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Recently, bulk-data or burst transmission has been the focus of research in wireless sensor networks. The aim is to benefit applications which require high throughput but small scale deployment. Healthcare applications and applications which are intended to support independent living for the elderly are typical examples. The main idea is to exclusively provide the channel for one transmitter only...
Wireless sensor networks supporting aggressive sampling in harsh environments (to deal with high packet loss or to provide reliable data from sensors such as 3D accelerometers and 3D gyroscopes) require transmission schemes which can achieve a relatively high throughput. Existing contention-based, low-power listening MAC protocols are not apt for these types of networks because their channel utilisation...
Several reproducible experiments are required before actual deployment of wireless sensor networks takes place if stable and predictable outcomes of protocols and data processing algorithms are desired. Considering the typical size of wireless sensor networks and the number of parameters that can be configured or tuned, conducting repeated and reproducible experiments can be both time consuming and...
Abstract-The energy consumption of a wireless sensor network affects its lifetime which in turn affects the scope and usefulness of the network. Most existing or proposed MAC protocols enable nodes to specify a duty cycle, so that they can sleep much of the time to save energy. However, only very few models exist to determine the appropriate time and duration of a sleep phase. Existing approaches...
Understanding fluctuations of link quality in a wireless sensor network is useful for different reasons. For example, nodes can determine when and for how long they should transmit packets, so that they can reduce packet loss rate and the cost of retransmission (delay as well as power consumption). However, because the quality of a link depends on many factors, it cannot be known except in a probabilistic...
One of the main challenges in Cognitive Radio Networks (CRNs) is that the dynamic radio environment affects the Quality of Service (QoS) requirements of Secondary Users (SUs). Another challenge is how to predict Primary Users (PUs) activities over licensed channels, to avoid interfering with PUs. So, there is a need to implement a Cognitive Engine (CE) as a self-organized entity in a CR that overcomes...
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