foysal haque

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.

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.

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 th...

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 twelv...

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. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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,...

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.

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 ...

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.

Waste collection and management is an integratedpart 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 systemalso 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 redu...

Water covers a greater part of the earth's surface. Even though we know very little about the underwater world as most parts of it remain unexplored. Oceans including other water bodies hold huge natural resources and also the aquatic lives. These are mostly unexplored and very few of those are known due to unsuited and hazardous environments for the human to explore. This vast underwater world can be monitored remotely from a distant location with much ease and less risk. To monitor water-bodies remotely in real-time, sensor networking has been playing a great role. It is needed to deploy a wireless sensor network over the volume which we want to surveil. For vast water bodies like oceans, rivers and large lakes, data is collected from the different heights of the water level which is sent to the surface sink. Unlike terrestrial communication, radio waves and other conventional mediums can't serve the purpose of underwater communication as they pose high attenuation and ver...

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.