Senscity was aimed at developing a wireless network infrastructure for the provision of services for a sustainable city. It addressed three major challenges:

  • A technical challenge: the development and testing of a city-wide, open and standardized wireless network infrastructure;
  • An economic challenge: allow the emergence of new services adapted to the needs of operators of urban services (e.g. waste collection, water management);
  • An environmental challenge: studying the positive impact of wireless sensor networks on sustainable development of the city.


Status: closed (2010-2012)
Consortium: Orange Labs, G-SCOP, G2Elab, LIG, MIND, Alcion Environnement, Azimut Monitoring, BH Technologies, Coronis, DotVision, E-GEE, NumTech, WebDyn. Funded by: French Ministry of Industry.
Keywords: Service ecodesign, Life cycle assessment (LCA), Information and Communication Technologies (ICT), Wireless sensor networks (WSN)


Are “smart” services better for the environment? Do services based on wireless sensor networks (WSN) generate less environmental impacts than they help to avoid? The environmental challenge of this project was to lay the theoretical foundations of an environmental assessment of optimization services and to provide guidance for their ecodesign.


This project delivered:

  • A theoretical framework helping to understand environmental impacts of optimization services. This required the development of an environmental impact model for information as well as a lifecycle model for complex technical infrastructures.
  • A comprehensive environmental assessment method extending based on these models as well as life cycle assessment (LCA) and wireless sensor network simulation.
  • Two wireless sensor simulation tools developed with Matlab and WSNet.
  • A comprehensive ecodesign method based on three levels of improvements:
    • equipment: reduce the environmental impacts of individual devices independently from their usage in an infrastructure;
    • infrastructure: reduce the use of individual devices in the infrastructure (i.e. use them less and make them consume less energy) independently from datasets to be generated;
    • information: reducing the use of the infrastructure for a given optimization performance, i.e. define the dataset to be collected that minimizes the solicitation of the infrastructure and maximizes the capacity to take effective optimization actions.


In one hand, the application of the developed method to a case of an optimization service for waste collection allowed us to conclude that environmental relevance of those services should not been taken for granted. The conducted studies confirmed the results of some preliminary studies that identified critical environmental impact transfers while applying WSN-based optimizations services.

In another hand, this project showed that there is a great room of improvement so that impact transfers could be avoided. Therefore, this project lead to the claim that optimization services shall systematically be the object of a multi-criteria environmental assessment and an eco-design process, before it can be asserted that they lead to environmental impact reduction.