},keywords={beamforming, Wi-Fi, compressed beamforming feedback, multiple-input multiple-output (MIMO)},location={Madrid, Spain},series={WiNTECH '23}}
arXiv
BeamSense: Rethinking Wireless Sensing with MU-MIMO Wi-Fi Beamforming Feedback
Haque, Khandaker Foysal, Zhang, Milin, Meneghello, Francesca, and Restuccia, Francesco
In this paper, we propose BeamSense– a completely novel approach to implement standard compliant Wi-Fi sensing applications. Wi-Fi sensing enables game-changing applications in remote healthcare, home entertainment, and home surveillance, among others. However, existing work leverages the manual extraction of channel state information (CSI) from Wi-Fi chips to classify activities, which is not supported by the Wi-Fi standard and hence requires the usage of specialized equipment. On the contrary, BeamSense leverages the standard-compliant beamforming feedback information (BFI) to characterize the propagation environment. Conversely from CSI, the BFI (i) can be easily recorded without any firmware modification, and (ii) captures the multiple channels between the access point and the stations, thus providing much better sensitivity. BeamSense includes a novel cross-domain few-shot learning (FSL) algorithm to handle unseen environments and subjects with few additional data points. We evaluate BeamSense through an extensive data collection campaign with three subjects performing twenty different activities in three different environments. We show that our BFI-based approach achieves about 10% more accuracy when compared to CSI-based prior work, while our FSL strategy improves accuracy by up to 30% and 80% when compared with state-of-the-art cross-domain algorithms.
@article{haque2023beamsense,title={BeamSense: Rethinking Wireless Sensing with MU-MIMO Wi-Fi Beamforming Feedback},author={Haque, Khandaker Foysal and Zhang, Milin and Meneghello, Francesca and Restuccia, Francesco},journal={arXiv preprint arXiv:2303.09687},abbr={arXiv},pdf={BeamSense.pdf},bibtex_show={true},selected={true},html={https://arxiv.org/abs/2303.09687},year={2023}}
2022
Journal
Comprehensive Performance Analysis of Zigbee Communication: An Experimental Approach with XBee S2C Module
Haque, Khandaker Foysal, Abdelgawad, Ahmed, and Yelamarthi, Kumar
The recent development of wireless communications has prompted many diversified applications in both industrial and medical sectors. Zigbee is a short-range wireless communication standard that is based on IEEE 802.15.4 and is vastly used in both indoor and outdoor applications. Its performance depends on networking parameters, such as baud rates, transmission power, data encryption, hopping, deployment environment, and transmission distances. For optimized network deployment, an extensive performance analysis is necessary. This would facilitate a clear understanding of the trade-offs of the network performance metrics, such as the packet delivery ratio (PDR), power consumption, network life, link quality, latency, and throughput. This work presents an extensive performance analysis of both the encrypted and unencrypted Zigbee with the stated metrics in a real-world testbed, deployed in both indoor and outdoor scenarios. The major contributions of this work include (i) evaluating the most optimized transmission power level of Zigbee, considering packet delivery ratio and network lifetime; (ii) formulating an algorithm to find the network lifetime from the measured current consumption of packet transmission; and (iii) identifying and quantizing the trade-offs of the multi-hop communication and data encryption with latency, transmission range, and throughput.
