AI inference consumes precious energy, drains batteries and shortens IoT lifetimes. Secondly, we explore AI-driven IoT which uses AI inference to characterize data harvested from IoT sensors. We found that model-driven management for FAAS can boost mission throughput by 10X and reduce costs by 87\%. Our model run on FAAS benchmarks predicted throughput with 4\% error across mission, software and hardware settings. Usage profiles can diverge greatly across edge management policies. Edge resources can affect whereįAAS fly and which data they sense. Firstly, we built a fully autonomous aerial system (FAAS) which uses a model-driven approach to manage edge resources in order to complete missions with feasible energy consumption. When deploying an autonomous application at edge, careful selection of these factors is very crucial. A variety of factors such as AI algorithm used, hardware resources available, internet capacity, latency and number of applications determine the amount of energy consumed at the edge. However, such AI inference consumes precious energy, drains batteries and shortens IoT lifetimes. Such systems deployed at the edge make use of low-powered edge devices and machine learning techniques in order to process inferences faster. For Experimental purpose, the questions related to technology, sports, biology, politics, science are queried in the Google and expected answer for the question is provided in the form of audio in our proposed system.Īutonomous systems such as self driving cars, smart traffic lights, smart homes and smart cameras are increasingly being deployed. Web pages retrieval, Snippets Extraction, Answer Filtering and Text to speech conversion are the processes involved in this proposed system. In order to address this problem, this paper provides a solution to provide an audio as output for all kind of search queries by using snippets of the web document. Though the search engine returns web links for all language search queries, the audio is given as output only for English language search queries. Especially for the voice based search query, the Google returns the web links as well as the audio as results in English language. For a search query the search engines like Google, Yahoo provides a list of web links as a result. QAS has many applications like retrieving information from the web, online examination, education, health care, sports, and geography. Semant ically driven snippet select ion for support ing focused web searches Iraklis Varlamis Download a PDF Pack of t he best relat ed papers Abstract-Question Answering System (QAS) is an information retrieval technique which provides a descriptive answer for the given question rather than web links.
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