Synthesis of Large-Scale Instant IoT Networks
user-5da93e5d530c70bec9508e2b(2021)
摘要
While most networks have long lifetimes, temporary network infrastructure is often useful for special events, pop-up retail, or disaster response. An
instant IoT
network is one that is rapidly constructed, used for a few days, then dismantled. We consider the synthesis of instant IoT networks in urban settings. This synthesis problem must satisfy complex and competing constraints: sensor coverage, line-of-sight visibility, and network connectivity. The central challenge in our synthesis problem is quickly
scaling
to large regions while producing cost-effective solutions. We explore two qualitatively different representations of the synthesis problems using satisfiability modulo convex optimization (SMC), and mixed-integer linear programming (MILP). The former is more expressive, for our problem, than the latter, but is less well-suited for solving optimization problems like ours. We show how to express our network synthesis in these frameworks. To scale to problem sizes beyond what these frameworks are capable of, we develop a
hierarchical synthesis
technique that independently synthesizes networks in sub-regions of the deployment area, then combines these. We find that, while MILP outperforms SMC in some settings for smaller problem sizes, the fact that SMC's expressivity matches our problem ensures that it uniformly generates better quality solutions at larger problem sizes.
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关键词
Scale (ratio),Distributed computing,Instant,Computer science,Internet of Things
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