Modeling Secured User’s Recent-Past History Tracking System Using Location Information for Controlling the Spread of COVID-19

Abstract Currently, the world is one under an umbrella of novel and pandemic disease called Covid-19 which is a deadly pandemic disease caused by a novel corona virus. As it is reported in worldometer up to the day of this report, the total deaths are around more than 6.5 million in the world and more than 7.5 thousand deaths in Ethiopia so that it has been observed as the world big challenge which knocks everybody’s door directly or indirectly. Therefore, all concerned bodies are struggling to stop or reduce it through many preventive techniques. One of the most effective techniques is utilizing digital technology to create awareness for community and tracking suspected users on behalf of infected persons. The main aim of this paper is to design user’s recent-past history tracking system using location information with the help of Global Positioning System (GPS). The designed model has two main components. The security and intelligent searching algorithms. In implementing the security system, the user authentication part has been implemented by using hashing technique called Security Hash Algorithm (SHA). The input for this authentication key is user’s current latitude, longitude, hour and minute having appended correctly with consideration of integral part of the parameters. Then, message encryption has been implemented by using Advanced Encryption Standard (AES). The encryption key used for AES is considering the half of left-hand side of result of authentication key whose key bit size is 256. So, the bit size for encryption key is 128 since the type of AES used here is AES-128. These both user authentication and message encryption have been implemented at the side of assumed client-side entity. Another component of our designed model is implementation of intelligent searching algorithm called Tabu Searching (TS) Algorithm. The algorithm is efficiently implemented by listing some further components like searching for Tabu List (TL), risky areas list, and solution area lists. Along with this implementation, TS uses two types of memory called recency memory (short term) memory used to store TL temporarily and long-term memory used to store problem instance, risky area list and solution list as well. The thing made our searching design called intelligent is since the searching mechanism is adaptive to problem instance size, the necessity or conditionality of searching for all problem instances depends on the result of risky area list and solution list. The document is organized into five parts as part one states about introduction, part two describes the related works and gaps in the existing works. The part three clearly describes about methodology which makes our dreams true in showing the right way for implementing the security and searching parts. Part four implements the methodology conceptually designed in chapter three by simulating it with Python programming language and the last part states about conclusion and future recommendation.