![](bg2.png)
ROLL 2009), a Working Group formed by the
Internet Engineering Task Force (IETF) in charge of
standardization and specifying the IP protocol
recognizes that factors like the routing tables
growth, constraints in routers technology and the
limitations of today's Internet addressing architecture
have driven the efforts in researching new
paradigms. Attempts to adapt the routing protocols
of infra-structured networks to cases of ad-hoc
networks are often inconsistent to address issues as:
frequent changes in topologies, poor link quality,
restricted bandwidth, and constraints on energy
resources. On the other hand, the ad-hoc and sensor
networks tend to proliferate as the number of smart
objects-based services on the Internet increase.
After this brief Introduction, Section 2 describes
the concept of a “Trellis Coded Network”- (TCNet)
to define the routing datagrams generated by each of
the nodes network, Section 3 presents the
performance of TCNet in terms of latency and
energy efficiency, Section 4 describes the
application of TCNet to the Sensor Network
Virtualization (SNV) and clustering scenarios and
Section 5 summarizes the conclusions and future
works.
2 TRELLIS CODED NETWORKS
The concept of a “Trellis Coded Network”- (TCNet)
changes conventional routing paradigms to enable
the development of QoS-aware packet forwarding
protocols in WSNs that are used to determine the
routing datagrams generated by each of the
network’s nodes offering the following advantages
that are compatible with the limited resources of
WSNs:
Elimination of routing tables;
Reduced latency by eliminating the route
request (RREQ) and route reply (RReply)
signaling packets employed, for example,
in AODV;
Implicit self-recovery mechanism in case
of failure.
The model is based on finite automata (Hopcroft
and Ulman 1955) or FSMs defined by a ''cross''
function (k
n
/ out
n
) where a sequence of input
symbols {k
n
} generates a sequence of output codes
{out
n
} as shown in Figure 1. The k
n
(t) is the input
symbol received at time t and generates the output
code out
n
(t). It is also assumed that at time t, a
transition occurs at the FSM from state i to state j. In
TCNet each state represents a network node and the
transition state indicates that the frame information
must be sent from node i to node j.
It is then possible to generate a specific route
along a set of nodes, defined by a desired
optimization criterion (latency, packet loss,
throughput, cost), by shifting an input sequence {k
n
}
in the FSM of the route’s first node, and informed in
the frame as a TCNet label.
Figure 1: Network node modeled by a state of a FSM.
A network node modeled as a state allows the
application of FSM principles in order to exploit the
analogy between networks and state diagrams to
define paths between nodes. This enables each node
to have full knowledge of the network by
implementing a paths generator machine (MM) of
low complexity (''XOR'' gates and shift registers) as
shown in Figure 2.
Figure 2: Example of a MM with the input sequence {k
n
}
generating an output sequence out
n
(t) = (c
1
,c
2
).
The TCNet architecture employs the Viterbi
algorithm (Proakis and Salehi 2008) proposed in
1967 for decoding convolutional codes based on the
trellis diagram to decide a sequence of branches to
be followed. The Viterbi algorithm decodes a
received sequence by evaluating the distance
between the sequence of received source symbols
and the weight of the path in the trellis, and
identifies the best sequence of branches as the one
that provides the minimum distance. This is done by
associating each branch with a number called branch
metrics, and looking for the path whose metrics sum
is minimum. This can be accomplished by means of
evaluating the maximum-likelihood (Gratzer, 1978)
and to produce an estimate of the received sequence
Novel IoT Applications Enabled by TCNet: Trellis Coded Network
621