Sifat Rezwan

Bio: Sifat Rezwan is an academic researcher from North South University. The author has contributed to research in topics: Inverter & Computer science. The author has an hindex of 2, co-authored 12 publications receiving 25 citations. Previous affiliations of Sifat Rezwan include Chosun University.

A Survey on Applications of Reinforcement Learning in Flying Ad-Hoc Networks

Abstract: Flying ad-hoc networks (FANET) are one of the most important branches of wireless ad-hoc networks, consisting of multiple unmanned air vehicles (UAVs) performing assigned tasks and communicating with each other. Nowadays FANETs are being used for commercial and civilian applications such as handling traffic congestion, remote data collection, remote sensing, network relaying, and delivering products. However, there are some major challenges, such as adaptive routing protocols, flight trajectory selection, energy limitations, charging, and autonomous deployment that need to be addressed in FANETs. Several researchers have been working for the last few years to resolve these problems. The main obstacles are the high mobility and unpredictable changes in the topology of FANETs. Hence, many researchers have introduced reinforcement learning (RL) algorithms in FANETs to overcome these shortcomings. In this study, we comprehensively surveyed and qualitatively compared the applications of RL in different scenarios of FANETs such as routing protocol, flight trajectory selection, relaying, and charging. We also discuss open research issues that can provide researchers with clear and direct insights for further research.

IoT Based Smart Inventory Management System for Kitchen Using Weight Sensors, LDR, LED, Arduino Mega and NodeMCU (ESP8266) Wi-Fi Module with Website and App

Abstract: Smart kitchen inventory management system (SIMS) is a system that is based on IoT, which will make managing kitchen, medicine, restaurant inventory more efficient and hassle free. This will not only notify users of their current inventory but also automatically order for new items if quantity gets low. Users can also manually order online to get any items delivered at their doorstep directly from their SIMS app. User can also generate list of a given timeframe so that user will be able to know about their expenditure. In addition, user can track their order status and order history through website. With the help of Smart Kitchen Inventory (SKI), a part of SIMS, people can forget about the hassle of grocery shopping as it can be operated from anywhere through the website or the android app and order anything they need anytime they want.

Artificial Intelligence Approaches for UAV Navigation: Recent Advances and Future Challenges

Abstract: Unmanned aerial vehicles (UAVs) applications have increased in popularity in recent years because of their ability to incorporate a wide variety of sensors while retaining cheap operating costs, easy deployment, and excellent mobility. However, controlling UAVs remotely in complex environments limits the capability of the UAVs and decreases the efficiency of the whole system. Therefore, many researchers are working on autonomous UAV navigation where UAVs can move and perform the assigned tasks based on their surroundings. With recent technological advancements, the application of artificial intelligence (AI) has proliferated. Autonomous UAV navigation is an example of an application in which AI plays a critical role in providing fundamental human control characteristics. Thus, many researchers have adopted different AI approaches to make autonomous UAV navigation more efficient. This paper comprehensively surveys and categorizes several AI approaches for autonomous UAV navigation implicated by several researchers. Different AI approaches comprise mathematical-based optimization and model-based learning approaches. The fundamentals, working principles, and main features of the different optimization-based and learning-based approaches are discussed in this paper. In addition, the characteristics, types, navigation models, and applications of UAVs are highlighted to make AI implementation understandable. Finally, the open research directions are discussed to provide researchers with clear and direct insights for further research.

Priority-Based Joint Resource Allocation With Deep Q-Learning for Heterogeneous NOMA Systems

Abstract: For heterogeneous demands in fifth-generation (5G) new radio (NR), a massive machine type communication (mMTC), enhanced mobile broadband (eMBB), and ultra-reliable and low-latency communication (URLLC) services have been introduced. To ensure these quality-of-service (QoS) requirements, non-orthogonal multiple access (NOMA) has been introduced in which multiple devices can be served from the same frequency by manipulating the power domain and successive interference cancellation (SIC) technique. To maximize the efficiency of NOMA systems, an optimal resource allocation, such as power allocation and channel assignment, is a key issue that needs to be solved. Although many researchers have proposed multiple solutions, there have been no studies addressing the 5G QoS requirements and three services that coexist in the same network. In this paper, we formulate an optimal power allocation scheme under Karush–Kuhn–Tucker (KKT) optimality conditions incorporating different NOMA constraints to maximize the channel sum-rate and system fairness. We then propose a priority-based channel assignment with a deep $Q$ -learning algorithm to maintain the 5G QoS requirements and increase the network performance. Finally, We conduct extensive simulations with respect to different system parameters and can confirm that the proposed scheme performs better than other existing schemes.

