Browsing by Author "Rankothge, W. H"

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ANDTi Virtual Assistant
(IEEE, 2022-06-21) Fathima Nihla, M.I.; Wicrama Arachchi, W. A. D. A; Dilini Subhani, K. G; Dissanayaka, D. M. I. K; Sanduni, W. T; Rankothge, W. H; Wariyapperuma, P. N; Kehelella, P. H
Due of the current COVID-19 pandemic crises, there is a worldwide need for quick medical findings. Furthermore, due to a lack of medical facilities and medical practitioners’ hectic schedules, several examinations must now be performed by the general public. Also because of the high rate of transmissibility of COVID-19, even asymptomatic patients can readily transfer the virus to others, faster detection is critical during the initial phase of COVID-19, which is early identification. The earlier a patient is detected; the better the virus’s spread may be stopped and the patient can undergo proper treatment. As the nationwide vaccination process is in its later part, it is obvious that the government will uplift its regulations and the employees will have to return to their workplaces or offices. As a solution to this upcoming urgency the authors would like to propose a solution to identify COVID- 19 patients in advance at corporate level. As an IoT based solution a device is supposed to be setup on top of each employee’s desk, which in return will be used to monitor the oxygen level, temperature, and heartbeat rate of the employees.
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AwareME: Public Awareness through Game-Based Learning
(IEEE, 2020-11-16) Dassanayake, D. K. M. P. M. M; Wijesinghe, S. N; Jayasiri, T. L. C; Keenawinna, K. A. R. T; Rankothge, W. H
It is widely recognized that a nation with minimum problems relating to areas such as health, environment, infrastructure, and technology is a developed country [1]. However, the developing/ lower-middle income countries need many improvements in the above-mentioned areas, as they are still lacking in those areas [1]. Apart from the risk associated with these problems, the main challenge faced by developing countries is, making the public aware of these problems. In this paper, we are proposing a mobile game-based learning platform: "AwareME" which focuses on following problems: (1) health awareness (dengue fever), (2) environmental awareness (garbage disposal), (3) cyber security awareness (social media) and (4) safety awareness (road safety). The "AwareME" platform includes quizzes, 2D/3D puzzle games, and 3D action games with activities to improve the cognitive skills and awareness of the public. We have provided the results of an initial performance evaluation of "AwareME" platform.
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AwareME: public awareness through game-based learning
(IEEE, 2020-11-16) Dassanayake, D. K. M. P. M. M; Wijesinghe, S. N; Jayasiri, T. L. C; Keenawinna, K. A. R. T; Rankothge, W. H; Gamage, N
It is widely recognized that a nation with minimum problems relating to areas such as health, environment, infrastructure, and technology is a developed country [1]. However, the developing/ lower-middle income countries need many improvements in the above-mentioned areas, as they are still lacking in those areas [1]. Apart from the risk associated with these problems, the main challenge faced by developing countries is, making the public aware of these problems. In this paper, we are proposing a mobile game-based learning platform: "AwareME" which focuses on following problems: (1) health awareness (dengue fever), (2) environmental awareness (garbage disposal), (3) cyber security awareness (social media) and (4) safety awareness (road safety). The "AwareME" platform includes quizzes, 2D/3D puzzle games, and 3D action games with activities to improve the cognitive skills and awareness of the public. We have provided the results of an initial performance evaluation of "AwareME" platform.
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A Deep Learning Model Optimized with Genetic Algorithms for Resource Allocation of Virtualized Network Functions
(IEEE, 2021-12-01) Rankothge, W. H; Gamage, N. D. U; Suhail, S. A. A; Ariyawansa, M. M. T. R.; Dewwiman, H. G. H; Senevirathne, M. D. B. P
Software Defined Networking (SDN) has gained a significant attention of Cloud Service providers (CSPs) for managing their network infrastructure. With the popularity of services such as virtualized applications and Virtualized Network Functions (VNFs), many organizations are outsourcing their entire data centers to the CSPs. From the perspective of CSPs, effective and efficient cloud resource management plays an important role, in terms of continuing a successful business model. This research focuses on proposing a resource allocation algorithm for a cloud platform where VNFs are offered as a service. It is a tier-based resource allocation approach, where different resource tiers are defined in terms of network bandwidth, processor speed, RAM, vCPUs and number of users. Once the client's request is submitted for VNFs, we have used a deep learning approach (a Keras model which was optimized using Genetic Algorithms) to forecast the most suitable resource tier. Our results show that the proposed resource allocation algorithms can forecasts the most suitable resource tier for given scenario, in the order of seconds, with high accuracy.
