Erik Björnemo

PhD Thesis, Uppsala University, ISBN 978-91-506-2043-6, Jan. 2009, 279 pages.

The thesis available in Pdf

Paper copies of the thesis can be obtained from Ylva Johansson, Signals and Systems Group, Uppsala University, Box 534, SE-75121 Uppsala, Sweden.

See also the thesis homepage at the Uppsala University database for Ph.D. theses.

Outline:

Imaginea set of small, self-contained, electronic devices equipped with sensors and the ability to communicate with each other without wires. These so-called sensor nodes can then together form a wireless sensor network.

Such a network can monitor a region or phenomenon of interest and provide useful information about it by combining measurements taken by individual sensor nodes and then communicated over the wireless interface. We are in this thesis concerned with energy efficient wireless communication and energy based compromises between sensing and communication.

Wireless sensors with computing capabilities facilitate a range of applications that have previously been infeasible, or at lest too expensive. For instance, in a research project on Great Duck Island (Ontario, Canada), the breeding behaviour of a bird, Leach's Storm Petrel, was monitored by the use of a wireless sensor network (Mainwaring et al., 2002). Sensor nodes equipped with infrared sensors detected the presence of birds inside their nesting burrows, while other sensors registered environmental parameters such as temperature, pressure and humidity. Without the wireless sensors, the April-to-October monitoring would have been practically infeasible.

Another application area gaining strong interest is that of structural health monitoring, in which wireless sensor networks are used to monitor structures such as bridges and nuclear power plants in order to detect damages or other changes in the structures. Yet other areas, to mention but a few, are health care, surveillance and security, wireless automation and military target tracking. Further examples can be found in Romer and Mattern (2004).

In many applications the key feature of a wireless sensor network is that it is just that, wireless. The use of wired node-to-node connections would in most applications constitute a severe complication, both practically and economically, in particular when hundreds of sensors are envisioned. But, while radio communication is a major enabling technology, the absence of wires is also the cause of one of the most prominent concerns, limited energy. Limited energy considerations is a nearly inescapable topic in wireless sensor network design as it imposes strict constraints on the network operation, and for this reason limited energy is the underlying theme of the present work.

Recharging batteries in a wireless sensor network is sometimes impossible due to the placement of the sensor nodes, but more commonly it is merely practically and/or economically infeasible. At any rate, it is widely recognised that, generally, energy is a strictly limited resource in wireless sensor networks and that the consequences of this limitation must be considered (Estrin et al., 2001, Shih et al., 2001, Sohrabi et al., 2000).

Ultimately, if we want to have the sensor network performing satisfactorily for as long as possible, the energy constrained operation of the sensor nodes forces us to compromise between different activities in the network. Compromises are needed on the node level as well as on the network level. Saving energy is tantamount to finding the best compromise, the best tradeoff, between different energy consuming activities and their design.

Based on the recognition in the literature that the radio is a prominent energy consumer (Raghunathan et al., 2002, Shih et al., 2001), we devote the present thesis largely to energy efficient radio communication and the related compromises.

Abstract:

Wireless sensor networks are attractive largely because they need no wired infrastructure. But precisely this feature makes them energy constrained, and the consequences of this hard energy constraint are the overall topic of this thesis.

We are in particular concerned with principles for energy efficient wireless communication and the energy-wise trade-off between sensing and radio communication. Radio transmission between sensors incurs both a fixed energy cost from radio circuit processing, and a variable energy cost related to the level of radiated energy. We here find that transmission techniques that are otherwise considered efficient consumes too much processing energy.

Currently available sensor node radios typically have a maximum output power that is too limited to benefit from transmission-efficient, but processing-intensive, techniques. Our results provide new design guidelines for the radio output power. With increasing transmission energy - with increasing distance - the considered techniques should be applied in the following order: output power control, polarisation receiver diversity, error correcting codes, multi-hop communication, and cooperative multiple-input multiple-output transmissions.

To assess the measurement capability of the network as a whole, and to facilitate a study of the sensing-communication trade-off, we devise a new metric: the network measurement capacity. It is based on the number of different measurement sequences that a network can provide, and is hence a measure of the network's readiness to meet a large number of possible events. Optimised multi-hop routing under this metric reveals that the energy consumed for sensing has decisive impact on the best multi-hop routes. We also find support for the use of hierarchical heterogeneous network structures.

Model parameter uncertainties have large impact on our results and we use probability theory as logic to include them consistently. Our analysis shows that common assumptions can give misleading results, and our analysis of radio channel measurements confirms the inadequacy of the Rayleigh fading channel model.

Keywords:

Wireless sensor network, communication under processing cost, wireless channels, fading, probability theory as logic, uncertainty, measurement capacity.