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level have been associated, nodes from the second one can start to send their
DSC packets. When choosing the best father, the nodes of a lower level listen
the channel to catch the information from the ACK and ACKASOC packets
from the nodes of the upper level. These packets include information about
the number of hops to the BS; actually, only this parameter has been taken
into consideration in the initial design. Once the network has been created, the
BS waits for a period of time, during which no new nodes are detected. The
BS node will then transmit a SYN packet indicating the beginning of the data
transmission phase. This process starts at the base station and a SYN packet
is propagated among successive levels. Figure 1 shows the sequence of actions
described for this scenario.
During the second phase, the nodes will turn ON and OFF, thus complet-
ing a duty cycle. This is to say, the parent node will wake up and each node
will transmit within the slot allocated during the set-up phase. In this way, we
limit the overhead due to the transient period every time the radio interface is
turned on.
4 Fuzzy Logic
Fuzzy logic is a decision system approach which works similarly to human con-
trol logic. It provides a simple way to arrive at a definite conclusion based
upon imprecise, vague or ambiguous input information. Furthermore, only a few
data samples are required in order to extract the final accurate result. Besides,
fuzzy logic is a handy technique since it uses human language to describe inputs
and outputs [12]. All these features make fuzzy logic appropriate for the parent
decision process in wireless sensor networks.
One of the frequently-used fuzzy-logic inference methods is Mamdani [13],
which consists of four phases: fuzzification, rule evaluation, combination or ag-
gregation of rules, and deffuzification. The input of a Mamdani fuzzy-logic sys-
tem is usually a crisp value. To allow this value to be processed by the system,
it has to be converted to natural language, that is, fuzzified. In this way, the
fuzzifier takes numeric values and converts them into fuzzy values which can be
processed by the inference system. These fuzzy values represent the membership
values of the input variables to the fuzzy sets. Once values have been fuzzified,
the inference system processes the fuzzy rules to get a fuzzy output. The third
step in the Mamdani inference method is the aggregation of all outputs, where
the outputs of each rule are combined to form a new fuzzy set. Finally, in the
deffuzification process, the new aggregated fuzzy set is converted to a number. In
summary, fuzzy logic is an ecient method for making decisions and it also has
the advantage of working with natural language and require low computational
capabilities.
5NtworkRouing
Routing is a key element in the operation of wireless sensor networks. Paths
from any node in the network to the base station must be defined in order to
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