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Opportunistic routing has gained much attention as a new and promising approach to relay packets in wireless mesh networks. The collaboration of intermediate nodes to deliver packets without a predetermined route, using the broadcast nature of wireless channel, increases the transmission reliability and network throughput. Nevertheless, such characteristics make the process of admission control even more challenging than in a traditional unipath routing protocol. In this paper, we propose an admission control mechanism for quality of service (QoS)-enabled opportunistic routing protocols. This mechanism performs admission control and resource reservation in multiple neighbors according to their importance for the relaying process. We also consider the influence of using thresholds for QoS parameters in order to improve the service provided to adaptive applications. Simulation results allow us to reach two conclusions: (i) the proposed mechanism achieves higher success rate than both a unipath-based and a multipath-based admission control mechanisms and (ii) thresholds allow high resource reservation even in network topologies with scarce resources. [ABSTRACT FROM AUTHOR]

Dynamic power management policies are essential for battery-powered consumer electronics devices, which are mostly equipped with power consuming network interfaces. Timeout-based power management policies are implemented on most operating systems, but the selection of the policy parameters generally resort to heuristic approaches and there is lack of support for network interfaces dynamic power management. Therefore, this paper introduces an innovative solution for timeout-based power management policies for network interfaces. The presented policy exploits statistical data analysis and the optimization of both power savings and performance. The effectiveness of the introduced policy is demonstrated by experimental results1. [ABSTRACT FROM PUBLISHER]

Minimization of power consumption is essential for battery-powered computing systems. This paper proposes an innovative solution for the processor power management problem. The proposed technique does not require accurate power consumption and performance model of the processor. The proposed solution exploits feedback control principles and the controller design is optimized for a given workload. Experimental results obtained from a case study are presented demonstrating, usefulness and the feasibility of the proposed solution. [ABSTRACT FROM AUTHOR]

Energy efficiency has become one of the key challenges for a large class of electronic systems. Longer time between battery recharges is highly desirable for battery-powered devices such as mobile phones, digital cameras, Internet tablets, and electronic organizers. Energy efficiency is also important for electronic systems powered from the electric grid since it may reduce power consumption and the cooling requirements. Power savings are possible because electronic systems generally have an idle state, when, for example, the processor can run in a low-power state. Thus, the correct estimation of the workload model plays an essential role in the decision of which and when a power state transition should be performed by the electronic system. This paper introduces a multisize sliding window workload estimation technique for dynamic power management (DPM) in nonstationary environments. This technique reduces both the effects of identification delay and sampling error present in the previous fixed-size sliding window approach. The system is modeled by discrete-time Markov chains and the model offers a rigorous mathematical formulation of the problem and allows one to obtain an excellent trade-off between performance and power consumption. [ABSTRACT FROM PUBLISHER]

Many human activities are tactile. Recognizing how a person touches an object or a surface surrounding them is an active area of research and it has generated keen interest within the interactive surface community. In this paper, we compare two machine learning techniques, namely Artificial Neural Network (ANN) and Hidden Markov Models (HMM), as they are some of the most common techniques with low computational cost used to classify an acoustic-based input. We employ a small and low-cost hardware design composed of a microphone, a stethoscope, a conditioning circuit, and a microcontroller. Together with an appropriate surface, we integrated these components into a passive gesture recognition input system for experimental evaluation. To perform the evaluation, we acquire the signals using a small microphone and send it through the microcontroller to MATLAB's toolboxes to implement and evaluate the ANN and HMM models. We also present the hardware and software implementation and discuss the advantages and limitations of these techniques in gesture recognition while using a simple alphabet of three geometrical figures: circle, square, and triangle. The results validate the robustness of the HMM technique that achieved a success rate of 90%, with a shorter training time than the ANN. [ABSTRACT FROM AUTHOR]