@article{haque2022comprehensive,abbr={Journal},title={Comprehensive Performance Analysis of Zigbee Communication: An Experimental Approach with XBee S2C Module},author={Haque, Khandaker Foysal and Abdelgawad, Ahmed and Yelamarthi, Kumar},journal={Sensors,},volume={22},number={9},pages={3245},year={2022},html={https://www.mdpi.com/1424-8220/22/9/3245},bibtex_show={true},publisher={Multidisciplinary Digital Publishing Institute},pdf={sensors_zigbee.pdf},selected={false}}
Journal
Convolutional-Neural-Network-Based Handwritten Character Recognition: An Approach with Massive Multisource Data
Saqib, Nazmus,
Haque, Khandaker Foysal, Yanambaka, Venkata Prasanth, and Abdelgawad, Ahmed
Neural networks have made big strides in image classification. Convolutional neural
networks (CNN) work successfully to run neural networks on direct images. Handwritten character
recognition (HCR) is now a very powerful tool to detect traffic signals, translate language, and extract
information from documents, etc. Although handwritten character recognition technology is in
use in the industry, present accuracy is not outstanding, which compromises both performance and
usability. Thus, the character recognition technologies in use are still not very reliable and need further
improvement to be extensively deployed for serious and reliable tasks. On this account, characters
of the English alphabet and digit recognition are performed by proposing a custom-tailored CNN
model with two different datasets of handwritten images, i.e., Kaggle and MNIST, respectively, which
are lightweight but achieve higher accuracies than state-of-the-art models. The best two models from
the total of twelve designed are proposed by altering hyper-parameters to observe which models
provide the best accuracy for which dataset. In addition, the classification reports (CRs) of these
two proposed models are extensively investigated considering the performance matrices, such as
precision, recall, specificity, and F1 score, which are obtained from the developed confusion matrix
(CM). To simulate a practical scenario, the dataset is kept unbalanced and three more averages for the
F measurement (micro, macro, and weighted) are calculated, which facilitates better understanding
of the performances of the models. The highest accuracy of 99.642% is achieved for digit recognition,
with the model using ‘RMSprop’, at a learning rate of 0.001, whereas the highest detection accuracy
for alphabet recognition is 99.563%, which is obtained with the proposed model using ‘ADAM’
optimizer at a learning rate of 0.00001. The macro F1 and weighted F1 scores for the best two models
are 0.998, 0.997:0.992, and 0.996, respectively, for digit and alphabet recognition.
@article{saqib2022convolutional,abbr={Journal},title={Convolutional-Neural-Network-Based Handwritten Character Recognition: An Approach with Massive Multisource Data},author={Saqib, Nazmus and Haque, Khandaker Foysal and Yanambaka, Venkata Prasanth and Abdelgawad, Ahmed},journal={Algorithms},volume={15},number={4},pages={129},year={2022},html={https://www.mdpi.com/1999-4893/15/4/129},publisher={MDPI},pdf={algorithms_mnist.pdf},bibtex_show={true},selected={false}}
2021
Conf.
D2D-LoRa Latency Analysis: An Indoor Application Perspective
Saqib, Nazmus,
Haque, Khandaker Foysal, Yelamarthi, Kumar, Yanambaka, Prasanath, and Abdelgawad, Ahmed
In 2021 IEEE 7th World Forum on Internet of Things (WF-IoT) 2021
LoRaWAN is one of the popular Internet of
Things (IoT) wireless technologies due to its versatility, long
transmission range, and low power communication capabilities.
