An approach for comprehensive Life Cycle Assessment and effective eco design of IoT systems: data-driven modeling of reference flows

Abstract

Abstract As we approach the limits of our technologies and the number of connected devices grows, scientists put more efforts to estimate and reduce the ecological damage of the Internet of Things. Unfortunately, environmental studies and eco design of IoT systems suffer from a major inconvenience so far: it does not put sensor data in the focus of attention. This paper aims to point out explicitly the essential role of this aspect for modeling reference flows and demonstrate its relevance for agile environmental assessment and sustainable design. Also, it aims to illustrate that such modeling process must happen in a comprehensive way. For this, our work relies on a case study addressing smart metering, and we proceed as follows. Based on available documentation and inspired by certain aspects of different technologies, we imagine the maximal capacities of key components, and we construct an unfavorable data flow scenario to get a rough idea of the reference flow and the long-term impact of our system during its use phase. Results from this procedure are later contrasted with results obtained from a packet traffic analysis, in which local and internet data flow are examined carefully. At the end, we verify the importance of data empirically, and we conclude that the reference flow and the impact contributors of a system could be affected not only by the local data transit but also by the complex interactions between edge devices and cloud resources. All our findings are discussed to produce generic guidelines for sustainable IoT systems.