Autonomy of devices / energy efficiency of WSN

Home/Autonomy of devices / energy efficiency of WSN
Autonomy of devices / energy efficiency of WSN 2017-08-28T08:51:42+00:00

In recent years, the number of wireless sensor network deployments for real life applications has rapidly increased. Still, the energy problem remains one of the major barriers somehow preventing the complete exploitation of this technology. Sensor nodes are typically powered by batteries with a limited lifetime and, even when additional energy can be harvested from the external environment (e.g. through solar cells or piezo-electric mechanisms), it remains a limited resource to be consumed judiciously. Efficient energy management is thus a key requirement for a credible design of a wireless sensor network.

The design of sustainable wireless sensor networks is a very challenging issue. On the one hand, energy-constrained sensors are expected to run autonomously for long periods. However, it may be cost-prohibitive to replace exhausted batteries or even impossible in hostile environments. On the other hand, unlike other networks, WSNs are designed for specific applications which range from small-size to large-scale monitoring. Thus, any WSN deployment has to satisfy a set of requirements that differs from one application to another.

The objective of SCOTT is to develop an advanced wireless intelligent sensors strategy based on energy harvesting systems that will offer many system engineering and operational advantages which can offer cost-effective solutions for a specific application in a domain.


While batteries represent the preferred low-cost energy storage technology, energy scavenging and harvesting devices are beginning to emerge as viable battery replacements in some applications. For example, power can be generated from temperature differences through thermoelectric and pyroelectric effects, kinetic motion of piezoelectric materials, photovoltaic cells that capture sunlight, or even the direct conversion of RF energy through specialized antennas and rectification. SCOTT will go in depth into the energy stored domain, expecting a continue fall in the coming years.

Harvesting-aware adaptive sampling (and, more generally, harvesting-aware power management) is a very interesting approach that promises to prolong the network lifetime to a virtually unlimited time. Model-based active sensing is also very interesting. However, in most cases, solutions based on this approach are computationally expensive, and must be implemented in a centralized way. Clustering, is one of those techniques that is very useful for WSNs in data aggregation and filtering. Selection of the most appropriate model is the key issue in the design of a model-based active sensing strategy.

Focused on wireless sensor networks in order to solve the energy constraint, to increase the level of security and precision and to expand autonomy for accuracy, feasibility and profitability reasons. It will be very useful to search the optimization of data routing and to limit unnecessary data sending and the collisions.

Depending on the application, the location where the WSNs nodes should be installed could be a challenge. For example, not all energy harvesting technologies are suitable for certain locations considering aspects such as space, luminosity, vibrations, etc. Also, limitations in terms of data traffic such as system noise, signal attenuation, response dynamics, power consumption, and effective conversion rates, etc.

The objectives of SCOTT in terms of measureable indicators are:

  • Develop a new energy efficient transceiver design based on impulse radio UWB technology for wireless in-vehicle networking in order to overcome the high power consumption of the current commercial solutions.

  • Develop a hybrid power generator and storage system using piezoelectric, solar and thermal energy harvesting with thin-film batteries. This system will be able to provide both energy and power density to a wireless sensor node.

  • Develop a high power energy harvesting (100W) system and the corresponding energy management and storage to really achieve an appropriate deployment of these infrastructures in remote areas or underground regions, such as railroad tracks.

  • Develop a dependable Wireless Sensor Node with energy harvesting for harsh environments, combining energy harvesting transducers, an energy processing power module, low power sensors and an energy aware microcontroller.

  • Investigate to what level fault injection can contribute to a fast release of large and complex systems. The output will be a description on how one should apply this technique and to what extend it will be successful in limiting the validation and verification of these systems.