In LoRaWAN, the data from the source to the destination is
routed through the gateways, increasing the communication
latency. The higher latency being a barrier in real-time
applications has prompted researchers to employ the physical
(PHY) layer of the LoRaWAN protocol -commonly termed as
LoRa, which utilizes the Device-to-Device (D2D) based
communication techniques to minimize the communication
latency. However, an extensive analysis of the D2D-LoRa is
needed for optimizing and better designing the network, which
is missing in the current literature. To address the void, this
paper analyzes the latency performance of the D2D based LoRa
by varying Spreading Factors (SF) and bandwidths and
explores the trade-offs with experimental deployment in a 110
m long indoor environment. The evaluation shows that with SF 7 and bandwidth 500 kHz, the communication latency is
minimum which is 33.67 ms at 0 m and 53 ms at 110 m for the
data packet of 13 bytes for each of the cases
@inproceedings{saqib2021d2d,abbr={Conf.},pdf={D2D_indoor.pdf},html={https://ieeexplore.ieee.org/abstract/document/9595324},title={D2D-LoRa Latency Analysis: An Indoor Application Perspective},author={Saqib, Nazmus and Haque, Khandaker Foysal and Yelamarthi, Kumar and Yanambaka, Prasanath and Abdelgawad, Ahmed},booktitle={2021 IEEE 7th World Forum on Internet of Things (WF-IoT)},pages={29--34},year={2021},selected={false},bibtex_show={true},organization={IEEE}}
Chapter
Prospects of Internet of Things (IoT) and Machine Learning to Fight Against COVID-19
Haque, Khandaker Foysal, and Abdelgawad, Ahmed
In 2021 IEEE 7th World Forum on Internet of Things (WF-IoT) 2021
IoT and Machine Learning has improved multi-fold in recent years and
they have been playing a great role in healthcare systems which includes detecting, screening and monitoring of the patients. IoT has been successfully detecting
different heart diseases, Alzheimer disease, helping autism patients and monitoring
patients’ health condition with much lesser cost but providing better efficiency, reliability and accuracy. IoT also has a great prospect in fighting against COVID-19. This
chapter discusses different aspects of IoT in aiding healthcare systems for detecting
and monitoring Coronavirus patients. Two such IoT based models are also designed
for automatic thermal monitoring and for measuring and real-time monitoring of
heart rate with wearable IoT devices. Convolutional Neural Networks (CNN) is a
Machine Learning algorithm that has been performing well in detecting many diseases including Coronary Artery Disease, Malaria, Alzheimer’s disease, different
dental diseases, and Parkinson’s disease. Like other cases, CNN has a substantial
prospect in detecting COVID-19 patients with medical images like chest X-rays and
CTs. Detecting Corona positive patients is very important in preventing the spread of
this virus. On this conquest, a CNN model is proposed to detect COVID-19 patients
from chest X-ray images. Two CNN models with different number of convolution
layers and three other models based on ResNet50, VGG-16 and VGG-19 are evaluated with comparative analytical analysis. The proposed model performs with an
accuracy of 97.5% and a precision of 97.5%. This model gives the Receiver Operating
Characteristic (ROC) curve area of 0.975 and F1-score of 97.5. It can be improved
further by increasing the dataset for training the model.
@incollection{haque2021prospects,abbr={Chapter},title={Prospects of Internet of Things (IoT) and Machine Learning to Fight Against COVID-19},author={Haque, Khandaker Foysal and Abdelgawad, Ahmed},booktitle={Advanced Systems for Biomedical Applications},pages={93--109},year={2021},selected={false},pdf={book_chapter_covid.pdf},html={https://link.springer.com/chapter/10.1007/978-3-030-71221-1_5},bibtex_show={true},publisher={Springer}}
2020
Conf.
A LoRa Based Reliable and Low Power Vehicle to Everything (V2X) Communication Architecture
The industrial development of the last few decades has prompt to increase in the number of vehicles multi-fold. With the increased number of vehicles on the road, safety has become one of the major concerns. Inter vehicular communication, specially Vehicle to Everything (V2X) communication can address these pressing issues including autonomous traffic systems and autonomous driving. Extensive research is going on to develop a reliable V2X communication architecture with different wireless technologies like Long Range (LoRa) communication, Zigbee, LTE, and 5G. The reliability and effectiveness of V2X communication greatly depend on communication architecture and the associated wireless technology. In this conquest, a LoRa based reliable, robust, and low power V2X communication architecture is proposed in this paper. The communication architecture is designed, implemented, and tested in a real world scenario to evaluate its reliability. Testing and analysis suggest a vehicle in the road can communicate reliably with roadside infrastructures at different speeds ranging from (10-30) Miles per Hour (MPH) with the proposed architecture. At 10 MPH, a vehicle sends one data packet of 40 bytes every 27 meters and at 30 MPH, it sends the same data packet every 53 meters with smooth transitioning from communicating with one infrastructure to another.
