Lecture Notes in Computer Science 4837

4 M. Kuty̷lowski
receiver should not be switched on all the time, so inevitably some messages
broadcasted could be unnoticed by the sensor, despite that the sensor was in the
proper range.
This problem is particularly visible when we concern warning function of sensors.
Detecting anomalies (which are the most crucial ways of detecting critical
situations) is not always equivalent with detecting certain numeric values, sometimes
it is their gradient or an abnormal pattern of values in some area. How to
detect abnormal patterns by a set of sensors without large communication is an
interesting question that involves issues of game theory, distributed algorithms
and communication complexity.
Heterogeneous Networks. In many practical applications we could share
the basic infrastructure composed by sensors. This helps to reduce deployment
costs and compose many virtual sensor networks out of the same set of devices.
Inevitably, we may come to a situation when a virtual sensor network consists
of devices that are deployed by more than one provider. Consequently, sensor
type and their configuration may differ, sometimes quite significantly.
Since sensors are weak devices, it might be an uneasy for them to emulate
different configurations in order to serve the needs of diverse virtual networks.
Therefore, algorithms and protocols designed for sensor networks should to certain
degree disregard particular features of the sensors - a good algorithm should
be as independent of the features of the sensors as possible. At the same time,
it should make use of any additional features available at certain nodes of the
network.
Standard algorithm design is not focused on such a situation: usually it is assumed
that in a distributed system all network units have comparable properties.
Therefore most of the algorithms for distributed systems have to be redesigned
or at least reconsidered before being deployed for sensor networks.
Network Evolution. Heterogenuity may arise for yet another important reason:
after some time new sensors become deployed in order to upgrade its functionality
or to replace malfunctioning or dead units. Location, functionality and
purpose of strong units may change as well. So finally we are faced with many
compatibility problems: new devices should work well within an old network,
however, they have to take adventage of newer technology in order to improve
the network performance.
The problem is a more difficult than in the classical networks. Sometimes it
is impossible to exchange fully the old units. Sometimes we share sensors with
another provider who can decide to make a replacement without our accord. The
problem is that communication protocols for sensor networks are not that stable
as for the classical networks, so a replacement may have profound consequences.
Since we have to assume that changes within the network may be frequent
and quite significant, and involve dramatically different hardware, we have to
design self-organization paradigms yielding algorithms that are robust to these
changes. We have to design algorithmic schemes (and not only data framework!)
that support upwards and downwards compatibility.