Database Reference
In-Depth Information
Making Sense of Sensors
It's easy to see why the availability of new and affordable technologies to store, access, and
analyze time series databases expands the possibilities in many sectors for measuring a wide
variety of physical parameters. One of the fastest growing areas for generating large-scale
time series data is in the use of sensors, both in familiar applications and in some new and
somewhat surprising uses.
In
Chapter 1
we considered the wide variety of sensor measurements collected on aircraft
throughout a flight. Trucking is another area in which the use of time series data from
sensors is expanding. Engine parameters, speed or acceleration, and location of the truck are
among the variables being recorded as a function of time for each individual truck
throughout its daily run. The data collected from these measurements can be used to address
some very practical and profitable questions. For example, there are potentially very large
tax savings when these data are analyzed to document actual road usage by each truck in a
fleet. Trucking companies generally are required to pay taxes according to how much they
drive on public roads. It's not just a matter of
how many
miles a truck drives; if it were, just
using the record on the odometer would be sufficient. Instead, it's a matter of knowing
which
miles the truck drives—in other words, how much each truck is driven on the taxable roads.
Trucks actually cover many miles off of these public roads, including moving through the
large loading areas of supply warehouses or traveling through the roads that run through
large landfills, in the case of waste-management vehicles.
If the trucking company is able to document their analysis of the position of each truck by
time as well as to the location relative to specific roads, it's possible for the road taxes for
each truck to be based on actual taxable road usage. Without this data and analysis, the taxes
will be based on odometer readings, which may be much higher. Being able to accurately
monitor overall engine performance is also a key economic issue in areas like Europe where
vehicles may be subject to a carbon tax that varies in different jurisdictions. Without accurate
records of location and engine operation, companies have to pay fees based on how much
carbon they
may
have emitted instead of how much they actually did emit.
It's not just trucking companies who have gotten “smart” in terms of sensor measurements.
Logistics are an important aspect of running a successful retail business, so knowing exactly
what is happening to each pallet of goods at different points in time is useful for tracking
goods, scheduling deliveries, and monitoring warehouse status. A smart pallet can be a
source of time series data that might record events of interest such as when the pallet was
filled with goods, when it was loaded or unloaded from a truck, when it was transferred into
storage in a warehouse, or even the environmental parameters involved, such as temperature.