@inproceedings{haque2020lora,abbr={Conf.},title={A LoRa Based Reliable and Low Power Vehicle to Everything (V2X) Communication Architecture},author={Haque, Khandaker Foysal and Abdelgawad, Ahmed and Yanambaka, Venkata P and Yelamarthi, Kumar},booktitle={2020 IEEE International Symposium on Smart Electronic Systems (iSES)(Formerly iNiS)},pages={177--182},html={https://ieeexplore.ieee.org/abstract/document/9426120},year={2020},organization={IEEE},pdf={v2v_conf.pdf},bibtex_show={true},selected={false}}
Journal
Lora architecture for v2x communication: An experimental evaluation with vehicles on the move
The industrial development of the last few decades has prompted an increase in the
number of vehicles by multiple folds. With the increased number of vehicles on the road, safety has
become one of the primary concerns. Inter vehicular communication, specially Vehicle to Everything
(V2X) communication can address these pressing issues including autonomous traffic systems and
autonomous driving. The reliability and effectiveness of V2X communication greatly depends on
communication architecture and the associated wireless technology. Addressing this challenge, a
device-to-device (D2D)-based reliable, robust, and energy-efficient V2X communication architecture
is proposed with LoRa wireless technology. The proposed system takes a D2D communication
approach to reduce the latency by offering direct vehicle-to-vehicle (V2V) and vehicle-to-infrastructure
(V2I) communication, rather than routing the data via the LoRa WAN server. Additionally, the
proposed architecture offers modularity and compact design, making it ideal for legacy systems
without requiring any additional hardware. Testing and analysis suggest the proposed system
can communicate reliably with roadside infrastructures and other vehicles at speeds ranging from
15–50 km per hour (kmph). The data packet consists of 12 bytes of metadata and 28 bytes of payload.
At 15 kmph, a vehicle sends one data packet every 25.9 m, and at 50 kmph, it sends the same data
packet every 53.34 m with reliable transitions.
@article{haque2020lorb,abbr={Journal},title={Lora architecture for v2x communication: An experimental evaluation with vehicles on the move},author={Haque, Khandaker Foysal and Abdelgawad, Ahmed and Yanambaka, Venkata Prasanth and Yelamarthi, Kumar},journal={Sensors},volume={20},number={23},pages={6876},year={2020},pdf={lora_V2X.pdf},selected={false},bibtex_show={true},html={https://www.mdpi.com/1424-8220/20/23/6876},publisher={MDPI}}
Conf.
Automatic detection of COVID-19 from chest X-ray images with convolutional neural networks
Haque, Khandaker Foysal, Haque, Fatin Farhan, Gandy, Lisa, and Abdelgawad, Ahmed
In 2020 international conference on computing, electronics & communications engineering (iCCECE) 2020
Deep Learning has improved multi-fold in recent
years and it has been playing a great role in image classification w hich a lso i ncludes m edical i maging. Convolutional
Neural Networks (CNN) has been performing well in detecting
many diseases including Coronary Artery Disease, Malaria,
Alzheimer’s disease, different dental diseases, and Parkinson’s
disease. Like other cases, CNN has a substantial prospect in
detecting COVID-19 patients with medical images like chest Xrays and CTs. Coronavirus or COVID-19 has been declared a
global pandemic by the World Health Organization (WHO). Till
July 11, 2020, the total COVID-19 confirmed c ases a re 1 2.32 M
and deaths are 0.556 M worldwide. Detecting Corona positive
patients is very important in preventing the spread of this virus.
