Gas Pollution: A Parametric Analysis Adopting Drone-based Evaluation and IOTs | Chapter 9 | Engineering Research: Perspectives on Recent Advances Vol. 12

 

In Nigeria, the Niger Delta region is faced with challenges of oil and gas exploitation. These activities increasingly affect human, aquatic life in the ocean, animals and the natural environment. Recent advancements in technology have introduced unmanned aerial vehicles (UAVs), commonly known as drones, as a viable and innovative solution to these challenges. This study presents a novel approach for monitoring air pollution with a drone and Internet of Things (IoT) technology. The specific objectives include developing a drone-based system capable of capturing gas pollution data, integrating various sensors to monitor environmental conditions and detect air pollutants (harmful gases) and developing a communication system for real-time data collection and dissemination. The design utilises sensors for the detection of hazardous gases and an ESP8266 module for real-time data transmission and cloud-based data presentation. The system facilitates sustainable environmental studies by providing access to areas that are hard or unsafe to reach, anytime. The research locations include Iko Town and Ukpenekang communities in Eastern Obolo LGA, Akwa Ibom State, Nigeria. Data collected during drone test flights was compared with traditional air quality monitoring stations to evaluate accuracy. The results show an affordable method for measurement of air quality in real time, especially in the challenging areas that are affected by oil and gas exploration, production and refining processes, such as the Niger Delta region of Nigeria. The findings demonstrate the feasibility of using drones and IOT for real-time environmental monitoring aimed at equipping researchers and policy makers with data to protect human lives, public health and the environment. In the course of this study, minor limitations were observed, including restricted flight duration and short range. Future research will investigate advancements in long-range communication protocols and the application of machine learning technology.

 

 

Author(s) Details

Bassey Okon
Department of Mechanical Engineering, Federal University of Technology, Ikot Abasi, Nigeria.

 

Ubong Ukommi

Department of Electrical and Electronic Engineering, Akwa Ibom State University, Ikot Akpaden, Nigeria.

 

Isaac Udoetor
Department of Electrical and Electronic Engineering, Akwa Ibom State University, Ikot Akpaden, Nigeria.

 

Enobong Akanimo
Department of Electrical and Electronic Engineering, Akwa Ibom State University, Ikot Akpaden, Nigeria.

 

Please see the book here :- https://doi.org/10.9734/bpi/erpra/v12/6960

 

Using Air-Drones in Chemical Warfare and Biological Warfare | Chapter 5 | Chemical Warfare and Environmental Warfare Operations

Air Drone applications in chemical warfare and biological warfare have great benefits for warfare. There are several types of drone applications in chemical warfare. The major task of this paper is to define the benefits of the development of drone technology in warfare using drone technology with different kinds of sensors to identify the locations of enemy troops, which will become effective early warning information. Air drones also can be used to attack enemy troops through chemical and biological attacks and to generate buffer zones between army troops and enemy troops. Drones are providing an economically effective solution. The main differences between agriculture spraying drones and chemical warfare drones are speed, maneuverability, and spraying methods. The spraying of pesticide solutions is different from spraying chemical agents such as HD, GB, or VX. This technology for warfare aims to reduce the need for manpower in military applications. Air drones reduce the use of chemical and biological agents in warfare. This reduces the chemical and biological materials used in warfare and then reduces the cost of warfare and also minimizes the environmental problems caused by the excessive use of chemical and biological agents.

 

Author (s) Details

 

Malik M.A. Fakron
University of Calabaria, Italy and University of Bright Star, Libya.

 

Please see the book here:- https://doi.org/10.9734/bpi/mono/978-93-49238-72-5/CH5

A Study on Optimization of Truck-Drone Parcel Delivery Using Metaheuristics | Chapter 12 | Recent Advances in Mathematical Research and Computer Science Vol. 5

 The TSP-D, a version of the travelling salesman problem in drone-based delivery systems, is the subject of this study. The TSP-D is a combinatorial optimization problem in which a truck and a drone work together to deliver packages to clients in the shortest time possible. Because determining the best answer is NP-hard, the number of problems that can be solved optimally is restricted. As a result, metaheuristics are employed to tackle the issue. Metaheuristics are adaptive and intelligent algorithms that have been proven to work in a variety of situations. This paper provides a hybrid meta-heuristic solution to the TSP-D issue, where the answer is based on the Concorde TSP solver's optimal TSP solution. The greedy, randomised adaptive search technique (GRASP) with two local search alternatives is then used to find the delivery routes for the truck and the drone. The application of self-adaptive selection when searching the neighbourhood in GRASP is the work's key contribution. The suggested method was evaluated on 200 cases from the publicly available "Instances of TSP with Drone" benchmark with various parameters. The results were compared to cutting-edge algorithms. In terms of tour duration, nonparametric statistical tests revealed that the proposed strategy is equivalent to rival algorithms (p = 0:074). When the drone and truck are travelling at the same speed ((alpha) = 1), the proposed method performs better or similarly.

Author(S) Details

Sarab AlMuhaideb
Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11362, Saudi Arabia.

Taghreed Alhussan
Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11362, Saudi Arabia.

Sara Alamri
Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11362, Saudi Arabia.

Yara Altwaijry
Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11362, Saudi Arabia.

Lujain Aljarbou
Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11362, Saudi Arabia.

Haifa Alrayes
Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11362, Saudi Arabia.

View Book:- https://stm.bookpi.org/RAMRCS-V5/article/view/4977

Determination of Appropriate Drone Flight Altitude for Horse Behavioural Observation | Chapter 10 | Research Aspects in Agriculture and Veterinary Science Vol. 4

 Drone technology has advanced recently, and their safety and operability have greatly improved, resulting in increased use in animal research. Drones of varying sizes and capabilities are commercially available and quite easy to purchase. This study used drone technology to watch horse behaviour and validate the proper horse–drone distance for airborne behavioural observations, demonstrating drone application in livestock management. The Phantom 4 Pro drone was used to record 11 horses between September and October 2017. To explore the horses' reactions to the drones and watch their behaviour, four flight altitudes were evaluated (60, 50, 40, and 30 m); the recording time at each altitude was 5 minutes. At whatever flight altitude, none of the horses demonstrated avoidance behaviour, and the observer was able to distinguish between any two horses. Foraging, moving, standing, recumbency, avoidance, and other actions were recorded. The most prevalent behaviour observed both personally and in the drone videos was foraging. All behavioural data from direct and drone video observations at all altitudes had substantial correlation coefficients (p 0.01). These findings suggest that both direct and recorded drone video observations may accurately detect horse behaviour. Finally, drones can be valuable for observing and documenting horse behaviour.

Author(S) Details

Tomoko Saitoh
Field Center of Animal Science and Agriculture, Obihiro University of Agriculture and Veterinary Medicine, Obihiro, 080-8555, Hokkaido, Japan.

Moyu Kobayashi
Field Center of Animal Science and Agriculture, Obihiro University of Agriculture and Veterinary Medicine, Obihiro, 080-8555, Hokkaido, Japan.

View Book:- https://stm.bookpi.org/RAAVS-V4/article/view/4852