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.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:668943 |
| Date | January 2015 |
| Creators | Bagale, Jiva Nath |
| Publisher | University of West London |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://repository.uwl.ac.uk/id/eprint/1279/ |
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