Low-Cost Reliable Blackout Sustainability of Wireless Sensor Networks with Energy Harvesting Systems
Conference: European Wireless 2011 - Sustainable Wireless Technologies
04/27/2011 - 04/29/2011 at Vienna, Austria
Proceedings: European Wireless 2011
Pages: 8Language: englishTyp: PDFPersonal VDE Members are entitled to a 10% discount on this title
Glatz, Philipp M.; Hörmann, Leander B.; Steger, Christian; Weiss, Reinhold (Institute for Technical Informatics, Graz University of Technology, Graz, Austria)
Wireless sensor network (WSN) motes are typically small and networked embedded systems. They are of limited computational power and suffer severe resource constraints - especially energy is scarce. With the advent of energy harvesting system (EHS) technology new opportunities open up for long-lived WSNs. Several prototypes of EHS-enhanced WSN platforms have recently been designed in a low-power and energy-efficient way. However, while the field starts to mature there are still a few lessons to be learned for building low-cost and robust systems. While the term low-cost may easily be defined by means of calculating what hardware and maintenance cost per end-user performance for a given business model, it is more complicated to define what is meant by the term robust. For perpetual systems one has to find a formal way of validating whether a system implementation - including all low power hardware and networking approaches - can sustain a given quality of service despite harsh environmental conditions or faulty operation. For the presented case study with the RiverMote hardware platform, we model its black-out sustainability (BOS). We can validate that with its robust low-power design and BOS of more than three weeks can be achieved. Perpetual operation with error detection, fall-back mechanisms, error reporting and self-healing of the system is possible. The system relies on more easy-to-validate system-level mechanisms than other approaches that concentrate on energy harvesting device pattern prediction and therefore have to assume robust forecasting of environmental conditions.