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Network-Aware Content Shaping, Energy Efficient Data Services and energy consumption modeling for Wireless Networks

In this project, we proposed to study techniques to transform and deliver diverse content adaptively so as to minimize the energy consumed by the wireless handheld. Besides the content transformation techniques themselves, we had to develop fast and accurate handheld energy modeling techniques that can be used to drive the content transformation algorithms.

In terms of energy modeling, we extended the energy model developed earlier for Hyper-Text Transport Protocol (HTTP)-based textual data communication, to include multimedia access,
specifically image access, and to consider dynamic channel variations. In addition to the energy spent in communicating the data, processing multimedia data at the handheld can constitute a significant portion of the total energy consumption. Hence, the computational energy model is extended to include the energy consumed in decompressing images. Next, we considered the effect of other image compression specific parameters as the inputs to the energy model in addition to the size of the objects accessed, as specified in our earlier model. Additionally, channel condition variations can affect the energy consumption significantly. For instance, when the channel condition, represented by the Signal-to-Noise Ratio (SNR) degrades, the energy
consumed in accessing the multimedia object increases. This is due the fact that the transmission power used and the number of retransmissions required to communicate increases under poor
channel conditions.

In order to consider the above enhancements, we measured energy consumption under different controlled conditions using our data acquisition platform. Based on the measured data, we performed regression analysis to develop the energy model considering different input variables identified before. The input parameters that we considered are the service specific parameters (volume of data requested, image size, image compression parameter) and networkrelated parameters (SNR). The resulting energy model is validated and used with adaptive image shaping techniques developed in a separate project. The use of the energy model in guiding the
adaptive image shaping technique leads to significant savings in energy consumption with minimal degradation to image quality. For example, for a medical image of size 160x160, our adaptive image delivery techniques consume 6.4J of energy compared to 11.6J without adaptation in Palm.Net access network, while degrading image quality from 40.3dB to 32.1dB. After studying the effect of network conditions on energy consumption, we have started to investigate the effect of another network condition, i.e., error patterns on application data. Below, we briefly describe the goal of the current efforts and the architecture proposed.

One significant bottleneck in enabling high quality multimedia applications is the dynamic error condition caused by wireless channel variation. In order to address the erroneous channel conditions, several link and physical layer error control techniques have been proposed to counter the presence of errors by introducing redundancies in transmission. However, the low layer techniques have high associated communication costs in terms of energy and latency due to the added data transmission. Additionally, these physical/link level techniques are oblivious to application requirements such as inherent error resiliency in application data.

We believe that the overhead of error control can be reduced significantly by adapting the error control mechanisms based on current context represented by application properties and wireless
channel conditions. We are developing a Context-aware Error Control (ConECt) framework that uses application level information to enable low-cost error control through proper selection and configuration of error control mechanisms. ConECt would enable trade-off between an
application’s Quality of Content (QoC) and communication cost in diverse wireless contexts.

Our approach to achieve the above goal consists of two functional steps: (1) characterization of the effect of different error profiles and error control mechanisms on application’s data in terms of Quality of Content (QoC) and communication cost (energy/latency/access cost), and (2) dynamic selection of the error control mechanism at runtime using the pre-characterized application’s error-effect models developed in the first step. The proposed ConECt framework uses current channel conditions to select appropriate error control mechanism in order to reduce communication cost without affecting QoC significantly. The framework is compatible with current wireless standards as it chooses an error control mechanism from the set of choices allowable by the standard specifications. For example, the majority of 3G wireless data standards currently have support for multiple channel coding algorithms (i.e. convolutional or turbo), and different coding rates at the physical layer. As the ConECt framework does not require any modification to the existing functionality, it can be deployed easily on current wireless data networks. We are currently evaluating the above framework under diverse channel conditions and data types.

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