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Prior work looks at factors such as battery characteristics, intelligent edge sensing considerations, planning, and robustness in isolation. But a global view of energy awareness that considers these factors and looks at various tradeoffs is essential. To this end, we present results from our detailed empirical study of battery charge-discharge characteristics and the impact of altitude and lighting on edge inference accuracy. Our energy models, derived from these observations, predict energy usage while performing various manoeuvres with an error of 5.6%, a 2.5X improvement over the state-of-the-art. Furthermore, we propose a holistic energy-aware multi-drone scheduling system that decreases the energy consumed by 21.14% and the mission times by 46.91% over state-of-the-art baselines. To achieve system robustness in the event of link or drone failure, we observe trends in Packet Delivery Ratio to propose a methodology to establish reliable communication between nodes. We release an open-source implementation of our system. Finally, we tie all of these pieces together using a people-counting case study.<\/jats:p>","DOI":"10.1145\/3641855","type":"journal-article","created":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T12:28:45Z","timestamp":1706012925000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Holistic Energy Awareness and Robustness for Intelligent Drones"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1147-2065","authenticated-orcid":false,"given":"Ravi Raj","family":"Saxena","sequence":"first","affiliation":[{"name":"Indian Institute of Science, Bengaluru, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3969-0139","authenticated-orcid":false,"given":"Joydeep","family":"Pal","sequence":"additional","affiliation":[{"name":"Indian Institute of Science, Bengaluru, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6112-5534","authenticated-orcid":false,"given":"Srinivasan","family":"Iyengar","sequence":"additional","affiliation":[{"name":"Microsoft Research India, Bengaluru, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4060-4883","authenticated-orcid":false,"given":"Bhawana","family":"Chhaglani","sequence":"additional","affiliation":[{"name":"University of Massachusetts Amherst, Amherst, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1617-0851","authenticated-orcid":false,"given":"Anurag","family":"Ghosh","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4315-4881","authenticated-orcid":false,"given":"Venkata N.","family":"Padmanabhan","sequence":"additional","affiliation":[{"name":"Microsoft Research India, Bengaluru, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2024-0197","authenticated-orcid":false,"given":"Prabhakar T.","family":"Venkata","sequence":"additional","affiliation":[{"name":"Indian Institute of Science, Bangalore, India"}]}],"member":"320","published-online":{"date-parts":[[2024,3,13]]},"reference":[{"key":"e_1_3_2_2_1","unstructured":"Federal Aviation Administration. 2016. 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