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Drones represent a significant technological shift at the convergence of on-demand cyber-physical systems and edge intelligence. However, realizing their full potential necessitates managing the limited energy resources carefully. 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. [ABSTRACT FROM AUTHOR]

For secure communications in wireless sensor network in adversarial conditions, one way is providing keys to sensor nodes before deployment. Two sensor nodes can communicate provided they have at least one key in common or there is a path between the two nodes where each pair of neighboring nodes on this path has at least one key in common. However node compromise poses severe security threats in sensor networks. Here we have considered dual BIBD design for the distribution of the keys such that any pair of sensors has exactly one key in common. In this scenario we study the probability of disconnection when a number of nodes, not more than the replication number of the keys are compromised and the resulting status of the network in terms of its resiliency properties. [ABSTRACT FROM AUTHOR]