9 May 2018 Large-scale parallel simulations of distributed detection algorithms for collaborative autonomous sensor networks
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Abstract
A key component of the Third Offset Strategy proposed by the United States Department of Defense is the use of unmanned autonomous systems to deter potential conflicts. Collaborative autonomy technologies are also being explored by the private sector, which is rapidly pushing towards the deployment of self-driving vehicles. For areas affected by disaster, autonomous drone swarms can assist with search and rescue operations by surveilling large regions quickly without exposing emergency responders to risk prematurely. A substantial amount of progress has been made in distributed sensing research over the last few years. However, simulation results for applications that require complex inter-agent communications have rarely been demonstrated at scale; these simulations are generally executed using tens or hundreds of agents rather than the thousands or tens of thousands envisioned for large autonomous swarms. We address this deficit here by presenting two contributions. First, we extend our previous work on efficient, distributed algorithms for weak radiation source detection to accommodate the use case of surveillance across a very wide area. We then demonstrate the efficacy of the proposed algorithms at scale using a parallelized version of the ns-3 discrete event simulator.
Conference Presentation
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Anton Y. Yen, Anton Y. Yen, Peter D. Barnes, Peter D. Barnes, Bhavya Kailkhura, Bhavya Kailkhura, Priyadip Ray, Priyadip Ray, Deepak Rajan, Deepak Rajan, Kathleen L. Schmidt, Kathleen L. Schmidt, Ryan A. Goldhahn, Ryan A. Goldhahn, } "Large-scale parallel simulations of distributed detection algorithms for collaborative autonomous sensor networks", Proc. SPIE 10652, Disruptive Technologies in Information Sciences, 106520G (9 May 2018); doi: 10.1117/12.2306545; https://doi.org/10.1117/12.2306545
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