Browsing by Subject "embedded devices"
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ItemInformation agents for mobile and embedded devices(World Scientific Publishing Co Pte Ltd, 2002-09-01) Finin, Tim; Joshi, Anupam; Kagal, Lalana; Ratsimor, Olga; Avancha, SasikanthThe pervasive computing environments of the near future will involve the interactions, coordination and cooperation of numerous, casually accessible, and often invisible computing devices. These devices, whether carried on our person or embedded in our homes, businesses and classrooms, will connect via wireless and wired links to one another and to the global networking infrastructure. The result will be a networking milieu with a new level of openness. The localized and dynamic nature of their interactions raises many new issues that draw on and challenge the disciplines of agents, distributed systems, and security. This paper describes recent work by the UMBC Ebiquity research group which addresses some of these issues. ItemOn the use of Deep Autoencoders for Efficient Embedded Reinforcement Learning(2019-03-25) Prakash, Bharat; Horton, Mark; Waytowich, Nicholas R.; Hairston, William David; Oates, Tim; Mohsenin, TinooshIn autonomous embedded systems, it is often vital to reduce the amount of actions taken in the real world and energy required to learn a policy. Training reinforcement learning agents from high dimensional image representations can be very expensive and time consuming. Autoencoders are deep neural network used to compress high dimensional data such as pixelated images into small latent representations. This compression model is vital to efficiently learn policies, especially when learning on embedded systems. We have implemented this model on the NVIDIA Jetson TX2 embedded GPU, and evaluated the power consumption, throughput, and energy consumption of the autoencoders for various CPU/GPU core combinations, frequencies, and model parameters. Additionally, we have shown the reconstructions generated by the autoencoder to analyze the quality of the generated compressed representation and also the performance of the reinforcement learning agent. Finally, we have presented an assessment of the viability of training these models on embedded systems and their usefulness in developing autonomous policies. Using autoencoders, we were able to achieve 4-5 × improved performance compared to a baseline RL agent with a convolutional feature extractor, while using less than 2W of power. ItemTowards a Pervasive Grid(IEEE, 2003-04-15) Hingne, Vipul; Joshi, Anupam; Finin, Tim; Kargupta, Hillol; Houstis, Elias N.The increase in the use of mobile & embedded devices, coupled with ad-hoc, short range wireless networking is enabling pervasive computing. This pervasive computing environment and the wired Grid infrastructure can be combined to make the Computation and Information Grid truly pervasive. This paper identifies some of the interesting research issues and challenges in creating such a pervasive Grid, and describes some preliminary work we have done to that end. We propose a runtime environment for the Pervasive Grid that utilizes a multi agent framework, and provides for discovery of services being offered by sensors, embedded and mobile devices, and their composition. The computation in this environment needs to be dynamically partitioned between the traditional Grid and elements that constitute the pervasive environment, like sensors, with limited computing and communication capabilities.