Symposia & Conferences
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Item A wireless sensor network based autonomous logistic trolley system for factory automation(International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Perera, M.S.H.; Prasadini, M.M.N.; Karunarathne, C.N.; Chathurangi, M.; Samarasinghe, P.; Jinasena, T.M.K.K.This paper presents the low cost autonomous logistic trolley for factory automation. Automation can be defined as the technology by which a process or procedure is performed without human assistance. Factory automation has automated the factory manufacturing process without human interaction. It will increase the efficiency of the manufacturing process, make the process highly accurate and because of less man power it will increase the revenue margin of the factory. The system primarily consists of a Arduino Uno board, ultrasound sensors, infrared proximity sensors, Bluetooth modules, DC motors, and Servo motors. This system mainly includes obstacle detection, line tracking, Bluetooth request transferring technologies and motion control technologies. This trolley is called up by a production desk of the factory if materials are needed, by sending a request. Trolley automatically deliveres the material to that exact production desk using its infrared sensors that are placed at both left and right sides of the vehicle. This can be used to identify each production line. This proposed autonomous trolley was experimented in different methods. It showed very accurate and successful results. This kind of autonomous trolley provides a wide range of benefits to a manufacturing process in a factory. This autonomous logistic trolley is low cost, less complicated and will increase efficiency of the manufacturing process of a factory.Item Semi-Automated Index based decision making engine for cyber foraging framework with minimal energy and performance cost(Faculty of Science, University of Kelaniya, Sri Lanka, 2016) Vekneswaran, P.; Dissanayake, N.R.Cyber Foraging is a concept to take advantage of the nearby underutilized computing resources (surrogates) to improve user experience of resource constraint handheld devices, by improving their performance while improving battery consumption. It’s not always beneficial to offload the tasks to the surrogates so there is a need for a decision making engine to only offload tasks, which will result in overall improvement in performance and battery consumption. There are many attempts to enable cyber foraging in smartphones, each having their own decision making mechanisms, some using fully automated Machine Learning Algorithms and Heuristic Data; and it has been proven inefficient even though with higher accuracy. Furthermore, these approaches fail to deliver accurate results when it comes to new applications without any historical data. The key drawback of them can be seen as the resource intensive decision making engine, which negatively effects on the overall performance and battery consumption of the device. We introduce a model for decision making engine, which takes context parameters like remaining battery, worst case complexity of the method, etc. into account, when making the choice to offload or not to the surrogate, during the runtime. The engine benchmarks the surrogate according to their capabilities such as: available JVM memory, accessible processing cores, which can be harnessed by the surrogate service used by the cyber foraging enabled mobile application using static policies. These indexes will then be used to compare with the worst case complexity value, which the developer has assigned to the identified intensive parts of the application. This approach will give a satisfactory amount of accuracy with extremely low cost of energy and higher performance. Further research is being done on addressing more complex use cases with more advanced algorithms towards a stable and reliable solution.