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An Empirical Study on Paddy Harvest and Rice Demand Prediction for an Optimal Distribution Plan
(wiley, 2022-03-04) Rankothge, W. H
This book provides the state-of-the-art applications of Machine Learning in IoT environment. The most common use cases for machine learning and IoT data are predictive maintenance, followed by analyzing CCTV surveillance, smart home applications, smart-healthcare, in-store ‘contextualized marketing’ and intelligent transportation systems. Readers will gain an insight into the integration of Machine Learning with IoT in various application domains.
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An Expermental Study on Load Balancing for a Software Defined Network based Virtualized Network Functions Platform
(IEEE, 2021-12-06) Senevirathne, M. D. B. P; Rankothge, W. H; Gamage, N. D. U; Ariyawansa, M. M. T. R; Dewwiman, H. G. H; Suhail, A
Network functionalities in conventional computer networks have been facilitated by implementing hardware middle-boxes. However, with the introduction of Virtualized Network Functions (VNFs) technologies, Cloud Service providers (CSPs) are able to offer VNFs as services to clients, along with general virtualized applications. CSPs provision and allocate resources to the VNFs, as required by the clients. An efficient cloud resource management approach plays an important role, in terms of continuing a successful CSP business model. To meet client Service Level Agreement (SLA) for Quality of Service (QoS), CSP is required to ensure that the virtualized entities are not overloaded with the processing, and workload is divided among virtualized entities adequately. Therefore, load balancing plays an important role, when offering VNFs as services to clients. This research focuses on exploring a load balancing algorithm for a cloud platform where VNFs are offered as a service. As the initial stage, we have used Weighted Round-Robin and Least Connection approaches and conducted an experimental study to compare the performances of the two approaches. Our results show that the weighted round robin algorithm performs better in terms of the workload distribution and average response time.
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Identification and Mitigation Tool For Cross-Site Request Forgery (CSRF)
(IEEE, 2020-12-01) Rankothge, W. H; Randeniya, S M. N
Most organizations use web applications for sharing resources and communication via the internet and information security is one of the biggest concerns in most organizations. Web applications are becoming vulnerable to threats and malicious attacks every day, which lead to violation of confidentiality, integrity, and availability of information assets.We have proposed and implemented a new automated tool for the identification and mitigation of Cross-Site Request Forgery (CSRF) vulnerability. A secret token pattern based has been used in the automated tool, which applies effective security mechanism on PHP based web applications, without damaging the content and its functionalities, where the authenticated users can perform web activities securely.
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Identification and Mitigation Tool for Sql Injection Attacks(SQLIA)
(IEEE, 2020-11-26) Rankothge, W. H; Randeniya, M; Samaranayaka, V
Structured Query Language Injection Attack (SQLIA) is a very frequent web security vulnerability. The attacker adds a malicious Structured Query Language (SQL) code to the input field of a web form, so that he can gain access to data or make unauthorized changes to data. A successful malicious SQL injection cause serious consequence to the victimized organization such as financial loss, reputation loss, compliance, and regulatory breaches. There have been several research works on detection and prevention of SQL injection attacks. However, still there is an absence of an advanced single tools for both identification and mitigation of SQL injection attacks. We have proposed an approach to identify and mitigate SQL injection attacks using a single tool and it allows software testers to identify the SQL injection vulnerabilities of their web applications during the testing stages. The proposed approach is based on parameterized queries and user input validation. Our results show that the tool provides 100% accurate and efficient results on identification and mitigation of SQL vulnerabilities.
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Interactive Solution to Improve Flood Awareness Among Public–Flood Run
(IEEE, 2019-12-10) Karunanayake, T; Dayarathne, P; Doratiyawa, C; Wickramanayake, A; Rankothge, W. H; Gamage, N
Flood is the most common natural disaster in Sri Lanka that causes a huge destruction annually [1]. Lack of awareness on flood among the public is one of the main reasons behind the huge destruction of lives and property.We have proposed a platform for flood awareness, named "Flood Run" which is an interactive 3D mobile game, with following modules: puzzle games, action games and memorizing games, adventure games and quizzes. These four modules consist of activities to develop the essential skills to improve flood awareness. This research used game-based learning and interactive game designing techniques. This research paper presents the performance evaluation of the four types of modules. The results show that, with the help of the provided solution, the expected skills of the people are improved, and through that flood awareness among public is improved.