A Minimalist Model of IoT based Sensor System for Sewage Treatment Plant Monitoring

Abstract: This paper presents an effective IoT approach to monitor a Sewage Treatment Plant (STP) status using a small set of critical sensors connected to Arduino microcontroller boards communicating using Wi-Fi networking technology. STP plants are very crucial to maintain environmental safety. The method discussed in this paper applies three critical sensors to measure the sewage plant’s status and sends the data to a cloud-based local server in real-time. These three sensors are temperature, turbidity and pH sensors. Besides, STP’s power consumption status is also monitored to identify any intentional shutdown of the plant. On the visualization side, a web-based application shows time-stamped data in different time scales of the changing status of the STP. The data are stored in a secured cloud system for further analysis. The sensor set used in the system has been proved effective to monitor operational status of STP plants and gives us a cost-effective decision-making tool for running a STP installation remotely. Our experience shows Arduino based Wi-Fi module used in our system is more cost-effective than a GSM based system for applications at underground levels.

A survey of cyber security threats and solutions for UAV communications and flying ad-hoc networks

Abstract: Unmanned aerial vehicles (UAVs) are a rapidly evolving technology, and being highly mobile, UAV systems are able to cooperate with each other to accomplish a wide range of different tasks. UAVs can be used in commercial applications, such as goods delivery, as well as in military surveillance. They can also operate in civil domains like search-and-rescue missions, that require multiple UAVs to collect location data as well as transmit video streams. However, the malicious use of UAVs began to emerge in recent years. The frequency of such attacks has been significantly increasing and their impact can have devastating effects. Hence, the relevant industries and standardisation bodies are exploring possibilities for securing UAV systems and networks. Our survey focuses on UAV security and privacy issues whilst establishing flying ad-hoc networks (FANETs) as well as on threats to the Internet of drones (IoD) infrastructure used to provide control and access over the Internet between UAVs and users. The goal of this survey is to categorise the versatile aspects of the UAV threat landscape and develop a classification approach based on different types of connections and nodes in FANETs and IoD. In particular, we categorise security and privacy threats on connections between UAVs, ground control stations, and personal pilot devices. All the most relevant threats and their corresponding defence mechanisms are classified using characteristics of the first four layers of the OSI model. We then analyse the conventional and novel UAV routing protocols, indicating their advantages and disadvantages from the cyber security perspective. To provide a deeper insight, the reviewed defence mechanisms have undergone a thorough examination of their security requirements and objectives such as availability, authentication, authorisation, confidentiality, integrity, privacy, and non-repudiation. Finally, we discuss the open research challenges, the limitations of current UAV standards, and provide possible future directions for research.

Artificial Intelligence in Disaster Risk Communication: A Systematic Literature Review

Abstract: Effective communication of disaster risks is crucial to provoking appropriate responses from citizens and emergency operators. With recent advancement in Artificial Intelligence (AI), several researchers have begun exploring machine learning techniques in improving disaster risk communication. This paper adopts a systematic literature approach to report on the various research activities involving the application of AI in disaster risk communication. The study found that research activities focus on two broad areas: (1) prediction and monitoring for early warning, and (2) information extraction and classification for situational awareness. These broad areas are discussed, including background information to help establish future applications of AI in disaster risk communication. The paper concludes with recommendations of several ways in which AI applications can have a broader role in disaster risk communication.

A Review of Flying Ad Hoc Networks: Key Characteristics, Applications, and Wireless Technologies

Abstract: Recent advances in unmanned aerial vehicles (UAVs), or drones, have made them able to communicate and collaborate, forming flying ad hoc networks (FANETs). FANETs are becoming popular in many application domains, including precision agriculture, goods delivery, construction, environment and climate monitoring, and military surveillance. These interesting new avenues for the use of UAVs are motivating researchers to rethink the existing research on FANETs. Therefore, this paper provides a comprehensive and thorough review of the different types of UAVs used in FANETs, their mobility models, main characteristics, and applications, as well as the routing protocols used in this type of network. Other important contributions of this paper include the investigation of emerging technologies integrated with FANETs.