The remarkable growth in the applications of low power wireless networks (LPWNs) in various disciplines such as health-care, wildlife monitoring, unmanned vehicles and the emerging Internet of Things (IoT) brings along various challenges. Such applications demand the transfer of large amounts of data in short durations. Unlike conventional medium access control protocols, which force each competing node to contend for each packet it transmits, bulk data transmission enables a node to exclusively use a channel for transferring a large amount of data in succession. Bulk data transmission is a technique in which a sender node is granted exclusive access of the channel in order to transmit all the packets accumulated in its buffer. However, there are two problems with this strategy: (1) For how long should bulk data transfer last if there are multiple contending nodes? (2) How should this strategy deal with the significant fluctuation in the quality of a low-power wireless link? Understanding link quality fluctuations in a wireless sensor network is useful for various reasons. For example, nodes can determine when and for how long they should transmit packets, so that they can reduce the packet loss rate and the cost of retransmission (delay as well as power consumption). However, the quality of a link depends on many factors, which cannot be known except in a probabilistic sense.
In this dissertation, I propose an efficient burst transmission scheme that measures and models the dynamic link quality fluctuations. Introducing a large empirical study at the beginning of this dissertation leads to a good understanding of the effect of external factors such as the environment (indoor,outdoor), Cross Technology Interference (CTI) and mobility of a sender node causing link quality to fluctuate. The analysis and observations of the empirical study establishes the basis on which the model for link quality estimation is built and designed. Here I propose three approaches to deal with different aspects of link quality fluctuation.
(i) Offline approach- long-term characteristics: The offline approach models the link quality fluctuations by taking into account a large set of data. To obtain such a data set, experiments were performed on the site under study for several weeks. It was observed that the link quality fluctuates considerably even in static deployment. Understanding the stable durations, good and bad alike contribute to the efficient transmission of packets. I propose
two offline approaches: (i) The first uses the conditional probability distribution function of signal-to-noise (SNR) fluctuation to estimate the expected reliable and unreliable period. (ii) The second uses k-mean clustering to characterise the link quality fluctuations into different states where the relationship between the states is defined by transitional probabilities. The advantages of employing an offline approach is (i) availability of sufficient memory, (ii) low computational cost, and (iii) possible use of a complex algorithm. However, these approaches can not deal with short-term link quality fluctuation.
(ii) Online approach- short-term characteristics: Unlike the offline approaches, an online approach models the link quality in real time and deals with short-term link quality fluctuation. However, this approach has some limitations, such as (i) limited memory space to store data, (ii) high computational cost, (iii) and employment of a simple algorithm to estimate the burst size. My proposed online approach uses adaptive history array to estimate the duration of good and bad states from the statistics of incoming acknowledgement packets. (iii) Hybrid approach- long-to-short-term characteristics: A hybrid approach combines both offline and online methods. I also take advantage of both offline and online models in my proposed hybrid approach. My aim is to characterise the long-term link quality fluctuation with statistics that are obtained offline and to employ the statistics of received acknowledgement packets in real-time to deal with short-term link quality fluctuations. The online statistics are used to fine-tune and calibrate the offline model. To evaluate the performance of my proposed approaches, I implement them in TinyOS and deploy them on TelosB sensor nodes. Furthermore, the proposed approaches in this thesis are compared with the state-of-the-art approaches. The thesis concludes by showing that
my approaches efficiently model the link quality fluctuation and propose correct burst size to achieve high throughput, reduce transmission delay, and power consumption under different channel conditions.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:31136 |
| Date | 17 September 2018 |
| Creators | Ansar, Zeeshan |
| Contributors | Dargie, Waltenegus, Seitz, Jochen, Schill, Alexander, Technische Universität Dresden |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
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