On this conquest, a CNN model is proposed to detect COVID-19
patients from chest X-ray images. This model is evaluated with
a comparative analysis of two other CNN models. The proposed
model performs with an accuracy of 97.56% and a precision of
95.34%. This model gives the Receiver Operating Characteristic
(ROC) curve area of 0.976 and F1-score of 97.61. It can be
improved further by increasing the dataset for training the model
@inproceedings{haque2020automatic,title={Automatic detection of COVID-19 from chest X-ray images with convolutional neural networks},abbr={Conf.},author={Haque, Khandaker Foysal and Haque, Fatin Farhan and Gandy, Lisa and Abdelgawad, Ahmed},booktitle={2020 international conference on computing, electronics \& communications engineering (iCCECE)},pages={125--130},year={2020},pdf={covid_conf.pdf},selected={false},html={https://ieeexplore.ieee.org/abstract/document/9231235},bibtex_show={true},organization={IEEE}}
Journal
A deep learning approach to detect COVID-19 patients from chest X-ray images
Deep Learning has improved multi-fold in recent years and it has been playing a great role in image classification which also includes medical imaging. Convolutional Neural Networks (CNNs) have been performing well in detecting many diseases including coronary artery disease, malaria, Alzheimer’s disease, different dental diseases, and Parkinson’s disease. Like other cases, CNN has a substantial prospect in detecting COVID-19 patients with medical images like chest X-rays and CTs. Coronavirus or COVID-19 has been declared a global pandemic by the World Health Organization (WHO). As of 8 August 2020, the total COVID-19 confirmed cases are 19.18 M and deaths are 0.716 M worldwide. Detecting Coronavirus positive patients is very important in preventing the spread of this virus. On this conquest, a CNN model is proposed to detect COVID-19 patients from chest X-ray images. Two more CNN models with different number of convolution layers and three other models based on pretrained ResNet50, VGG-16 and VGG-19 are evaluated with comparative analytical analysis. All six models are trained and validated with Dataset 1 and Dataset 2. Dataset 1 has 201 normal and 201 COVID-19 chest X-rays whereas Dataset 2 is comparatively larger with 659 normal and 295 COVID-19 chest X-ray images. The proposed model performs with an accuracy of 98.3% and a precision of 96.72% with Dataset 2. This model gives the Receiver Operating Characteristic (ROC) curve area of 0.983 and F1- score of 98.3 with Dataset 2. Moreover, this work shows a comparative analysis of how change in convolutional layers and increase in dataset affect classifying performances.
@article{haque2020deep,title={A deep learning approach to detect COVID-19 patients from chest X-ray images},abbr={Journal},author={Haque, Khandaker Foysal and Abdelgawad, Ahmed},journal={AI},volume={1},number={3},pages={27},year={2020},selected={false},pdf={covid_journal.pdf},html={https://www.mdpi.com/2673-2688/1/3/27/htm},bibtex_show={true},publisher={MDPI}}
Journal
Advancement of routing protocols and applications of underwater wireless sensor network (UWSN)—A survey
Haque, Khandaker Foysal, Kabir, K Habibul, and Abdelgawad, Ahmed
Water covers a greater part of the Earth’s surface. However, little knowledge has been
achieved regarding the underwater world as most parts of it remain unexplored. Oceans, including
other water bodies, hold substantial natural resources and also the aquatic lives. These are mostly
undiscovered and unknown due to the unsuited and hazardous underwater environments for
the human. This inspires the unmanned exploration of these dicey environments. Neither unmanned
exploration nor the distant real-time monitoring is possible without deploying Underwater Wireless
Sensor Network (UWSN). Consequently, UWSN has drawn the interests of the researchers recently.
This vast underwater world is possible to be monitored remotely from a distant location with much
ease and less risk. The UWSN is required to be deployed over the volume of the water body to
monitor and surveil. For vast water bodies like oceans, rivers and large lakes, data is collected from
the different heights/depths of the water level which is then delivered to the surface sinks. Unlike
terrestrial communication and radio waves, conventional mediums do not serve the purpose of
underwater communication due to their high attenuation and low underwater-transmission range.
