End-to-end energy models for Edge Cloud-based IoT platforms: Application to data stream analysis in IoT

Yunbo Li, Anne Cécile Orgerie, Ivan Rodero, Betsegaw Lemma Amersho, Manish Parashar, Jean Marc Menaud

Research output: Contribution to journalArticlepeer-review

60 Scopus citations

Abstract

Internet of Things (IoT) is bringing an increasing number of connected devices that have a direct impact on the growth of data and energy-hungry services. These services are relying on Cloud infrastructures for storage and computing capabilities, transforming their architecture into more a distributed one based on edge facilities provided by Internet Service Providers (ISP). Yet, between the IoT device, communication network and Cloud infrastructure, it is unclear which part is the largest in terms of energy consumption. In this paper, we provide end-to-end energy models for Edge Cloud-based IoT platforms. These models are applied to a concrete scenario: data stream analysis produced by cameras embedded on vehicles. The validation combines measurements on real test-beds running the targeted application and simulations on well-known simulators for studying the scaling-up with an increasing number of IoT devices. Our results show that, for our scenario, the edge Cloud part embedding the computing resources consumes 3 times more than the IoT part comprising the IoT devices and the wireless access point.

Original languageEnglish (US)
Pages (from-to)667-678
Number of pages12
JournalFuture Generation Computer Systems
Volume87
DOIs
StatePublished - Oct 2018

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

Keywords

  • Data stream analysis
  • Edge Cloud computing
  • End-to-end energy model
  • Energy-efficiency
  • IoT

Fingerprint

Dive into the research topics of 'End-to-end energy models for Edge Cloud-based IoT platforms: Application to data stream analysis in IoT'. Together they form a unique fingerprint.

Cite this