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Internet of Things Based Automatic System for the Traffic Violation
(IEEE, 2021-12-10) Nizzad, A. R. M; Sameer, U. M; Suhath, S. M; Mohamed Nafrees, A. C; Rankothge, W. H; Kehelella, P. H.; Mansoor, C. M. M.
An efficient and effective motor traffic management is crucial for any Intelligent Transportation System (ITS) to reduce traffic violations. Scientific evidence suggests that exceeding the speed limit is the most important factor that impacts the severity, fatality and other risks associated with motor vehicle collisions. However, due to the lack of proper technology used in the transport sector, there are possibilities for traffic law violators get unnoticed. Therefore, this study provides a solution based on Internet of Things with Image Processing technology to process the vehicle registration number to uniquely identify the vehicles that are violating traffic laws. The outcome confirms that the architecture is viable for the low-cost automation of traffic fine for the speed violation. In addition, it is recommended to use more robust mechanism to capture the real time speed of any fast-moving vehicle. In conclusion, the proposed architecture with slight modification can be deployed for many commercial test cases such as traffic management, parking, vehicle counting, data privacy, data security and more.
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NetEye: Network Monitoring for a Software Defined Network based Virtualized Network Functions Platform
(IEEE, 2021-12-01) Rankothge, W. H; Gamage, N. D. U; Ariyawansa, M. M. T. R; Suhail, S. A. A; Dewwiman, H. G. H.; Senevirathne, M. D. B. P
With the introduction of Virtualized Network Functions Virtualization (VNFs), Cloud Service Providers (CSPs) allocate resources and deploy network functions as virtualized entities in the cloud. With the dynamic changes in the traffic and workload, initially allocated resources have to be increased or decreased to maintain the Service Level Agreement (SLA). Therefore, CSPs rely on network monitoring approaches to maintain an effective and efficient resource management process. However, the monitoring process itself creates an overhead to the performance of the network. Monitoring algorithms consume the CPU and memory resources of the cloud infrastructure during their execution. Therefore, selecting an appropriate monitoring approach is important, especially in a resource-constrained network. In this research, we have explored two monitoring approaches: continuous and periodic, and compared their performances in terms of memory and CPU utilization.
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Network Traffic Prediction for a Software Defined Network Based Virtualized Security Functions Platform
(IEEE, 2021-12-06) Jayasinghe, D; Rankothge, W. H; Gamage, N. D. U; Gamage, T. C. T; Amarasinghe, D. A. H. M; Uwanpriya, S. D. L. S
Software-Defined Networking (SDN) has become a popular and widely used approach with Cloud Service Providers (CSPs). With the introduction of Virtualized Security Functions (VSFs), and offering them as a service, CSPs are exploring effective and efficient approaches for resource management in the cloud infrastructure, considering specific requirements of VSFs. Network traffic prediction is an important component of cloud resource management, as prediction helps CSPs to take necessary proactive management actions, specifically for VSFs. This research focuses on introducing an algorithm to predict the network traffic traverse via a cloud platform where VSFs are offered as a service, by using the Auto-Regressive Integrated Moving Average (ARIMA) model. In this paper, the implementation and performance of the traffic prediction algorithm are presented. The results show that the network traffic in cloud environments can be effectively predicted by using the introduced algorithm with an accuracy of 96.49%.
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Plant recognition system based on Neural Networks
(IEEE, 2013-01-23) Rankothge, W. H; Dissanayake, D. M. S. B; Gunathilaka, U. V. K. T; Gunarathna, S. A. C. M; Mudalige, C. M; Thilakumara, R. P
With the evolution of technologies, people have adopted their day today lives to utilize the benefits of highly advanced technologies. Artificial Intelligence and Neural Networks are playing major roles in this process and they have been involved in fields of medicine, automobiles, aeronautical science, military and many more. Unfortunately very little concern is devoted to the botanical science field, especially in taxonomic researches of plants. Even today, identification and classification of unknown plant species are performed manually by expert personnel who are very few in number. It takes a long time and the results are not very accurate. Advanced Plant Identification System (APIS) is an intelligent system which has the ability to identify tree species from photographs of their leaves and it provides more accurate results within less time.
