Bagale, Jiva Nath (2015) On the performance of emerging wireless mesh networks. Doctoral thesis, University of West London.
Bagale_-_Thesis_(September_2015).pdf - Submitted Version
Wireless networks are increasingly used within pervasive computing. The recent
development of low-cost sensors coupled with the decline in prices of
embedded hardware and improvements in low-power low-rate wireless networks
has made them ubiquitous. The sensors are becoming smaller and
smarter enabling them to be embedded inside tiny hardware. They are already
being used in various areas such as health care, industrial automation
and environment monitoring. Thus, the data to be communicated can include
room temperature, heart beat, user’s activities or seismic events. Such networks
have been deployed in wide range areas and various levels of scale.
The deployment can include only a couple of sensors inside human body or
hundreds of sensors monitoring the environment.
The sensors are capable of generating a huge amount of information when
data is sensed regularly. The information has to be communicated to a central
node in the sensor network or to the Internet. The sensor may be connected
directly to the central node but it may also be connected via other sensor
nodes acting as intermediate routers/forwarders. The bandwidth of a typical
wireless sensor network is already small and the use of forwarders to pass
the data to the central node decreases the network capacity even further.
Wireless networks consist of high packet loss ratio along with the low network
bandwidth. The data transfer time from the sensor nodes to the central node
increases with network size. Thus it becomes challenging to regularly communicate
the sensed data especially when the network grows in size. Due to
this problem, it is very difficult to create a scalable sensor network which can
regularly communicate sensor data.
The problem can be tackled either by improving the available network
bandwidth or by reducing the amount of data communicated in the network.
It is not possible to improve the network bandwidth as power limitation on the
devices restricts the use of faster network standards. Also it is not acceptable
to reduce the quality of the sensed data leading to loss of information before communication.
However the data can be modified without losing any
information using compression techniques and the processing power of embedded
devices are improving to make it possible.
In this research, the challenges and impacts of data compression on embedded
devices is studied with an aim to improve the network performance
and the scalability of sensor networks. In order to evaluate this, firstly messaging
protocols which are suitable for embedded devices are studied and
a messaging model to communicate sensor data is determined. Then data
compression techniques which can be implemented on devices with limited
resources and are suitable to compress typical sensor data are studied. Although
compression can reduce the amount of data to be communicated over
a wireless network, the time and energy costs of the process must be considered
to justify the benefits. In other words, the combined compression and
data transfer time must also be smaller than the uncompressed data transfer
time. Also the compression and data transfer process must consume less
energy than the uncompressed data transfer process. The network communication
is known to be more expensive than the on-device computation in
terms of energy consumption. A data sharing system is created to study the
time and energy consumption trade-off of compression techniques. A mathematical
model is also used to study the impact of compression on the overall
network performance of various scale of sensor networks.
|Item Type:||Thesis (Doctoral)|
|Depositing User:||Marzena Dybkowska|
|Date Deposited:||20 Oct 2015 13:55|
|Last Modified:||22 Feb 2016 20:15|
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