Parameter Selection At Run-Time To Optimize Energy Efficiency

Energy efficiency is vital for a mobile terminal. In this paper we investigate how to choose the right parameter settings at run-time so that the energy consumption is minimized while satisfying the required level of service. To use a real world example, the energy consumption of a third generation telephone WCDMA downlink receiver with turbo decoding forward error correction is considered. A trade-off is made between the number of fingers of a rake receiver and the number of iterations of the turbo decoder. A simulation environment is constructed to simulate the system. In this paper we present graphs, with which the trade-off can be easily made

QoS scheduling for energy-efficient wireless communication

Presents a QoS (quality of service) scheduler that assigns bandwidth over a wireless channel such that the amount of energy spent by a mobile device is minimized, while maintaining the QoS of the connections. Energy efficiency is an important issue for mobile computers, since they must rely on their batteries. We have designed and implemented an energy-efficient architecture and MAC protocol for wireless multimedia traffic. The scheduling is based on two mechanisms: (1) short-term transmission frame scheduling that concatenates the uplink and downlink traffic of a mobile device, and (2) long-term scheduling that tries to collect traffic as much as possible within the QoS requirements of the connections. The result is that the transceiver can be in a low-power operating mode for an extended period of time and that the number of operating mode transitions is reduce

Using Unsupervised Learning to Improve the Naive Bayes Classifier for Wireless Sensor Networks

Online processing is essential for many sensor network applications. Sensor nodes can sample far more data than what can practically be transmitted using state of the art sensor network radios. Online processing, however, is complicated due to limited resources of individual nodes. The naive Bayes classifier is an algorithm proven to be suitable for online classification on Wireless Sensor Networks. In this paper, we investigate a new technique to improve the naive Bayes classifier while maintaining sensor network compatibility. We propose the application of unsupervised learning techniques to enhance the probability density estimation needed for naive Bayes, thereby achieving the benefits of binning histogram probability density estimation without the related memory requirements. Using an offline experimental dataset, we demonstrate the possibility of matching the performance of the binning histogram approach within the constraints provided by Wireless Sensor Network hardware. We validate the feasibility of our approach using an implementation based on Arduino Nano hardware combined with NRF24L01+ radios

A Request-TDMA Multiple-Access Scheme for Wireless Multimedia Networks

This paper describes a cellular multiple-access scheme based on TDMA for multimedia communication networks. The scheme proposes an admission control of two different multimedia application stream types: real-time and non-real-time. We do not consider interference between cells. The proposed protocol, that is based on TDMA, exploits the available bandwidth fully. The throughput per mobile station is higher compared to other multiple-access protocols, it offers low latency for both real-time and non-real-time communication and the unused reserved bandwidth is reallocated for non-real-time communication. Furthermore, the throughput and latency remain stable under high loads

Audio source location for a digital TV-Director

Three algorithms are presented for location of audio sources using standard workstations and a minimal amount of resources. The audio source location is based on time-delay estimation. The algorithms use general human speech properties and straightforward heuristics on human speaker behaviour to acquire accurate and efficient estimation of delays

The Moby Dick architecture

This document is deliverable II.3.1, and presents the the MobyDick architecture. The introduction outlines the main areas of use and the main characteristics of the Pocket Companion, and ends up with the overall demands we put on the architecture. In chapter 2 Quality of Service (QoS) as we use it in MobyDick QoS is presented. It gives the foundation the adaptability used in the chapters to follow. In chapter 3 an overview of the software architecture of the Pocket Companion is presented. The security architecture of the Pocket Companion is presented in chapter 4, and the consistency architecture is presented in chapter 5. In chapter 6, the system\ud architecture of the Pocket Companion is outlined