Instead, an acoustic medium is able to transmit data in underwater more efficiently and reliably in
comparison to other mediums. To transmit and relay the data reliably from the bottom of the sea
to the sinks at the surface, multi-hop communication is utilized with different schemes. For seabed
to surface sink communication, leading researchers proposed different routing protocols. The goal
of these routing protocols is to make underwater communications more reliable, energy-efficient
and delay efficient. This paper surveys the advancement of some of the routing protocols which
eventually helps in finding the most efficient routing protocol and some recent applications for the
UWSN. This work also summarizes the remaining challenging issues and the future trends of those
considered routing protocols. This survey encourages further research efforts to improve the routing
protocols of UWSN for enhanced underwater monitoring and exploration.
@article{haque2020advancement,abbr={Journal},title={Advancement of routing protocols and applications of underwater wireless sensor network (UWSN)—A survey},author={Haque, Khandaker Foysal and Kabir, K Habibul and Abdelgawad, Ahmed},journal={Journal of Sensor and Actuator Networks},volume={9},number={2},pages={19},year={2020},selected={false},pdf={UWSN_journal.pdf},bibtex_show={true},html={https://www.mdpi.com/2224-2708/9/2/19},publisher={MDPI}}
Conf.
An IoT based efficient waste collection system with smart bins
Haque, Khandaker Foysal, Zabin, Rifat, Yelamarthi, Kumar, Yanambaka, Prasanth, and Abdelgawad, Ahmed
In 2020 IEEE 6th World Forum on Internet of Things (WF-IoT) 2020
Waste collection and management is an integrated
part of both city and village life. Lack of optimized and efficient
waste collection system vastly affect public health and costs
more. The prevailing traditional waste collection system is neither
optimized nor efficient. Internet of Things (IoT) has been playing
a great role in making human life easier by making systems
smart, adequate and self-sufficient. Thus, this paper proposes an
IoT based efficient waste collection system with smart bins. It
does real-time monitoring of the waste bins and determines which
bins are to emptied in every cycle of waste collection. The system
also presents an enhanced navigation system that shows the best
route to collect wastes from the selected bins. Four waste bins
are assumed in the city of Mount Pleasant, Michigan at random
location. The proposed system decreases the travel distance by
30.76% on an average in the assumed scenario, compared to the
traditional waste collection system. Thus it reduces the fuel cost
and human labor making the system optimized and efficient by
enabling real-time monitoring and enhanced navigation
@inproceedings{haque2020iot,abbr={Conf.},title={An IoT based efficient waste collection system with smart bins},author={Haque, Khandaker Foysal and Zabin, Rifat and Yelamarthi, Kumar and Yanambaka, Prasanth and Abdelgawad, Ahmed},booktitle={2020 IEEE 6th World Forum on Internet of Things (WF-IoT)},pages={1--5},year={2020},pdf={smart_bins.pdf},bibtex_show={true},selected={false},html={https://ieeexplore.ieee.org/abstract/document/9221251},organization={IEEE}}
Routing Protocol for Low-Power and Lossy Networks (RPL) is an IPv6 routing protocol that is standardized
for the Internet of Things (IoT) by Internet-Engineering Task
Force (IETF). RPL forms a tree-like topology which is based
on different optimizing process called Objective Function (OF).
In most cases, IoT has to deal with low power devices and lossy
networks. So, the major constraints of the RPL are limited power
source, network life time and reliability of the network. OFs
depend on different metrics like Expected Transmission Count
(ETX), Energy, Received Signal Strength Indicator (RSSI) for
route optimization. In this work, the ETX and Energy based OF
have been evaluated in terms of energy-efficiency and reliability.
