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wide physical area. The benefits, which are offered by these ubiquitous
applications and their WSNs, are easily integrated and embedded within
the surroundings and require minimal human interventions. The WSN
supports continuous data collection from a distributed network of station-
ary and mobile sensor devices deployed in the surroundings. The collected
data can be processed either locally (within the individual sensors) or glob-
ally (in other central nodes, such as base stations), and fused into an interac-
tive virtual environmental monitoring model that can be accessed remotely
by humans.
We strongly believe that the deployment of WSN offers several advan-
tages, and solves some of the spatial and temporal data collection. The WSN
can cover a larger physical area with minimum human effort than it cur-
rently handles by the manual coverage process (spatial drawback). Also,
WSN offers an automatic periodical data collection, which corresponds to
all possible events occurring in the monitoring area (temporal drawback).
Due to the nonstop data collection process by WSN, it presents a challeng-
ing problem to the collection centers (or base stations [BS]). On a regular
basis, sensors transmit their collected data to a BS; however, the BS has cer-
tain process and storage capacities. Eventually the BS will reach its limit
with respect to the processing capacity if there are too many sensors in the
WSN transmitting data. At the same time, the BS will reach its limit with
respect to the storage capacity if the ubiquitous application of WSN captures
multimedia data, such as a collection of video clips, audio, or still-images.
The multimedia data requires a lot of memory especially for high-quality
resolutions.
In this chapter, we present a model for data aggregation based on mapping
all tasks within the wireless sensor network into a super task-flow graph
(STFG), and a scheduling methodology that regulates the three tasks within
each sensor (sense, process, and transmit) in order to balance the workload
at the base stations. The sense task estimates a physical phenomenon, such as
a temperature, light intensity, humidity, and so forth. The process task man-
ages all activities within a sensor. The transmit task sends the collected data
to the base station. We utilized two algorithms: as soon as possible (ASAP)
and as late as possible (ALAP) to determine boundaries on how early and
late to schedule all sensor tasks, keeping in mind the threshold on the BS
with respect to the process and storage capacities.
5.2 Structure of a Sensor and Its Related Work
The main components of a sensor consist of a sensing unit, processing unit,
communication unit, and power unit as shown in Figure 5.1.