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Real-time decision optimization platform for airline operations
(IEEE, 2020-12-10) Weerasinghe, P. S. R; Ranasinghe, R. A. M. D. K; Mahanthe, M. M. V. R. B; Samarakoon, P. G. C. B; Rankothge, W. H; Kasthurirathna, D
With close to 4 billion origin-destination passenger journeys worldwide, airline operations have become a crucial factor in the global economy. With the increasing number of journeys and passengers, managing the daily operations of airlines have become a complicated task. We have proposed a real-time decision optimization platform for airline operations with the following subsystems: (1) determine the optimum path for a flight, (2) optimum fleet assignment, (3) optimum gate allocation, (4) optimum crew allocation. We have used an approximation (heuristics) based optimization approach: Genetic Programming (GP) to implement the modules. The results of our proposed platform illustrate that, the decision-making process of Airline Operations Control Center (AOCC) can be optimized, and dynamic change requirements can be accommodated.
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Smart Platform for Cloud Service Providers
(IEEE, 2019-12-05) Dharmapriya, W. A. S. P; Supipi, K. G; Ravindu Nimesh, G. G; Muhandiram, M. A. B. K; Rankothge, W. H; Gamage, N
Cloud computing offers many types of computer related services without the direct active management of their users. Cloud Service Providers (CSPs) are responsible to manage these services such as placement of services in the cloud, resource allocation, network monitoring etc. The cloud service provider is required to monitor the network traffic, predict the dynamic traffic changes, and scale out the resources accordingly. We have proposed a platform for cloud service providers that automates the cloud management related services with following modules: (1) traffic monitoring, (2) traffic prediction, (3) virtual service instances placement and (4) traffic load balancing. We have used continuous and periodic approaches for traffic monitoring, Auto-Regressive Integrated Moving Average (ARIMA) model for traffic prediction, Randomized Weighted Majority Algorithm (RWMA) for virtual service instances placement and a threshold-based approach for load balancing. In this paper, we are presenting the performances of our cloud management platform, specially an evaluation of the algorithms used in above mentioned modules. Our results show that, using our proposed modules, the cloud management related services can be automated efficiently and reliably.
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SmartCop: an automated platform to mitigate the impact of road accidents
(IEEE, 2020-12-01) Sewwandi, A. K. T; Dissanayake, D. M. K. P; Navanjani, D. H. K. H; Shangavie, R; Rankothge, W. H; Gamage, N
Road accidents have become one of the major issues both locally and globally as they cause many deaths, injuries, fatalities, and economic deprivation. Major reasons for the rapid increase in road accidents are not only the negligence of public unawareness, but also the improper scheduling and enforcement of traffic police officers to control the traffic. In this paper, we have proposed the SmartCop platform to mitigate the impact of road accidents with four modules: (1) predict road accidents, (2) recommend and schedule police officers, (3) enhance road accidents prevention awareness using a game-based learning approach, and (4) enhance road accidents response awareness using a game-based learning approach. We have used supervised/unsupervised learning, optimization techniques, and game-based learning approaches to implement the above-mentioned modules. Our results show that, using our proposed modules, the road safety related services can be automated efficiently and effectively.
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Towards an Efficient and Secure Blood Bank Management System
(IEEE, 2020-12-01) Sandaruwan, P. A. J; Dolapihilla, U.D.L; Karunathilaka, D. W. N. R; Wijayaweera, W. A. D. T. L; Rankothge, W. H; Gamage, N.D.U
A blood bank plays an important role in a hospital as well as in a country, ensuring safe and timely blood transfusions. However, there are several challenges faced by blood banks around the world, specifically when securing the blood supply chain. Reducing the supply-demand imbalance, protecting the data privacy of donors as well as receivers, are some of them. Therefore, there is a timely requirement for an effective and secure management system for the blood bank. We have proposed a management platform for the blood bank operations with the following modules: (1) forecast blood demand, (2) suggest blood donation campaign locations and (3) secure blood supply chain. The proposed platform has been implemented using techniques such as Long Short-Term Memory (LSTM), k-means clustering, Geographic Information Systems (GIS), and blockchain. Our results show that using our proposed modules, we can minimize the imbalance between supply and demand of blood, find the most suitable donor in an emergency, and enhance the privacy of data.