For one sink and nine senders, the simulated average power
consumption is 1.291 mW and 1.56 mW respectively, for ETX
OF and Energy OF. On the other hand, the average hop count for
ETX OF is 1.89, which is 3.01 for Energy OF. Thus, ETX OF is
more energy-efficient but it is not reliable as it takes fewer hops
with long distances. Moreover, it does not take load balancing and
link quality into account. However, Energy OF is more reliable
due to short hops, but it is not energy efficient and sometimes it
might take unnecessary hops
@inproceedings{9221450,author={Haque, Khandaker Foysal and Abdelgawad, Ahmed and Yanambaka, Venkata P and Yelamarthi, Kumar},abbr={Conf.},booktitle={2020 IEEE 6th World Forum on Internet of Things (WF-IoT)},title={An Energy-Efficient and Reliable RPL for IoT},year={2020},volume={},number={},pages={1-2},bibtex_show={true},pdf={rpl.pdf},selected={false},html={https://ieeexplore.ieee.org/abstract/document/9221450},doi={10.1109/WF-IoT48130.2020.9221450}}
2019
Conf.
An optimized stand-alone green hybrid grid system for an offshore Island, Saint Martin, Bangladesh
Haque, Khandaker Foysal, Saqib, Nazmus, and Rahman, Md Shamim
In 2019 International Conference on Energy and Power Engineering (ICEPE) 2019
Saint Martin’s island is the largest offshore island
of Bangladesh which is one of the most beautiful tourist spots in
the world. But as the island is far away from the mainland,
it is not connected to the main grid of the country. This
paper proposes an optimized stand-alone green hybrid system
to supply electricity for the inhabitants & tourists of the island.
Considering 1000 households for all of its inhabitants and 200
hotel rooms for tourists, the average daily load is 1135.82
kWh/day with an annual peak load of 227.76 kW. The aim of
this paper is to design the most cost efficient optimized standalone green hybrid system which provides zero emission and
100% renewable fraction. HOMER(Hybrid Optimization Model
for Multiple Energy Resources) is used to design this system.
The simulation results show that a hybrid system with 659 kW
PV array, 3073 strings of batteries, 245 kW converter forms the
most optimized stand-alone system with COE(Cost of Energy)
of 0.266 and NPC(Net Present Cost) of 1379832. Significantly,
energy cost of the proposed system is viable in context with socioeconomic condition of the country which will eventually provide
the power solution maintaining the scenic beauty of the island.
@inproceedings{haque2019optimized,abbr={Conf.},title={An optimized stand-alone green hybrid grid system for an offshore Island, Saint Martin, Bangladesh},author={Haque, Khandaker Foysal and Saqib, Nazmus and Rahman, Md Shamim},booktitle={2019 International Conference on Energy and Power Engineering (ICEPE)},pages={1--5},year={2019},bibtex_show={true},selected={false},pdf={hybrid_grid.pdf},html={https://ieeexplore.ieee.org/abstract/document/8726596},organization={IEEE}}
Conf.
Analysis of Grid Integrated PV System as Home RES with Net Metering Scheme
To meet the increased demand of electricity, PV
system is being used as home RES (Renewable Energy Source)
throughout the world. In this paper, a grid integrated PV system
has been proposed with net metering scheme. A home of 149 sq.
meter in Dhaka city is considered whose average daily load is
11.27 kWh/day with an annual peak load of 1.21 kW. According to
DESCO (Dhaka Electric Supply Company Limited), for the span
of last one year (July, 2017-July, 2018) the monthly electricity
usage of this home varies from 401-600 units (kWh) with a Cost
of Energy (COE) of $0.1. Simulation and analysis of the proposed
system shows that the Cost of Energy (COE) and Net present Cost
(NPC) of the proposed system can be reduced to a great extent
with the application of net metering scheme which also improves
the renewable fraction of the system.
@inproceedings{saqib2019analysis,title={Analysis of Grid Integrated PV System as Home RES with Net Metering Scheme},abbr={Conf.},author={Saqib, Nazmus and Haque, Khandaker Foysal and Zabin, Rifat and Preonto, Sayed Nahian},booktitle={2019 International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST)},pages={395--399},year={2019},pdf={integrated_pv.pdf},bibtex_show={true},selected={false},html={https://ieeexplore.ieee.org/abstract/document/8644098},organization={IEEE}}