I.              
CONTIKI

Contiki has been the go-to operating system for
implementing Internet of Things, since its inception in the year 2002, because
it is an open source operating sytem which is lightweight and mature. It allows
rapid prototyping and easy shift between different hardware platforms. This OS
is preferred mainly because the developer need not design the underlying
operating system for the internet-connected devices.Using this platform, almost
any device can be connected to the internet even when the device is placed
underground or in an enclosed space. The inventor of Contiki has managed to fit
an entire operating system , including a graphical user interface, networking
software and web browser into less than 30 kilobytes of space, which makes it
easier to run on small, low-powered chips. Devices with limited memory, power,
processing power and communication bandwidth can be run using Contiki.

The
Contiki system includes a network simulator called Cooja, which simulates
networks of Contiki motes (sensors). Contiki provides IPv6 networking for
relaying datagrams across network boundaries. It contains the Routing Protocol
for Low power and Lossy Networks (RPL), an Internet Protocol (IP) optimized for
wireless sensor networks. Contiki is implemented in the C language and can be
ported to a number of micro controllers such as MSP430, Atmel AVR, Arduino,
CC2430, etc. The applications are implemented using Constrained Application
Protocol (CoAP) which is an application layer protocol that provides power
efficient operation through low radio duty cycling mechanism. CoAP adopts
patterns from HTTP but unlike the latter, CoAP uses UDP.

II.            
COMPARISON OF
TOPOLOGIES

The remote sensors (motes) can be arranged in a
number of topologies according to the required application. The mote type
determines the type of sensor hardware and which Contiki applications are to be
simulated. In the case of Smart City concept, the major concern would be power
consumption and the transmission-reception delay. To determine the topology in
which power consumption is minimum, motes were placed in different topologies
with different transmission ranges and payload. The three major topologies are
linear, ellipse and random. In this example, IPv6 routing with RPL is
considered. Therefore, arpl-border-router with three clients and servers are
simulated using Cooja simulator. Figure 1.shows the various topologies

 

 

Figure 1a.Linear topology

1a. Linear                                     1b.Ellipse                                             
1c. Random

To
evaluate the power consumption, Tmote Sky mote was used in different topologies
and the following graph (Figure 2.) was obtained from the average power values
of each topology. From Figure 2. it is inferred that power consumed during
transmission and reception between motes is less in random topology.

Figure 2. Power for various topologies

Transmission
range of the various motes also affect the amount of power consumed. Therefore,
we increase the transmission as well as interference ranges of the motes to
examine the power consumption. From figure 3.it can be observed that for
various transmission ranges, the motes in random topology has consumed less
power over its counterparts.

Figure 3. Range Vs Power

The
next concern is the amount of data, ie., payload, sent by the servers during
transmission of signals. Figure 4.plots the power consumed by the nodes with
increasing payload. It can be seen that for random topology, the power consumed
decreases drastically with increase in payload. Therefore, random topology can
be used even when large amount of server data has to be transimitted.

Figure 4. Payload variation

The payload also
affects the end-to-end delay of the nodes. Hence, an experiment is conducted by
increasing the payload and determining the corresponding power changes. From
figure 5, it can be inferred that as the payload increases,
transmission-reception delay also increases. When the topologies are compared,
linear topology has been found to have lesser delay.

Figure 5. Payload Vs Delay

From all the above
comparisons, it can be observed that random topology can be used for practical
applications.

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