Nature-inspired? Optimization in the era of IoT: Particle Swarm Optimization (PSO) applied to Indoor-Distributed Antenna Systems (I-DAS)

H. Hildmann, D. Y. Atia, D. Ruta, K. Poon, A. F. Isakovic

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

5 Scopus citations

Abstract

The Internet of Things (IoT) refers to a network type structure (like the Internet) that connects (unique) objects and things. Sensors and devices, integrated into all types of objects, are increasingly connected through wireless networks and, ultimately, the Internet. Already the existing ICT infrastructure accounts for roughly 10% of the global power consumption. By the year 2020, this network will contain 50 billion connected devices and the global IoT/machine-to-machine (M2M) communication market will have a volume of $ 0.5 trillion. Pervasive indoor wireless access has almost become a standard in the first world, but ensuring a thorough and economically sound wireless signal coverage throughout buildings is not a trivial problem. In-building Distributed Antenna Systems (I-DAS) extend wireless access from the base station to distributed antennas through a complex network of coaxial cables and power splitters. For high rise buildings and other multi-unit complexes, the initial cost of I-DAS (cabling and splitters) and the running costs of powering the network are quite significant, motivating the need to optimize the design of I-DAS networks. We propose to utilize Particle Swarm Optimization (PSO) to provide near-optimal network topology. Our PSO model uses Prüfer code representation to efficiently traverse through different spanning tree solutions. Our approach is scalable and robust, capable of producing I-DAS design advice for buildings beyond one hundred floors. We demonstrate that our model is capable of obtaining optimal solutions for small buildings and near-optimal solutions for tall buildings.

Original languageBritish English
Title of host publicationThe IoT Physical Layer
Subtitle of host publicationDesign and Implementation
Pages171-192
Number of pages22
ISBN (Electronic)9783319931005
DOIs
StatePublished - 1 Jan 2018

Keywords

  • Distributed antenna systems
  • Heuristics
  • Multi-agent systems
  • Nature-Inspired optimization
  • Particle swarm optimization

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