Karunarathne, Lakmali ORCID: https://orcid.org/0009-0000-7720-7817, Ganesan, Swathi ORCID: https://orcid.org/0000-0002-6278-2090 and Somasiri, Nalinda ORCID: https://orcid.org/0000-0001-6311-2251 (2023) Business during COVID: An IOT (Internet of Things) based Automated Sand Truck Management Solution. In: 5th International Conference on Internet of Things (CIoT 2023), 17 June 2023, Sydney, Australia. (Unpublished)

Text (Business during COVID: An IOT (Internet of Things) based Automated Sand Truck Management Solution) Business during COVID 19- AUTOMATED SAND TRUCK MANAGEMENT SYSTEM.docx - Accepted Version Download (8MB)

Abstract

As a result of the development in computing technologies have begun to believe the human expectations on these needs in the different sort of components. By the result of that, the diverse types of systems are started to design and implement by the people who has used with their knowledge to do something innovated. The computer technology offers computerized system to prevent the capability of having errors on sensitive data. Apart from that, the manual system is used for sand transporting which has been controlled by the Geological Survey and Mines Bureau of Sri Lanka (GSMB). The permit sheet is offered by the GSMB to the truck owners which has transported the sand via the trucks. The permit sheet is having a manual process. The gross weight of the truck with sand is depended on the variations of the trucks. As a solution, the trucks which transport the sand are referred to the specific checkpoint that measuring the exact weight while the Radio Frequency Identification (RFID) card is detecting the truck details. Vehicle recognition using RFID card is the efficient solution to get rid of having short comes such as parking large scale trucks near the checkpoint, getting much time to sign their permit sheets from the specific police officer in the checkpoint etc. Not only the GSMB but also, it is possible to apply to the many fields for the purpose of reducing traffic issues, roadblocks, and parking management systems. This process can apply which typically the vehicle number is used to get the current information in daily as an alert message to the owner's account. Using the RFID technology to confirm the exact information of the selected truck or if there is any error, then it will automatically inform to the owner via an alert message with the GPS current location of the truck. The project eSand Transport System with IOT(eSTSI ) has been carried out to implement the sand transport system which will perform as the real time surface for the GSMB or any company which relates to the detecting the specific vehicle using RFID Card that allows you to provide the secure and accurate data such as gross weight with the sand and the truck, viewing the details of the owner when the RFID card is detected, sending alerts as messages through the app which has been connected with the firebase, viewing the schedule of the selected truck with the date and the destination and displaying the location once the truck is passed the checkpoint. The main functionalities of the eSTSI are to identify the truck with the correct information via the RFID card that retrieves the data who has enrolled with the app which stores the data in the firebase. The expected services are aimed to provide by this system. The COVID 19 which has arrived from the Wuhan, Hubei province China to the world at the beginning of the 2020 and it has spread all over world with making enormous number of damages to each country’s health and the economy. As a result of this, people are facing to challenges which has affected by the new virus called as COVID 19 and searching the best solutions to prevent this pandemic situation as possible as much to make the world where it has used to be before the COVID 19 virus. The COVID 19 is spread through hands, eyes and mouth because of that people use to sanitize or wash their hands after touching a surface when in a public place.

Item Type:	Conference or Workshop Item (Paper)
Status:	Unpublished
Subjects:	Q Science > Q Science (General) > Q325 Machine learning
School/Department:	London Campus
URI:	https://ray.yorksj.ac.uk/id/eprint/7901

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