当前位置: X-MOL 学术arXiv.cs.CC › 论文详情
Rapid Top-Down Synthesis of Large-Scale IoT Networks
arXiv - CS - Computational Complexity Pub Date : 2020-02-11 , DOI: arxiv-2002.04244
Pradipta Ghosh; Jonathan Bunton; Dimitrios Pylorof; Marcos Vieira; Kevin Chan; Ramesh Govindan; Gaurav Sukhatme; Paulo Tabuada; Gunjan Verma

Advances in optimization and constraint satisfaction techniques, together with the availability of elastic computing resources, have spurred interest in large-scale network verification and synthesis. Motivated by this, we consider the top-down synthesis of ad-hoc IoT networks for disaster response and search and rescue operations. 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 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, and, to scale to problem sizes beyond what these frameworks are capable of, 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.
更新日期:2020-02-12

 

全部期刊列表>>
化学/材料学中国作者研究精选
ACS材料视界
南京大学
自然科研论文编辑服务
剑桥大学-
中国科学院大学化学科学学院
南开大学化学院周其林
课题组网站
X-MOL
北京大学分子工程苏南研究院
华东师范大学分子机器及功能材料
中山大学化学工程与技术学院
试剂库存
天合科研
down
wechat
bug